From 2d356dc0cc72d80f687d66e5b08e3d4053be903b Mon Sep 17 00:00:00 2001 From: "Mr. For Example" <62230687+MrForExample@users.noreply.github.com> Date: Wed, 3 Jul 2024 11:17:41 -0600 Subject: [PATCH] Big Code Refactor --- .vscode/settings.json | 3 +- Dockerfile | 2 +- MVs_Algorithms/DiffRastMesh/__init__.py | 0 MVs_Algorithms/DiffRastMesh/diff_mesh.py | 162 + .../DiffRastMesh/diff_mesh_renderer.py | 160 + MVs_Algorithms/FlexiCubes/__init__.py | 0 MVs_Algorithms/FlexiCubes/flexicubes.py | 579 + .../FlexiCubes/flexicubes_renderer.py | 74 + .../FlexiCubes/flexicubes_trainer.py | 213 + MVs_Algorithms/FlexiCubes/loss.py | 95 + MVs_Algorithms/FlexiCubes/tables.py | 791 + MVs_Algorithms/FlexiCubes/util.py | 93 + MVs_Algorithms/GaussianSplatting/main_3DGS.py | 232 + .../GaussianSplatting/main_3DGS_renderer.py | 982 + MVs_Algorithms/NeRF/Instant_NGP.py | 205 + ...RM_Img_to_Multi-views_to_CCMs_to_Mesh.json | 150 +- _Example_Workflows/Era3D_to_InstantMesh.json | 93 +- ...to_3DGS_to_3DMesh(DMTet and 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gen_3d_modules/TripoSR/models/tokenizers/triplane.py create mode 100644 gen_3d_modules/TripoSR/models/transformer/attention.py create mode 100644 gen_3d_modules/TripoSR/models/transformer/basic_transformer_block.py create mode 100644 gen_3d_modules/TripoSR/models/transformer/transformer_1d.py create mode 100644 gen_3d_modules/TripoSR/system.py create mode 100644 gen_3d_modules/TripoSR/utils.py create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_left_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_right_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_left_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_right_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_left_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_right_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_top_RT.txt create mode 100644 gen_3d_modules/Wonder3D/data/normal_utils.py create mode 100644 gen_3d_modules/Wonder3D/data/objaverse_dataset.py create mode 100644 gen_3d_modules/Wonder3D/data/single_image_dataset.py create mode 100644 gen_3d_modules/Wonder3D/models/transformer_mv2d.py create mode 100644 gen_3d_modules/Wonder3D/models/unet_mv2d_blocks.py create mode 100644 gen_3d_modules/Wonder3D/models/unet_mv2d_condition.py create mode 100644 gen_3d_modules/Wonder3D/pipelines/pipeline_mvdiffusion_image.py create mode 100644 gen_3d_modules/Wonder3D/utils/misc.py create mode 100644 gen_3d_modules/Zero123Plus/model.py create mode 100644 gen_3d_modules/Zero123Plus/pipeline.py create mode 100644 shared_utils/log_utils.py diff --git a/.vscode/settings.json b/.vscode/settings.json index d0e17ac..13cd9e2 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,6 +1,7 @@ { "cmake.sourceDirectory": "C:/Users/reall/Softwares/ComfyUI_windows_portable/ComfyUI/custom_nodes/ComfyUI-3D-Pack/diff-gaussian-rasterization", "python.analysis.extraPaths": [ - "./gen_3d_modules" + "./gen_3d_modules", + "./MVs_Algorithms" ] } \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 01187e3..f977674 100644 --- a/Dockerfile +++ b/Dockerfile @@ -63,7 +63,7 @@ RUN pip install --no-cache -r requirements.txt WORKDIR /app/custom_nodes/ComfyUI-3D-Pack/ COPY --chown=user:user requirements.txt requirements_post.txt ./ COPY --chown=user:user simple-knn/ simple-knn/ -COPY --chown=user:user tgs/ tgs/ +COPY --chown=user:user Gen_3D_Modules/TriplaneGaussian/ Gen_3D_Modules/TriplaneGaussian/ RUN pip install --no-cache -r requirements.txt \ # post requirements installation require gpu, setup # `nvidia-container-runtime`, for docker, see diff --git a/MVs_Algorithms/DiffRastMesh/__init__.py b/MVs_Algorithms/DiffRastMesh/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/MVs_Algorithms/DiffRastMesh/diff_mesh.py b/MVs_Algorithms/DiffRastMesh/diff_mesh.py new file mode 100644 index 0000000..cb8f314 --- /dev/null +++ b/MVs_Algorithms/DiffRastMesh/diff_mesh.py @@ -0,0 +1,162 @@ +import random +import tqdm + +import torch +import torch.nn as nn +import torch.nn.functional as F +from kiui.mesh_utils import clean_mesh, decimate_mesh +from kiui.mesh_utils import laplacian_smooth_loss, normal_consistency +from pytorch_msssim import SSIM, MS_SSIM + +import comfy.utils + +from .diff_mesh_renderer import DiffRastRenderer + +from shared_utils.camera_utils import BaseCameraController +from shared_utils.image_utils import prepare_torch_img + +class DiffMeshCameraController(BaseCameraController): + + def get_render_result(self, render_pose, bg_color, **kwargs): + ref_cam = (render_pose, self.cam.perspective) + return self.renderer.render(*ref_cam, self.cam.H, self.cam.W, ssaa=1, bg_color=bg_color, **kwargs) #ssaa = min(2.0, max(0.125, 2 * np.random.random())) + +class DiffMesh: + + def __init__( + self, + mesh, + training_iterations, + batch_size, + texture_learning_rate, + train_mesh_geometry, + geometry_learning_rate, + ms_ssim_loss_weight, + remesh_after_n_iteration, + invert_bg_prob, + force_cuda_rasterize + ): + self.device = torch.device("cuda") + + self.train_mesh_geometry = train_mesh_geometry + self.remesh_after_n_iteration = remesh_after_n_iteration + + # prepare main components for optimization + self.renderer = DiffRastRenderer(mesh, force_cuda_rasterize).to(self.device) + + self.optimizer = torch.optim.Adam(self.renderer.get_params(texture_learning_rate, train_mesh_geometry, geometry_learning_rate)) + #self.ssim_loss = SSIM(data_range=1, size_average=True, channel=3) + self.ms_ssim_loss = MS_SSIM(data_range=1, size_average=True, channel=3) + self.lambda_ssim = ms_ssim_loss_weight + + self.training_iterations = training_iterations + + self.batch_size = batch_size + + self.invert_bg_prob = invert_bg_prob + + def prepare_training(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy): + self.ref_imgs_num = len(reference_images) + + self.ref_size_H = reference_images[0].shape[0] + self.ref_size_W = reference_images[0].shape[1] + + # default camera settings + self.cam_controller = DiffMeshCameraController( + self.renderer, self.ref_size_W, self.ref_size_H, reference_orbit_camera_fovy, self.invert_bg_prob, None, self.device + ) + + self.all_ref_cam_poses = reference_orbit_camera_poses + + # prepare reference images and masks + ref_imgs_torch_list = [] + ref_masks_torch_list = [] + for i in range(self.ref_imgs_num): + ref_imgs_torch_list.append(prepare_torch_img(reference_images[i].unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device)) + ref_masks_torch_list.append(prepare_torch_img(reference_masks[i].unsqueeze(2).unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device)) + + self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0) + self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0) + + def training(self, decimate_target=5e4): + starter = torch.cuda.Event(enable_timing=True) + ender = torch.cuda.Event(enable_timing=True) + starter.record() + + ref_imgs_masked = [] + for i in range(self.ref_imgs_num): + ref_imgs_masked.append((self.ref_imgs_torch[i] * self.ref_masks_torch[i]).unsqueeze(0)) + + ref_imgs_num_minus_1 = self.ref_imgs_num-1 + + comfy_pbar = comfy.utils.ProgressBar(self.training_iterations) + + for step in tqdm.trange(self.training_iterations): + + ### calculate loss between reference and rendered image from known view + loss = 0 + masked_rendered_img_batch = [] + masked_ref_img_batch = [] + for _ in range(self.batch_size): + + i = random.randint(0, ref_imgs_num_minus_1) + + out = self.cam_controller.render_at_pose(self.all_ref_cam_poses[i]) + + image = out["image"] # [H, W, 3] in [0, 1] + image = image.permute(2, 0, 1).contiguous() # [3, H, W] in [0, 1] + + image_masked = (image * self.ref_masks_torch[i]).unsqueeze(0) + + masked_rendered_img_batch.append(image_masked) + masked_ref_img_batch.append(ref_imgs_masked[i]) + + masked_rendered_img_batch_torch = torch.cat(masked_rendered_img_batch, dim=0) + masked_ref_img_batch_torch = torch.cat(masked_ref_img_batch, dim=0) + + # rgb loss + loss += (1 - self.lambda_ssim) * F.mse_loss(masked_rendered_img_batch_torch, masked_ref_img_batch_torch) + + # D-SSIM loss + # [1, 3, H, W] in [0, 1] + #loss += self.lambda_ssim * (1 - self.ssim_loss(X, Y)) + loss += self.lambda_ssim * (1 - self.ms_ssim_loss(masked_ref_img_batch_torch, masked_rendered_img_batch_torch)) + + # Regularization loss + if self.train_mesh_geometry: + current_v = self.renderer.mesh.v + self.renderer.v_offsets + loss += 0.01 * laplacian_smooth_loss(current_v, self.renderer.mesh.f) + loss += 0.001 * normal_consistency(current_v, self.renderer.mesh.f) + loss += 0.1 * (self.renderer.v_offsets ** 2).sum(-1).mean() + + # remesh periodically + if step > 0 and step % self.remesh_after_n_iteration == 0: + vertices = (self.renderer.mesh.v + self.renderer.v_offsets).detach().cpu().numpy() + triangles = self.renderer.mesh.f.detach().cpu().numpy() + vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.01) + if triangles.shape[0] > decimate_target: + vertices, triangles = decimate_mesh(vertices, triangles, decimate_target, optimalplacement=False) + self.renderer.mesh.v = torch.from_numpy(vertices).contiguous().float().to(self.device) + self.renderer.mesh.f = torch.from_numpy(triangles).contiguous().int().to(self.device) + self.renderer.v_offsets = nn.Parameter(torch.zeros_like(self.renderer.mesh.v)).to(self.device) + + # optimize step + loss.backward() + self.optimizer.step() + self.optimizer.zero_grad() + + comfy_pbar.update_absolute(step + 1) + + torch.cuda.synchronize() + + self.need_update = True + + print(f"Step: {step}") + + self.renderer.update_mesh() + + ender.record() + #t = starter.elapsed_time(ender) + + def get_mesh_and_texture(self): + return (self.renderer.mesh, self.renderer.mesh.albedo, ) \ No newline at end of file diff --git a/MVs_Algorithms/DiffRastMesh/diff_mesh_renderer.py b/MVs_Algorithms/DiffRastMesh/diff_mesh_renderer.py new file mode 100644 index 0000000..d404149 --- /dev/null +++ b/MVs_Algorithms/DiffRastMesh/diff_mesh_renderer.py @@ -0,0 +1,160 @@ +import os +import math + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import nvdiffrast.torch as dr + +from kiui.op import inverse_sigmoid + +from mesh_processer.mesh import safe_normalize + +def scale_img_nhwc(x, size, mag='bilinear', min='bilinear'): + assert (x.shape[1] >= size[0] and x.shape[2] >= size[1]) or (x.shape[1] < size[0] and x.shape[2] < size[1]), "Trying to magnify image in one dimension and minify in the other" + y = x.permute(0, 3, 1, 2) # NHWC -> NCHW + if x.shape[1] > size[0] and x.shape[2] > size[1]: # Minification, previous size was bigger + y = torch.nn.functional.interpolate(y, size, mode=min) + else: # Magnification + if mag == 'bilinear' or mag == 'bicubic': + y = torch.nn.functional.interpolate(y, size, mode=mag, align_corners=True) + else: + y = torch.nn.functional.interpolate(y, size, mode=mag) + return y.permute(0, 2, 3, 1).contiguous() # NCHW -> NHWC + +def scale_img_hwc(x, size, mag='bilinear', min='bilinear'): + return scale_img_nhwc(x[None, ...], size, mag, min)[0] + +def scale_img_nhw(x, size, mag='bilinear', min='bilinear'): + return scale_img_nhwc(x[..., None], size, mag, min)[..., 0] + +def scale_img_hw(x, size, mag='bilinear', min='bilinear'): + return scale_img_nhwc(x[None, ..., None], size, mag, min)[0, ..., 0] + +def make_divisible(x, m=8): + return int(math.ceil(x / m) * m) + +class DiffRastRenderer(nn.Module): + def __init__(self, mesh, force_cuda_rast): + + super().__init__() + + self.mesh = mesh + + if force_cuda_rast or os.name != 'nt': + self.glctx = dr.RasterizeCudaContext() + else: + self.glctx = dr.RasterizeGLContext() + + # extract trainable parameters + self.v_offsets = nn.Parameter(torch.zeros_like(self.mesh.v), requires_grad=True) + self.raw_albedo = nn.Parameter(inverse_sigmoid(self.mesh.albedo), requires_grad=True) + + self.train_geo = False + + def get_params(self, texture_lr, train_geo, geom_lr): + + params = [ + {'params': self.raw_albedo, 'lr': texture_lr}, + ] + + self.train_geo = train_geo + if train_geo: + params.append({'params': self.v_offsets, 'lr': geom_lr}) + + return params + + def update_mesh(self): + self.mesh.v = (self.mesh.v + self.v_offsets).detach() + self.mesh.albedo = torch.sigmoid(self.raw_albedo.detach()) + + def render(self, pose, proj, h0, w0, ssaa=1, bg_color=1, texture_filter='linear', + optional_render_types=['depth', 'normal']): + + # do super-sampling + if ssaa != 1: + h = make_divisible(h0 * ssaa, 8) + w = make_divisible(w0 * ssaa, 8) + else: + h, w = h0, w0 + + results = {} + + # get v + if self.train_geo: + v = self.mesh.v + self.v_offsets # [N, 3] + else: + v = self.mesh.v + + pose = torch.from_numpy(pose.astype(np.float32)).to(v.device) + proj = torch.from_numpy(proj.astype(np.float32)).to(v.device) + + # get v_clip and render rgb + v_cam = torch.matmul(F.pad(v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0) + v_clip = v_cam @ proj.T + + rast, rast_db = dr.rasterize(self.glctx, v_clip, self.mesh.f, (h, w)) + + #alpha = (rast[0, ..., 3:] > 0).float() # [H, W, 1] + alpha = torch.clamp(rast[..., -1:], 0, 1).contiguous() # [1, H, W, 1] + alpha = dr.antialias(alpha, rast, v_clip, self.mesh.f).clamp(0, 1).squeeze(0) # [H, W, 1] important to enable gradients! + + # render albedo + texc, texc_db = dr.interpolate(self.mesh.vt.unsqueeze(0).contiguous(), rast, self.mesh.ft, rast_db=rast_db, diff_attrs='all') + albedo = dr.texture(self.raw_albedo.unsqueeze(0), texc, uv_da=texc_db, filter_mode=texture_filter) # [1, H, W, 3] + albedo = torch.sigmoid(albedo) + + # render depth + if 'depth' in optional_render_types: + depth, _ = dr.interpolate(-v_cam[..., [2]], rast, self.mesh.f) # [1, H, W, 1] + depth = depth.squeeze(0) # [H, W, 1] + + # get vn and render normal + if 'normal' in optional_render_types: + if self.train_geo: + i0, i1, i2 = self.mesh.f[:, 0].long(), self.mesh.f[:, 1].long(), self.mesh.f[:, 2].long() + v0, v1, v2 = v[i0, :], v[i1, :], v[i2, :] + + face_normals = torch.cross(v1 - v0, v2 - v0) + face_normals = safe_normalize(face_normals) + + vn = torch.zeros_like(v) + vn.scatter_add_(0, i0[:, None].repeat(1,3), face_normals) + vn.scatter_add_(0, i1[:, None].repeat(1,3), face_normals) + vn.scatter_add_(0, i2[:, None].repeat(1,3), face_normals) + + vn = torch.where(torch.sum(vn * vn, -1, keepdim=True) > 1e-20, vn, torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device)) + else: + vn = self.mesh.vn + + normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, self.mesh.fn) + normal = safe_normalize(normal[0]) + + # rotated normal (where [0, 0, 1] always faces camera) + rot_normal = normal @ pose[:3, :3] + viewcos = rot_normal[..., [2]] + + # antialias + albedo = dr.antialias(albedo, rast, v_clip, self.mesh.f).squeeze(0).contiguous() # [H, W, 3] + albedo = alpha * albedo + (1 - alpha) * bg_color + + # ssaa + if ssaa != 1: + albedo = scale_img_hwc(albedo, (h0, w0)) + alpha = scale_img_hwc(alpha, (h0, w0)) + if 'depth' in optional_render_types: + depth = scale_img_hwc(depth, (h0, w0)) + if 'normal' in optional_render_types: + normal = scale_img_hwc(normal, (h0, w0)) + viewcos = scale_img_hwc(viewcos, (h0, w0)) + + results['image'] = albedo.clamp(0, 1) + results['alpha'] = alpha + if 'depth' in optional_render_types: + results['depth'] = depth + if 'normal' in optional_render_types: + results['normal'] = (normal + 1) / 2 + results['viewcos'] = viewcos + + return results \ No newline at end of file diff --git a/MVs_Algorithms/FlexiCubes/__init__.py b/MVs_Algorithms/FlexiCubes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/MVs_Algorithms/FlexiCubes/flexicubes.py b/MVs_Algorithms/FlexiCubes/flexicubes.py new file mode 100644 index 0000000..26d7b91 --- /dev/null +++ b/MVs_Algorithms/FlexiCubes/flexicubes.py @@ -0,0 +1,579 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import torch +from .tables import * + +__all__ = [ + 'FlexiCubes' +] + + +class FlexiCubes: + """ + This class implements the FlexiCubes method for extracting meshes from scalar fields. + It maintains a series of lookup tables and indices to support the mesh extraction process. + FlexiCubes, a differentiable variant of the Dual Marching Cubes (DMC) scheme, enhances + the geometric fidelity and mesh quality of reconstructed meshes by dynamically adjusting + the surface representation through gradient-based optimization. + + During instantiation, the class loads DMC tables from a file and transforms them into + PyTorch tensors on the specified device. + + Attributes: + device (str): Specifies the computational device (default is "cuda"). + dmc_table (torch.Tensor): Dual Marching Cubes (DMC) table that encodes the edges + associated with each dual vertex in 256 Marching Cubes (MC) configurations. + num_vd_table (torch.Tensor): Table holding the number of dual vertices in each of + the 256 MC configurations. + check_table (torch.Tensor): Table resolving ambiguity in cases C16 and C19 + of the DMC configurations. + tet_table (torch.Tensor): Lookup table used in tetrahedralizing the isosurface. + quad_split_1 (torch.Tensor): Indices for splitting a quad into two triangles + along one diagonal. + quad_split_2 (torch.Tensor): Alternative indices for splitting a quad into + two triangles along the other diagonal. + quad_split_train (torch.Tensor): Indices for splitting a quad into four triangles + during training by connecting all edges to their midpoints. + cube_corners (torch.Tensor): Defines the positions of a standard unit cube's + eight corners in 3D space, ordered starting from the origin (0,0,0), + moving along the x-axis, then y-axis, and finally z-axis. + Used as a blueprint for generating a voxel grid. + cube_corners_idx (torch.Tensor): Cube corners indexed as powers of 2, used + to retrieve the case id. + cube_edges (torch.Tensor): Edge connections in a cube, listed in pairs. + Used to retrieve edge vertices in DMC. + edge_dir_table (torch.Tensor): A mapping tensor that associates edge indices with + their corresponding axis. For instance, edge_dir_table[0] = 0 indicates that the + first edge is oriented along the x-axis. + dir_faces_table (torch.Tensor): A tensor that maps the corresponding axis of shared edges + across four adjacent cubes to the shared faces of these cubes. For instance, + dir_faces_table[0] = [5, 4] implies that for four cubes sharing an edge along + the x-axis, the first and second cubes share faces indexed as 5 and 4, respectively. + This tensor is only utilized during isosurface tetrahedralization. + adj_pairs (torch.Tensor): + A tensor containing index pairs that correspond to neighboring cubes that share the same edge. + qef_reg_scale (float): + The scaling factor applied to the regularization loss to prevent issues with singularity + when solving the QEF. This parameter is only used when a 'grad_func' is specified. + weight_scale (float): + The scale of weights in FlexiCubes. Should be between 0 and 1. + """ + + def __init__(self, device="cuda", qef_reg_scale=1e-3, weight_scale=0.99): + + self.device = device + self.dmc_table = torch.tensor(dmc_table, dtype=torch.long, device=device, requires_grad=False) + self.num_vd_table = torch.tensor(num_vd_table, + dtype=torch.long, device=device, requires_grad=False) + self.check_table = torch.tensor( + check_table, + dtype=torch.long, device=device, requires_grad=False) + + self.tet_table = torch.tensor(tet_table, dtype=torch.long, device=device, requires_grad=False) + self.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=device, requires_grad=False) + self.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=device, requires_grad=False) + self.quad_split_train = torch.tensor( + [0, 1, 1, 2, 2, 3, 3, 0], dtype=torch.long, device=device, requires_grad=False) + + self.cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [ + 1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.float, device=device) + self.cube_corners_idx = torch.pow(2, torch.arange(8, requires_grad=False)) + self.cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6, + 2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, device=device, requires_grad=False) + + self.edge_dir_table = torch.tensor([0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1], + dtype=torch.long, device=device) + self.dir_faces_table = torch.tensor([ + [[5, 4], [3, 2], [4, 5], [2, 3]], + [[5, 4], [1, 0], [4, 5], [0, 1]], + [[3, 2], [1, 0], [2, 3], [0, 1]] + ], dtype=torch.long, device=device) + self.adj_pairs = torch.tensor([0, 1, 1, 3, 3, 2, 2, 0], dtype=torch.long, device=device) + self.qef_reg_scale = qef_reg_scale + self.weight_scale = weight_scale + + def construct_voxel_grid(self, res): + """ + Generates a voxel grid based on the specified resolution. + + Args: + res (int or list[int]): The resolution of the voxel grid. If an integer + is provided, it is used for all three dimensions. If a list or tuple + of 3 integers is provided, they define the resolution for the x, + y, and z dimensions respectively. + + Returns: + (torch.Tensor, torch.Tensor): Returns the vertices and the indices of the + cube corners (index into vertices) of the constructed voxel grid. + The vertices are centered at the origin, with the length of each + dimension in the grid being one. + """ + base_cube_f = torch.arange(8).to(self.device) + if isinstance(res, int): + res = (res, res, res) + voxel_grid_template = torch.ones(res, device=self.device) + + res = torch.tensor([res], dtype=torch.float, device=self.device) + coords = torch.nonzero(voxel_grid_template).float() / res # N, 3 + verts = (self.cube_corners.unsqueeze(0) / res + coords.unsqueeze(1)).reshape(-1, 3) + cubes = (base_cube_f.unsqueeze(0) + + torch.arange(coords.shape[0], device=self.device).unsqueeze(1) * 8).reshape(-1) + + verts_rounded = torch.round(verts * 10**5) / (10**5) + verts_unique, inverse_indices = torch.unique(verts_rounded, dim=0, return_inverse=True) + cubes = inverse_indices[cubes.reshape(-1)].reshape(-1, 8) + + return verts_unique - 0.5, cubes + + def __call__(self, x_nx3, s_n, cube_fx8, res, beta_fx12=None, alpha_fx8=None, + gamma_f=None, training=False, output_tetmesh=False, grad_func=None): + r""" + Main function for mesh extraction from scalar field using FlexiCubes. This function converts + discrete signed distance fields, encoded on voxel grids and additional per-cube parameters, + to triangle or tetrahedral meshes using a differentiable operation as described in + `Flexible Isosurface Extraction for Gradient-Based Mesh Optimization`_. FlexiCubes enhances + mesh quality and geometric fidelity by adjusting the surface representation based on gradient + optimization. The output surface is differentiable with respect to the input vertex positions, + scalar field values, and weight parameters. + + If you intend to extract a surface mesh from a fixed Signed Distance Field without the + optimization of parameters, it is suggested to provide the "grad_func" which should + return the surface gradient at any given 3D position. When grad_func is provided, the process + to determine the dual vertex position adapts to solve a Quadratic Error Function (QEF), as + described in the `Manifold Dual Contouring`_ paper, and employs an smart splitting strategy. + Please note, this approach is non-differentiable. + + For more details and example usage in optimization, refer to the + `Flexible Isosurface Extraction for Gradient-Based Mesh Optimization`_ SIGGRAPH 2023 paper. + + Args: + x_nx3 (torch.Tensor): Coordinates of the voxel grid vertices, can be deformed. + s_n (torch.Tensor): Scalar field values at each vertex of the voxel grid. Negative values + denote that the corresponding vertex resides inside the isosurface. This affects + the directions of the extracted triangle faces and volume to be tetrahedralized. + cube_fx8 (torch.Tensor): Indices of 8 vertices for each cube in the voxel grid. + res (int or list[int]): The resolution of the voxel grid. If an integer is provided, it + is used for all three dimensions. If a list or tuple of 3 integers is provided, they + specify the resolution for the x, y, and z dimensions respectively. + beta_fx12 (torch.Tensor, optional): Weight parameters for the cube edges to adjust dual + vertices positioning. Defaults to uniform value for all edges. + alpha_fx8 (torch.Tensor, optional): Weight parameters for the cube corners to adjust dual + vertices positioning. Defaults to uniform value for all vertices. + gamma_f (torch.Tensor, optional): Weight parameters to control the splitting of + quadrilaterals into triangles. Defaults to uniform value for all cubes. + training (bool, optional): If set to True, applies differentiable quad splitting for + training. Defaults to False. + output_tetmesh (bool, optional): If set to True, outputs a tetrahedral mesh, otherwise, + outputs a triangular mesh. Defaults to False. + grad_func (callable, optional): A function to compute the surface gradient at specified + 3D positions (input: Nx3 positions). The function should return gradients as an Nx3 + tensor. If None, the original FlexiCubes algorithm is utilized. Defaults to None. + + Returns: + (torch.Tensor, torch.LongTensor, torch.Tensor): Tuple containing: + - Vertices for the extracted triangular/tetrahedral mesh. + - Faces for the extracted triangular/tetrahedral mesh. + - Regularizer L_dev, computed per dual vertex. + + .. _Flexible Isosurface Extraction for Gradient-Based Mesh Optimization: + https://research.nvidia.com/labs/toronto-ai/flexicubes/ + .. _Manifold Dual Contouring: + https://people.engr.tamu.edu/schaefer/research/dualsimp_tvcg.pdf + """ + + surf_cubes, occ_fx8 = self._identify_surf_cubes(s_n, cube_fx8) + if surf_cubes.sum() == 0: + return torch.zeros( + (0, 3), + device=self.device), torch.zeros( + (0, 4), + dtype=torch.long, device=self.device) if output_tetmesh else torch.zeros( + (0, 3), + dtype=torch.long, device=self.device), torch.zeros( + (0), + device=self.device) + beta_fx12, alpha_fx8, gamma_f = self._normalize_weights(beta_fx12, alpha_fx8, gamma_f, surf_cubes) + + case_ids = self._get_case_id(occ_fx8, surf_cubes, res) + + surf_edges, idx_map, edge_counts, surf_edges_mask = self._identify_surf_edges(s_n, cube_fx8, surf_cubes) + + vd, L_dev, vd_gamma, vd_idx_map = self._compute_vd( + x_nx3, cube_fx8[surf_cubes], surf_edges, s_n, case_ids, beta_fx12, alpha_fx8, gamma_f, idx_map, grad_func) + vertices, faces, s_edges, edge_indices = self._triangulate( + s_n, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, grad_func) + if not output_tetmesh: + return vertices, faces, L_dev + else: + vertices, tets = self._tetrahedralize( + x_nx3, s_n, cube_fx8, vertices, faces, surf_edges, s_edges, vd_idx_map, case_ids, edge_indices, + surf_cubes, training) + return vertices, tets, L_dev + + def _compute_reg_loss(self, vd, ue, edge_group_to_vd, vd_num_edges): + """ + Regularizer L_dev as in Equation 8 + """ + dist = torch.norm(ue - torch.index_select(input=vd, index=edge_group_to_vd, dim=0), dim=-1) + mean_l2 = torch.zeros_like(vd[:, 0]) + mean_l2 = (mean_l2).index_add_(0, edge_group_to_vd, dist) / vd_num_edges.squeeze(1).float() + mad = (dist - torch.index_select(input=mean_l2, index=edge_group_to_vd, dim=0)).abs() + return mad + + def _normalize_weights(self, beta_fx12, alpha_fx8, gamma_f, surf_cubes): + """ + Normalizes the given weights to be non-negative. If input weights are None, it creates and returns a set of weights of ones. + """ + n_cubes = surf_cubes.shape[0] + + if beta_fx12 is not None: + beta_fx12 = (torch.tanh(beta_fx12) * self.weight_scale + 1) + else: + beta_fx12 = torch.ones((n_cubes, 12), dtype=torch.float, device=self.device) + + if alpha_fx8 is not None: + alpha_fx8 = (torch.tanh(alpha_fx8) * self.weight_scale + 1) + else: + alpha_fx8 = torch.ones((n_cubes, 8), dtype=torch.float, device=self.device) + + if gamma_f is not None: + gamma_f = torch.sigmoid(gamma_f) * self.weight_scale + (1 - self.weight_scale)/2 + else: + gamma_f = torch.ones((n_cubes), dtype=torch.float, device=self.device) + + return beta_fx12[surf_cubes], alpha_fx8[surf_cubes], gamma_f[surf_cubes] + + @torch.no_grad() + def _get_case_id(self, occ_fx8, surf_cubes, res): + """ + Obtains the ID of topology cases based on cell corner occupancy. This function resolves the + ambiguity in the Dual Marching Cubes (DMC) configurations as described in Section 1.3 of the + supplementary material. It should be noted that this function assumes a regular grid. + """ + case_ids = (occ_fx8[surf_cubes] * self.cube_corners_idx.to(self.device).unsqueeze(0)).sum(-1) + + problem_config = self.check_table.to(self.device)[case_ids] + to_check = problem_config[..., 0] == 1 + problem_config = problem_config[to_check] + if not isinstance(res, (list, tuple)): + res = [res, res, res] + + # The 'problematic_configs' only contain configurations for surface cubes. Next, we construct a 3D array, + # 'problem_config_full', to store configurations for all cubes (with default config for non-surface cubes). + # This allows efficient checking on adjacent cubes. + problem_config_full = torch.zeros(list(res) + [5], device=self.device, dtype=torch.long) + vol_idx = torch.nonzero(problem_config_full[..., 0] == 0) # N, 3 + vol_idx_problem = vol_idx[surf_cubes][to_check] + problem_config_full[vol_idx_problem[..., 0], vol_idx_problem[..., 1], vol_idx_problem[..., 2]] = problem_config + vol_idx_problem_adj = vol_idx_problem + problem_config[..., 1:4] + + within_range = ( + vol_idx_problem_adj[..., 0] >= 0) & ( + vol_idx_problem_adj[..., 0] < res[0]) & ( + vol_idx_problem_adj[..., 1] >= 0) & ( + vol_idx_problem_adj[..., 1] < res[1]) & ( + vol_idx_problem_adj[..., 2] >= 0) & ( + vol_idx_problem_adj[..., 2] < res[2]) + + vol_idx_problem = vol_idx_problem[within_range] + vol_idx_problem_adj = vol_idx_problem_adj[within_range] + problem_config = problem_config[within_range] + problem_config_adj = problem_config_full[vol_idx_problem_adj[..., 0], + vol_idx_problem_adj[..., 1], vol_idx_problem_adj[..., 2]] + # If two cubes with cases C16 and C19 share an ambiguous face, both cases are inverted. + to_invert = (problem_config_adj[..., 0] == 1) + idx = torch.arange(case_ids.shape[0], device=self.device)[to_check][within_range][to_invert] + case_ids.index_put_((idx,), problem_config[to_invert][..., -1]) + return case_ids + + @torch.no_grad() + def _identify_surf_edges(self, s_n, cube_fx8, surf_cubes): + """ + Identifies grid edges that intersect with the underlying surface by checking for opposite signs. As each edge + can be shared by multiple cubes, this function also assigns a unique index to each surface-intersecting edge + and marks the cube edges with this index. + """ + occ_n = s_n < 0 + all_edges = cube_fx8[surf_cubes][:, self.cube_edges].reshape(-1, 2) + unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True) + + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 + + surf_edges_mask = mask_edges[_idx_map] + counts = counts[_idx_map] + + mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=cube_fx8.device) * -1 + mapping[mask_edges] = torch.arange(mask_edges.sum(), device=cube_fx8.device) + # Shaped as [number of cubes x 12 edges per cube]. This is later used to map a cube edge to the unique index + # for a surface-intersecting edge. Non-surface-intersecting edges are marked with -1. + idx_map = mapping[_idx_map] + surf_edges = unique_edges[mask_edges] + return surf_edges, idx_map, counts, surf_edges_mask + + @torch.no_grad() + def _identify_surf_cubes(self, s_n, cube_fx8): + """ + Identifies grid cubes that intersect with the underlying surface by checking if the signs at + all corners are not identical. + """ + occ_n = s_n < 0 + occ_fx8 = occ_n[cube_fx8.reshape(-1)].reshape(-1, 8) + _occ_sum = torch.sum(occ_fx8, -1) + surf_cubes = (_occ_sum > 0) & (_occ_sum < 8) + return surf_cubes, occ_fx8 + + def _linear_interp(self, edges_weight, edges_x): + """ + Computes the location of zero-crossings on 'edges_x' using linear interpolation with 'edges_weight'. + """ + edge_dim = edges_weight.dim() - 2 + assert edges_weight.shape[edge_dim] == 2 + edges_weight = torch.cat([torch.index_select(input=edges_weight, index=torch.tensor(1, device=self.device), dim=edge_dim), - + torch.index_select(input=edges_weight, index=torch.tensor(0, device=self.device), dim=edge_dim)], edge_dim) + denominator = edges_weight.sum(edge_dim) + ue = (edges_x * edges_weight).sum(edge_dim) / denominator + return ue + + def _solve_vd_QEF(self, p_bxnx3, norm_bxnx3, c_bx3=None): + p_bxnx3 = p_bxnx3.reshape(-1, 7, 3) + norm_bxnx3 = norm_bxnx3.reshape(-1, 7, 3) + c_bx3 = c_bx3.reshape(-1, 3) + A = norm_bxnx3 + B = ((p_bxnx3) * norm_bxnx3).sum(-1, keepdims=True) + + A_reg = (torch.eye(3, device=p_bxnx3.device) * self.qef_reg_scale).unsqueeze(0).repeat(p_bxnx3.shape[0], 1, 1) + B_reg = (self.qef_reg_scale * c_bx3).unsqueeze(-1) + A = torch.cat([A, A_reg], 1) + B = torch.cat([B, B_reg], 1) + dual_verts = torch.linalg.lstsq(A, B).solution.squeeze(-1) + return dual_verts + + def _compute_vd(self, x_nx3, surf_cubes_fx8, surf_edges, s_n, case_ids, beta_fx12, alpha_fx8, gamma_f, idx_map, grad_func): + """ + Computes the location of dual vertices as described in Section 4.2 + """ + alpha_nx12x2 = torch.index_select(input=alpha_fx8, index=self.cube_edges, dim=1).reshape(-1, 12, 2) + surf_edges_x = torch.index_select(input=x_nx3, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 3) + surf_edges_s = torch.index_select(input=s_n, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 1) + zero_crossing = self._linear_interp(surf_edges_s, surf_edges_x) + + idx_map = idx_map.reshape(-1, 12) + num_vd = torch.index_select(input=self.num_vd_table, index=case_ids, dim=0) + edge_group, edge_group_to_vd, edge_group_to_cube, vd_num_edges, vd_gamma = [], [], [], [], [] + + total_num_vd = 0 + vd_idx_map = torch.zeros((case_ids.shape[0], 12), dtype=torch.long, device=self.device, requires_grad=False) + if grad_func is not None: + normals = torch.nn.functional.normalize(grad_func(zero_crossing), dim=-1) + vd = [] + for num in torch.unique(num_vd): + cur_cubes = (num_vd == num) # consider cubes with the same numbers of vd emitted (for batching) + curr_num_vd = cur_cubes.sum() * num + curr_edge_group = self.dmc_table[case_ids[cur_cubes], :num].reshape(-1, num * 7) + curr_edge_group_to_vd = torch.arange( + curr_num_vd, device=self.device).unsqueeze(-1).repeat(1, 7) + total_num_vd + total_num_vd += curr_num_vd + curr_edge_group_to_cube = torch.arange(idx_map.shape[0], device=self.device)[ + cur_cubes].unsqueeze(-1).repeat(1, num * 7).reshape_as(curr_edge_group) + + curr_mask = (curr_edge_group != -1) + edge_group.append(torch.masked_select(curr_edge_group, curr_mask)) + edge_group_to_vd.append(torch.masked_select(curr_edge_group_to_vd.reshape_as(curr_edge_group), curr_mask)) + edge_group_to_cube.append(torch.masked_select(curr_edge_group_to_cube, curr_mask)) + vd_num_edges.append(curr_mask.reshape(-1, 7).sum(-1, keepdims=True)) + vd_gamma.append(torch.masked_select(gamma_f, cur_cubes).unsqueeze(-1).repeat(1, num).reshape(-1)) + + if grad_func is not None: + with torch.no_grad(): + cube_e_verts_idx = idx_map[cur_cubes] + curr_edge_group[~curr_mask] = 0 + + verts_group_idx = torch.gather(input=cube_e_verts_idx, dim=1, index=curr_edge_group) + verts_group_idx[verts_group_idx == -1] = 0 + verts_group_pos = torch.index_select( + input=zero_crossing, index=verts_group_idx.reshape(-1), dim=0).reshape(-1, num.item(), 7, 3) + v0 = x_nx3[surf_cubes_fx8[cur_cubes][:, 0]].reshape(-1, 1, 1, 3).repeat(1, num.item(), 1, 1) + curr_mask = curr_mask.reshape(-1, num.item(), 7, 1) + verts_centroid = (verts_group_pos * curr_mask).sum(2) / (curr_mask.sum(2)) + + normals_bx7x3 = torch.index_select(input=normals, index=verts_group_idx.reshape(-1), dim=0).reshape( + -1, num.item(), 7, + 3) + curr_mask = curr_mask.squeeze(2) + vd.append(self._solve_vd_QEF((verts_group_pos - v0) * curr_mask, normals_bx7x3 * curr_mask, + verts_centroid - v0.squeeze(2)) + v0.reshape(-1, 3)) + edge_group = torch.cat(edge_group) + edge_group_to_vd = torch.cat(edge_group_to_vd) + edge_group_to_cube = torch.cat(edge_group_to_cube) + vd_num_edges = torch.cat(vd_num_edges) + vd_gamma = torch.cat(vd_gamma) + + if grad_func is not None: + vd = torch.cat(vd) + L_dev = torch.zeros([1], device=self.device) + else: + vd = torch.zeros((total_num_vd, 3), device=self.device) + beta_sum = torch.zeros((total_num_vd, 1), device=self.device) + + idx_group = torch.gather(input=idx_map.reshape(-1), dim=0, index=edge_group_to_cube * 12 + edge_group) + + x_group = torch.index_select(input=surf_edges_x, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 3) + s_group = torch.index_select(input=surf_edges_s, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 1) + + zero_crossing_group = torch.index_select( + input=zero_crossing, index=idx_group.reshape(-1), dim=0).reshape(-1, 3) + + alpha_group = torch.index_select(input=alpha_nx12x2.reshape(-1, 2), dim=0, + index=edge_group_to_cube * 12 + edge_group).reshape(-1, 2, 1) + ue_group = self._linear_interp(s_group * alpha_group, x_group) + + beta_group = torch.gather(input=beta_fx12.reshape(-1), dim=0, + index=edge_group_to_cube * 12 + edge_group).reshape(-1, 1) + beta_sum = beta_sum.index_add_(0, index=edge_group_to_vd, source=beta_group) + vd = vd.index_add_(0, index=edge_group_to_vd, source=ue_group * beta_group) / beta_sum + L_dev = self._compute_reg_loss(vd, zero_crossing_group, edge_group_to_vd, vd_num_edges) + + v_idx = torch.arange(vd.shape[0], device=self.device) # + total_num_vd + + vd_idx_map = (vd_idx_map.reshape(-1)).scatter(dim=0, index=edge_group_to_cube * + 12 + edge_group, src=v_idx[edge_group_to_vd]) + + return vd, L_dev, vd_gamma, vd_idx_map + + def _triangulate(self, s_n, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, grad_func): + """ + Connects four neighboring dual vertices to form a quadrilateral. The quadrilaterals are then split into + triangles based on the gamma parameter, as described in Section 4.3. + """ + with torch.no_grad(): + group_mask = (edge_counts == 4) & surf_edges_mask # surface edges shared by 4 cubes. + group = idx_map.reshape(-1)[group_mask] + vd_idx = vd_idx_map[group_mask] + edge_indices, indices = torch.sort(group, stable=True) + quad_vd_idx = vd_idx[indices].reshape(-1, 4) + + # Ensure all face directions point towards the positive SDF to maintain consistent winding. + s_edges = s_n[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1)].reshape(-1, 2) + flip_mask = s_edges[:, 0] > 0 + quad_vd_idx = torch.cat((quad_vd_idx[flip_mask][:, [0, 1, 3, 2]], + quad_vd_idx[~flip_mask][:, [2, 3, 1, 0]])) + if grad_func is not None: + # when grad_func is given, split quadrilaterals along the diagonals with more consistent gradients. + with torch.no_grad(): + vd_gamma = torch.nn.functional.normalize(grad_func(vd), dim=-1) + quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3) + gamma_02 = (quad_gamma[:, 0] * quad_gamma[:, 2]).sum(-1, keepdims=True) + gamma_13 = (quad_gamma[:, 1] * quad_gamma[:, 3]).sum(-1, keepdims=True) + else: + quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4) + gamma_02 = torch.index_select(input=quad_gamma, index=torch.tensor( + 0, device=self.device), dim=1) * torch.index_select(input=quad_gamma, index=torch.tensor(2, device=self.device), dim=1) + gamma_13 = torch.index_select(input=quad_gamma, index=torch.tensor( + 1, device=self.device), dim=1) * torch.index_select(input=quad_gamma, index=torch.tensor(3, device=self.device), dim=1) + if not training: + mask = (gamma_02 > gamma_13).squeeze(1) + faces = torch.zeros((quad_gamma.shape[0], 6), dtype=torch.long, device=quad_vd_idx.device) + faces[mask] = quad_vd_idx[mask][:, self.quad_split_1] + faces[~mask] = quad_vd_idx[~mask][:, self.quad_split_2] + faces = faces.reshape(-1, 3) + else: + vd_quad = torch.index_select(input=vd, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3) + vd_02 = (torch.index_select(input=vd_quad, index=torch.tensor(0, device=self.device), dim=1) + + torch.index_select(input=vd_quad, index=torch.tensor(2, device=self.device), dim=1)) / 2 + vd_13 = (torch.index_select(input=vd_quad, index=torch.tensor(1, device=self.device), dim=1) + + torch.index_select(input=vd_quad, index=torch.tensor(3, device=self.device), dim=1)) / 2 + weight_sum = (gamma_02 + gamma_13) + 1e-8 + vd_center = ((vd_02 * gamma_02.unsqueeze(-1) + vd_13 * gamma_13.unsqueeze(-1)) / + weight_sum.unsqueeze(-1)).squeeze(1) + vd_center_idx = torch.arange(vd_center.shape[0], device=self.device) + vd.shape[0] + vd = torch.cat([vd, vd_center]) + faces = quad_vd_idx[:, self.quad_split_train].reshape(-1, 4, 2) + faces = torch.cat([faces, vd_center_idx.reshape(-1, 1, 1).repeat(1, 4, 1)], -1).reshape(-1, 3) + return vd, faces, s_edges, edge_indices + + def _tetrahedralize( + self, x_nx3, s_n, cube_fx8, vertices, faces, surf_edges, s_edges, vd_idx_map, case_ids, edge_indices, + surf_cubes, training): + """ + Tetrahedralizes the interior volume to produce a tetrahedral mesh, as described in Section 4.5. + """ + occ_n = s_n < 0 + occ_fx8 = occ_n[cube_fx8.reshape(-1)].reshape(-1, 8) + occ_sum = torch.sum(occ_fx8, -1) + + inside_verts = x_nx3[occ_n] + mapping_inside_verts = torch.ones((occ_n.shape[0]), dtype=torch.long, device=self.device) * -1 + mapping_inside_verts[occ_n] = torch.arange(occ_n.sum(), device=self.device) + vertices.shape[0] + """ + For each grid edge connecting two grid vertices with different + signs, we first form a four-sided pyramid by connecting one + of the grid vertices with four mesh vertices that correspond + to the grid edge and then subdivide the pyramid into two tetrahedra + """ + inside_verts_idx = mapping_inside_verts[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1, 2)[ + s_edges < 0]] + if not training: + inside_verts_idx = inside_verts_idx.unsqueeze(1).expand(-1, 2).reshape(-1) + else: + inside_verts_idx = inside_verts_idx.unsqueeze(1).expand(-1, 4).reshape(-1) + + tets_surface = torch.cat([faces, inside_verts_idx.unsqueeze(-1)], -1) + """ + For each grid edge connecting two grid vertices with the + same sign, the tetrahedron is formed by the two grid vertices + and two vertices in consecutive adjacent cells + """ + inside_cubes = (occ_sum == 8) + inside_cubes_center = x_nx3[cube_fx8[inside_cubes].reshape(-1)].reshape(-1, 8, 3).mean(1) + inside_cubes_center_idx = torch.arange( + inside_cubes_center.shape[0], device=inside_cubes.device) + vertices.shape[0] + inside_verts.shape[0] + + surface_n_inside_cubes = surf_cubes | inside_cubes + edge_center_vertex_idx = torch.ones(((surface_n_inside_cubes).sum(), 13), + dtype=torch.long, device=x_nx3.device) * -1 + surf_cubes = surf_cubes[surface_n_inside_cubes] + inside_cubes = inside_cubes[surface_n_inside_cubes] + edge_center_vertex_idx[surf_cubes, :12] = vd_idx_map.reshape(-1, 12) + edge_center_vertex_idx[inside_cubes, 12] = inside_cubes_center_idx + + all_edges = cube_fx8[surface_n_inside_cubes][:, self.cube_edges].reshape(-1, 2) + unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True) + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 2 + mask = mask_edges[_idx_map] + counts = counts[_idx_map] + mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=self.device) * -1 + mapping[mask_edges] = torch.arange(mask_edges.sum(), device=self.device) + idx_map = mapping[_idx_map] + + group_mask = (counts == 4) & mask + group = idx_map.reshape(-1)[group_mask] + edge_indices, indices = torch.sort(group) + cube_idx = torch.arange((_idx_map.shape[0] // 12), dtype=torch.long, + device=self.device).unsqueeze(1).expand(-1, 12).reshape(-1)[group_mask] + edge_idx = torch.arange((12), dtype=torch.long, device=self.device).unsqueeze( + 0).expand(_idx_map.shape[0] // 12, -1).reshape(-1)[group_mask] + # Identify the face shared by the adjacent cells. + cube_idx_4 = cube_idx[indices].reshape(-1, 4) + edge_dir = self.edge_dir_table[edge_idx[indices]].reshape(-1, 4)[..., 0] + shared_faces_4x2 = self.dir_faces_table[edge_dir].reshape(-1) + cube_idx_4x2 = cube_idx_4[:, self.adj_pairs].reshape(-1) + # Identify an edge of the face with different signs and + # select the mesh vertex corresponding to the identified edge. + case_ids_expand = torch.ones((surface_n_inside_cubes).sum(), dtype=torch.long, device=x_nx3.device) * 255 + case_ids_expand[surf_cubes] = case_ids + cases = case_ids_expand[cube_idx_4x2] + quad_edge = edge_center_vertex_idx[cube_idx_4x2, self.tet_table[cases, shared_faces_4x2]].reshape(-1, 2) + mask = (quad_edge == -1).sum(-1) == 0 + inside_edge = mapping_inside_verts[unique_edges[mask_edges][edge_indices].reshape(-1)].reshape(-1, 2) + tets_inside = torch.cat([quad_edge, inside_edge], -1)[mask] + + tets = torch.cat([tets_surface, tets_inside]) + vertices = torch.cat([vertices, inside_verts, inside_cubes_center]) + return vertices, tets diff --git a/MVs_Algorithms/FlexiCubes/flexicubes_renderer.py b/MVs_Algorithms/FlexiCubes/flexicubes_renderer.py new file mode 100644 index 0000000..2ca47c8 --- /dev/null +++ b/MVs_Algorithms/FlexiCubes/flexicubes_renderer.py @@ -0,0 +1,74 @@ +import os +import numpy as np +import torch + +import nvdiffrast.torch as dr +from kiui.cam import orbit_camera + +from FlexiCubes import util + +class FlexiCubesRenderer: + def __init__(self, force_cuda_rast): + + if force_cuda_rast or os.name != 'nt': + self.glctx = dr.RasterizeCudaContext() + else: + self.glctx = dr.RasterizeGLContext() + + def get_rotate_camera(self, itr, fovy = np.deg2rad(45), iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda"): + proj_mtx = util.perspective(fovy, iter_res[1] / iter_res[0], cam_near_far[0], cam_near_far[1]) + + # Smooth rotation for display. + ang = (itr / 10) * np.pi * 2 + mv = util.translate(0, 0, -cam_radius) @ (util.rotate_x(-0.4) @ util.rotate_y(ang)) + mvp = proj_mtx @ mv + return mv.to(device), mvp.to(device) + + def get_orbit_camera(self, azimuth=0, elevation=0, fovy=45, iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda"): + aspect = iter_res[1] / iter_res[0] + fovy = np.deg2rad(fovy) + proj_mtx = util.perspective(fovy, aspect, cam_near_far[0], cam_near_far[1]) + #fovx = 2 * np.arctan(np.tan(fovy / 2) * aspect) + #proj_mtx = get_projection_matrix(cam_near_far[0], cam_near_far[1], fovx, -fovy, z_sign=-1.0) + + # Smooth rotation for display. + #mv = util.translate(0, 0, -cam_radius) @ (util.rotate_x(-np.de2rad(elevation)) @ util.rotate_y(np.deg2rad(azimuth))) + mv = torch.tensor(np.linalg.inv(orbit_camera(-elevation, azimuth, cam_radius))) + mvp = proj_mtx @ mv + return mv.to(device), mvp.to(device) + + def render_mesh(self, mesh, mv, mvp, iter_res, depth_min=-5.5, depth_max=-0.5, return_types=["mask", "depth", "normal"], white_bg=False): + ''' + The rendering function used to produce the results in the paper. + ''' + v_pos_clip = util.xfm_points(mesh.vertices.unsqueeze(0), mvp) # Rotate it to camera coordinates + rast, db = dr.rasterize( + dr.RasterizeGLContext(), v_pos_clip, mesh.faces.int(), iter_res) + + alpha_index = rast[..., -1:] > 0 + inv_alpha_index = rast[..., -1:] <= 0 + alpha = alpha_index.float() + + out_dict = {} + for type in return_types: + if type == "mask" : + img = dr.antialias(alpha, rast, v_pos_clip, mesh.faces.int()) + elif type == "depth": + v_pos_cam = util.xfm_points(mesh.vertices.unsqueeze(0), mv) + img, _ = util.interpolate(v_pos_cam[..., [2]].contiguous(), rast, mesh.faces.int()) + img_masked = img[alpha_index] + img_masked = torch.clamp(img_masked, min=depth_min, max=depth_max) + img_masked = (img_masked - depth_min) / (depth_max - depth_min) + img[alpha_index] = img_masked + img[inv_alpha_index] = 0 + elif type == "normal" : + normal_indices = (torch.arange(0, mesh.nrm.shape[0], dtype=torch.int64, device='cuda')[:, None]).repeat(1, 3) + img, _ = util.interpolate(mesh.nrm.unsqueeze(0).contiguous(), rast, normal_indices.int()) + elif type == "vertex_normal": + img, _ = util.interpolate(mesh.v_nrm.unsqueeze(0).contiguous(), rast, mesh.faces.int()) + img = dr.antialias((img + 1) * 0.5, rast, v_pos_clip, mesh.faces.int()) + if white_bg: + bg = torch.ones_like(img) + img = torch.lerp(bg, img, alpha) + out_dict[type] = img + return out_dict diff --git a/MVs_Algorithms/FlexiCubes/flexicubes_trainer.py b/MVs_Algorithms/FlexiCubes/flexicubes_trainer.py new file mode 100644 index 0000000..41ba267 --- /dev/null +++ b/MVs_Algorithms/FlexiCubes/flexicubes_trainer.py @@ -0,0 +1,213 @@ +import os +import imageio + +import torch +import numpy as np + +import tqdm +import comfy.utils + +from .flexicubes_renderer import FlexiCubesRenderer +from .flexicubes import FlexiCubes +from .util import SimpleMesh +from .loss import sdf_reg_loss + +from shared_utils.camera_utils import OrbitCamera +from mesh_processer.mesh import Mesh + + +def lr_schedule(iter): + return max(0.0, 10**(-(iter)*0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs. + +class FlexiCubesTrainer: + + def __init__( + self, + training_iterations, + batch_size, + learning_rate, + voxel_grids_resolution, + depth_near=0.5, + depth_far=5.5, + mask_loss_weight=1.0, + depth_loss_weight=100.0, + normal_loss_weight=1.0, + sdf_regularizer_weight=0.2, + remove_floaters_weight=0.5, + cube_stabilizer_weight=0.1, + force_cuda_rast=False, + device='cuda' + ): + self.device = torch.device(device) + + self.renderer = FlexiCubesRenderer(force_cuda_rast) + + # Create and initialize FlexiCubes + self.voxel_grid_res = voxel_grids_resolution + self.fc = FlexiCubes(self.device) + self.x_nx3, self.cube_fx8 = self.fc.construct_voxel_grid(self.voxel_grid_res) + self.x_nx3 *= 2 # scale up the grid so that it's larger than the target object + + self.sdf = torch.rand_like(self.x_nx3[:,0]) - 0.1 # randomly init SDF + self.sdf = torch.nn.Parameter(self.sdf.clone().detach(), requires_grad=True) + # set per-cube learnable weights to zeros + self.weight = torch.zeros((self.cube_fx8.shape[0], 21), dtype=torch.float, device=self.device) + self.weight = torch.nn.Parameter(self.weight.clone().detach(), requires_grad=True) + self.deform = torch.nn.Parameter(torch.zeros_like(self.x_nx3), requires_grad=True) + + # Retrieve all the edges of the voxel grid; these edges will be utilized to + # compute the regularization loss in subsequent steps of the process. + all_edges = self.cube_fx8[:, self.fc.cube_edges].reshape(-1, 2) + self.grid_edges = torch.unique(all_edges, dim=0) + + # Setup optimizer + self.optimizer = torch.optim.Adam([self.sdf, self.weight, self.deform], lr=learning_rate) + self.scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lambda x: lr_schedule(x)) + + self.training_iterations = training_iterations + self.batch_size = batch_size + + self.depth_near = -depth_near + self.depth_far = -depth_far + + self.mask_loss_weight = mask_loss_weight + self.depth_loss_weight = depth_loss_weight + self.normal_loss_weight = normal_loss_weight + self.sdf_regularizer_weight = sdf_regularizer_weight + self.remove_floaters_weight = remove_floaters_weight + self.cube_stabilizer_weight = cube_stabilizer_weight + + def prepare_training(self, reference_depth_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, reference_normals=None): + self.ref_imgs_num = len(reference_depth_images) + + self.all_ref_cam_poses = reference_orbit_camera_poses + self.ref_cam_fovy = reference_orbit_camera_fovy + + self.ref_size_H = reference_depth_images[0].shape[0] + self.ref_size_W = reference_depth_images[0].shape[1] + + self.cam = OrbitCamera(self.ref_size_W, self.ref_size_H, fovy=reference_orbit_camera_fovy) + + # prepare reference images and masks + self.ref_depth_imgs_torch = reference_depth_images[:, :, :, 0].unsqueeze(3).to(self.device) # (N, H, W, C) -> (N, H, W, 1) + self.ref_masks_torch = reference_masks.unsqueeze(3).to(self.device) + + if reference_normals is not None: + self.ref_normal_imgs_torch = (reference_normals * 2 - 1).to(self.device) # change value from [0, 1] -> [-1, 1] + else: + self.ref_normal_imgs_torch = None + + # prepare reference camera projection matrix for all camera poses + mv_all = [] + mvp_all = [] + for pose in self.all_ref_cam_poses: + mv, mvp = self.renderer.get_orbit_camera(pose[2], pose[1], cam_radius=pose[0], device=self.device) + mv_all.append(mv) + mvp_all.append(mvp) + self.mv_all = torch.stack(mv_all).to(self.device) + self.mvp_all = torch.stack(mvp_all).to(self.device) + self.camposes_len = len(self.all_ref_cam_poses) + + def training(self): + starter = torch.cuda.Event(enable_timing=True) + ender = torch.cuda.Event(enable_timing=True) + starter.record() + + comfy_pbar = comfy.utils.ProgressBar(self.training_iterations) + + if self.ref_normal_imgs_torch is not None: + return_types = ["mask", "depth", "normal"] + else: + return_types = ["mask", "depth"] + + for step in tqdm.trange(self.training_iterations): + # sample random render & camera pose from multi-views + batch_index = np.random.randint(0, self.camposes_len, size=self.batch_size) + mv = self.mv_all[batch_index, :, :] + mvp = self.mvp_all[batch_index, :, :] + + target_mask = self.ref_masks_torch[batch_index, :, :, :] + target_depth = self.ref_depth_imgs_torch[batch_index, :, :, :] + + # extract and render FlexiCubes mesh + grid_verts = self.x_nx3 + (2-1e-8) / (self.voxel_grid_res * 2) * torch.tanh(self.deform) + vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20], + gamma_f=self.weight[:,20], training=True) + flexicubes_mesh = SimpleMesh(vertices, faces) + if self.ref_normal_imgs_torch is not None: + flexicubes_mesh.auto_normals() + buffers = self.renderer.render_mesh(flexicubes_mesh, mv, mvp, (self.ref_size_H, self.ref_size_W), self.depth_far, self.depth_near, return_types) + + t_iter = step / self.training_iterations + # evaluate reconstruction loss + # mask & depth shape: (N, H, W, 1) + mask_loss = (buffers['mask'] - target_mask).abs().mean() * self.mask_loss_weight + depth_loss = (((((buffers['depth'] - target_depth)* target_mask)**2).sum(-1)+1e-8)).sqrt().mean() * self.depth_loss_weight + total_loss = mask_loss + depth_loss + if self.ref_normal_imgs_torch is not None: + target_normal = self.ref_normal_imgs_torch[batch_index, :, :, :] + normal_loss = (((((buffers['normal'] - (target_normal))* target_mask)**2).sum(-1)+1e-8)).sqrt().mean() * self.normal_loss_weight * t_iter + total_loss += normal_loss + + sdf_weight = self.sdf_regularizer_weight - (self.sdf_regularizer_weight - self.sdf_regularizer_weight/20)*min(1.0, 4.0 * t_iter) + reg_loss = sdf_reg_loss(self.sdf, self.grid_edges).mean() * sdf_weight # Loss to eliminate internal floaters that are not visible + reg_loss += L_dev.mean() * self.remove_floaters_weight + reg_loss += (self.weight[:,:20]).abs().mean() * self.cube_stabilizer_weight + total_loss += reg_loss + + # optimize step + total_loss.backward() + self.optimizer.step() + self.scheduler.step() + self.optimizer.zero_grad() + + comfy_pbar.update_absolute(step + 1) + + #self.test_save(step, mv, mvp, grid_verts, total_loss, out_dir="C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\FlexiCubes_Output\\Test_Normals") + + ender.record() + + def get_mesh(self): + grid_verts = self.x_nx3 + (2-1e-8) / (self.voxel_grid_res * 2) * torch.tanh(self.deform) + vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20], + gamma_f=self.weight[:,20], training=False) + + v = vertices.detach().contiguous().float().to(self.device) + f = faces.detach().contiguous().float().to(self.device) + mesh = Mesh(v=v, f=f, device=self.device) + mesh.auto_normal() + mesh.auto_uv() + + # Trimesh seems output better mesh + #import trimesh + #import os + #mesh_np = trimesh.Trimesh(vertices = vertices.detach().cpu().numpy(), faces=faces.detach().cpu().numpy(), process=False) + #mesh_np.export(os.path.join("C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\FlexiCubes_Output", 'output_trimesh.obj')) + + return mesh + + def test_save(self, step, mv, mvp, grid_verts, total_loss, out_dir, save_interval=20, display_res=[1024, 1024]): + if (step % save_interval == 0 or step == (self.training_iterations-1)): # save normal image for visualization + with torch.no_grad(): + # extract mesh with training=False + vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20], + gamma_f=self.weight[:,20], training=False) + flexicubes_mesh = SimpleMesh(vertices, faces) + flexicubes_mesh.auto_normals() # compute face normals for visualization + + mv, mvp = self.renderer.get_rotate_camera(step//save_interval, iter_res=display_res, device=self.device) + mv = mv.unsqueeze(0) + mvp = mvp.unsqueeze(0) + val_buffers = self.renderer.render_mesh(flexicubes_mesh, mv, mvp, display_res, return_types=["normal"], white_bg=True) + val_image = ((val_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8) + #val_image = (val_buffers["depth"][0].detach().cpu().numpy()*255).astype(np.uint8) + #val_repeat = np.repeat(val_image, 4, axis=2) + #val_repeat[:, :, 3] = 255 + + #gt_image = (gt_image[0].detach().cpu().numpy()*255).astype(np.uint8) + #gt_repeat = np.repeat(gt_image, 4, axis=2) + #gt_repeat[:, :, 3] = 255 + + imageio.imwrite(os.path.join(out_dir, '{:04d}.png'.format(step)), val_image) + #imageio.imwrite(os.path.join(out_dir, '{:04d}.png'.format(step)), np.concatenate([val_repeat, gt_repeat], 1)) + print(f"Optimization Step [{step}/{self.training_iterations}], Loss: {total_loss.item():.4f}") \ No newline at end of file diff --git a/MVs_Algorithms/FlexiCubes/loss.py b/MVs_Algorithms/FlexiCubes/loss.py new file mode 100644 index 0000000..ba507f0 --- /dev/null +++ b/MVs_Algorithms/FlexiCubes/loss.py @@ -0,0 +1,95 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import torch +import torch_scatter + +############################################################################### +# Pytorch implementation of the developability regularizer introduced in paper +# "Developability of Triangle Meshes" by Stein et al. +############################################################################### +def mesh_developable_reg(mesh): + + verts = mesh.vertices + tris = mesh.faces + + device = verts.device + V = verts.shape[0] + F = tris.shape[0] + + POS_EPS = 1e-6 + REL_EPS = 1e-6 + + def normalize(vecs): + return vecs / (torch.linalg.norm(vecs, dim=-1, keepdim=True) + POS_EPS) + + tri_pos = verts[tris] + + vert_normal_covariance_sum = torch.zeros((V, 9), device=device) + vert_area = torch.zeros(V, device=device) + vert_degree = torch.zeros(V, dtype=torch.int32, device=device) + + for iC in range(3): # loop over three corners of each triangle + + # gather tri verts + pRoot = tri_pos[:, iC, :] + pA = tri_pos[:, (iC + 1) % 3, :] + pB = tri_pos[:, (iC + 2) % 3, :] + + # compute the corner angle & normal + vA = pA - pRoot + vAn = normalize(vA) + vB = pB - pRoot + vBn = normalize(vB) + area_normal = torch.linalg.cross(vA, vB, dim=-1) + face_area = 0.5 * torch.linalg.norm(area_normal, dim=-1) + normal = normalize(area_normal) + corner_angle = torch.acos(torch.clamp(torch.sum(vAn * vBn, dim=-1), min=-1., max=1.)) + + # add up the contribution to the covariance matrix + outer = normal[:, :, None] @ normal[:, None, :] + contrib = corner_angle[:, None] * outer.reshape(-1, 9) + + # scatter the result to the appropriate matrices + vert_normal_covariance_sum = torch_scatter.scatter_add(src=contrib, + index=tris[:, iC], + dim=-2, + out=vert_normal_covariance_sum) + + vert_area = torch_scatter.scatter_add(src=face_area / 3., + index=tris[:, iC], + dim=-1, + out=vert_area) + + vert_degree = torch_scatter.scatter_add(src=torch.ones(F, dtype=torch.int32, device=device), + index=tris[:, iC], + dim=-1, + out=vert_degree) + + # The energy is the smallest eigenvalue of the outer-product matrix + vert_normal_covariance_sum = vert_normal_covariance_sum.reshape( + -1, 3, 3) # reshape to a batch of matrices + vert_normal_covariance_sum = vert_normal_covariance_sum + torch.eye( + 3, device=device)[None, :, :] * REL_EPS + + min_eigvals = torch.min(torch.linalg.eigvals(vert_normal_covariance_sum).abs(), dim=-1).values + + # Mask out degree-3 vertices + vert_area = torch.where(vert_degree == 3, torch.tensor(0, dtype=vert_area.dtype,device=vert_area.device), vert_area) + + # Adjust the vertex area weighting so it is unit-less, and 1 on average + vert_area = vert_area * (V / torch.sum(vert_area, dim=-1, keepdim=True)) + + return vert_area * min_eigvals + +def sdf_reg_loss(sdf, all_edges): + sdf_f1x6x2 = sdf[all_edges.reshape(-1)].reshape(-1,2) + mask = torch.sign(sdf_f1x6x2[...,0]) != torch.sign(sdf_f1x6x2[...,1]) + sdf_f1x6x2 = sdf_f1x6x2[mask] + sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[...,0], (sdf_f1x6x2[...,1] > 0).float()) + \ + torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[...,1], (sdf_f1x6x2[...,0] > 0).float()) + return sdf_diff \ No newline at end of file diff --git a/MVs_Algorithms/FlexiCubes/tables.py b/MVs_Algorithms/FlexiCubes/tables.py new file mode 100644 index 0000000..5873e77 --- /dev/null +++ b/MVs_Algorithms/FlexiCubes/tables.py @@ -0,0 +1,791 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +dmc_table = [ +[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], +[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], 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1, 2, 1, 2, 1, 1, 1, 1, 1, +1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, +1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, +1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0] +check_table = [ +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 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0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, 1, 152], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, 1, 145], +[1, 0, 0, 1, 144], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 137], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 1, 0, 133], +[1, 0, 1, 0, 132], +[1, 1, 0, 0, 131], +[1, 1, 0, 0, 130], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, 1, 100], +[0, 0, 0, 0, 0], +[1, 0, 0, 1, 98], +[0, 0, 0, 0, 0], +[1, 0, 0, 1, 96], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 1, 0, 88], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 82], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 1, 0, 74], +[0, 0, 0, 0, 0], +[1, 0, 1, 0, 72], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 70], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 67], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 65], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 1, 0, 0, 56], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 52], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 1, 0, 0, 44], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 1, 0, 0, 40], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 38], +[1, 0, -1, 0, 37], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 33], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 28], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 26], +[1, 0, 0, -1, 25], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, -1, 0, 0, 20], +[0, 0, 0, 0, 0], +[1, 0, -1, 0, 18], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 9], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[1, 0, 0, -1, 6], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0], +[0, 0, 0, 0, 0] +] +tet_table = [ +[-1, -1, -1, -1, -1, -1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[4, 4, 4, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, -1], +[1, 1, 1, 1, 1, 1], +[4, 4, 4, 4, 4, 4], +[0, 4, 0, 4, 4, -1], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[5, 5, 5, 5, 5, 5], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, -1, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, -1, 2, 4, 4, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 4, 4, 2], +[1, 1, 1, 1, 1, 1], +[2, 4, 2, 4, 4, 2], +[0, 4, 0, 4, 4, 0], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 5, 2, 5, 5, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[0, 1, 1, -1, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[4, 1, 1, 4, 4, 1], +[0, 1, 1, 0, 0, 1], +[4, 0, 0, 4, 4, 0], +[2, 2, 2, 2, 2, 2], +[-1, 1, 1, 4, 4, 1], +[0, 1, 1, 4, 4, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[5, 1, 1, 5, 5, 1], +[0, 1, 1, 0, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[8, 8, 8, 8, 8, 8], +[1, 1, 1, 4, 4, 1], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 4, 4, 1], +[0, 4, 0, 4, 4, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 5, 5, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[5, 5, 5, 5, 5, 5], +[6, 6, 6, 6, 6, 6], +[6, -1, 0, 6, 0, 6], +[6, 0, 0, 6, 0, 6], +[6, 1, 1, 6, 1, 6], +[4, 4, 4, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[6, 4, -1, 6, 4, 6], +[6, 4, 0, 6, 4, 6], +[6, 0, 0, 6, 0, 6], +[6, 1, 1, 6, 1, 6], +[5, 5, 5, 5, 5, 5], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 2, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[2, 4, 2, 2, 4, 2], +[0, 4, 0, 4, 4, 0], +[2, 0, 2, 2, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[6, 1, 1, 6, -1, 6], +[6, 1, 1, 6, 0, 6], +[6, 0, 0, 6, 0, 6], +[6, 2, 2, 6, 2, 6], +[4, 1, 1, 4, 4, 1], +[0, 1, 1, 0, 0, 1], +[4, 0, 0, 4, 4, 4], +[2, 2, 2, 2, 2, 2], +[6, 1, 1, 6, 4, 6], +[6, 1, 1, 6, 4, 6], +[6, 0, 0, 6, 0, 6], +[6, 2, 2, 6, 2, 6], +[5, 1, 1, 5, 5, 1], +[0, 1, 1, 0, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[6, 6, 6, 6, 6, 6], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 4, 1], +[0, 4, 0, 4, 4, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 5, 0, 5, 0, 5], +[5, 5, 5, 5, 5, 5], +[5, 5, 5, 5, 5, 5], +[0, 5, 0, 5, 0, 5], +[-1, 5, 0, 5, 0, 5], +[1, 5, 1, 5, 1, 5], +[4, 5, -1, 5, 4, 5], +[0, 5, 0, 5, 0, 5], +[4, 5, 0, 5, 4, 5], +[1, 5, 1, 5, 1, 5], +[4, 4, 4, 4, 4, 4], +[0, 4, 0, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[6, 6, 6, 6, 6, 6], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 5, 2, 5, -1, 5], +[0, 5, 0, 5, 0, 5], +[2, 5, 2, 5, 0, 5], +[1, 5, 1, 5, 1, 5], +[2, 5, 2, 5, 4, 5], +[0, 5, 0, 5, 0, 5], +[2, 5, 2, 5, 4, 5], +[1, 5, 1, 5, 1, 5], +[2, 4, 2, 4, 4, 2], +[0, 4, 0, 4, 4, 4], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 6, 2, 6, 6, 2], +[0, 0, 0, 0, 0, 0], +[2, 0, 2, 0, 0, 2], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[0, 1, 1, 1, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[4, 1, 1, 1, 4, 1], +[0, 1, 1, 1, 0, 1], +[4, 0, 0, 4, 4, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[5, 5, 5, 5, 5, 5], +[1, 1, 1, 1, 4, 1], +[0, 0, 0, 0, 0, 0], +[4, 0, 0, 4, 4, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[6, 0, 0, 6, 0, 6], +[0, 0, 0, 0, 0, 0], +[6, 6, 6, 6, 6, 6], +[5, 5, 5, 5, 5, 5], +[5, 5, 0, 5, 0, 5], +[5, 5, 0, 5, 0, 5], +[5, 5, 1, 5, 1, 5], +[4, 4, 4, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[4, 4, 0, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[4, 4, 4, 4, 4, 4], +[4, 4, 0, 4, 4, 4], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[8, 8, 8, 8, 8, 8], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 0, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 1, 1, 4, 4, 1], +[2, 2, 2, 2, 2, 2], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 0, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[2, 4, 2, 4, 4, 2], +[1, 1, 1, 1, 1, 1], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[2, 2, 2, 2, 2, 2], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[5, 5, 5, 5, 5, 5], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[4, 4, 4, 4, 4, 4], +[1, 1, 1, 1, 1, 1], +[0, 0, 0, 0, 0, 0], +[0, 0, 0, 0, 0, 0], +[12, 12, 12, 12, 12, 12] +] diff --git a/MVs_Algorithms/FlexiCubes/util.py b/MVs_Algorithms/FlexiCubes/util.py new file mode 100644 index 0000000..e7de64f --- /dev/null +++ b/MVs_Algorithms/FlexiCubes/util.py @@ -0,0 +1,93 @@ +# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. +import numpy as np +import torch +import nvdiffrast.torch as dr + +############################################################################### +# Functions adapted from https://github.com/NVlabs/nvdiffrec +############################################################################### + +def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + return torch.sum(x*y, -1, keepdim=True) + +def length(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor: + return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN + +def safe_normalize(x: torch.Tensor, eps: float =1e-8) -> torch.Tensor: + return x / length(x, eps) + +def perspective(fovy=0.7854, aspect=1.0, n=0.1, f=1000.0, device=None): + y = np.tan(fovy / 2) + return torch.tensor([[1/(y*aspect), 0, 0, 0], + [ 0, 1/-y, 0, 0], + [ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)], + [ 0, 0, -1, 0]], dtype=torch.float32, device=device) + +def translate(x, y, z, device=None): + return torch.tensor([[1, 0, 0, x], + [0, 1, 0, y], + [0, 0, 1, z], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + +@torch.no_grad() +def random_rotation_translation(t, device=None): + m = np.random.normal(size=[3, 3]) + m[1] = np.cross(m[0], m[2]) + m[2] = np.cross(m[0], m[1]) + m = m / np.linalg.norm(m, axis=1, keepdims=True) + m = np.pad(m, [[0, 1], [0, 1]], mode='constant') + m[3, 3] = 1.0 + m[:3, 3] = np.random.uniform(-t, t, size=[3]) + return torch.tensor(m, dtype=torch.float32, device=device) + +def rotate_x(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[1, 0, 0, 0], + [0, c, s, 0], + [0, -s, c, 0], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + +def rotate_y(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[ c, 0, s, 0], + [ 0, 1, 0, 0], + [-s, 0, c, 0], + [ 0, 0, 0, 1]], dtype=torch.float32, device=device) + +class SimpleMesh: + def __init__(self, vertices, faces): + self.vertices = vertices + self.faces = faces + + def auto_normals(self): + v0 = self.vertices[self.faces[:, 0], :] + v1 = self.vertices[self.faces[:, 1], :] + v2 = self.vertices[self.faces[:, 2], :] + nrm = safe_normalize(torch.cross(v1 - v0, v2 - v0, dim=-1)) + self.nrm = nrm + +def xfm_points(points, matrix): + '''Transform points. + Args: + points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3] + matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4] + use_python: Use PyTorch's torch.matmul (for validation) + Returns: + Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4]. + ''' + out = torch.matmul( + torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2)) + if torch.is_anomaly_enabled(): + assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN" + return out + +def interpolate(attr, rast, attr_idx, rast_db=None): + return dr.interpolate( + attr, rast, attr_idx, rast_db=rast_db, + diff_attrs=None if rast_db is None else 'all') \ No newline at end of file diff --git a/MVs_Algorithms/GaussianSplatting/main_3DGS.py b/MVs_Algorithms/GaussianSplatting/main_3DGS.py new file mode 100644 index 0000000..c98cd32 --- /dev/null +++ b/MVs_Algorithms/GaussianSplatting/main_3DGS.py @@ -0,0 +1,232 @@ +import random +import tqdm + +import torch +import torch.nn.functional as F +from pytorch_msssim import SSIM, MS_SSIM + +import comfy.utils + +from .main_3DGS_renderer import GaussianSplattingRenderer + +from shared_utils.camera_utils import BaseCameraController, MiniCam, calculate_fovX, get_projection_matrix +from shared_utils.image_utils import prepare_torch_img + +class GSParams: + def __init__( + self, + training_iterations=30_000, + batch_size=1, + lambda_ssim=0.2, + lambda_alpha=3, + lambda_offset=0, + lambda_offset_opacity=0, + invert_bg_prob = 0.5, + feature_lr=0.0025, + opacity_lr=0.05, + scaling_lr=0.005, + rotation_lr=0.001, + position_lr_init=0.00016, + position_lr_final=0.0000016, + position_lr_delay_mult=0.01, + position_lr_max_steps=30_000, + num_pts=10_000, + K=3, + percent_dense=0.01, + density_start_iter=500, + density_end_iter=15_000, + densification_interval=100, + opacity_reset_interval=3000, + densify_grad_threshold=0.0002, + sh_degree=3 + ): + + # training params + self.training_iterations = training_iterations + self.batch_size = batch_size + self.lambda_ssim = lambda_ssim + self.lambda_alpha = lambda_alpha + self.lambda_offset = lambda_offset + self.lambda_offset_opacity = lambda_offset_opacity + self.invert_bg_prob = invert_bg_prob + + # learning params + self.feature_lr = feature_lr + self.opacity_lr = opacity_lr + self.scaling_lr = scaling_lr + self.rotation_lr = rotation_lr + self.position_lr_init = position_lr_init + self.position_lr_final = position_lr_final + self.position_lr_delay_mult = position_lr_delay_mult + self.position_lr_max_steps = position_lr_max_steps + + # densify and prune params + self.num_pts = num_pts + self.K = K + self.percent_dense = percent_dense + self.density_start_iter=density_start_iter + self.density_end_iter=density_end_iter + self.densification_interval=densification_interval + self.opacity_reset_interval=opacity_reset_interval + self.densify_grad_threshold=densify_grad_threshold + + # other gaussian params + self.sh_degree = sh_degree + +class GaussianSplattingCameraController(BaseCameraController): + def get_render_result(self, render_pose, bg_color, **kwargs): + render_cam = MiniCam(render_pose, self.cam.W, self.cam.H, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, self.projection_matrix) + return self.renderer.render(render_cam, bg_color=bg_color, **kwargs) + + def post_init(self): + self.projection_matrix = get_projection_matrix(self.cam.near, self.cam.far, self.cam.fovx, self.cam.fovy).transpose(0, 1).cuda() + +class GaussianSplatting3D: + + def __init__(self, gs_params=None, init_input=None, device='cuda'): + self.device = torch.device(device) + + # prepare renderer for optimization + if gs_params is None: + gs_params = GSParams() + + self.renderer = GaussianSplattingRenderer(sh_degree=gs_params.sh_degree) + self.renderer.initialize(init_input, num_pts=gs_params.num_pts) + + # setup training + self.renderer.gaussians.training_setup(gs_params) + # do not do progressive sh-level + self.renderer.gaussians.active_sh_degree = self.renderer.gaussians.max_sh_degree + self.optimizer = self.renderer.gaussians.optimizer + #self.ssim_loss = SSIM(data_range=1, size_average=True, channel=3) + self.ms_ssim_loss = MS_SSIM(data_range=1, size_average=True, channel=3) + + self.gs_params = gs_params + + def prepare_training(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy): + self.ref_imgs_num = len(reference_images) + + self.ref_size_H = reference_images[0].shape[0] + self.ref_size_W = reference_images[0].shape[1] + + # default camera settings + self.cam_controller = GaussianSplattingCameraController( + self.renderer, self.ref_size_W, self.ref_size_H, reference_orbit_camera_fovy, self.gs_params.invert_bg_prob, None, self.device + ) + + self.all_ref_cam_poses = reference_orbit_camera_poses + + # prepare reference images and masks + ref_imgs_torch_list = [] + ref_masks_torch_list = [] + for i in range(self.ref_imgs_num): + ref_imgs_torch_list.append(prepare_torch_img(reference_images[i].unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device)) + ref_masks_torch_list.append(prepare_torch_img(reference_masks[i].unsqueeze(2).unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device)) + + self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0) + self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0) + + def training(self): + starter = torch.cuda.Event(enable_timing=True) + ender = torch.cuda.Event(enable_timing=True) + starter.record() + + ref_imgs_masked = [] + for i in range(self.ref_imgs_num): + ref_imgs_masked.append((self.ref_imgs_torch[i] * self.ref_masks_torch[i]).unsqueeze(0)) + + ref_imgs_num_minus_1 = self.ref_imgs_num-1 + + comfy_pbar = comfy.utils.ProgressBar(self.gs_params.training_iterations) + + for step in tqdm.trange(self.gs_params.training_iterations): + + ### calculate loss between reference and rendered image from known view + loss = 0 + masked_rendered_img_batch = [] + masked_ref_img_batch = [] + masks_batch = [] + ref_masks_batch = [] + + step_ratio = min(1, step / self.gs_params.training_iterations) + + # update lr + self.renderer.gaussians.update_learning_rate(step) + + loss = 0 + + for _ in range(self.gs_params.batch_size): + ### calculate loss between reference and rendered image from known view + + i = random.randint(0, ref_imgs_num_minus_1) + + out = self.cam_controller.render_at_pose(self.all_ref_cam_poses[i]) + + image = out["image"] # [3, H, W] in [0, 1] + mask = out["alpha"] # [1, H, W] in [0, 1] + + ref_mask = self.ref_masks_torch[i] + image_masked = (image * ref_mask).unsqueeze(0) + + masked_rendered_img_batch.append(image_masked) + masked_ref_img_batch.append(ref_imgs_masked[i]) + masks_batch.append(mask.unsqueeze(0)) + ref_masks_batch.append(ref_mask.unsqueeze(0)) + + masked_rendered_img_batch_torch = torch.cat(masked_rendered_img_batch, dim=0) + masked_ref_img_batch_torch = torch.cat(masked_ref_img_batch, dim=0) + masks_batch_torch = torch.cat(masks_batch, dim=0) + ref_masks_batch_torch = torch.cat(ref_masks_batch, dim=0) + + #loss_scaler = self.gs_params.loss_scale * step_ratio + + # rgb loss + loss += (1 - self.gs_params.lambda_ssim) * F.l1_loss(masked_rendered_img_batch_torch, masked_ref_img_batch_torch) + + # alpha loss + loss += self.gs_params.lambda_alpha * F.mse_loss(masks_batch_torch, ref_masks_batch_torch) + + # D-SSIM loss + # [1, 3, H, W] in [0, 1] + #loss += self.lambda_ssim * (1 - self.ssim_loss(X, Y)) + loss += self.gs_params.lambda_ssim * (1 - self.ms_ssim_loss(masked_ref_img_batch_torch, masked_rendered_img_batch_torch)) + + if self.gs_params.lambda_offset > 0: + # Reference offset loss + offset_norm = self.renderer.gaussians.get_xyz_offset.norm(dim=-1, keepdim=True) + loss += self.gs_params.lambda_offset * torch.mean(offset_norm) + + if self.gs_params.lambda_offset_opacity > 0: + # Alpha penalty loss + loss += self.gs_params.lambda_offset_opacity * torch.mean(offset_norm.detach() * self.renderer.gaussians.get_opacity) + + + # optimize step + loss.backward() + self.optimizer.step() + self.optimizer.zero_grad() + + # densify and prune + if step >= self.gs_params.density_start_iter and step <= self.gs_params.density_end_iter: + viewspace_point_tensor, visibility_filter, radii = out["viewspace_points"], out["visibility_filter"], out["radii"] + self.renderer.gaussians.max_radii2D[visibility_filter] = torch.max(self.renderer.gaussians.max_radii2D[visibility_filter], radii[visibility_filter]) + self.renderer.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter) + + if step % self.gs_params.densification_interval == 0: + self.renderer.gaussians.densify_and_prune(self.gs_params.densify_grad_threshold, min_opacity=0.005, extent=4, max_screen_size=1) + #self.renderer.gaussians.densify_and_prune_by_compatness(self.gs_params.K, min_opacity=0.01, extent=4, max_screen_size=1) + + #self.renderer.gaussians.densify_by_clone_and_split(self.gs_params.densify_grad_threshold, extent=4) + #self.renderer.gaussians.densify_by_compatness(self.gs_params.K) + #self.renderer.gaussians.prune(min_opacity=0.01, extent=4, max_screen_size=1, max_offset=0.1) + + if step % self.gs_params.opacity_reset_interval == 0: + self.renderer.gaussians.reset_opacity() + + comfy_pbar.update_absolute(step + 1) + + ender.record() + torch.cuda.synchronize() + #t = starter.elapsed_time(ender) + + self.need_update = True \ No newline at end of file diff --git a/MVs_Algorithms/GaussianSplatting/main_3DGS_renderer.py b/MVs_Algorithms/GaussianSplatting/main_3DGS_renderer.py new file mode 100644 index 0000000..7cdbf8b --- /dev/null +++ b/MVs_Algorithms/GaussianSplatting/main_3DGS_renderer.py @@ -0,0 +1,982 @@ +import os +import math +import random +import numpy as np +from typing import Optional +from torchtyping import TensorType +from plyfile import PlyData, PlyElement + +import torch +from torch import nn +import torch.nn.functional as F + +pytorch3d_capable = True +try: + import pytorch3d + from pytorch3d.ops import knn_points +except ImportError: + pytorch3d_capable = False + +from kornia.geometry.conversions import ( + quaternion_to_rotation_matrix, +) + +from diff_gaussian_rasterization import ( + GaussianRasterizationSettings, + GaussianRasterizer, +) +from simple_knn._C import distCUDA2 + +from kiui.op import inverse_sigmoid + +from shared_utils.sh_utils import eval_sh, SH2RGB, RGB2SH +from mesh_processer.mesh import Mesh, PointCloud +from mesh_processer.mesh_utils import construct_list_of_gs_attributes, write_gs_ply, read_gs_ply + +def get_expon_lr_func( + lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 +): + + def helper(step): + if lr_init == lr_final: + # constant lr, ignore other params + return lr_init + if step < 0 or (lr_init == 0.0 and lr_final == 0.0): + # Disable this parameter + return 0.0 + if lr_delay_steps > 0: + # A kind of reverse cosine decay. + delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( + 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) + ) + else: + delay_rate = 1.0 + t = np.clip(step / max_steps, 0, 1) + log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return delay_rate * log_lerp + + return helper + + +def strip_lowerdiag(L): + uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") + + uncertainty[:, 0] = L[:, 0, 0] + uncertainty[:, 1] = L[:, 0, 1] + uncertainty[:, 2] = L[:, 0, 2] + uncertainty[:, 3] = L[:, 1, 1] + uncertainty[:, 4] = L[:, 1, 2] + uncertainty[:, 5] = L[:, 2, 2] + return uncertainty + +def strip_symmetric(sym): + return strip_lowerdiag(sym) + +def gaussian_3d_coeff(xyzs, covs): + # xyzs: [N, 3] + # covs: [N, 6] + x, y, z = xyzs[:, 0], xyzs[:, 1], xyzs[:, 2] + a, b, c, d, e, f = covs[:, 0], covs[:, 1], covs[:, 2], covs[:, 3], covs[:, 4], covs[:, 5] + + # eps must be small enough !!! + inv_det = 1 / (a * d * f + 2 * e * c * b - e**2 * a - c**2 * d - b**2 * f + 1e-24) + inv_a = (d * f - e**2) * inv_det + inv_b = (e * c - b * f) * inv_det + inv_c = (e * b - c * d) * inv_det + inv_d = (a * f - c**2) * inv_det + inv_e = (b * c - e * a) * inv_det + inv_f = (a * d - b**2) * inv_det + + power = -0.5 * (x**2 * inv_a + y**2 * inv_d + z**2 * inv_f) - x * y * inv_b - x * z * inv_c - y * z * inv_e + + power[power > 0] = -1e10 # abnormal values... make weights 0 + + return torch.exp(power) + +def build_rotation(r): + norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) + + q = r / norm[:, None] + + R = torch.zeros((q.size(0), 3, 3), device='cuda') + + r = q[:, 0] + x = q[:, 1] + y = q[:, 2] + z = q[:, 3] + + R[:, 0, 0] = 1 - 2 * (y*y + z*z) + R[:, 0, 1] = 2 * (x*y - r*z) + R[:, 0, 2] = 2 * (x*z + r*y) + R[:, 1, 0] = 2 * (x*y + r*z) + R[:, 1, 1] = 1 - 2 * (x*x + z*z) + R[:, 1, 2] = 2 * (y*z - r*x) + R[:, 2, 0] = 2 * (x*z - r*y) + R[:, 2, 1] = 2 * (y*z + r*x) + R[:, 2, 2] = 1 - 2 * (x*x + y*y) + return R + +def build_scaling_rotation(s, r): + L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") + R = build_rotation(r) + + L[:,0,0] = s[:,0] + L[:,1,1] = s[:,1] + L[:,2,2] = s[:,2] + + L = R @ L + return L + +def random_point_in_triangle(v0, v1, v2): + """ + Given three vertices v0, v1, v2, sample a point uniformly in the triangle + Algorithm Reference: + https://math.stackexchange.com/questions/538458/how-to-sample-points-on-a-triangle-surface-in-3d + https://stackoverflow.com/questions/47410054/generate-random-locations-within-a-triangular-domain + """ + + r1 = random.random() + r2 = random.random() + + s1 = math.sqrt(r1) + return v0 * (1.0 - s1) + v1 * (1.0 - r2) * s1 + v2 * r2 * s1# + +def find_points_within_radius(query_points, vertex_points, d): + """ + Finds vertex points within a given radius for each query point. + + Args: + query_points (torch.Tensor): Tensor of shape (P1, 3) representing query points. + vertex_points (torch.Tensor): Tensor of shape (P2, 3) representing vertex points. + d (float): Radius within which to search for vertex points. + + Returns: + List[List[int]]: A list of lists, where each inner list contains indices of vertex points + within the radius for the corresponding query point. + """ + num_query_points = query_points.shape[0] + + # Calculate pairwise distances between query points and vertex points + distances = torch.norm(query_points[:, None] - vertex_points, dim=2) + + # Create a mask indicating which vertex points are within the radius + mask = distances <= d + + # Collect indices of vertex points within the radius for each query point + result = [] + for i in range(num_query_points): + indices_within_radius = torch.nonzero(mask[i]).squeeze().tolist() + result.append(indices_within_radius) + + return result + +@torch.no_grad() +def K_nearest_neighbors( + points: torch.Tensor, + K: int, + query: Optional[torch.Tensor] = None, + return_dist=False, +): + if not pytorch3d_capable: + raise ImportError("pytorch3d is not installed, which is required for KNN") + + # query/points: Tensor of shape (N, P1/P2, D) giving a batch of N point clouds, each containing up to P1/P2 points of dimension D + if query is None: + query = points + dist, idx, nn = knn_points(query[None, ...], points[None, ...], K=K, return_nn=True) + + # idx: Tensor of shape (N, P1, K) + # nn: Tensor of shape (N, P1, K, D) + + # take the index 1 since index 0 is the point itself + if not return_dist: + return nn[0, :, 1:, :], idx[0, :, 1:] + else: + return nn[0, :, 1:, :], idx[0, :, 1:], dist[0, :, 1:] + + +def distance_to_gaussian_surface(points, svec, rotmat, query): + """ + Calculate the radius of the gaussian along the direction, which determined by offset between points and query + Calculation using Mahalanobis distance + + Args: + points (Tensor): gaussians position of shape (*, 3) + svec (Tensor): gaussians scale vector of shape (*, 3) + rotmat : the rotation matrix of shape (*, 3, 3) + query (Tensor): query positions of shape (*, 3) + + Returns: + Distance of shape (*, 1): + """ + offset_dir = query - points + + offset_dir = torch.einsum("bij,bj->bi", rotmat.transpose(-1, -2), offset_dir) + offset_dir = F.normalize(offset_dir, dim=-1) + z = offset_dir[..., 2] + y = offset_dir[..., 1] + x = offset_dir[..., 0] + r_xy = torch.sqrt(x**2 + y**2 + 1e-10) + cos_theta = z + sin_theta = r_xy + cos_phi = x / r_xy + sin_phi = y / r_xy + + d2 = svec[..., 0] ** 2 * cos_phi**2 + svec[..., 1] ** 2 * sin_phi**2 + #r2 = svec[..., 2] ** 2 * cos_theta**2 + d2**2 * sin_theta**2 + r2 = svec[..., 2] ** 2 * cos_theta**2 + d2 * sin_theta**2 # same as: squared_dist = np.dot(diff.T, np.dot(np.linalg.inv(covariance), diff)) + + """ + Alternatively: + + def gaussian_squared_distance(query_point, mean, covariance): + # Calculate the difference vector + diff = query_point - mean + + # Compute the squared Mahalanobis distance + inv_covariance = np.linalg.inv(covariance) + squared_dist = np.dot(diff.T, np.dot(inv_covariance, diff)) + + return squared_dist + + # Example usage + mean = np.array([x0, y0, z0]) # Gaussian mean (origin position) + covariance = np.diag([sigma_x**2, sigma_y**2, sigma_z**2]) # Covariance matrix + + query_point = np.array([x_q, y_q, z_q]) # Query point + squared_dist_to_gaussian = gaussian_squared_distance(query_point, mean, covariance) + + print(f"Squared distance to Gaussian surface: {squared_dist_to_gaussian:.4f}") + """ + + return torch.sqrt(r2 + 1e-10) + +def qvec2rotmat_batched(qvec: TensorType["N", 4]): + return quaternion_to_rotation_matrix(qvec) + +class GaussianModel: + + def setup_functions(self): + def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): + L = build_scaling_rotation(scaling_modifier * scaling, rotation) + actual_covariance = L @ L.transpose(1, 2) + symm = strip_symmetric(actual_covariance) + return symm + + self.scaling_activation = torch.exp + self.scaling_inverse_activation = torch.log + + self.covariance_activation = build_covariance_from_scaling_rotation + + self.opacity_activation = torch.sigmoid + self.inverse_opacity_activation = inverse_sigmoid + + self.rotation_activation = torch.nn.functional.normalize + + + def __init__(self, sh_degree : int): + self.active_sh_degree = 0 + self.max_sh_degree = sh_degree + self.init_xyz = torch.empty(0) + self._xyz = torch.empty(0) + self._features_dc = torch.empty(0) # diffuse color + self._features_rest = torch.empty(0) # spherical harmonic coefficients + self._scaling = torch.empty(0) + self._rotation = torch.empty(0) # (w, x, y, z) + self._opacity = torch.empty(0) + self.max_radii2D = torch.empty(0) + self.xyz_gradient_accum = torch.empty(0) + self.denom = torch.empty(0) + self.optimizer = None + self.percent_dense = 0 + self.spatial_lr_scale = 0 + self.setup_functions() + + def capture(self): + return ( + self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + self.xyz_gradient_accum, + self.denom, + self.optimizer.state_dict(), + self.spatial_lr_scale, + ) + + def restore(self, model_args, training_args): + (self.active_sh_degree, + self._xyz, + self._features_dc, + self._features_rest, + self._scaling, + self._rotation, + self._opacity, + self.max_radii2D, + xyz_gradient_accum, + denom, + opt_dict, + self.spatial_lr_scale) = model_args + self.training_setup(training_args) + self.xyz_gradient_accum = xyz_gradient_accum + self.denom = denom + self.optimizer.load_state_dict(opt_dict) + + @property + def get_gaussians_num(self): + return self.init_xyz.shape[0] + + @property + def get_scaling(self): + return self.scaling_activation(self._scaling) + + @property + def get_rotation(self): + return self.rotation_activation(self._rotation) + + @property + def rotmat(self): + return qvec2rotmat_batched(self._rotation) + + @property + def get_xyz_offset(self): + return self.init_xyz - self._xyz + + @property + def get_xyz(self): + return self._xyz + + @property + def get_features(self): + features_dc = self._features_dc + features_rest = self._features_rest + return torch.cat((features_dc, features_rest), dim=1) + + @property + def get_opacity(self): + return self.opacity_activation(self._opacity) + + @torch.no_grad() + def extract_fields(self, resolution=128, num_blocks=16, relax_ratio=1.5): + # resolution: resolution of field + + block_size = 2 / num_blocks + + assert resolution % block_size == 0 + split_size = resolution // num_blocks + + opacities = self.get_opacity + + # pre-filter low opacity gaussians to save computation + mask = (opacities > 0.005).squeeze(1) + + opacities = opacities[mask] + xyzs = self.get_xyz[mask] + stds = self.get_scaling[mask] + + # normalize to ~ [-1, 1] + mn, mx = xyzs.amin(0), xyzs.amax(0) + self.center = (mn + mx) / 2 + self.scale = 1.8 / (mx - mn).amax().item() + + xyzs = (xyzs - self.center) * self.scale + stds = stds * self.scale + + covs = self.covariance_activation(stds, 1, self._rotation[mask]) + + # tile + device = opacities.device + occ = torch.zeros([resolution] * 3, dtype=torch.float32, device=device) + + X = torch.linspace(-1, 1, resolution).split(split_size) + Y = torch.linspace(-1, 1, resolution).split(split_size) + Z = torch.linspace(-1, 1, resolution).split(split_size) + + + # loop blocks (assume max size of gaussian is small than relax_ratio * block_size !!!) + for xi, xs in enumerate(X): + for yi, ys in enumerate(Y): + for zi, zs in enumerate(Z): + xx, yy, zz = torch.meshgrid(xs, ys, zs) + # sample points [M, 3] + pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).to(device) + # in-tile gaussians mask + vmin, vmax = pts.amin(0), pts.amax(0) + vmin -= block_size * relax_ratio + vmax += block_size * relax_ratio + mask = (xyzs < vmax).all(-1) & (xyzs > vmin).all(-1) + # if hit no gaussian, continue to next block + if not mask.any(): + continue + mask_xyzs = xyzs[mask] # [L, 3] + mask_covs = covs[mask] # [L, 6] + mask_opas = opacities[mask].view(1, -1) # [L, 1] --> [1, L] + + # query per point-gaussian pair. + g_pts = pts.unsqueeze(1).repeat(1, mask_covs.shape[0], 1) - mask_xyzs.unsqueeze(0) # [M, L, 3] + g_covs = mask_covs.unsqueeze(0).repeat(pts.shape[0], 1, 1) # [M, L, 6] + + # batch on gaussian to avoid OOM + batch_g = 1024 + val = 0 + for start in range(0, g_covs.shape[1], batch_g): + end = min(start + batch_g, g_covs.shape[1]) + w = gaussian_3d_coeff(g_pts[:, start:end].reshape(-1, 3), g_covs[:, start:end].reshape(-1, 6)).reshape(pts.shape[0], -1) # [M, l] + val += (mask_opas[:, start:end] * w).sum(-1) + + occ[xi * split_size: xi * split_size + len(xs), + yi * split_size: yi * split_size + len(ys), + zi * split_size: zi * split_size + len(zs)] = val.reshape(len(xs), len(ys), len(zs)) + + return occ + + def get_covariance(self, scaling_modifier = 1, gaussain_idx = None): + if gaussain_idx is None: + return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation) + else: + return self.covariance_activation(self.get_scaling[gaussain_idx], scaling_modifier, self._rotation[gaussain_idx]) + + def oneupSHdegree(self): + if self.active_sh_degree < self.max_sh_degree: + self.active_sh_degree += 1 + + def create_from_pcd(self, pcd : PointCloud, spatial_lr_scale : float = 1): + self.spatial_lr_scale = spatial_lr_scale + fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() + fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) + features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() + features[:, :3, 0 ] = fused_color + features[:, 3:, 1:] = 0.0 + + print("Number of points at initialisation : ", fused_point_cloud.shape[0]) + + dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) + scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) + rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") + rots[:, 0] = 1 + + opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) + + self.init_xyz = fused_point_cloud.clone().detach() + self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) + self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) + self._scaling = nn.Parameter(scales.requires_grad_(True)) + self._rotation = nn.Parameter(rots.requires_grad_(True)) + self._opacity = nn.Parameter(opacities.requires_grad_(True)) + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def training_setup(self, training_args): + self.percent_dense = training_args.percent_dense + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + + l = [ + {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, + {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"}, + {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"}, + {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"}, + {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"}, + {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"} + ] + + self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) + self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale, + lr_final=training_args.position_lr_final*self.spatial_lr_scale, + lr_delay_mult=training_args.position_lr_delay_mult, + max_steps=training_args.position_lr_max_steps) + + def update_learning_rate(self, iteration): + ''' Learning rate scheduling per step ''' + for param_group in self.optimizer.param_groups: + if param_group["name"] == "xyz": + lr = self.xyz_scheduler_args(iteration) + param_group['lr'] = lr + return lr + + def reset_opacity(self): + opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01)) + optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity") + self._opacity = optimizable_tensors["opacity"] + + def get_points_cloud(self): + xyz = self._xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + shs = self.get_features.detach().cpu().numpy() + pcd = PointCloud(points=xyz, colors=SH2RGB(shs), normals=normals) + return pcd + + def to_ply(self): + xyz = self._xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + opacities = self._opacity.detach().cpu().numpy() + scale = self._scaling.detach().cpu().numpy() + rotation = self._rotation.detach().cpu().numpy() + + return write_gs_ply(xyz, normals, f_dc, f_rest, opacities, scale, rotation, construct_list_of_gs_attributes(self._features_dc, self._features_rest, self._scaling, self._rotation)) + + def create_from_ply(self, plydata): + xyz, features_dc, features_extra, opacities, scales, rots = read_gs_ply(plydata) + + self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True)) + self.init_xyz = self._xyz.clone().detach() + self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) + self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True)) + self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True)) + self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True)) + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + self.active_sh_degree = self.max_sh_degree + + def create_from_mesh(self, mesh, num_pts): + """ + Sample gaussians uniformly for each face, this method can capture the structure of the 3D meshes well even for the fine structure e.g. fingers + """ + # In .obj file, first index is 1 instead of zero + vertices = np.concatenate([[[0., 0., 0.]], mesh.v.detach().cpu().numpy()]) + #normals = np.concatenate([[[0., 0., 0.]], mesh.n.detach().cpu().numpy()]) + + all_points = [] + num_pts_per_face = math.ceil(num_pts / mesh.f.shape[0]) + for triangle_face in mesh.f.detach().cpu().numpy(): + v0 = vertices[triangle_face[0]] + v1 = vertices[triangle_face[1]] + v2 = vertices[triangle_face[2]] + for i in range(num_pts_per_face): + all_points.append(random_point_in_triangle(v0, v1, v2)) + + num_pts = len(all_points) + + xyz = np.array(all_points) + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = PointCloud( + points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) + ) + self.create_from_pcd(pcd, 10) + + def create_from_uv_data(self, uv_grids_data): + """ + Idealy this method should capture the most information for the texture, + but depends on the UV of 3D meshes, fine structure like fingers could have too little gaussians to form the proper structure, + also the number of gaussians is too big even for 1K texture + """ + if uv_grids_data is not None: + uv_coords, uv_coords_3d, uv_normals_3d = uv_grids_data + + num_pts = len(uv_coords_3d) + xyz = np.array(uv_coords_3d) + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = PointCloud( + points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) + ) + self.create_from_pcd(pcd, 10) + + def replace_tensor_to_optimizer(self, tensor, name): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + if group["name"] == name: + stored_state = self.optimizer.state.get(group['params'][0], None) + stored_state["exp_avg"] = torch.zeros_like(tensor) + stored_state["exp_avg_sq"] = torch.zeros_like(tensor) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(tensor.requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def _prune_optimizer(self, mask): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + stored_state["exp_avg"] = stored_state["exp_avg"][mask] + stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask] + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True))) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + return optimizable_tensors + + def prune_points(self, mask): + valid_points_mask = ~mask + optimizable_tensors = self._prune_optimizer(valid_points_mask) + + self.init_xyz = self.init_xyz[valid_points_mask] + + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] + + self.denom = self.denom[valid_points_mask] + self.max_radii2D = self.max_radii2D[valid_points_mask] + + def cat_tensors_to_optimizer(self, tensors_dict): + optimizable_tensors = {} + for group in self.optimizer.param_groups: + assert len(group["params"]) == 1 + extension_tensor = tensors_dict[group["name"]] + stored_state = self.optimizer.state.get(group['params'][0], None) + if stored_state is not None: + + stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0) + stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0) + + del self.optimizer.state[group['params'][0]] + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + self.optimizer.state[group['params'][0]] = stored_state + + optimizable_tensors[group["name"]] = group["params"][0] + else: + group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) + optimizable_tensors[group["name"]] = group["params"][0] + + return optimizable_tensors + + def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation): + new_params = { + "xyz": new_xyz, + "f_dc": new_features_dc, + "f_rest": new_features_rest, + "opacity": new_opacities, + "scaling" : new_scaling, + "rotation" : new_rotation + } + + self.densify_with_new_params(new_params) + + def densify_with_new_params(self, new_params): + optimizable_tensors = self.cat_tensors_to_optimizer(new_params) + self._xyz = optimizable_tensors["xyz"] + self._features_dc = optimizable_tensors["f_dc"] + self._features_rest = optimizable_tensors["f_rest"] + self._opacity = optimizable_tensors["opacity"] + self._scaling = optimizable_tensors["scaling"] + self._rotation = optimizable_tensors["rotation"] + + self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") + self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") + + def densify_and_split(self, grads, grad_threshold, scene_extent, N=2): + n_init_points = self.get_xyz.shape[0] + # Extract points that satisfy the gradient condition + padded_grad = torch.zeros((n_init_points), device="cuda") + padded_grad[:grads.shape[0]] = grads.squeeze() + selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent + ) + + stds = self.get_scaling[selected_pts_mask].repeat(N,1) + means =torch.zeros((stds.size(0), 3),device="cuda") + samples = torch.normal(mean=means, std=stds) + rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1) + new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1) + new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N)) + new_rotation = self._rotation[selected_pts_mask].repeat(N,1) + new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1) + new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1) + new_opacity = self._opacity[selected_pts_mask].repeat(N,1) + + # Update reference position for gaussians + self.init_xyz = torch.cat((self.init_xyz, self.init_xyz[selected_pts_mask].repeat(N,1)), dim=0) + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation) + + prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool))) + self.prune_points(prune_filter) + + def densify_and_clone(self, grads, grad_threshold, scene_extent): + # Extract points that satisfy the gradient condition + selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False) + selected_pts_mask = torch.logical_and(selected_pts_mask, + torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent + ) + + new_xyz = self._xyz[selected_pts_mask] + new_features_dc = self._features_dc[selected_pts_mask] + new_features_rest = self._features_rest[selected_pts_mask] + new_opacities = self._opacity[selected_pts_mask] + new_scaling = self._scaling[selected_pts_mask] + new_rotation = self._rotation[selected_pts_mask] + + # Update reference position for gaussians + self.init_xyz = torch.cat((self.init_xyz, self.init_xyz[selected_pts_mask]), dim=0) + + self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation) + + def densify_by_compatnes_with_idx(self, idx): + nn_scaling = self._scaling[idx] + nn_rotmat = self.rotmat[idx] + nn_pos = self._xyz[idx] + + nn_gaussian_surface_dist = distance_to_gaussian_surface( + nn_pos, nn_scaling, nn_rotmat, self._xyz + ) + gaussian_surface_dist = distance_to_gaussian_surface( + self._xyz, self._scaling, self.rotmat, nn_pos + ) + + dist_to_nn = torch.norm(nn_pos - self._xyz, dim=-1) + selected_pts_mask = (gaussian_surface_dist + nn_gaussian_surface_dist) < dist_to_nn + new_direction = (nn_pos - self._xyz) / dist_to_nn[..., None] + new_xyz = ( + self._xyz + + new_direction * (dist_to_nn + gaussian_surface_dist - nn_gaussian_surface_dist)[..., None] + / 2.0 + )[selected_pts_mask] + new_features_dc = self._features_dc[selected_pts_mask] + new_features_rest = self._features_rest[selected_pts_mask] + new_raw_alpha = self._opacity[selected_pts_mask] + new_raw_svec = self.scaling_inverse_activation( + torch.ones_like(self._scaling[selected_pts_mask]) + * (dist_to_nn - gaussian_surface_dist - nn_gaussian_surface_dist)[selected_pts_mask][..., None] + / 6.0 + ) + # print(torch.ones_like(self.svec.data[selected_pts_mask]).shape) + # print( + # (dist_to_nn - gaussian_surface_dist - nn_gaussian_surface_dist)[selected_pts_mask].shape + # ) + new_qvec = self._rotation[selected_pts_mask] + + # Update reference position for gaussians + self.init_xyz = torch.cat((self.init_xyz, self.init_xyz[selected_pts_mask]), dim=0) + + new_params = { + "xyz": new_xyz, + "f_dc": new_features_dc, + "f_rest": new_features_rest, + "opacity": new_raw_alpha, + "scaling": new_raw_svec, + "rotation": new_qvec + } + + return new_params + + def densify_by_compatness(self, K=1): + _, idx = K_nearest_neighbors(self._xyz, K=K+1) + + new_params_list = [] + for i in range(K): + new_params = self.densify_by_compatnes_with_idx(idx[:, i]) + new_params_list.append(new_params) + new_params = {} + for key in new_params_list[0].keys(): + new_params[key] = torch.cat([p[key] for p in new_params_list], dim=0) + + self.densify_with_new_params(new_params) + + #num_densified = new_params["xyz"].shape[0] + + def densify_by_clone_and_split(self, max_grad, extent): + grads = self.xyz_gradient_accum / self.denom + grads[grads.isnan()] = 0.0 + + self.densify_and_clone(grads, max_grad, extent) + self.densify_and_split(grads, max_grad, extent) + + def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size): + self.densify_by_clone_and_split(max_grad, extent) + self.prune(min_opacity, extent, max_screen_size) + + def densify_and_prune_by_compatness(self, K, min_opacity, extent, max_screen_size): + self.densify_by_compatness(K) + self.prune(min_opacity, extent, max_screen_size) + + def add_densification_stats(self, viewspace_point_tensor, update_filter): + self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True) + self.denom[update_filter] += 1 + + def prune(self, min_opacity, extent, max_screen_size, max_offset=0): + prune_mask = (self.get_opacity < min_opacity).squeeze() + if max_screen_size: + big_points_vs = self.max_radii2D > max_screen_size + big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent + #big_points_os = self.get_xyz_offset.norm(dim=1) > max_offset + #prune_mask = torch.logical_or(torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws), big_points_os) + prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws) + self.prune_points(prune_mask) + + torch.cuda.empty_cache() + +class GaussianSplattingRenderer: + def __init__(self, sh_degree=3, white_background=True, radius=1): + + self.sh_degree = sh_degree + self.white_background = white_background + self.radius = radius + + self.gaussians = GaussianModel(sh_degree) + + self.bg_color = torch.tensor( + [1, 1, 1] if white_background else [0, 0, 0], + dtype=torch.float32, + device="cuda", + ) + + def initialize(self, input=None, num_pts=5000, radius=0.5): + # load checkpoint + if isinstance(input, Mesh): + # load from 3D mesh + self.gaussians.create_from_mesh(input, num_pts) + elif isinstance(input, PlyData): + self.gaussians.create_from_ply(input) + elif isinstance(input, PointCloud): + # load from a provided pcd + self.gaussians.create_from_pcd(input, 1) + elif input is None: + # init from random point cloud + + phis = np.random.random((num_pts,)) * 2 * np.pi + costheta = np.random.random((num_pts,)) * 2 - 1 + thetas = np.arccos(costheta) + mu = np.random.random((num_pts,)) + radius = radius * np.cbrt(mu) + x = radius * np.sin(thetas) * np.cos(phis) + y = radius * np.sin(thetas) * np.sin(phis) + z = radius * np.cos(thetas) + xyz = np.stack((x, y, z), axis=1) + # xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 + + shs = np.random.random((num_pts, 3)) / 255.0 + pcd = PointCloud( + points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) + ) + self.gaussians.create_from_pcd(pcd, 10) + else: + self.gaussians.create_from_uv_data(input) + + def render( + self, + viewpoint_camera, + scaling_modifier=1.0, + gaussain_idx=None, + bg_color=None, + override_color=None, + compute_cov3D_python=False, + convert_SHs_python=False, + ): + # Set up rasterization configuration + tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) + tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) + + raster_settings = GaussianRasterizationSettings( + image_height=int(viewpoint_camera.image_height), + image_width=int(viewpoint_camera.image_width), + tanfovx=tanfovx, + tanfovy=tanfovy, + bg=self.bg_color if bg_color is None else bg_color, + scale_modifier=scaling_modifier, + viewmatrix=viewpoint_camera.world_view_transform, + projmatrix=viewpoint_camera.full_proj_transform, + sh_degree=self.gaussians.active_sh_degree, + campos=viewpoint_camera.camera_center, + prefiltered=False, + debug=False, + ) + + rasterizer = GaussianRasterizer(raster_settings=raster_settings) + + gaussians_xyz = self.gaussians.get_xyz + gaussians_features = self.gaussians.get_features + gaussians_opacity = self.gaussians.get_opacity + + # If precomputed 3d covariance is provided, use it. If not, then it will be computed from + # scaling / rotation by the rasterizer. + gaussians_scales = None + gaussians_rotations = None + cov3D_precomp = None + if compute_cov3D_python: + cov3D_precomp = self.gaussians.get_covariance(scaling_modifier, gaussain_idx) + else: + gaussians_scales = self.gaussians.get_scaling + gaussians_rotations = self.gaussians.get_rotation + + if gaussain_idx is not None: + gaussians_xyz = gaussians_xyz[gaussain_idx] + gaussians_features = gaussians_features[gaussain_idx] + gaussians_opacity = gaussians_opacity[gaussain_idx] + if cov3D_precomp is None: + gaussians_scales = gaussians_scales[gaussain_idx] + gaussians_rotations = gaussians_rotations[gaussain_idx] + + # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means + screenspace_points = ( + torch.zeros_like( + gaussians_xyz, + dtype=gaussians_xyz.dtype, + requires_grad=True, + device="cuda", + ) + + 0 + ) + try: + screenspace_points.retain_grad() + except: + pass + + # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors + # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. + shs = None + colors_precomp = None + if override_color is None: + if convert_SHs_python: + shs_view = self.gaussians_features.transpose(1, 2).view( + -1, 3, (self.gaussians.max_sh_degree + 1) ** 2 + ) + dir_pp = gaussians_xyz - viewpoint_camera.camera_center.repeat( + gaussians_features.shape[0], 1 + ) + dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True) + sh2rgb = eval_sh( + self.gaussians.active_sh_degree, shs_view, dir_pp_normalized + ) + colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) + else: + shs = gaussians_features + else: + colors_precomp = override_color + + # Rasterize visible Gaussians to image, obtain their radii (on screen). + rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( + means3D=gaussians_xyz, + means2D=screenspace_points, + shs=shs, + colors_precomp=colors_precomp, + opacities=gaussians_opacity, + scales=gaussians_scales, + rotations=gaussians_rotations, + cov3D_precomp=cov3D_precomp, + ) + + rendered_image = rendered_image.clamp(0, 1) + + # Those Gaussians that were frustum culled or had a radius of 0 were not visible. + # They will be excluded from value updates used in the splitting criteria. + return { + "image": rendered_image, # rgb + "depth": rendered_depth, + "alpha": rendered_alpha, + "viewspace_points": screenspace_points, + "visibility_filter": radii > 0, + "radii": radii, + } \ No newline at end of file diff --git a/MVs_Algorithms/NeRF/Instant_NGP.py b/MVs_Algorithms/NeRF/Instant_NGP.py new file mode 100644 index 0000000..39a4b63 --- /dev/null +++ b/MVs_Algorithms/NeRF/Instant_NGP.py @@ -0,0 +1,205 @@ +import tqdm +import random +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import nerfacc + +import comfy.utils + +from pytorch_msssim import SSIM, MS_SSIM + +from kiui.op import safe_normalize +from kiui.cam import orbit_camera +from kiui.nn import MLP, trunc_exp + +from shared_utils.image_utils import prepare_torch_img + +class InstantNGP(nn.Module): + def __init__(self, resolution=128, device="cuda"): + super().__init__() + from kiui.gridencoder import GridEncoder + + self.device = torch.device(device) + self.ref_size_H = resolution + self.ref_size_W = resolution + + self.render_step_size = 5e-3 + self.aabb = torch.tensor([-1.0, -1.0, -1.0, 1.0, 1.0, 1.0], device=self.device) + self.estimator = nerfacc.OccGridEstimator(roi_aabb=self.aabb, resolution=64, levels=1) + + self.encoder_density = GridEncoder(num_levels=12) # VMEncoder(output_dim=16, mode='sum') + self.encoder = GridEncoder(num_levels=12) + self.mlp_density = MLP(self.encoder_density.output_dim, 1, 32, 2, bias=False) + self.mlp = MLP(self.encoder.output_dim, 3, 32, 2, bias=False) + + def get_rays(self, pose, h, w, fovy, opengl=True): + + x, y = torch.meshgrid( + torch.arange(w, device=pose.device), + torch.arange(h, device=pose.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + + cx = w * 0.5 + cy = h * 0.5 + + focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy)) + + camera_dirs = F.pad( + torch.stack( + [ + (x - cx + 0.5) / focal, + (y - cy + 0.5) / focal * (-1.0 if opengl else 1.0), + ], + dim=-1, + ), + (0, 1), + value=(-1.0 if opengl else 1.0), + ) # [hw, 3] + + rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1) # [hw, 3] + rays_o = pose[:3, 3].unsqueeze(0).expand_as(rays_d) # [hw, 3] + + rays_o = rays_o.view(h, w, 3) + rays_d = safe_normalize(rays_d).view(h, w, 3) + + return rays_o, rays_d + + def get_color(self, xs): + return torch.sigmoid(self.mlp(self.encoder(xs.to(self.device)))) + + def get_density(self, xs): + # xs: [..., 3] + xs = xs.to(self.device) + prefix = xs.shape[:-1] + xs = xs.view(-1, 3) + feats = self.encoder_density(xs) + density = trunc_exp(self.mlp_density(feats)) + density = density.view(*prefix, 1) + return density + + def prepare_training(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy): + self.ref_imgs_num = len(reference_images) + + self.all_ref_cam_poses = reference_orbit_camera_poses + self.ref_cam_fovy = reference_orbit_camera_fovy + + # prepare reference images and masks + ref_imgs_torch_list = [] + ref_masks_torch_list = [] + for i in range(self.ref_imgs_num): + ref_imgs_torch_list.append(prepare_torch_img(reference_images[i].unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device)) + ref_masks_torch_list.append(prepare_torch_img(reference_masks[i].unsqueeze(2).unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device)) + + self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0) # [N, 3, H, W] + self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0).squeeze(1) # [N, H, W] + + def render_nerf(self, pose, bg_color=1): + + pose = torch.from_numpy(pose.astype(np.float32)).to(self.device) + + # get rays + rays_o, rays_d = self.get_rays(pose, self.ref_size_H, self.ref_size_W, self.ref_cam_fovy) + hw = rays_o.shape[0] * rays_o.shape[1] + rays_o = rays_o.view(hw, 3) + rays_d = rays_d.view(hw, 3) + + # update occ grid + if self.training: + def occ_eval_fn(xs): + sigmas = self.get_density(xs) + return self.render_step_size * sigmas + + self.estimator.update_every_n_steps(self.render_step, occ_eval_fn=occ_eval_fn, occ_thre=0.01, n=8) + self.render_step += 1 + + # render + def sigma_fn(t_starts, t_ends, ray_indices): + t_origins = rays_o[ray_indices] + t_dirs = rays_d[ray_indices] + xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 + sigmas = self.get_density(xs) + return sigmas.squeeze(-1) + + with torch.no_grad(): + ray_indices, t_starts, t_ends = self.estimator.sampling( + rays_o, + rays_d, + sigma_fn=sigma_fn, + near_plane=0.01, + far_plane=100, + render_step_size=self.render_step_size, + stratified=self.training, + cone_angle=0, + ) + + t_origins = rays_o[ray_indices] + t_dirs = rays_d[ray_indices] + xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 + sigmas = self.get_density(xs).squeeze(-1) + rgbs = torch.sigmoid(self.mlp(self.encoder(xs))) + + n_rays=rays_o.shape[0] + weights, t, alphas = nerfacc.render_weight_from_density(t_starts, t_ends, sigmas, ray_indices=ray_indices, n_rays=n_rays) + color = nerfacc.accumulate_along_rays(weights, values=rgbs, ray_indices=ray_indices, n_rays=n_rays) + alpha = nerfacc.accumulate_along_rays(weights, values=None, ray_indices=ray_indices, n_rays=n_rays) + + color = color + (1.0 - alpha) * bg_color + + color = color.view(self.ref_size_H, self.ref_size_W, 3).clamp(0, 1).permute(2, 0, 1).contiguous() + alpha = alpha.view(self.ref_size_H, self.ref_size_W).clamp(0, 1).contiguous() + + return color, alpha + + def fit_nerf(self, iters=512, bg_color=1): + + optimizer = torch.optim.Adam([ + {'params': self.encoder_density.parameters(), 'lr': 1e-2}, + {'params': self.encoder.parameters(), 'lr': 1e-2}, + {'params': self.mlp_density.parameters(), 'lr': 1e-3}, + {'params': self.mlp.parameters(), 'lr': 1e-3}, + ]) + + print(f"[INFO] fitting nerf...") + self.render_step = 0 + + ref_imgs_num_minus_1 = self.ref_imgs_num-1 + + comfy_pbar = comfy.utils.ProgressBar(iters) + pbar = tqdm.trange(iters) + for step in pbar: + + i = random.randint(0, ref_imgs_num_minus_1) + + radius, elevation, azimuth, center_X, center_Y, center_Z = self.all_ref_cam_poses[i] + + orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32) + pose = orbit_camera(elevation, azimuth, radius, target=orbit_target) + + image_gt = self.ref_imgs_torch[i] # [3, H, W] + alpha_gt = self.ref_masks_torch[i] # [H, W] + image_pred, alpha_pred = self.render_nerf(pose, bg_color) + + # if i % 200 == 0: + # kiui.vis.plot_image(image_gt, alpha_gt, image_pred, alpha_pred) + + loss_mse = F.mse_loss(image_pred, image_gt) + 0.1 * F.mse_loss(alpha_pred, alpha_gt) + loss = loss_mse #+ 0.1 * self.encoder_density.tv_loss() #+ 0.0001 * self.encoder_density.density_loss() + #loss += self.lambda_ssim * (1 - self.ms_ssim_loss(image_gt, image_pred)) + + loss.backward() + self.encoder_density.grad_total_variation(1e-8) + + optimizer.step() + optimizer.zero_grad() + + pbar.set_description(f"NeRF Fitting Loss = {loss_mse.item():.6f}") + comfy_pbar.update_absolute(step + 1) + + torch.cuda.synchronize() + + print(f"[INFO] finished fitting nerf!") \ No newline at end of file diff --git a/_Example_Workflows/CRM_Img_to_Multi-views_to_CCMs_to_Mesh.json b/_Example_Workflows/CRM_Img_to_Multi-views_to_CCMs_to_Mesh.json index b91a1ea..166e09e 100644 --- a/_Example_Workflows/CRM_Img_to_Multi-views_to_CCMs_to_Mesh.json +++ b/_Example_Workflows/CRM_Img_to_Multi-views_to_CCMs_to_Mesh.json @@ -260,39 +260,6 @@ "Node name for S&R": "PreviewImage" } }, - { - "id": 5, - "type": "[Comfy3D] Load CRM MVDiffusion Model", - "pos": [ - 820, - 60 - ], - "size": { - "0": 515.006103515625, - "1": 88.70651245117188 - }, - "flags": {}, - "order": 1, - "mode": 0, - "outputs": [ - { - "name": "crm_mvdiffusion_sampler", - "type": "CRM_MVDIFFUSION_SAMPLER", - "links": [ - 4 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Load CRM MVDiffusion Model" - }, - "widgets_values": [ - "ccm-diffusion.pth", - "configs/crm_configs/sd_v2_base_ipmv_chin8_zero_snr.yaml" - ] - }, { "id": 3, "type": "[Comfy3D] CRM Images MVDiffusion Model", @@ -336,6 +303,12 @@ ], "shape": 3, "slot_index": 0 + }, + { + "name": "orbit_camposes", + "type": "ORBIT_CAMPOSES", + "links": null, + "shape": 3 } ], "properties": { @@ -463,39 +436,6 @@ "CRMTest\\CCMs\\cat" ] }, - { - "id": 2, - "type": "[Comfy3D] Load CRM MVDiffusion Model", - "pos": [ - -102, - 131 - ], - "size": { - "0": 489.21466064453125, - "1": 95.50737762451172 - }, - "flags": {}, - "order": 2, - "mode": 0, - "outputs": [ - { - "name": "crm_mvdiffusion_sampler", - "type": "CRM_MVDIFFUSION_SAMPLER", - "links": [ - 1 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Load CRM MVDiffusion Model" - }, - "widgets_values": [ - "pixel-diffusion.pth", - "configs/crm_configs/sd_v2_base_ipmv_zero_SNR.yaml" - ] - }, { "id": 20, "type": "InvertMask", @@ -581,7 +521,7 @@ "1": 545.7767944335938 }, "flags": {}, - "order": 3, + "order": 1, "mode": 0, "outputs": [ { @@ -738,6 +678,72 @@ "-z", false ] + }, + { + "id": 2, + "type": "[Comfy3D] Load CRM MVDiffusion Model", + "pos": [ + -102, + 131 + ], + "size": { + "0": 489.21466064453125, + "1": 95.50737762451172 + }, + "flags": {}, + "order": 2, + "mode": 0, + "outputs": [ + { + "name": "crm_mvdiffusion_sampler", + "type": "CRM_MVDIFFUSION_SAMPLER", + "links": [ + 1 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Load CRM MVDiffusion Model" + }, + "widgets_values": [ + "pixel-diffusion.pth", + "sd_v2_base_ipmv_zero_SNR.yaml" + ] + }, + { + "id": 5, + "type": "[Comfy3D] Load CRM MVDiffusion Model", + "pos": [ + 820, + 60 + ], + "size": { + "0": 515.006103515625, + "1": 88.70651245117188 + }, + "flags": {}, + "order": 3, + "mode": 0, + "outputs": [ + { + "name": "crm_mvdiffusion_sampler", + "type": "CRM_MVDIFFUSION_SAMPLER", + "links": [ + 4 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Load CRM MVDiffusion Model" + }, + "widgets_values": [ + "ccm-diffusion.pth", + "sd_v2_base_ipmv_chin8_zero_snr.yaml" + ] } ], "links": [ @@ -928,6 +934,14 @@ ], "groups": [], "config": {}, - "extra": {}, + "extra": { + "ds": { + "scale": 0.6830134553650706, + "offset": [ + -848.1038004423184, + 33.36692580651105 + ] + } + }, "version": 0.4 } \ No newline at end of file diff --git a/_Example_Workflows/Era3D_to_InstantMesh.json b/_Example_Workflows/Era3D_to_InstantMesh.json index 119d746..f451081 100644 --- a/_Example_Workflows/Era3D_to_InstantMesh.json +++ b/_Example_Workflows/Era3D_to_InstantMesh.json @@ -200,45 +200,6 @@ 4 ] }, - { - "id": 26, - "type": "[Comfy3D] Save 3D Mesh", - "pos": [ - 2354.019611278979, - 107.94257514767159 - ], - "size": { - "0": 315, - "1": 58 - }, - "flags": {}, - "order": 9, - "mode": 0, - "inputs": [ - { - "name": "mesh", - "type": "MESH", - "link": 44 - } - ], - "outputs": [ - { - "name": "save_path", - "type": "STRING", - "links": [ - 45 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Save 3D Mesh" - }, - "widgets_values": [ - "InstantMesh_test\\Priest_Cat.obj" - ] - }, { "id": 1, "type": "[Comfy3D] Load Diffusers Pipeline", @@ -248,7 +209,7 @@ ], "size": { "0": 383.9324645996094, - "1": 109.8599853515625 + "1": 130 }, "flags": {}, "order": 1, @@ -270,7 +231,8 @@ "widgets_values": [ "Era3DPipeline", "pengHTYX/MacLab-Era3D-512-6view", - "" + "", + true ] }, { @@ -359,8 +321,8 @@ 109 ], "size": [ - 627.5795474783936, - 527.5795474783936 + 600, + 500 ], "flags": {}, "order": 10, @@ -432,6 +394,45 @@ "properties": { "Node name for S&R": "PreviewImage" } + }, + { + "id": 26, + "type": "[Comfy3D] Save 3D Mesh", + "pos": [ + 2354.019611278979, + 107.94257514767159 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [ + { + "name": "mesh", + "type": "MESH", + "link": 44 + } + ], + "outputs": [ + { + "name": "save_path", + "type": "STRING", + "links": [ + 45 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Save 3D Mesh" + }, + "widgets_values": [ + "InstantMesh_test\\Priest_Cat.glb" + ] } ], "links": [ @@ -536,10 +537,10 @@ "config": {}, "extra": { "ds": { - "scale": 0.620921323059155, + "scale": 0.751314800901578, "offset": [ - -581.8092565730503, - 135.36562552877655 + -963.3190573317725, + 219.52911008737303 ] } }, diff --git a/_Example_Workflows/Multi-View-Images_to_3DGS_to_3DMesh(DMTet and DiffRast).json b/_Example_Workflows/Multi-View-Images_to_3DGS_to_3DMesh(DMTet and DiffRast).json index b06cdc1..64094a9 100644 --- a/_Example_Workflows/Multi-View-Images_to_3DGS_to_3DMesh(DMTet and DiffRast).json +++ b/_Example_Workflows/Multi-View-Images_to_3DGS_to_3DMesh(DMTet and DiffRast).json @@ -780,7 +780,7 @@ }, { "id": 47, - "type": "[Comfy3D] Gaussian Splatting", + "type": "[Comfy3D] Gaussian Splatting 3D", "pos": [ 1415, 52 @@ -838,7 +838,7 @@ } ], "properties": { - "Node name for S&R": "[Comfy3D] Gaussian Splatting" + "Node name for S&R": "[Comfy3D] Gaussian Splatting 3D" }, "widgets_values": [ 49.1, diff --git a/_Example_Workflows/Multi-View-Images_to_Instant-NGP_to_3DMesh.json b/_Example_Workflows/Multi-View-Images_to_Instant-NGP_to_3DMesh.json index 8264dc0..e64f1e0 100644 --- a/_Example_Workflows/Multi-View-Images_to_Instant-NGP_to_3DMesh.json +++ b/_Example_Workflows/Multi-View-Images_to_Instant-NGP_to_3DMesh.json @@ -1,6 +1,6 @@ { - "last_node_id": 22, - "last_link_id": 41, + "last_node_id": 27, + "last_link_id": 46, "nodes": [ { "id": 6, @@ -38,60 +38,6 @@ "Node name for S&R": "MaskToImage" } }, - { - "id": 7, - "type": "PreviewImage", - "pos": [ - 1740, - 740 - ], - "size": { - "0": 215.39028930664062, - "1": 246 - }, - "flags": { - "collapsed": false - }, - "order": 12, - "mode": 0, - "inputs": [ - { - "name": "images", - "type": "IMAGE", - "link": 4 - } - ], - "properties": { - "Node name for S&R": "PreviewImage" - } - }, - { - "id": 13, - "type": "PreviewImage", - "pos": [ - 1485, - -69 - ], - "size": { - "0": 215.39028930664062, - "1": 246 - }, - "flags": { - "collapsed": false - }, - "order": 7, - "mode": 0, - "inputs": [ - { - "name": "images", - "type": "IMAGE", - "link": 17 - } - ], - "properties": { - "Node name for S&R": "PreviewImage" - } - }, { "id": 10, "type": "[Comfy3D] Preview 3DMesh", @@ -161,68 +107,6 @@ "horizontal": false } }, - { - "id": 2, - "type": "[Comfy3D] Gaussian Splatting Orbit Renderer", - "pos": [ - 807, - 289 - ], - "size": { - "0": 556.800048828125, - "1": 198 - }, - "flags": {}, - "order": 5, - "mode": 0, - "inputs": [ - { - "name": "gs_ply", - "type": "GS_PLY", - "link": 16 - }, - { - "name": "render_orbit_camera_poses", - "type": "ORBIT_CAMPOSES", - "link": 5 - } - ], - "outputs": [ - { - "name": "rendered_gs_images", - "type": "IMAGE", - "links": [ - 17, - 21, - 32 - ], - "shape": 3, - "slot_index": 0 - }, - { - "name": "rendered_gs_masks", - "type": "MASK", - "links": [ - 6, - 23, - 33 - ], - "shape": 3, - "slot_index": 1 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Gaussian Splatting Orbit Renderer" - }, - "widgets_values": [ - 1024, - 1024, - 49.1, - 1, - 1, - 1 - ] - }, { "id": 5, "type": "[Comfy3D] Stack Orbit Camera Poses", @@ -309,79 +193,6 @@ 0.1 ] }, - { - "id": 12, - "type": "[Comfy3D] Switch 3DGS Axis", - "pos": [ - 466, - 219 - ], - "size": { - "0": 210, - "1": 106 - }, - "flags": {}, - "order": 3, - "mode": 0, - "inputs": [ - { - "name": "gs_ply", - "type": "GS_PLY", - "link": 15 - } - ], - "outputs": [ - { - "name": "switched_gs_ply", - "type": "GS_PLY", - "links": [ - 16 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Switch 3DGS Axis" - }, - "widgets_values": [ - "+x", - "-y", - "-z" - ] - }, - { - "id": 1, - "type": "[Comfy3D] Load 3DGS", - "pos": [ - 104, - 218 - ], - "size": { - "0": 315, - "1": 58 - }, - "flags": {}, - "order": 1, - "mode": 0, - "outputs": [ - { - "name": "gs_ply", - "type": "GS_PLY", - "links": [ - 15 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Load 3DGS" - }, - "widgets_values": [ - "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\LGMTest\\CatCoffee.ply" - ] - }, { "id": 8, "type": "Reroute", @@ -526,45 +337,6 @@ "horizontal": false } }, - { - "id": 19, - "type": "[Comfy3D] Save 3D Mesh", - "pos": [ - 2945, - 188 - ], - "size": { - "0": 315, - "1": 58 - }, - "flags": {}, - "order": 16, - "mode": 0, - "inputs": [ - { - "name": "mesh", - "type": "MESH", - "link": 41 - } - ], - "outputs": [ - { - "name": "save_path", - "type": "STRING", - "links": [ - 25 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Save 3D Mesh" - }, - "widgets_values": [ - "Instant_NGP_Test/CatCoffee_1.obj" - ] - }, { "id": 11, "type": "[Comfy3D] Save 3D Mesh", @@ -604,6 +376,79 @@ "Instant_NGP_Test/CatCoffee.obj" ] }, + { + "id": 1, + "type": "[Comfy3D] Load 3DGS", + "pos": [ + 104, + 218 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 1, + "mode": 0, + "outputs": [ + { + "name": "gs_ply", + "type": "GS_PLY", + "links": [ + 15 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Load 3DGS" + }, + "widgets_values": [ + "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\custom_nodes\\ComfyUI-3D-Pack\\_Example_Workflows\\_Example_Inputs_Files\\Meshes\\CatCoffee.ply" + ] + }, + { + "id": 12, + "type": "[Comfy3D] Switch 3DGS Axis", + "pos": [ + 466, + 219 + ], + "size": { + "0": 210, + "1": 106 + }, + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "name": "gs_ply", + "type": "GS_PLY", + "link": 15 + } + ], + "outputs": [ + { + "name": "switched_gs_ply", + "type": "GS_PLY", + "links": [ + 16 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Switch 3DGS Axis" + }, + "widgets_values": [ + "+x", + "-y", + "-z" + ] + }, { "id": 18, "type": "[Comfy3D] Preview 3DMesh", @@ -637,60 +482,42 @@ ] }, { - "id": 21, - "type": "[Comfy3D] Instant NGP", + "id": 19, + "type": "[Comfy3D] Save 3D Mesh", "pos": [ - 1570, - 288 + 2945, + 188 ], "size": { - "0": 418.1999816894531, - "1": 266 + "0": 315, + "1": 58 }, "flags": {}, - "order": 11, + "order": 16, "mode": 0, "inputs": [ { - "name": "reference_image", - "type": "IMAGE", - "link": 32 - }, - { - "name": "reference_mask", - "type": "MASK", - "link": 33 - }, - { - "name": "reference_orbit_camera_poses", - "type": "ORBIT_CAMPOSES", - "link": 34 + "name": "mesh", + "type": "MESH", + "link": 41 } ], "outputs": [ { - "name": "mesh", - "type": "MESH", + "name": "save_path", + "type": "STRING", "links": [ - 36, - 39 + 25 ], "shape": 3, "slot_index": 0 } ], "properties": { - "Node name for S&R": "[Comfy3D] Instant NGP" + "Node name for S&R": "[Comfy3D] Save 3D Mesh" }, "widgets_values": [ - 49.1, - 1024, - 128, - 256, - 128, - 10, - 1024, - false + "Instant_NGP_Test/CatCoffee_1.obj" ] }, { @@ -702,7 +529,7 @@ ], "size": { "0": 531.5999755859375, - "1": 358 + "1": 406 }, "flags": {}, "order": 14, @@ -760,8 +587,183 @@ 0.0001, 0.5, 512, + 0, false ] + }, + { + "id": 13, + "type": "PreviewImage", + "pos": [ + 1506, + -109 + ], + "size": { + "0": 215.39028930664062, + "1": 246 + }, + "flags": { + "collapsed": false + }, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 17 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 7, + "type": "PreviewImage", + "pos": [ + 1742, + 740 + ], + "size": { + "0": 215.39028930664062, + "1": 246 + }, + "flags": { + "collapsed": false + }, + "order": 12, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 4 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 21, + "type": "[Comfy3D] Instant NGP", + "pos": [ + 1570, + 288 + ], + "size": { + "0": 418.1999816894531, + "1": 290 + }, + "flags": {}, + "order": 11, + "mode": 0, + "inputs": [ + { + "name": "reference_image", + "type": "IMAGE", + "link": 32 + }, + { + "name": "reference_mask", + "type": "MASK", + "link": 33 + }, + { + "name": "reference_orbit_camera_poses", + "type": "ORBIT_CAMPOSES", + "link": 34 + } + ], + "outputs": [ + { + "name": "mesh", + "type": "MESH", + "links": [ + 36, + 39 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Instant NGP" + }, + "widgets_values": [ + 49.1, + 1024, + 128, + 256, + 128, + 10, + 1024, + 1, + false + ] + }, + { + "id": 2, + "type": "[Comfy3D] Gaussian Splatting Orbit Renderer", + "pos": [ + 807, + 289 + ], + "size": { + "0": 556.800048828125, + "1": 198 + }, + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": "gs_ply", + "type": "GS_PLY", + "link": 16 + }, + { + "name": "render_orbit_camera_poses", + "type": "ORBIT_CAMPOSES", + "link": 5 + } + ], + "outputs": [ + { + "name": "rendered_gs_images", + "type": "IMAGE", + "links": [ + 17, + 21, + 32 + ], + "shape": 3, + "slot_index": 0 + }, + { + "name": "rendered_gs_masks", + "type": "MASK", + "links": [ + 6, + 23, + 33 + ], + "shape": 3, + "slot_index": 1 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Gaussian Splatting Orbit Renderer" + }, + "widgets_values": [ + 1024, + 1024, + 49.1, + 1, + 1, + 1 + ] } ], "links": [ @@ -944,6 +946,14 @@ ], "groups": [], "config": {}, - "extra": {}, + "extra": { + "ds": { + "scale": 0.6934334949441353, + "offset": [ + -1086.556448784114, + 243.62758422318373 + ] + } + }, "version": 0.4 } \ No newline at end of file diff --git a/_Example_Workflows/Render_Mesh_and_3DGS_Example.json b/_Example_Workflows/Render_Mesh_and_3DGS_Example.json index 8661141..895fd46 100644 --- a/_Example_Workflows/Render_Mesh_and_3DGS_Example.json +++ b/_Example_Workflows/Render_Mesh_and_3DGS_Example.json @@ -1,13 +1,207 @@ { - "last_node_id": 9, - "last_link_id": 6, + "last_node_id": 18, + "last_link_id": 16, "nodes": [ + { + "id": 7, + "type": "[Comfy3D] Gaussian Splatting Orbit Renderer", + "pos": [ + 730, + 720 + ], + "size": { + "0": 556.800048828125, + "1": 242 + }, + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": "gs_ply", + "type": "GS_PLY", + "link": 15 + }, + { + "name": "render_orbit_camera_poses", + "type": "ORBIT_CAMPOSES", + "link": 4 + } + ], + "outputs": [ + { + "name": "rendered_gs_images", + "type": "IMAGE", + "links": [ + 6 + ], + "shape": 3, + "slot_index": 0 + }, + { + "name": "rendered_gs_masks", + "type": "MASK", + "links": [ + 10 + ], + "shape": 3, + "slot_index": 1 + }, + { + "name": "rendered_gs_depths", + "type": "IMAGE", + "links": [ + 7 + ], + "shape": 3, + "slot_index": 2 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Gaussian Splatting Orbit Renderer" + }, + "widgets_values": [ + 1024, + 1024, + 49.1, + 0, + 0, + 0 + ] + }, + { + "id": 15, + "type": "MaskToImage", + "pos": [ + 1236, + -152 + ], + "size": { + "0": 210, + "1": 26 + }, + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "mask", + "type": "MASK", + "link": 12 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 13 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "MaskToImage" + } + }, + { + "id": 16, + "type": "PreviewImage", + "pos": [ + 1482, + -312 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 14, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 13 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 2, + "type": "PreviewImage", + "pos": [ + 1155, + -469 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 1 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 17, + "type": "[Comfy3D] Switch 3DGS Axis", + "pos": [ + 459, + 1120 + ], + "size": { + "0": 210, + "1": 106 + }, + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "name": "gs_ply", + "type": "GS_PLY", + "link": 14 + } + ], + "outputs": [ + { + "name": "switched_gs_ply", + "type": "GS_PLY", + "links": [ + 15 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Switch 3DGS Axis" + }, + "widgets_values": [ + "+x", + "-y", + "-z" + ] + }, { "id": 6, "type": "[Comfy3D] Load 3D Mesh", "pos": [ 337, - 232 + 231 ], "size": { "0": 315, @@ -31,10 +225,316 @@ "Node name for S&R": "[Comfy3D] Load 3D Mesh" }, "widgets_values": [ - "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\input\\Meshes\\Cammy_Uniform.obj", + "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\custom_nodes\\ComfyUI-3D-Pack\\_Example_Workflows\\_Example_Inputs_Files\\Meshes\\Cammy_Uniform.obj", false, false, false, + false + ] + }, + { + "id": 8, + "type": "[Comfy3D] Load 3DGS", + "pos": [ + 100, + 1123 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 1, + "mode": 0, + "outputs": [ + { + "name": "gs_ply", + "type": "GS_PLY", + "links": [ + 14 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Load 3DGS" + }, + "widgets_values": [ + "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\custom_nodes\\ComfyUI-3D-Pack\\_Example_Workflows\\_Example_Inputs_Files\\Meshes\\CatCoffee.ply" + ] + }, + { + "id": 12, + "type": "PreviewImage", + "pos": [ + 1310, + 340 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 9 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 14, + "type": "MaskToImage", + "pos": [ + 1411, + 940 + ], + "size": { + "0": 210, + "1": 26 + }, + "flags": {}, + "order": 12, + "mode": 0, + "inputs": [ + { + "name": "mask", + "type": "MASK", + "link": 10 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 11 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "MaskToImage" + } + }, + { + "id": 13, + "type": "PreviewImage", + "pos": [ + 1665, + 1017 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 15, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 11 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 10, + "type": "PreviewImage", + "pos": [ + 1391, + 1348 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 13, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 7 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 9, + "type": "PreviewImage", + "pos": [ + 1381, + 638 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 11, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 6 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 18, + "type": "PreviewImage", + "pos": [ + 1583, + 337 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 16 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 11, + "type": "PreviewImage", + "pos": [ + 1440, + 18 + ], + "size": { + "0": 210, + "1": 246 + }, + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 8 + } + ], + "properties": { + "Node name for S&R": "PreviewImage" + } + }, + { + "id": 1, + "type": "[Comfy3D] Mesh Orbit Renderer", + "pos": [ + 734, + 330 + ], + "size": { + "0": 395, + "1": 330 + }, + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [ + { + "name": "mesh", + "type": "MESH", + "link": 3 + }, + { + "name": "render_orbit_camera_poses", + "type": "ORBIT_CAMPOSES", + "link": 2 + } + ], + "outputs": [ + { + "name": "rendered_mesh_images", + "type": "IMAGE", + "links": [ + 1 + ], + "shape": 3, + "slot_index": 0 + }, + { + "name": "rendered_mesh_masks", + "type": "MASK", + "links": [ + 12 + ], + "shape": 3, + "slot_index": 1 + }, + { + "name": "all_rendered_depths", + "type": "IMAGE", + "links": [ + 8 + ], + "shape": 3, + "slot_index": 2 + }, + { + "name": "all_rendered_normals", + "type": "IMAGE", + "links": [ + 9 + ], + "shape": 3, + "slot_index": 3 + }, + { + "name": "all_rendered_viewcos", + "type": "IMAGE", + "links": [ + 16 + ], + "shape": 3, + "slot_index": 4 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Mesh Orbit Renderer" + }, + "widgets_values": [ + 1024, + 1024, + 49.1, + 1, + 1, + 1, + false, + true, true ] }, @@ -50,7 +550,7 @@ "1": 586 }, "flags": {}, - "order": 1, + "order": 2, "mode": 0, "outputs": [ { @@ -112,7 +612,7 @@ 45, 0, -0.01, - 12, + 24, 0, 0, 0.1, @@ -123,199 +623,6 @@ 0, 0.1 ] - }, - { - "id": 7, - "type": "[Comfy3D] Gaussian Splatting Orbit Renderer", - "pos": [ - 730, - 720 - ], - "size": { - "0": 556.800048828125, - "1": 198 - }, - "flags": {}, - "order": 4, - "mode": 0, - "inputs": [ - { - "name": "gs_ply", - "type": "GS_PLY", - "link": 5 - }, - { - "name": "render_orbit_camera_poses", - "type": "ORBIT_CAMPOSES", - "link": 4 - } - ], - "outputs": [ - { - "name": "rendered_gs_images", - "type": "IMAGE", - "links": [ - 6 - ], - "shape": 3, - "slot_index": 0 - }, - { - "name": "rendered_gs_masks", - "type": "MASK", - "links": null, - "shape": 3 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Gaussian Splatting Orbit Renderer" - }, - "widgets_values": [ - 1024, - 1024, - 49.1, - 0, - 0, - 0 - ] - }, - { - "id": 9, - "type": "PreviewImage", - "pos": [ - 1361, - 657 - ], - "size": { - "0": 210, - "1": 246 - }, - "flags": {}, - "order": 6, - "mode": 0, - "inputs": [ - { - "name": "images", - "type": "IMAGE", - "link": 6 - } - ], - "properties": { - "Node name for S&R": "PreviewImage" - } - }, - { - "id": 1, - "type": "[Comfy3D] Mesh Orbit Renderer", - "pos": [ - 734, - 330 - ], - "size": { - "0": 395, - "1": 247 - }, - "flags": {}, - "order": 3, - "mode": 0, - "inputs": [ - { - "name": "mesh", - "type": "MESH", - "link": 3 - }, - { - "name": "render_orbit_camera_poses", - "type": "ORBIT_CAMPOSES", - "link": 2 - } - ], - "outputs": [ - { - "name": "rendered_mesh_images", - "type": "IMAGE", - "links": [ - 1 - ], - "shape": 3, - "slot_index": 0 - }, - { - "name": "rendered_mesh_masks", - "type": "MASK", - "links": null, - "shape": 3 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Mesh Orbit Renderer" - }, - "widgets_values": [ - 1024, - 1024, - 49.1, - 0, - 0, - 0, - false - ] - }, - { - "id": 2, - "type": "PreviewImage", - "pos": [ - 1197, - 230 - ], - "size": { - "0": 210, - "1": 246 - }, - "flags": {}, - "order": 5, - "mode": 0, - "inputs": [ - { - "name": "images", - "type": "IMAGE", - "link": 1 - } - ], - "properties": { - "Node name for S&R": "PreviewImage" - } - }, - { - "id": 8, - "type": "[Comfy3D] Load 3DGS", - "pos": [ - 329, - 1139 - ], - "size": { - "0": 315, - "1": 58 - }, - "flags": {}, - "order": 2, - "mode": 0, - "outputs": [ - { - "name": "gs_ply", - "type": "GS_PLY", - "links": [ - 5 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Load 3DGS" - }, - "widgets_values": [ - "C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\input\\Meshes\\3DGS_TGS.ply" - ] } ], "links": [ @@ -352,18 +659,90 @@ "ORBIT_CAMPOSES" ], [ - 5, + 6, + 7, + 0, + 9, + 0, + "IMAGE" + ], + [ + 7, + 7, + 2, + 10, + 0, + "IMAGE" + ], + [ 8, + 1, + 2, + 11, + 0, + "IMAGE" + ], + [ + 9, + 1, + 3, + 12, + 0, + "IMAGE" + ], + [ + 10, + 7, + 1, + 14, + 0, + "MASK" + ], + [ + 11, + 14, + 0, + 13, + 0, + "IMAGE" + ], + [ + 12, + 1, + 1, + 15, + 0, + "MASK" + ], + [ + 13, + 15, + 0, + 16, + 0, + "IMAGE" + ], + [ + 14, + 8, + 0, + 17, + 0, + "GS_PLY" + ], + [ + 15, + 17, 0, 7, 0, "GS_PLY" ], [ - 6, - 7, - 0, - 9, + 16, + 1, + 4, + 18, 0, "IMAGE" ] @@ -372,10 +751,10 @@ "config": {}, "extra": { "ds": { - "scale": 0.6830134553650709, + "scale": 0.5644739300537776, "offset": [ - 232.77218799488367, - -20.214945769251532 + 176.76143377062772, + 55.35863740645868 ] } }, diff --git a/_Example_Workflows/Zero123Plus_to_InstantMesh.json b/_Example_Workflows/Zero123Plus_to_InstantMesh.json index 5dfddc1..d6b4794 100644 --- a/_Example_Workflows/Zero123Plus_to_InstantMesh.json +++ b/_Example_Workflows/Zero123Plus_to_InstantMesh.json @@ -168,7 +168,7 @@ ], "size": { "0": 383.9324645996094, - "1": 109.8599853515625 + "1": 130 }, "flags": {}, "order": 1, @@ -190,7 +190,8 @@ "widgets_values": [ "Zero123PlusPipeline", "sudo-ai/zero123plus-v1.2", - "" + "", + true ] }, { @@ -337,45 +338,6 @@ 1024 ] }, - { - "id": 9, - "type": "[Comfy3D] Save 3D Mesh", - "pos": [ - 2534, - 158 - ], - "size": { - "0": 315, - "1": 58 - }, - "flags": {}, - "order": 10, - "mode": 0, - "inputs": [ - { - "name": "mesh", - "type": "MESH", - "link": 14 - } - ], - "outputs": [ - { - "name": "save_path", - "type": "STRING", - "links": [ - 11 - ], - "shape": 3, - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "[Comfy3D] Save 3D Mesh" - }, - "widgets_values": [ - "InstantMesh_test\\Mesh_Cat.obj" - ] - }, { "id": 10, "type": "[Comfy3D] Preview 3DMesh", @@ -474,6 +436,45 @@ "properties": { "Node name for S&R": "PreviewImage" } + }, + { + "id": 9, + "type": "[Comfy3D] Save 3D Mesh", + "pos": [ + 2534, + 158 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [ + { + "name": "mesh", + "type": "MESH", + "link": 14 + } + ], + "outputs": [ + { + "name": "save_path", + "type": "STRING", + "links": [ + 11 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "[Comfy3D] Save 3D Mesh" + }, + "widgets_values": [ + "InstantMesh_test\\Mesh_Cat.glb" + ] } ], "links": [ @@ -586,10 +587,10 @@ "config": {}, "extra": { "ds": { - "scale": 0.8264462809917354, + "scale": 0.6830134553650709, "offset": [ - -788.9517631212848, - 67.47620178525084 + -1395.2063656748421, + 247.10279463694602 ] } }, diff --git a/__init__.py b/__init__.py index 6e8c4d6..332a861 100644 --- a/__init__.py +++ b/__init__.py @@ -5,16 +5,19 @@ from pyhocon import ConfigFactory import logging ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack") -MODULE_PATH = os.path.join(ROOT_PATH, "gen_3d_modules") +MODULE_PATH = os.path.join(ROOT_PATH, "Gen_3D_Modules") +MV_ALGO_PATH = os.path.join(ROOT_PATH, "MVs_Algorithms") + sys.path.append(ROOT_PATH) sys.path.append(MODULE_PATH) +sys.path.append(MV_ALGO_PATH) import shutil import __main__ import importlib import inspect from .webserver.server import server, set_web_conf -from .utils import setup_logger +from .shared_utils.log_utils import setup_logger # Common formatter for simplicity, adjust as needed common_formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s') diff --git a/algorithms/nerf_marching_cubes_converter.py b/algorithms/nerf_marching_cubes_converter.py index 1f52ed7..f97de5e 100644 --- a/algorithms/nerf_marching_cubes_converter.py +++ b/algorithms/nerf_marching_cubes_converter.py @@ -15,9 +15,9 @@ from kiui.cam import orbit_camera, get_perspective from kiui.nn import MLP, trunc_exp from ..mesh_processer.mesh import Mesh -from ..lgm.core.options import Options -from ..lgm.core.gs import GaussianRenderer -from ..lgm.core.utils import get_rays +from LGM.core.options import Options +from LGM.core.gs import GaussianRenderer +from LGM.core.utils import get_rays # Triple renderer of gaussians, gaussian, and diso mesh. # gaussian --> nerf --> mesh diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/controlnet-tile/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/controlnet-tile/config.json new file mode 100644 index 0000000..754ad3a --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/controlnet-tile/config.json @@ -0,0 +1,52 @@ +{ + "_class_name": "ControlNetModel", + "_diffusers_version": "0.27.2", + "_name_or_path": "lllyasviel/control_v11f1e_sd15_tile", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "class_embed_type": null, + "conditioning_channels": 3, + "conditioning_embedding_out_channels": [ + 16, + 32, + 96, + 256 + ], + "controlnet_conditioning_channel_order": "rgb", + "cross_attention_dim": 768, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "global_pool_conditions": false, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "only_cross_attention": false, + "projection_class_embeddings_input_dim": null, + "resnet_time_scale_shift": "default", + "transformer_layers_per_block": 1, + "upcast_attention": false, + "use_linear_projection": false +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/feature_extractor/preprocessor_config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/feature_extractor/preprocessor_config.json new file mode 100644 index 0000000..c7f2d86 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/feature_extractor/preprocessor_config.json @@ -0,0 +1,44 @@ +{ + "_valid_processor_keys": [ + "images", + "do_resize", + "size", + "resample", + "do_center_crop", + "crop_size", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "do_convert_rgb", + "return_tensors", + "data_format", + "input_data_format" + ], + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_processor_type": "CLIPImageProcessor", + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + } +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/image_encoder/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/image_encoder/config.json new file mode 100644 index 0000000..c6cfa18 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/image_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "architectures": [ + "CLIPVisionModelWithProjection" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.39.3" +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/model_index.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/model_index.json new file mode 100644 index 0000000..afe0b22 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/model_index.json @@ -0,0 +1,31 @@ +{ + "_class_name": "StableDiffusionImage2MVCustomPipeline", + "_diffusers_version": "0.27.2", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "condition_offset": true, + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "image_encoder": [ + "transformers", + "CLIPVisionModelWithProjection" + ], + "requires_safety_checker": true, + "safety_checker": [ + null, + null + ], + "scheduler": [ + "diffusers", + "DDIMScheduler" + ], + "unet": [ + "diffusers", + "UNet2DConditionModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/scheduler/scheduler_config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/scheduler/scheduler_config.json new file mode 100644 index 0000000..788d423 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/scheduler/scheduler_config.json @@ -0,0 +1,20 @@ +{ + "_class_name": "DDIMScheduler", + "_diffusers_version": "0.27.2", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": false, + "clip_sample_range": 1.0, + "dynamic_thresholding_ratio": 0.995, + "num_train_timesteps": 1000, + "prediction_type": "epsilon", + "rescale_betas_zero_snr": false, + "sample_max_value": 1.0, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "steps_offset": 1, + "thresholding": false, + "timestep_spacing": "leading", + "trained_betas": null +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/unet/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/unet/config.json new file mode 100644 index 0000000..851f2e1 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/unet/config.json @@ -0,0 +1,68 @@ +{ + "_class_name": "UnifieldWrappedUNet", + "_diffusers_version": "0.27.2", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "attention_type": "default", + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "center_input_sample": false, + "class_embed_type": null, + "class_embeddings_concat": false, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 768, + "cross_attention_norm": null, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "dropout": 0.0, + "dual_cross_attention": false, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 8, + "layers_per_block": 2, + "mid_block_only_cross_attention": null, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": 8, + "only_cross_attention": false, + "out_channels": 4, + "projection_class_embeddings_input_dim": null, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": false, + "resnet_time_scale_shift": "default", + "reverse_transformer_layers_per_block": null, + "sample_size": 64, + "time_cond_proj_dim": null, + "time_embedding_act_fn": null, + "time_embedding_dim": null, + "time_embedding_type": "positional", + "timestep_post_act": null, + "transformer_layers_per_block": 1, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "upcast_attention": false, + "use_linear_projection": false +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/vae/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/vae/config.json new file mode 100644 index 0000000..5dac932 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2mvimage/vae/config.json @@ -0,0 +1,34 @@ +{ + "_class_name": "AutoencoderKL", + "_diffusers_version": "0.27.2", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "act_fn": "silu", + "block_out_channels": [ + 128, + 256, + 512, + 512 + ], + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D" + ], + "force_upcast": true, + "in_channels": 3, + "latent_channels": 4, + "latents_mean": null, + "latents_std": null, + "layers_per_block": 2, + "norm_num_groups": 32, + "out_channels": 3, + "sample_size": 256, + "scaling_factor": 0.18215, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D" + ] +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/feature_extractor/preprocessor_config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/feature_extractor/preprocessor_config.json new file mode 100644 index 0000000..c7f2d86 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/feature_extractor/preprocessor_config.json @@ -0,0 +1,44 @@ +{ + "_valid_processor_keys": [ + "images", + "do_resize", + "size", + "resample", + "do_center_crop", + "crop_size", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "do_convert_rgb", + "return_tensors", + "data_format", + "input_data_format" + ], + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_processor_type": "CLIPImageProcessor", + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + } +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/image_encoder/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/image_encoder/config.json new file mode 100644 index 0000000..2a2fb07 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/image_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "architectures": [ + "CLIPVisionModelWithProjection" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "bfloat16", + "transformers_version": "4.39.3" +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/model_index.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/model_index.json new file mode 100644 index 0000000..5a6a160 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/model_index.json @@ -0,0 +1,31 @@ +{ + "_class_name": "StableDiffusionImageCustomPipeline", + "_diffusers_version": "0.27.2", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "image_encoder": [ + "transformers", + "CLIPVisionModelWithProjection" + ], + "noisy_cond_latents": false, + "requires_safety_checker": true, + "safety_checker": [ + null, + null + ], + "scheduler": [ + "diffusers", + "EulerAncestralDiscreteScheduler" + ], + "unet": [ + "diffusers", + "UNet2DConditionModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/scheduler/scheduler_config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/scheduler/scheduler_config.json new file mode 100644 index 0000000..35d882b --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/scheduler/scheduler_config.json @@ -0,0 +1,16 @@ +{ + "_class_name": "EulerAncestralDiscreteScheduler", + "_diffusers_version": "0.27.2", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": false, + "num_train_timesteps": 1000, + "prediction_type": "epsilon", + "rescale_betas_zero_snr": false, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "steps_offset": 1, + "timestep_spacing": "linspace", + "trained_betas": null +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/unet/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/unet/config.json new file mode 100644 index 0000000..0212cd9 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/unet/config.json @@ -0,0 +1,68 @@ +{ + "_class_name": "UnifieldWrappedUNet", + "_diffusers_version": "0.27.2", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "attention_type": "default", + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "center_input_sample": false, + "class_embed_type": null, + "class_embeddings_concat": false, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 768, + "cross_attention_norm": null, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "dropout": 0.0, + "dual_cross_attention": false, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_only_cross_attention": null, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "only_cross_attention": false, + "out_channels": 4, + "projection_class_embeddings_input_dim": null, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": false, + "resnet_time_scale_shift": "default", + "reverse_transformer_layers_per_block": null, + "sample_size": 64, + "time_cond_proj_dim": null, + "time_embedding_act_fn": null, + "time_embedding_dim": null, + "time_embedding_type": "positional", + "timestep_post_act": null, + "transformer_layers_per_block": 1, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "upcast_attention": false, + "use_linear_projection": false +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/vae/config.json b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/vae/config.json new file mode 100644 index 0000000..5dac932 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/image2normal/vae/config.json @@ -0,0 +1,34 @@ +{ + "_class_name": "AutoencoderKL", + "_diffusers_version": "0.27.2", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "act_fn": "silu", + "block_out_channels": [ + 128, + 256, + 512, + 512 + ], + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D" + ], + "force_upcast": true, + "in_channels": 3, + "latent_channels": 4, + "latents_mean": null, + "latents_std": null, + "layers_per_block": 2, + "norm_num_groups": 32, + "out_channels": 3, + "sample_size": 256, + "scaling_factor": 0.18215, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D" + ] +} diff --git a/checkpoints/Diffusers/Wuvin/Unique3D/v1-inference.yaml b/checkpoints/Diffusers/Wuvin/Unique3D/v1-inference.yaml new file mode 100644 index 0000000..d4effe5 --- /dev/null +++ b/checkpoints/Diffusers/Wuvin/Unique3D/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/.gitattributes b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/README.md b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/README.md new file mode 100644 index 0000000..620d147 --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/README.md @@ -0,0 +1,18 @@ +--- +license: openrail +--- +# MVDream-diffusers Model Card +This is a port of https://huggingface.co/Peng-Wang/ImageDream into diffusers. + +For usage, please check: https://github.com/ashawkey/mvdream_diffusers +## Citation +``` +@article{wang2023imagedream, + title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation}, + author={Wang, Peng and Shi, Yichun}, + journal={arXiv preprint arXiv:2312.02201}, + year={2023} +} +``` +## Misuse, Malicious Use, and Out-of-Scope Use +The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/feature_extractor/preprocessor_config.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/feature_extractor/preprocessor_config.json new file mode 100644 index 0000000..73fe891 --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/feature_extractor/preprocessor_config.json @@ -0,0 +1,29 @@ +{ + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "feature_extractor_type": "CLIPFeatureExtractor", + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_processor_type": "CLIPImageProcessor", + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + }, + "use_square_size": false +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/image_encoder/config.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/image_encoder/config.json new file mode 100644 index 0000000..81081d9 --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/image_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", + "architectures": [ + "CLIPVisionModel" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float16", + "transformers_version": "4.35.2" +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/model_index.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/model_index.json new file mode 100644 index 0000000..e5c1919 --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/model_index.json @@ -0,0 +1,33 @@ +{ + "_class_name": "MVDreamPipeline", + "_diffusers_version": "0.25.0", + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "image_encoder": [ + "transformers", + "CLIPVisionModel" + ], + "requires_safety_checker": false, + "scheduler": [ + "diffusers", + "DDIMScheduler" + ], + "text_encoder": [ + "transformers", + "CLIPTextModel" + ], + "tokenizer": [ + "transformers", + "CLIPTokenizer" + ], + "unet": [ + "mv_unet", + "MultiViewUNetModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/pipeline_mvdream.py b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/pipeline_mvdream.py new file mode 100644 index 0000000..25eee28 --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/pipeline_mvdream.py @@ -0,0 +1,558 @@ +import torch +import torch.nn.functional as F +import inspect +import numpy as np +from typing import Callable, List, Optional, Union +from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModel, CLIPImageProcessor +from diffusers import AutoencoderKL, DiffusionPipeline +from diffusers.utils import ( + deprecate, + is_accelerate_available, + is_accelerate_version, + logging, +) +from diffusers.configuration_utils import FrozenDict +from diffusers.schedulers import DDIMScheduler +from diffusers.utils.torch_utils import randn_tensor + +from mv_unet import MultiViewUNetModel, get_camera + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class MVDreamPipeline(DiffusionPipeline): + + _optional_components = ["feature_extractor", "image_encoder"] + + def __init__( + self, + vae: AutoencoderKL, + unet: MultiViewUNetModel, + tokenizer: CLIPTokenizer, + text_encoder: CLIPTextModel, + scheduler: DDIMScheduler, + # imagedream variant + feature_extractor: CLIPImageProcessor, + image_encoder: CLIPVisionModel, + requires_safety_checker: bool = False, + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: # type: ignore + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " # type: ignore + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate( + "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: # type: ignore + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate( + "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + unet=unet, + scheduler=scheduler, + tokenizer=tokenizer, + text_encoder=text_encoder, + feature_extractor=feature_extractor, + image_encoder=image_encoder, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. + + When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in + several steps. This is useful to save a large amount of memory and to allow the processing of larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + Note that offloading happens on a submodule basis. Memory savings are higher than with + `enable_model_cpu_offload`, but performance is lower. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): + from accelerate import cpu_offload + else: + raise ImportError( + "`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher" + ) + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + def enable_model_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError( + "`enable_model_offload` requires `accelerate v0.17.0` or higher." + ) + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + hook = None + for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: + _, hook = cpu_offload_with_hook( + cpu_offloaded_model, device, prev_module_hook=hook + ) + + # We'll offload the last model manually. + self.final_offload_hook = hook + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance: bool, + negative_prompt=None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + raise ValueError( + f"`prompt` should be either a string or a list of strings, but got {type(prompt)}." + ) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + prompt_embeds = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view( + bs_embed * num_images_per_prompt, seq_len, -1 + ) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to( + dtype=self.text_encoder.dtype, device=device + ) + + negative_prompt_embeds = negative_prompt_embeds.repeat( + 1, num_images_per_prompt, 1 + ) + negative_prompt_embeds = negative_prompt_embeds.view( + batch_size * num_images_per_prompt, seq_len, -1 + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + return prompt_embeds + + def decode_latents(self, latents): + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents).sample + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_channels_latents, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def encode_image(self, image, device, num_images_per_prompt): + dtype = next(self.image_encoder.parameters()).dtype + + if image.dtype == np.float32: + image = (image * 255).astype(np.uint8) + + image = self.feature_extractor(image, return_tensors="pt").pixel_values + image = image.to(device=device, dtype=dtype) + + image_embeds = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] + image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) + + return torch.zeros_like(image_embeds), image_embeds + + def encode_image_latents(self, image, device, num_images_per_prompt): + + dtype = next(self.image_encoder.parameters()).dtype + + image = torch.from_numpy(image).unsqueeze(0).permute(0, 3, 1, 2).to(device=device) # [1, 3, H, W] + image = 2 * image - 1 + image = F.interpolate(image, (256, 256), mode='bilinear', align_corners=False) + image = image.to(dtype=dtype) + + posterior = self.vae.encode(image).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor # [B, C, H, W] + latents = latents.repeat_interleave(num_images_per_prompt, dim=0) + + return torch.zeros_like(latents), latents + + @torch.no_grad() + def __call__( + self, + prompt: str = "", + image: Optional[np.ndarray] = None, + height: int = 256, + width: int = 256, + num_inference_steps: int = 50, + guidance_scale: float = 7.0, + negative_prompt: str = "", + num_images_per_prompt: int = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + output_type: Optional[str] = "numpy", # pil, numpy, latents + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + num_frames: int = 4, + device=torch.device("cuda:0"), + ): + self.unet = self.unet.to(device=device) + self.vae = self.vae.to(device=device) + self.text_encoder = self.text_encoder.to(device=device) + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # imagedream variant + if image is not None: + assert isinstance(image, np.ndarray) and image.dtype == np.float32 + self.image_encoder = self.image_encoder.to(device=device) + image_embeds_neg, image_embeds_pos = self.encode_image(image, device, num_images_per_prompt) + image_latents_neg, image_latents_pos = self.encode_image_latents(image, device, num_images_per_prompt) + + _prompt_embeds = self._encode_prompt( + prompt=prompt, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + ) # type: ignore + prompt_embeds_neg, prompt_embeds_pos = _prompt_embeds.chunk(2) + + # Prepare latent variables + actual_num_frames = num_frames if image is None else num_frames + 1 + latents: torch.Tensor = self.prepare_latents( + actual_num_frames * num_images_per_prompt, + 4, + height, + width, + prompt_embeds_pos.dtype, + device, + generator, + None, + ) + + if image is not None: + camera = get_camera(num_frames, elevation=5, extra_view=True).to(dtype=latents.dtype, device=device) + else: + camera = get_camera(num_frames, elevation=15, extra_view=False).to(dtype=latents.dtype, device=device) + camera = camera.repeat_interleave(num_images_per_prompt, dim=0) + + # Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + multiplier = 2 if do_classifier_free_guidance else 1 + latent_model_input = torch.cat([latents] * multiplier) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + unet_inputs = { + 'x': latent_model_input, + 'timesteps': torch.tensor([t] * actual_num_frames * multiplier, dtype=latent_model_input.dtype, device=device), + 'context': torch.cat([prompt_embeds_neg] * actual_num_frames + [prompt_embeds_pos] * actual_num_frames), + 'num_frames': actual_num_frames, + 'camera': torch.cat([camera] * multiplier), + } + + if image is not None: + unet_inputs['ip'] = torch.cat([image_embeds_neg] * actual_num_frames + [image_embeds_pos] * actual_num_frames) + unet_inputs['ip_img'] = torch.cat([image_latents_neg] + [image_latents_pos]) # no repeat + + # predict the noise residual + noise_pred = self.unet.forward(**unet_inputs) + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents: torch.Tensor = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs, return_dict=False + )[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) # type: ignore + + # Post-processing + if output_type == "latent": + image = latents + elif output_type == "pil": + image = self.decode_latents(latents) + image = self.numpy_to_pil(image) + else: # numpy + image = self.decode_latents(latents) + + # Offload last model to CPU + if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: + self.final_offload_hook.offload() + + return image diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/scheduler/scheduler_config.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/scheduler/scheduler_config.json new file mode 100644 index 0000000..441edcd --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/scheduler/scheduler_config.json @@ -0,0 +1,19 @@ +{ + "_class_name": "DDIMScheduler", + "_diffusers_version": "0.25.0", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": false, + "clip_sample_range": 1.0, + "dynamic_thresholding_ratio": 0.995, + "num_train_timesteps": 1000, + "prediction_type": "epsilon", + "rescale_betas_zero_snr": false, + "sample_max_value": 1.0, + "set_alpha_to_one": false, + "steps_offset": 1, + "thresholding": false, + "timestep_spacing": "leading", + "trained_betas": null +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/text_encoder/config.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/text_encoder/config.json new file mode 100644 index 0000000..ec9b92b --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/text_encoder/config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "stabilityai/stable-diffusion-2-1", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 23, + "pad_token_id": 1, + "projection_dim": 512, + "torch_dtype": "float16", + "transformers_version": "4.35.2", + "vocab_size": 49408 +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/tokenizer/merges.txt b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/tokenizer/merges.txt new file mode 100644 index 0000000..76e821f --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e 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255, + "Ń": 511 +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/unet/config.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/unet/config.json new file mode 100644 index 0000000..d7bd7d1 --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/unet/config.json @@ -0,0 +1,25 @@ +{ + "_class_name": "MultiViewUNetModel", + "_diffusers_version": "0.25.0", + "attention_resolutions": [ + 4, + 2, + 1 + ], + "camera_dim": 16, + "channel_mult": [ + 1, + 2, + 4, + 4 + ], + "context_dim": 1024, + "image_size": 32, + "in_channels": 4, + "ip_dim": 16, + "model_channels": 320, + "num_head_channels": 64, + "num_res_blocks": 2, + "out_channels": 4, + "transformer_depth": 1 +} diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/unet/mv_unet.py b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/unet/mv_unet.py new file mode 100644 index 0000000..60e2ddb --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/unet/mv_unet.py @@ -0,0 +1,1005 @@ +import math +import numpy as np +from inspect import isfunction +from typing import Optional, Any, List + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from diffusers.configuration_utils import ConfigMixin +from diffusers.models.modeling_utils import ModelMixin + +# require xformers! +import xformers +import xformers.ops + +from kiui.cam import orbit_camera + +def get_camera( + num_frames, elevation=15, azimuth_start=0, azimuth_span=360, blender_coord=True, extra_view=False, +): + angle_gap = azimuth_span / num_frames + cameras = [] + for azimuth in np.arange(azimuth_start, azimuth_span + azimuth_start, angle_gap): + + pose = orbit_camera(-elevation, azimuth, radius=1) # kiui's elevation is negated, [4, 4] + + # opengl to blender + if blender_coord: + pose[2] *= -1 + pose[[1, 2]] = pose[[2, 1]] + + cameras.append(pose.flatten()) + + if extra_view: + cameras.append(np.zeros_like(cameras[0])) + + return torch.from_numpy(np.stack(cameras, axis=0)).float() # [num_frames, 16] + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps[:, None] * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + # import pdb; pdb.set_trace() + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def default(val, d): + if val is not None: + return val + return d() if isfunction(d) else d + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__( + self, + query_dim, + context_dim=None, + heads=8, + dim_head=64, + dropout=0.0, + ip_dim=0, + ip_weight=1, + ): + super().__init__() + + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.ip_dim = ip_dim + self.ip_weight = ip_weight + + if self.ip_dim > 0: + self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None): + q = self.to_q(x) + context = default(context, x) + + if self.ip_dim > 0: + # context： [B, 77 + 16(ip), 1024] + token_len = context.shape[1] + context_ip = context[:, -self.ip_dim :, :] + k_ip = self.to_k_ip(context_ip) + v_ip = self.to_v_ip(context_ip) + context = context[:, : (token_len - self.ip_dim), :] + + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + if self.ip_dim > 0: + k_ip, v_ip = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (k_ip, v_ip), + ) + # actually compute the attention, what we cannot get enough of + out_ip = xformers.ops.memory_efficient_attention( + q, k_ip, v_ip, attn_bias=None, op=self.attention_op + ) + out = out + self.ip_weight * out_ip + + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + + +class BasicTransformerBlock3D(nn.Module): + + def __init__( + self, + dim, + n_heads, + d_head, + context_dim, + dropout=0.0, + gated_ff=True, + ip_dim=0, + ip_weight=1, + ): + super().__init__() + + self.attn1 = MemoryEfficientCrossAttention( + query_dim=dim, + context_dim=None, # self-attention + heads=n_heads, + dim_head=d_head, + dropout=dropout, + ) + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = MemoryEfficientCrossAttention( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + # ip only applies to cross-attention + ip_dim=ip_dim, + ip_weight=ip_weight, + ) + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + + def forward(self, x, context=None, num_frames=1): + x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous() + x = self.attn1(self.norm1(x), context=None) + x + x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous() + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer3D(nn.Module): + + def __init__( + self, + in_channels, + n_heads, + d_head, + context_dim, # cross attention input dim + depth=1, + dropout=0.0, + ip_dim=0, + ip_weight=1, + ): + super().__init__() + + if not isinstance(context_dim, list): + context_dim = [context_dim] + + self.in_channels = in_channels + + inner_dim = n_heads * d_head + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock3D( + inner_dim, + n_heads, + d_head, + context_dim=context_dim[d], + dropout=dropout, + ip_dim=ip_dim, + ip_weight=ip_weight, + ) + for d in range(depth) + ] + ) + + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + + + def forward(self, x, context=None, num_frames=1): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i], num_frames=num_frames) + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + + return x + x_in + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q, k, v = map( + lambda t: t.reshape(b, t.shape[1], self.heads, -1) + .transpose(1, 2) + .reshape(b, self.heads, t.shape[1], -1) + .contiguous(), + (q, k, v), + ) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + ): + super().__init__() + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5) + self.proj_in = nn.Linear(embedding_dim, dim) + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, dim * ff_mult, bias=False), + nn.GELU(), + nn.Linear(dim * ff_mult, dim, bias=False), + ) + ] + ) + ) + + def forward(self, x): + latents = self.latents.repeat(x.size(0), 1, 1) + x = self.proj_in(x) + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) + + +class CondSequential(nn.Sequential): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, num_frames=1): + for layer in self: + if isinstance(layer, ResBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer3D): + x = layer(x, context, num_frames=num_frames) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd( + dims, self.channels, self.out_channels, 3, padding=padding + ) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, + self.channels, + self.out_channels, + 3, + stride=stride, + padding=padding, + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(nn.Module): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + nn.GroupNorm(32, channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + nn.GroupNorm(32, self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class MultiViewUNetModel(ModelMixin, ConfigMixin): + """ + The full multi-view UNet model with attention, timestep embedding and camera embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + :param camera_dim: dimensionality of camera input. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + transformer_depth=1, + context_dim=None, + n_embed=None, + num_attention_blocks=None, + adm_in_channels=None, + camera_dim=None, + ip_dim=0, # imagedream uses ip_dim > 0 + ip_weight=1.0, + **kwargs, + ): + super().__init__() + assert context_dim is not None + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert ( + num_head_channels != -1 + ), "Either num_heads or num_head_channels has to be set" + + if num_head_channels == -1: + assert ( + num_heads != -1 + ), "Either num_heads or num_head_channels has to be set" + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError( + "provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult" + ) + self.num_res_blocks = num_res_blocks + + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all( + map( + lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], + range(len(num_attention_blocks)), + ) + ) + print( + f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set." + ) + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + self.ip_dim = ip_dim + self.ip_weight = ip_weight + + if self.ip_dim > 0: + self.image_embed = Resampler( + dim=context_dim, + depth=4, + dim_head=64, + heads=12, + num_queries=ip_dim, # num token + embedding_dim=1280, + output_dim=context_dim, + ff_mult=4, + ) + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + nn.Linear(model_channels, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + if camera_dim is not None: + time_embed_dim = model_channels * 4 + self.camera_embed = nn.Sequential( + nn.Linear(camera_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(self.num_classes, time_embed_dim) + elif self.num_classes == "continuous": + # print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + nn.Linear(adm_in_channels, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + CondSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers: List[Any] = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + + if num_attention_blocks is None or nr < num_attention_blocks[level]: + layers.append( + SpatialTransformer3D( + ch, + num_heads, + dim_head, + context_dim=context_dim, + depth=transformer_depth, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight, + ) + ) + self.input_blocks.append(CondSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + CondSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + + self.middle_block = CondSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ), + SpatialTransformer3D( + ch, + num_heads, + dim_head, + context_dim=context_dim, + depth=transformer_depth, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + + if num_attention_blocks is None or i < num_attention_blocks[level]: + layers.append( + SpatialTransformer3D( + ch, + num_heads, + dim_head, + context_dim=context_dim, + depth=transformer_depth, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight, + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(CondSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + nn.GroupNorm(32, ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + nn.GroupNorm(32, ch), + conv_nd(dims, model_channels, n_embed, 1), + # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward( + self, + x, + timesteps=None, + context=None, + y=None, + camera=None, + num_frames=1, + ip=None, + ip_img=None, + **kwargs, + ): + """ + Apply the model to an input batch. + :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views). + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :param num_frames: a integer indicating number of frames for tensor reshaping. + :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views). + """ + assert ( + x.shape[0] % num_frames == 0 + ), "input batch size must be dividable by num_frames!" + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + + hs = [] + + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y is not None + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + # Add camera embeddings + if camera is not None: + emb = emb + self.camera_embed(camera) + + # imagedream variant + if self.ip_dim > 0: + x[(num_frames - 1) :: num_frames, :, :, :] = ip_img # place at [4, 9] + ip_emb = self.image_embed(ip) + context = torch.cat((context, ip_emb), 1) + + h = x + for module in self.input_blocks: + h = module(h, emb, context, num_frames=num_frames) + hs.append(h) + h = self.middle_block(h, emb, context, num_frames=num_frames) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context, num_frames=num_frames) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) \ No newline at end of file diff --git a/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/vae/config.json b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/vae/config.json new file mode 100644 index 0000000..ae5747e --- /dev/null +++ b/checkpoints/Diffusers/ashawkey/imagedream-ipmv-diffusers/vae/config.json @@ -0,0 +1,31 @@ +{ + "_class_name": "AutoencoderKL", + "_diffusers_version": "0.25.0", + "act_fn": "silu", + "block_out_channels": [ + 128, + 256, + 512, + 512 + ], + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D" + ], + "force_upcast": true, + "in_channels": 3, + "latent_channels": 4, + "layers_per_block": 2, + "norm_num_groups": 32, + "out_channels": 3, + "sample_size": 256, + "scaling_factor": 0.18215, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D" + ] +} diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/.gitattributes b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack 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merge=lfs -text diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/README.md b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/README.md new file mode 100644 index 0000000..3b0f0e9 --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/README.md @@ -0,0 +1,3 @@ +--- +license: agpl-3.0 +--- diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/feature_extractor/preprocessor_config.json b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/feature_extractor/preprocessor_config.json new file mode 100644 index 0000000..0d9d33b --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/feature_extractor/preprocessor_config.json @@ -0,0 +1,28 @@ +{ + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "feature_extractor_type": "CLIPFeatureExtractor", + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_processor_type": "CLIPImageProcessor", + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + } +} diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/image_encoder/config.json b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/image_encoder/config.json new file mode 100644 index 0000000..3b69e48 --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/image_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "architectures": [ + "CLIPVisionModelWithProjection" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float16", + "transformers_version": "4.34.1" +} diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/model_index.json b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/model_index.json new file mode 100644 index 0000000..7681c2f --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/model_index.json @@ -0,0 +1,29 @@ +{ + "_class_name": "MVDiffusionImagePipeline", + "_diffusers_version": "0.19.3", + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "image_encoder": [ + "transformers", + "CLIPVisionModelWithProjection" + ], + "requires_safety_checker": true, + "safety_checker": [ + null, + null + ], + "scheduler": [ + "diffusers", + "DDIMScheduler" + ], + "unet": [ + "Wonder3D.models.unet_mv2d_condition", + "UNetMV2DConditionModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/scheduler/scheduler_config.json b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/scheduler/scheduler_config.json new file mode 100644 index 0000000..93da17b --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/scheduler/scheduler_config.json @@ -0,0 +1,20 @@ +{ + "_class_name": "DDIMScheduler", + "_diffusers_version": "0.19.3", + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": false, + "clip_sample_range": 1.0, + "dynamic_thresholding_ratio": 0.995, + "num_train_timesteps": 1000, + "prediction_type": "epsilon", + "rescale_betas_zero_snr": false, + "sample_max_value": 1.0, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "steps_offset": 1, + "thresholding": false, + "timestep_spacing": "leading", + "trained_betas": null +} diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/unet/config.json b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/unet/config.json new file mode 100644 index 0000000..64655ad --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/unet/config.json @@ -0,0 +1,74 @@ +{ + "_class_name": "UNetMV2DConditionModel", + "_diffusers_version": "0.19.3", + "_name_or_path": "/home/xiaoxiao/Workplace/wonder3Dplus/ckpts", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": 8, + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "cd_attention_last": false, + "cd_attention_mid": true, + "center_input_sample": false, + "class_embed_type": "projection", + "class_embeddings_concat": false, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 768, + "cross_attention_norm": null, + "cross_domain_attention": false, + "down_block_types": [ + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "dual_cross_attention": false, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 8, + "joint_attention": false, + "joint_attention_twice": true, + "layers_per_block": 2, + "mid_block_only_cross_attention": null, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlockMV2DCrossAttn", + "multiview_attention": true, + "mvcd_attention": false, + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "num_views": 6, + "only_cross_attention": false, + "out_channels": 4, + "projection_class_embeddings_input_dim": 10, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": false, + "resnet_time_scale_shift": "default", + "sample_size": 32, + "sparse_mv_attention": false, + "time_cond_proj_dim": null, + "time_embedding_act_fn": null, + "time_embedding_dim": null, + "time_embedding_type": "positional", + "timestep_post_act": null, + "transformer_layers_per_block": 1, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlockMV2D", + "CrossAttnUpBlockMV2D", + "CrossAttnUpBlockMV2D" + ], + "upcast_attention": false, + "use_linear_projection": false +} diff --git a/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/vae/config.json b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/vae/config.json new file mode 100644 index 0000000..6c38401 --- /dev/null +++ b/checkpoints/Diffusers/flamehaze1115/wonder3d-v1.0/vae/config.json @@ -0,0 +1,32 @@ +{ + "_class_name": "AutoencoderKL", + "_diffusers_version": "0.19.3", + "_name_or_path": "lambdalabs/sd-image-variations-diffusers", + "act_fn": "silu", + "block_out_channels": [ + 128, + 256, + 512, + 512 + ], + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D" + ], + "force_upcast": true, + "in_channels": 3, + "latent_channels": 4, + "layers_per_block": 2, + "norm_num_groups": 32, + "out_channels": 3, + "sample_size": 256, + "scaling_factor": 0.18215, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D" + ] +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/.gitattributes b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs 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+*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/README.md b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/README.md new file mode 100644 index 0000000..154df82 --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/README.md @@ -0,0 +1,3 @@ +--- +license: apache-2.0 +--- diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/feature_extractor/preprocessor_config.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/feature_extractor/preprocessor_config.json new file mode 100644 index 0000000..fb1692f --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/feature_extractor/preprocessor_config.json @@ -0,0 +1,27 @@ +{ + "crop_size": { + "height": 224, + "width": 224 + }, + "do_center_crop": true, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.48145466, + 0.4578275, + 0.40821073 + ], + "image_processor_type": "CLIPImageProcessor", + "image_std": [ + 0.26862954, + 0.26130258, + 0.27577711 + ], + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "shortest_edge": 224 + } +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_encoder/config.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_encoder/config.json new file mode 100644 index 0000000..8acd629 --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "stabilityai/stable-diffusion-2-1-unclip", + "architectures": [ + "CLIPVisionModelWithProjection" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float16", + "transformers_version": "4.37.2" +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_noising_scheduler/scheduler_config.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_noising_scheduler/scheduler_config.json new file mode 100644 index 0000000..87af594 --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_noising_scheduler/scheduler_config.json @@ -0,0 +1,19 @@ +{ + "_class_name": "DDPMScheduler", + "_diffusers_version": "0.26.0.dev0", + "beta_end": 0.02, + "beta_schedule": "squaredcos_cap_v2", + "beta_start": 0.0001, + "clip_sample": true, + "clip_sample_range": 1.0, + "dynamic_thresholding_ratio": 0.995, + "num_train_timesteps": 1000, + "prediction_type": "epsilon", + "rescale_betas_zero_snr": false, + "sample_max_value": 1.0, + "steps_offset": 0, + "thresholding": false, + "timestep_spacing": "leading", + "trained_betas": null, + "variance_type": "fixed_small" +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_normalizer/config.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_normalizer/config.json new file mode 100644 index 0000000..5007254 --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/image_normalizer/config.json @@ -0,0 +1,6 @@ +{ + "_class_name": "StableUnCLIPImageNormalizer", + "_diffusers_version": "0.26.0.dev0", + "_name_or_path": "stabilityai/stable-diffusion-2-1-unclip", + "embedding_dim": 1024 +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/model_index.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/model_index.json new file mode 100644 index 0000000..ea704f1 --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/model_index.json @@ -0,0 +1,40 @@ +{ + "_class_name": "StableUnCLIPImg2ImgPipeline", + "_diffusers_version": "0.26.0.dev0", + "feature_extractor": [ + "transformers", + "CLIPImageProcessor" + ], + "image_encoder": [ + "transformers", + "CLIPVisionModelWithProjection" + ], + "image_noising_scheduler": [ + "diffusers", + "DDPMScheduler" + ], + "image_normalizer": [ + "stable_diffusion", + "StableUnCLIPImageNormalizer" + ], + "scheduler": [ + "diffusers", + "DDIMScheduler" + ], + "text_encoder": [ + "transformers", + "CLIPTextModel" + ], + "tokenizer": [ + "transformers", + "CLIPTokenizer" + ], + "unet": [ + "Era3D.mvdiffusion.models.unet_mv2d_condition", + "UNetMV2DConditionModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/scheduler/scheduler_config.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/scheduler/scheduler_config.json new file mode 100644 index 0000000..e52d1cb --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/scheduler/scheduler_config.json @@ -0,0 +1,20 @@ +{ + "_class_name": "DDIMScheduler", + "_diffusers_version": "0.26.0.dev0", + "beta_end": 0.012, + "beta_schedule": "linear", + "beta_start": 0.00085, + "clip_sample": false, + "clip_sample_range": 1.0, + "dynamic_thresholding_ratio": 0.995, + "num_train_timesteps": 1000, + "prediction_type": "v_prediction", + "rescale_betas_zero_snr": false, + "sample_max_value": 1.0, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "steps_offset": 1, + "thresholding": false, + "timestep_spacing": "leading", + "trained_betas": null +} diff --git a/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/text_encoder/config.json b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/text_encoder/config.json new file mode 100644 index 0000000..120e7db --- /dev/null +++ b/checkpoints/Diffusers/pengHTYX/MacLab-Era3D-512-6view/text_encoder/config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "stabilityai/stable-diffusion-2-1-unclip", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 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255, + "Ń": 511 +} diff --git a/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/unet/config.json b/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/unet/config.json new file mode 100644 index 0000000..cacbdd9 --- /dev/null +++ b/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/unet/config.json @@ -0,0 +1,73 @@ +{ + "_class_name": "UNet2DConditionModel", + "_diffusers_version": "0.24.0", + "_name_or_path": "D:\\.cache\\huggingface\\hub\\models--stabilityai--stable-diffusion-2\\snapshots\\1e128c8891e52218b74cde8f26dbfc701cb99d79\\unet", + "act_fn": "silu", + "addition_embed_type": null, + "addition_embed_type_num_heads": 64, + "addition_time_embed_dim": null, + "attention_head_dim": [ + 5, + 10, + 20, + 20 + ], + "attention_type": "default", + "block_out_channels": [ + 320, + 640, + 1280, + 1280 + ], + "center_input_sample": false, + "class_embed_type": null, + "class_embeddings_concat": false, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 1024, + "cross_attention_norm": null, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "downsample_padding": 1, + "dropout": 0.0, + "dual_cross_attention": false, + "encoder_hid_dim": null, + "encoder_hid_dim_type": null, + "flip_sin_to_cos": true, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 2, + "mid_block_only_cross_attention": null, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_attention_heads": null, + "num_class_embeds": null, + "only_cross_attention": false, + "out_channels": 4, + "projection_class_embeddings_input_dim": null, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": false, + "resnet_time_scale_shift": "default", + "reverse_transformer_layers_per_block": null, + "sample_size": 96, + "time_cond_proj_dim": null, + "time_embedding_act_fn": null, + "time_embedding_dim": null, + "time_embedding_type": "positional", + "timestep_post_act": null, + "transformer_layers_per_block": 1, + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "upcast_attention": false, + "use_linear_projection": true +} diff --git a/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/vae/config.json b/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/vae/config.json new file mode 100644 index 0000000..96a7aaa --- /dev/null +++ b/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/vae/config.json @@ -0,0 +1,32 @@ +{ + "_class_name": "AutoencoderKL", + "_diffusers_version": "0.24.0", + "_name_or_path": "D:\\.cache\\huggingface\\hub\\models--sudo-ai--zero123plus-v1.1\\snapshots\\36df7de980afd15f80b2e1a4e9a920d7020e2654\\vae", + "act_fn": "silu", + "block_out_channels": [ + 128, + 256, + 512, + 512 + ], + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D" + ], + "force_upcast": true, + "in_channels": 3, + "latent_channels": 4, + "layers_per_block": 2, + "norm_num_groups": 32, + "out_channels": 3, + "sample_size": 256, + "scaling_factor": 0.18215, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D" + ] +} diff --git a/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/vision_encoder/config.json b/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/vision_encoder/config.json new file mode 100644 index 0000000..5e55ea1 --- /dev/null +++ b/checkpoints/Diffusers/sudo-ai/zero123plus-v1.2/vision_encoder/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "D:\\.cache\\huggingface\\hub\\models--sudo-ai--zero123plus-v1.1\\snapshots\\36df7de980afd15f80b2e1a4e9a920d7020e2654\\vision_encoder", + "architectures": [ + "CLIPVisionModelWithProjection" + ], + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float16", + "transformers_version": "4.36.0" +} diff --git a/configs/InstantMesh_configs/instant_mesh_base.yaml b/configs/InstantMesh_configs/instant_mesh_base.yaml index ff069ad..63a053c 100644 --- a/configs/InstantMesh_configs/instant_mesh_base.yaml +++ b/configs/InstantMesh_configs/instant_mesh_base.yaml @@ -1,5 +1,5 @@ model_config: - target: instant_mesh.models.lrm_mesh.InstantMesh + target: InstantMesh.models.lrm_mesh.InstantMesh params: encoder_feat_dim: 768 encoder_freeze: false diff --git a/configs/InstantMesh_configs/instant_mesh_large.yaml b/configs/InstantMesh_configs/instant_mesh_large.yaml index fafb5dc..8822dad 100644 --- a/configs/InstantMesh_configs/instant_mesh_large.yaml +++ b/configs/InstantMesh_configs/instant_mesh_large.yaml @@ -1,5 +1,5 @@ model_config: - target: instant_mesh.models.lrm_mesh.InstantMesh + target: InstantMesh.models.lrm_mesh.InstantMesh params: encoder_feat_dim: 768 encoder_freeze: false diff --git a/configs/InstantMesh_configs/instant_nerf_base.yaml b/configs/InstantMesh_configs/instant_nerf_base.yaml index c939e25..ec786d9 100644 --- a/configs/InstantMesh_configs/instant_nerf_base.yaml +++ b/configs/InstantMesh_configs/instant_nerf_base.yaml @@ -1,5 +1,5 @@ model_config: - target: instant_mesh.models.lrm.InstantNeRF + target: InstantMesh.models.lrm.InstantNeRF params: encoder_feat_dim: 768 encoder_freeze: false diff --git a/configs/InstantMesh_configs/instant_nerf_large.yaml b/configs/InstantMesh_configs/instant_nerf_large.yaml index 3604618..d16a4cc 100644 --- a/configs/InstantMesh_configs/instant_nerf_large.yaml +++ b/configs/InstantMesh_configs/instant_nerf_large.yaml @@ -1,5 +1,5 @@ model_config: - target: instant_mesh.models.lrm.InstantNeRF + target: InstantMesh.models.lrm.InstantNeRF params: encoder_feat_dim: 768 encoder_freeze: false diff --git a/configs/TriplaneGaussian_config.yaml b/configs/TriplaneGaussian_config.yaml new file mode 100644 index 0000000..8b08398 --- /dev/null +++ b/configs/TriplaneGaussian_config.yaml @@ -0,0 +1,150 @@ +data: + background_color: [1.0, 1.0, 1.0] + cond_width: 252 # multiply of 14 + cond_height: 252 + + relative_pose: true + + num_workers: 16 + eval_batch_size: 1 + eval_height: 512 + eval_width: 512 + +system: + camera_embedder_cls: TriplaneGaussian.models.networks.MLP + camera_embedder: + dim_in: 25 # c2w + [fx, fy, cx, cy] + dim_out: 768 + n_neurons: 768 + n_hidden_layers: 1 + activation: silu + + image_feature: + out_dim: 773 + + image_tokenizer_cls: TriplaneGaussian.models.tokenizers.image.DINOV2SingleImageTokenizer + image_tokenizer: + pretrained_model_name_or_path: "facebook/dinov2-base" + width: ${data.cond_width} + height: ${data.cond_height} + + modulation: true + modulation_zero_init: true + modulation_single_layer: true + modulation_cond_dim: ${system.camera_embedder.dim_out} # c2w + intrinsic + + freeze_backbone_params: false + enable_memory_efficient_attention: ${system.backbone.enable_memory_efficient_attention} + enable_gradient_checkpointing: ${system.backbone.gradient_checkpointing} + + tokenizer_cls: TriplaneGaussian.models.tokenizers.triplane.TriplaneLearnablePositionalEmbedding + tokenizer: + plane_size: 32 + num_channels: 512 + + backbone_cls: TriplaneGaussian.models.transformers.Transformer1D + backbone: + in_channels: ${system.tokenizer.num_channels} + num_attention_heads: 8 + attention_head_dim: 64 + num_layers: 10 + cross_attention_dim: 768 # hard-code, =DINO feature dim + + norm_type: "layer_norm" + + enable_memory_efficient_attention: false + gradient_checkpointing: false + + post_processor_cls: TriplaneGaussian.models.networks.TriplaneUpsampleNetwork + post_processor: + in_channels: ${system.tokenizer.num_channels} + out_channels: 80 + + pointcloud_generator_cls: TriplaneGaussian.models.pointclouds.simplepoint.SimplePointGenerator + pointcloud_generator: + camera_embedder_cls: TriplaneGaussian.models.networks.MLP + camera_embedder: + dim_in: 25 # c2w + [fx, fy, cx, cy] + dim_out: 768 + n_neurons: 768 + n_hidden_layers: 1 + activation: silu + + image_tokenizer_cls: TriplaneGaussian.models.tokenizers.image.DINOV2SingleImageTokenizer + image_tokenizer: + pretrained_model_name_or_path: "facebook/dinov2-base" + width: ${data.cond_width} + height: ${data.cond_height} + + modulation: true + modulation_zero_init: true + modulation_single_layer: true + modulation_cond_dim: ${system.camera_embedder.dim_out} # c2w + intrinsic + + freeze_backbone_params: true + enable_memory_efficient_attention: ${system.backbone.enable_memory_efficient_attention} + enable_gradient_checkpointing: false + + tokenizer_cls: TriplaneGaussian.models.tokenizers.point.PointLearnablePositionalEmbedding + tokenizer: + num_pcl: 2048 + num_channels: 512 + + backbone_cls: TriplaneGaussian.models.transformers.Transformer1D + backbone: + in_channels: ${system.pointcloud_generator.tokenizer.num_channels} + num_attention_heads: 8 + attention_head_dim: 64 + num_layers: 10 + cross_attention_dim: 768 # hard-code, =DINO feature dim + + norm_type: "layer_norm" + + enable_memory_efficient_attention: ${system.backbone.enable_memory_efficient_attention} + gradient_checkpointing: ${system.backbone.gradient_checkpointing} + + post_processor_cls: TriplaneGaussian.models.networks.PointOutLayer + post_processor: + in_channels: 512 + out_channels: 3 + + pointcloud_upsampling_cls: TriplaneGaussian.models.snowflake.model_spdpp.SnowflakeModelSPDPP + pointcloud_upsampling: + input_channels: 768 + dim_feat: 128 + num_p0: 2048 + radius: 1 + bounding: true + use_fps: true + up_factors: [2,4] + token_type: "image_token" + + pointcloud_encoder_cls: TriplaneGaussian.models.pointclouds.pointnet.LocalPoolPointnet + pointcloud_encoder: + input_channels: 776 # 3 + 3 + 768 + 1 + 1 [xyz, local features] + c_dim: ${system.tokenizer.num_channels} + hidden_dim: 128 + plane_size: ${system.tokenizer.plane_size} + n_blocks: 5 + radius: ${system.renderer.radius} + + renderer_cls: TriplaneGaussian.models.renderer.GS3DRenderer + renderer: + sh_degree: 3 + radius: 0.6 + mlp_network_config: + n_neurons: ${system.renderer.gs_out.in_channels} + n_hidden_layers: 2 + activation: silu + gs_out: + in_channels: 128 + xyz_offset: true + restrict_offset: true + use_rgb: false + feature_channels: + xyz: 3 + scaling: 3 + rotation: 4 + opacity: 1 + shs: ${shsdim:${system.renderer.sh_degree}} + clip_scaling: 0.2 \ No newline at end of file diff --git a/configs/TripoSR_config.yaml b/configs/TripoSR_config.yaml new file mode 100644 index 0000000..2b691e7 --- /dev/null +++ b/configs/TripoSR_config.yaml @@ -0,0 +1,38 @@ +cond_image_size: 512 + +image_tokenizer_cls: TripoSR.models.tokenizers.image.DINOSingleImageTokenizer +image_tokenizer: + pretrained_model_name_or_path: "facebook/dino-vitb16" + +tokenizer_cls: TripoSR.models.tokenizers.triplane.Triplane1DTokenizer +tokenizer: + plane_size: 32 + num_channels: 1024 + +backbone_cls: TripoSR.models.transformer.transformer_1d.Transformer1D +backbone: + in_channels: ${tokenizer.num_channels} + num_attention_heads: 16 + attention_head_dim: 64 + num_layers: 16 + cross_attention_dim: 768 + +post_processor_cls: TripoSR.models.network_utils.TriplaneUpsampleNetwork +post_processor: + in_channels: 1024 + out_channels: 40 + +decoder_cls: TripoSR.models.network_utils.NeRFMLP +decoder: + in_channels: 120 # 3 * 40 + n_neurons: 64 + n_hidden_layers: 9 + activation: silu + +renderer_cls: TripoSR.models.nerf_renderer.TriplaneNeRFRenderer +renderer: + radius: 0.87 # slightly larger than 0.5 * sqrt(3) + feature_reduction: concat + density_activation: exp + density_bias: -1.0 + num_samples_per_ray: 128 \ No newline at end of file diff --git a/configs/crm_configs/sd_v2_base_ipmv_chin8_zero_snr.yaml b/configs/crm_configs/sd_v2_base_ipmv_chin8_zero_snr.yaml index d8a5b5c..53a9938 100644 --- a/configs/crm_configs/sd_v2_base_ipmv_chin8_zero_snr.yaml +++ b/configs/crm_configs/sd_v2_base_ipmv_chin8_zero_snr.yaml @@ -1,5 +1,5 @@ model: - target: crm.imagedream.ldm.interface.LatentDiffusionInterface + target: CRM.imagedream.ldm.interface.LatentDiffusionInterface params: linear_start: 0.00085 linear_end: 0.0120 @@ -9,7 +9,7 @@ model: zero_snr: true unet_config: - target: crm.imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModelStage2 + target: CRM.imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModelStage2 params: image_size: 32 # unused in_channels: 8 @@ -31,7 +31,7 @@ model: ip_mode: "local_resample" vae_config: - target: crm.imagedream.ldm.models.autoencoder.AutoencoderKL + target: CRM.imagedream.ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -55,7 +55,7 @@ model: target: torch.nn.Identity clip_config: - target: crm.imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + target: CRM.imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" @@ -63,7 +63,7 @@ model: sampler: - target: crm.libs.sample.ImageDreamDiffusionStage2 + target: CRM.libs.sample.ImageDreamDiffusionStage2 params: mode: pixel num_frames: 6 diff --git a/configs/crm_configs/sd_v2_base_ipmv_zero_SNR.yaml b/configs/crm_configs/sd_v2_base_ipmv_zero_SNR.yaml index 15159d6..3f15c9b 100644 --- a/configs/crm_configs/sd_v2_base_ipmv_zero_SNR.yaml +++ b/configs/crm_configs/sd_v2_base_ipmv_zero_SNR.yaml @@ -1,5 +1,5 @@ model: - target: crm.imagedream.ldm.interface.LatentDiffusionInterface + target: CRM.imagedream.ldm.interface.LatentDiffusionInterface params: linear_start: 0.00085 linear_end: 0.0120 @@ -9,7 +9,7 @@ model: zero_snr: true unet_config: - target: crm.imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModel + target: CRM.imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModel params: image_size: 32 # unused in_channels: 4 @@ -31,7 +31,7 @@ model: ip_mode: "local_resample" vae_config: - target: crm.imagedream.ldm.models.autoencoder.AutoencoderKL + target: CRM.imagedream.ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss @@ -55,14 +55,14 @@ model: target: torch.nn.Identity clip_config: - target: crm.imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + target: CRM.imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" ip_mode: "local_resample" sampler: - target: crm.libs.sample.ImageDreamDiffusion + target: CRM.libs.sample.ImageDreamDiffusion params: mode: pixel num_frames: 7 diff --git a/configs/era3d_config.yaml b/configs/era3d_config.yaml index eef18d9..9a2b475 100644 --- a/configs/era3d_config.yaml +++ b/configs/era3d_config.yaml @@ -1,7 +1,7 @@ num_views: 6 dataset: - prompt_embeds_path: era3d/mvdiffusion/data/fixed_prompt_embeds_6view + prompt_embeds_path: Gen_3D_Modules/Era3D/mvdiffusion/data/fixed_prompt_embeds_6view num_views: ${num_views} bg_color: 'white' img_wh: [512, 512] diff --git a/gen_3d_modules/CRM/imagedream/__init__.py b/gen_3d_modules/CRM/imagedream/__init__.py new file mode 100644 index 0000000..1ba541c --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/__init__.py @@ -0,0 +1 @@ +from .model_zoo import build_model diff --git a/gen_3d_modules/CRM/imagedream/camera_utils.py b/gen_3d_modules/CRM/imagedream/camera_utils.py new file mode 100644 index 0000000..e3a5d80 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/camera_utils.py @@ -0,0 +1,99 @@ +import numpy as np +import torch + + +def create_camera_to_world_matrix(elevation, azimuth): + elevation = np.radians(elevation) + azimuth = np.radians(azimuth) + # Convert elevation and azimuth angles to Cartesian coordinates on a unit sphere + x = np.cos(elevation) * np.sin(azimuth) + y = np.sin(elevation) + z = np.cos(elevation) * np.cos(azimuth) + + # Calculate camera position, target, and up vectors + camera_pos = np.array([x, y, z]) + target = np.array([0, 0, 0]) + up = np.array([0, 1, 0]) + + # Construct view matrix + forward = target - camera_pos + forward /= np.linalg.norm(forward) + right = np.cross(forward, up) + right /= np.linalg.norm(right) + new_up = np.cross(right, forward) + new_up /= np.linalg.norm(new_up) + cam2world = np.eye(4) + cam2world[:3, :3] = np.array([right, new_up, -forward]).T + cam2world[:3, 3] = camera_pos + return cam2world + + +def convert_opengl_to_blender(camera_matrix): + if isinstance(camera_matrix, np.ndarray): + # Construct transformation matrix to convert from OpenGL space to Blender space + flip_yz = np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) + camera_matrix_blender = np.dot(flip_yz, camera_matrix) + else: + # Construct transformation matrix to convert from OpenGL space to Blender space + flip_yz = torch.tensor( + [[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]] + ) + if camera_matrix.ndim == 3: + flip_yz = flip_yz.unsqueeze(0) + camera_matrix_blender = torch.matmul(flip_yz.to(camera_matrix), camera_matrix) + return camera_matrix_blender + + +def normalize_camera(camera_matrix): + """normalize the camera location onto a unit-sphere""" + if isinstance(camera_matrix, np.ndarray): + camera_matrix = camera_matrix.reshape(-1, 4, 4) + translation = camera_matrix[:, :3, 3] + translation = translation / ( + np.linalg.norm(translation, axis=1, keepdims=True) + 1e-8 + ) + camera_matrix[:, :3, 3] = translation + else: + camera_matrix = camera_matrix.reshape(-1, 4, 4) + translation = camera_matrix[:, :3, 3] + translation = translation / ( + torch.norm(translation, dim=1, keepdim=True) + 1e-8 + ) + camera_matrix[:, :3, 3] = translation + return camera_matrix.reshape(-1, 16) + + +def get_camera( + num_frames, + elevation=15, + azimuth_start=0, + azimuth_span=360, + blender_coord=True, + extra_view=False, +): + angle_gap = azimuth_span / num_frames + cameras = [] + for azimuth in np.arange(azimuth_start, azimuth_span + azimuth_start, angle_gap): + camera_matrix = create_camera_to_world_matrix(elevation, azimuth) + if blender_coord: + camera_matrix = convert_opengl_to_blender(camera_matrix) + cameras.append(camera_matrix.flatten()) + + if extra_view: + dim = len(cameras[0]) + cameras.append(np.zeros(dim)) + return torch.tensor(np.stack(cameras, 0)).float() + + +def get_camera_for_index(data_index): + """ + 按照当前我们的数据格式, 以000为正对我们的情况: + 000是正面, ev: 0, azimuth: 0 + 001是左边, ev: 0, azimuth: -90 + 002是下面, ev: -90, azimuth: 0 + 003是背面, ev: 0, azimuth: 180 + 004是右边, ev: 0, azimuth: 90 + 005是上面, ev: 90, azimuth: 0 + """ + params = [(0, 0), (0, -90), (-90, 0), (0, 180), (0, 90), (90, 0)] + return get_camera(1, *params[data_index]) \ No newline at end of file diff --git a/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv.yaml b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv.yaml new file mode 100644 index 0000000..234fa01 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv.yaml @@ -0,0 +1,61 @@ +model: + target: imagedream.ldm.interface.LatentDiffusionInterface + params: + linear_start: 0.00085 + linear_end: 0.0120 + timesteps: 1000 + scale_factor: 0.18215 + parameterization: "eps" + + unet_config: + target: imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: False + legacy: False + camera_dim: 16 + with_ip: True + ip_dim: 16 # ip token length + ip_mode: "local_resample" + + vae_config: + target: imagedream.ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + clip_config: + target: imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + ip_mode: "local_resample" diff --git a/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_ch8.yaml b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_ch8.yaml new file mode 100644 index 0000000..1979885 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_ch8.yaml @@ -0,0 +1,61 @@ +model: + target: imagedream.ldm.interface.LatentDiffusionInterface + params: + linear_start: 0.00085 + linear_end: 0.0120 + timesteps: 1000 + scale_factor: 0.18215 + parameterization: "eps" + + unet_config: + target: imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModel + params: + image_size: 32 # unused + in_channels: 8 + out_channels: 8 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: False + legacy: False + camera_dim: 16 + with_ip: True + ip_dim: 16 # ip token length + ip_mode: "local_resample" + + vae_config: + target: imagedream.ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + clip_config: + target: imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + ip_mode: "local_resample" diff --git a/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_chin8.yaml b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_chin8.yaml new file mode 100644 index 0000000..b6010de --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_chin8.yaml @@ -0,0 +1,61 @@ +model: + target: imagedream.ldm.interface.LatentDiffusionInterface + params: + linear_start: 0.00085 + linear_end: 0.0120 + timesteps: 1000 + scale_factor: 0.18215 + parameterization: "eps" + + unet_config: + target: imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModelStage2 + params: + image_size: 32 # unused + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: False + legacy: False + camera_dim: 16 + with_ip: True + ip_dim: 16 # ip token length + ip_mode: "local_resample" + + vae_config: + target: imagedream.ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + clip_config: + target: imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + ip_mode: "local_resample" diff --git a/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_local.yaml b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_local.yaml new file mode 100644 index 0000000..f50e728 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/configs/sd_v2_base_ipmv_local.yaml @@ -0,0 +1,62 @@ +model: + target: imagedream.ldm.interface.LatentDiffusionInterface + params: + linear_start: 0.00085 + linear_end: 0.0120 + timesteps: 1000 + scale_factor: 0.18215 + parameterization: "eps" + + unet_config: + target: imagedream.ldm.modules.diffusionmodules.openaimodel.MultiViewUNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: False + legacy: False + camera_dim: 16 + with_ip: True + ip_dim: 16 # ip token length + ip_mode: "local_resample" + ip_weight: 1.0 # adjust for similarity to image + + vae_config: + target: imagedream.ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + clip_config: + target: imagedream.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + ip_mode: "local_resample" diff --git a/gen_3d_modules/CRM/imagedream/ldm/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/interface.py b/gen_3d_modules/CRM/imagedream/ldm/interface.py new file mode 100644 index 0000000..716c0a2 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/interface.py @@ -0,0 +1,206 @@ +from typing import List +from functools import partial + +import numpy as np +import torch +import torch.nn as nn + +from .modules.diffusionmodules.util import ( + make_beta_schedule, + extract_into_tensor, + enforce_zero_terminal_snr, + noise_like, +) +from .util import exists, default, instantiate_from_config +from .modules.distributions.distributions import DiagonalGaussianDistribution + + +class DiffusionWrapper(nn.Module): + def __init__(self, diffusion_model): + super().__init__() + self.diffusion_model = diffusion_model + + def forward(self, *args, **kwargs): + return self.diffusion_model(*args, **kwargs) + + +class LatentDiffusionInterface(nn.Module): + """a simple interface class for LDM inference""" + + def __init__( + self, + unet_config, + clip_config, + vae_config, + parameterization="eps", + scale_factor=0.18215, + beta_schedule="linear", + timesteps=1000, + linear_start=0.00085, + linear_end=0.0120, + cosine_s=8e-3, + given_betas=None, + zero_snr=False, + *args, + **kwargs, + ): + super().__init__() + + unet = instantiate_from_config(unet_config) + self.model = DiffusionWrapper(unet) + self.clip_model = instantiate_from_config(clip_config) + self.vae_model = instantiate_from_config(vae_config) + + self.parameterization = parameterization + self.scale_factor = scale_factor + self.register_schedule( + given_betas=given_betas, + beta_schedule=beta_schedule, + timesteps=timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + zero_snr=zero_snr + ) + + def register_schedule( + self, + given_betas=None, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + zero_snr=False + ): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule( + beta_schedule, + timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + ) + if zero_snr: + print("--- using zero snr---") + betas = enforce_zero_terminal_snr(betas).numpy() + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1]) + + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert ( + alphas_cumprod.shape[0] == self.num_timesteps + ), "alphas have to be defined for each timestep" + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer("betas", to_torch(betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod)) + ) + self.register_buffer( + "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod)) + ) + eps = 1e-8 # Added this epsilon to avoid divide by zero errors + self.register_buffer( + "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / (alphas_cumprod + eps))) + ) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / (alphas_cumprod + eps) - 1)) + ) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + self.v_posterior = 0 + posterior_variance = (1 - self.v_posterior) * betas * ( + 1.0 - alphas_cumprod_prev + ) / (1.0 - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer("posterior_variance", to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer( + "posterior_log_variance_clipped", + to_torch(np.log(np.maximum(posterior_variance, 1e-20))), + ) + self.register_buffer( + "posterior_mean_coef1", + to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)), + ) + self.register_buffer( + "posterior_mean_coef2", + to_torch( + (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod) + ), + ) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * noise + ) + + def get_v(self, x, noise, t): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + ) + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + * noise + ) + + def predict_start_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def predict_eps_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) + * x_t + ) + + def apply_model(self, x_noisy, t, cond, **kwargs): + assert isinstance(cond, dict), "cond has to be a dictionary" + return self.model(x_noisy, t, **cond, **kwargs) + + def get_learned_conditioning(self, prompts: List[str]): + return self.clip_model(prompts) + + def get_learned_image_conditioning(self, images): + return self.clip_model.forward_image(images) + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError( + f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented" + ) + return self.scale_factor * z + + def encode_first_stage(self, x): + return self.vae_model.encode(x) + + def decode_first_stage(self, z): + z = 1.0 / self.scale_factor * z + return self.vae_model.decode(z) diff --git a/gen_3d_modules/CRM/imagedream/ldm/models/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/models/autoencoder.py b/gen_3d_modules/CRM/imagedream/ldm/models/autoencoder.py new file mode 100644 index 0000000..8941041 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/models/autoencoder.py @@ -0,0 +1,270 @@ +import torch +import torch.nn.functional as F +from contextlib import contextmanager + +from ..modules.diffusionmodules.model import Encoder, Decoder +from ..modules.distributions.distributions import DiagonalGaussianDistribution + +from ..util import instantiate_from_config +from ..modules.ema import LitEma + + +class AutoencoderKL(torch.nn.Module): + def __init__( + self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ema_decay=None, + learn_logvar=False, + ): + super().__init__() + self.learn_logvar = learn_logvar + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels) == int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + self.use_ema = ema_decay is not None + if self.use_ema: + self.ema_decay = ema_decay + assert 0.0 < ema_decay < 1.0 + self.model_ema = LitEma(self, decay=ema_decay) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss( + inputs, + reconstructions, + posterior, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + self.log( + "aeloss", + aeloss, + prog_bar=True, + logger=True, + on_step=True, + on_epoch=True, + ) + self.log_dict( + log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False + ) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss( + inputs, + reconstructions, + posterior, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + + self.log( + "discloss", + discloss, + prog_bar=True, + logger=True, + on_step=True, + on_epoch=True, + ) + self.log_dict( + log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False + ) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, postfix=""): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss( + inputs, + reconstructions, + posterior, + 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val" + postfix, + ) + + discloss, log_dict_disc = self.loss( + inputs, + reconstructions, + posterior, + 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val" + postfix, + ) + + self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + ae_params_list = ( + list(self.encoder.parameters()) + + list(self.decoder.parameters()) + + list(self.quant_conv.parameters()) + + list(self.post_quant_conv.parameters()) + ) + if self.learn_logvar: + print(f"{self.__class__.__name__}: Learning logvar") + ae_params_list.append(self.loss.logvar) + opt_ae = torch.optim.Adam(ae_params_list, lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam( + self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9) + ) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + if log_ema or self.use_ema: + with self.ema_scope(): + xrec_ema, posterior_ema = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec_ema.shape[1] > 3 + xrec_ema = self.to_rgb(xrec_ema) + log["samples_ema"] = self.decode( + torch.randn_like(posterior_ema.sample()) + ) + log["reconstructions_ema"] = xrec_ema + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x diff --git a/gen_3d_modules/CRM/imagedream/ldm/models/diffusion/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/models/diffusion/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/models/diffusion/ddim.py b/gen_3d_modules/CRM/imagedream/ldm/models/diffusion/ddim.py new file mode 100644 index 0000000..9bd9c84 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/models/diffusion/ddim.py @@ -0,0 +1,437 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +import comfy.utils + +from ...modules.diffusionmodules.util import ( + make_ddim_sampling_parameters, + make_ddim_timesteps, + noise_like, + extract_into_tensor, +) + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule( + self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True + ): + self.ddim_timesteps = make_ddim_timesteps( + ddim_discr_method=ddim_discretize, + num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps, + verbose=verbose, + ) + alphas_cumprod = self.model.alphas_cumprod + assert ( + alphas_cumprod.shape[0] == self.ddpm_num_timesteps + ), "alphas have to be defined for each timestep" + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer("betas", to_torch(self.model.betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer( + "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev) + ) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer( + "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", + to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())), + ) + self.register_buffer( + "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", + to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)), + ) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( + alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta, + verbose=verbose, + ) + self.register_buffer("ddim_sigmas", ddim_sigmas) + self.register_buffer("ddim_alphas", ddim_alphas) + self.register_buffer("ddim_alphas_prev", ddim_alphas_prev) + self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) + / (1 - self.alphas_cumprod) + * (1 - self.alphas_cumprod / self.alphas_cumprod_prev) + ) + self.register_buffer( + "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps + ) + + @torch.no_grad() + def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0.0, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs, + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print( + f"Warning: Got {cbs} conditionings but batch-size is {batch_size}" + ) + else: + if conditioning.shape[0] != batch_size: + print( + f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}" + ) + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + + samples, intermediates = self.ddim_sampling( + conditioning, + size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, + x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + **kwargs, + ) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling( + self, + cond, + shape, + x_T=None, + ddim_use_original_steps=False, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + log_every_t=100, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + **kwargs, + ): + """ + when inference time: all values of parameter + cond.keys(): dict_keys(['context', 'camera', 'num_frames', 'ip', 'ip_img']) + shape: (5, 4, 32, 32) + x_T: None + ddim_use_original_steps: False + timesteps: None + callback: None + quantize_denoised: False + mask: None + image_callback: None + log_every_t: 100 + temperature: 1.0 + noise_dropout: 0.0 + score_corrector: None + corrector_kwargs: None + unconditional_guidance_scale: 5 + unconditional_conditioning.keys(): dict_keys(['context', 'camera', 'num_frames', 'ip', 'ip_img']) + kwargs: {} + """ + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) # shape: torch.Size([5, 4, 32, 32]) mean: -0.00, std: 1.00, min: -3.64, max: 3.94 + else: + img = x_T + + if timesteps is None: # equal with set time step in hf + timesteps = ( + self.ddpm_num_timesteps + if ddim_use_original_steps + else self.ddim_timesteps + ) + elif timesteps is not None and not ddim_use_original_steps: + subset_end = ( + int( + min(timesteps / self.ddim_timesteps.shape[0], 1) + * self.ddim_timesteps.shape[0] + ) + - 1 + ) + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {"x_inter": [img], "pred_x0": [img]} + time_range = ( # reversed timesteps + reversed(range(0, timesteps)) + if ddim_use_original_steps + else np.flip(timesteps) + ) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps) + + comfy_pbar = comfy.utils.ProgressBar(total_steps) + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample( + x0, ts + ) # TODO: deterministic forward pass? + img = img_orig * mask + (1.0 - mask) * img + + outs = self.p_sample_ddim( + img, + cond, + ts, + index=index, + use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, + temperature=temperature, + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + **kwargs, + ) + img, pred_x0 = outs + if callback: + callback(i) + if img_callback: + img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates["x_inter"].append(img) + intermediates["pred_x0"].append(pred_x0) + + comfy_pbar.update_absolute(i + 1) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + dynamic_threshold=None, + **kwargs, + ): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.0: + model_output = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + elif isinstance(c[k], torch.Tensor): + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + assert c[k] == unconditional_conditioning[k] + c_in[k] = c[k] + elif isinstance(c, list): + c_in = list() + assert isinstance(unconditional_conditioning, list) + for i in range(len(c)): + c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) + else: + c_in = torch.cat([unconditional_conditioning, c]) + model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + model_output = model_uncond + unconditional_guidance_scale * ( + model_t - model_uncond + ) + + + if self.model.parameterization == "v": + print("using v!") + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + + if score_corrector is not None: + assert self.model.parameterization == "eps", "not implemented" + e_t = score_corrector.modify_score( + self.model, e_t, x, t, c, **corrector_kwargs + ) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = ( + self.model.alphas_cumprod_prev + if use_original_steps + else self.ddim_alphas_prev + ) + sqrt_one_minus_alphas = ( + self.model.sqrt_one_minus_alphas_cumprod + if use_original_steps + else self.ddim_sqrt_one_minus_alphas + ) + sigmas = ( + self.model.ddim_sigmas_for_original_num_steps + if use_original_steps + else self.ddim_sigmas + ) + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full( + (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device + ) + + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + + if dynamic_threshold is not None: + raise NotImplementedError() + + # direction pointing to x_t + dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return ( + extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise + ) + + @torch.no_grad() + def decode( + self, + x_latent, + cond, + t_start, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + use_original_steps=False, + **kwargs, + ): + timesteps = ( + np.arange(self.ddpm_num_timesteps) + if use_original_steps + else self.ddim_timesteps + ) + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + + iterator = tqdm(time_range, desc="Decoding image", total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full( + (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long + ) + x_dec, _ = self.p_sample_ddim( + x_dec, + cond, + ts, + index=index, + use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + **kwargs, + ) + return x_dec diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/attention.py b/gen_3d_modules/CRM/imagedream/ldm/modules/attention.py new file mode 100644 index 0000000..0693788 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/attention.py @@ -0,0 +1,456 @@ +from inspect import isfunction +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat +from typing import Optional, Any + +from .diffusionmodules.util import checkpoint + + +try: + import xformers + import xformers.ops + + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + +# CrossAttn precision handling +import os + +_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32") + + +def exists(val): + return val is not None + + +def uniq(arr): + return {el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b (h w) c") + k = rearrange(k, "b c h w -> b c (h w)") + w_ = torch.einsum("bij,bjk->bik", q, k) + + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, "b c h w -> b c (h w)") + w_ = rearrange(w_, "b i j -> b j i") + h_ = torch.einsum("bij,bjk->bik", v, w_) + h_ = rearrange(h_, "b c (h w) -> b c h w", h=h) + h_ = self.proj_out(h_) + + return x + h_ + + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs): + super().__init__() + print( + f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " + f"{heads} heads." + ) + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.with_ip = kwargs.get("with_ip", False) + if self.with_ip and (context_dim is not None): + self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.ip_dim= kwargs.get("ip_dim", 16) + self.ip_weight = kwargs.get("ip_weight", 1.0) + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None, mask=None): + q = self.to_q(x) + + has_ip = self.with_ip and (context is not None) + if has_ip: + # context dim [(b frame_num), (77 + img_token), 1024] + token_len = context.shape[1] + context_ip = context[:, -self.ip_dim:, :] + k_ip = self.to_k_ip(context_ip) + v_ip = self.to_v_ip(context_ip) + context = context[:, :(token_len - self.ip_dim), :] + + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + if has_ip: + k_ip, v_ip = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (k_ip, v_ip), + ) + # actually compute the attention, what we cannot get enough of + out_ip = xformers.ops.memory_efficient_attention( + q, k_ip, v_ip, attn_bias=None, op=self.attention_op + ) + out = out + self.ip_weight * out_ip + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + disable_self_attn=False, + **kwargs + ): + super().__init__() + assert XFORMERS_IS_AVAILBLE, "xformers is not available" + attn_cls = MemoryEfficientCrossAttention + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls( + query_dim=dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, + ) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + **kwargs + ) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint( + self._forward, (x, context), self.parameters(), self.checkpoint + ) + + def _forward(self, x, context=None): + x = ( + self.attn1( + self.norm1(x), context=context if self.disable_self_attn else None + ) + + x + ) + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + context_dim=None, + disable_self_attn=False, + use_linear=False, + use_checkpoint=True, + **kwargs + ): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + if not use_linear: + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim[d], + disable_self_attn=disable_self_attn, + checkpoint=use_checkpoint, + **kwargs + ) + for d in range(depth) + ] + ) + if not use_linear: + self.proj_out = zero_module( + nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + ) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i]) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class BasicTransformerBlock3D(BasicTransformerBlock): + def forward(self, x, context=None, num_frames=1): + return checkpoint( + self._forward, (x, context, num_frames), self.parameters(), self.checkpoint + ) + + def _forward(self, x, context=None, num_frames=1): + x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous() + x = ( + self.attn1( + self.norm1(x), + context=context if self.disable_self_attn else None + ) + + x + ) + x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous() + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer3D(nn.Module): + """3D self-attention""" + + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + context_dim=None, + disable_self_attn=False, + use_linear=False, + use_checkpoint=True, + **kwargs + ): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + if not use_linear: + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock3D( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim[d], + disable_self_attn=disable_self_attn, + checkpoint=use_checkpoint, + **kwargs + ) + for d in range(depth) + ] + ) + if not use_linear: + self.proj_out = zero_module( + nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + ) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + self.use_linear = use_linear + + def forward(self, x, context=None, num_frames=1): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i], num_frames=num_frames) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/adaptors.py b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/adaptors.py new file mode 100644 index 0000000..547e7f4 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/adaptors.py @@ -0,0 +1,163 @@ +# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + #(bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class ImageProjModel(torch.nn.Module): + """Projection Model""" + def __init__(self, + cross_attention_dim=1024, + clip_embeddings_dim=1024, + clip_extra_context_tokens=4): + super().__init__() + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + + # from 1024 -> 4 * 1024 + self.proj = torch.nn.Linear( + clip_embeddings_dim, + self.clip_extra_context_tokens * cross_attention_dim) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + embeds = image_embeds + clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +class SimpleReSampler(nn.Module): + def __init__(self, embedding_dim=1280, output_dim=1024): + super().__init__() + self.proj_out = nn.Linear(embedding_dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + def forward(self, latents): + """ + latents: B 256 N + """ + latents = self.proj_out(latents) + return self.norm_out(latents) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + ): + super().__init__() + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + self.proj_in = nn.Linear(embedding_dim, dim) + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, + dim_head=dim_head, + heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, x): + latents = self.latents.repeat(x.size(0), 1, 1) + x = self.proj_in(x) + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) + + +if __name__ == '__main__': + resampler = Resampler(embedding_dim=1280) + resampler = SimpleReSampler(embedding_dim=1280) + tensor = torch.rand(4, 257, 1280) + embed = resampler(tensor) + # embed = (tensor) + print(embed.shape) diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/model.py b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000..090df6b --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,1018 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +from typing import Optional, Any + +from ..attention import MemoryEfficientCrossAttention + +try: + import xformers + import xformers.ops + + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + print("No module 'xformers'. Proceeding without it.") + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm( + num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512, + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) # b,hw,c + k = k.reshape(b, c, h * w) # b,c,hw + w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +class MemoryEfficientAttnBlock(nn.Module): + """ + Uses xformers efficient implementation, + see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + Note: this is a single-head self-attention operation + """ + + # + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.attention_op: Optional[Any] = None + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + B, C, H, W = q.shape + q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v)) + + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(B, t.shape[1], 1, C) + .permute(0, 2, 1, 3) + .reshape(B * 1, t.shape[1], C) + .contiguous(), + (q, k, v), + ) + out = xformers.ops.memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + out = ( + out.unsqueeze(0) + .reshape(B, 1, out.shape[1], C) + .permute(0, 2, 1, 3) + .reshape(B, out.shape[1], C) + ) + out = rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C) + out = self.proj_out(out) + return x + out + + +class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): + def forward(self, x, context=None, mask=None): + b, c, h, w = x.shape + x = rearrange(x, "b c h w -> b (h w) c") + out = super().forward(x, context=context, mask=mask) + out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c) + return x + out + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): + assert attn_type in [ + "vanilla", + "vanilla-xformers", + "memory-efficient-cross-attn", + "linear", + "none", + ], f"attn_type {attn_type} unknown" + if XFORMERS_IS_AVAILBLE and attn_type == "vanilla": + attn_type = "vanilla-xformers" + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + assert attn_kwargs is None + return AttnBlock(in_channels) + elif attn_type == "vanilla-xformers": + print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") + return MemoryEfficientAttnBlock(in_channels) + elif type == "memory-efficient-cross-attn": + attn_kwargs["query_dim"] = in_channels + return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + raise NotImplementedError() + + +class Model(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + use_timestep=True, + use_linear_attn=False, + attn_type="vanilla", + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch * 4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList( + [ + torch.nn.Linear(self.ch, self.temb_ch), + torch.nn.Linear(self.temb_ch, self.temb_ch), + ] + ) + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + skip_in = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + if i_block == self.num_res_blocks: + skip_in = ch * in_ch_mult[i_level] + block.append( + ResnetBlock( + in_channels=block_in + skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x, t=None, context=None): + # assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb + ) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + double_z=True, + use_linear_attn=False, + attn_type="vanilla", + **ignore_kwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + give_pre_end=False, + tanh_out=False, + use_linear_attn=False, + attn_type="vanilla", + **ignorekwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList( + [ + nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock( + in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, + dropout=0.0, + ), + ResnetBlock( + in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, + dropout=0.0, + ), + ResnetBlock( + in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, + dropout=0.0, + ), + nn.Conv2d(2 * in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True), + ] + ) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1, 2, 3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + ch, + num_res_blocks, + resolution, + ch_mult=(2, 2), + dropout=0.0, + ): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d( + in_channels, mid_channels, kernel_size=3, stride=1, padding=1 + ) + self.res_block1 = nn.ModuleList( + [ + ResnetBlock( + in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0, + ) + for _ in range(depth) + ] + ) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList( + [ + ResnetBlock( + in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0, + ) + for _ in range(depth) + ] + ) + + self.conv_out = nn.Conv2d( + mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate( + x, + size=( + int(round(x.shape[2] * self.factor)), + int(round(x.shape[3] * self.factor)), + ), + ) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__( + self, + in_channels, + ch, + resolution, + out_ch, + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + ch_mult=(1, 2, 4, 8), + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder( + in_channels=in_channels, + num_res_blocks=num_res_blocks, + ch=ch, + ch_mult=ch_mult, + z_channels=intermediate_chn, + double_z=False, + resolution=resolution, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + out_ch=None, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=intermediate_chn, + mid_channels=intermediate_chn, + out_channels=out_ch, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__( + self, + z_channels, + out_ch, + resolution, + num_res_blocks, + attn_resolutions, + ch, + ch_mult=(1, 2, 4, 8), + dropout=0.0, + resamp_with_conv=True, + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + tmp_chn = z_channels * ch_mult[-1] + self.decoder = Decoder( + out_ch=out_ch, + z_channels=tmp_chn, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + in_channels=None, + num_res_blocks=num_res_blocks, + ch_mult=ch_mult, + resolution=resolution, + ch=ch, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=z_channels, + mid_channels=tmp_chn, + out_channels=tmp_chn, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size // in_size)) + 1 + factor_up = 1.0 + (out_size % in_size) + print( + f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}" + ) + self.rescaler = LatentRescaler( + factor=factor_up, + in_channels=in_channels, + mid_channels=2 * in_channels, + out_channels=in_channels, + ) + self.decoder = Decoder( + out_ch=out_channels, + resolution=out_size, + z_channels=in_channels, + num_res_blocks=2, + attn_resolutions=[], + in_channels=None, + ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)], + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print( + f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" + ) + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=4, stride=2, padding=1 + ) + + def forward(self, x, scale_factor=1.0): + if scale_factor == 1.0: + return x + else: + x = torch.nn.functional.interpolate( + x, mode=self.mode, align_corners=False, scale_factor=scale_factor + ) + return x diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/openaimodel.py b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000..ba8347a --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,1135 @@ +from abc import abstractmethod +import math + +import numpy as np +import torch +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from CRM.imagedream.ldm.modules.diffusionmodules.util import ( + checkpoint, + conv_nd, + linear, + avg_pool_nd, + zero_module, + normalization, + timestep_embedding, + convert_module_to_f16, + convert_module_to_f32 +) +from CRM.imagedream.ldm.modules.attention import ( + SpatialTransformer, + SpatialTransformer3D, + exists +) +from CRM.imagedream.ldm.modules.diffusionmodules.adaptors import ( + Resampler, + ImageProjModel +) + +## go +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter( + th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5 + ) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, num_frames=1): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer3D): + x = layer(x, context, num_frames=num_frames) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd( + dims, self.channels, self.out_channels, 3, padding=padding + ) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class TransposedUpsample(nn.Module): + "Learned 2x upsampling without padding" + + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d( + self.channels, self.out_channels, kernel_size=ks, stride=2 + ) + + def forward(self, x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, + self.channels, + self.out_channels, + 3, + stride=stride, + padding=padding, + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint( + self._forward, (x,), self.parameters(), True + ) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! + # return pt_checkpoint(self._forward, x) # pytorch + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial**2) * c + model.total_ops += th.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class Timestep(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + return timestep_embedding(t, self.dim) + + +class MultiViewUNetModel(nn.Module): + """ + The full multi-view UNet model with attention, timestep embedding and camera embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + :param camera_dim: dimensionality of camera input. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + use_bf16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + camera_dim=None, + with_ip=False, # wether add image prompt images + ip_dim=0, # number of extra token, 4 for global 16 for local + ip_weight=1.0, # weight for image prompt context + ip_mode="local_resample", # which mode of adaptor, global or local + ): + super().__init__() + if use_spatial_transformer: + assert ( + context_dim is not None + ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..." + + if context_dim is not None: + assert ( + use_spatial_transformer + ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..." + from omegaconf.listconfig import ListConfig + + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert ( + num_head_channels != -1 + ), "Either num_heads or num_head_channels has to be set" + + if num_head_channels == -1: + assert ( + num_heads != -1 + ), "Either num_heads or num_head_channels has to be set" + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError( + "provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult" + ) + self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all( + map( + lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], + range(len(num_attention_blocks)), + ) + ) + print( + f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set." + ) + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.dtype = th.bfloat16 if use_bf16 else self.dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + self.with_ip = with_ip # wether there is image prompt + self.ip_dim = ip_dim # num of extra token, 4 for global 16 for local + self.ip_weight = ip_weight + assert ip_mode in ["global", "local_resample"] + self.ip_mode = ip_mode # which mode of adaptor + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if camera_dim is not None: + time_embed_dim = model_channels * 4 + self.camera_embed = nn.Sequential( + linear(camera_dim, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + linear(adm_in_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + ) + else: + raise ValueError() + + if self.with_ip and (context_dim is not None) and ip_dim > 0: + if self.ip_mode == "local_resample": + # ip-adapter-plus + hidden_dim = 1280 + self.image_embed = Resampler( + dim=context_dim, + depth=4, + dim_head=64, + heads=12, + num_queries=ip_dim, # num token + embedding_dim=hidden_dim, + output_dim=context_dim, + ff_mult=4, + ) + elif self.ip_mode == "global": + self.image_embed = ImageProjModel( + cross_attention_dim=context_dim, + clip_extra_context_tokens=ip_dim) + else: + raise ValueError(f"{self.ip_mode} is not supported") + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ( + ch // num_heads + if use_spatial_transformer + else num_head_channels + ) + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if ( + not exists(num_attention_blocks) + or nr < num_attention_blocks[level] + ): + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer3D( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + with_ip=self.with_ip, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer3D( # always uses a self-attn + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + with_ip=self.with_ip, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ( + ch // num_heads + if use_spatial_transformer + else num_head_channels + ) + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if ( + not exists(num_attention_blocks) + or i < num_attention_blocks[level] + ): + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer3D( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + with_ip=self.with_ip, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + self.output_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + self.output_blocks.apply(convert_module_to_f32) + + def forward( + self, + x, + timesteps=None, + context=None, + y=None, + camera=None, + num_frames=1, + **kwargs, + ): + """ + Apply the model to an input batch. + :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views). + :param timesteps: a 1-D batch of timesteps. + :param context: a dict conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional, default None. + :param num_frames: a integer indicating number of frames for tensor reshaping. + :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views). + """ + assert ( + x.shape[0] % num_frames == 0 + ), "[UNet] input batch size must be dividable by num_frames!" + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) # shape: torch.Size([B, 320]) mean: 0.18, std: 0.68, min: -1.00, max: 1.00 + emb = self.time_embed(t_emb) # shape: torch.Size([B, 1280]) mean: 0.12, std: 0.57, min: -5.73, max: 6.51 + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + # Add camera embeddings + if camera is not None: + assert camera.shape[0] == emb.shape[0] + # camera embed: shape: torch.Size([B, 1280]) mean: -0.02, std: 0.27, min: -7.23, max: 2.04 + emb = emb + self.camera_embed(camera) + ip = kwargs.get("ip", None) + ip_img = kwargs.get("ip_img", None) + + if ip_img is not None: + x[(num_frames-1)::num_frames, :, :, :] = ip_img + + if ip is not None: + ip_emb = self.image_embed(ip) # shape: torch.Size([B, 16, 1024]) mean: -0.00, std: 1.00, min: -11.65, max: 7.31 + context = torch.cat((context, ip_emb), 1) # shape: torch.Size([B, 93, 1024]) mean: -0.00, std: 1.00, min: -11.65, max: 7.31 + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context, num_frames=num_frames) + hs.append(h) + h = self.middle_block(h, emb, context, num_frames=num_frames) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context, num_frames=num_frames) + h = h.type(x.dtype) # shape: torch.Size([10, 320, 32, 32]) mean: -0.67, std: 3.96, min: -42.74, max: 25.58 + if self.predict_codebook_ids: # False + return self.id_predictor(h) + else: + return self.out(h) # shape: torch.Size([10, 4, 32, 32]) mean: -0.00, std: 0.91, min: -3.65, max: 3.93 + + + + +class MultiViewUNetModelStage2(MultiViewUNetModel): + """ + The full multi-view UNet model with attention, timestep embedding and camera embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + :param camera_dim: dimensionality of camera input. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + use_bf16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + camera_dim=None, + with_ip=False, # wether add image prompt images + ip_dim=0, # number of extra token, 4 for global 16 for local + ip_weight=1.0, # weight for image prompt context + ip_mode="local_resample", # which mode of adaptor, global or local + ): + super().__init__( + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout, + channel_mult, + conv_resample, + dims, + num_classes, + use_checkpoint, + use_fp16, + use_bf16, + num_heads, + num_head_channels, + num_heads_upsample, + use_scale_shift_norm, + resblock_updown, + use_new_attention_order, + use_spatial_transformer, + transformer_depth, + context_dim, + n_embed, + legacy, + disable_self_attentions, + num_attention_blocks, + disable_middle_self_attn, + use_linear_in_transformer, + adm_in_channels, + camera_dim, + with_ip, + ip_dim, + ip_weight, + ip_mode, + ) + + def forward( + self, + x, + timesteps=None, + context=None, + y=None, + camera=None, + num_frames=1, + **kwargs, + ): + """ + Apply the model to an input batch. + :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views). + :param timesteps: a 1-D batch of timesteps. + :param context: a dict conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional, default None. + :param num_frames: a integer indicating number of frames for tensor reshaping. + :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views). + """ + assert ( + x.shape[0] % num_frames == 0 + ), "[UNet] input batch size must be dividable by num_frames!" + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) # shape: torch.Size([B, 320]) mean: 0.18, std: 0.68, min: -1.00, max: 1.00 + emb = self.time_embed(t_emb) # shape: torch.Size([B, 1280]) mean: 0.12, std: 0.57, min: -5.73, max: 6.51 + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + # Add camera embeddings + if camera is not None: + assert camera.shape[0] == emb.shape[0] + # camera embed: shape: torch.Size([B, 1280]) mean: -0.02, std: 0.27, min: -7.23, max: 2.04 + emb = emb + self.camera_embed(camera) + ip = kwargs.get("ip", None) + ip_img = kwargs.get("ip_img", None) + pixel_images = kwargs.get("pixel_images", None) + + if ip_img is not None: + x[(num_frames-1)::num_frames, :, :, :] = ip_img + + x = torch.cat((x, pixel_images), dim=1) + + if ip is not None: + ip_emb = self.image_embed(ip) # shape: torch.Size([B, 16, 1024]) mean: -0.00, std: 1.00, min: -11.65, max: 7.31 + context = torch.cat((context, ip_emb), 1) # shape: torch.Size([B, 93, 1024]) mean: -0.00, std: 1.00, min: -11.65, max: 7.31 + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context, num_frames=num_frames) + hs.append(h) + h = self.middle_block(h, emb, context, num_frames=num_frames) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context, num_frames=num_frames) + h = h.type(x.dtype) # shape: torch.Size([10, 320, 32, 32]) mean: -0.67, std: 3.96, min: -42.74, max: 25.58 + if self.predict_codebook_ids: # False + return self.id_predictor(h) + else: + return self.out(h) # shape: torch.Size([10, 4, 32, 32]) mean: -0.00, std: 0.91, min: -3.65, max: 3.93 + \ No newline at end of file diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/util.py b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000..665af3e --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,353 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat +import importlib + + +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def make_beta_schedule( + schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3 +): + if schedule == "linear": + betas = ( + torch.linspace( + linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64 + ) + ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace( + linear_start, linear_end, n_timestep, dtype=torch.float64 + ) + elif schedule == "sqrt": + betas = ( + torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + ** 0.5 + ) + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + +def enforce_zero_terminal_snr(betas): + betas = torch.tensor(betas) if not isinstance(betas, torch.Tensor) else betas + # Convert betas to alphas_bar_sqrt + alphas =1 - betas + alphas_bar = alphas.cumprod(0) + alphas_bar_sqrt = alphas_bar.sqrt() + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + # Shift so last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + # Scale so first timestep is back to old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt ** 2 + alphas = alphas_bar[1:] / alphas_bar[:-1] + alphas = torch.cat ([alphas_bar[0:1], alphas]) + betas = 1 - alphas + return betas + + +def make_ddim_timesteps( + ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True +): + if ddim_discr_method == "uniform": + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == "quad": + ddim_timesteps = ( + (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2 + ).astype(int) + else: + raise NotImplementedError( + f'There is no ddim discretization method called "{ddim_discr_method}"' + ) + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f"Selected timesteps for ddim sampler: {steps_out}") + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt( + (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev) + ) + if verbose: + print( + f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}" + ) + print( + f"For the chosen value of eta, which is {eta}, " + f"this results in the following sigma_t schedule for ddim sampler {sigmas}" + ) + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + # import pdb; pdb.set_trace() + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {"c_concat": [c_concat], "c_crossattn": [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat( + shape[0], *((1,) * (len(shape) - 1)) + ) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +# dummy replace +def convert_module_to_f16(l): + """ + Convert primitive modules to float16. + """ + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + +def convert_module_to_f32(l): + """ + Convert primitive modules to float32, undoing convert_module_to_f16(). + """ + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): + l.weight.data = l.weight.data.float() + if l.bias is not None: + l.bias.data = l.bias.data.float() diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/distributions/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/distributions/distributions.py b/gen_3d_modules/CRM/imagedream/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000..0b61f03 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/distributions/distributions.py @@ -0,0 +1,102 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to( + device=self.parameters.device + ) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to( + device=self.parameters.device + ) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/ema.py b/gen_3d_modules/CRM/imagedream/ldm/modules/ema.py new file mode 100644 index 0000000..97b5ae2 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/ema.py @@ -0,0 +1,86 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError("Decay must be between 0 and 1") + + self.m_name2s_name = {} + self.register_buffer("decay", torch.tensor(decay, dtype=torch.float32)) + self.register_buffer( + "num_updates", + torch.tensor(0, dtype=torch.int) + if use_num_upates + else torch.tensor(-1, dtype=torch.int), + ) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace(".", "") + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def reset_num_updates(self): + del self.num_updates + self.register_buffer("num_updates", torch.tensor(0, dtype=torch.int)) + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_( + one_minus_decay * (shadow_params[sname] - m_param[key]) + ) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/encoders/__init__.py b/gen_3d_modules/CRM/imagedream/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/imagedream/ldm/modules/encoders/modules.py b/gen_3d_modules/CRM/imagedream/ldm/modules/encoders/modules.py new file mode 100644 index 0000000..8efe14f --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/modules/encoders/modules.py @@ -0,0 +1,329 @@ +import torch +import torch.nn as nn +from torch.utils.checkpoint import checkpoint + +from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel + +import numpy as np +import open_clip +from PIL import Image +from ...util import default, count_params + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class IdentityEncoder(AbstractEncoder): + def encode(self, x): + return x + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key="class", ucg_rate=0.1): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + self.n_classes = n_classes + self.ucg_rate = ucg_rate + + def forward(self, batch, key=None, disable_dropout=False): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + if self.ucg_rate > 0.0 and not disable_dropout: + mask = 1.0 - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) + c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1) + c = c.long() + c = self.embedding(c) + return c + + def get_unconditional_conditioning(self, bs, device="cuda"): + uc_class = ( + self.n_classes - 1 + ) # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) + uc = torch.ones((bs,), device=device) * uc_class + uc = {self.key: uc} + return uc + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + + def __init__( + self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True + ): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +class FrozenCLIPEmbedder(AbstractEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + + LAYERS = ["last", "pooled", "hidden"] + + def __init__( + self, + version="openai/clip-vit-large-patch14", + device="cuda", + max_length=77, + freeze=True, + layer="last", + layer_idx=None, + ): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = layer_idx + if layer == "hidden": + assert layer_idx is not None + assert 0 <= abs(layer_idx) <= 12 + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer( + input_ids=tokens, output_hidden_states=self.layer == "hidden" + ) + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + return z + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPEmbedder(AbstractEncoder, nn.Module): + """ + Uses the OpenCLIP transformer encoder for text + """ + + LAYERS = [ + # "pooled", + "last", + "penultimate", + ] + + def __init__( + self, + arch="ViT-H-14", + version="laion2b_s32b_b79k", + device="cuda", + max_length=77, + freeze=True, + layer="last", + ip_mode=None + ): + """_summary_ + + Args: + ip_mode (str, optional): what is the image promcessing mode. Defaults to None. + + """ + super().__init__() + assert layer in self.LAYERS + model, _, preprocess = open_clip.create_model_and_transforms( + arch, device=torch.device("cpu"), pretrained=version + ) + if ip_mode is None: + del model.visual + + self.model = model + self.preprocess = preprocess + self.device = device + self.max_length = max_length + self.ip_mode = ip_mode + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = open_clip.tokenize(text) + z = self.encode_with_transformer(tokens.to(self.device)) + return z + + def forward_image(self, pil_image): + if isinstance(pil_image, Image.Image): + pil_image = [pil_image] + if isinstance(pil_image, torch.Tensor): + pil_image = pil_image.cpu().numpy() + if isinstance(pil_image, np.ndarray): + if pil_image.ndim == 3: + pil_image = pil_image[None, :, :, :] + pil_image = [Image.fromarray(x) for x in pil_image] + + images = [] + for image in pil_image: + images.append(self.preprocess(image).to(self.device)) + + image = torch.stack(images, 0) # to [b, 3, h, w] + if self.ip_mode == "global": + image_features = self.model.encode_image(image) + image_features /= image_features.norm(dim=-1, keepdim=True) + elif "local" in self.ip_mode: + image_features = self.encode_image_with_transformer(image) + + return image_features # b, l + + def encode_image_with_transformer(self, x): + visual = self.model.visual + x = visual.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [visual.class_embedding.to(x.dtype) + \ + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + visual.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + # x = visual.patch_dropout(x) + x = visual.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + hidden = self.image_transformer_forward(x) + x = hidden[-2].permute(1, 0, 2) # LND -> NLD + return x + + def image_transformer_forward(self, x): + encoder_states = () + trans = self.model.visual.transformer + for r in trans.resblocks: + if trans.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + x = checkpoint(r, x, None, None, None) + else: + x = r(x, attn_mask=None) + encoder_states = encoder_states + (x, ) + return encoder_states + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if ( + self.model.transformer.grad_checkpointing + and not torch.jit.is_scripting() + ): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenCLIPT5Encoder(AbstractEncoder): + def __init__( + self, + clip_version="openai/clip-vit-large-patch14", + t5_version="google/t5-v1_1-xl", + device="cuda", + clip_max_length=77, + t5_max_length=77, + ): + super().__init__() + self.clip_encoder = FrozenCLIPEmbedder( + clip_version, device, max_length=clip_max_length + ) + self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) + print( + f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, " + f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params." + ) + + def encode(self, text): + return self(text) + + def forward(self, text): + clip_z = self.clip_encoder.encode(text) + t5_z = self.t5_encoder.encode(text) + return [clip_z, t5_z] diff --git a/gen_3d_modules/CRM/imagedream/ldm/util.py b/gen_3d_modules/CRM/imagedream/ldm/util.py new file mode 100644 index 0000000..f541504 --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/ldm/util.py @@ -0,0 +1,226 @@ +import importlib + +import random +import torch +import numpy as np +from collections import abc + +import multiprocessing as mp +from threading import Thread +from queue import Queue + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype("data/DejaVuSans.ttf", size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join( + xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc) + ) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + # import pdb; pdb.set_trace() + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + # import pdb; pdb.set_trace() + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): + # create dummy dataset instance + + # run prefetching + if idx_to_fn: + res = func(data, worker_id=idx) + else: + res = func(data) + Q.put([idx, res]) + Q.put("Done") + + +def parallel_data_prefetch( + func: callable, + data, + n_proc, + target_data_type="ndarray", + cpu_intensive=True, + use_worker_id=False, +): + # if target_data_type not in ["ndarray", "list"]: + # raise ValueError( + # "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray." + # ) + if isinstance(data, np.ndarray) and target_data_type == "list": + raise ValueError("list expected but function got ndarray.") + elif isinstance(data, abc.Iterable): + if isinstance(data, dict): + print( + f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' + ) + data = list(data.values()) + if target_data_type == "ndarray": + data = np.asarray(data) + else: + data = list(data) + else: + raise TypeError( + f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." + ) + + if cpu_intensive: + Q = mp.Queue(1000) + proc = mp.Process + else: + Q = Queue(1000) + proc = Thread + # spawn processes + if target_data_type == "ndarray": + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate(np.array_split(data, n_proc)) + ] + else: + step = ( + int(len(data) / n_proc + 1) + if len(data) % n_proc != 0 + else int(len(data) / n_proc) + ) + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate( + [data[i : i + step] for i in range(0, len(data), step)] + ) + ] + processes = [] + for i in range(n_proc): + p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) + processes += [p] + + # start processes + print(f"Start prefetching...") + import time + + start = time.time() + gather_res = [[] for _ in range(n_proc)] + try: + for p in processes: + p.start() + + k = 0 + while k < n_proc: + # get result + res = Q.get() + if res == "Done": + k += 1 + else: + gather_res[res[0]] = res[1] + + except Exception as e: + print("Exception: ", e) + for p in processes: + p.terminate() + + raise e + finally: + for p in processes: + p.join() + print(f"Prefetching complete. [{time.time() - start} sec.]") + + if target_data_type == "ndarray": + if not isinstance(gather_res[0], np.ndarray): + return np.concatenate([np.asarray(r) for r in gather_res], axis=0) + + # order outputs + return np.concatenate(gather_res, axis=0) + elif target_data_type == "list": + out = [] + for r in gather_res: + out.extend(r) + return out + else: + return gather_res + +def set_seed(seed=None): + random.seed(seed) + np.random.seed(seed) + if seed is not None: + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + +def add_random_background(image, bg_color=None): + bg_color = np.random.rand() * 255 if bg_color is None else bg_color + image = np.array(image) + rgb, alpha = image[..., :3], image[..., 3:] + alpha = alpha.astype(np.float32) / 255.0 + image_new = rgb * alpha + bg_color * (1 - alpha) + return Image.fromarray(image_new.astype(np.uint8)) \ No newline at end of file diff --git a/gen_3d_modules/CRM/imagedream/model_zoo.py b/gen_3d_modules/CRM/imagedream/model_zoo.py new file mode 100644 index 0000000..2bfb48a --- /dev/null +++ b/gen_3d_modules/CRM/imagedream/model_zoo.py @@ -0,0 +1,64 @@ +""" Utiliy functions to load pre-trained models more easily """ +import os +import pkg_resources +from omegaconf import OmegaConf + +import torch +from huggingface_hub import hf_hub_download + +from CRM.imagedream.ldm.util import instantiate_from_config + + +PRETRAINED_MODELS = { + "sd-v2.1-base-4view-ipmv": { + "config": "sd_v2_base_ipmv.yaml", + "repo_id": "Peng-Wang/ImageDream", + "filename": "sd-v2.1-base-4view-ipmv.pt", + }, + "sd-v2.1-base-4view-ipmv-local": { + "config": "sd_v2_base_ipmv_local.yaml", + "repo_id": "Peng-Wang/ImageDream", + "filename": "sd-v2.1-base-4view-ipmv-local.pt", + }, +} + + +def get_config_file(config_path): + cfg_file = pkg_resources.resource_filename( + "imagedream", os.path.join("configs", config_path) + ) + if not os.path.exists(cfg_file): + raise RuntimeError(f"Config {config_path} not available!") + return cfg_file + + +def build_model(model_name, config_path=None, ckpt_path=None, cache_dir=None): + if (config_path is not None) and (ckpt_path is not None): + config = OmegaConf.load(config_path) + model = instantiate_from_config(config.model) + model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False) + return model + + if not model_name in PRETRAINED_MODELS: + raise RuntimeError( + f"Model name {model_name} is not a pre-trained model. Available models are:\n- " + + "\n- ".join(PRETRAINED_MODELS.keys()) + ) + model_info = PRETRAINED_MODELS[model_name] + + # Instiantiate the model + print(f"Loading model from config: {model_info['config']}") + config_file = get_config_file(model_info["config"]) + config = OmegaConf.load(config_file) + model = instantiate_from_config(config.model) + + # Load pre-trained checkpoint from huggingface + if not ckpt_path: + ckpt_path = hf_hub_download( + repo_id=model_info["repo_id"], + filename=model_info["filename"], + cache_dir=cache_dir, + ) + print(f"Loading model from cache file: {ckpt_path}") + model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False) + return model diff --git a/gen_3d_modules/CRM/libs/base_utils.py b/gen_3d_modules/CRM/libs/base_utils.py new file mode 100644 index 0000000..9233461 --- /dev/null +++ b/gen_3d_modules/CRM/libs/base_utils.py @@ -0,0 +1,84 @@ +import numpy as np +import cv2 +import torch +import numpy as np +from PIL import Image + + +def instantiate_from_config(config): + if not "target" in config: + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + import importlib + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def tensor_detail(t): + assert type(t) == torch.Tensor + print(f"shape: {t.shape} mean: {t.mean():.2f}, std: {t.std():.2f}, min: {t.min():.2f}, max: {t.max():.2f}") + + + +def drawRoundRec(draw, color, x, y, w, h, r): + drawObject = draw + + '''Rounds''' + drawObject.ellipse((x, y, x + r, y + r), fill=color) + drawObject.ellipse((x + w - r, y, x + w, y + r), fill=color) + drawObject.ellipse((x, y + h - r, x + r, y + h), fill=color) + drawObject.ellipse((x + w - r, y + h - r, x + w, y + h), fill=color) + + '''rec.s''' + drawObject.rectangle((x + r / 2, y, x + w - (r / 2), y + h), fill=color) + drawObject.rectangle((x, y + r / 2, x + w, y + h - (r / 2)), fill=color) + + +def do_resize_content(original_image: Image, scale_rate): + # resize image content wile retain the original image size + if scale_rate != 1: + # Calculate the new size after rescaling + new_size = tuple(int(dim * scale_rate) for dim in original_image.size) + # Resize the image while maintaining the aspect ratio + resized_image = original_image.resize(new_size) + # Create a new image with the original size and black background + padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0)) + paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2) + padded_image.paste(resized_image, paste_position) + return padded_image + else: + return original_image + +def add_stroke(img, color=(255, 255, 255), stroke_radius=3): + # color in R, G, B format + if isinstance(img, Image.Image): + assert img.mode == "RGBA" + img = cv2.cvtColor(np.array(img), cv2.COLOR_RGBA2BGRA) + else: + assert img.shape[2] == 4 + gray = img[:,:, 3] + ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY) + contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) + res = cv2.drawContours(img, contours,-1, tuple(color)[::-1] + (255,), stroke_radius) + return Image.fromarray(cv2.cvtColor(res,cv2.COLOR_BGRA2RGBA)) + +def make_blob(image_size=(512, 512), sigma=0.2): + """ + make 2D blob image with: + I(x, y)=1-\exp \left(-\frac{(x-H / 2)^2+(y-W / 2)^2}{2 \sigma^2 HS}\right) + """ + import numpy as np + H, W = image_size + x = np.arange(0, W, 1, float) + y = np.arange(0, H, 1, float) + x, y = np.meshgrid(x, y) + x0 = W // 2 + y0 = H // 2 + img = 1 - np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2 * H * W)) + return (img * 255).astype(np.uint8) \ No newline at end of file diff --git a/gen_3d_modules/CRM/libs/sample.py b/gen_3d_modules/CRM/libs/sample.py new file mode 100644 index 0000000..1650870 --- /dev/null +++ b/gen_3d_modules/CRM/libs/sample.py @@ -0,0 +1,380 @@ +import numpy as np +import torch +from CRM.imagedream.camera_utils import get_camera_for_index +from CRM.imagedream.ldm.util import set_seed, add_random_background +from CRM.libs.base_utils import do_resize_content +from CRM.imagedream.ldm.models.diffusion.ddim import DDIMSampler +from torchvision import transforms as T + + +class ImageDreamDiffusion: + def __init__( + self, + model, + device, + dtype, + mode, + num_frames, + camera_views, + ref_position, + random_background=False, + offset_noise=False, + resize_rate=1, + image_size=256, + seed=1234, + ) -> None: + assert mode in ["pixel", "local"] + size = image_size + self.seed = seed + batch_size = max(4, num_frames) + + neg_texts = "uniform low no texture ugly, boring, bad anatomy, blurry, pixelated, obscure, unnatural colors, poor lighting, dull, and unclear." + uc = model.get_learned_conditioning([neg_texts]).to(device) + sampler = DDIMSampler(model) + + # pre-compute camera matrices + camera = [get_camera_for_index(i).squeeze() for i in camera_views] + camera[ref_position] = torch.zeros_like(camera[ref_position]) # set ref camera to zero + camera = torch.stack(camera) + camera = camera.repeat(batch_size // num_frames, 1).to(device) + + self.image_transform = T.Compose( + [ + T.Resize((size, size)), + T.ToTensor(), # scale image pixels from [0,255] to [0,1] values + T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ] + ) + self.dtype = dtype + self.ref_position = ref_position + self.mode = mode + self.random_background = random_background + self.resize_rate = resize_rate + self.num_frames = num_frames + self.size = size + self.device = device + self.batch_size = batch_size + self.model = model + self.sampler = sampler + self.uc = uc + self.camera = camera + self.offset_noise = offset_noise + + @staticmethod + def i2i( + model, + image_size, + prompt, + uc, + sampler, + ip=None, + step=20, + scale=5.0, + batch_size=8, + ddim_eta=0.0, + dtype=torch.float32, + device="cuda", + camera=None, + num_frames=4, + pixel_control=False, + transform=None, + offset_noise=False, + ): + """ The function supports additional image prompt. + Args: + model (_type_): the image dream model + image_size (_type_): size of diffusion output (standard 256) + prompt (_type_): text prompt for the image (prompt in type str) + uc (_type_): unconditional vector (tensor in shape [1, 77, 1024]) + sampler (_type_): imagedream.ldm.models.diffusion.ddim.DDIMSampler + ip (Image, optional): the image prompt. Defaults to None. + step (int, optional): _description_. Defaults to 20. + scale (float, optional): _description_. Defaults to 7.5. + batch_size (int, optional): _description_. Defaults to 8. + ddim_eta (float, optional): _description_. Defaults to 0.0. + dtype (_type_, optional): _description_. Defaults to torch.float32. + device (str, optional): _description_. Defaults to "cuda". + camera (_type_, optional): camera info in tensor, shape: torch.Size([5, 16]) mean: 0.11, std: 0.49, min: -1.00, max: 1.00 + num_frames (int, optional): _num of frames (views) to generate + pixel_control: whether to use pixel conditioning. Defaults to False, True when using pixel mode + transform: Compose( + Resize(size=(256, 256), interpolation=bilinear, max_size=None, antialias=warn) + ToTensor() + Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)) + ) + """ + ip_raw = ip + if type(prompt) != list: + prompt = [prompt] + with torch.no_grad(), torch.autocast(device_type=torch.device(device).type, dtype=dtype): + c = model.get_learned_conditioning(prompt).to( + device + ) # shape: torch.Size([1, 77, 1024]) mean: -0.17, std: 1.02, min: -7.50, max: 13.05 + c_ = {"context": c.repeat(batch_size, 1, 1)} # batch_size + uc_ = {"context": uc.repeat(batch_size, 1, 1)} + + if camera is not None: + c_["camera"] = uc_["camera"] = ( + camera # shape: torch.Size([5, 16]) mean: 0.11, std: 0.49, min: -1.00, max: 1.00 + ) + c_["num_frames"] = uc_["num_frames"] = num_frames + + if ip is not None: + ip_embed = model.get_learned_image_conditioning(ip).to( + device + ) # shape: torch.Size([1, 257, 1280]) mean: 0.06, std: 0.53, min: -6.83, max: 11.12 + ip_ = ip_embed.repeat(batch_size, 1, 1) + c_["ip"] = ip_ + uc_["ip"] = torch.zeros_like(ip_) + + if pixel_control: + assert camera is not None + ip = transform(ip).to( + device + ) # shape: torch.Size([3, 256, 256]) mean: 0.33, std: 0.37, min: -1.00, max: 1.00 + ip_img = model.get_first_stage_encoding( + model.encode_first_stage(ip[None, :, :, :]) + ) # shape: torch.Size([1, 4, 32, 32]) mean: 0.23, std: 0.77, min: -4.42, max: 3.55 + c_["ip_img"] = ip_img + uc_["ip_img"] = torch.zeros_like(ip_img) + + shape = [4, image_size // 8, image_size // 8] # [4, 32, 32] + if offset_noise: + ref = transform(ip_raw).to(device) + ref_latent = model.get_first_stage_encoding(model.encode_first_stage(ref[None, :, :, :])) + ref_mean = ref_latent.mean(dim=(-1, -2), keepdim=True) + time_steps = torch.randint(model.num_timesteps - 1, model.num_timesteps, (batch_size,), device=device) + x_T = model.q_sample(torch.ones([batch_size] + shape, device=device) * ref_mean, time_steps) + + samples_ddim, _ = ( + sampler.sample( # shape: torch.Size([5, 4, 32, 32]) mean: 0.29, std: 0.85, min: -3.38, max: 4.43 + S=step, + conditioning=c_, + batch_size=batch_size, + shape=shape, + verbose=False, + unconditional_guidance_scale=scale, + unconditional_conditioning=uc_, + eta=ddim_eta, + x_T=x_T if offset_noise else None, + ) + ) + + x_sample = model.decode_first_stage(samples_ddim) + x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = x_sample.permute(0, 2, 3, 1) # (N, H, W, 3) in [0, 1] + + return x_sample + + def diffuse(self, t, ip, n_test=2): + set_seed(self.seed) + ip = do_resize_content(ip, self.resize_rate) + if self.random_background: + ip = add_random_background(ip) + + images = [] + for _ in range(n_test): + img = self.i2i( + self.model, + self.size, + t, + self.uc, + self.sampler, + ip=ip, + step=50, + scale=5, + batch_size=self.batch_size, + ddim_eta=0.0, + dtype=self.dtype, + device=self.device, + camera=self.camera, + num_frames=self.num_frames, + pixel_control=(self.mode == "pixel"), + transform=self.image_transform, + offset_noise=self.offset_noise, + ) + img = np.concatenate(img, 1) + img = np.concatenate((img, ip.resize((self.size, self.size))), axis=1) + images.append(img) + set_seed() # unset random and numpy seed + return images + + +class ImageDreamDiffusionStage2: + def __init__( + self, + model, + device, + dtype, + num_frames, + camera_views, + ref_position, + random_background=False, + offset_noise=False, + resize_rate=1, + mode="pixel", + image_size=256, + seed=1234, + ) -> None: + assert mode in ["pixel", "local"] + + size = image_size + self.seed = seed + batch_size = max(4, num_frames) + + neg_texts = "uniform low no texture ugly, boring, bad anatomy, blurry, pixelated, obscure, unnatural colors, poor lighting, dull, and unclear." + uc = model.get_learned_conditioning([neg_texts]).to(device) + sampler = DDIMSampler(model) + + # pre-compute camera matrices + camera = [get_camera_for_index(i).squeeze() for i in camera_views] + if ref_position is not None: + camera[ref_position] = torch.zeros_like(camera[ref_position]) # set ref camera to zero + camera = torch.stack(camera) + camera = camera.repeat(batch_size // num_frames, 1).to(device) + + self.image_transform = T.Compose( + [ + T.Resize((size, size)), + T.ToTensor(), + T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ] + ) + + self.dtype = dtype + self.mode = mode + self.ref_position = ref_position + self.random_background = random_background + self.resize_rate = resize_rate + self.num_frames = num_frames + self.size = size + self.device = device + self.batch_size = batch_size + self.model = model + self.sampler = sampler + self.uc = uc + self.camera = camera + self.offset_noise = offset_noise + + @staticmethod + def i2iStage2( + model, + image_size, + prompt, + uc, + sampler, + pixel_images, + ip=None, + step=20, + scale=5.0, + batch_size=8, + ddim_eta=0.0, + dtype=torch.float32, + device="cuda", + camera=None, + num_frames=4, + pixel_control=False, + transform=None, + offset_noise=False, + ): + ip_raw = ip + if type(prompt) != list: + prompt = [prompt] + with torch.no_grad(), torch.autocast(device_type=torch.device(device).type, dtype=dtype): + c = model.get_learned_conditioning(prompt).to( + device + ) # shape: torch.Size([1, 77, 1024]) mean: -0.17, std: 1.02, min: -7.50, max: 13.05 + c_ = {"context": c.repeat(batch_size, 1, 1)} # batch_size + uc_ = {"context": uc.repeat(batch_size, 1, 1)} + + if camera is not None: + c_["camera"] = uc_["camera"] = ( + camera # shape: torch.Size([5, 16]) mean: 0.11, std: 0.49, min: -1.00, max: 1.00 + ) + c_["num_frames"] = uc_["num_frames"] = num_frames + + if ip is not None: + ip_embed = model.get_learned_image_conditioning(ip).to( + device + ) # shape: torch.Size([1, 257, 1280]) mean: 0.06, std: 0.53, min: -6.83, max: 11.12 + ip_ = ip_embed.repeat(batch_size, 1, 1) + c_["ip"] = ip_ + uc_["ip"] = torch.zeros_like(ip_) + + if pixel_control: + assert camera is not None + + transed_pixel_images = torch.stack([transform(i).to(device) for i in pixel_images]) + latent_pixel_images = model.get_first_stage_encoding(model.encode_first_stage(transed_pixel_images)) + + c_["pixel_images"] = latent_pixel_images + uc_["pixel_images"] = torch.zeros_like(latent_pixel_images) + + shape = [4, image_size // 8, image_size // 8] # [4, 32, 32] + if offset_noise: + ref = transform(ip_raw).to(device) + ref_latent = model.get_first_stage_encoding(model.encode_first_stage(ref[None, :, :, :])) + ref_mean = ref_latent.mean(dim=(-1, -2), keepdim=True) + time_steps = torch.randint(model.num_timesteps - 1, model.num_timesteps, (batch_size,), device=device) + x_T = model.q_sample(torch.ones([batch_size] + shape, device=device) * ref_mean, time_steps) + + samples_ddim, _ = ( + sampler.sample( # shape: torch.Size([5, 4, 32, 32]) mean: 0.29, std: 0.85, min: -3.38, max: 4.43 + S=step, + conditioning=c_, + batch_size=batch_size, + shape=shape, + verbose=False, + unconditional_guidance_scale=scale, + unconditional_conditioning=uc_, + eta=ddim_eta, + x_T=x_T if offset_noise else None, + ) + ) + x_sample = model.decode_first_stage(samples_ddim) + x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = x_sample.permute(0, 2, 3, 1) # (N, H, W, 3) in [0, 1] + + return x_sample + + @torch.no_grad() + def diffuse(self, t, ip, pixel_images, n_test=2): + set_seed(self.seed) + ip = do_resize_content(ip, self.resize_rate) + pixel_images = [do_resize_content(i, self.resize_rate) for i in pixel_images] + + if self.random_background: + bg_color = np.random.rand() * 255 + ip = add_random_background(ip, bg_color) + pixel_images = [add_random_background(i, bg_color) for i in pixel_images] + + images = [] + for _ in range(n_test): + img = self.i2iStage2( + self.model, + self.size, + t, + self.uc, + self.sampler, + pixel_images=pixel_images, + ip=ip, + step=50, + scale=5, + batch_size=self.batch_size, + ddim_eta=0.0, + dtype=self.dtype, + device=self.device, + camera=self.camera, + num_frames=self.num_frames, + pixel_control=(self.mode == "pixel"), + transform=self.image_transform, + offset_noise=self.offset_noise, + ) + img = np.concatenate(img, 1) + img = np.concatenate( + (img, ip.resize((self.size, self.size)), *[i.resize((self.size, self.size)) for i in pixel_images]), + axis=1, + ) + images.append(img) + set_seed() # unset random and numpy seed + return images diff --git a/gen_3d_modules/CRM/model/__init__.py b/gen_3d_modules/CRM/model/__init__.py new file mode 100644 index 0000000..f4f1c23 --- /dev/null +++ b/gen_3d_modules/CRM/model/__init__.py @@ -0,0 +1 @@ +from CRM.model.crm.model import ConvolutionalReconstructionModel \ No newline at end of file diff --git a/gen_3d_modules/CRM/model/archs/__init__.py b/gen_3d_modules/CRM/model/archs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/model/archs/decoders/__init__.py b/gen_3d_modules/CRM/model/archs/decoders/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/gen_3d_modules/CRM/model/archs/decoders/__init__.py @@ -0,0 +1 @@ + diff --git a/gen_3d_modules/CRM/model/archs/decoders/shape_texture_net.py b/gen_3d_modules/CRM/model/archs/decoders/shape_texture_net.py new file mode 100644 index 0000000..706d95c --- /dev/null +++ b/gen_3d_modules/CRM/model/archs/decoders/shape_texture_net.py @@ -0,0 +1,62 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class TetTexNet(nn.Module): + def __init__(self, plane_reso=64, padding=0.1, fea_concat=True): + super().__init__() + # self.c_dim = c_dim + self.plane_reso = plane_reso + self.padding = padding + self.fea_concat = fea_concat + + def forward(self, rolled_out_feature, query): + # rolled_out_feature: rolled-out triplane feature + # query: queried xyz coordinates (should be scaled consistently to ptr cloud) + + plane_reso = self.plane_reso + + triplane_feature = dict() + triplane_feature['xy'] = rolled_out_feature[:, :, :, 0: plane_reso] + triplane_feature['yz'] = rolled_out_feature[:, :, :, plane_reso: 2 * plane_reso] + triplane_feature['zx'] = rolled_out_feature[:, :, :, 2 * plane_reso:] + + query_feature_xy = self.sample_plane_feature(query, triplane_feature['xy'], 'xy') + query_feature_yz = self.sample_plane_feature(query, triplane_feature['yz'], 'yz') + query_feature_zx = self.sample_plane_feature(query, triplane_feature['zx'], 'zx') + + if self.fea_concat: + query_feature = torch.cat((query_feature_xy, query_feature_yz, query_feature_zx), dim=1) + else: + query_feature = query_feature_xy + query_feature_yz + query_feature_zx + + output = query_feature.permute(0, 2, 1) + + return output + + # uses values from plane_feature and pixel locations from vgrid to interpolate feature + def sample_plane_feature(self, query, plane_feature, plane): + # CYF note: + # for pretraining, query are uniformly sampled positions w.i. [-scale, scale] + # for training, query are essentially tetrahedra grid vertices, which are + # also within [-scale, scale] in the current version! + # xy range [-scale, scale] + if plane == 'xy': + xy = query[:, :, [0, 1]] + elif plane == 'yz': + xy = query[:, :, [1, 2]] + elif plane == 'zx': + xy = query[:, :, [2, 0]] + else: + raise ValueError("Error! Invalid plane type!") + + xy = xy[:, :, None].float() + # not seem necessary to rescale the grid, because from + # https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html, + # it specifies sampling locations normalized by plane_feature's spatial dimension, + # which is within [-scale, scale] as specified by encoder's calling of coordinate2index() + vgrid = 1.0 * xy + sampled_feat = F.grid_sample(plane_feature, vgrid, padding_mode='border', align_corners=True, mode='bilinear').squeeze(-1) + + return sampled_feat diff --git a/gen_3d_modules/CRM/model/archs/mlp_head.py b/gen_3d_modules/CRM/model/archs/mlp_head.py new file mode 100644 index 0000000..7b87ff5 --- /dev/null +++ b/gen_3d_modules/CRM/model/archs/mlp_head.py @@ -0,0 +1,40 @@ +import torch.nn as nn +import torch.nn.functional as F + + +class SdfMlp(nn.Module): + def __init__(self, input_dim, hidden_dim=512, bias=True): + super().__init__() + self.input_dim = input_dim + self.hidden_dim = hidden_dim + + self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) + self.fc2 = nn.Linear(hidden_dim, hidden_dim, bias=bias) + self.fc3 = nn.Linear(hidden_dim, 4, bias=bias) + + + def forward(self, input): + x = F.relu(self.fc1(input)) + x = F.relu(self.fc2(x)) + out = self.fc3(x) + return out + + +class RgbMlp(nn.Module): + def __init__(self, input_dim, hidden_dim=512, bias=True): + super().__init__() + self.input_dim = input_dim + self.hidden_dim = hidden_dim + + self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) + self.fc2 = nn.Linear(hidden_dim, hidden_dim, bias=bias) + self.fc3 = nn.Linear(hidden_dim, 3, bias=bias) + + def forward(self, input): + x = F.relu(self.fc1(input)) + x = F.relu(self.fc2(x)) + out = self.fc3(x) + + return out + + \ No newline at end of file diff --git a/gen_3d_modules/CRM/model/archs/unet.py b/gen_3d_modules/CRM/model/archs/unet.py new file mode 100644 index 0000000..e43b72b --- /dev/null +++ b/gen_3d_modules/CRM/model/archs/unet.py @@ -0,0 +1,53 @@ +''' +Codes are from: +https://github.com/jaxony/unet-pytorch/blob/master/model.py +''' + +import torch +import torch.nn as nn +from diffusers import UNet2DModel +import einops +class UNetPP(nn.Module): + ''' + Wrapper for UNet in diffusers + ''' + def __init__(self, in_channels): + super(UNetPP, self).__init__() + self.in_channels = in_channels + self.unet = UNet2DModel( + sample_size=[256, 256*3], + in_channels=in_channels, + out_channels=32, + layers_per_block=2, + block_out_channels=(64, 128, 128, 128*2, 128*2, 128*4, 128*4), + down_block_types=( + "DownBlock2D", + "DownBlock2D", + "DownBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + "DownBlock2D", + ), + up_block_types=( + "UpBlock2D", + "AttnUpBlock2D", + "AttnUpBlock2D", + "AttnUpBlock2D", + "UpBlock2D", + "UpBlock2D", + "UpBlock2D", + ), + ) + + self.unet.enable_xformers_memory_efficient_attention() + if in_channels > 12: + self.learned_plane = torch.nn.parameter.Parameter(torch.zeros([1,in_channels-12,256,256*3])) + + def forward(self, x, t=256): + learned_plane = self.learned_plane + if x.shape[1] < self.in_channels: + learned_plane = einops.repeat(learned_plane, '1 C H W -> B C H W', B=x.shape[0]).to(x.device) + x = torch.cat([x, learned_plane], dim = 1) + return self.unet(x, t).sample + diff --git a/gen_3d_modules/CRM/model/crm/model.py b/gen_3d_modules/CRM/model/crm/model.py new file mode 100644 index 0000000..504a22f --- /dev/null +++ b/gen_3d_modules/CRM/model/crm/model.py @@ -0,0 +1,194 @@ +import torch.nn as nn +import torch +import torch.nn.functional as F + +import numpy as np + +import cv2 +import trimesh +import nvdiffrast.torch as dr + +from CRM.model.archs.decoders.shape_texture_net import TetTexNet +from CRM.model.archs.unet import UNetPP +from CRM.util.renderer import Renderer +from CRM.model.archs.mlp_head import SdfMlp, RgbMlp +import xatlas + +from mesh_processer.mesh import Mesh + + +class Dummy: + pass + +class ConvolutionalReconstructionModel(nn.Module): + def __init__(self, specs): + super(ConvolutionalReconstructionModel, self).__init__() + + self.specs = specs + # configs + input_specs = specs["Input"] + self.input = Dummy() + self.input.scale = input_specs['scale'] + self.input.resolution = input_specs['resolution'] + self.tet_grid_size = input_specs['tet_grid_size'] + self.camera_angle_num = input_specs['camera_angle_num'] + + self.arch = Dummy() + self.arch.fea_concat = specs["ArchSpecs"]["fea_concat"] + self.arch.mlp_bias = specs["ArchSpecs"]["mlp_bias"] + + self.dec = Dummy() + self.dec.c_dim = specs["DecoderSpecs"]["c_dim"] + self.dec.plane_resolution = specs["DecoderSpecs"]["plane_resolution"] + + self.geo_type = specs["Train"].get("geo_type", "flex") # "dmtet" or "flex" + + self.unet2 = UNetPP(in_channels=self.dec.c_dim) + + mlp_chnl_s = 3 if self.arch.fea_concat else 1 # 3 for queried triplane feature concatenation + self.decoder = TetTexNet(plane_reso=self.dec.plane_resolution, fea_concat=self.arch.fea_concat) + + if self.geo_type == "flex": + self.weightMlp = nn.Sequential( + nn.Linear(mlp_chnl_s * 32 * 8, 512), + nn.SiLU(), + nn.Linear(512, 21)) + + self.sdfMlp = SdfMlp(mlp_chnl_s * 32, 512, bias=self.arch.mlp_bias) + self.rgbMlp = RgbMlp(mlp_chnl_s * 32, 512, bias=self.arch.mlp_bias) + self.renderer = Renderer(tet_grid_size=self.tet_grid_size, camera_angle_num=self.camera_angle_num, + scale=self.input.scale, geo_type = self.geo_type) + + + self.spob = True if specs['Pretrain']['mode'] is None else False # whether to add sphere + self.radius = specs['Pretrain']['radius'] # used when spob + + self.denoising = True + from diffusers import DDIMScheduler + self.scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base", subfolder="scheduler") + + def decode(self, data, triplane_feature2): + if self.geo_type == "flex": + tet_verts = self.renderer.flexicubes.verts.unsqueeze(0) + tet_indices = self.renderer.flexicubes.indices + + dec_verts = self.decoder(triplane_feature2, tet_verts) + out = self.sdfMlp(dec_verts) + + weight = None + if self.geo_type == "flex": + grid_feat = torch.index_select(input=dec_verts, index=self.renderer.flexicubes.indices.reshape(-1),dim=1) + grid_feat = grid_feat.reshape(dec_verts.shape[0], self.renderer.flexicubes.indices.shape[0], self.renderer.flexicubes.indices.shape[1] * dec_verts.shape[-1]) + weight = self.weightMlp(grid_feat) + weight = weight * 0.1 + + pred_sdf, deformation = out[..., 0], out[..., 1:] + if self.spob: + pred_sdf = pred_sdf + self.radius - torch.sqrt((tet_verts**2).sum(-1)) + + _, verts, faces = self.renderer(data, pred_sdf, deformation, tet_verts, tet_indices, weight= weight) + return verts[0].unsqueeze(0), faces[0].int() + + def export_mesh(self, data, out_dir, ind, device=None, tri_fea_2 = None): + verts = data['verts'] + faces = data['faces'] + + dec_verts = self.decoder(tri_fea_2, verts.unsqueeze(0)) + colors = self.rgbMlp(dec_verts).squeeze().detach().cpu().numpy() + # Expect predicted colors value range from [-1, 1] + colors = (colors * 0.5 + 0.5).clip(0, 1) + + verts = verts.squeeze().cpu().numpy() + faces = faces[..., [2, 1, 0]].squeeze().cpu().numpy() + + # export the final mesh + with torch.no_grad(): + mesh = trimesh.Trimesh(verts, faces, vertex_colors=colors, process=False) # important, process=True leads to seg fault... + mesh.export(out_dir / f'{ind}.obj') + + @torch.no_grad() + def get_mesh_wt_uv(self, ctx, data, device, res, tri_fea_2=None): + + mesh_v = data['verts'].squeeze().cpu().numpy() + mesh_pos_idx = data['faces'].squeeze().cpu().numpy() + + def interpolate(attr, rast, attr_idx, rast_db=None): + return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, + diff_attrs=None if rast_db is None else 'all') + + vmapping, indices, uvs = xatlas.parametrize(mesh_v, mesh_pos_idx) + + mesh_v = torch.tensor(mesh_v, dtype=torch.float32, device=device) + mesh_pos_idx = torch.tensor(mesh_pos_idx, dtype=torch.int64, device=device) + + # Convert to tensors + indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64) + + uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device) + mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device) + # mesh_v_tex. ture + uv_clip = uvs[None, ...] * 2.0 - 1.0 + + # pad to four component coordinate + uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1) + + # rasterize + rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), res) + + # Interpolate world space position + gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int()) + mask = rast[..., 3:4] > 0 + + # return uvs, mesh_tex_idx, gb_pos, mask + gb_pos_unsqz = gb_pos.view(-1, 3) + mask_unsqz = mask.view(-1) + tex_unsqz = torch.zeros_like(gb_pos_unsqz) + 1 + + gb_mask_pos = gb_pos_unsqz[mask_unsqz] + + gb_mask_pos = gb_mask_pos[None, ] + + with torch.no_grad(): + + dec_verts = self.decoder(tri_fea_2, gb_mask_pos) + colors = self.rgbMlp(dec_verts).squeeze() + + # Expect predicted colors value range from [-1, 1] + lo, hi = (-1, 1) + colors = (colors - lo) * (255 / (hi - lo)) + colors = colors.clip(0, 255) + + tex_unsqz[mask_unsqz] = colors + + tex = tex_unsqz.view(res + (3,)) + + verts = mesh_v.squeeze().cpu().numpy() + faces = mesh_pos_idx[..., [2, 1, 0]].squeeze().cpu().numpy() + # faces = mesh_pos_idx + # faces = faces.detach().cpu().numpy() + # faces = faces[..., [2, 1, 0]] + indices = indices[..., [2, 1, 0]] + + verts = verts[:, [0, 2, 1]] + verts[:, 2] *= -1 + + #uvs[:, 1] = 1 - uvs[:, 1] + + # Get albedo texture map + img = np.asarray(tex.data.cpu().numpy(), dtype=np.float32) + mask = np.sum(img.astype(float), axis=-1, keepdims=True) + mask = (mask <= 3.0).astype(float) + kernel = np.ones((3, 3), 'uint8') + dilate_img = cv2.dilate(img, kernel, iterations=1) + img = img * (1 - mask) + dilate_img * mask + img = img.clip(0, 255).astype(np.uint8) / 255 + + mesh_v = torch.tensor(verts, dtype=torch.float32, device=device) + mesh_f = torch.tensor(faces, dtype=torch.int32, device=device) + mesh_vt = uvs.clone().detach() + mesh_ft = torch.tensor(indices.astype(np.int32), dtype=torch.int32, device=device) + albedo = torch.tensor(img, dtype=torch.float32, device=device) + + mesh = Mesh(v=mesh_v, f=mesh_f, vt=mesh_vt, ft=mesh_ft, albedo=albedo, device=device) + mesh.auto_normal() + return mesh \ No newline at end of file diff --git a/gen_3d_modules/CRM/model/crm/sampler.py b/gen_3d_modules/CRM/model/crm/sampler.py new file mode 100644 index 0000000..4f6909f --- /dev/null +++ b/gen_3d_modules/CRM/model/crm/sampler.py @@ -0,0 +1,185 @@ +import numpy as np +import torch +import time +import nvdiffrast.torch as dr +from PIL import Image + +from CRM.util.utils import get_tri + +class CRMSampler: + @classmethod + def process_pixel_img( + cls, + pixel_img: Image.Image, + bg_color = "#7F7F7F" + ): + # expand image to 1:1 squared and add fixed background color + width, height = pixel_img.size + new_size = (max(width, height), max(width, height)) + new_image = Image.new("RGBA", new_size, bg_color) + paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2) + new_image.paste(pixel_img, paste_position) + + # given an RGBA image fixed background color, alpha channel is used as mask to add background color + background = Image.new("RGBA", new_image.size, bg_color) + return Image.alpha_composite(background, new_image).convert("RGB") + + @classmethod + def stage1_sample( + cls, + stage1_sampler, + pixel_img: Image.Image, + prompt="3D assets", + neg_texts="uniform low no texture ugly, boring, bad anatomy, blurry, pixelated, obscure, unnatural colors, poor lighting, dull, and unclear.", + seed=0, + scale=5, + step=50, + ddim_eta=0.0 + ): + """ + The first stage was condition on single pixel image, gererate multi-view pixel image, based on the v2pp config + """ + + stage1_sampler.seed = seed + + uc = stage1_sampler.model.get_learned_conditioning([neg_texts]).to(stage1_sampler.device) + stage1_images = stage1_sampler.i2i( + stage1_sampler.model, + stage1_sampler.size, + prompt, + uc=uc, + sampler=stage1_sampler.sampler, + ip=pixel_img, + step=step, + scale=scale, + batch_size=stage1_sampler.batch_size, + ddim_eta=ddim_eta, + dtype=stage1_sampler.dtype, + device=stage1_sampler.device, + camera=stage1_sampler.camera, + num_frames=stage1_sampler.num_frames, + pixel_control=(stage1_sampler.mode == "pixel"), + transform=stage1_sampler.image_transform, + offset_noise=stage1_sampler.offset_noise, + ) + + stage1_images = stage1_images[:-1] # remove reference view + return stage1_images # (N, H, W, 3) in [0, 1] + + @classmethod + def stage2_sample( + cls, + stage2_sampler, + pixel_img: Image.Image, + stage1_images, + prompt="3D assets", + neg_texts="uniform low no texture ugly, boring, bad anatomy, blurry, pixelated, obscure, unnatural colors, poor lighting, dull, and unclear.", + seed=0, + scale=5, + step=50 + ): + """ + The second stage was condition on multiview pixel image generated by the first stage, generate the final image, based on the stage2-test config + """ + + stage2_sampler.seed = seed + + # Convert torch image to PIL.Image + stage1_images = list((255.0 * stage1_images.cpu().numpy()).astype(np.uint8)) + stage1_images = [Image.fromarray(img) for img in stage1_images] + + uc = stage2_sampler.model.get_learned_conditioning([neg_texts]).to(stage2_sampler.device) + stage2_images = stage2_sampler.i2iStage2( + stage2_sampler.model, + stage2_sampler.size, + prompt, + uc=uc, + sampler=stage2_sampler.sampler, + pixel_images=stage1_images, + ip=pixel_img, + step=step, + scale=scale, + batch_size=stage2_sampler.batch_size, + ddim_eta=0.0, + dtype=stage2_sampler.dtype, + device=stage2_sampler.device, + camera=stage2_sampler.camera, + num_frames=stage2_sampler.num_frames, + pixel_control=(stage2_sampler.mode == "pixel"), + transform=stage2_sampler.image_transform, + offset_noise=stage2_sampler.offset_noise, + ) + + return stage2_images # (N, H, W, 3) in [0, 1] + + @classmethod + def generate3d(cls, crm_model, rgb, ccm, device): + + color_tri = torch.from_numpy(rgb) + xyz_tri = torch.from_numpy(ccm[:,:,(2,1,0)]) + color = color_tri.permute(2,0,1) + xyz = xyz_tri.permute(2,0,1) + + + def get_imgs(color): + # color : [C, H, W*6] + color_list = [] + color_list.append(color[:,:,256*5:256*(1+5)]) + for i in range(0,5): + color_list.append(color[:,:,256*i:256*(1+i)]) + return torch.stack(color_list, dim=0)# [6, C, H, W] + + triplane_color = get_imgs(color).permute(0,2,3,1).unsqueeze(0).to(device)# [1, 6, H, W, C] + + color = get_imgs(color) + xyz = get_imgs(xyz) + + color = get_tri(color, dim=0, blender= True, scale = 1).unsqueeze(0) + xyz = get_tri(xyz, dim=0, blender= True, scale = 1, fix= True).unsqueeze(0) + + triplane = torch.cat([color,xyz],dim=1).to(device) + # 3D visualize + crm_model.eval() + glctx = dr.RasterizeCudaContext() + + if crm_model.denoising == True: + tnew = 20 + tnew = torch.randint(tnew, tnew+1, [triplane.shape[0]], dtype=torch.long, device=triplane.device) + noise_new = torch.randn_like(triplane) *0.5+0.5 + triplane = crm_model.scheduler.add_noise(triplane, noise_new, tnew) + start_time = time.time() + with torch.no_grad(): + triplane_feature2 = crm_model.unet2(triplane,tnew) + end_time = time.time() + elapsed_time = end_time - start_time + print(f"Running unet takes {elapsed_time}s") + else: + triplane_feature2 = crm_model.unet2(triplane) + + + with torch.no_grad(): + data_config = { + 'resolution': [1024, 1024], + "triview_color": triplane_color.to(device), + } + + verts, faces = crm_model.decode(data_config, triplane_feature2) + + data_config['verts'] = verts[0] + data_config['faces'] = faces + + + from kiui.mesh_utils import clean_mesh + verts, faces = clean_mesh(data_config['verts'].squeeze().cpu().numpy().astype(np.float32), data_config['faces'].squeeze().cpu().numpy().astype(np.int32), repair = False, remesh=False, remesh_size=0.005) + data_config['verts'] = torch.from_numpy(verts).cuda().contiguous() + data_config['faces'] = torch.from_numpy(faces).cuda().contiguous() + + start_time = time.time() + + mesh = crm_model.get_mesh_wt_uv(glctx, data_config, device, res=(1024,1024), tri_fea_2=triplane_feature2) + + end_time = time.time() + elapsed_time = end_time - start_time + print(f"Extracting mesh & texture takes {elapsed_time}s") + + return mesh \ No newline at end of file diff --git a/gen_3d_modules/CRM/util/__init__.py b/gen_3d_modules/CRM/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/CRM/util/flexicubes_geometry.py b/gen_3d_modules/CRM/util/flexicubes_geometry.py new file mode 100644 index 0000000..8bc8622 --- /dev/null +++ b/gen_3d_modules/CRM/util/flexicubes_geometry.py @@ -0,0 +1,116 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +from FlexiCubes.flexicubes import FlexiCubes +# from dmtet import sdf_reg_loss_batch +import torch.nn.functional as F + +def get_center_boundary_index(grid_res, device): + v = torch.zeros((grid_res + 1, grid_res + 1, grid_res + 1), dtype=torch.bool, device=device) + v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = True + center_indices = torch.nonzero(v.reshape(-1)) + + v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = False + v[:2, ...] = True + v[-2:, ...] = True + v[:, :2, ...] = True + v[:, -2:, ...] = True + v[:, :, :2] = True + v[:, :, -2:] = True + boundary_indices = torch.nonzero(v.reshape(-1)) + return center_indices, boundary_indices + +############################################################################### +# Geometry interface +############################################################################### +class FlexiCubesGeometry(object): + def __init__( + self, grid_res=64, scale=2.0, device='cuda', renderer=None, + render_type='neural_render', args=None): + super(FlexiCubesGeometry, self).__init__() + self.grid_res = grid_res + self.device = device + self.args = args + self.fc = FlexiCubes(device, weight_scale=0.5) + self.verts, self.indices = self.fc.construct_voxel_grid(grid_res) + if isinstance(scale, list): + self.verts[:, 0] = self.verts[:, 0] * scale[0] + self.verts[:, 1] = self.verts[:, 1] * scale[1] + self.verts[:, 2] = self.verts[:, 2] * scale[1] + else: + self.verts = self.verts * scale + + all_edges = self.indices[:, self.fc.cube_edges].reshape(-1, 2) + self.all_edges = torch.unique(all_edges, dim=0) + + # Parameters used for fix boundary sdf + self.center_indices, self.boundary_indices = get_center_boundary_index(self.grid_res, device) + self.renderer = renderer + self.render_type = render_type + + def getAABB(self): + return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values + + def get_mesh(self, v_deformed_nx3, sdf_n, weight_n=None, with_uv=False, indices=None, is_training=False): + if indices is None: + indices = self.indices + + verts, faces, v_reg_loss = self.fc(v_deformed_nx3, sdf_n, indices, self.grid_res, + beta_fx12=weight_n[:, :12], alpha_fx8=weight_n[:, 12:20], + gamma_f=weight_n[:, 20], training=is_training + ) + return verts, faces, v_reg_loss + + + def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False): + return_value = dict() + if self.render_type == 'neural_render': + tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth = self.renderer.render_mesh( + mesh_v_nx3.unsqueeze(dim=0), + mesh_f_fx3.int(), + camera_mv_bx4x4, + mesh_v_nx3.unsqueeze(dim=0), + resolution=resolution, + device=self.device, + hierarchical_mask=hierarchical_mask + ) + + return_value['tex_pos'] = tex_pos + return_value['mask'] = mask + return_value['hard_mask'] = hard_mask + return_value['rast'] = rast + return_value['v_pos_clip'] = v_pos_clip + return_value['mask_pyramid'] = mask_pyramid + return_value['depth'] = depth + else: + raise NotImplementedError + + return return_value + + def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256): + # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1 + v_list = [] + f_list = [] + n_batch = v_deformed_bxnx3.shape[0] + all_render_output = [] + for i_batch in range(n_batch): + verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch]) + v_list.append(verts_nx3) + f_list.append(faces_fx3) + render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution) + all_render_output.append(render_output) + + # Concatenate all render output + return_keys = all_render_output[0].keys() + return_value = dict() + for k in return_keys: + value = [v[k] for v in all_render_output] + return_value[k] = value + # We can do concatenation outside of the render + return return_value diff --git a/gen_3d_modules/CRM/util/renderer.py b/gen_3d_modules/CRM/util/renderer.py new file mode 100644 index 0000000..72e80c9 --- /dev/null +++ b/gen_3d_modules/CRM/util/renderer.py @@ -0,0 +1,49 @@ + +import torch +import torch.nn as nn +import nvdiffrast.torch as dr +from .flexicubes_geometry import FlexiCubesGeometry + +class Renderer(nn.Module): + def __init__(self, tet_grid_size, camera_angle_num, scale, geo_type): + super().__init__() + + self.tet_grid_size = tet_grid_size + self.camera_angle_num = camera_angle_num + self.scale = scale + self.geo_type = geo_type + self.glctx = dr.RasterizeCudaContext() + + if self.geo_type == "flex": + self.flexicubes = FlexiCubesGeometry(grid_res = self.tet_grid_size) + + def forward(self, data, sdf, deform, verts, tets, training=False, weight = None): + + results = {} + + deform = torch.tanh(deform) / self.tet_grid_size * self.scale / 0.95 + if self.geo_type == "flex": + deform = deform *0.5 + + v_deformed = verts + deform + + verts_list = [] + faces_list = [] + reg_list = [] + n_shape = verts.shape[0] + for i in range(n_shape): + verts_i, faces_i, reg_i = self.flexicubes.get_mesh(v_deformed[i], sdf[i].squeeze(dim=-1), + with_uv=False, indices=tets, weight_n=weight[i], is_training=training) + + verts_list.append(verts_i) + faces_list.append(faces_i) + reg_list.append(reg_i) + verts = verts_list + faces = faces_list + + flexicubes_surface_reg = torch.cat(reg_list).mean() + flexicubes_weight_reg = (weight ** 2).mean() + results["flex_surf_loss"] = flexicubes_surface_reg + results["flex_weight_loss"] = flexicubes_weight_reg + + return results, verts, faces \ No newline at end of file diff --git a/gen_3d_modules/CRM/util/utils.py b/gen_3d_modules/CRM/util/utils.py new file mode 100644 index 0000000..e4cb35e --- /dev/null +++ b/gen_3d_modules/CRM/util/utils.py @@ -0,0 +1,194 @@ +import numpy as np +import torch +import random + + +# Reworked so this matches gluPerspective / glm::perspective, using fovy +def perspective(fovx=0.7854, aspect=1.0, n=0.1, f=1000.0, device=None): + # y = np.tan(fovy / 2) + x = np.tan(fovx / 2) + return torch.tensor([[1/x, 0, 0, 0], + [ 0, -aspect/x, 0, 0], + [ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)], + [ 0, 0, -1, 0]], dtype=torch.float32, device=device) + + +def translate(x, y, z, device=None): + return torch.tensor([[1, 0, 0, x], + [0, 1, 0, y], + [0, 0, 1, z], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + + +def rotate_x(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[1, 0, 0, 0], + [0, c, -s, 0], + [0, s, c, 0], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + + +def rotate_y(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[ c, 0, s, 0], + [ 0, 1, 0, 0], + [-s, 0, c, 0], + [ 0, 0, 0, 1]], dtype=torch.float32, device=device) + + +def rotate_z(a, device=None): + s, c = np.sin(a), np.cos(a) + return torch.tensor([[c, -s, 0, 0], + [s, c, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1]], dtype=torch.float32, device=device) + +@torch.no_grad() +def batch_random_rotation_translation(b, t, device=None): + m = np.random.normal(size=[b, 3, 3]) + m[:, 1] = np.cross(m[:, 0], m[:, 2]) + m[:, 2] = np.cross(m[:, 0], m[:, 1]) + m = m / np.linalg.norm(m, axis=2, keepdims=True) + m = np.pad(m, [[0, 0], [0, 1], [0, 1]], mode='constant') + m[:, 3, 3] = 1.0 + m[:, :3, 3] = np.random.uniform(-t, t, size=[b, 3]) + return torch.tensor(m, dtype=torch.float32, device=device) + +@torch.no_grad() +def random_rotation_translation(t, device=None): + m = np.random.normal(size=[3, 3]) + m[1] = np.cross(m[0], m[2]) + m[2] = np.cross(m[0], m[1]) + m = m / np.linalg.norm(m, axis=1, keepdims=True) + m = np.pad(m, [[0, 1], [0, 1]], mode='constant') + m[3, 3] = 1.0 + m[:3, 3] = np.random.uniform(-t, t, size=[3]) + return torch.tensor(m, dtype=torch.float32, device=device) + + +@torch.no_grad() +def random_rotation(device=None): + m = np.random.normal(size=[3, 3]) + m[1] = np.cross(m[0], m[2]) + m[2] = np.cross(m[0], m[1]) + m = m / np.linalg.norm(m, axis=1, keepdims=True) + m = np.pad(m, [[0, 1], [0, 1]], mode='constant') + m[3, 3] = 1.0 + m[:3, 3] = np.array([0,0,0]).astype(np.float32) + return torch.tensor(m, dtype=torch.float32, device=device) + + +def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + return torch.sum(x*y, -1, keepdim=True) + + +def length(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor: + return torch.sqrt(torch.clamp(dot(x,x), min=eps)) # Clamp to avoid nan gradients because grad(sqrt(0)) = NaN + + +def safe_normalize(x: torch.Tensor, eps: float =1e-20) -> torch.Tensor: + return x / length(x, eps) + + +def lr_schedule(iter, warmup_iter, scheduler_decay): + if iter < warmup_iter: + return iter / warmup_iter + return max(0.0, 10 ** ( + -(iter - warmup_iter) * scheduler_decay)) + + +def trans_depth(depth): + depth = depth[0].detach().cpu().numpy() + valid = depth > 0 + depth[valid] -= depth[valid].min() + depth[valid] = ((depth[valid] / depth[valid].max()) * 255) + return depth.astype('uint8') + + +def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): + assert isinstance(input, torch.Tensor) + if posinf is None: + posinf = torch.finfo(input.dtype).max + if neginf is None: + neginf = torch.finfo(input.dtype).min + assert nan == 0 + return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) + + +def load_item(filepath): + with open(filepath, 'r') as f: + items = [name.strip() for name in f.readlines()] + return set(items) + +def load_prompt(filepath): + uuid2prompt = {} + with open(filepath, 'r') as f: + for line in f.readlines(): + list_line = line.split(',') + uuid2prompt[list_line[0]] = ','.join(list_line[1:]).strip() + return uuid2prompt + +def resize_and_center_image(image_tensor, scale=0.95, c = 0, shift = 0, rgb=False, aug_shift = 0): + if scale == 1: + return image_tensor + B, C, H, W = image_tensor.shape + new_H, new_W = int(H * scale), int(W * scale) + resized_image = torch.nn.functional.interpolate(image_tensor, size=(new_H, new_W), mode='bilinear', align_corners=False).squeeze(0) + background = torch.zeros_like(image_tensor) + c + start_y, start_x = (H - new_H) // 2, (W - new_W) // 2 + if shift == 0: + background[:, :, start_y:start_y + new_H, start_x:start_x + new_W] = resized_image + else: + for i in range(B): + randx = random.randint(-shift, shift) + randy = random.randint(-shift, shift) + if rgb == True: + if i == 0 or i==2 or i==4: + randx = 0 + randy = 0 + background[i, :, start_y+randy:start_y + new_H+randy, start_x+randx:start_x + new_W+randx] = resized_image[i] + if aug_shift == 0: + return background + for i in range(B): + for j in range(C): + background[i, j, :, :] += (random.random() - 0.5)*2 * aug_shift / 255 + return background + +def get_tri(triview_color, dim = 1, blender=True, c = 0, scale=0.95, shift = 0, fix = False, rgb=False, aug_shift = 0): + # triview_color: [6,C,H,W] + # rgb is useful when shift is not 0 + triview_color = resize_and_center_image(triview_color, scale=scale, c = c, shift=shift,rgb=rgb, aug_shift = aug_shift) + if blender is False: + triview_color0 = torch.rot90(triview_color[0],k=2,dims=[1,2]) + triview_color1 = torch.rot90(triview_color[4],k=1,dims=[1,2]).flip(2).flip(1) + triview_color2 = torch.rot90(triview_color[5],k=1,dims=[1,2]).flip(2) + triview_color3 = torch.rot90(triview_color[3],k=2,dims=[1,2]).flip(2) + triview_color4 = torch.rot90(triview_color[1],k=3,dims=[1,2]).flip(1) + triview_color5 = torch.rot90(triview_color[2],k=3,dims=[1,2]).flip(1).flip(2) + else: + triview_color0 = torch.rot90(triview_color[2],k=2,dims=[1,2]) + triview_color1 = torch.rot90(triview_color[4],k=0,dims=[1,2]).flip(2).flip(1) + triview_color2 = torch.rot90(torch.rot90(triview_color[0],k=3,dims=[1,2]).flip(2), k=2,dims=[1,2]) + triview_color3 = torch.rot90(torch.rot90(triview_color[5],k=2,dims=[1,2]).flip(2), k=2,dims=[1,2]) + triview_color4 = torch.rot90(triview_color[1],k=2,dims=[1,2]).flip(1).flip(1).flip(2) + triview_color5 = torch.rot90(triview_color[3],k=1,dims=[1,2]).flip(1).flip(2) + if fix == True: + triview_color0[1] = triview_color0[1] * 0 + triview_color0[2] = triview_color0[2] * 0 + triview_color3[1] = triview_color3[1] * 0 + triview_color3[2] = triview_color3[2] * 0 + + triview_color1[0] = triview_color1[0] * 0 + triview_color1[1] = triview_color1[1] * 0 + triview_color4[0] = triview_color4[0] * 0 + triview_color4[1] = triview_color4[1] * 0 + + triview_color2[0] = triview_color2[0] * 0 + triview_color2[2] = triview_color2[2] * 0 + triview_color5[0] = triview_color5[0] * 0 + triview_color5[2] = triview_color5[2] * 0 + color_tensor1_gt = torch.cat((triview_color0, triview_color1, triview_color2), dim=2) + color_tensor2_gt = torch.cat((triview_color3, triview_color4, triview_color5), dim=2) + color_tensor_gt = torch.cat((color_tensor1_gt, color_tensor2_gt), dim = dim) + return color_tensor_gt + diff --git a/gen_3d_modules/Era3D/mvdiffusion/data/dataset.py b/gen_3d_modules/Era3D/mvdiffusion/data/dataset.py new file mode 100644 index 0000000..f4e5ba7 --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/data/dataset.py @@ -0,0 +1,138 @@ +# import decord +# decord.bridge.set_bridge('torch') + +from torch.utils.data import Dataset +from einops import rearrange +from typing import Literal, Tuple, Optional, Any +import glob +import os +import json +import random +import cv2 +import math +import numpy as np +import torch +from PIL import Image + + +class MVDiffusionDatasetV1(Dataset): + def __init__( + self, + root_dir: str, + num_views: int, + bg_color: Any, + img_wh: Tuple[int, int], + validation: bool = False, + num_validation_samples: int = 64, + num_samples: Optional[int] = None, + caption_path: Optional[str] = None, + elevation_range_deg: Tuple[float,float] = (-90, 90), + azimuth_range_deg: Tuple[float, float] = (0, 360), + ): + self.all_obj_paths = sorted(glob.glob(os.path.join(root_dir, "*/*"))) + if not validation: + self.all_obj_paths = self.all_obj_paths[:-num_validation_samples] + else: + self.all_obj_paths = self.all_obj_paths[-num_validation_samples:] + if num_samples is not None: + self.all_obj_paths = self.all_obj_paths[:num_samples] + self.all_obj_ids = [os.path.basename(path) for path in self.all_obj_paths] + self.num_views = num_views + self.bg_color = bg_color + self.img_wh = img_wh + + def get_bg_color(self): + if self.bg_color == 'white': + bg_color = np.array([1., 1., 1.], dtype=np.float32) + elif self.bg_color == 'black': + bg_color = np.array([0., 0., 0.], dtype=np.float32) + elif self.bg_color == 'gray': + bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32) + elif self.bg_color == 'random': + bg_color = np.random.rand(3) + elif isinstance(self.bg_color, float): + bg_color = np.array([self.bg_color] * 3, dtype=np.float32) + else: + raise NotImplementedError + return bg_color + + def load_image(self, img_path, bg_color, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + img = np.array(Image.open(img_path).resize(self.img_wh)) + img = img.astype(np.float32) / 255. # [0, 1] + assert img.shape[-1] == 4 # RGBA + + alpha = img[...,3:4] + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img + + def __len__(self): + return len(self.all_obj_ids) + + def __getitem__(self, index): + obj_path = self.all_obj_paths[index] + obj_id = self.all_obj_ids[index] + with open(os.path.join(obj_path, 'meta.json')) as f: + meta = json.loads(f.read()) + + num_views_all = len(meta['locations']) + num_groups = num_views_all // self.num_views + + # random a set of 4 views + # the data is arranged in ascending order of the azimuth angle + group_ids = random.sample(range(num_groups), k=2) + cond_group_id, tgt_group_id = group_ids + cond_location = meta['locations'][cond_group_id * self.num_views + random.randint(0, self.num_views - 1)] + tgt_locations = meta['locations'][tgt_group_id * self.num_views : tgt_group_id * self.num_views + self.num_views] + # random an order + start_id = random.randint(0, self.num_views - 1) + tgt_locations = tgt_locations[start_id:] + tgt_locations[:start_id] + + cond_elevation = cond_location['elevation'] + cond_azimuth = cond_location['azimuth'] + tgt_elevations = [loc['elevation'] for loc in tgt_locations] + tgt_azimuths = [loc['azimuth'] for loc in tgt_locations] + + elevations = [ele - cond_elevation for ele in tgt_elevations] + azimuths = [(azi - cond_azimuth) % (math.pi * 2) for azi in tgt_azimuths] + elevations = torch.as_tensor(elevations).float() + azimuths = torch.as_tensor(azimuths).float() + elevations_cond = torch.as_tensor([cond_elevation] * self.num_views).float() + + bg_color = self.get_bg_color() + img_tensors_in = [ + self.load_image(os.path.join(obj_path, cond_location['frames'][0]['name']), bg_color, return_type='pt').permute(2, 0, 1) + ] * self.num_views + img_tensors_out = [] + for loc in tgt_locations: + img_path = os.path.join(obj_path, loc['frames'][0]['name']) + img_tensor = self.load_image(img_path, bg_color, return_type="pt").permute(2, 0, 1) + img_tensors_out.append(img_tensor) + img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W) + img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W) + + camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3) + + return { + 'elevations_cond': elevations_cond, + 'elevations_cond_deg': torch.rad2deg(elevations_cond), + 'elevations': elevations, + 'azimuths': azimuths, + 'elevations_deg': torch.rad2deg(elevations), + 'azimuths_deg': torch.rad2deg(azimuths), + 'imgs_in': img_tensors_in, + 'imgs_out': img_tensors_out, + 'camera_embeddings': camera_embeddings + } + diff --git a/gen_3d_modules/Era3D/mvdiffusion/data/dataset_nc.py b/gen_3d_modules/Era3D/mvdiffusion/data/dataset_nc.py new file mode 100644 index 0000000..c6a8158 --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/data/dataset_nc.py @@ -0,0 +1,178 @@ +# import decord +# decord.bridge.set_bridge('torch') + +from torch.utils.data import Dataset +from einops import rearrange +from typing import Literal, Tuple, Optional, Any +import glob +import os +import json +import random +import cv2 +import math +import numpy as np +import torch +from PIL import Image +from .normal_utils import trans_normal, img2normal, normal2img + +""" +load normal and color images together +""" +class MVDiffusionDatasetV2(Dataset): + def __init__( + self, + root_dir: str, + num_views: int, + bg_color: Any, + img_wh: Tuple[int, int], + validation: bool = False, + num_validation_samples: int = 64, + num_samples: Optional[int] = None, + caption_path: Optional[str] = None, + elevation_range_deg: Tuple[float,float] = (-90, 90), + azimuth_range_deg: Tuple[float, float] = (0, 360), + ): + self.all_obj_paths = sorted(glob.glob(os.path.join(root_dir, "*/*"))) + if not validation: + self.all_obj_paths = self.all_obj_paths[:-num_validation_samples] + else: + self.all_obj_paths = self.all_obj_paths[-num_validation_samples:] + if num_samples is not None: + self.all_obj_paths = self.all_obj_paths[:num_samples] + self.all_obj_ids = [os.path.basename(path) for path in self.all_obj_paths] + self.num_views = num_views + self.bg_color = bg_color + self.img_wh = img_wh + + def get_bg_color(self): + if self.bg_color == 'white': + bg_color = np.array([1., 1., 1.], dtype=np.float32) + elif self.bg_color == 'black': + bg_color = np.array([0., 0., 0.], dtype=np.float32) + elif self.bg_color == 'gray': + bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32) + elif self.bg_color == 'random': + bg_color = np.random.rand(3) + elif isinstance(self.bg_color, float): + bg_color = np.array([self.bg_color] * 3, dtype=np.float32) + else: + raise NotImplementedError + return bg_color + + def load_image(self, img_path, bg_color, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + img = np.array(Image.open(img_path).resize(self.img_wh)) + img = img.astype(np.float32) / 255. # [0, 1] + assert img.shape[-1] == 4 # RGBA + + alpha = img[...,3:4] + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img, alpha + + def load_normal(self, img_path, bg_color, alpha, RT_w2c=None, RT_w2c_cond=None, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + normal = np.array(Image.open(img_path).resize(self.img_wh)) + + assert normal.shape[-1] == 3 # RGB + + normal = trans_normal(img2normal(normal), RT_w2c, RT_w2c_cond) + img = normal2img(normal) + + img = img.astype(np.float32) / 255. # [0, 1] + + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img + + def __len__(self): + return len(self.all_obj_ids) + + def __getitem__(self, index): + obj_path = self.all_obj_paths[index] + obj_id = self.all_obj_ids[index] + with open(os.path.join(obj_path, 'meta.json')) as f: + meta = json.loads(f.read()) + + num_views_all = len(meta['locations']) + num_groups = num_views_all // self.num_views + + # random a set of 4 views + # the data is arranged in ascending order of the azimuth angle + group_ids = random.sample(range(num_groups), k=2) + cond_group_id, tgt_group_id = group_ids + cond_location = meta['locations'][cond_group_id * self.num_views + random.randint(0, self.num_views - 1)] + tgt_locations = meta['locations'][tgt_group_id * self.num_views : tgt_group_id * self.num_views + self.num_views] + # random an order + start_id = random.randint(0, self.num_views - 1) + tgt_locations = tgt_locations[start_id:] + tgt_locations[:start_id] + + cond_elevation = cond_location['elevation'] + cond_azimuth = cond_location['azimuth'] + cond_c2w = cond_location['transform_matrix'] + cond_w2c = np.linalg.inv(cond_c2w) + tgt_elevations = [loc['elevation'] for loc in tgt_locations] + tgt_azimuths = [loc['azimuth'] for loc in tgt_locations] + tgt_c2ws = [loc['transform_matrix'] for loc in tgt_locations] + tgt_w2cs = [np.linalg.inv(loc['transform_matrix']) for loc in tgt_locations] + + elevations = [ele - cond_elevation for ele in tgt_elevations] + azimuths = [(azi - cond_azimuth) % (math.pi * 2) for azi in tgt_azimuths] + elevations = torch.as_tensor(elevations).float() + azimuths = torch.as_tensor(azimuths).float() + elevations_cond = torch.as_tensor([cond_elevation] * self.num_views).float() + + bg_color = self.get_bg_color() + img_tensors_in = [ + self.load_image(os.path.join(obj_path, cond_location['frames'][0]['name']), bg_color, return_type='pt')[0].permute(2, 0, 1) + ] * self.num_views + img_tensors_out = [] + normal_tensors_out = [] + for loc, tgt_w2c in zip(tgt_locations, tgt_w2cs): + img_path = os.path.join(obj_path, loc['frames'][0]['name']) + img_tensor, alpha = self.load_image(img_path, bg_color, return_type="pt") + img_tensor = img_tensor.permute(2, 0, 1) + img_tensors_out.append(img_tensor) + + normal_path = os.path.join(obj_path, loc['frames'][1]['name']) + normal_tensor = self.load_normal(normal_path, bg_color, alpha, RT_w2c=tgt_w2c, RT_w2c_cond=cond_w2c, return_type="pt").permute(2, 0, 1) + normal_tensors_out.append(normal_tensor) + img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W) + img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W) + normal_tensors_out = torch.stack(normal_tensors_out, dim=0).float() # (Nv, 3, H, W) + + camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3) + + return { + 'elevations_cond': elevations_cond, + 'elevations_cond_deg': torch.rad2deg(elevations_cond), + 'elevations': elevations, + 'azimuths': azimuths, + 'elevations_deg': torch.rad2deg(elevations), + 'azimuths_deg': torch.rad2deg(azimuths), + 'imgs_in': img_tensors_in, + 'imgs_out': img_tensors_out, + 'normals_out': normal_tensors_out, + 'camera_embeddings': camera_embeddings + } + diff --git a/gen_3d_modules/Era3D/mvdiffusion/data/fixed_prompt_embeds_6view/clr_embeds.pt b/gen_3d_modules/Era3D/mvdiffusion/data/fixed_prompt_embeds_6view/clr_embeds.pt new file mode 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zCON~OZhdkN0gcs>@JqvDPt%rr3(no#g3tXp;LUm{Jj$Eh$P=5SRkCZ+*NJzY!OT*7 zJ+}*l;WqG2sIQl%->HYOO63vL$}!5RQJG8SDxRlITapGH7&kQ`Wn~H(8bX#d=IGOKE$lY)+3uqZ zU@mYy%qhl4`l`Pq)&rc(o5q~4kI)Y~>nj&q)mI>O5ibXOf`i^Sa3yxbCLFlIj(6{{ z{LamozBDv{ryP*ZnQNME1f1(Ru1s3ul!CE&*OL0BOcwhJVY^LAB)quy6W6U{nX>Q5 za#6)5RWA0=ty{+uC5sg+TejH$T51*(i%pm~W&E(OhmIUSeB_r?Q)i5vGIh+v32j=i z#RmNUF7ERG9Kq@IQN4@fSN!MJtz*PLcmI714|@$dvmdYjbF1~wAO3Cm_apjp*tB6O zlg54hpZoZq|6z@P+x~C7_P_U$>iEw+{Ldq-`EPsZHo5YD?BQSUOSg`tODFy7sSQgc y|Mx~E{`K4cyFdP~^)q-!|15aBGXLCfpYmVp5&!qGQl= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids): + removed_text = tokenizer.batch_decode( + untruncated_ids[:, tokenizer.model_max_length - 1 : -1] + ) + if hasattr(text_encoder.config, "use_attention_mask") and text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=attention_mask,) + prompt_embeds = prompt_embeds[0].detach().cpu() + print(prompt_embeds.shape) + + + # print(prompt_embeds.dtype) + if id == 0: + torch.save(prompt_embeds, f'./{path}/clr_embeds.pt') + else: + torch.save(prompt_embeds, f'./{path}/normal_embeds.pt') + print('done') + + +def generate_img_embeds(): + path = './fixed_prompt_embeds_persp2ortho' + os.makedirs(path, exist_ok=True) + text_prompt = ["a orthogonal renderining image of 3D models"] + print(text_prompt) + text_inputs = tokenizer(text_prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt").to(device) + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(text_prompt, padding="longest", return_tensors="pt").input_ids + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids): + removed_text = tokenizer.batch_decode( + untruncated_ids[:, tokenizer.model_max_length - 1 : -1] + ) + if hasattr(text_encoder.config, "use_attention_mask") and text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=attention_mask,) + prompt_embeds = prompt_embeds[0].detach().cpu() + print(prompt_embeds.shape) + + # print(prompt_embeds.dtype) + + torch.save(prompt_embeds, f'./{path}/embeds.pt') + print('done') + +generate_img_embeds() \ No newline at end of file diff --git a/gen_3d_modules/Era3D/mvdiffusion/data/normal_utils.py b/gen_3d_modules/Era3D/mvdiffusion/data/normal_utils.py new file mode 100644 index 0000000..1a39008 --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/data/normal_utils.py @@ -0,0 +1,78 @@ +import numpy as np +def deg2rad(deg): + return deg*np.pi/180 + +def inv_RT(RT): + # RT_h = np.concatenate([RT, np.array([[0,0,0,1]])], axis=0) + RT_inv = np.linalg.inv(RT) + + return RT_inv[:3, :] +def camNormal2worldNormal(rot_c2w, camNormal): + H,W,_ = camNormal.shape + normal_img = np.matmul(rot_c2w[None, :, :], camNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3]) + + return normal_img + +def worldNormal2camNormal(rot_w2c, normal_map_world): + H,W,_ = normal_map_world.shape + # normal_img = np.matmul(rot_w2c[None, :, :], worldNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3]) + + # faster version + # Reshape the normal map into a 2D array where each row represents a normal vector + normal_map_flat = normal_map_world.reshape(-1, 3) + + # Transform the normal vectors using the transformation matrix + normal_map_camera_flat = np.dot(normal_map_flat, rot_w2c.T) + + # Reshape the transformed normal map back to its original shape + normal_map_camera = normal_map_camera_flat.reshape(normal_map_world.shape) + + return normal_map_camera + +def trans_normal(normal, RT_w2c, RT_w2c_target): + + # normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal) + # normal_target_cam = worldNormal2camNormal(RT_w2c_target[:3,:3], normal_world) + + relative_RT = np.matmul(RT_w2c_target[:3,:3], np.linalg.inv(RT_w2c[:3,:3])) + return worldNormal2camNormal(relative_RT[:3,:3], normal) + +def trans_normal_complex(normal, RT_w2c, RT_w2c_rela_to_cond): + # camview -> world -> condview + normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal) + # debug_normal_world = normal2img(normal_world) + + # relative_RT = np.matmul(RT_w2c_rela_to_cond[:3,:3], np.linalg.inv(RT_w2c[:3,:3])) + normal_target_cam = worldNormal2camNormal(RT_w2c_rela_to_cond[:3,:3], normal_world) + # normal_condview = normal2img(normal_target_cam) + return normal_target_cam +def img2normal(img): + return (img/255.)*2-1 + +def normal2img(normal): + return np.uint8((normal*0.5+0.5)*255) + +def norm_normalize(normal, dim=-1): + + normal = normal/(np.linalg.norm(normal, axis=dim, keepdims=True)+1e-6) + + return normal + +def plot_grid_images(images, row, col, path=None): + import cv2 + """ + Args: + images: np.array [B, H, W, 3] + row: + col: + save_path: + + Returns: + + """ + images = images.detach().cpu().numpy() + assert row * col == images.shape[0] + images = np.vstack([np.hstack(images[r * col:(r + 1) * col]) for r in range(row)]) + if path: + cv2.imwrite(path, images[:,:,::-1] * 255) + return images \ No newline at end of file diff --git a/gen_3d_modules/Era3D/mvdiffusion/data/single_image_dataset.py b/gen_3d_modules/Era3D/mvdiffusion/data/single_image_dataset.py new file mode 100644 index 0000000..03c580a --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/data/single_image_dataset.py @@ -0,0 +1,248 @@ +from typing import Dict +import numpy as np +from omegaconf import DictConfig, ListConfig +import torch +from torch.utils.data import Dataset +from pathlib import Path +import json +from PIL import Image +from torchvision import transforms +from einops import rearrange +from typing import Literal, Tuple, Optional, Any +import cv2 +import random + +import json +import os, sys +import math + +from glob import glob + +import PIL.Image +from .normal_utils import trans_normal, normal2img, img2normal +import pdb + +import cv2 +import numpy as np + +def add_margin(pil_img, color=0, size=256): + width, height = pil_img.size + result = Image.new(pil_img.mode, (size, size), color) + result.paste(pil_img, ((size - width) // 2, (size - height) // 2)) + return result + +def scale_and_place_object(image, scale_factor): + assert np.shape(image)[-1]==4 # RGBA + + # Extract the alpha channel (transparency) and the object (RGB channels) + alpha_channel = image[:, :, 3] + + # Find the bounding box coordinates of the object + coords = cv2.findNonZero(alpha_channel) + x, y, width, height = cv2.boundingRect(coords) + + # Calculate the scale factor for resizing + original_height, original_width = image.shape[:2] + + if width > height: + size = width + original_size = original_width + else: + size = height + original_size = original_height + + scale_factor = min(scale_factor, size / (original_size+0.0)) + + new_size = scale_factor * original_size + scale_factor = new_size / size + + # Calculate the new size based on the scale factor + new_width = int(width * scale_factor) + new_height = int(height * scale_factor) + + center_x = original_width // 2 + center_y = original_height // 2 + + paste_x = center_x - (new_width // 2) + paste_y = center_y - (new_height // 2) + + # Resize the object (RGB channels) to the new size + rescaled_object = cv2.resize(image[y:y+height, x:x+width], (new_width, new_height)) + + # Create a new RGBA image with the resized image + new_image = np.zeros((original_height, original_width, 4), dtype=np.uint8) + + new_image[paste_y:paste_y + new_height, paste_x:paste_x + new_width] = rescaled_object + + return new_image + +class SingleImageDataset(Dataset): + def __init__(self, + num_views: int, + img_wh: Tuple[int, int], + bg_color: str, + crop_size: int = 224, + single_image: Optional[PIL.Image.Image] = None, + images_dir: Optional[str] = None, + num_validation_samples: Optional[int] = None, + filepaths: Optional[list] = None, + cond_type: Optional[str] = None, + prompt_embeds_path: Optional[str] = None, + gt_path: Optional[str] = None, + dtype: Optional[torch.dtype] = torch.float16, + ) -> None: + """Create a dataset from a folder of images. + If you pass in a root directory it will be searched for images + ending in ext (ext can be a list) + """ + self.images_dir = images_dir + self.num_views = num_views + self.img_wh = img_wh + self.crop_size = crop_size + self.bg_color = bg_color + self.cond_type = cond_type + self.gt_path = gt_path + self.dtype = dtype + + + if single_image is None: + if filepaths is None: + # Get a list of all files in the directory + file_list = os.listdir(self.images_dir) + else: + file_list = filepaths + + # Filter the files that end with .png or .jpg + self.file_list = [file for file in file_list if file.endswith(('.png', '.jpg', '.webp'))] + else: + self.file_list = None + + # load all images + self.all_images = [] + self.all_alphas = [] + bg_color = self.get_bg_color() + + if single_image is not None: + image, alpha = self.load_image(None, bg_color, return_type='pt', Imagefile=single_image) + self.all_images.append(image) + self.all_alphas.append(alpha) + else: + for file in self.file_list: + print(os.path.join(self.images_dir, file)) + image, alpha = self.load_image(os.path.join(self.images_dir, file), bg_color, return_type='pt') + self.all_images.append(image) + self.all_alphas.append(alpha) + + + self.all_images = self.all_images[:num_validation_samples] + self.all_alphas = self.all_alphas[:num_validation_samples] + + try: + self.normal_text_embeds = torch.load(f'{prompt_embeds_path}/normal_embeds.pt') + self.color_text_embeds = torch.load(f'{prompt_embeds_path}/clr_embeds.pt') # 4view + except: + self.color_text_embeds = torch.load(f'{prompt_embeds_path}/embeds.pt') + self.normal_text_embeds = None + + def __len__(self): + return len(self.all_images) + + def get_bg_color(self): + if self.bg_color == 'white': + bg_color = np.array([1., 1., 1.], dtype=np.float32) + elif self.bg_color == 'black': + bg_color = np.array([0., 0., 0.], dtype=np.float32) + elif self.bg_color == 'gray': + bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32) + elif self.bg_color == 'random': + bg_color = np.random.rand(3) + elif isinstance(self.bg_color, float): + bg_color = np.array([self.bg_color] * 3, dtype=np.float32) + else: + raise NotImplementedError + return bg_color + + + def load_image(self, img_path, bg_color, return_type='np', Imagefile=None): + # pil always returns uint8 + if Imagefile is None: + image_input = Image.open(img_path) + else: + image_input = Imagefile + image_size = self.img_wh[0] + + if self.crop_size!=-1: + alpha_np = np.asarray(image_input)[:, :, 3] + coords = np.stack(np.nonzero(alpha_np), 1)[:, (1, 0)] + min_x, min_y = np.min(coords, 0) + max_x, max_y = np.max(coords, 0) + ref_img_ = image_input.crop((min_x, min_y, max_x, max_y)) + h, w = ref_img_.height, ref_img_.width + scale = self.crop_size / max(h, w) + h_, w_ = int(scale * h), int(scale * w) + ref_img_ = ref_img_.resize((w_, h_)) + image_input = add_margin(ref_img_, size=image_size) + else: + image_input = add_margin(image_input, size=max(image_input.height, image_input.width)) + image_input = image_input.resize((image_size, image_size)) + + # img = scale_and_place_object(img, self.scale_ratio) + img = np.array(image_input) + img = img.astype(np.float32) / 255. # [0, 1] + assert img.shape[-1] == 4 # RGBA + + alpha = img[...,3:4] + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + alpha = torch.from_numpy(alpha) + else: + raise NotImplementedError + + return img, alpha + + + def __getitem__(self, index): + image = self.all_images[index%len(self.all_images)] + alpha = self.all_alphas[index%len(self.all_images)] + if self.file_list is not None: + filename = self.file_list[index%len(self.all_images)].replace(".png", "") + else: + filename = 'null' + img_tensors_in = [ + image.permute(2, 0, 1) + ] * self.num_views + + alpha_tensors_in = [ + alpha.permute(2, 0, 1) + ] * self.num_views + + img_tensors_in = torch.stack(img_tensors_in, dim=0).to(self.dtype) # (Nv, 3, H, W) + alpha_tensors_in = torch.stack(alpha_tensors_in, dim=0).to(self.dtype) # (Nv, 3, H, W) + + if self.gt_path is not None: + gt_image = self.gt_images[index%len(self.all_images)] + gt_alpha = self.gt_alpha[index%len(self.all_images)] + gt_img_tensors_in = [gt_image.permute(2, 0, 1) ] * self.num_views + gt_alpha_tensors_in = [gt_alpha.permute(2, 0, 1) ] * self.num_views + gt_img_tensors_in = torch.stack(gt_img_tensors_in, dim=0).to(self.dtype) + gt_alpha_tensors_in = torch.stack(gt_alpha_tensors_in, dim=0).to(self.dtype) + + normal_prompt_embeddings = self.normal_text_embeds if hasattr(self, 'normal_text_embeds') else None + color_prompt_embeddings = self.color_text_embeds if hasattr(self, 'color_text_embeds') else None + + out = { + 'imgs_in': img_tensors_in, + 'alphas': alpha_tensors_in, + 'normal_prompt_embeddings': normal_prompt_embeddings, + 'color_prompt_embeddings': color_prompt_embeddings, + 'filename': filename, + } + + return out + + + diff --git a/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_image.py b/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_image.py new file mode 100644 index 0000000..68d8b4d --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_image.py @@ -0,0 +1,1029 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import ImagePositionalEmbeddings +from diffusers.utils import BaseOutput, deprecate +from diffusers.utils.torch_utils import maybe_allow_in_graph +from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention +from diffusers.models.embeddings import PatchEmbed +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils.import_utils import is_xformers_available + +from einops import rearrange, repeat +import pdb +import random + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + +def my_repeat(tensor, num_repeats): + """ + Repeat a tensor along a given dimension + """ + if len(tensor.shape) == 3: + return repeat(tensor, "b d c -> (b v) d c", v=num_repeats) + elif len(tensor.shape) == 4: + return repeat(tensor, "a b d c -> (a v) b d c", v=num_repeats) + + +@dataclass +class TransformerMV2DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + + +class TransformerMV2DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + num_views: int = 1, + cd_attention_last: bool=False, + cd_attention_mid: bool=False, + multiview_attention: bool=True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + if self.is_input_continuous: + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = LoRACompatibleLinear(in_channels, inner_dim) + else: + self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" + assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" + + self.height = sample_size + self.width = sample_size + self.num_vector_embeds = num_vector_embeds + self.num_latent_pixels = self.height * self.width + + self.latent_image_embedding = ImagePositionalEmbeddings( + num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width + ) + elif self.is_input_patches: + assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" + + self.height = sample_size + self.width = sample_size + + self.patch_size = patch_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicMVTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + if self.is_input_continuous: + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = LoRACompatibleLinear(inner_dim, in_channels) + else: + self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + self.norm_out = nn.LayerNorm(inner_dim) + self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) + elif self.is_input_patches: + self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) + self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + if self.is_input_continuous: + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + elif self.is_input_vectorized: + hidden_states = self.latent_image_embedding(hidden_states) + elif self.is_input_patches: + hidden_states = self.pos_embed(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + elif self.is_input_vectorized: + hidden_states = self.norm_out(hidden_states) + logits = self.out(hidden_states) + # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) + logits = logits.permute(0, 2, 1) + + # log(p(x_0)) + output = F.log_softmax(logits.double(), dim=1).float() + elif self.is_input_patches: + # TODO: cleanup! + conditioning = self.transformer_blocks[0].norm1.emb( + timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] + hidden_states = self.proj_out_2(hidden_states) + + # unpatchify + height = width = int(hidden_states.shape[1] ** 0.5) + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + if not return_dict: + return (output,) + + return TransformerMV2DModelOutput(sample=output) + + +@maybe_allow_in_graph +class BasicMVTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool = False + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + self.multiview_attention = multiview_attention + self.sparse_mv_attention = sparse_mv_attention + self.mvcd_attention = mvcd_attention + + self.attn1 = CustomAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=MVAttnProcessor() + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + self.num_views = num_views + + self.cd_attention_last = cd_attention_last + + if self.cd_attention_last: + # Joint task -Attn + self.attn_joint_last = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + nn.init.zeros_(self.attn_joint_last.to_out[0].weight.data) + self.norm_joint_last = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + + self.cd_attention_mid = cd_attention_mid + + if self.cd_attention_mid: + print("cross-domain attn in the middle") + # Joint task -Attn + self.attn_joint_mid = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + nn.init.zeros_(self.attn_joint_mid.to_out[0].weight.data) + self.norm_joint_mid = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ): + assert attention_mask is None # not supported yet + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + num_views=self.num_views, + multiview_attention=self.multiview_attention, + sparse_mv_attention=self.sparse_mv_attention, + mvcd_attention=self.mvcd_attention, + **cross_attention_kwargs, + ) + + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # joint attention twice + if self.cd_attention_mid: + norm_hidden_states = ( + self.norm_joint_mid(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_mid(hidden_states) + ) + hidden_states = self.attn_joint_mid(norm_hidden_states) + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + if self.cd_attention_last: + norm_hidden_states = ( + self.norm_joint_last(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_last(hidden_states) + ) + hidden_states = self.attn_joint_last(norm_hidden_states) + hidden_states + + return hidden_states + + +class CustomAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersMVAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + + +class CustomJointAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersJointAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + +class MVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1, + multiview_attention=True + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320]) + # pdb.set_trace() + # multi-view self-attention + if multiview_attention: + if num_views <= 6: + # after use xformer; possible to train with 6 views + key = rearrange(key, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0) + value = rearrange(value, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0) + else:# apply sparse attention + pass + # print("use sparse attention") + # # seems that the sparse random sampling cause problems + # # don't use random sampling, just fix the indexes + # onekey = rearrange(key, "(b t) d c -> b t d c", t=num_views) + # onevalue = rearrange(value, "(b t) d c -> b t d c", t=num_views) + # allkeys = [] + # allvalues = [] + # all_indexes = { + # 0 : [0, 2, 3, 4], + # 1: [0, 1, 3, 5], + # 2: [0, 2, 3, 4], + # 3: [0, 2, 3, 4], + # 4: [0, 2, 3, 4], + # 5: [0, 1, 3, 5] + # } + # for jj in range(num_views): + # # valid_index = [x for x in range(0, num_views) if x!= jj] + # # indexes = random.sample(valid_index, 3) + [jj] + [0] + # indexes = all_indexes[jj] + + # indexes = torch.tensor(indexes).long().to(key.device) + # allkeys.append(onekey[:, indexes]) + # allvalues.append(onevalue[:, indexes]) + # keys = torch.stack(allkeys, dim=1) # checked, should be dim=1 + # values = torch.stack(allvalues, dim=1) + # key = rearrange(keys, 'b t f d c -> (b t) (f d) c') + # value = rearrange(values, 'b t f d c -> (b t) (f d) c') + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersMVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1., + multiview_attention=True, + sparse_mv_attention=False, + mvcd_attention=False, + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key_raw = attn.to_k(encoder_hidden_states) + value_raw = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320]) + # pdb.set_trace() + # multi-view self-attention + if multiview_attention: + if not sparse_mv_attention: + key = my_repeat(rearrange(key_raw, "(b t) d c -> b (t d) c", t=num_views), num_views) + value = my_repeat(rearrange(value_raw, "(b t) d c -> b (t d) c", t=num_views), num_views) + else: + key_front = my_repeat(rearrange(key_raw, "(b t) d c -> b t d c", t=num_views)[:, 0, :, :], num_views) # [(b t), d, c] + value_front = my_repeat(rearrange(value_raw, "(b t) d c -> b t d c", t=num_views)[:, 0, :, :], num_views) + key = torch.cat([key_front, key_raw], dim=1) # shape (b t) (2 d) c + value = torch.cat([value_front, value_raw], dim=1) + + if mvcd_attention: + # memory efficient, cross domain attention + key_0, key_1 = torch.chunk(key_raw, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value_raw, dim=0, chunks=2) + key_cross = torch.concat([key_1, key_0], dim=0) + value_cross = torch.concat([value_1, value_0], dim=0) # shape (b t) d c + key = torch.cat([key, key_cross], dim=1) + value = torch.cat([value, value_cross], dim=1) # shape (b t) (t+1 d) c + else: + # print("don't use multiview attention.") + key = key_raw + value = value_raw + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + + +class XFormersJointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value, dim=0, chunks=2) + key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c + value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c + key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c + value = torch.cat([value]*2, dim=0) # (2 b t) 2d c + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class JointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value, dim=0, chunks=2) + key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c + value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c + key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c + value = torch.cat([value]*2, dim=0) # (2 b t) 2d c + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + diff --git a/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_rowwise.py b/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_rowwise.py new file mode 100644 index 0000000..5bf9643 --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_rowwise.py @@ -0,0 +1,978 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import ImagePositionalEmbeddings +from diffusers.utils import BaseOutput, deprecate +from diffusers.utils.torch_utils import maybe_allow_in_graph +from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention +from diffusers.models.embeddings import PatchEmbed +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils.import_utils import is_xformers_available + +from einops import rearrange +import pdb +import random +import math + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +@dataclass +class TransformerMV2DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + + +class TransformerMV2DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + num_views: int = 1, + cd_attention_last: bool=False, + cd_attention_mid: bool=False, + multiview_attention: bool=True, + sparse_mv_attention: bool = True, # not used + mvcd_attention: bool=False + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + if self.is_input_continuous: + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = LoRACompatibleLinear(in_channels, inner_dim) + else: + self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" + assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" + + self.height = sample_size + self.width = sample_size + self.num_vector_embeds = num_vector_embeds + self.num_latent_pixels = self.height * self.width + + self.latent_image_embedding = ImagePositionalEmbeddings( + num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width + ) + elif self.is_input_patches: + assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" + + self.height = sample_size + self.width = sample_size + + self.patch_size = patch_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicMVTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + mvcd_attention=mvcd_attention + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + if self.is_input_continuous: + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = LoRACompatibleLinear(inner_dim, in_channels) + else: + self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + self.norm_out = nn.LayerNorm(inner_dim) + self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) + elif self.is_input_patches: + self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) + self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + if self.is_input_continuous: + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + elif self.is_input_vectorized: + hidden_states = self.latent_image_embedding(hidden_states) + elif self.is_input_patches: + hidden_states = self.pos_embed(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + elif self.is_input_vectorized: + hidden_states = self.norm_out(hidden_states) + logits = self.out(hidden_states) + # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) + logits = logits.permute(0, 2, 1) + + # log(p(x_0)) + output = F.log_softmax(logits.double(), dim=1).float() + elif self.is_input_patches: + # TODO: cleanup! + conditioning = self.transformer_blocks[0].norm1.emb( + timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] + hidden_states = self.proj_out_2(hidden_states) + + # unpatchify + height = width = int(hidden_states.shape[1] ** 0.5) + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + if not return_dict: + return (output,) + + return TransformerMV2DModelOutput(sample=output) + + +@maybe_allow_in_graph +class BasicMVTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + mvcd_attention: bool = False, + rowwise_attention: bool = True + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + self.multiview_attention = multiview_attention + self.mvcd_attention = mvcd_attention + self.rowwise_attention = multiview_attention and rowwise_attention + + # rowwise multiview attention + + print('INFO: using row wise attention...') + + self.attn1 = CustomAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=MVAttnProcessor() + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + self.num_views = num_views + + self.cd_attention_last = cd_attention_last + + if self.cd_attention_last: + # Joint task -Attn + self.attn_joint = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + nn.init.zeros_(self.attn_joint.to_out[0].weight.data) + self.norm_joint = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + + self.cd_attention_mid = cd_attention_mid + + if self.cd_attention_mid: + print("joint twice") + # Joint task -Attn + self.attn_joint_twice = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + nn.init.zeros_(self.attn_joint_twice.to_out[0].weight.data) + self.norm_joint_twice = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ): + assert attention_mask is None # not supported yet + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + multiview_attention=self.multiview_attention, + mvcd_attention=self.mvcd_attention, + num_views=self.num_views, + **cross_attention_kwargs, + ) + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # joint attention twice + if self.cd_attention_mid: + norm_hidden_states = ( + self.norm_joint_twice(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_twice(hidden_states) + ) + hidden_states = self.attn_joint_twice(norm_hidden_states) + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + if self.cd_attention_last: + norm_hidden_states = ( + self.norm_joint(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint(hidden_states) + ) + hidden_states = self.attn_joint(norm_hidden_states) + hidden_states + + return hidden_states + + +class CustomAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersMVAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + + +class CustomJointAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersJointAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + +class MVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1, + multiview_attention=True + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + height = int(math.sqrt(sequence_length)) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320]) + # pdb.set_trace() + # multi-view self-attention + key = rearrange(key, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + value = rearrange(value, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + query = rearrange(query, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) # torch.Size([192, 384, 320]) + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + hidden_states = rearrange(hidden_states, "(b h) (v w) c -> (b v) (h w) c", v=num_views, h=height) + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersMVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1, + multiview_attention=True, + mvcd_attention=False, + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + height = int(math.sqrt(sequence_length)) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + print('Warning: using group norm, pay attention to use it in row-wise attention') + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key_raw = attn.to_k(encoder_hidden_states) + value_raw = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + # pdb.set_trace() + + key = rearrange(key_raw, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + value = rearrange(value_raw, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + query = rearrange(query, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) # torch.Size([192, 384, 320]) + if mvcd_attention: + # memory efficient, cross domain attention + key_0, key_1 = torch.chunk(key_raw, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value_raw, dim=0, chunks=2) + key_cross = torch.concat([key_1, key_0], dim=0) + value_cross = torch.concat([value_1, value_0], dim=0) # shape (b t) d c + key = torch.cat([key, key_cross], dim=1) + value = torch.cat([value, value_cross], dim=1) # shape (b t) (t+1 d) c + + + query = attn.head_to_batch_dim(query) # torch.Size([960, 384, 64]) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + # print(hidden_states.shape) + hidden_states = rearrange(hidden_states, "(b h) (v w) c -> (b v) (h w) c", v=num_views, h=height) + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersJointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value, dim=0, chunks=2) + key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c + value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c + key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c + value = torch.cat([value]*2, dim=0) # (2 b t) 2d c + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class JointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value, dim=0, chunks=2) + key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c + value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c + key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c + value = torch.cat([value]*2, dim=0) # (2 b t) 2d c + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states \ No newline at end of file diff --git a/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_self_rowwise.py b/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_self_rowwise.py new file mode 100644 index 0000000..4eca703 --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/models/transformer_mv2d_self_rowwise.py @@ -0,0 +1,1038 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import ImagePositionalEmbeddings +from diffusers.utils import BaseOutput, deprecate +from diffusers.utils.torch_utils import maybe_allow_in_graph +from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention +from diffusers.models.embeddings import PatchEmbed +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils.import_utils import is_xformers_available + +from einops import rearrange +import pdb +import random +import math + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +@dataclass +class TransformerMV2DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + + +class TransformerMV2DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + num_views: int = 1, + cd_attention_mid: bool=False, + cd_attention_last: bool=False, + multiview_attention: bool=True, + sparse_mv_attention: bool = True, # not used + mvcd_attention: bool=False, + use_dino: bool=False + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + if self.is_input_continuous: + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = LoRACompatibleLinear(in_channels, inner_dim) + else: + self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" + assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" + + self.height = sample_size + self.width = sample_size + self.num_vector_embeds = num_vector_embeds + self.num_latent_pixels = self.height * self.width + + self.latent_image_embedding = ImagePositionalEmbeddings( + num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width + ) + elif self.is_input_patches: + assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" + + self.height = sample_size + self.width = sample_size + + self.patch_size = patch_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicMVTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + if self.is_input_continuous: + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = LoRACompatibleLinear(inner_dim, in_channels) + else: + self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + self.norm_out = nn.LayerNorm(inner_dim) + self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) + elif self.is_input_patches: + self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) + self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + dino_feature: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + if self.is_input_continuous: + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + elif self.is_input_vectorized: + hidden_states = self.latent_image_embedding(hidden_states) + elif self.is_input_patches: + hidden_states = self.pos_embed(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + dino_feature=dino_feature, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + elif self.is_input_vectorized: + hidden_states = self.norm_out(hidden_states) + logits = self.out(hidden_states) + # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) + logits = logits.permute(0, 2, 1) + + # log(p(x_0)) + output = F.log_softmax(logits.double(), dim=1).float() + elif self.is_input_patches: + # TODO: cleanup! + conditioning = self.transformer_blocks[0].norm1.emb( + timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] + hidden_states = self.proj_out_2(hidden_states) + + # unpatchify + height = width = int(hidden_states.shape[1] ** 0.5) + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + if not return_dict: + return (output,) + + return TransformerMV2DModelOutput(sample=output) + + +@maybe_allow_in_graph +class BasicMVTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + mvcd_attention: bool = False, + rowwise_attention: bool = True, + use_dino: bool = False + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + self.multiview_attention = multiview_attention + self.mvcd_attention = mvcd_attention + self.cd_attention_mid = cd_attention_mid + self.rowwise_attention = multiview_attention and rowwise_attention + + if mvcd_attention and (not cd_attention_mid): + # add cross domain attn to self attn + self.attn1 = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + else: + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention + ) + # 1.1 rowwise multiview attention + if self.rowwise_attention: + # print('INFO: using self+row_wise mv attention...') + self.norm_mv = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn_mv = CustomAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=MVAttnProcessor() + ) + nn.init.zeros_(self.attn_mv.to_out[0].weight.data) + else: + self.norm_mv = None + self.attn_mv = None + + # # 1.2 rowwise cross-domain attn + # if mvcd_attention: + # self.attn_joint = CustomJointAttention( + # query_dim=dim, + # heads=num_attention_heads, + # dim_head=attention_head_dim, + # dropout=dropout, + # bias=attention_bias, + # cross_attention_dim=cross_attention_dim if only_cross_attention else None, + # upcast_attention=upcast_attention, + # processor=JointAttnProcessor() + # ) + # nn.init.zeros_(self.attn_joint.to_out[0].weight.data) + # self.norm_joint = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + # else: + # self.attn_joint = None + # self.norm_joint = None + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + self.num_views = num_views + + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + dino_feature: Optional[torch.FloatTensor] = None + ): + assert attention_mask is None # not supported yet + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + # multiview_attention=self.multiview_attention, + # mvcd_attention=self.mvcd_attention, + **cross_attention_kwargs, + ) + + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # import pdb;pdb.set_trace() + # 1.1 row wise multiview attention + if self.rowwise_attention: + norm_hidden_states = ( + self.norm_mv(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_mv(hidden_states) + ) + attn_output = self.attn_mv( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + num_views=self.num_views, + multiview_attention=self.multiview_attention, + cd_attention_mid=self.cd_attention_mid, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states + + +class CustomAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersMVAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + + +class CustomJointAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersJointAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + +class MVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1, + cd_attention_mid=False + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + height = int(math.sqrt(sequence_length)) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320]) + # pdb.set_trace() + # multi-view self-attention + def transpose(tensor): + tensor = rearrange(tensor, "(b v) (h w) c -> b v h w c", v=num_views, h=height) + tensor_0, tensor_1 = torch.chunk(tensor, dim=0, chunks=2) # b v h w c + tensor = torch.cat([tensor_0, tensor_1], dim=3) # b v h 2w c + tensor = rearrange(tensor, "b v h w c -> (b h) (v w) c", v=num_views, h=height) + return tensor + + if cd_attention_mid: + key = transpose(key) + value = transpose(value) + query = transpose(query) + else: + key = rearrange(key, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + value = rearrange(value, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + query = rearrange(query, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) # torch.Size([192, 384, 320]) + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + if cd_attention_mid: + hidden_states = rearrange(hidden_states, "(b h) (v w) c -> b v h w c", v=num_views, h=height) + hidden_states_0, hidden_states_1 = torch.chunk(hidden_states, dim=3, chunks=2) # b v h w c + hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=0) # 2b v h w c + hidden_states = rearrange(hidden_states, "b v h w c -> (b v) (h w) c", v=num_views, h=height) + else: + hidden_states = rearrange(hidden_states, "(b h) (v w) c -> (b v) (h w) c", v=num_views, h=height) + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersMVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1, + multiview_attention=True, + cd_attention_mid=False + ): + # print(num_views) + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + height = int(math.sqrt(sequence_length)) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + print('Warning: using group norm, pay attention to use it in row-wise attention') + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key_raw = attn.to_k(encoder_hidden_states) + value_raw = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + # pdb.set_trace() + def transpose(tensor): + tensor = rearrange(tensor, "(b v) (h w) c -> b v h w c", v=num_views, h=height) + tensor_0, tensor_1 = torch.chunk(tensor, dim=0, chunks=2) # b v h w c + tensor = torch.cat([tensor_0, tensor_1], dim=3) # b v h 2w c + tensor = rearrange(tensor, "b v h w c -> (b h) (v w) c", v=num_views, h=height) + return tensor + # print(mvcd_attention) + # import pdb;pdb.set_trace() + if cd_attention_mid: + key = transpose(key_raw) + value = transpose(value_raw) + query = transpose(query) + else: + key = rearrange(key_raw, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + value = rearrange(value_raw, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) + query = rearrange(query, "(b v) (h w) c -> (b h) (v w) c", v=num_views, h=height) # torch.Size([192, 384, 320]) + + + query = attn.head_to_batch_dim(query) # torch.Size([960, 384, 64]) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if cd_attention_mid: + hidden_states = rearrange(hidden_states, "(b h) (v w) c -> b v h w c", v=num_views, h=height) + hidden_states_0, hidden_states_1 = torch.chunk(hidden_states, dim=3, chunks=2) # b v h w c + hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=0) # 2b v h w c + hidden_states = rearrange(hidden_states, "b v h w c -> (b v) (h w) c", v=num_views, h=height) + else: + hidden_states = rearrange(hidden_states, "(b h) (v w) c -> (b v) (h w) c", v=num_views, h=height) + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersJointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + def transpose(tensor): + tensor_0, tensor_1 = torch.chunk(tensor, dim=0, chunks=2) # bv hw c + tensor = torch.cat([tensor_0, tensor_1], dim=1) # bv 2hw c + return tensor + key = transpose(key) + value = transpose(value) + query = transpose(query) + # from icecream import ic + # ic(key.shape, value.shape, query.shape) + # import pdb;pdb.set_trace() + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + hidden_states_normal, hidden_states_color = torch.chunk(hidden_states, dim=1, chunks=2) + hidden_states = torch.cat([hidden_states_normal, hidden_states_color], dim=0) # 2bv hw c + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class JointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + def transpose(tensor): + tensor_0, tensor_1 = torch.chunk(tensor, dim=0, chunks=2) # bv hw c + tensor = torch.cat([tensor_0, tensor_1], dim=1) # bv 2hw c + return tensor + key = transpose(key) + value = transpose(value) + query = transpose(query) + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + hidden_states = torch.cat([hidden_states[:, 0], hidden_states[:, 1]], dim=0) # 2bv hw c + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states \ No newline at end of file diff --git a/gen_3d_modules/Era3D/mvdiffusion/models/unet_mv2d_blocks.py b/gen_3d_modules/Era3D/mvdiffusion/models/unet_mv2d_blocks.py new file mode 100644 index 0000000..928443f --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/models/unet_mv2d_blocks.py @@ -0,0 +1,971 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.utils import is_torch_version, logging +from diffusers.models.normalization import AdaGroupNorm +from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 +from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel +from diffusers.models.resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D + +from diffusers.models.unets.unet_2d_blocks import DownBlock2D, ResnetDownsampleBlock2D, AttnDownBlock2D, CrossAttnDownBlock2D, SimpleCrossAttnDownBlock2D, SkipDownBlock2D, AttnSkipDownBlock2D, DownEncoderBlock2D, AttnDownEncoderBlock2D, KDownBlock2D, KCrossAttnDownBlock2D +from diffusers.models.unets.unet_2d_blocks import UpBlock2D, ResnetUpsampleBlock2D, CrossAttnUpBlock2D, SimpleCrossAttnUpBlock2D, AttnUpBlock2D, SkipUpBlock2D, AttnSkipUpBlock2D, UpDecoderBlock2D, AttnUpDecoderBlock2D, KUpBlock2D, KCrossAttnUpBlock2D + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + transformer_layers_per_block=1, + num_attention_heads=None, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + resnet_skip_time_act=False, + resnet_out_scale_factor=1.0, + cross_attention_norm=None, + attention_head_dim=None, + downsample_type=None, + num_views=1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + selfattn_block: str = "custom", + mvcd_attention: bool=False, + use_dino: bool = False +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock2D": + return DownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "ResnetDownsampleBlock2D": + return ResnetDownsampleBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + ) + elif down_block_type == "AttnDownBlock2D": + if add_downsample is False: + downsample_type = None + else: + downsample_type = downsample_type or "conv" # default to 'conv' + return AttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + downsample_type=downsample_type, + ) + elif down_block_type == "CrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") + return CrossAttnDownBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + # custom MV2D attention block + elif down_block_type == "CrossAttnDownBlockMV2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMV2D") + return CrossAttnDownBlockMV2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + selfattn_block=selfattn_block, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + elif down_block_type == "SimpleCrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") + return SimpleCrossAttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif down_block_type == "SkipDownBlock2D": + return SkipDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "AttnSkipDownBlock2D": + return AttnSkipDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "DownEncoderBlock2D": + return DownEncoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "AttnDownEncoderBlock2D": + return AttnDownEncoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "KDownBlock2D": + return KDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + ) + elif down_block_type == "KCrossAttnDownBlock2D": + return KCrossAttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + add_self_attention=True if not add_downsample else False, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + transformer_layers_per_block=1, + num_attention_heads=None, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + resnet_skip_time_act=False, + resnet_out_scale_factor=1.0, + cross_attention_norm=None, + attention_head_dim=None, + upsample_type=None, + num_views=1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + selfattn_block: str = "custom", + mvcd_attention: bool=False, + use_dino: bool = False +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock2D": + return UpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "ResnetUpsampleBlock2D": + return ResnetUpsampleBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + ) + elif up_block_type == "CrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") + return CrossAttnUpBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + # custom MV2D attention block + elif up_block_type == "CrossAttnUpBlockMV2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMV2D") + return CrossAttnUpBlockMV2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + selfattn_block=selfattn_block, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + elif up_block_type == "SimpleCrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") + return SimpleCrossAttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif up_block_type == "AttnUpBlock2D": + if add_upsample is False: + upsample_type = None + else: + upsample_type = upsample_type or "conv" # default to 'conv' + + return AttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + upsample_type=upsample_type, + ) + elif up_block_type == "SkipUpBlock2D": + return SkipUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "AttnSkipUpBlock2D": + return AttnSkipUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "UpDecoderBlock2D": + return UpDecoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + elif up_block_type == "AttnUpDecoderBlock2D": + return AttnUpDecoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + elif up_block_type == "KUpBlock2D": + return KUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + ) + elif up_block_type == "KCrossAttnUpBlock2D": + return KCrossAttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + ) + + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlockMV2DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + selfattn_block: str = "custom", + mvcd_attention: bool=False, + use_dino: bool = False + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + if selfattn_block == "custom": + from .transformer_mv2d import TransformerMV2DModel + elif selfattn_block == "rowwise": + from .transformer_mv2d_rowwise import TransformerMV2DModel + elif selfattn_block == "self_rowwise": + from .transformer_mv2d_self_rowwise import TransformerMV2DModel + else: + raise NotImplementedError + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for _ in range(num_layers): + if not dual_cross_attention: + attentions.append( + TransformerMV2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + ) + else: + raise NotImplementedError + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + dino_feature: Optional[torch.FloatTensor] = None + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + dino_feature=dino_feature, + return_dict=False, + )[0] + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnUpBlockMV2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + selfattn_block: str = "custom", + mvcd_attention: bool=False, + use_dino: bool = False + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + if selfattn_block == "custom": + from .transformer_mv2d import TransformerMV2DModel + elif selfattn_block == "rowwise": + from .transformer_mv2d_rowwise import TransformerMV2DModel + elif selfattn_block == "self_rowwise": + from .transformer_mv2d_self_rowwise import TransformerMV2DModel + else: + raise NotImplementedError + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + TransformerMV2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + ) + else: + raise NotImplementedError + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + dino_feature: Optional[torch.FloatTensor] = None + ): + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + dino_feature, + None, # timestep + None, # class_labels + cross_attention_kwargs, + attention_mask, + encoder_attention_mask, + **ckpt_kwargs, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + dino_feature=dino_feature, + return_dict=False, + )[0] + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class CrossAttnDownBlockMV2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + selfattn_block: str = "custom", + mvcd_attention: bool=False, + use_dino: bool = False + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + if selfattn_block == "custom": + from .transformer_mv2d import TransformerMV2DModel + elif selfattn_block == "rowwise": + from .transformer_mv2d_rowwise import TransformerMV2DModel + elif selfattn_block == "self_rowwise": + from .transformer_mv2d_self_rowwise import TransformerMV2DModel + else: + raise NotImplementedError + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + TransformerMV2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + ) + else: + raise NotImplementedError + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + dino_feature: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + additional_residuals=None, + ): + output_states = () + + blocks = list(zip(self.resnets, self.attentions)) + + for i, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + dino_feature, + None, # timestep + None, # class_labels + cross_attention_kwargs, + attention_mask, + encoder_attention_mask, + **ckpt_kwargs, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + dino_feature=dino_feature, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + hidden_states = hidden_states + additional_residuals + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + diff --git a/gen_3d_modules/Era3D/mvdiffusion/models/unet_mv2d_condition.py b/gen_3d_modules/Era3D/mvdiffusion/models/unet_mv2d_condition.py new file mode 100644 index 0000000..7bd9f9b --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/models/unet_mv2d_condition.py @@ -0,0 +1,1679 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union +import os + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor +from diffusers.models.embeddings import ( + GaussianFourierProjection, + ImageHintTimeEmbedding, + ImageProjection, + ImageTimeEmbedding, + TextImageProjection, + TextImageTimeEmbedding, + TextTimeEmbedding, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.modeling_utils import ModelMixin, load_state_dict, _load_state_dict_into_model +from diffusers.models.unets.unet_2d_blocks import ( + CrossAttnDownBlock2D, + CrossAttnUpBlock2D, + DownBlock2D, + UNetMidBlock2DCrossAttn, + UNetMidBlock2DSimpleCrossAttn, + UpBlock2D, +) +from diffusers.utils import ( + CONFIG_NAME, + FLAX_WEIGHTS_NAME, + SAFETENSORS_WEIGHTS_NAME, + WEIGHTS_NAME, + _add_variant, + _get_model_file, + deprecate, + is_torch_version, + logging, +) +from diffusers.utils.import_utils import is_accelerate_available +from diffusers.utils.hub_utils import HF_HUB_OFFLINE +from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE +DIFFUSERS_CACHE = HUGGINGFACE_HUB_CACHE + +from diffusers import __version__ +from .unet_mv2d_blocks import ( + CrossAttnDownBlockMV2D, + CrossAttnUpBlockMV2D, + UNetMidBlockMV2DCrossAttn, + get_down_block, + get_up_block, +) +from einops import rearrange, repeat + +from diffusers import __version__ + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNetMV2DConditionOutput(BaseOutput): + """ + The output of [`UNet2DConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class ResidualBlock(nn.Module): + def __init__(self, dim): + super(ResidualBlock, self).__init__() + self.linear1 = nn.Linear(dim, dim) + self.activation = nn.SiLU() + self.linear2 = nn.Linear(dim, dim) + + def forward(self, x): + identity = x + out = self.linear1(x) + out = self.activation(out) + out = self.linear2(out) + out += identity + out = self.activation(out) + return out + +class ResidualLiner(nn.Module): + def __init__(self, in_features, out_features, dim, act=None, num_block=1): + super(ResidualLiner, self).__init__() + self.linear_in = nn.Sequential(nn.Linear(in_features, dim), nn.SiLU()) + + blocks = nn.ModuleList() + for _ in range(num_block): + blocks.append(ResidualBlock(dim)) + self.blocks = blocks + + self.linear_out = nn.Linear(dim, out_features) + self.act = act + + def forward(self, x): + out = self.linear_in(x) + for block in self.blocks: + out = block(out) + out = self.linear_out(out) + if self.act is not None: + out = self.act(out) + return out + +class BasicConvBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride=1): + super(BasicConvBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.norm1 = nn.GroupNorm(num_groups=8, num_channels=in_channels, affine=True) + self.act = nn.SiLU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.norm2 = nn.GroupNorm(num_groups=8, num_channels=in_channels, affine=True) + self.downsample = nn.Sequential() + if stride != 1 or in_channels != out_channels: + self.downsample = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), + nn.GroupNorm(num_groups=8, num_channels=in_channels, affine=True) + ) + + def forward(self, x): + identity = x + out = self.conv1(x) + out = self.norm1(out) + out = self.act(out) + out = self.conv2(out) + out = self.norm2(out) + out += self.downsample(identity) + out = self.act(out) + return out + +class UNetMV2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. + flip_sin_to_cos (`bool`, *optional*, defaults to `False`): + Whether to flip the sin to cos in the time embedding. + freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): + The tuple of downsample blocks to use. + mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): + Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or + `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): + The tuple of upsample blocks to use. + only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): + Whether to include self-attention in the basic transformer blocks, see + [`~models.attention.BasicTransformerBlock`]. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. + mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. + act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. + norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. + If `None`, normalization and activation layers is skipped in post-processing. + norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + encoder_hid_dim (`int`, *optional*, defaults to None): + If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` + dimension to `cross_attention_dim`. + encoder_hid_dim_type (`str`, *optional*, defaults to `None`): + If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text + embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. + attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. + num_attention_heads (`int`, *optional*): + The number of attention heads. If not defined, defaults to `attention_head_dim` + resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config + for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. + class_embed_type (`str`, *optional*, defaults to `None`): + The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, + `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. + addition_embed_type (`str`, *optional*, defaults to `None`): + Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or + "text". "text" will use the `TextTimeEmbedding` layer. + addition_time_embed_dim: (`int`, *optional*, defaults to `None`): + Dimension for the timestep embeddings. + num_class_embeds (`int`, *optional*, defaults to `None`): + Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing + class conditioning with `class_embed_type` equal to `None`. + time_embedding_type (`str`, *optional*, defaults to `positional`): + The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. + time_embedding_dim (`int`, *optional*, defaults to `None`): + An optional override for the dimension of the projected time embedding. + time_embedding_act_fn (`str`, *optional*, defaults to `None`): + Optional activation function to use only once on the time embeddings before they are passed to the rest of + the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. + timestep_post_act (`str`, *optional*, defaults to `None`): + The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. + time_cond_proj_dim (`int`, *optional*, defaults to `None`): + The dimension of `cond_proj` layer in the timestep embedding. + conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. + conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. + projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when + `class_embed_type="projection"`. Required when `class_embed_type="projection"`. + class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time + embeddings with the class embeddings. + mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): + Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If + `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the + `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` + otherwise. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlockMV2DCrossAttn", + up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D"), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: Union[int, Tuple[int]] = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: Union[int, Tuple[int]] = 1280, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int]]] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: int = 1.0, + time_embedding_type: str = "positional", + time_embedding_dim: Optional[int] = None, + time_embedding_act_fn: Optional[str] = None, + timestep_post_act: Optional[str] = None, + time_cond_proj_dim: Optional[int] = None, + conv_in_kernel: int = 3, + conv_out_kernel: int = 3, + projection_class_embeddings_input_dim: Optional[int] = None, + projection_camera_embeddings_input_dim: Optional[int] = None, + class_embeddings_concat: bool = False, + mid_block_only_cross_attention: Optional[bool] = None, + cross_attention_norm: Optional[str] = None, + addition_embed_type_num_heads=64, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + selfattn_block: str = "custom", + mvcd_attention: bool = False, + regress_elevation: bool = False, + regress_focal_length: bool = False, + num_regress_blocks: int = 4, + use_dino: bool = False, + addition_downsample: bool = False, + addition_channels: Optional[Tuple[int]] = (1280, 1280, 1280), + ): + super().__init__() + + self.sample_size = sample_size + self.num_views = num_views + self.mvcd_attention = mvcd_attention + if num_attention_heads is not None: + raise ValueError( + "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." + ) + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + + # input + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in = nn.Conv2d( + in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding + ) + + # time + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 + if time_embed_dim % 2 != 0: + raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") + self.time_proj = GaussianFourierProjection( + time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + post_act_fn=timestep_post_act, + cond_proj_dim=time_cond_proj_dim, + ) + + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 + self.encoder_hid_proj = ImageProjection( + image_embed_dim=encoder_hid_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + elif class_embed_type == "simple_projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" + ) + self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) + else: + self.class_embedding = None + + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + elif addition_embed_type == "image": + # Kandinsky 2.2 + self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) + elif addition_embed_type == "image_hint": + # Kandinsky 2.2 ControlNet + self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) + elif addition_embed_type is not None: + raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") + + if time_embedding_act_fn is None: + self.time_embed_act = None + else: + self.time_embed_act = get_activation(time_embedding_act_fn) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = only_cross_attention + + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = False + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + if class_embeddings_concat: + # The time embeddings are concatenated with the class embeddings. The dimension of the + # time embeddings passed to the down, middle, and up blocks is twice the dimension of the + # regular time embeddings + blocks_time_embed_dim = time_embed_dim * 2 + else: + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + selfattn_block=selfattn_block, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock2DCrossAttn": + self.mid_block = UNetMidBlock2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + # custom MV2D attention block + elif mid_block_type == "UNetMidBlockMV2DCrossAttn": + self.mid_block = UNetMidBlockMV2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + selfattn_block=selfattn_block, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": + self.mid_block = UNetMidBlock2DSimpleCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + cross_attention_dim=cross_attention_dim[-1], + attention_head_dim=attention_head_dim[-1], + resnet_groups=norm_num_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + only_cross_attention=mid_block_only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif mid_block_type is None: + self.mid_block = None + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + self.addition_downsample = addition_downsample + if self.addition_downsample: + inc = block_out_channels[-1] + self.downsample = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.conv_block = nn.ModuleList() + self.conv_block.append(BasicConvBlock(inc, addition_channels[0], stride=1)) + for dim_ in addition_channels[1:-1]: + self.conv_block.append(BasicConvBlock(dim_, dim_, stride=1)) + self.conv_block.append(BasicConvBlock(dim_, inc)) + self.addition_conv_out = nn.Conv2d(inc, inc, kernel_size=1, bias=False) + nn.init.zeros_(self.addition_conv_out.weight.data) + self.addition_act_out = nn.SiLU() + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + + self.regress_elevation = regress_elevation + self.regress_focal_length = regress_focal_length + if regress_elevation or regress_focal_length: + self.pool = nn.AdaptiveAvgPool2d((1, 1)) + self.camera_embedding = TimestepEmbedding(projection_camera_embeddings_input_dim, time_embed_dim=time_embed_dim) + + regress_in_dim = block_out_channels[-1]*2 if mvcd_attention else block_out_channels + + if regress_elevation: + self.elevation_regressor = ResidualLiner(regress_in_dim, 1, 1280, act=None, num_block=num_regress_blocks) + if regress_focal_length: + self.focal_regressor = ResidualLiner(regress_in_dim, 1, 1280, act=None, num_block=num_regress_blocks) + ''' + self.regress_elevation = regress_elevation + self.regress_focal_length = regress_focal_length + if regress_elevation and (not regress_focal_length): + print("Regressing elevation") + cam_dim = 1 + elif regress_focal_length and (not regress_elevation): + print("Regressing focal length") + cam_dim = 6 + elif regress_elevation and regress_focal_length: + print("Regressing both elevation and focal length") + cam_dim = 7 + else: + cam_dim = 0 + assert projection_camera_embeddings_input_dim == 2*cam_dim, "projection_camera_embeddings_input_dim should be 2*cam_dim" + if regress_elevation or regress_focal_length: + self.elevation_regressor = nn.ModuleList([ + nn.Linear(block_out_channels[-1], 1280), + nn.SiLU(), + nn.Linear(1280, 1280), + nn.SiLU(), + nn.Linear(1280, cam_dim) + ]) + self.pool = nn.AdaptiveAvgPool2d((1, 1)) + self.focal_act = nn.Softmax(dim=-1) + self.camera_embedding = TimestepEmbedding(projection_camera_embeddings_input_dim, time_embed_dim=time_embed_dim) + ''' + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + selfattn_block=selfattn_block, + mvcd_attention=mvcd_attention, + use_dino=use_dino + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_num_groups is not None: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + + self.conv_act = get_activation(act_fn) + + else: + self.conv_norm_out = None + self.conv_act = None + + conv_out_padding = (conv_out_kernel - 1) // 2 + self.conv_out = nn.Conv2d( + block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock2D, CrossAttnDownBlockMV2D, DownBlock2D, CrossAttnUpBlock2D, CrossAttnUpBlockMV2D, UpBlock2D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + dino_feature: Optional[torch.Tensor] = None, + return_dict: bool = True, + vis_max_min: bool = False, + ) -> Union[UNetMV2DConditionOutput, Tuple]: + r""" + The [`UNet2DConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. + encoder_attention_mask (`torch.Tensor`): + A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If + `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, + which adds large negative values to the attention scores corresponding to "discard" tokens. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + record_max_min = {} + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None: + encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 0. center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # there might be better ways to encapsulate this. + class_labels = class_labels.to(dtype=sample.dtype) + + class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) + if self.config.class_embeddings_concat: + emb = torch.cat([emb, class_emb], dim=-1) + else: + emb = emb + class_emb + + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + elif self.config.addition_embed_type == "text_image": + # Kandinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + + image_embs = added_cond_kwargs.get("image_embeds") + text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) + aug_emb = self.add_embedding(text_embs, image_embs) + elif self.config.addition_embed_type == "text_time": + # SDXL - style + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + elif self.config.addition_embed_type == "image": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + aug_emb = self.add_embedding(image_embs) + elif self.config.addition_embed_type == "image_hint": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + hint = added_cond_kwargs.get("hint") + aug_emb, hint = self.add_embedding(image_embs, hint) + sample = torch.cat([sample, hint], dim=1) + + emb = emb + aug_emb if aug_emb is not None else emb + emb_pre_act = emb + if self.time_embed_act is not None: + emb = self.time_embed_act(emb) + + if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": + # Kadinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(image_embeds) + # 2. pre-process + sample = self.conv_in(sample) + # 3. down + + is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None + is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None + + down_block_res_samples = (sample,) + for i, downsample_block in enumerate(self.down_blocks): + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # For t2i-adapter CrossAttnDownBlock2D + additional_residuals = {} + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + dino_feature=dino_feature, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + **additional_residuals, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + if is_adapter and len(down_block_additional_residuals) > 0: + sample += down_block_additional_residuals.pop(0) + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + if self.addition_downsample: + global_sample = sample + global_sample = self.downsample(global_sample) + for layer in self.conv_block: + global_sample = layer(global_sample) + global_sample = self.addition_act_out(self.addition_conv_out(global_sample)) + global_sample = self.upsample(global_sample) + # 4. mid + if self.mid_block is not None: + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + dino_feature=dino_feature, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + # 4.1 regress elevation and focal length + # # predict elevation -> embed -> projection -> add to time emb + if self.regress_elevation or self.regress_focal_length: + pool_embeds = self.pool(sample.detach()).squeeze(-1).squeeze(-1) # (2B, C) + if self.mvcd_attention: + pool_embeds_normal, pool_embeds_color = torch.chunk(pool_embeds, 2, dim=0) + pool_embeds = torch.cat([pool_embeds_normal, pool_embeds_color], dim=-1) # (B, 2C) + pose_pred = [] + if self.regress_elevation: + ele_pred = self.elevation_regressor(pool_embeds) + ele_pred = rearrange(ele_pred, '(b v) c -> b v c', v=self.num_views) + ele_pred = torch.mean(ele_pred, dim=1) + pose_pred.append(ele_pred) # b, c + + if self.regress_focal_length: + focal_pred = self.focal_regressor(pool_embeds) + focal_pred = rearrange(focal_pred, '(b v) c -> b v c', v=self.num_views) + focal_pred = torch.mean(focal_pred, dim=1) + pose_pred.append(focal_pred) + pose_pred = torch.cat(pose_pred, dim=-1) + # 'e_de_da_sincos', (B, 2) + pose_embeds = torch.cat([ + torch.sin(pose_pred), + torch.cos(pose_pred) + ], dim=-1) + pose_embeds = self.camera_embedding(pose_embeds) + pose_embeds = torch.repeat_interleave(pose_embeds, self.num_views, 0) + if self.mvcd_attention: + pose_embeds = torch.cat([pose_embeds,] * 2, dim=0) + + emb = pose_embeds + emb_pre_act + if self.time_embed_act is not None: + emb = self.time_embed_act(emb) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + if self.addition_downsample: + sample = sample + global_sample + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + dino_feature=dino_feature, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + if torch.isnan(sample).any() or torch.isinf(sample).any(): + print("NAN in sample, stop training.") + exit() + # 6. post-process + if self.conv_norm_out: + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + if not return_dict: + return (sample, pose_pred) + if self.regress_elevation or self.regress_focal_length: + return UNetMV2DConditionOutput(sample=sample), pose_pred + else: + return UNetMV2DConditionOutput(sample=sample) + + + @classmethod + def from_pretrained_2d( + cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], + camera_embedding_type: str, num_views: int, sample_size: int, + zero_init_conv_in: bool = True, zero_init_camera_projection: bool = False, + projection_camera_embeddings_input_dim: int=2, + cd_attention_last: bool = False, num_regress_blocks: int = 4, + cd_attention_mid: bool = False, multiview_attention: bool = True, + sparse_mv_attention: bool = False, selfattn_block: str = 'custom', mvcd_attention: bool = False, + in_channels: int = 8, out_channels: int = 4, unclip: bool = False, regress_elevation: bool = False, regress_focal_length: bool = False, + init_mvattn_with_selfattn: bool= False, use_dino: bool = False, addition_downsample: bool = False, + **kwargs + ): + r""" + Instantiate a pretrained PyTorch model from a pretrained model configuration. + + The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To + train the model, set it back in training mode with `model.train()`. + + Parameters: + pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): + Can be either: + + - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on + the Hub. + - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved + with [`~ModelMixin.save_pretrained`]. + + cache_dir (`Union[str, os.PathLike]`, *optional*): + Path to a directory where a downloaded pretrained model configuration is cached if the standard cache + is not used. + torch_dtype (`str` or `torch.dtype`, *optional*): + Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the + dtype is automatically derived from the model's weights. + force_download (`bool`, *optional*, defaults to `False`): + Whether or not to force the (re-)download of the model weights and configuration files, overriding the + cached versions if they exist. + resume_download (`bool`, *optional*, defaults to `False`): + Whether or not to resume downloading the model weights and configuration files. If set to `False`, any + incompletely downloaded files are deleted. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. + output_loading_info (`bool`, *optional*, defaults to `False`): + Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. + local_files_only(`bool`, *optional*, defaults to `False`): + Whether to only load local model weights and configuration files or not. If set to `True`, the model + won't be downloaded from the Hub. + use_auth_token (`str` or *bool*, *optional*): + The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from + `diffusers-cli login` (stored in `~/.huggingface`) is used. + revision (`str`, *optional*, defaults to `"main"`): + The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier + allowed by Git. + from_flax (`bool`, *optional*, defaults to `False`): + Load the model weights from a Flax checkpoint save file. + subfolder (`str`, *optional*, defaults to `""`): + The subfolder location of a model file within a larger model repository on the Hub or locally. + mirror (`str`, *optional*): + Mirror source to resolve accessibility issues if you're downloading a model in China. We do not + guarantee the timeliness or safety of the source, and you should refer to the mirror site for more + information. + device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): + A map that specifies where each submodule should go. It doesn't need to be defined for each + parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the + same device. + + Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For + more information about each option see [designing a device + map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). + max_memory (`Dict`, *optional*): + A dictionary device identifier for the maximum memory. Will default to the maximum memory available for + each GPU and the available CPU RAM if unset. + offload_folder (`str` or `os.PathLike`, *optional*): + The path to offload weights if `device_map` contains the value `"disk"`. + offload_state_dict (`bool`, *optional*): + If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if + the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` + when there is some disk offload. + low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): + Speed up model loading only loading the pretrained weights and not initializing the weights. This also + tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. + Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this + argument to `True` will raise an error. + variant (`str`, *optional*): + Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when + loading `from_flax`. + use_safetensors (`bool`, *optional*, defaults to `None`): + If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the + `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors` + weights. If set to `False`, `safetensors` weights are not loaded. + + + + To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with + `huggingface-cli login`. You can also activate the special + ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a + firewalled environment. + + + + Example: + + ```py + from diffusers import UNet2DConditionModel + + unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + ``` + + If you get the error message below, you need to finetune the weights for your downstream task: + + ```bash + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated + You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. + ``` + """ + cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) + ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) + force_download = kwargs.pop("force_download", False) + from_flax = kwargs.pop("from_flax", False) + resume_download = kwargs.pop("resume_download", False) + proxies = kwargs.pop("proxies", None) + output_loading_info = kwargs.pop("output_loading_info", False) + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) + use_auth_token = kwargs.pop("use_auth_token", None) + revision = kwargs.pop("revision", None) + torch_dtype = kwargs.pop("torch_dtype", None) + subfolder = kwargs.pop("subfolder", None) + device_map = kwargs.pop("device_map", None) + max_memory = kwargs.pop("max_memory", None) + offload_folder = kwargs.pop("offload_folder", None) + offload_state_dict = kwargs.pop("offload_state_dict", False) + variant = kwargs.pop("variant", None) + use_safetensors = kwargs.pop("use_safetensors", None) + + if use_safetensors: + raise ValueError( + "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors" + ) + + allow_pickle = False + if use_safetensors is None: + use_safetensors = True + allow_pickle = True + + if device_map is not None and not is_accelerate_available(): + raise NotImplementedError( + "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" + " `device_map=None`. You can install accelerate with `pip install accelerate`." + ) + + # Check if we can handle device_map and dispatching the weights + if device_map is not None and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `device_map=None`." + ) + + # Load config if we don't provide a configuration + config_path = pretrained_model_name_or_path + + user_agent = { + "diffusers": __version__, + "file_type": "model", + "framework": "pytorch", + } + + # load config + config, unused_kwargs, commit_hash = cls.load_config( + config_path, + cache_dir=cache_dir, + return_unused_kwargs=True, + return_commit_hash=True, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + device_map=device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + user_agent=user_agent, + **kwargs, + ) + + # modify config + config["_class_name"] = cls.__name__ + config['in_channels'] = in_channels + config['out_channels'] = out_channels + config['sample_size'] = sample_size # training resolution + config['num_views'] = num_views + config['cd_attention_last'] = cd_attention_last + config['cd_attention_mid'] = cd_attention_mid + config['multiview_attention'] = multiview_attention + config['sparse_mv_attention'] = sparse_mv_attention + config['selfattn_block'] = selfattn_block + config['mvcd_attention'] = mvcd_attention + config["down_block_types"] = [ + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "DownBlock2D" + ] + config['mid_block_type'] = "UNetMidBlockMV2DCrossAttn" + config["up_block_types"] = [ + "UpBlock2D", + "CrossAttnUpBlockMV2D", + "CrossAttnUpBlockMV2D", + "CrossAttnUpBlockMV2D" + ] + + + config['regress_elevation'] = regress_elevation # true + config['regress_focal_length'] = regress_focal_length # true + config['projection_camera_embeddings_input_dim'] = projection_camera_embeddings_input_dim # 2 for elevation and 10 for focal_length + config['use_dino'] = use_dino + config['num_regress_blocks'] = num_regress_blocks + config['addition_downsample'] = addition_downsample + # load model + model_file = None + if from_flax: + raise NotImplementedError + else: + if use_safetensors: + try: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + except IOError as e: + if not allow_pickle: + raise e + pass + if model_file is None: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + + model = cls.from_config(config, **unused_kwargs) + import copy + state_dict_pretrain = load_state_dict(model_file, variant=variant) + state_dict = copy.deepcopy(state_dict_pretrain) + + if init_mvattn_with_selfattn: + for key in state_dict_pretrain: + if 'attn1' in key: + key_mv = key.replace('attn1', 'attn_mv') + state_dict[key_mv] = state_dict_pretrain[key] + if 'to_out.0.weight' in key: + nn.init.zeros_(state_dict[key_mv].data) + if 'transformer_blocks' in key and 'norm1' in key: # in case that initialize the norm layer in resnet block + key_mv = key.replace('norm1', 'norm_mv') + state_dict[key_mv] = state_dict_pretrain[key] + # del state_dict_pretrain + + model._convert_deprecated_attention_blocks(state_dict) + + conv_in_weight = state_dict['conv_in.weight'] + conv_out_weight = state_dict['conv_out.weight'] + model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model_2d( + model, + state_dict, + model_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=True, + ) + if any([key == 'conv_in.weight' for key, _, _ in mismatched_keys]): + # initialize from the original SD structure + model.conv_in.weight.data[:,:4] = conv_in_weight + + # whether to place all zero to new layers? + if zero_init_conv_in: + model.conv_in.weight.data[:,4:] = 0. + + if any([key == 'conv_out.weight' for key, _, _ in mismatched_keys]): + # initialize from the original SD structure + model.conv_out.weight.data[:,:4] = conv_out_weight + if out_channels == 8: # copy for the last 4 channels + model.conv_out.weight.data[:, 4:] = conv_out_weight + + if zero_init_camera_projection: # true + params = [p for p in model.camera_embedding.parameters()] + torch.nn.init.zeros_(params[-1].data) + + loading_info = { + "missing_keys": missing_keys, + "unexpected_keys": unexpected_keys, + "mismatched_keys": mismatched_keys, + "error_msgs": error_msgs, + } + + if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." + ) + elif torch_dtype is not None: + model = model.to(torch_dtype) + + model.register_to_config(_name_or_path=pretrained_model_name_or_path) + + # Set model in evaluation mode to deactivate DropOut modules by default + model.eval() + if output_loading_info: + return model, loading_info + return model + + @classmethod + def _load_pretrained_model_2d( + cls, + model, + state_dict, + resolved_archive_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=False, + ): + # Retrieve missing & unexpected_keys + model_state_dict = model.state_dict() + loaded_keys = list(state_dict.keys()) + + expected_keys = list(model_state_dict.keys()) + + original_loaded_keys = loaded_keys + + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + # Make sure we are able to load base models as well as derived models (with heads) + model_to_load = model + + def _find_mismatched_keys( + state_dict, + model_state_dict, + loaded_keys, + ignore_mismatched_sizes, + ): + mismatched_keys = [] + if ignore_mismatched_sizes: + for checkpoint_key in loaded_keys: + model_key = checkpoint_key + + if ( + model_key in model_state_dict + and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape + ): + mismatched_keys.append( + (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) + ) + del state_dict[checkpoint_key] + return mismatched_keys + + if state_dict is not None: + # Whole checkpoint + mismatched_keys = _find_mismatched_keys( + state_dict, + model_state_dict, + original_loaded_keys, + ignore_mismatched_sizes, + ) + error_msgs = _load_state_dict_into_model(model_to_load, state_dict) + + if len(error_msgs) > 0: + error_msg = "\n\t".join(error_msgs) + if "size mismatch" in error_msg: + error_msg += ( + "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method." + ) + raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}") + + if len(unexpected_keys) > 0: + logger.warning( + f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when" + f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are" + f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task" + " or with another architecture (e.g. initializing a BertForSequenceClassification model from a" + " BertForPreTraining model).\n- This IS NOT expected if you are initializing" + f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly" + " identical (initializing a BertForSequenceClassification model from a" + " BertForSequenceClassification model)." + ) + else: + logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n") + if len(missing_keys) > 0: + logger.warning( + f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" + f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably" + " TRAIN this model on a down-stream task to be able to use it for predictions and inference." + ) + elif len(mismatched_keys) == 0: + logger.info( + f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at" + f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the" + f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions" + " without further training." + ) + if len(mismatched_keys) > 0: + mismatched_warning = "\n".join( + [ + f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" + for key, shape1, shape2 in mismatched_keys + ] + ) + logger.warning( + f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" + f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not" + f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be" + " able to use it for predictions and inference." + ) + + return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs + diff --git a/gen_3d_modules/Era3D/mvdiffusion/pipelines/pipeline_mvdiffusion_unclip.py b/gen_3d_modules/Era3D/mvdiffusion/pipelines/pipeline_mvdiffusion_unclip.py new file mode 100644 index 0000000..c465393 --- /dev/null +++ b/gen_3d_modules/Era3D/mvdiffusion/pipelines/pipeline_mvdiffusion_unclip.py @@ -0,0 +1,637 @@ +import inspect +import warnings +from typing import Callable, List, Optional, Union, Dict, Any +import PIL +import torch +from packaging import version +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPFeatureExtractor, CLIPTokenizer, CLIPTextModel +from diffusers.utils.import_utils import is_accelerate_available +from diffusers.configuration_utils import FrozenDict +from diffusers.image_processor import VaeImageProcessor +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.models.embeddings import get_timestep_embedding +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import deprecate, logging +from diffusers.utils.torch_utils import randn_tensor +from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput +from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer +import os +import torchvision.transforms.functional as TF +from einops import rearrange +import comfy.utils +logger = logging.get_logger(__name__) + +class StableUnCLIPImg2ImgPipeline(DiffusionPipeline): + """ + Pipeline for text-guided image to image generation using stable unCLIP. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + feature_extractor ([`CLIPFeatureExtractor`]): + Feature extractor for image pre-processing before being encoded. + image_encoder ([`CLIPVisionModelWithProjection`]): + CLIP vision model for encoding images. + image_normalizer ([`StableUnCLIPImageNormalizer`]): + Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image + embeddings after the noise has been applied. + image_noising_scheduler ([`KarrasDiffusionSchedulers`]): + Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined + by `noise_level` in `StableUnCLIPPipeline.__call__`. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`KarrasDiffusionSchedulers`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + """ + # image encoding components + feature_extractor: CLIPFeatureExtractor + image_encoder: CLIPVisionModelWithProjection + # image noising components + image_normalizer: StableUnCLIPImageNormalizer + image_noising_scheduler: KarrasDiffusionSchedulers + # regular denoising components + tokenizer: CLIPTokenizer + text_encoder: CLIPTextModel + unet: UNet2DConditionModel + scheduler: KarrasDiffusionSchedulers + vae: AutoencoderKL + + def __init__( + self, + # image encoding components + feature_extractor: CLIPFeatureExtractor, + image_encoder: CLIPVisionModelWithProjection, + # image noising components + image_normalizer: StableUnCLIPImageNormalizer, + image_noising_scheduler: KarrasDiffusionSchedulers, + # regular denoising components + tokenizer: CLIPTokenizer, + text_encoder: CLIPTextModel, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + # vae + vae: AutoencoderKL, + num_views: int = 4, + ): + super().__init__() + + self.register_modules( + feature_extractor=feature_extractor, + image_encoder=image_encoder, + image_normalizer=image_normalizer, + image_noising_scheduler=image_noising_scheduler, + tokenizer=tokenizer, + text_encoder=text_encoder, + unet=unet, + scheduler=scheduler, + vae=vae, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.num_views: int = num_views + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's + models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only + when their specific submodule has its `forward` method called. + """ + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + # TODO: self.image_normalizer.{scale,unscale} are not covered by the offload hooks, so they fails if added to the list + models = [ + self.image_encoder, + self.text_encoder, + self.unet, + self.vae, + ] + for cpu_offloaded_model in models: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + @property + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + if do_classifier_free_guidance: + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + normal_prompt_embeds, color_prompt_embeds = torch.chunk(prompt_embeds, 2, dim=0) + + prompt_embeds = torch.cat([normal_prompt_embeds, normal_prompt_embeds, color_prompt_embeds, color_prompt_embeds], 0) + + return prompt_embeds + + def _encode_image( + self, + image_pil, + device, + num_images_per_prompt, + do_classifier_free_guidance, + noise_level: int=0, + generator: Optional[torch.Generator] = None + ): + dtype = next(self.image_encoder.parameters()).dtype + # ______________________________clip image embedding______________________________ + image = self.feature_extractor(images=image_pil, return_tensors="pt").pixel_values + image = image.to(device=device, dtype=dtype) + image_embeds = self.image_encoder(image).image_embeds + + image_embeds = self.noise_image_embeddings( + image_embeds=image_embeds, + noise_level=noise_level, + generator=generator, + ) + # duplicate image embeddings for each generation per prompt, using mps friendly method + # image_embeds = image_embeds.unsqueeze(1) + # note: the condition input is same + image_embeds = image_embeds.repeat(num_images_per_prompt, 1) + + if do_classifier_free_guidance: + normal_image_embeds, color_image_embeds = torch.chunk(image_embeds, 2, dim=0) + negative_prompt_embeds = torch.zeros_like(normal_image_embeds) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + image_embeds = torch.cat([negative_prompt_embeds, normal_image_embeds, negative_prompt_embeds, color_image_embeds], 0) + + # _____________________________vae input latents__________________________________________________ + image_pt = torch.stack([TF.to_tensor(img) for img in image_pil], dim=0).to(device) + image_pt = image_pt * 2.0 - 1.0 + image_latents = self.vae.encode(image_pt).latent_dist.mode() * self.vae.config.scaling_factor + # Note: repeat differently from official pipelines + image_latents = image_latents.repeat(num_images_per_prompt, 1, 1, 1) + + if do_classifier_free_guidance: + normal_image_latents, color_image_latents = torch.chunk(image_latents, 2, dim=0) + image_latents = torch.cat([torch.zeros_like(normal_image_latents), normal_image_latents, + torch.zeros_like(color_image_latents), color_image_latents], 0) + + return image_embeds, image_latents + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents + def decode_latents(self, latents): + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents).sample + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + image, + height, + width, + callback_steps, + noise_level, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + + if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps: + raise ValueError( + f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive." + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_unclip.StableUnCLIPPipeline.noise_image_embeddings + def noise_image_embeddings( + self, + image_embeds: torch.Tensor, + noise_level: int, + noise: Optional[torch.FloatTensor] = None, + generator: Optional[torch.Generator] = None, + ): + """ + Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher + `noise_level` increases the variance in the final un-noised images. + + The noise is applied in two ways + 1. A noise schedule is applied directly to the embeddings + 2. A vector of sinusoidal time embeddings are appended to the output. + + In both cases, the amount of noise is controlled by the same `noise_level`. + + The embeddings are normalized before the noise is applied and un-normalized after the noise is applied. + """ + if noise is None: + noise = randn_tensor( + image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype + ) + + noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device) + + image_embeds = self.image_normalizer.scale(image_embeds) + + image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise) + + image_embeds = self.image_normalizer.unscale(image_embeds) + + noise_level = get_timestep_embedding( + timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0 + ) + + # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors, + # but we might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + noise_level = noise_level.to(image_embeds.dtype) + + image_embeds = torch.cat((image_embeds, noise_level), 1) + + return image_embeds + + @torch.no_grad() + # @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + image: Union[torch.FloatTensor, PIL.Image.Image], + prompt: Union[str, List[str]], + prompt_embeds: torch.FloatTensor = None, + dino_feature: torch.FloatTensor = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 20, + guidance_scale: float = 10, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[torch.Generator] = None, + latents: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + noise_level: int = 0, + image_embeds: Optional[torch.FloatTensor] = None, + return_elevation_focal: Optional[bool] = False, + gt_img_in: Optional[torch.FloatTensor] = None, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch. The image will be encoded to its CLIP embedding which + the unet will be conditioned on. Note that the image is _not_ encoded by the vae and then used as the + latents in the denoising process such as in the standard stable diffusion text guided image variation + process. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 20): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 10.0): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under + `self.processor` in + [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). + noise_level (`int`, *optional*, defaults to `0`): + The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in + the final un-noised images. See `StableUnCLIPPipeline.noise_image_embeddings` for details. + image_embeds (`torch.FloatTensor`, *optional*): + Pre-generated CLIP embeddings to condition the unet on. Note that these are not latents to be used in + the denoising process. If you want to provide pre-generated latents, pass them to `__call__` as + `latents`. + + Examples: + + Returns: + [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is + True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt=prompt, + image=image, + height=height, + width=width, + callback_steps=callback_steps, + noise_level=noise_level + ) + + # 2. Define call parameters + if isinstance(image, list): + batch_size = len(image) + elif isinstance(image, torch.Tensor): + batch_size = image.shape[0] + assert batch_size >= self.num_views and batch_size % self.num_views == 0 + elif isinstance(image, PIL.Image.Image): + image = [image]*self.num_views*2 + batch_size = self.num_views*2 + + if isinstance(prompt, str): + prompt = [prompt] * self.num_views * 2 + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale != 1.0 + + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + prompt_embeds = self._encode_prompt( + prompt=prompt, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + lora_scale=text_encoder_lora_scale, + ) + + + # 4. Encoder input image + if isinstance(image, list): + image_pil = image + elif isinstance(image, torch.Tensor): + image_pil = [TF.to_pil_image(image[i]) for i in range(image.shape[0])] + noise_level = torch.tensor([noise_level], device=device) + image_embeds, image_latents = self._encode_image( + image_pil=image_pil, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + noise_level=noise_level, + generator=generator, + ) + + # 5. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 6. Prepare latent variables + num_channels_latents = self.unet.config.out_channels + if gt_img_in is not None: + latents = gt_img_in * self.scheduler.init_noise_sigma + else: + latents = self.prepare_latents( + batch_size=batch_size, + num_channels_latents=num_channels_latents, + height=height, + width=width, + dtype=prompt_embeds.dtype, + device=device, + generator=generator, + latents=latents, + ) + + # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + eles, focals = [], [] + # 8. Denoising loop + comfy_pbar = comfy.utils.ProgressBar(num_inference_steps) + for i, t in enumerate(self.progress_bar(timesteps)): + if do_classifier_free_guidance: + normal_latents, color_latents = torch.chunk(latents, 2, dim=0) + latent_model_input = torch.cat([normal_latents, normal_latents, color_latents, color_latents], 0) + else: + latent_model_input = latents + latent_model_input = torch.cat([ + latent_model_input, image_latents + ], dim=1) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + unet_out = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + dino_feature=dino_feature, + class_labels=image_embeds, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False) + + noise_pred = unet_out[0] + if return_elevation_focal: + uncond_pose, pose = torch.chunk(unet_out[1], 2, 0) + pose = uncond_pose + guidance_scale * (pose - uncond_pose) + ele = pose[:, 0].detach().cpu().numpy() # b + eles.append(ele) + focal = pose[:, 1].detach().cpu().numpy() + focals.append(focal) + + # perform guidance + if do_classifier_free_guidance: + normal_noise_pred_uncond, normal_noise_pred_text, color_noise_pred_uncond, color_noise_pred_text = torch.chunk(noise_pred, 4, dim=0) + + noise_pred_uncond, noise_pred_text = torch.cat([normal_noise_pred_uncond, color_noise_pred_uncond], 0), torch.cat([normal_noise_pred_text, color_noise_pred_text], 0) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + comfy_pbar.update_absolute(i + 1) + + # 9. Post-processing + if not output_type == "latent": + if num_channels_latents == 8: + latents = torch.cat([latents[:, :4], latents[:, 4:]], dim=0) + with torch.no_grad(): + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + else: + image = latents + + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload last model to CPU + # if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: + # self.final_offload_hook.offload() + if not return_dict: + return (image, ) + if return_elevation_focal: + return ImagePipelineOutput(images=image), eles, focals + else: + return ImagePipelineOutput(images=image) diff --git a/gen_3d_modules/Era3D/utils/misc.py b/gen_3d_modules/Era3D/utils/misc.py new file mode 100644 index 0000000..634b399 --- /dev/null +++ b/gen_3d_modules/Era3D/utils/misc.py @@ -0,0 +1,44 @@ +import os +from omegaconf import OmegaConf +from packaging import version +from typing import Dict, Optional, List +from dataclasses import dataclass + + +# ============ Register OmegaConf Recolvers ============= # +OmegaConf.register_new_resolver('calc_exp_lr_decay_rate', lambda factor, n: factor**(1./n), replace=True) +OmegaConf.register_new_resolver('add', lambda a, b: a + b, replace=True) +OmegaConf.register_new_resolver('sub', lambda a, b: a - b, replace=True) +OmegaConf.register_new_resolver('mul', lambda a, b: a * b, replace=True) +OmegaConf.register_new_resolver('div', lambda a, b: a / b, replace=True) +OmegaConf.register_new_resolver('idiv', lambda a, b: a // b, replace=True) +OmegaConf.register_new_resolver('basename', lambda p: os.path.basename(p), replace=True) +# ======================================================= # + + +@dataclass +class TestConfig: + num_views: int + dataset: Dict + + +def load_config(*yaml_files): + yaml_confs = [OmegaConf.load(f) for f in yaml_files] + conf = OmegaConf.merge(*yaml_confs) + OmegaConf.resolve(conf) + + schema = OmegaConf.structured(TestConfig) + conf = OmegaConf.merge(schema, conf) + return conf + + +def config_to_primitive(config, resolve=True): + return OmegaConf.to_container(config, resolve=resolve) + + +def dump_config(path, config): + with open(path, 'w') as fp: + OmegaConf.save(config=config, f=fp) + +def parse_version(ver): + return version.parse(ver) diff --git a/gen_3d_modules/InstantMesh/__init__.py b/gen_3d_modules/InstantMesh/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/InstantMesh/data/__init__.py b/gen_3d_modules/InstantMesh/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/InstantMesh/data/objaverse.py b/gen_3d_modules/InstantMesh/data/objaverse.py new file mode 100644 index 0000000..1030ebe --- /dev/null +++ b/gen_3d_modules/InstantMesh/data/objaverse.py @@ -0,0 +1,325 @@ +import os, sys +import math +import json +import importlib +from pathlib import Path + +import cv2 +import random +import numpy as np +from PIL import Image +import webdataset as wds +import pytorch_lightning as pl + +import torch +import torch.nn.functional as F +from torch.utils.data import Dataset +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from torchvision import transforms + +from InstantMesh.utils.train_util import instantiate_from_config +from InstantMesh.utils.camera_util import ( + FOV_to_intrinsics, + center_looking_at_camera_pose, + get_circular_camera_poses, +) + + +class DataModuleFromConfig(pl.LightningDataModule): + def __init__( + self, + batch_size=8, + num_workers=4, + train=None, + validation=None, + test=None, + **kwargs, + ): + super().__init__() + + self.batch_size = batch_size + self.num_workers = num_workers + + self.dataset_configs = dict() + if train is not None: + self.dataset_configs['train'] = train + if validation is not None: + self.dataset_configs['validation'] = validation + if test is not None: + self.dataset_configs['test'] = test + + def setup(self, stage): + + if stage in ['fit']: + self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) + else: + raise NotImplementedError + + def train_dataloader(self): + + sampler = DistributedSampler(self.datasets['train']) + return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler) + + def val_dataloader(self): + + sampler = DistributedSampler(self.datasets['validation']) + return wds.WebLoader(self.datasets['validation'], batch_size=1, num_workers=self.num_workers, shuffle=False, sampler=sampler) + + def test_dataloader(self): + + return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False) + + +class ObjaverseData(Dataset): + def __init__(self, + root_dir='objaverse/', + meta_fname='valid_paths.json', + input_image_dir='rendering_random_32views', + target_image_dir='rendering_random_32views', + input_view_num=6, + target_view_num=4, + total_view_n=32, + fov=50, + camera_rotation=True, + validation=False, + ): + self.root_dir = Path(root_dir) + self.input_image_dir = input_image_dir + self.target_image_dir = target_image_dir + + self.input_view_num = input_view_num + self.target_view_num = target_view_num + self.total_view_n = total_view_n + self.fov = fov + self.camera_rotation = camera_rotation + + with open(os.path.join(root_dir, meta_fname)) as f: + filtered_dict = json.load(f) + paths = filtered_dict['good_objs'] + self.paths = paths + + self.depth_scale = 6.0 + + total_objects = len(self.paths) + print('============= length of dataset %d =============' % len(self.paths)) + + def __len__(self): + return len(self.paths) + + def load_im(self, path, color): + ''' + replace background pixel with random color in rendering + ''' + pil_img = Image.open(path) + + image = np.asarray(pil_img, dtype=np.float32) / 255. + alpha = image[:, :, 3:] + image = image[:, :, :3] * alpha + color * (1 - alpha) + + image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float() + alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float() + return image, alpha + + def __getitem__(self, index): + while True: + input_image_path = os.path.join(self.root_dir, self.input_image_dir, self.paths[index]) + target_image_path = os.path.join(self.root_dir, self.target_image_dir, self.paths[index]) + + indices = np.random.choice(range(self.total_view_n), self.input_view_num + self.target_view_num, replace=False) + input_indices = indices[:self.input_view_num] + target_indices = indices[self.input_view_num:] + + '''background color, default: white''' + bg_white = [1., 1., 1.] + bg_black = [0., 0., 0.] + + image_list = [] + alpha_list = [] + depth_list = [] + normal_list = [] + pose_list = [] + + try: + input_cameras = np.load(os.path.join(input_image_path, 'cameras.npz'))['cam_poses'] + for idx in input_indices: + image, alpha = self.load_im(os.path.join(input_image_path, '%03d.png' % idx), bg_white) + normal, _ = self.load_im(os.path.join(input_image_path, '%03d_normal.png' % idx), bg_black) + depth = cv2.imread(os.path.join(input_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale + depth = torch.from_numpy(depth).unsqueeze(0) + pose = input_cameras[idx] + pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0) + + image_list.append(image) + alpha_list.append(alpha) + depth_list.append(depth) + normal_list.append(normal) + pose_list.append(pose) + + target_cameras = np.load(os.path.join(target_image_path, 'cameras.npz'))['cam_poses'] + for idx in target_indices: + image, alpha = self.load_im(os.path.join(target_image_path, '%03d.png' % idx), bg_white) + normal, _ = self.load_im(os.path.join(target_image_path, '%03d_normal.png' % idx), bg_black) + depth = cv2.imread(os.path.join(target_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale + depth = torch.from_numpy(depth).unsqueeze(0) + pose = target_cameras[idx] + pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0) + + image_list.append(image) + alpha_list.append(alpha) + depth_list.append(depth) + normal_list.append(normal) + pose_list.append(pose) + + except Exception as e: + print(e) + index = np.random.randint(0, len(self.paths)) + continue + + break + + images = torch.stack(image_list, dim=0).float() # (6+V, 3, H, W) + alphas = torch.stack(alpha_list, dim=0).float() # (6+V, 1, H, W) + depths = torch.stack(depth_list, dim=0).float() # (6+V, 1, H, W) + normals = torch.stack(normal_list, dim=0).float() # (6+V, 3, H, W) + w2cs = torch.from_numpy(np.stack(pose_list, axis=0)).float() # (6+V, 4, 4) + c2ws = torch.linalg.inv(w2cs).float() + + normals = normals * 2.0 - 1.0 + normals = F.normalize(normals, dim=1) + normals = (normals + 1.0) / 2.0 + normals = torch.lerp(torch.zeros_like(normals), normals, alphas) + + # random rotation along z axis + if self.camera_rotation: + degree = np.random.uniform(0, math.pi * 2) + rot = torch.tensor([ + [np.cos(degree), -np.sin(degree), 0, 0], + [np.sin(degree), np.cos(degree), 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ]).unsqueeze(0).float() + c2ws = torch.matmul(rot, c2ws) + + # rotate normals + N, _, H, W = normals.shape + normals = normals * 2.0 - 1.0 + normals = torch.matmul(rot[:, :3, :3], normals.view(N, 3, -1)).view(N, 3, H, W) + normals = F.normalize(normals, dim=1) + normals = (normals + 1.0) / 2.0 + normals = torch.lerp(torch.zeros_like(normals), normals, alphas) + + # random scaling + if np.random.rand() < 0.5: + scale = np.random.uniform(0.7, 1.1) + c2ws[:, :3, 3] *= scale + depths *= scale + + # instrinsics of perspective cameras + K = FOV_to_intrinsics(self.fov) + Ks = K.unsqueeze(0).repeat(self.input_view_num + self.target_view_num, 1, 1).float() + + data = { + 'input_images': images[:self.input_view_num], # (6, 3, H, W) + 'input_alphas': alphas[:self.input_view_num], # (6, 1, H, W) + 'input_depths': depths[:self.input_view_num], # (6, 1, H, W) + 'input_normals': normals[:self.input_view_num], # (6, 3, H, W) + 'input_c2ws': c2ws[:self.input_view_num], # (6, 4, 4) + 'input_Ks': Ks[:self.input_view_num], # (6, 3, 3) + + # lrm generator input and supervision + 'target_images': images[self.input_view_num:], # (V, 3, H, W) + 'target_alphas': alphas[self.input_view_num:], # (V, 1, H, W) + 'target_depths': depths[self.input_view_num:], # (V, 1, H, W) + 'target_normals': normals[self.input_view_num:], # (V, 3, H, W) + 'target_c2ws': c2ws[self.input_view_num:], # (V, 4, 4) + 'target_Ks': Ks[self.input_view_num:], # (V, 3, 3) + } + return data + + +class ValidationData(Dataset): + def __init__(self, + root_dir='objaverse/', + input_view_num=6, + input_image_size=320, + fov=30, + ): + self.root_dir = Path(root_dir) + self.input_view_num = input_view_num + self.input_image_size = input_image_size + self.fov = fov + + self.paths = sorted(os.listdir(self.root_dir)) + print('============= length of dataset %d =============' % len(self.paths)) + + cam_distance = 4.0 + azimuths = np.array([30, 90, 150, 210, 270, 330]) + elevations = np.array([20, -10, 20, -10, 20, -10]) + azimuths = np.deg2rad(azimuths) + elevations = np.deg2rad(elevations) + + x = cam_distance * np.cos(elevations) * np.cos(azimuths) + y = cam_distance * np.cos(elevations) * np.sin(azimuths) + z = cam_distance * np.sin(elevations) + + cam_locations = np.stack([x, y, z], axis=-1) + cam_locations = torch.from_numpy(cam_locations).float() + c2ws = center_looking_at_camera_pose(cam_locations) + self.c2ws = c2ws.float() + self.Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(6, 1, 1).float() + + render_c2ws = get_circular_camera_poses(M=8, radius=cam_distance, elevation=20.0) + render_Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(render_c2ws.shape[0], 1, 1) + self.render_c2ws = render_c2ws.float() + self.render_Ks = render_Ks.float() + + def __len__(self): + return len(self.paths) + + def load_im(self, path, color): + ''' + replace background pixel with random color in rendering + ''' + pil_img = Image.open(path) + pil_img = pil_img.resize((self.input_image_size, self.input_image_size), resample=Image.BICUBIC) + + image = np.asarray(pil_img, dtype=np.float32) / 255. + if image.shape[-1] == 4: + alpha = image[:, :, 3:] + image = image[:, :, :3] * alpha + color * (1 - alpha) + else: + alpha = np.ones_like(image[:, :, :1]) + + image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float() + alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float() + return image, alpha + + def __getitem__(self, index): + # load data + input_image_path = os.path.join(self.root_dir, self.paths[index]) + + '''background color, default: white''' + bkg_color = [1.0, 1.0, 1.0] + + image_list = [] + alpha_list = [] + + for idx in range(self.input_view_num): + image, alpha = self.load_im(os.path.join(input_image_path, f'{idx:03d}.png'), bkg_color) + image_list.append(image) + alpha_list.append(alpha) + + images = torch.stack(image_list, dim=0).float() + alphas = torch.stack(alpha_list, dim=0).float() + + data = { + 'input_images': images, + 'input_alphas': alphas, + 'input_c2ws': self.c2ws, + 'input_Ks': self.Ks, + + 'render_c2ws': self.render_c2ws, + 'render_Ks': self.render_Ks, + } + return data diff --git a/gen_3d_modules/InstantMesh/data/objaverse_zero123plus.py b/gen_3d_modules/InstantMesh/data/objaverse_zero123plus.py new file mode 100644 index 0000000..57dbd08 --- /dev/null +++ b/gen_3d_modules/InstantMesh/data/objaverse_zero123plus.py @@ -0,0 +1,124 @@ +import os +import json +import numpy as np +import webdataset as wds +import pytorch_lightning as pl +import torch +from torch.utils.data import Dataset +from torch.utils.data.distributed import DistributedSampler +from PIL import Image +from pathlib import Path + +from InstantMesh.utils.train_util import instantiate_from_config + + +class DataModuleFromConfig(pl.LightningDataModule): + def __init__( + self, + batch_size=8, + num_workers=4, + train=None, + validation=None, + test=None, + **kwargs, + ): + super().__init__() + + self.batch_size = batch_size + self.num_workers = num_workers + + self.dataset_configs = dict() + if train is not None: + self.dataset_configs['train'] = train + if validation is not None: + self.dataset_configs['validation'] = validation + if test is not None: + self.dataset_configs['test'] = test + + def setup(self, stage): + + if stage in ['fit']: + self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) + else: + raise NotImplementedError + + def train_dataloader(self): + + sampler = DistributedSampler(self.datasets['train']) + return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler) + + def val_dataloader(self): + + sampler = DistributedSampler(self.datasets['validation']) + return wds.WebLoader(self.datasets['validation'], batch_size=4, num_workers=self.num_workers, shuffle=False, sampler=sampler) + + def test_dataloader(self): + + return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False) + + +class ObjaverseData(Dataset): + def __init__(self, + root_dir='objaverse/', + meta_fname='valid_paths.json', + image_dir='rendering_zero123plus', + validation=False, + ): + self.root_dir = Path(root_dir) + self.image_dir = image_dir + + with open(os.path.join(root_dir, meta_fname)) as f: + lvis_dict = json.load(f) + paths = [] + for k in lvis_dict.keys(): + paths.extend(lvis_dict[k]) + self.paths = paths + + total_objects = len(self.paths) + if validation: + self.paths = self.paths[-16:] # used last 16 as validation + else: + self.paths = self.paths[:-16] + print('============= length of dataset %d =============' % len(self.paths)) + + def __len__(self): + return len(self.paths) + + def load_im(self, path, color): + pil_img = Image.open(path) + + image = np.asarray(pil_img, dtype=np.float32) / 255. + alpha = image[:, :, 3:] + image = image[:, :, :3] * alpha + color * (1 - alpha) + + image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float() + alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float() + return image, alpha + + def __getitem__(self, index): + while True: + image_path = os.path.join(self.root_dir, self.image_dir, self.paths[index]) + + '''background color, default: white''' + bkg_color = [1., 1., 1.] + + img_list = [] + try: + for idx in range(7): + img, alpha = self.load_im(os.path.join(image_path, '%03d.png' % idx), bkg_color) + img_list.append(img) + + except Exception as e: + print(e) + index = np.random.randint(0, len(self.paths)) + continue + + break + + imgs = torch.stack(img_list, dim=0).float() + + data = { + 'cond_imgs': imgs[0], # (3, H, W) + 'target_imgs': imgs[1:], # (6, 3, H, W) + } + return data diff --git a/gen_3d_modules/InstantMesh/model.py b/gen_3d_modules/InstantMesh/model.py new file mode 100644 index 0000000..aaa4ace --- /dev/null +++ b/gen_3d_modules/InstantMesh/model.py @@ -0,0 +1,303 @@ +import os +import numpy as np +import torch +import torch.nn.functional as F +from torchvision.transforms import v2 +from torchvision.utils import make_grid, save_image +from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity +import pytorch_lightning as pl +from einops import rearrange, repeat + +from InstantMesh.utils.train_util import instantiate_from_config + + +class MVRecon(pl.LightningModule): + def __init__( + self, + lrm_generator_config, + lrm_path=None, + input_size=256, + render_size=192, + ): + super(MVRecon, self).__init__() + + self.input_size = input_size + self.render_size = render_size + + # init modules + self.lrm_generator = instantiate_from_config(lrm_generator_config) + if lrm_path is not None: + lrm_ckpt = torch.load(lrm_path) + self.lrm_generator.load_state_dict(lrm_ckpt['weights'], strict=False) + + self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg') + + self.validation_step_outputs = [] + + def on_fit_start(self): + if self.global_rank == 0: + os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True) + os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True) + + def prepare_batch_data(self, batch): + lrm_generator_input = {} + render_gt = {} # for supervision + + # input images + images = batch['input_images'] + images = v2.functional.resize( + images, self.input_size, interpolation=3, antialias=True).clamp(0, 1) + + lrm_generator_input['images'] = images.to(self.device) + + # input cameras and render cameras + input_c2ws = batch['input_c2ws'].flatten(-2) + input_Ks = batch['input_Ks'].flatten(-2) + target_c2ws = batch['target_c2ws'].flatten(-2) + target_Ks = batch['target_Ks'].flatten(-2) + render_cameras_input = torch.cat([input_c2ws, input_Ks], dim=-1) + render_cameras_target = torch.cat([target_c2ws, target_Ks], dim=-1) + render_cameras = torch.cat([render_cameras_input, render_cameras_target], dim=1) + + input_extrinsics = input_c2ws[:, :, :12] + input_intrinsics = torch.stack([ + input_Ks[:, :, 0], input_Ks[:, :, 4], + input_Ks[:, :, 2], input_Ks[:, :, 5], + ], dim=-1) + cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1) + + # add noise to input cameras + cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02 + + lrm_generator_input['cameras'] = cameras.to(self.device) + lrm_generator_input['render_cameras'] = render_cameras.to(self.device) + + # target images + target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1) + target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1) + target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1) + + # random crop + render_size = np.random.randint(self.render_size, 513) + target_images = v2.functional.resize( + target_images, render_size, interpolation=3, antialias=True).clamp(0, 1) + target_depths = v2.functional.resize( + target_depths, render_size, interpolation=0, antialias=True) + target_alphas = v2.functional.resize( + target_alphas, render_size, interpolation=0, antialias=True) + + crop_params = v2.RandomCrop.get_params( + target_images, output_size=(self.render_size, self.render_size)) + target_images = v2.functional.crop(target_images, *crop_params) + target_depths = v2.functional.crop(target_depths, *crop_params)[:, :, 0:1] + target_alphas = v2.functional.crop(target_alphas, *crop_params)[:, :, 0:1] + + lrm_generator_input['render_size'] = render_size + lrm_generator_input['crop_params'] = crop_params + + render_gt['target_images'] = target_images.to(self.device) + render_gt['target_depths'] = target_depths.to(self.device) + render_gt['target_alphas'] = target_alphas.to(self.device) + + return lrm_generator_input, render_gt + + def prepare_validation_batch_data(self, batch): + lrm_generator_input = {} + + # input images + images = batch['input_images'] + images = v2.functional.resize( + images, self.input_size, interpolation=3, antialias=True).clamp(0, 1) + + lrm_generator_input['images'] = images.to(self.device) + + input_c2ws = batch['input_c2ws'].flatten(-2) + input_Ks = batch['input_Ks'].flatten(-2) + + input_extrinsics = input_c2ws[:, :, :12] + input_intrinsics = torch.stack([ + input_Ks[:, :, 0], input_Ks[:, :, 4], + input_Ks[:, :, 2], input_Ks[:, :, 5], + ], dim=-1) + cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1) + + lrm_generator_input['cameras'] = cameras.to(self.device) + + render_c2ws = batch['render_c2ws'].flatten(-2) + render_Ks = batch['render_Ks'].flatten(-2) + render_cameras = torch.cat([render_c2ws, render_Ks], dim=-1) + + lrm_generator_input['render_cameras'] = render_cameras.to(self.device) + lrm_generator_input['render_size'] = 384 + lrm_generator_input['crop_params'] = None + + return lrm_generator_input + + def forward_lrm_generator( + self, + images, + cameras, + render_cameras, + render_size=192, + crop_params=None, + chunk_size=1, + ): + planes = torch.utils.checkpoint.checkpoint( + self.lrm_generator.forward_planes, + images, + cameras, + use_reentrant=False, + ) + frames = [] + for i in range(0, render_cameras.shape[1], chunk_size): + frames.append( + torch.utils.checkpoint.checkpoint( + self.lrm_generator.synthesizer, + planes, + cameras=render_cameras[:, i:i+chunk_size], + render_size=render_size, + crop_params=crop_params, + use_reentrant=False + ) + ) + frames = { + k: torch.cat([r[k] for r in frames], dim=1) + for k in frames[0].keys() + } + return frames + + def forward(self, lrm_generator_input): + images = lrm_generator_input['images'] + cameras = lrm_generator_input['cameras'] + render_cameras = lrm_generator_input['render_cameras'] + render_size = lrm_generator_input['render_size'] + crop_params = lrm_generator_input['crop_params'] + + out = self.forward_lrm_generator( + images, + cameras, + render_cameras, + render_size=render_size, + crop_params=crop_params, + chunk_size=1, + ) + render_images = torch.clamp(out['images_rgb'], 0.0, 1.0) + render_depths = out['images_depth'] + render_alphas = torch.clamp(out['images_weight'], 0.0, 1.0) + + out = { + 'render_images': render_images, + 'render_depths': render_depths, + 'render_alphas': render_alphas, + } + return out + + def training_step(self, batch, batch_idx): + lrm_generator_input, render_gt = self.prepare_batch_data(batch) + + render_out = self.forward(lrm_generator_input) + + loss, loss_dict = self.compute_loss(render_out, render_gt) + + self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) + + if self.global_step % 1000 == 0 and self.global_rank == 0: + B, N, C, H, W = render_gt['target_images'].shape + N_in = lrm_generator_input['images'].shape[1] + + input_images = v2.functional.resize( + lrm_generator_input['images'], (H, W), interpolation=3, antialias=True).clamp(0, 1) + input_images = torch.cat( + [input_images, torch.ones(B, N-N_in, C, H, W).to(input_images)], dim=1) + + input_images = rearrange( + input_images, 'b n c h w -> b c h (n w)') + target_images = rearrange( + render_gt['target_images'], 'b n c h w -> b c h (n w)') + render_images = rearrange( + render_out['render_images'], 'b n c h w -> b c h (n w)') + target_alphas = rearrange( + repeat(render_gt['target_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + render_alphas = rearrange( + repeat(render_out['render_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + target_depths = rearrange( + repeat(render_gt['target_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + render_depths = rearrange( + repeat(render_out['render_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + MAX_DEPTH = torch.max(target_depths) + target_depths = target_depths / MAX_DEPTH * target_alphas + render_depths = render_depths / MAX_DEPTH + + grid = torch.cat([ + input_images, + target_images, render_images, + target_alphas, render_alphas, + target_depths, render_depths, + ], dim=-2) + grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1)) + + save_image(grid, os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png')) + + return loss + + def compute_loss(self, render_out, render_gt): + # NOTE: the rgb value range of OpenLRM is [0, 1] + render_images = render_out['render_images'] + target_images = render_gt['target_images'].to(render_images) + render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 + target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 + + loss_mse = F.mse_loss(render_images, target_images) + loss_lpips = 2.0 * self.lpips(render_images, target_images) + + render_alphas = render_out['render_alphas'] + target_alphas = render_gt['target_alphas'] + loss_mask = F.mse_loss(render_alphas, target_alphas) + + loss = loss_mse + loss_lpips + loss_mask + + prefix = 'train' + loss_dict = {} + loss_dict.update({f'{prefix}/loss_mse': loss_mse}) + loss_dict.update({f'{prefix}/loss_lpips': loss_lpips}) + loss_dict.update({f'{prefix}/loss_mask': loss_mask}) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + lrm_generator_input = self.prepare_validation_batch_data(batch) + + render_out = self.forward(lrm_generator_input) + render_images = render_out['render_images'] + render_images = rearrange(render_images, 'b n c h w -> b c h (n w)') + + self.validation_step_outputs.append(render_images) + + def on_validation_epoch_end(self): + images = torch.cat(self.validation_step_outputs, dim=-1) + + all_images = self.all_gather(images) + all_images = rearrange(all_images, 'r b c h w -> (r b) c h w') + + if self.global_rank == 0: + image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png') + + grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1)) + save_image(grid, image_path) + print(f"Saved image to {image_path}") + + self.validation_step_outputs.clear() + + def configure_optimizers(self): + lr = self.learning_rate + + params = [] + + params.append({"params": self.lrm_generator.parameters(), "lr": lr, "weight_decay": 0.01 }) + + optimizer = torch.optim.AdamW(params, lr=lr, betas=(0.90, 0.95)) + scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 3000, eta_min=lr/10) + + return {'optimizer': optimizer, 'lr_scheduler': scheduler} diff --git a/gen_3d_modules/InstantMesh/model_mesh.py b/gen_3d_modules/InstantMesh/model_mesh.py new file mode 100644 index 0000000..c406d7a --- /dev/null +++ b/gen_3d_modules/InstantMesh/model_mesh.py @@ -0,0 +1,325 @@ +import os +import numpy as np +import torch +import torch.nn.functional as F +from torchvision.transforms import v2 +from torchvision.utils import make_grid, save_image +from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity +import pytorch_lightning as pl +from einops import rearrange, repeat + +from InstantMesh.utils.train_util import instantiate_from_config + + +# Regulrarization loss for FlexiCubes +def sdf_reg_loss_batch(sdf, all_edges): + sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2) + mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1]) + sdf_f1x6x2 = sdf_f1x6x2[mask] + sdf_diff = F.binary_cross_entropy_with_logits( + sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \ + F.binary_cross_entropy_with_logits( + sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float()) + return sdf_diff + + +class MVRecon(pl.LightningModule): + def __init__( + self, + lrm_generator_config, + input_size=256, + render_size=512, + init_ckpt=None, + ): + super(MVRecon, self).__init__() + + self.input_size = input_size + self.render_size = render_size + + # init modules + self.lrm_generator = instantiate_from_config(lrm_generator_config) + + self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg') + + # Load weights from pretrained MVRecon model, and use the mlp + # weights to initialize the weights of sdf and rgb mlps. + if init_ckpt is not None: + sd = torch.load(init_ckpt, map_location='cpu')['state_dict'] + sd = {k: v for k, v in sd.items() if k.startswith('lrm_generator')} + sd_fc = {} + for k, v in sd.items(): + if k.startswith('lrm_generator.synthesizer.decoder.net.'): + if k.startswith('lrm_generator.synthesizer.decoder.net.6.'): # last layer + # Here we assume the density filed's isosurface threshold is t, + # we reverse the sign of density filed to initialize SDF field. + # -(w*x + b - t) = (-w)*x + (t - b) + if 'weight' in k: + sd_fc[k.replace('net.', 'net_sdf.')] = -v[0:1] + else: + sd_fc[k.replace('net.', 'net_sdf.')] = 10.0 - v[0:1] + sd_fc[k.replace('net.', 'net_rgb.')] = v[1:4] + else: + sd_fc[k.replace('net.', 'net_sdf.')] = v + sd_fc[k.replace('net.', 'net_rgb.')] = v + else: + sd_fc[k] = v + sd_fc = {k.replace('lrm_generator.', ''): v for k, v in sd_fc.items()} + # missing `net_deformation` and `net_weight` parameters + self.lrm_generator.load_state_dict(sd_fc, strict=False) + print(f'Loaded weights from {init_ckpt}') + + self.validation_step_outputs = [] + + def on_fit_start(self): + device = torch.device(f'cuda:{self.global_rank}') + self.lrm_generator.init_flexicubes_geometry(device) + if self.global_rank == 0: + os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True) + os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True) + + def prepare_batch_data(self, batch): + lrm_generator_input = {} + render_gt = {} + + # input images + images = batch['input_images'] + images = v2.functional.resize( + images, self.input_size, interpolation=3, antialias=True).clamp(0, 1) + + lrm_generator_input['images'] = images.to(self.device) + + # input cameras and render cameras + input_c2ws = batch['input_c2ws'] + input_Ks = batch['input_Ks'] + target_c2ws = batch['target_c2ws'] + + render_c2ws = torch.cat([input_c2ws, target_c2ws], dim=1) + render_w2cs = torch.linalg.inv(render_c2ws) + + input_extrinsics = input_c2ws.flatten(-2) + input_extrinsics = input_extrinsics[:, :, :12] + input_intrinsics = input_Ks.flatten(-2) + input_intrinsics = torch.stack([ + input_intrinsics[:, :, 0], input_intrinsics[:, :, 4], + input_intrinsics[:, :, 2], input_intrinsics[:, :, 5], + ], dim=-1) + cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1) + + # add noise to input_cameras + cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02 + + lrm_generator_input['cameras'] = cameras.to(self.device) + lrm_generator_input['render_cameras'] = render_w2cs.to(self.device) + + # target images + target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1) + target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1) + target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1) + target_normals = torch.cat([batch['input_normals'], batch['target_normals']], dim=1) + + render_size = self.render_size + target_images = v2.functional.resize( + target_images, render_size, interpolation=3, antialias=True).clamp(0, 1) + target_depths = v2.functional.resize( + target_depths, render_size, interpolation=0, antialias=True) + target_alphas = v2.functional.resize( + target_alphas, render_size, interpolation=0, antialias=True) + target_normals = v2.functional.resize( + target_normals, render_size, interpolation=3, antialias=True) + + lrm_generator_input['render_size'] = render_size + + render_gt['target_images'] = target_images.to(self.device) + render_gt['target_depths'] = target_depths.to(self.device) + render_gt['target_alphas'] = target_alphas.to(self.device) + render_gt['target_normals'] = target_normals.to(self.device) + + return lrm_generator_input, render_gt + + def prepare_validation_batch_data(self, batch): + lrm_generator_input = {} + + # input images + images = batch['input_images'] + images = v2.functional.resize( + images, self.input_size, interpolation=3, antialias=True).clamp(0, 1) + + lrm_generator_input['images'] = images.to(self.device) + + # input cameras + input_c2ws = batch['input_c2ws'].flatten(-2) + input_Ks = batch['input_Ks'].flatten(-2) + + input_extrinsics = input_c2ws[:, :, :12] + input_intrinsics = torch.stack([ + input_Ks[:, :, 0], input_Ks[:, :, 4], + input_Ks[:, :, 2], input_Ks[:, :, 5], + ], dim=-1) + cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1) + + lrm_generator_input['cameras'] = cameras.to(self.device) + + # render cameras + render_c2ws = batch['render_c2ws'] + render_w2cs = torch.linalg.inv(render_c2ws) + + lrm_generator_input['render_cameras'] = render_w2cs.to(self.device) + lrm_generator_input['render_size'] = 384 + + return lrm_generator_input + + def forward_lrm_generator(self, images, cameras, render_cameras, render_size=512): + planes = torch.utils.checkpoint.checkpoint( + self.lrm_generator.forward_planes, + images, + cameras, + use_reentrant=False, + ) + out = self.lrm_generator.forward_geometry( + planes, + render_cameras, + render_size, + ) + return out + + def forward(self, lrm_generator_input): + images = lrm_generator_input['images'] + cameras = lrm_generator_input['cameras'] + render_cameras = lrm_generator_input['render_cameras'] + render_size = lrm_generator_input['render_size'] + + out = self.forward_lrm_generator( + images, cameras, render_cameras, render_size=render_size) + + return out + + def training_step(self, batch, batch_idx): + lrm_generator_input, render_gt = self.prepare_batch_data(batch) + + render_out = self.forward(lrm_generator_input) + + loss, loss_dict = self.compute_loss(render_out, render_gt) + + self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) + + if self.global_step % 1000 == 0 and self.global_rank == 0: + B, N, C, H, W = render_gt['target_images'].shape + N_in = lrm_generator_input['images'].shape[1] + + target_images = rearrange( + render_gt['target_images'], 'b n c h w -> b c h (n w)') + render_images = rearrange( + render_out['img'], 'b n c h w -> b c h (n w)') + target_alphas = rearrange( + repeat(render_gt['target_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + render_alphas = rearrange( + repeat(render_out['mask'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + target_depths = rearrange( + repeat(render_gt['target_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + render_depths = rearrange( + repeat(render_out['depth'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)') + target_normals = rearrange( + render_gt['target_normals'], 'b n c h w -> b c h (n w)') + render_normals = rearrange( + render_out['normal'], 'b n c h w -> b c h (n w)') + MAX_DEPTH = torch.max(target_depths) + target_depths = target_depths / MAX_DEPTH * target_alphas + render_depths = render_depths / MAX_DEPTH + + grid = torch.cat([ + target_images, render_images, + target_alphas, render_alphas, + target_depths, render_depths, + target_normals, render_normals, + ], dim=-2) + grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1)) + + image_path = os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png') + save_image(grid, image_path) + print(f"Saved image to {image_path}") + + return loss + + def compute_loss(self, render_out, render_gt): + # NOTE: the rgb value range of OpenLRM is [0, 1] + render_images = render_out['img'] + target_images = render_gt['target_images'].to(render_images) + render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 + target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0 + loss_mse = F.mse_loss(render_images, target_images) + loss_lpips = 2.0 * self.lpips(render_images, target_images) + + render_alphas = render_out['mask'] + target_alphas = render_gt['target_alphas'] + loss_mask = F.mse_loss(render_alphas, target_alphas) + + render_depths = render_out['depth'] + target_depths = render_gt['target_depths'] + loss_depth = 0.5 * F.l1_loss(render_depths[target_alphas>0], target_depths[target_alphas>0]) + + render_normals = render_out['normal'] * 2.0 - 1.0 + target_normals = render_gt['target_normals'] * 2.0 - 1.0 + similarity = (render_normals * target_normals).sum(dim=-3).abs() + normal_mask = target_alphas.squeeze(-3) + loss_normal = 1 - similarity[normal_mask>0].mean() + loss_normal = 0.2 * loss_normal + + # flexicubes regularization loss + sdf = render_out['sdf'] + sdf_reg_loss = render_out['sdf_reg_loss'] + sdf_reg_loss_entropy = sdf_reg_loss_batch(sdf, self.lrm_generator.geometry.all_edges).mean() * 0.01 + _, flexicubes_surface_reg, flexicubes_weights_reg = sdf_reg_loss + flexicubes_surface_reg = flexicubes_surface_reg.mean() * 0.5 + flexicubes_weights_reg = flexicubes_weights_reg.mean() * 0.1 + + loss_reg = sdf_reg_loss_entropy + flexicubes_surface_reg + flexicubes_weights_reg + + loss = loss_mse + loss_lpips + loss_mask + loss_depth + loss_normal + loss_reg + + prefix = 'train' + loss_dict = {} + loss_dict.update({f'{prefix}/loss_mse': loss_mse}) + loss_dict.update({f'{prefix}/loss_lpips': loss_lpips}) + loss_dict.update({f'{prefix}/loss_mask': loss_mask}) + loss_dict.update({f'{prefix}/loss_normal': loss_normal}) + loss_dict.update({f'{prefix}/loss_depth': loss_depth}) + loss_dict.update({f'{prefix}/loss_reg_sdf': sdf_reg_loss_entropy}) + loss_dict.update({f'{prefix}/loss_reg_surface': flexicubes_surface_reg}) + loss_dict.update({f'{prefix}/loss_reg_weights': flexicubes_weights_reg}) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + lrm_generator_input = self.prepare_validation_batch_data(batch) + + render_out = self.forward(lrm_generator_input) + render_images = render_out['img'] + render_images = rearrange(render_images, 'b n c h w -> b c h (n w)') + + self.validation_step_outputs.append(render_images) + + def on_validation_epoch_end(self): + images = torch.cat(self.validation_step_outputs, dim=-1) + + all_images = self.all_gather(images) + all_images = rearrange(all_images, 'r b c h w -> (r b) c h w') + + if self.global_rank == 0: + image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png') + + grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1)) + save_image(grid, image_path) + print(f"Saved image to {image_path}") + + self.validation_step_outputs.clear() + + def configure_optimizers(self): + lr = self.learning_rate + + optimizer = torch.optim.AdamW( + self.lrm_generator.parameters(), lr=lr, betas=(0.90, 0.95), weight_decay=0.01) + scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 100000, eta_min=0) + + return {'optimizer': optimizer, 'lr_scheduler': scheduler} \ No newline at end of file diff --git a/gen_3d_modules/InstantMesh/models/__init__.py b/gen_3d_modules/InstantMesh/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/InstantMesh/models/decoder/__init__.py b/gen_3d_modules/InstantMesh/models/decoder/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/InstantMesh/models/decoder/transformer.py b/gen_3d_modules/InstantMesh/models/decoder/transformer.py new file mode 100644 index 0000000..d8e628c --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/decoder/transformer.py @@ -0,0 +1,123 @@ +# Copyright (c) 2023, Zexin He +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn + + +class BasicTransformerBlock(nn.Module): + """ + Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks. + """ + # use attention from torch.nn.MultiHeadAttention + # Block contains a cross-attention layer, a self-attention layer, and a MLP + def __init__( + self, + inner_dim: int, + cond_dim: int, + num_heads: int, + eps: float, + attn_drop: float = 0., + attn_bias: bool = False, + mlp_ratio: float = 4., + mlp_drop: float = 0., + ): + super().__init__() + + self.norm1 = nn.LayerNorm(inner_dim) + self.cross_attn = nn.MultiheadAttention( + embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim, + dropout=attn_drop, bias=attn_bias, batch_first=True) + self.norm2 = nn.LayerNorm(inner_dim) + self.self_attn = nn.MultiheadAttention( + embed_dim=inner_dim, num_heads=num_heads, + dropout=attn_drop, bias=attn_bias, batch_first=True) + self.norm3 = nn.LayerNorm(inner_dim) + self.mlp = nn.Sequential( + nn.Linear(inner_dim, int(inner_dim * mlp_ratio)), + nn.GELU(), + nn.Dropout(mlp_drop), + nn.Linear(int(inner_dim * mlp_ratio), inner_dim), + nn.Dropout(mlp_drop), + ) + + def forward(self, x, cond): + # x: [N, L, D] + # cond: [N, L_cond, D_cond] + x = x + self.cross_attn(self.norm1(x), cond, cond)[0] + before_sa = self.norm2(x) + x = x + self.self_attn(before_sa, before_sa, before_sa)[0] + x = x + self.mlp(self.norm3(x)) + return x + + +class TriplaneTransformer(nn.Module): + """ + Transformer with condition that generates a triplane representation. + + Reference: + Timm: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L486 + """ + def __init__( + self, + inner_dim: int, + image_feat_dim: int, + triplane_low_res: int, + triplane_high_res: int, + triplane_dim: int, + num_layers: int, + num_heads: int, + eps: float = 1e-6, + ): + super().__init__() + + # attributes + self.triplane_low_res = triplane_low_res + self.triplane_high_res = triplane_high_res + self.triplane_dim = triplane_dim + + # modules + # initialize pos_embed with 1/sqrt(dim) * N(0, 1) + self.pos_embed = nn.Parameter(torch.randn(1, 3*triplane_low_res**2, inner_dim) * (1. / inner_dim) ** 0.5) + self.layers = nn.ModuleList([ + BasicTransformerBlock( + inner_dim=inner_dim, cond_dim=image_feat_dim, num_heads=num_heads, eps=eps) + for _ in range(num_layers) + ]) + self.norm = nn.LayerNorm(inner_dim, eps=eps) + self.deconv = nn.ConvTranspose2d(inner_dim, triplane_dim, kernel_size=2, stride=2, padding=0) + + def forward(self, image_feats): + # image_feats: [N, L_cond, D_cond] + + N = image_feats.shape[0] + H = W = self.triplane_low_res + L = 3 * H * W + + x = self.pos_embed.repeat(N, 1, 1) # [N, L, D] + for layer in self.layers: + x = layer(x, image_feats) + x = self.norm(x) + + # separate each plane and apply deconv + x = x.view(N, 3, H, W, -1) + x = torch.einsum('nihwd->indhw', x) # [3, N, D, H, W] + x = x.contiguous().view(3*N, -1, H, W) # [3*N, D, H, W] + x = self.deconv(x) # [3*N, D', H', W'] + x = x.view(3, N, *x.shape[-3:]) # [3, N, D', H', W'] + x = torch.einsum('indhw->nidhw', x) # [N, 3, D', H', W'] + x = x.contiguous() + + return x diff --git a/gen_3d_modules/InstantMesh/models/encoder/__init__.py b/gen_3d_modules/InstantMesh/models/encoder/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/InstantMesh/models/encoder/dino.py b/gen_3d_modules/InstantMesh/models/encoder/dino.py new file mode 100644 index 0000000..684444c --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/encoder/dino.py @@ -0,0 +1,550 @@ +# coding=utf-8 +# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViT model.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Set, Tuple, Union + +import torch +from torch import nn + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, +) +from transformers import PreTrainedModel, ViTConfig +from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer + + +class ViTEmbeddings(nn.Module): + """ + Construct the CLS token, position and patch embeddings. Optionally, also the mask token. + """ + + def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None + self.patch_embeddings = ViTPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + h0 = height // self.config.patch_size + w0 = width // self.config.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + h0, w0 = h0 + 0.1, w0 + 0.1 + patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)), + mode="bicubic", + align_corners=False, + ) + assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: Optional[torch.BoolTensor] = None, + interpolate_pos_encoding: bool = False, + ) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + + if bool_masked_pos is not None: + seq_length = embeddings.shape[1] + mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) + # replace the masked visual tokens by mask_tokens + mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) + embeddings = embeddings * (1.0 - mask) + mask_tokens * mask + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings + + +class ViTPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + if not interpolate_pos_encoding: + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +class ViTSelfAttention(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class ViTSelfOutput(nn.Module): + """ + The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class ViTAttention(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.attention = ViTSelfAttention(config) + self.output = ViTSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class ViTIntermediate(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +class ViTOutput(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class ViTLayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ViTAttention(config) + self.intermediate = ViTIntermediate(config) + self.output = ViTOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(config.hidden_size, 4 * config.hidden_size, bias=True) + ) + nn.init.constant_(self.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.adaLN_modulation[-1].bias, 0) + + def forward( + self, + hidden_states: torch.Tensor, + adaln_input: torch.Tensor = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + shift_msa, scale_msa, shift_mlp, scale_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) + + self_attention_outputs = self.attention( + modulate(self.layernorm_before(hidden_states), shift_msa, scale_msa), # in ViT, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states + + # in ViT, layernorm is also applied after self-attention + layer_output = modulate(self.layernorm_after(hidden_states), shift_mlp, scale_mlp) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +class ViTEncoder(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + adaln_input: torch.Tensor = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + adaln_input, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, adaln_input, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ViTPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + _no_split_modules = ["ViTEmbeddings", "ViTLayer"] + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid + # `trunc_normal_cpu` not implemented in `half` issues + module.weight.data = nn.init.trunc_normal_( + module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range + ).to(module.weight.dtype) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, ViTEmbeddings): + module.position_embeddings.data = nn.init.trunc_normal_( + module.position_embeddings.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.position_embeddings.dtype) + + module.cls_token.data = nn.init.trunc_normal_( + module.cls_token.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.cls_token.dtype) + + +class ViTModel(ViTPreTrainedModel): + def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): + super().__init__(config) + self.config = config + + self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token) + self.encoder = ViTEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pooler = ViTPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> ViTPatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + adaln_input: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) + expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype + if pixel_values.dtype != expected_dtype: + pixel_values = pixel_values.to(expected_dtype) + + embedding_output = self.embeddings( + pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding + ) + + encoder_outputs = self.encoder( + embedding_output, + adaln_input=adaln_input, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class ViTPooler(nn.Module): + def __init__(self, config: ViTConfig): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output \ No newline at end of file diff --git a/gen_3d_modules/InstantMesh/models/encoder/dino_wrapper.py b/gen_3d_modules/InstantMesh/models/encoder/dino_wrapper.py new file mode 100644 index 0000000..e84fd51 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/encoder/dino_wrapper.py @@ -0,0 +1,80 @@ +# Copyright (c) 2023, Zexin He +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch.nn as nn +from transformers import ViTImageProcessor +from einops import rearrange, repeat +from .dino import ViTModel + + +class DinoWrapper(nn.Module): + """ + Dino v1 wrapper using huggingface transformer implementation. + """ + def __init__(self, model_name: str, freeze: bool = True): + super().__init__() + self.model, self.processor = self._build_dino(model_name) + self.camera_embedder = nn.Sequential( + nn.Linear(16, self.model.config.hidden_size, bias=True), + nn.SiLU(), + nn.Linear(self.model.config.hidden_size, self.model.config.hidden_size, bias=True) + ) + if freeze: + self._freeze() + + def forward(self, image, camera): + # image: [B, N, C, H, W] + # camera: [B, N, D] + # RGB image with [0,1] scale and properly sized + if image.ndim == 5: + image = rearrange(image, 'b n c h w -> (b n) c h w') + dtype = image.dtype + inputs = self.processor( + images=image.float(), + return_tensors="pt", + do_rescale=False, + do_resize=False, + ).to(self.model.device).to(dtype) + # embed camera + N = camera.shape[1] + camera_embeddings = self.camera_embedder(camera) + camera_embeddings = rearrange(camera_embeddings, 'b n d -> (b n) d') + embeddings = camera_embeddings + # This resampling of positional embedding uses bicubic interpolation + outputs = self.model(**inputs, adaln_input=embeddings, interpolate_pos_encoding=True) + last_hidden_states = outputs.last_hidden_state + return last_hidden_states + + def _freeze(self): + print(f"======== Freezing DinoWrapper ========") + self.model.eval() + for name, param in self.model.named_parameters(): + param.requires_grad = False + + @staticmethod + def _build_dino(model_name: str, proxy_error_retries: int = 3, proxy_error_cooldown: int = 5): + import requests + try: + model = ViTModel.from_pretrained(model_name, add_pooling_layer=False) + processor = ViTImageProcessor.from_pretrained(model_name) + return model, processor + except requests.exceptions.ProxyError as err: + if proxy_error_retries > 0: + print(f"Huggingface ProxyError: Retrying in {proxy_error_cooldown} seconds...") + import time + time.sleep(proxy_error_cooldown) + return DinoWrapper._build_dino(model_name, proxy_error_retries - 1, proxy_error_cooldown) + else: + raise err diff --git a/gen_3d_modules/InstantMesh/models/geometry/__init__.py b/gen_3d_modules/InstantMesh/models/geometry/__init__.py new file mode 100644 index 0000000..89e9a6c --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. diff --git a/gen_3d_modules/InstantMesh/models/geometry/camera/__init__.py b/gen_3d_modules/InstantMesh/models/geometry/camera/__init__.py new file mode 100644 index 0000000..c5c7082 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/camera/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +from torch import nn + + +class Camera(nn.Module): + def __init__(self): + super(Camera, self).__init__() + pass diff --git a/gen_3d_modules/InstantMesh/models/geometry/camera/perspective_camera.py b/gen_3d_modules/InstantMesh/models/geometry/camera/perspective_camera.py new file mode 100644 index 0000000..7dcab0d --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/camera/perspective_camera.py @@ -0,0 +1,35 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +from . import Camera +import numpy as np + + +def projection(x=0.1, n=1.0, f=50.0, near_plane=None): + if near_plane is None: + near_plane = n + return np.array( + [[n / x, 0, 0, 0], + [0, n / -x, 0, 0], + [0, 0, -(f + near_plane) / (f - near_plane), -(2 * f * near_plane) / (f - near_plane)], + [0, 0, -1, 0]]).astype(np.float32) + + +class PerspectiveCamera(Camera): + def __init__(self, fovy=49.0, device='cuda'): + super(PerspectiveCamera, self).__init__() + self.device = device + focal = np.tan(fovy / 180.0 * np.pi * 0.5) + self.proj_mtx = torch.from_numpy(projection(x=focal, f=1000.0, n=1.0, near_plane=0.1)).to(self.device).unsqueeze(dim=0) + + def project(self, points_bxnx4): + out = torch.matmul( + points_bxnx4, + torch.transpose(self.proj_mtx, 1, 2)) + return out diff --git a/gen_3d_modules/InstantMesh/models/geometry/render/__init__.py b/gen_3d_modules/InstantMesh/models/geometry/render/__init__.py new file mode 100644 index 0000000..483cfab --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/render/__init__.py @@ -0,0 +1,8 @@ +import torch + +class Renderer(): + def __init__(self): + pass + + def forward(self): + pass \ No newline at end of file diff --git a/gen_3d_modules/InstantMesh/models/geometry/render/neural_render.py b/gen_3d_modules/InstantMesh/models/geometry/render/neural_render.py new file mode 100644 index 0000000..4734644 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/render/neural_render.py @@ -0,0 +1,121 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +import torch.nn.functional as F +import nvdiffrast.torch as dr +from . import Renderer + +_FG_LUT = None + + +def interpolate(attr, rast, attr_idx, rast_db=None): + return dr.interpolate( + attr.contiguous(), rast, attr_idx, rast_db=rast_db, + diff_attrs=None if rast_db is None else 'all') + + +def xfm_points(points, matrix, use_python=True): + '''Transform points. + Args: + points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3] + matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4] + use_python: Use PyTorch's torch.matmul (for validation) + Returns: + Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4]. + ''' + out = torch.matmul(torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2)) + if torch.is_anomaly_enabled(): + assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN" + return out + + +def dot(x, y): + return torch.sum(x * y, -1, keepdim=True) + + +def compute_vertex_normal(v_pos, t_pos_idx): + i0 = t_pos_idx[:, 0] + i1 = t_pos_idx[:, 1] + i2 = t_pos_idx[:, 2] + + v0 = v_pos[i0, :] + v1 = v_pos[i1, :] + v2 = v_pos[i2, :] + + face_normals = torch.cross(v1 - v0, v2 - v0) + + # Splat face normals to vertices + v_nrm = torch.zeros_like(v_pos) + v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals) + v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals) + v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals) + + # Normalize, replace zero (degenerated) normals with some default value + v_nrm = torch.where( + dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm) + ) + v_nrm = F.normalize(v_nrm, dim=1) + assert torch.all(torch.isfinite(v_nrm)) + + return v_nrm + + +class NeuralRender(Renderer): + def __init__(self, device='cuda', camera_model=None): + super(NeuralRender, self).__init__() + self.device = device + self.ctx = dr.RasterizeCudaContext(device=device) + self.projection_mtx = None + self.camera = camera_model + + def render_mesh( + self, + mesh_v_pos_bxnx3, + mesh_t_pos_idx_fx3, + camera_mv_bx4x4, + mesh_v_feat_bxnxd, + resolution=256, + spp=1, + device='cuda', + hierarchical_mask=False + ): + assert not hierarchical_mask + + mtx_in = torch.tensor(camera_mv_bx4x4, dtype=torch.float32, device=device) if not torch.is_tensor(camera_mv_bx4x4) else camera_mv_bx4x4 + v_pos = xfm_points(mesh_v_pos_bxnx3, mtx_in) # Rotate it to camera coordinates + v_pos_clip = self.camera.project(v_pos) # Projection in the camera + + v_nrm = compute_vertex_normal(mesh_v_pos_bxnx3[0], mesh_t_pos_idx_fx3.long()) # vertex normals in world coordinates + + # Render the image, + # Here we only return the feature (3D location) at each pixel, which will be used as the input for neural render + num_layers = 1 + mask_pyramid = None + assert mesh_t_pos_idx_fx3.shape[0] > 0 # Make sure we have shapes + mesh_v_feat_bxnxd = torch.cat([mesh_v_feat_bxnxd.repeat(v_pos.shape[0], 1, 1), v_pos], dim=-1) # Concatenate the pos + + with dr.DepthPeeler(self.ctx, v_pos_clip, mesh_t_pos_idx_fx3, [resolution * spp, resolution * spp]) as peeler: + for _ in range(num_layers): + rast, db = peeler.rasterize_next_layer() + gb_feat, _ = interpolate(mesh_v_feat_bxnxd, rast, mesh_t_pos_idx_fx3) + + hard_mask = torch.clamp(rast[..., -1:], 0, 1) + antialias_mask = dr.antialias( + hard_mask.clone().contiguous(), rast, v_pos_clip, + mesh_t_pos_idx_fx3) + + depth = gb_feat[..., -2:-1] + ori_mesh_feature = gb_feat[..., :-4] + + normal, _ = interpolate(v_nrm[None, ...], rast, mesh_t_pos_idx_fx3) + normal = dr.antialias(normal.clone().contiguous(), rast, v_pos_clip, mesh_t_pos_idx_fx3) + normal = F.normalize(normal, dim=-1) + normal = torch.lerp(torch.zeros_like(normal), (normal + 1.0) / 2.0, hard_mask.float()) # black background + + return ori_mesh_feature, antialias_mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal diff --git a/gen_3d_modules/InstantMesh/models/geometry/rep_3d/__init__.py b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/__init__.py new file mode 100644 index 0000000..a3d5628 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +import numpy as np + + +class Geometry(): + def __init__(self): + pass + + def forward(self): + pass diff --git a/gen_3d_modules/InstantMesh/models/geometry/rep_3d/dmtet.py b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/dmtet.py new file mode 100644 index 0000000..b6a7093 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/dmtet.py @@ -0,0 +1,504 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +import numpy as np +import os +from . import Geometry +from .dmtet_utils import get_center_boundary_index +import torch.nn.functional as F + + +############################################################################### +# DMTet utility functions +############################################################################### +def create_mt_variable(device): + triangle_table = torch.tensor( + [ + [-1, -1, -1, -1, -1, -1], + [1, 0, 2, -1, -1, -1], + [4, 0, 3, -1, -1, -1], + [1, 4, 2, 1, 3, 4], + [3, 1, 5, -1, -1, -1], + [2, 3, 0, 2, 5, 3], + [1, 4, 0, 1, 5, 4], + [4, 2, 5, -1, -1, -1], + [4, 5, 2, -1, -1, -1], + [4, 1, 0, 4, 5, 1], + [3, 2, 0, 3, 5, 2], + [1, 3, 5, -1, -1, -1], + [4, 1, 2, 4, 3, 1], + [3, 0, 4, -1, -1, -1], + [2, 0, 1, -1, -1, -1], + [-1, -1, -1, -1, -1, -1] + ], dtype=torch.long, device=device) + + num_triangles_table = torch.tensor([0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long, device=device) + base_tet_edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=device) + v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=device)) + return triangle_table, num_triangles_table, base_tet_edges, v_id + + +def sort_edges(edges_ex2): + with torch.no_grad(): + order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long() + order = order.unsqueeze(dim=1) + a = torch.gather(input=edges_ex2, index=order, dim=1) + b = torch.gather(input=edges_ex2, index=1 - order, dim=1) + return torch.stack([a, b], -1) + + +############################################################################### +# marching tetrahedrons (differentiable) +############################################################################### + +def marching_tets(pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id): + with torch.no_grad(): + occ_n = sdf_n > 0 + occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4) + occ_sum = torch.sum(occ_fx4, -1) + valid_tets = (occ_sum > 0) & (occ_sum < 4) + occ_sum = occ_sum[valid_tets] + + # find all vertices + all_edges = tet_fx4[valid_tets][:, base_tet_edges].reshape(-1, 2) + all_edges = sort_edges(all_edges) + unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True) + + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 + mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=sdf_n.device) * -1 + mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=sdf_n.device) + idx_map = mapping[idx_map] # map edges to verts + + interp_v = unique_edges[mask_edges] # .long() + edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3) + edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1) + edges_to_interp_sdf[:, -1] *= -1 + + denominator = edges_to_interp_sdf.sum(1, keepdim=True) + + edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator + verts = (edges_to_interp * edges_to_interp_sdf).sum(1) + + idx_map = idx_map.reshape(-1, 6) + + tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1) + num_triangles = num_triangles_table[tetindex] + + # Generate triangle indices + faces = torch.cat( + ( + torch.gather( + input=idx_map[num_triangles == 1], dim=1, + index=triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1, 3), + torch.gather( + input=idx_map[num_triangles == 2], dim=1, + index=triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1, 3), + ), dim=0) + return verts, faces + + +def create_tetmesh_variables(device='cuda'): + tet_table = torch.tensor( + [[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], + [0, 4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1], + [1, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1], + [1, 0, 8, 7, 0, 5, 8, 7, 0, 5, 6, 8], + [2, 5, 7, 9, -1, -1, -1, -1, -1, -1, -1, -1], + [2, 0, 9, 7, 0, 4, 9, 7, 0, 4, 6, 9], + [2, 1, 9, 5, 1, 4, 9, 5, 1, 4, 8, 9], + [6, 0, 1, 2, 6, 1, 2, 8, 6, 8, 2, 9], + [3, 6, 8, 9, -1, -1, -1, -1, -1, -1, -1, -1], + [3, 0, 9, 8, 0, 4, 9, 8, 0, 4, 5, 9], + [3, 1, 9, 6, 1, 4, 9, 6, 1, 4, 7, 9], + [5, 0, 1, 3, 5, 1, 3, 7, 5, 7, 3, 9], + [3, 2, 8, 6, 2, 5, 8, 6, 2, 5, 7, 8], + [4, 0, 2, 3, 4, 2, 3, 7, 4, 7, 3, 8], + [4, 1, 2, 3, 4, 2, 3, 5, 4, 5, 3, 6], + [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]], dtype=torch.long, device=device) + num_tets_table = torch.tensor([0, 1, 1, 3, 1, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 0], dtype=torch.long, device=device) + return tet_table, num_tets_table + + +def marching_tets_tetmesh( + pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id, + return_tet_mesh=False, ori_v=None, num_tets_table=None, tet_table=None): + with torch.no_grad(): + occ_n = sdf_n > 0 + occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4) + occ_sum = torch.sum(occ_fx4, -1) + valid_tets = (occ_sum > 0) & (occ_sum < 4) + occ_sum = occ_sum[valid_tets] + + # find all vertices + all_edges = tet_fx4[valid_tets][:, base_tet_edges].reshape(-1, 2) + all_edges = sort_edges(all_edges) + unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True) + + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 + mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=sdf_n.device) * -1 + mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=sdf_n.device) + idx_map = mapping[idx_map] # map edges to verts + + interp_v = unique_edges[mask_edges] # .long() + edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3) + edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1) + edges_to_interp_sdf[:, -1] *= -1 + + denominator = edges_to_interp_sdf.sum(1, keepdim=True) + + edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator + verts = (edges_to_interp * edges_to_interp_sdf).sum(1) + + idx_map = idx_map.reshape(-1, 6) + + tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1) + num_triangles = num_triangles_table[tetindex] + + # Generate triangle indices + faces = torch.cat( + ( + torch.gather( + input=idx_map[num_triangles == 1], dim=1, + index=triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1, 3), + torch.gather( + input=idx_map[num_triangles == 2], dim=1, + index=triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1, 3), + ), dim=0) + if not return_tet_mesh: + return verts, faces + occupied_verts = ori_v[occ_n] + mapping = torch.ones((pos_nx3.shape[0]), dtype=torch.long, device="cuda") * -1 + mapping[occ_n] = torch.arange(occupied_verts.shape[0], device="cuda") + tet_fx4 = mapping[tet_fx4.reshape(-1)].reshape((-1, 4)) + + idx_map = torch.cat([tet_fx4[valid_tets] + verts.shape[0], idx_map], -1) # t x 10 + tet_verts = torch.cat([verts, occupied_verts], 0) + num_tets = num_tets_table[tetindex] + + tets = torch.cat( + ( + torch.gather(input=idx_map[num_tets == 1], dim=1, index=tet_table[tetindex[num_tets == 1]][:, :4]).reshape( + -1, + 4), + torch.gather(input=idx_map[num_tets == 3], dim=1, index=tet_table[tetindex[num_tets == 3]][:, :12]).reshape( + -1, + 4), + ), dim=0) + # add fully occupied tets + fully_occupied = occ_fx4.sum(-1) == 4 + tet_fully_occupied = tet_fx4[fully_occupied] + verts.shape[0] + tets = torch.cat([tets, tet_fully_occupied]) + + return verts, faces, tet_verts, tets + + +############################################################################### +# Compact tet grid +############################################################################### + +def compact_tets(pos_nx3, sdf_n, tet_fx4): + with torch.no_grad(): + # Find surface tets + occ_n = sdf_n > 0 + occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4) + occ_sum = torch.sum(occ_fx4, -1) + valid_tets = (occ_sum > 0) & (occ_sum < 4) # one value per tet, these are the surface tets + + valid_vtx = tet_fx4[valid_tets].reshape(-1) + unique_vtx, idx_map = torch.unique(valid_vtx, dim=0, return_inverse=True) + new_pos = pos_nx3[unique_vtx] + new_sdf = sdf_n[unique_vtx] + new_tets = idx_map.reshape(-1, 4) + return new_pos, new_sdf, new_tets + + +############################################################################### +# Subdivide volume +############################################################################### + +def batch_subdivide_volume(tet_pos_bxnx3, tet_bxfx4, grid_sdf): + device = tet_pos_bxnx3.device + # get new verts + tet_fx4 = tet_bxfx4[0] + edges = [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3] + all_edges = tet_fx4[:, edges].reshape(-1, 2) + all_edges = sort_edges(all_edges) + unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True) + idx_map = idx_map + tet_pos_bxnx3.shape[1] + all_values = torch.cat([tet_pos_bxnx3, grid_sdf], -1) + mid_points_pos = all_values[:, unique_edges.reshape(-1)].reshape( + all_values.shape[0], -1, 2, + all_values.shape[-1]).mean(2) + new_v = torch.cat([all_values, mid_points_pos], 1) + new_v, new_sdf = new_v[..., :3], new_v[..., 3] + + # get new tets + + idx_a, idx_b, idx_c, idx_d = tet_fx4[:, 0], tet_fx4[:, 1], tet_fx4[:, 2], tet_fx4[:, 3] + idx_ab = idx_map[0::6] + idx_ac = idx_map[1::6] + idx_ad = idx_map[2::6] + idx_bc = idx_map[3::6] + idx_bd = idx_map[4::6] + idx_cd = idx_map[5::6] + + tet_1 = torch.stack([idx_a, idx_ab, idx_ac, idx_ad], dim=1) + tet_2 = torch.stack([idx_b, idx_bc, idx_ab, idx_bd], dim=1) + tet_3 = torch.stack([idx_c, idx_ac, idx_bc, idx_cd], dim=1) + tet_4 = torch.stack([idx_d, idx_ad, idx_cd, idx_bd], dim=1) + tet_5 = torch.stack([idx_ab, idx_ac, idx_ad, idx_bd], dim=1) + tet_6 = torch.stack([idx_ab, idx_ac, idx_bd, idx_bc], dim=1) + tet_7 = torch.stack([idx_cd, idx_ac, idx_bd, idx_ad], dim=1) + tet_8 = torch.stack([idx_cd, idx_ac, idx_bc, idx_bd], dim=1) + + tet_np = torch.cat([tet_1, tet_2, tet_3, tet_4, tet_5, tet_6, tet_7, tet_8], dim=0) + tet_np = tet_np.reshape(1, -1, 4).expand(tet_pos_bxnx3.shape[0], -1, -1) + tet = tet_np.long().to(device) + + return new_v, tet, new_sdf + + +############################################################################### +# Adjacency +############################################################################### +def tet_to_tet_adj_sparse(tet_tx4): + # include self connection!!!!!!!!!!!!!!!!!!! + with torch.no_grad(): + t = tet_tx4.shape[0] + device = tet_tx4.device + idx_array = torch.LongTensor( + [0, 1, 2, + 1, 0, 3, + 2, 3, 0, + 3, 2, 1]).to(device).reshape(4, 3).unsqueeze(0).expand(t, -1, -1) # (t, 4, 3) + + # get all faces + all_faces = torch.gather(input=tet_tx4.unsqueeze(1).expand(-1, 4, -1), index=idx_array, dim=-1).reshape( + -1, + 3) # (tx4, 3) + all_faces_tet_idx = torch.arange(t, device=device).unsqueeze(-1).expand(-1, 4).reshape(-1) + # sort and group + all_faces_sorted, _ = torch.sort(all_faces, dim=1) + + all_faces_unique, inverse_indices, counts = torch.unique( + all_faces_sorted, dim=0, return_counts=True, + return_inverse=True) + tet_face_fx3 = all_faces_unique[counts == 2] + counts = counts[inverse_indices] # tx4 + valid = (counts == 2) + + group = inverse_indices[valid] + # print (inverse_indices.shape, group.shape, all_faces_tet_idx.shape) + _, indices = torch.sort(group) + all_faces_tet_idx_grouped = all_faces_tet_idx[valid][indices] + tet_face_tetidx_fx2 = torch.stack([all_faces_tet_idx_grouped[::2], all_faces_tet_idx_grouped[1::2]], dim=-1) + + tet_adj_idx = torch.cat([tet_face_tetidx_fx2, torch.flip(tet_face_tetidx_fx2, [1])]) + adj_self = torch.arange(t, device=tet_tx4.device) + adj_self = torch.stack([adj_self, adj_self], -1) + tet_adj_idx = torch.cat([tet_adj_idx, adj_self]) + + tet_adj_idx = torch.unique(tet_adj_idx, dim=0) + values = torch.ones( + tet_adj_idx.shape[0], device=tet_tx4.device).float() + adj_sparse = torch.sparse.FloatTensor( + tet_adj_idx.t(), values, torch.Size([t, t])) + + # normalization + neighbor_num = 1.0 / torch.sparse.sum( + adj_sparse, dim=1).to_dense() + values = torch.index_select(neighbor_num, 0, tet_adj_idx[:, 0]) + adj_sparse = torch.sparse.FloatTensor( + tet_adj_idx.t(), values, torch.Size([t, t])) + return adj_sparse + + +############################################################################### +# Compact grid +############################################################################### + +def get_tet_bxfx4x3(bxnxz, bxfx4): + n_batch, z = bxnxz.shape[0], bxnxz.shape[2] + gather_input = bxnxz.unsqueeze(2).expand( + n_batch, bxnxz.shape[1], 4, z) + gather_index = bxfx4.unsqueeze(-1).expand( + n_batch, bxfx4.shape[1], 4, z).long() + tet_bxfx4xz = torch.gather( + input=gather_input, dim=1, index=gather_index) + + return tet_bxfx4xz + + +def shrink_grid(tet_pos_bxnx3, tet_bxfx4, grid_sdf): + with torch.no_grad(): + assert tet_pos_bxnx3.shape[0] == 1 + + occ = grid_sdf[0] > 0 + occ_sum = get_tet_bxfx4x3(occ.unsqueeze(0).unsqueeze(-1), tet_bxfx4).reshape(-1, 4).sum(-1) + mask = (occ_sum > 0) & (occ_sum < 4) + + # build connectivity graph + adj_matrix = tet_to_tet_adj_sparse(tet_bxfx4[0]) + mask = mask.float().unsqueeze(-1) + + # Include a one ring of neighbors + for i in range(1): + mask = torch.sparse.mm(adj_matrix, mask) + mask = mask.squeeze(-1) > 0 + + mapping = torch.zeros((tet_pos_bxnx3.shape[1]), device=tet_pos_bxnx3.device, dtype=torch.long) + new_tet_bxfx4 = tet_bxfx4[:, mask].long() + selected_verts_idx = torch.unique(new_tet_bxfx4) + new_tet_pos_bxnx3 = tet_pos_bxnx3[:, selected_verts_idx] + mapping[selected_verts_idx] = torch.arange(selected_verts_idx.shape[0], device=tet_pos_bxnx3.device) + new_tet_bxfx4 = mapping[new_tet_bxfx4.reshape(-1)].reshape(new_tet_bxfx4.shape) + new_grid_sdf = grid_sdf[:, selected_verts_idx] + return new_tet_pos_bxnx3, new_tet_bxfx4, new_grid_sdf + + +############################################################################### +# Regularizer +############################################################################### + +def sdf_reg_loss(sdf, all_edges): + sdf_f1x6x2 = sdf[all_edges.reshape(-1)].reshape(-1, 2) + mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1]) + sdf_f1x6x2 = sdf_f1x6x2[mask] + sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits( + sdf_f1x6x2[..., 0], + (sdf_f1x6x2[..., 1] > 0).float()) + \ + torch.nn.functional.binary_cross_entropy_with_logits( + sdf_f1x6x2[..., 1], + (sdf_f1x6x2[..., 0] > 0).float()) + return sdf_diff + + +def sdf_reg_loss_batch(sdf, all_edges): + sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2) + mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1]) + sdf_f1x6x2 = sdf_f1x6x2[mask] + sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \ + torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float()) + return sdf_diff + + +############################################################################### +# Geometry interface +############################################################################### +class DMTetGeometry(Geometry): + def __init__( + self, grid_res=64, scale=2.0, device='cuda', renderer=None, + render_type='neural_render', args=None): + super(DMTetGeometry, self).__init__() + self.grid_res = grid_res + self.device = device + self.args = args + tets = np.load('data/tets/%d_compress.npz' % (grid_res)) + self.verts = torch.from_numpy(tets['vertices']).float().to(self.device) + # Make sure the tet is zero-centered and length is equal to 1 + length = self.verts.max(dim=0)[0] - self.verts.min(dim=0)[0] + length = length.max() + mid = (self.verts.max(dim=0)[0] + self.verts.min(dim=0)[0]) / 2.0 + self.verts = (self.verts - mid.unsqueeze(dim=0)) / length + if isinstance(scale, list): + self.verts[:, 0] = self.verts[:, 0] * scale[0] + self.verts[:, 1] = self.verts[:, 1] * scale[1] + self.verts[:, 2] = self.verts[:, 2] * scale[1] + else: + self.verts = self.verts * scale + self.indices = torch.from_numpy(tets['tets']).long().to(self.device) + self.triangle_table, self.num_triangles_table, self.base_tet_edges, self.v_id = create_mt_variable(self.device) + self.tet_table, self.num_tets_table = create_tetmesh_variables(self.device) + # Parameters for regularization computation + edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=self.device) + all_edges = self.indices[:, edges].reshape(-1, 2) + all_edges_sorted = torch.sort(all_edges, dim=1)[0] + self.all_edges = torch.unique(all_edges_sorted, dim=0) + + # Parameters used for fix boundary sdf + self.center_indices, self.boundary_indices = get_center_boundary_index(self.verts) + self.renderer = renderer + self.render_type = render_type + + def getAABB(self): + return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values + + def get_mesh(self, v_deformed_nx3, sdf_n, with_uv=False, indices=None): + if indices is None: + indices = self.indices + verts, faces = marching_tets( + v_deformed_nx3, sdf_n, indices, self.triangle_table, + self.num_triangles_table, self.base_tet_edges, self.v_id) + faces = torch.cat( + [faces[:, 0:1], + faces[:, 2:3], + faces[:, 1:2], ], dim=-1) + return verts, faces + + def get_tet_mesh(self, v_deformed_nx3, sdf_n, with_uv=False, indices=None): + if indices is None: + indices = self.indices + verts, faces, tet_verts, tets = marching_tets_tetmesh( + v_deformed_nx3, sdf_n, indices, self.triangle_table, + self.num_triangles_table, self.base_tet_edges, self.v_id, return_tet_mesh=True, + num_tets_table=self.num_tets_table, tet_table=self.tet_table, ori_v=v_deformed_nx3) + faces = torch.cat( + [faces[:, 0:1], + faces[:, 2:3], + faces[:, 1:2], ], dim=-1) + return verts, faces, tet_verts, tets + + def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False): + return_value = dict() + if self.render_type == 'neural_render': + tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth = self.renderer.render_mesh( + mesh_v_nx3.unsqueeze(dim=0), + mesh_f_fx3.int(), + camera_mv_bx4x4, + mesh_v_nx3.unsqueeze(dim=0), + resolution=resolution, + device=self.device, + hierarchical_mask=hierarchical_mask + ) + + return_value['tex_pos'] = tex_pos + return_value['mask'] = mask + return_value['hard_mask'] = hard_mask + return_value['rast'] = rast + return_value['v_pos_clip'] = v_pos_clip + return_value['mask_pyramid'] = mask_pyramid + return_value['depth'] = depth + else: + raise NotImplementedError + + return return_value + + def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256): + # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1 + v_list = [] + f_list = [] + n_batch = v_deformed_bxnx3.shape[0] + all_render_output = [] + for i_batch in range(n_batch): + verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch]) + v_list.append(verts_nx3) + f_list.append(faces_fx3) + render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution) + all_render_output.append(render_output) + + # Concatenate all render output + return_keys = all_render_output[0].keys() + return_value = dict() + for k in return_keys: + value = [v[k] for v in all_render_output] + return_value[k] = value + # We can do concatenation outside of the render + return return_value diff --git a/gen_3d_modules/InstantMesh/models/geometry/rep_3d/dmtet_utils.py b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/dmtet_utils.py new file mode 100644 index 0000000..8d466a9 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/dmtet_utils.py @@ -0,0 +1,20 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch + + +def get_center_boundary_index(verts): + length_ = torch.sum(verts ** 2, dim=-1) + center_idx = torch.argmin(length_) + boundary_neg = verts == verts.max() + boundary_pos = verts == verts.min() + boundary = torch.bitwise_or(boundary_pos, boundary_neg) + boundary = torch.sum(boundary.float(), dim=-1) + boundary_idx = torch.nonzero(boundary) + return center_idx, boundary_idx.squeeze(dim=-1) diff --git a/gen_3d_modules/InstantMesh/models/geometry/rep_3d/extract_texture_map.py b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/extract_texture_map.py new file mode 100644 index 0000000..a5d62bb --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/extract_texture_map.py @@ -0,0 +1,40 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +import xatlas +import numpy as np +import nvdiffrast.torch as dr + + +# ============================================================================================== +def interpolate(attr, rast, attr_idx, rast_db=None): + return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all') + + +def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution): + vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy()) + + # Convert to tensors + indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64) + + uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device) + mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device) + # mesh_v_tex. ture + uv_clip = uvs[None, ...] * 2.0 - 1.0 + + # pad to four component coordinate + uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1) + + # rasterize + rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution)) + + # Interpolate world space position + gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int()) + mask = rast[..., 3:4] > 0 + return uvs, mesh_tex_idx, gb_pos, mask diff --git a/gen_3d_modules/InstantMesh/models/geometry/rep_3d/flexicubes_geometry.py b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/flexicubes_geometry.py new file mode 100644 index 0000000..e22fcd7 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/geometry/rep_3d/flexicubes_geometry.py @@ -0,0 +1,120 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +import numpy as np +import os +from . import Geometry +from FlexiCubes.flexicubes import FlexiCubes +from .dmtet import sdf_reg_loss_batch +import torch.nn.functional as F + +def get_center_boundary_index(grid_res, device): + v = torch.zeros((grid_res + 1, grid_res + 1, grid_res + 1), dtype=torch.bool, device=device) + v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = True + center_indices = torch.nonzero(v.reshape(-1)) + + v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = False + v[:2, ...] = True + v[-2:, ...] = True + v[:, :2, ...] = True + v[:, -2:, ...] = True + v[:, :, :2] = True + v[:, :, -2:] = True + boundary_indices = torch.nonzero(v.reshape(-1)) + return center_indices, boundary_indices + +############################################################################### +# Geometry interface +############################################################################### +class FlexiCubesGeometry(Geometry): + def __init__( + self, grid_res=64, scale=2.0, device='cuda', renderer=None, + render_type='neural_render', args=None): + super(FlexiCubesGeometry, self).__init__() + self.grid_res = grid_res + self.device = device + self.args = args + self.fc = FlexiCubes(device, weight_scale=0.5) + self.verts, self.indices = self.fc.construct_voxel_grid(grid_res) + if isinstance(scale, list): + self.verts[:, 0] = self.verts[:, 0] * scale[0] + self.verts[:, 1] = self.verts[:, 1] * scale[1] + self.verts[:, 2] = self.verts[:, 2] * scale[1] + else: + self.verts = self.verts * scale + + all_edges = self.indices[:, self.fc.cube_edges].reshape(-1, 2) + self.all_edges = torch.unique(all_edges, dim=0) + + # Parameters used for fix boundary sdf + self.center_indices, self.boundary_indices = get_center_boundary_index(self.grid_res, device) + self.renderer = renderer + self.render_type = render_type + + def getAABB(self): + return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values + + def get_mesh(self, v_deformed_nx3, sdf_n, weight_n=None, with_uv=False, indices=None, is_training=False): + if indices is None: + indices = self.indices + + verts, faces, v_reg_loss = self.fc(v_deformed_nx3, sdf_n, indices, self.grid_res, + beta_fx12=weight_n[:, :12], alpha_fx8=weight_n[:, 12:20], + gamma_f=weight_n[:, 20], training=is_training + ) + return verts, faces, v_reg_loss + + + def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False): + return_value = dict() + if self.render_type == 'neural_render': + tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal = self.renderer.render_mesh( + mesh_v_nx3.unsqueeze(dim=0), + mesh_f_fx3.int(), + camera_mv_bx4x4, + mesh_v_nx3.unsqueeze(dim=0), + resolution=resolution, + device=self.device, + hierarchical_mask=hierarchical_mask + ) + + return_value['tex_pos'] = tex_pos + return_value['mask'] = mask + return_value['hard_mask'] = hard_mask + return_value['rast'] = rast + return_value['v_pos_clip'] = v_pos_clip + return_value['mask_pyramid'] = mask_pyramid + return_value['depth'] = depth + return_value['normal'] = normal + else: + raise NotImplementedError + + return return_value + + def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256): + # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1 + v_list = [] + f_list = [] + n_batch = v_deformed_bxnx3.shape[0] + all_render_output = [] + for i_batch in range(n_batch): + verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch]) + v_list.append(verts_nx3) + f_list.append(faces_fx3) + render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution) + all_render_output.append(render_output) + + # Concatenate all render output + return_keys = all_render_output[0].keys() + return_value = dict() + for k in return_keys: + value = [v[k] for v in all_render_output] + return_value[k] = value + # We can do concatenation outside of the render + return return_value diff --git a/gen_3d_modules/InstantMesh/models/lrm.py b/gen_3d_modules/InstantMesh/models/lrm.py new file mode 100644 index 0000000..2128bac --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/lrm.py @@ -0,0 +1,209 @@ +# Copyright (c) 2023, Zexin He +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import torch +import torch.nn as nn +import mcubes +import nvdiffrast.torch as dr +from einops import rearrange, repeat + +from .encoder.dino_wrapper import DinoWrapper +from .decoder.transformer import TriplaneTransformer +from .renderer.synthesizer import TriplaneSynthesizer +from ..utils.mesh_util import xatlas_uvmap + + +class InstantNeRF(nn.Module): + """ + Full model of the large reconstruction model. + """ + def __init__( + self, + encoder_freeze: bool = False, + encoder_model_name: str = 'facebook/dino-vitb16', + encoder_feat_dim: int = 768, + transformer_dim: int = 1024, + transformer_layers: int = 16, + transformer_heads: int = 16, + triplane_low_res: int = 32, + triplane_high_res: int = 64, + triplane_dim: int = 80, + rendering_samples_per_ray: int = 128, + ): + super().__init__() + + # modules + self.encoder = DinoWrapper( + model_name=encoder_model_name, + freeze=encoder_freeze, + ) + + self.transformer = TriplaneTransformer( + inner_dim=transformer_dim, + num_layers=transformer_layers, + num_heads=transformer_heads, + image_feat_dim=encoder_feat_dim, + triplane_low_res=triplane_low_res, + triplane_high_res=triplane_high_res, + triplane_dim=triplane_dim, + ) + + self.synthesizer = TriplaneSynthesizer( + triplane_dim=triplane_dim, + samples_per_ray=rendering_samples_per_ray, + ) + + def forward_planes(self, images, cameras): + # images: [B, V, C_img, H_img, W_img] + # cameras: [B, V, 16] + B = images.shape[0] + + # encode images + image_feats = self.encoder(images, cameras) + image_feats = rearrange(image_feats, '(b v) l d -> b (v l) d', b=B) + + # transformer generating planes + planes = self.transformer(image_feats) + + return planes + + def forward_synthesizer(self, planes, render_cameras, render_size: int): + render_results = self.synthesizer( + planes, + render_cameras, + render_size, + ) + return render_results + + def forward(self, images, cameras, render_cameras, render_size: int): + # images: [B, V, C_img, H_img, W_img] + # cameras: [B, V, 16] + # render_cameras: [B, M, D_cam_render] + # render_size: int + B, M = render_cameras.shape[:2] + + planes = self.forward_planes(images, cameras) + + # render target views + render_results = self.synthesizer(planes, render_cameras, render_size) + + return { + 'planes': planes, + **render_results, + } + + def get_texture_prediction(self, planes, tex_pos, hard_mask=None): + ''' + Predict Texture given triplanes + :param planes: the triplane feature map + :param tex_pos: Position we want to query the texture field + :param hard_mask: 2D silhoueete of the rendered image + ''' + tex_pos = torch.cat(tex_pos, dim=0) + if not hard_mask is None: + tex_pos = tex_pos * hard_mask.float() + batch_size = tex_pos.shape[0] + tex_pos = tex_pos.reshape(batch_size, -1, 3) + ################### + # We use mask to get the texture location (to save the memory) + if hard_mask is not None: + n_point_list = torch.sum(hard_mask.long().reshape(hard_mask.shape[0], -1), dim=-1) + sample_tex_pose_list = [] + max_point = n_point_list.max() + expanded_hard_mask = hard_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3) > 0.5 + for i in range(tex_pos.shape[0]): + tex_pos_one_shape = tex_pos[i][expanded_hard_mask[i]].reshape(1, -1, 3) + if tex_pos_one_shape.shape[1] < max_point: + tex_pos_one_shape = torch.cat( + [tex_pos_one_shape, torch.zeros( + 1, max_point - tex_pos_one_shape.shape[1], 3, + device=tex_pos_one_shape.device, dtype=torch.float32)], dim=1) + sample_tex_pose_list.append(tex_pos_one_shape) + tex_pos = torch.cat(sample_tex_pose_list, dim=0) + + tex_feat = torch.utils.checkpoint.checkpoint( + self.synthesizer.forward_points, + planes, + tex_pos, + use_reentrant=False, + )['rgb'] + + if hard_mask is not None: + final_tex_feat = torch.zeros( + planes.shape[0], hard_mask.shape[1] * hard_mask.shape[2], tex_feat.shape[-1], device=tex_feat.device) + expanded_hard_mask = hard_mask.reshape(hard_mask.shape[0], -1, 1).expand(-1, -1, final_tex_feat.shape[-1]) > 0.5 + for i in range(planes.shape[0]): + final_tex_feat[i][expanded_hard_mask[i]] = tex_feat[i][:n_point_list[i]].reshape(-1) + tex_feat = final_tex_feat + + return tex_feat.reshape(planes.shape[0], hard_mask.shape[1], hard_mask.shape[2], tex_feat.shape[-1]) + + def extract_mesh( + self, + planes: torch.Tensor, + mesh_resolution: int = 256, + mesh_threshold: int = 10.0, + use_texture_map: bool = False, + texture_resolution: int = 1024, + **kwargs, + ): + ''' + Extract a 3D mesh from triplane nerf. Only support batch_size 1. + :param planes: triplane features + :param mesh_resolution: marching cubes resolution + :param mesh_threshold: iso-surface threshold + :param use_texture_map: use texture map or vertex color + :param texture_resolution: the resolution of texture map + ''' + assert planes.shape[0] == 1 + device = planes.device + + grid_out = self.synthesizer.forward_grid( + planes=planes, + grid_size=mesh_resolution, + ) + + vertices, faces = mcubes.marching_cubes( + grid_out['sigma'].squeeze(0).squeeze(-1).cpu().numpy(), + mesh_threshold, + ) + vertices = vertices / (mesh_resolution - 1) * 2 - 1 + + if not use_texture_map: + # query vertex colors + vertices_tensor = torch.tensor(vertices, dtype=torch.float32, device=device).unsqueeze(0) + vertices_colors = self.synthesizer.forward_points( + planes, vertices_tensor)['rgb'].squeeze(0).cpu().numpy() + vertices_colors = (vertices_colors * 255).astype(np.uint8) + + return vertices, faces, vertices_colors + + # use x-atlas to get uv mapping for the mesh + vertices = torch.tensor(vertices, dtype=torch.float32, device=device) + faces = torch.tensor(faces.astype(int), dtype=torch.long, device=device) + + ctx = dr.RasterizeCudaContext(device=device) + uvs, mesh_tex_idx, gb_pos, tex_hard_mask = xatlas_uvmap( + ctx, vertices, faces, resolution=texture_resolution) + tex_hard_mask = tex_hard_mask.float() + + # query the texture field to get the RGB color for texture map + tex_feat = self.get_texture_prediction( + planes, [gb_pos], tex_hard_mask) + background_feature = torch.zeros_like(tex_feat) + img_feat = torch.lerp(background_feature, tex_feat, tex_hard_mask) + texture_map = img_feat.permute(0, 3, 1, 2).squeeze(0) + + return vertices, faces, uvs, mesh_tex_idx, texture_map \ No newline at end of file diff --git a/gen_3d_modules/InstantMesh/models/lrm_mesh.py b/gen_3d_modules/InstantMesh/models/lrm_mesh.py new file mode 100644 index 0000000..cb6e351 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/lrm_mesh.py @@ -0,0 +1,382 @@ +# Copyright (c) 2023, Tencent Inc +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import torch +import torch.nn as nn +import nvdiffrast.torch as dr +from einops import rearrange, repeat + +from .encoder.dino_wrapper import DinoWrapper +from .decoder.transformer import TriplaneTransformer +from .renderer.synthesizer_mesh import TriplaneSynthesizer +from .geometry.camera.perspective_camera import PerspectiveCamera +from .geometry.render.neural_render import NeuralRender +from .geometry.rep_3d.flexicubes_geometry import FlexiCubesGeometry +from ..utils.mesh_util import xatlas_uvmap + + +class InstantMesh(nn.Module): + """ + Full model of the large reconstruction model. + """ + def __init__( + self, + encoder_freeze: bool = False, + encoder_model_name: str = 'facebook/dino-vitb16', + encoder_feat_dim: int = 768, + transformer_dim: int = 1024, + transformer_layers: int = 16, + transformer_heads: int = 16, + triplane_low_res: int = 32, + triplane_high_res: int = 64, + triplane_dim: int = 80, + rendering_samples_per_ray: int = 128, + grid_res: int = 128, + grid_scale: float = 2.0, + ): + super().__init__() + + # attributes + self.grid_res = grid_res + self.grid_scale = grid_scale + self.deformation_multiplier = 4.0 + + # modules + self.encoder = DinoWrapper( + model_name=encoder_model_name, + freeze=encoder_freeze, + ) + + self.transformer = TriplaneTransformer( + inner_dim=transformer_dim, + num_layers=transformer_layers, + num_heads=transformer_heads, + image_feat_dim=encoder_feat_dim, + triplane_low_res=triplane_low_res, + triplane_high_res=triplane_high_res, + triplane_dim=triplane_dim, + ) + + self.synthesizer = TriplaneSynthesizer( + triplane_dim=triplane_dim, + samples_per_ray=rendering_samples_per_ray, + ) + + def init_flexicubes_geometry(self, device, fovy=50.0): + camera = PerspectiveCamera(fovy=fovy, device=device) + renderer = NeuralRender(device, camera_model=camera) + self.geometry = FlexiCubesGeometry( + grid_res=self.grid_res, + scale=self.grid_scale, + renderer=renderer, + render_type='neural_render', + device=device, + ) + + def forward_planes(self, images, cameras): + # images: [B, V, C_img, H_img, W_img] + # cameras: [B, V, 16] + B = images.shape[0] + + # encode images + image_feats = self.encoder(images, cameras) + image_feats = rearrange(image_feats, '(b v) l d -> b (v l) d', b=B) + + # decode triplanes + planes = self.transformer(image_feats) + + return planes + + def get_sdf_deformation_prediction(self, planes): + ''' + Predict SDF and deformation for tetrahedron vertices + :param planes: triplane feature map for the geometry + ''' + init_position = self.geometry.verts.unsqueeze(0).expand(planes.shape[0], -1, -1) + + # Step 1: predict the SDF and deformation + sdf, deformation, weight = torch.utils.checkpoint.checkpoint( + self.synthesizer.get_geometry_prediction, + planes, + init_position, + self.geometry.indices, + use_reentrant=False, + ) + + # Step 2: Normalize the deformation to avoid the flipped triangles. + deformation = 1.0 / (self.grid_res * self.deformation_multiplier) * torch.tanh(deformation) + sdf_reg_loss = torch.zeros(sdf.shape[0], device=sdf.device, dtype=torch.float32) + + #### + # Step 3: Fix some sdf if we observe empty shape (full positive or full negative) + sdf_bxnxnxn = sdf.reshape((sdf.shape[0], self.grid_res + 1, self.grid_res + 1, self.grid_res + 1)) + sdf_less_boundary = sdf_bxnxnxn[:, 1:-1, 1:-1, 1:-1].reshape(sdf.shape[0], -1) + pos_shape = torch.sum((sdf_less_boundary > 0).int(), dim=-1) + neg_shape = torch.sum((sdf_less_boundary < 0).int(), dim=-1) + zero_surface = torch.bitwise_or(pos_shape == 0, neg_shape == 0) + if torch.sum(zero_surface).item() > 0: + update_sdf = torch.zeros_like(sdf[0:1]) + max_sdf = sdf.max() + min_sdf = sdf.min() + update_sdf[:, self.geometry.center_indices] += (1.0 - min_sdf) # greater than zero + update_sdf[:, self.geometry.boundary_indices] += (-1 - max_sdf) # smaller than zero + new_sdf = torch.zeros_like(sdf) + for i_batch in range(zero_surface.shape[0]): + if zero_surface[i_batch]: + new_sdf[i_batch:i_batch + 1] += update_sdf + update_mask = (new_sdf == 0).float() + # Regulraization here is used to push the sdf to be a different sign (make it not fully positive or fully negative) + sdf_reg_loss = torch.abs(sdf).mean(dim=-1).mean(dim=-1) + sdf_reg_loss = sdf_reg_loss * zero_surface.float() + sdf = sdf * update_mask + new_sdf * (1 - update_mask) + + # Step 4: Here we remove the gradient for the bad sdf (full positive or full negative) + final_sdf = [] + final_def = [] + for i_batch in range(zero_surface.shape[0]): + if zero_surface[i_batch]: + final_sdf.append(sdf[i_batch: i_batch + 1].detach()) + final_def.append(deformation[i_batch: i_batch + 1].detach()) + else: + final_sdf.append(sdf[i_batch: i_batch + 1]) + final_def.append(deformation[i_batch: i_batch + 1]) + sdf = torch.cat(final_sdf, dim=0) + deformation = torch.cat(final_def, dim=0) + return sdf, deformation, sdf_reg_loss, weight + + def get_geometry_prediction(self, planes=None): + ''' + Function to generate mesh with give triplanes + :param planes: triplane features + ''' + # Step 1: first get the sdf and deformation value for each vertices in the tetrahedon grid. + sdf, deformation, sdf_reg_loss, weight = self.get_sdf_deformation_prediction(planes) + v_deformed = self.geometry.verts.unsqueeze(dim=0).expand(sdf.shape[0], -1, -1) + deformation + tets = self.geometry.indices + n_batch = planes.shape[0] + v_list = [] + f_list = [] + flexicubes_surface_reg_list = [] + + # Step 2: Using marching tet to obtain the mesh + for i_batch in range(n_batch): + verts, faces, flexicubes_surface_reg = self.geometry.get_mesh( + v_deformed[i_batch], + sdf[i_batch].squeeze(dim=-1), + with_uv=False, + indices=tets, + weight_n=weight[i_batch].squeeze(dim=-1), + is_training=self.training, + ) + flexicubes_surface_reg_list.append(flexicubes_surface_reg) + v_list.append(verts) + f_list.append(faces) + + flexicubes_surface_reg = torch.cat(flexicubes_surface_reg_list).mean() + flexicubes_weight_reg = (weight ** 2).mean() + + return v_list, f_list, sdf, deformation, v_deformed, (sdf_reg_loss, flexicubes_surface_reg, flexicubes_weight_reg) + + def get_texture_prediction(self, planes, tex_pos, hard_mask=None): + ''' + Predict Texture given triplanes + :param planes: the triplane feature map + :param tex_pos: Position we want to query the texture field + :param hard_mask: 2D silhoueete of the rendered image + ''' + tex_pos = torch.cat(tex_pos, dim=0) + if not hard_mask is None: + tex_pos = tex_pos * hard_mask.float() + batch_size = tex_pos.shape[0] + tex_pos = tex_pos.reshape(batch_size, -1, 3) + ################### + # We use mask to get the texture location (to save the memory) + if hard_mask is not None: + n_point_list = torch.sum(hard_mask.long().reshape(hard_mask.shape[0], -1), dim=-1) + sample_tex_pose_list = [] + max_point = n_point_list.max() + expanded_hard_mask = hard_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3) > 0.5 + for i in range(tex_pos.shape[0]): + tex_pos_one_shape = tex_pos[i][expanded_hard_mask[i]].reshape(1, -1, 3) + if tex_pos_one_shape.shape[1] < max_point: + tex_pos_one_shape = torch.cat( + [tex_pos_one_shape, torch.zeros( + 1, max_point - tex_pos_one_shape.shape[1], 3, + device=tex_pos_one_shape.device, dtype=torch.float32)], dim=1) + sample_tex_pose_list.append(tex_pos_one_shape) + tex_pos = torch.cat(sample_tex_pose_list, dim=0) + + tex_feat = torch.utils.checkpoint.checkpoint( + self.synthesizer.get_texture_prediction, + planes, + tex_pos, + use_reentrant=False, + ) + + if hard_mask is not None: + final_tex_feat = torch.zeros( + planes.shape[0], hard_mask.shape[1] * hard_mask.shape[2], tex_feat.shape[-1], device=tex_feat.device) + expanded_hard_mask = hard_mask.reshape(hard_mask.shape[0], -1, 1).expand(-1, -1, final_tex_feat.shape[-1]) > 0.5 + for i in range(planes.shape[0]): + final_tex_feat[i][expanded_hard_mask[i]] = tex_feat[i][:n_point_list[i]].reshape(-1) + tex_feat = final_tex_feat + + return tex_feat.reshape(planes.shape[0], hard_mask.shape[1], hard_mask.shape[2], tex_feat.shape[-1]) + + def render_mesh(self, mesh_v, mesh_f, cam_mv, render_size=256): + ''' + Function to render a generated mesh with nvdiffrast + :param mesh_v: List of vertices for the mesh + :param mesh_f: List of faces for the mesh + :param cam_mv: 4x4 rotation matrix + :return: + ''' + return_value_list = [] + for i_mesh in range(len(mesh_v)): + return_value = self.geometry.render_mesh( + mesh_v[i_mesh], + mesh_f[i_mesh].int(), + cam_mv[i_mesh], + resolution=render_size, + hierarchical_mask=False + ) + return_value_list.append(return_value) + + return_keys = return_value_list[0].keys() + return_value = dict() + for k in return_keys: + value = [v[k] for v in return_value_list] + return_value[k] = value + + mask = torch.cat(return_value['mask'], dim=0) + hard_mask = torch.cat(return_value['hard_mask'], dim=0) + tex_pos = return_value['tex_pos'] + depth = torch.cat(return_value['depth'], dim=0) + normal = torch.cat(return_value['normal'], dim=0) + return mask, hard_mask, tex_pos, depth, normal + + def forward_geometry(self, planes, render_cameras, render_size=256): + ''' + Main function of our Generator. It first generate 3D mesh, then render it into 2D image + with given `render_cameras`. + :param planes: triplane features + :param render_cameras: cameras to render generated 3D shape + ''' + B, NV = render_cameras.shape[:2] + + # Generate 3D mesh first + mesh_v, mesh_f, sdf, deformation, v_deformed, sdf_reg_loss = self.get_geometry_prediction(planes) + + # Render the mesh into 2D image (get 3d position of each image plane) + cam_mv = render_cameras + run_n_view = cam_mv.shape[1] + antilias_mask, hard_mask, tex_pos, depth, normal = self.render_mesh(mesh_v, mesh_f, cam_mv, render_size=render_size) + + tex_hard_mask = hard_mask + tex_pos = [torch.cat([pos[i_view:i_view + 1] for i_view in range(run_n_view)], dim=2) for pos in tex_pos] + tex_hard_mask = torch.cat( + [torch.cat( + [tex_hard_mask[i * run_n_view + i_view: i * run_n_view + i_view + 1] + for i_view in range(run_n_view)], dim=2) + for i in range(planes.shape[0])], dim=0) + + # Querying the texture field to predict the texture feature for each pixel on the image + tex_feat = self.get_texture_prediction(planes, tex_pos, tex_hard_mask) + background_feature = torch.ones_like(tex_feat) # white background + + # Merge them together + img_feat = tex_feat * tex_hard_mask + background_feature * (1 - tex_hard_mask) + + # We should split it back to the original image shape + img_feat = torch.cat( + [torch.cat( + [img_feat[i:i + 1, :, render_size * i_view: render_size * (i_view + 1)] + for i_view in range(run_n_view)], dim=0) for i in range(len(tex_pos))], dim=0) + + img = img_feat.clamp(0, 1).permute(0, 3, 1, 2).unflatten(0, (B, NV)) + antilias_mask = antilias_mask.permute(0, 3, 1, 2).unflatten(0, (B, NV)) + depth = -depth.permute(0, 3, 1, 2).unflatten(0, (B, NV)) # transform negative depth to positive + normal = normal.permute(0, 3, 1, 2).unflatten(0, (B, NV)) + + out = { + 'img': img, + 'mask': antilias_mask, + 'depth': depth, + 'normal': normal, + 'sdf': sdf, + 'mesh_v': mesh_v, + 'mesh_f': mesh_f, + 'sdf_reg_loss': sdf_reg_loss, + } + return out + + def forward(self, images, cameras, render_cameras, render_size: int): + # images: [B, V, C_img, H_img, W_img] + # cameras: [B, V, 16] + # render_cameras: [B, M, D_cam_render] + # render_size: int + B, M = render_cameras.shape[:2] + + planes = self.forward_planes(images, cameras) + out = self.forward_geometry(planes, render_cameras, render_size=render_size) + + return { + 'planes': planes, + **out + } + + def extract_mesh( + self, + planes: torch.Tensor, + use_texture_map: bool = False, + texture_resolution: int = 1024, + **kwargs, + ): + ''' + Extract a 3D mesh from FlexiCubes. Only support batch_size 1. + :param planes: triplane features + :param use_texture_map: use texture map or vertex color + :param texture_resolution: the resolution of texure map + ''' + assert planes.shape[0] == 1 + device = planes.device + + # predict geometry first + mesh_v, mesh_f, sdf, deformation, v_deformed, sdf_reg_loss = self.get_geometry_prediction(planes) + vertices, faces = mesh_v[0], mesh_f[0] + + if not use_texture_map: + # query vertex colors + vertices_tensor = vertices.unsqueeze(0) + vertices_colors = self.synthesizer.get_texture_prediction( + planes, vertices_tensor).clamp(0, 1).squeeze(0).cpu().numpy() + vertices_colors = (vertices_colors * 255).astype(np.uint8) + + return vertices.cpu().numpy(), faces.cpu().numpy(), vertices_colors + + # use x-atlas to get uv mapping for the mesh + ctx = dr.RasterizeCudaContext(device=device) + uvs, mesh_tex_idx, gb_pos, tex_hard_mask = xatlas_uvmap( + self.geometry.renderer.ctx, vertices, faces, resolution=texture_resolution) + tex_hard_mask = tex_hard_mask.float() + + # query the texture field to get the RGB color for texture map + tex_feat = self.get_texture_prediction( + planes, [gb_pos], tex_hard_mask) + background_feature = torch.zeros_like(tex_feat) + img_feat = torch.lerp(background_feature, tex_feat, tex_hard_mask) + texture_map = img_feat.permute(0, 3, 1, 2).squeeze(0) + + return vertices, faces, uvs, mesh_tex_idx, texture_map \ No newline at end of file diff --git a/gen_3d_modules/InstantMesh/models/renderer/__init__.py b/gen_3d_modules/InstantMesh/models/renderer/__init__.py new file mode 100644 index 0000000..2c772e4 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/__init__.py @@ -0,0 +1,9 @@ +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. diff --git a/gen_3d_modules/InstantMesh/models/renderer/synthesizer.py b/gen_3d_modules/InstantMesh/models/renderer/synthesizer.py new file mode 100644 index 0000000..8db9fbd --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/synthesizer.py @@ -0,0 +1,203 @@ +# ORIGINAL LICENSE +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# Modified by Jiale Xu +# The modifications are subject to the same license as the original. + + +import itertools +import torch +import torch.nn as nn + +from .utils.renderer import ImportanceRenderer +from .utils.ray_sampler import RaySampler + + +class OSGDecoder(nn.Module): + """ + Triplane decoder that gives RGB and sigma values from sampled features. + Using ReLU here instead of Softplus in the original implementation. + + Reference: + EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112 + """ + def __init__(self, n_features: int, + hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU): + super().__init__() + self.net = nn.Sequential( + nn.Linear(3 * n_features, hidden_dim), + activation(), + *itertools.chain(*[[ + nn.Linear(hidden_dim, hidden_dim), + activation(), + ] for _ in range(num_layers - 2)]), + nn.Linear(hidden_dim, 1 + 3), + ) + # init all bias to zero + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.zeros_(m.bias) + + def forward(self, sampled_features, ray_directions): + # Aggregate features by mean + # sampled_features = sampled_features.mean(1) + # Aggregate features by concatenation + _N, n_planes, _M, _C = sampled_features.shape + sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C) + x = sampled_features + + N, M, C = x.shape + x = x.contiguous().view(N*M, C) + + x = self.net(x) + x = x.view(N, M, -1) + rgb = torch.sigmoid(x[..., 1:])*(1 + 2*0.001) - 0.001 # Uses sigmoid clamping from MipNeRF + sigma = x[..., 0:1] + + return {'rgb': rgb, 'sigma': sigma} + + +class TriplaneSynthesizer(nn.Module): + """ + Synthesizer that renders a triplane volume with planes and a camera. + + Reference: + EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L19 + """ + + DEFAULT_RENDERING_KWARGS = { + 'ray_start': 'auto', + 'ray_end': 'auto', + 'box_warp': 2., + 'white_back': True, + 'disparity_space_sampling': False, + 'clamp_mode': 'softplus', + 'sampler_bbox_min': -1., + 'sampler_bbox_max': 1., + } + + def __init__(self, triplane_dim: int, samples_per_ray: int): + super().__init__() + + # attributes + self.triplane_dim = triplane_dim + self.rendering_kwargs = { + **self.DEFAULT_RENDERING_KWARGS, + 'depth_resolution': samples_per_ray // 2, + 'depth_resolution_importance': samples_per_ray // 2, + } + + # renderings + self.renderer = ImportanceRenderer() + self.ray_sampler = RaySampler() + + # modules + self.decoder = OSGDecoder(n_features=triplane_dim) + + def forward(self, planes, cameras, render_size=128, crop_params=None): + # planes: (N, 3, D', H', W') + # cameras: (N, M, D_cam) + # render_size: int + assert planes.shape[0] == cameras.shape[0], "Batch size mismatch for planes and cameras" + N, M = cameras.shape[:2] + + cam2world_matrix = cameras[..., :16].view(N, M, 4, 4) + intrinsics = cameras[..., 16:25].view(N, M, 3, 3) + + # Create a batch of rays for volume rendering + ray_origins, ray_directions = self.ray_sampler( + cam2world_matrix=cam2world_matrix.reshape(-1, 4, 4), + intrinsics=intrinsics.reshape(-1, 3, 3), + render_size=render_size, + ) + assert N*M == ray_origins.shape[0], "Batch size mismatch for ray_origins" + assert ray_origins.dim() == 3, "ray_origins should be 3-dimensional" + + # Crop rays if crop_params is available + if crop_params is not None: + ray_origins = ray_origins.reshape(N*M, render_size, render_size, 3) + ray_directions = ray_directions.reshape(N*M, render_size, render_size, 3) + i, j, h, w = crop_params + ray_origins = ray_origins[:, i:i+h, j:j+w, :].reshape(N*M, -1, 3) + ray_directions = ray_directions[:, i:i+h, j:j+w, :].reshape(N*M, -1, 3) + + # Perform volume rendering + rgb_samples, depth_samples, weights_samples = self.renderer( + planes.repeat_interleave(M, dim=0), self.decoder, ray_origins, ray_directions, self.rendering_kwargs, + ) + + # Reshape into 'raw' neural-rendered image + if crop_params is not None: + Himg, Wimg = crop_params[2:] + else: + Himg = Wimg = render_size + rgb_images = rgb_samples.permute(0, 2, 1).reshape(N, M, rgb_samples.shape[-1], Himg, Wimg).contiguous() + depth_images = depth_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg) + weight_images = weights_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg) + + out = { + 'images_rgb': rgb_images, + 'images_depth': depth_images, + 'images_weight': weight_images, + } + return out + + def forward_grid(self, planes, grid_size: int, aabb: torch.Tensor = None): + # planes: (N, 3, D', H', W') + # grid_size: int + # aabb: (N, 2, 3) + if aabb is None: + aabb = torch.tensor([ + [self.rendering_kwargs['sampler_bbox_min']] * 3, + [self.rendering_kwargs['sampler_bbox_max']] * 3, + ], device=planes.device, dtype=planes.dtype).unsqueeze(0).repeat(planes.shape[0], 1, 1) + assert planes.shape[0] == aabb.shape[0], "Batch size mismatch for planes and aabb" + N = planes.shape[0] + + # create grid points for triplane query + grid_points = [] + for i in range(N): + grid_points.append(torch.stack(torch.meshgrid( + torch.linspace(aabb[i, 0, 0], aabb[i, 1, 0], grid_size, device=planes.device), + torch.linspace(aabb[i, 0, 1], aabb[i, 1, 1], grid_size, device=planes.device), + torch.linspace(aabb[i, 0, 2], aabb[i, 1, 2], grid_size, device=planes.device), + indexing='ij', + ), dim=-1).reshape(-1, 3)) + cube_grid = torch.stack(grid_points, dim=0).to(planes.device) + + features = self.forward_points(planes, cube_grid) + + # reshape into grid + features = { + k: v.reshape(N, grid_size, grid_size, grid_size, -1) + for k, v in features.items() + } + return features + + def forward_points(self, planes, points: torch.Tensor, chunk_size: int = 2**20): + # planes: (N, 3, D', H', W') + # points: (N, P, 3) + N, P = points.shape[:2] + + # query triplane in chunks + outs = [] + for i in range(0, points.shape[1], chunk_size): + chunk_points = points[:, i:i+chunk_size] + + # query triplane + chunk_out = self.renderer.run_model_activated( + planes=planes, + decoder=self.decoder, + sample_coordinates=chunk_points, + sample_directions=torch.zeros_like(chunk_points), + options=self.rendering_kwargs, + ) + outs.append(chunk_out) + + # concatenate the outputs + point_features = { + k: torch.cat([out[k] for out in outs], dim=1) + for k in outs[0].keys() + } + return point_features diff --git a/gen_3d_modules/InstantMesh/models/renderer/synthesizer_mesh.py b/gen_3d_modules/InstantMesh/models/renderer/synthesizer_mesh.py new file mode 100644 index 0000000..dc31838 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/synthesizer_mesh.py @@ -0,0 +1,141 @@ +# ORIGINAL LICENSE +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# Modified by Jiale Xu +# The modifications are subject to the same license as the original. + +import itertools +import torch +import torch.nn as nn + +from .utils.renderer import generate_planes, project_onto_planes, sample_from_planes + + +class OSGDecoder(nn.Module): + """ + Triplane decoder that gives RGB and sigma values from sampled features. + Using ReLU here instead of Softplus in the original implementation. + + Reference: + EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112 + """ + def __init__(self, n_features: int, + hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU): + super().__init__() + + self.net_sdf = nn.Sequential( + nn.Linear(3 * n_features, hidden_dim), + activation(), + *itertools.chain(*[[ + nn.Linear(hidden_dim, hidden_dim), + activation(), + ] for _ in range(num_layers - 2)]), + nn.Linear(hidden_dim, 1), + ) + self.net_rgb = nn.Sequential( + nn.Linear(3 * n_features, hidden_dim), + activation(), + *itertools.chain(*[[ + nn.Linear(hidden_dim, hidden_dim), + activation(), + ] for _ in range(num_layers - 2)]), + nn.Linear(hidden_dim, 3), + ) + self.net_deformation = nn.Sequential( + nn.Linear(3 * n_features, hidden_dim), + activation(), + *itertools.chain(*[[ + nn.Linear(hidden_dim, hidden_dim), + activation(), + ] for _ in range(num_layers - 2)]), + nn.Linear(hidden_dim, 3), + ) + self.net_weight = nn.Sequential( + nn.Linear(8 * 3 * n_features, hidden_dim), + activation(), + *itertools.chain(*[[ + nn.Linear(hidden_dim, hidden_dim), + activation(), + ] for _ in range(num_layers - 2)]), + nn.Linear(hidden_dim, 21), + ) + + # init all bias to zero + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.zeros_(m.bias) + + def get_geometry_prediction(self, sampled_features, flexicubes_indices): + _N, n_planes, _M, _C = sampled_features.shape + sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C) + + sdf = self.net_sdf(sampled_features) + deformation = self.net_deformation(sampled_features) + + grid_features = torch.index_select(input=sampled_features, index=flexicubes_indices.reshape(-1), dim=1) + grid_features = grid_features.reshape( + sampled_features.shape[0], flexicubes_indices.shape[0], flexicubes_indices.shape[1] * sampled_features.shape[-1]) + weight = self.net_weight(grid_features) * 0.1 + + return sdf, deformation, weight + + def get_texture_prediction(self, sampled_features): + _N, n_planes, _M, _C = sampled_features.shape + sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C) + + rgb = self.net_rgb(sampled_features) + rgb = torch.sigmoid(rgb)*(1 + 2*0.001) - 0.001 # Uses sigmoid clamping from MipNeRF + + return rgb + + +class TriplaneSynthesizer(nn.Module): + """ + Synthesizer that renders a triplane volume with planes and a camera. + + Reference: + EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L19 + """ + + DEFAULT_RENDERING_KWARGS = { + 'ray_start': 'auto', + 'ray_end': 'auto', + 'box_warp': 2., + 'white_back': True, + 'disparity_space_sampling': False, + 'clamp_mode': 'softplus', + 'sampler_bbox_min': -1., + 'sampler_bbox_max': 1., + } + + def __init__(self, triplane_dim: int, samples_per_ray: int): + super().__init__() + + # attributes + self.triplane_dim = triplane_dim + self.rendering_kwargs = { + **self.DEFAULT_RENDERING_KWARGS, + 'depth_resolution': samples_per_ray // 2, + 'depth_resolution_importance': samples_per_ray // 2, + } + + # modules + self.plane_axes = generate_planes() + self.decoder = OSGDecoder(n_features=triplane_dim) + + def get_geometry_prediction(self, planes, sample_coordinates, flexicubes_indices): + plane_axes = self.plane_axes.to(planes.device) + sampled_features = sample_from_planes( + plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=self.rendering_kwargs['box_warp']) + + sdf, deformation, weight = self.decoder.get_geometry_prediction(sampled_features, flexicubes_indices) + return sdf, deformation, weight + + def get_texture_prediction(self, planes, sample_coordinates): + plane_axes = self.plane_axes.to(planes.device) + sampled_features = sample_from_planes( + plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=self.rendering_kwargs['box_warp']) + + rgb = self.decoder.get_texture_prediction(sampled_features) + return rgb diff --git a/gen_3d_modules/InstantMesh/models/renderer/utils/__init__.py b/gen_3d_modules/InstantMesh/models/renderer/utils/__init__.py new file mode 100644 index 0000000..2c772e4 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/utils/__init__.py @@ -0,0 +1,9 @@ +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. diff --git a/gen_3d_modules/InstantMesh/models/renderer/utils/math_utils.py b/gen_3d_modules/InstantMesh/models/renderer/utils/math_utils.py new file mode 100644 index 0000000..4cf9d2b --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/utils/math_utils.py @@ -0,0 +1,118 @@ +# MIT License + +# Copyright (c) 2022 Petr Kellnhofer + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import torch + +def transform_vectors(matrix: torch.Tensor, vectors4: torch.Tensor) -> torch.Tensor: + """ + Left-multiplies MxM @ NxM. Returns NxM. + """ + res = torch.matmul(vectors4, matrix.T) + return res + + +def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor: + """ + Normalize vector lengths. + """ + return vectors / (torch.norm(vectors, dim=-1, keepdim=True)) + +def torch_dot(x: torch.Tensor, y: torch.Tensor): + """ + Dot product of two tensors. + """ + return (x * y).sum(-1) + + +def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor, box_side_length): + """ + Author: Petr Kellnhofer + Intersects rays with the [-1, 1] NDC volume. + Returns min and max distance of entry. + Returns -1 for no intersection. + https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection + """ + o_shape = rays_o.shape + rays_o = rays_o.detach().reshape(-1, 3) + rays_d = rays_d.detach().reshape(-1, 3) + + + bb_min = [-1*(box_side_length/2), -1*(box_side_length/2), -1*(box_side_length/2)] + bb_max = [1*(box_side_length/2), 1*(box_side_length/2), 1*(box_side_length/2)] + bounds = torch.tensor([bb_min, bb_max], dtype=rays_o.dtype, device=rays_o.device) + is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device) + + # Precompute inverse for stability. + invdir = 1 / rays_d + sign = (invdir < 0).long() + + # Intersect with YZ plane. + tmin = (bounds.index_select(0, sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0] + tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0] + + # Intersect with XZ plane. + tymin = (bounds.index_select(0, sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1] + tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1] + + # Resolve parallel rays. + is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False + + # Use the shortest intersection. + tmin = torch.max(tmin, tymin) + tmax = torch.min(tmax, tymax) + + # Intersect with XY plane. + tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2] + tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2] + + # Resolve parallel rays. + is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False + + # Use the shortest intersection. + tmin = torch.max(tmin, tzmin) + tmax = torch.min(tmax, tzmax) + + # Mark invalid. + tmin[torch.logical_not(is_valid)] = -1 + tmax[torch.logical_not(is_valid)] = -2 + + return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1) + + +def linspace(start: torch.Tensor, stop: torch.Tensor, num: int): + """ + Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive. + Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch. + """ + # create a tensor of 'num' steps from 0 to 1 + steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1) + + # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings + # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript + # "cannot statically infer the expected size of a list in this contex", hence the code below + for i in range(start.ndim): + steps = steps.unsqueeze(-1) + + # the output starts at 'start' and increments until 'stop' in each dimension + out = start[None] + steps * (stop - start)[None] + + return out diff --git a/gen_3d_modules/InstantMesh/models/renderer/utils/ray_marcher.py b/gen_3d_modules/InstantMesh/models/renderer/utils/ray_marcher.py new file mode 100644 index 0000000..ea1db43 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/utils/ray_marcher.py @@ -0,0 +1,72 @@ +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +# +# Modified by Jiale Xu +# The modifications are subject to the same license as the original. + + +""" +The ray marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths. +Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!) +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class MipRayMarcher2(nn.Module): + def __init__(self, activation_factory): + super().__init__() + self.activation_factory = activation_factory + + def run_forward(self, colors, densities, depths, rendering_options, normals=None): + dtype = colors.dtype + deltas = depths[:, :, 1:] - depths[:, :, :-1] + colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2 + densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2 + depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2 + + # using factory mode for better usability + densities_mid = self.activation_factory(rendering_options)(densities_mid).to(dtype) + + density_delta = densities_mid * deltas + + alpha = 1 - torch.exp(-density_delta).to(dtype) + + alpha_shifted = torch.cat([torch.ones_like(alpha[:, :, :1]), 1-alpha + 1e-10], -2) + weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1] + weights = weights.to(dtype) + + composite_rgb = torch.sum(weights * colors_mid, -2) + weight_total = weights.sum(2) + # composite_depth = torch.sum(weights * depths_mid, -2) / weight_total + composite_depth = torch.sum(weights * depths_mid, -2) + + # clip the composite to min/max range of depths + composite_depth = torch.nan_to_num(composite_depth, float('inf')).to(dtype) + composite_depth = torch.clamp(composite_depth, torch.min(depths), torch.max(depths)) + + if rendering_options.get('white_back', False): + composite_rgb = composite_rgb + 1 - weight_total + + # rendered value scale is 0-1, comment out original mipnerf scaling + # composite_rgb = composite_rgb * 2 - 1 # Scale to (-1, 1) + + return composite_rgb, composite_depth, weights + + + def forward(self, colors, densities, depths, rendering_options, normals=None): + if normals is not None: + composite_rgb, composite_depth, composite_normals, weights = self.run_forward(colors, densities, depths, rendering_options, normals) + return composite_rgb, composite_depth, composite_normals, weights + + composite_rgb, composite_depth, weights = self.run_forward(colors, densities, depths, rendering_options) + return composite_rgb, composite_depth, weights diff --git a/gen_3d_modules/InstantMesh/models/renderer/utils/ray_sampler.py b/gen_3d_modules/InstantMesh/models/renderer/utils/ray_sampler.py new file mode 100644 index 0000000..ae5151d --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/utils/ray_sampler.py @@ -0,0 +1,141 @@ +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +# +# Modified by Jiale Xu +# The modifications are subject to the same license as the original. + + +""" +The ray sampler is a module that takes in camera matrices and resolution and batches of rays. +Expects cam2world matrices that use the OpenCV camera coordinate system conventions. +""" + +import torch + +class RaySampler(torch.nn.Module): + def __init__(self): + super().__init__() + self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None + + + def forward(self, cam2world_matrix, intrinsics, render_size): + """ + Create batches of rays and return origins and directions. + + cam2world_matrix: (N, 4, 4) + intrinsics: (N, 3, 3) + render_size: int + + ray_origins: (N, M, 3) + ray_dirs: (N, M, 2) + """ + + dtype = cam2world_matrix.dtype + device = cam2world_matrix.device + N, M = cam2world_matrix.shape[0], render_size**2 + cam_locs_world = cam2world_matrix[:, :3, 3] + fx = intrinsics[:, 0, 0] + fy = intrinsics[:, 1, 1] + cx = intrinsics[:, 0, 2] + cy = intrinsics[:, 1, 2] + sk = intrinsics[:, 0, 1] + + uv = torch.stack(torch.meshgrid( + torch.arange(render_size, dtype=dtype, device=device), + torch.arange(render_size, dtype=dtype, device=device), + indexing='ij', + )) + uv = uv.flip(0).reshape(2, -1).transpose(1, 0) + uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1) + + x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size) + y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size) + z_cam = torch.ones((N, M), dtype=dtype, device=device) + + x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1)*sk.unsqueeze(-1)/fy.unsqueeze(-1) - sk.unsqueeze(-1)*y_cam/fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam + y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam + + cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1).to(dtype) + + _opencv2blender = torch.tensor([ + [1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0], + [0, 0, 0, 1], + ], dtype=dtype, device=device).unsqueeze(0).repeat(N, 1, 1) + + cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender) + + world_rel_points = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3] + + ray_dirs = world_rel_points - cam_locs_world[:, None, :] + ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2).to(dtype) + + ray_origins = cam_locs_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1) + + return ray_origins, ray_dirs + + +class OrthoRaySampler(torch.nn.Module): + def __init__(self): + super().__init__() + self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None + + + def forward(self, cam2world_matrix, ortho_scale, render_size): + """ + Create batches of rays and return origins and directions. + + cam2world_matrix: (N, 4, 4) + ortho_scale: float + render_size: int + + ray_origins: (N, M, 3) + ray_dirs: (N, M, 3) + """ + + N, M = cam2world_matrix.shape[0], render_size**2 + + uv = torch.stack(torch.meshgrid( + torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device), + torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device), + indexing='ij', + )) + uv = uv.flip(0).reshape(2, -1).transpose(1, 0) + uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1) + + x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size) + y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size) + z_cam = torch.zeros((N, M), device=cam2world_matrix.device) + + x_lift = (x_cam - 0.5) * ortho_scale + y_lift = (y_cam - 0.5) * ortho_scale + + cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1) + + _opencv2blender = torch.tensor([ + [1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0], + [0, 0, 0, 1], + ], dtype=torch.float32, device=cam2world_matrix.device).unsqueeze(0).repeat(N, 1, 1) + + cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender) + + ray_origins = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3] + + ray_dirs_cam = torch.stack([ + torch.zeros((N, M), device=cam2world_matrix.device), + torch.zeros((N, M), device=cam2world_matrix.device), + torch.ones((N, M), device=cam2world_matrix.device), + ], dim=-1) + ray_dirs = torch.bmm(cam2world_matrix[:, :3, :3], ray_dirs_cam.permute(0, 2, 1)).permute(0, 2, 1) + + return ray_origins, ray_dirs diff --git a/gen_3d_modules/InstantMesh/models/renderer/utils/renderer.py b/gen_3d_modules/InstantMesh/models/renderer/utils/renderer.py new file mode 100644 index 0000000..95c4c72 --- /dev/null +++ b/gen_3d_modules/InstantMesh/models/renderer/utils/renderer.py @@ -0,0 +1,323 @@ +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: LicenseRef-NvidiaProprietary +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. +# +# Modified by Jiale Xu +# The modifications are subject to the same license as the original. + + +""" +The renderer is a module that takes in rays, decides where to sample along each +ray, and computes pixel colors using the volume rendering equation. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .ray_marcher import MipRayMarcher2 +from . import math_utils + + +def generate_planes(): + """ + Defines planes by the three vectors that form the "axes" of the + plane. Should work with arbitrary number of planes and planes of + arbitrary orientation. + + Bugfix reference: https://github.com/NVlabs/eg3d/issues/67 + """ + return torch.tensor([[[1, 0, 0], + [0, 1, 0], + [0, 0, 1]], + [[1, 0, 0], + [0, 0, 1], + [0, 1, 0]], + [[0, 0, 1], + [0, 1, 0], + [1, 0, 0]]], dtype=torch.float32) + +def project_onto_planes(planes, coordinates): + """ + Does a projection of a 3D point onto a batch of 2D planes, + returning 2D plane coordinates. + + Takes plane axes of shape n_planes, 3, 3 + # Takes coordinates of shape N, M, 3 + # returns projections of shape N*n_planes, M, 2 + """ + N, M, C = coordinates.shape + n_planes, _, _ = planes.shape + coordinates = coordinates.unsqueeze(1).expand(-1, n_planes, -1, -1).reshape(N*n_planes, M, 3) + inv_planes = torch.linalg.inv(planes).unsqueeze(0).expand(N, -1, -1, -1).reshape(N*n_planes, 3, 3) + projections = torch.bmm(coordinates, inv_planes) + return projections[..., :2] + +def sample_from_planes(plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', box_warp=None): + assert padding_mode == 'zeros' + N, n_planes, C, H, W = plane_features.shape + _, M, _ = coordinates.shape + plane_features = plane_features.view(N*n_planes, C, H, W) + dtype = plane_features.dtype + + coordinates = (2/box_warp) * coordinates # add specific box bounds + + projected_coordinates = project_onto_planes(plane_axes, coordinates).unsqueeze(1) + output_features = torch.nn.functional.grid_sample( + plane_features, + projected_coordinates.to(dtype), + mode=mode, + padding_mode=padding_mode, + align_corners=False, + ).permute(0, 3, 2, 1).reshape(N, n_planes, M, C) + return output_features + +def sample_from_3dgrid(grid, coordinates): + """ + Expects coordinates in shape (batch_size, num_points_per_batch, 3) + Expects grid in shape (1, channels, H, W, D) + (Also works if grid has batch size) + Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels) + """ + batch_size, n_coords, n_dims = coordinates.shape + sampled_features = torch.nn.functional.grid_sample( + grid.expand(batch_size, -1, -1, -1, -1), + coordinates.reshape(batch_size, 1, 1, -1, n_dims), + mode='bilinear', + padding_mode='zeros', + align_corners=False, + ) + N, C, H, W, D = sampled_features.shape + sampled_features = sampled_features.permute(0, 4, 3, 2, 1).reshape(N, H*W*D, C) + return sampled_features + +class ImportanceRenderer(torch.nn.Module): + """ + Modified original version to filter out-of-box samples as TensoRF does. + + Reference: + TensoRF: https://github.com/apchenstu/TensoRF/blob/main/models/tensorBase.py#L277 + """ + def __init__(self): + super().__init__() + self.activation_factory = self._build_activation_factory() + self.ray_marcher = MipRayMarcher2(self.activation_factory) + self.plane_axes = generate_planes() + + def _build_activation_factory(self): + def activation_factory(options: dict): + if options['clamp_mode'] == 'softplus': + return lambda x: F.softplus(x - 1) # activation bias of -1 makes things initialize better + else: + assert False, "Renderer only supports `clamp_mode`=`softplus`!" + return activation_factory + + def _forward_pass(self, depths: torch.Tensor, ray_directions: torch.Tensor, ray_origins: torch.Tensor, + planes: torch.Tensor, decoder: nn.Module, rendering_options: dict): + """ + Additional filtering is applied to filter out-of-box samples. + Modifications made by Zexin He. + """ + + # context related variables + batch_size, num_rays, samples_per_ray, _ = depths.shape + device = depths.device + + # define sample points with depths + sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3) + sample_coordinates = (ray_origins.unsqueeze(-2) + depths * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3) + + # filter out-of-box samples + mask_inbox = \ + (rendering_options['sampler_bbox_min'] <= sample_coordinates) & \ + (sample_coordinates <= rendering_options['sampler_bbox_max']) + mask_inbox = mask_inbox.all(-1) + + # forward model according to all samples + _out = self.run_model(planes, decoder, sample_coordinates, sample_directions, rendering_options) + + # set out-of-box samples to zeros(rgb) & -inf(sigma) + SAFE_GUARD = 3 + DATA_TYPE = _out['sigma'].dtype + colors_pass = torch.zeros(batch_size, num_rays * samples_per_ray, 3, device=device, dtype=DATA_TYPE) + densities_pass = torch.nan_to_num(torch.full((batch_size, num_rays * samples_per_ray, 1), -float('inf'), device=device, dtype=DATA_TYPE)) / SAFE_GUARD + colors_pass[mask_inbox], densities_pass[mask_inbox] = _out['rgb'][mask_inbox], _out['sigma'][mask_inbox] + + # reshape back + colors_pass = colors_pass.reshape(batch_size, num_rays, samples_per_ray, colors_pass.shape[-1]) + densities_pass = densities_pass.reshape(batch_size, num_rays, samples_per_ray, densities_pass.shape[-1]) + + return colors_pass, densities_pass + + def forward(self, planes, decoder, ray_origins, ray_directions, rendering_options): + # self.plane_axes = self.plane_axes.to(ray_origins.device) + + if rendering_options['ray_start'] == rendering_options['ray_end'] == 'auto': + ray_start, ray_end = math_utils.get_ray_limits_box(ray_origins, ray_directions, box_side_length=rendering_options['box_warp']) + is_ray_valid = ray_end > ray_start + if torch.any(is_ray_valid).item(): + ray_start[~is_ray_valid] = ray_start[is_ray_valid].min() + ray_end[~is_ray_valid] = ray_start[is_ray_valid].max() + depths_coarse = self.sample_stratified(ray_origins, ray_start, ray_end, rendering_options['depth_resolution'], rendering_options['disparity_space_sampling']) + else: + # Create stratified depth samples + depths_coarse = self.sample_stratified(ray_origins, rendering_options['ray_start'], rendering_options['ray_end'], rendering_options['depth_resolution'], rendering_options['disparity_space_sampling']) + + # Coarse Pass + colors_coarse, densities_coarse = self._forward_pass( + depths=depths_coarse, ray_directions=ray_directions, ray_origins=ray_origins, + planes=planes, decoder=decoder, rendering_options=rendering_options) + + # Fine Pass + N_importance = rendering_options['depth_resolution_importance'] + if N_importance > 0: + _, _, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options) + + depths_fine = self.sample_importance(depths_coarse, weights, N_importance) + + colors_fine, densities_fine = self._forward_pass( + depths=depths_fine, ray_directions=ray_directions, ray_origins=ray_origins, + planes=planes, decoder=decoder, rendering_options=rendering_options) + + all_depths, all_colors, all_densities = self.unify_samples(depths_coarse, colors_coarse, densities_coarse, + depths_fine, colors_fine, densities_fine) + + rgb_final, depth_final, weights = self.ray_marcher(all_colors, all_densities, all_depths, rendering_options) + else: + rgb_final, depth_final, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options) + + return rgb_final, depth_final, weights.sum(2) + + def run_model(self, planes, decoder, sample_coordinates, sample_directions, options): + plane_axes = self.plane_axes.to(planes.device) + sampled_features = sample_from_planes(plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=options['box_warp']) + + out = decoder(sampled_features, sample_directions) + if options.get('density_noise', 0) > 0: + out['sigma'] += torch.randn_like(out['sigma']) * options['density_noise'] + return out + + def run_model_activated(self, planes, decoder, sample_coordinates, sample_directions, options): + out = self.run_model(planes, decoder, sample_coordinates, sample_directions, options) + out['sigma'] = self.activation_factory(options)(out['sigma']) + return out + + def sort_samples(self, all_depths, all_colors, all_densities): + _, indices = torch.sort(all_depths, dim=-2) + all_depths = torch.gather(all_depths, -2, indices) + all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1])) + all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1)) + return all_depths, all_colors, all_densities + + def unify_samples(self, depths1, colors1, densities1, depths2, colors2, densities2, normals1=None, normals2=None): + all_depths = torch.cat([depths1, depths2], dim = -2) + all_colors = torch.cat([colors1, colors2], dim = -2) + all_densities = torch.cat([densities1, densities2], dim = -2) + + if normals1 is not None and normals2 is not None: + all_normals = torch.cat([normals1, normals2], dim = -2) + else: + all_normals = None + + _, indices = torch.sort(all_depths, dim=-2) + all_depths = torch.gather(all_depths, -2, indices) + all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1])) + all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1)) + + if all_normals is not None: + all_normals = torch.gather(all_normals, -2, indices.expand(-1, -1, -1, all_normals.shape[-1])) + return all_depths, all_colors, all_normals, all_densities + + return all_depths, all_colors, all_densities + + def sample_stratified(self, ray_origins, ray_start, ray_end, depth_resolution, disparity_space_sampling=False): + """ + Return depths of approximately uniformly spaced samples along rays. + """ + N, M, _ = ray_origins.shape + if disparity_space_sampling: + depths_coarse = torch.linspace(0, + 1, + depth_resolution, + device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1) + depth_delta = 1/(depth_resolution - 1) + depths_coarse += torch.rand_like(depths_coarse) * depth_delta + depths_coarse = 1./(1./ray_start * (1. - depths_coarse) + 1./ray_end * depths_coarse) + else: + if type(ray_start) == torch.Tensor: + depths_coarse = math_utils.linspace(ray_start, ray_end, depth_resolution).permute(1,2,0,3) + depth_delta = (ray_end - ray_start) / (depth_resolution - 1) + depths_coarse += torch.rand_like(depths_coarse) * depth_delta[..., None] + else: + depths_coarse = torch.linspace(ray_start, ray_end, depth_resolution, device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1) + depth_delta = (ray_end - ray_start)/(depth_resolution - 1) + depths_coarse += torch.rand_like(depths_coarse) * depth_delta + + return depths_coarse + + def sample_importance(self, z_vals, weights, N_importance): + """ + Return depths of importance sampled points along rays. See NeRF importance sampling for more. + """ + with torch.no_grad(): + batch_size, num_rays, samples_per_ray, _ = z_vals.shape + + z_vals = z_vals.reshape(batch_size * num_rays, samples_per_ray) + weights = weights.reshape(batch_size * num_rays, -1) # -1 to account for loss of 1 sample in MipRayMarcher + + # smooth weights + weights = torch.nn.functional.max_pool1d(weights.unsqueeze(1), 2, 1, padding=1) + weights = torch.nn.functional.avg_pool1d(weights, 2, 1).squeeze() + weights = weights + 0.01 + + z_vals_mid = 0.5 * (z_vals[: ,:-1] + z_vals[: ,1:]) + importance_z_vals = self.sample_pdf(z_vals_mid, weights[:, 1:-1], + N_importance).detach().reshape(batch_size, num_rays, N_importance, 1) + return importance_z_vals + + def sample_pdf(self, bins, weights, N_importance, det=False, eps=1e-5): + """ + Sample @N_importance samples from @bins with distribution defined by @weights. + Inputs: + bins: (N_rays, N_samples_+1) where N_samples_ is "the number of coarse samples per ray - 2" + weights: (N_rays, N_samples_) + N_importance: the number of samples to draw from the distribution + det: deterministic or not + eps: a small number to prevent division by zero + Outputs: + samples: the sampled samples + """ + N_rays, N_samples_ = weights.shape + weights = weights + eps # prevent division by zero (don't do inplace op!) + pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_) + cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function + cdf = torch.cat([torch.zeros_like(cdf[: ,:1]), cdf], -1) # (N_rays, N_samples_+1) + # padded to 0~1 inclusive + + if det: + u = torch.linspace(0, 1, N_importance, device=bins.device) + u = u.expand(N_rays, N_importance) + else: + u = torch.rand(N_rays, N_importance, device=bins.device) + u = u.contiguous() + + inds = torch.searchsorted(cdf, u, right=True) + below = torch.clamp_min(inds-1, 0) + above = torch.clamp_max(inds, N_samples_) + + inds_sampled = torch.stack([below, above], -1).view(N_rays, 2*N_importance) + cdf_g = torch.gather(cdf, 1, inds_sampled).view(N_rays, N_importance, 2) + bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2) + + denom = cdf_g[...,1]-cdf_g[...,0] + denom[denom 0 and radius > 0 + + elevation = np.deg2rad(elevation) + + camera_positions = [] + for i in range(M): + azimuth = 2 * np.pi * i / M + x = radius * np.cos(elevation) * np.cos(azimuth) + y = radius * np.cos(elevation) * np.sin(azimuth) + z = radius * np.sin(elevation) + camera_positions.append([x, y, z]) + camera_positions = np.array(camera_positions) + camera_positions = torch.from_numpy(camera_positions).float() + extrinsics = center_looking_at_camera_pose(camera_positions) + return extrinsics + +def FOV_to_intrinsics(fov, device='cpu'): + """ + Creates a 3x3 camera intrinsics matrix from the camera field of view, specified in degrees. + Note the intrinsics are returned as normalized by image size, rather than in pixel units. + Assumes principal point is at image center. + """ + focal_length = 0.5 / np.tan(np.deg2rad(fov) * 0.5) + intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device) + return intrinsics + +def oribt_camera_poses_to_input_cameras(azimuths, elevations, batch_size=1, radius=4.0, fov=30.0): + """ + Args: + azimuths (list) + elevations (list) + radius (float or list) + """ + # (0, 180/-180, 0) -> (0, 180, 360) + azimuths = [360 + angle if angle < 0 else angle for angle in azimuths] + azimuths = np.array(azimuths).astype(float) + elevations = -np.array(elevations).astype(float) + if isinstance(radius, list): + radius = np.array(radius).astype(float) + + c2ws = spherical_camera_pose(azimuths, elevations, radius) + c2ws = c2ws.float().flatten(-2) + + Ks = FOV_to_intrinsics(fov).unsqueeze(0).repeat(azimuths.shape[0], 1, 1).float().flatten(-2) + + extrinsics = c2ws[:, :12] + intrinsics = torch.stack([Ks[:, 0], Ks[:, 4], Ks[:, 2], Ks[:, 5]], dim=-1) + cameras = torch.cat([extrinsics, intrinsics], dim=-1) + + return cameras.unsqueeze(0).repeat(batch_size, 1, 1) diff --git a/gen_3d_modules/InstantMesh/utils/infer_util.py b/gen_3d_modules/InstantMesh/utils/infer_util.py new file mode 100644 index 0000000..f2faf2b --- /dev/null +++ b/gen_3d_modules/InstantMesh/utils/infer_util.py @@ -0,0 +1,97 @@ +import os +import imageio +import rembg +import torch +import numpy as np +import PIL.Image +from PIL import Image +from typing import Any + + +def remove_background(image: PIL.Image.Image, + rembg_session: Any = None, + force: bool = False, + **rembg_kwargs, +) -> PIL.Image.Image: + do_remove = True + if image.mode == "RGBA" and image.getextrema()[3][0] < 255: + do_remove = False + do_remove = do_remove or force + if do_remove: + image = rembg.remove(image, session=rembg_session, **rembg_kwargs) + return image + + +def resize_foreground( + image: PIL.Image.Image, + ratio: float, +) -> PIL.Image.Image: + image = np.array(image) + assert image.shape[-1] == 4 + alpha = np.where(image[..., 3] > 0) + y1, y2, x1, x2 = ( + alpha[0].min(), + alpha[0].max(), + alpha[1].min(), + alpha[1].max(), + ) + # crop the foreground + fg = image[y1:y2, x1:x2] + # pad to square + size = max(fg.shape[0], fg.shape[1]) + ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2 + ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0 + new_image = np.pad( + fg, + ((ph0, ph1), (pw0, pw1), (0, 0)), + mode="constant", + constant_values=((0, 0), (0, 0), (0, 0)), + ) + + # compute padding according to the ratio + new_size = int(new_image.shape[0] / ratio) + # pad to size, double side + ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2 + ph1, pw1 = new_size - size - ph0, new_size - size - pw0 + new_image = np.pad( + new_image, + ((ph0, ph1), (pw0, pw1), (0, 0)), + mode="constant", + constant_values=((0, 0), (0, 0), (0, 0)), + ) + new_image = PIL.Image.fromarray(new_image) + return new_image + + +def images_to_video( + images: torch.Tensor, + output_path: str, + fps: int = 30, +) -> None: + # images: (N, C, H, W) + video_dir = os.path.dirname(output_path) + video_name = os.path.basename(output_path) + os.makedirs(video_dir, exist_ok=True) + + frames = [] + for i in range(len(images)): + frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8) + assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \ + f"Frame shape mismatch: {frame.shape} vs {images.shape}" + assert frame.min() >= 0 and frame.max() <= 255, \ + f"Frame value out of range: {frame.min()} ~ {frame.max()}" + frames.append(frame) + imageio.mimwrite(output_path, np.stack(frames), fps=fps, quality=10) + + +def save_video( + frames: torch.Tensor, + output_path: str, + fps: int = 30, +) -> None: + # images: (N, C, H, W) + frames = [(frame.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8) for frame in frames] + writer = imageio.get_writer(output_path, fps=fps) + for frame in frames: + writer.append_data(frame) + writer.close() \ No newline at end of file diff --git a/gen_3d_modules/InstantMesh/utils/mesh_util.py b/gen_3d_modules/InstantMesh/utils/mesh_util.py new file mode 100644 index 0000000..0ec4663 --- /dev/null +++ b/gen_3d_modules/InstantMesh/utils/mesh_util.py @@ -0,0 +1,181 @@ +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited. + +import torch +import xatlas +import trimesh +import cv2 +import numpy as np +import nvdiffrast.torch as dr +from PIL import Image + + +def save_obj(pointnp_px3, facenp_fx3, colornp_px3, fpath): + + pointnp_px3 = pointnp_px3 @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]]) + facenp_fx3 = facenp_fx3[:, [2, 1, 0]] + + mesh = trimesh.Trimesh( + vertices=pointnp_px3, + faces=facenp_fx3, + vertex_colors=colornp_px3, + ) + mesh.export(fpath, 'obj') + + +def save_glb(pointnp_px3, facenp_fx3, colornp_px3, fpath): + + pointnp_px3 = pointnp_px3 @ np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]]) + + mesh = trimesh.Trimesh( + vertices=pointnp_px3, + faces=facenp_fx3, + vertex_colors=colornp_px3, + ) + mesh.export(fpath, 'glb') + + +def save_obj_with_mtl(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, texmap_hxwx3, fname): + import os + fol, na = os.path.split(fname) + na, _ = os.path.splitext(na) + + matname = '%s/%s.mtl' % (fol, na) + fid = open(matname, 'w') + fid.write('newmtl material_0\n') + fid.write('Kd 1 1 1\n') + fid.write('Ka 0 0 0\n') + fid.write('Ks 0.4 0.4 0.4\n') + fid.write('Ns 10\n') + fid.write('illum 2\n') + fid.write('map_Kd %s.png\n' % na) + fid.close() + #### + + fid = open(fname, 'w') + fid.write('mtllib %s.mtl\n' % na) + + for pidx, p in enumerate(pointnp_px3): + pp = p + fid.write('v %f %f %f\n' % (pp[0], pp[1], pp[2])) + + for pidx, p in enumerate(tcoords_px2): + pp = p + fid.write('vt %f %f\n' % (pp[0], pp[1])) + + fid.write('usemtl material_0\n') + for i, f in enumerate(facenp_fx3): + f1 = f + 1 + f2 = facetex_fx3[i] + 1 + fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2])) + fid.close() + + # save texture map + lo, hi = 0, 1 + img = np.asarray(texmap_hxwx3, dtype=np.float32) + img = (img - lo) * (255 / (hi - lo)) + img = img.clip(0, 255) + mask = np.sum(img.astype(np.float32), axis=-1, keepdims=True) + mask = (mask <= 3.0).astype(np.float32) + kernel = np.ones((3, 3), 'uint8') + dilate_img = cv2.dilate(img, kernel, iterations=1) + img = img * (1 - mask) + dilate_img * mask + img = img.clip(0, 255).astype(np.uint8) + Image.fromarray(np.ascontiguousarray(img[::-1, :, :]), 'RGB').save(f'{fol}/{na}.png') + + +def loadobj(meshfile): + v = [] + f = [] + meshfp = open(meshfile, 'r') + for line in meshfp.readlines(): + data = line.strip().split(' ') + data = [da for da in data if len(da) > 0] + if len(data) != 4: + continue + if data[0] == 'v': + v.append([float(d) for d in data[1:]]) + if data[0] == 'f': + data = [da.split('/')[0] for da in data] + f.append([int(d) for d in data[1:]]) + meshfp.close() + + # torch need int64 + facenp_fx3 = np.array(f, dtype=np.int64) - 1 + pointnp_px3 = np.array(v, dtype=np.float32) + return pointnp_px3, facenp_fx3 + + +def loadobjtex(meshfile): + v = [] + vt = [] + f = [] + ft = [] + meshfp = open(meshfile, 'r') + for line in meshfp.readlines(): + data = line.strip().split(' ') + data = [da for da in data if len(da) > 0] + if not ((len(data) == 3) or (len(data) == 4) or (len(data) == 5)): + continue + if data[0] == 'v': + assert len(data) == 4 + + v.append([float(d) for d in data[1:]]) + if data[0] == 'vt': + if len(data) == 3 or len(data) == 4: + vt.append([float(d) for d in data[1:3]]) + if data[0] == 'f': + data = [da.split('/') for da in data] + if len(data) == 4: + f.append([int(d[0]) for d in data[1:]]) + ft.append([int(d[1]) for d in data[1:]]) + elif len(data) == 5: + idx1 = [1, 2, 3] + data1 = [data[i] for i in idx1] + f.append([int(d[0]) for d in data1]) + ft.append([int(d[1]) for d in data1]) + idx2 = [1, 3, 4] + data2 = [data[i] for i in idx2] + f.append([int(d[0]) for d in data2]) + ft.append([int(d[1]) for d in data2]) + meshfp.close() + + # torch need int64 + facenp_fx3 = np.array(f, dtype=np.int64) - 1 + ftnp_fx3 = np.array(ft, dtype=np.int64) - 1 + pointnp_px3 = np.array(v, dtype=np.float32) + uvs = np.array(vt, dtype=np.float32) + return pointnp_px3, facenp_fx3, uvs, ftnp_fx3 + + +# ============================================================================================== +def interpolate(attr, rast, attr_idx, rast_db=None): + return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all') + + +def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution): + vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy()) + + # Convert to tensors + indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64) + + uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device) + mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device) + # mesh_v_tex. ture + uv_clip = uvs[None, ...] * 2.0 - 1.0 + + # pad to four component coordinate + uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1) + + # rasterize + rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution)) + + # Interpolate world space position + gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int()) + mask = rast[..., 3:4] > 0 + return uvs, mesh_tex_idx, gb_pos, mask diff --git a/gen_3d_modules/InstantMesh/utils/train_util.py b/gen_3d_modules/InstantMesh/utils/train_util.py new file mode 100644 index 0000000..2e65421 --- /dev/null +++ b/gen_3d_modules/InstantMesh/utils/train_util.py @@ -0,0 +1,26 @@ +import importlib + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) diff --git a/gen_3d_modules/LGM/acc_configs/gpu1.yaml b/gen_3d_modules/LGM/acc_configs/gpu1.yaml new file mode 100644 index 0000000..bbbabf2 --- /dev/null +++ b/gen_3d_modules/LGM/acc_configs/gpu1.yaml @@ -0,0 +1,15 @@ +compute_environment: LOCAL_MACHINE +debug: false +distributed_type: 'NO' +downcast_bf16: 'no' +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 1 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/gen_3d_modules/LGM/acc_configs/gpu4.yaml b/gen_3d_modules/LGM/acc_configs/gpu4.yaml new file mode 100644 index 0000000..6f4c538 --- /dev/null +++ b/gen_3d_modules/LGM/acc_configs/gpu4.yaml @@ -0,0 +1,15 @@ +compute_environment: LOCAL_MACHINE +debug: false +distributed_type: MULTI_GPU +downcast_bf16: 'no' +machine_rank: 0 +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/gen_3d_modules/LGM/acc_configs/gpu6.yaml b/gen_3d_modules/LGM/acc_configs/gpu6.yaml new file mode 100644 index 0000000..32ef41c --- /dev/null +++ b/gen_3d_modules/LGM/acc_configs/gpu6.yaml @@ -0,0 +1,15 @@ +compute_environment: LOCAL_MACHINE +debug: false +distributed_type: MULTI_GPU +downcast_bf16: 'no' +machine_rank: 0 +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 6 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/gen_3d_modules/LGM/acc_configs/gpu8.yaml b/gen_3d_modules/LGM/acc_configs/gpu8.yaml new file mode 100644 index 0000000..46b5c35 --- /dev/null +++ b/gen_3d_modules/LGM/acc_configs/gpu8.yaml @@ -0,0 +1,15 @@ +compute_environment: LOCAL_MACHINE +debug: false +distributed_type: MULTI_GPU +downcast_bf16: 'no' +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/gen_3d_modules/LGM/core/__init__.py b/gen_3d_modules/LGM/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/LGM/core/attention.py b/gen_3d_modules/LGM/core/attention.py new file mode 100644 index 0000000..8e74717 --- /dev/null +++ b/gen_3d_modules/LGM/core/attention.py @@ -0,0 +1,158 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the Apache License, Version 2.0 +# found in the LICENSE file in the root directory of this source tree. + +# References: +# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py +# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py + +import os +import logging + +from torch import Tensor +from torch import nn +from .utils import setup_logger + +setup_logger('', logging.INFO, [logging.INFO, logging.WARNING], [logging.ERROR, logging.CRITICAL], logging.Formatter('%(message)s')) + +XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None +try: + if XFORMERS_ENABLED: + from xformers.ops import memory_efficient_attention, unbind + + XFORMERS_AVAILABLE = True + logging.warn("xFormers is available (Attention)") + else: + logging.warn("xFormers is disabled (Attention)") + raise ImportError +except ImportError: + XFORMERS_AVAILABLE = False + logging.warn("xFormers is not available (Attention)") + +class Attention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x: Tensor) -> Tensor: + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + + q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] + attn = q @ k.transpose(-2, -1) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class MemEffAttention(Attention): + def forward(self, x: Tensor, attn_bias=None) -> Tensor: + if not XFORMERS_AVAILABLE: + if attn_bias is not None: + raise AssertionError("xFormers is required for using nested tensors") + return super().forward(x) + + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + + q, k, v = unbind(qkv, 2) + + x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) + x = x.reshape([B, N, C]) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class CrossAttention(nn.Module): + def __init__( + self, + dim: int, + dim_q: int, + dim_k: int, + dim_v: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super().__init__() + self.dim = dim + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.to_q = nn.Linear(dim_q, dim, bias=qkv_bias) + self.to_k = nn.Linear(dim_k, dim, bias=qkv_bias) + self.to_v = nn.Linear(dim_v, dim, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: + # q: [B, N, Cq] + # k: [B, M, Ck] + # v: [B, M, Cv] + # return: [B, N, C] + + B, N, _ = q.shape + M = k.shape[1] + + q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, N, C/nh] + k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh] + v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh] + + attn = q @ k.transpose(-2, -1) # [B, nh, N, M] + + attn = attn.softmax(dim=-1) # [B, nh, N, M] + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) # [B, nh, N, M] @ [B, nh, M, C/nh] --> [B, nh, N, C/nh] --> [B, N, nh, C/nh] --> [B, N, C] + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class MemEffCrossAttention(CrossAttention): + def forward(self, q: Tensor, k: Tensor, v: Tensor, attn_bias=None) -> Tensor: + if not XFORMERS_AVAILABLE: + if attn_bias is not None: + raise AssertionError("xFormers is required for using nested tensors") + return super().forward(x) + + B, N, _ = q.shape + M = k.shape[1] + + q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads) # [B, N, nh, C/nh] + k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh] + v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh] + + x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) + x = x.reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x diff --git a/gen_3d_modules/LGM/core/gs.py b/gen_3d_modules/LGM/core/gs.py new file mode 100644 index 0000000..73c42ae --- /dev/null +++ b/gen_3d_modules/LGM/core/gs.py @@ -0,0 +1,186 @@ +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from diff_gaussian_rasterization import ( + GaussianRasterizationSettings, + GaussianRasterizer, +) + +from LGM.core.options import Options + +import kiui + +class GaussianRenderer: + def __init__(self, opt: Options): + + self.opt = opt + self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device="cuda") + + # intrinsics + self.tan_half_fov = np.tan(0.5 * np.deg2rad(self.opt.fovy)) + self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32) + self.proj_matrix[0, 0] = 1 / self.tan_half_fov + self.proj_matrix[1, 1] = 1 / self.tan_half_fov + self.proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear) + self.proj_matrix[3, 2] = - (opt.zfar * opt.znear) / (opt.zfar - opt.znear) + self.proj_matrix[2, 3] = 1 + + def render(self, gaussians, cam_view, cam_view_proj, cam_pos, bg_color=None, scale_modifier=1): + # gaussians: [B, N, 14] + # cam_view, cam_view_proj: [B, V, 4, 4] + # cam_pos: [B, V, 3] + + device = gaussians.device + B, V = cam_view.shape[:2] + + # loop of loop... + images = [] + alphas = [] + for b in range(B): + + # pos, opacity, scale, rotation, shs + means3D = gaussians[b, :, 0:3].contiguous().float() + opacity = gaussians[b, :, 3:4].contiguous().float() + scales = gaussians[b, :, 4:7].contiguous().float() + rotations = gaussians[b, :, 7:11].contiguous().float() + rgbs = gaussians[b, :, 11:].contiguous().float() # [N, 3] + + for v in range(V): + + # render novel views + view_matrix = cam_view[b, v].float() + view_proj_matrix = cam_view_proj[b, v].float() + campos = cam_pos[b, v].float() + + raster_settings = GaussianRasterizationSettings( + image_height=self.opt.output_size, + image_width=self.opt.output_size, + tanfovx=self.tan_half_fov, + tanfovy=self.tan_half_fov, + bg=self.bg_color if bg_color is None else bg_color, + scale_modifier=scale_modifier, + viewmatrix=view_matrix, + projmatrix=view_proj_matrix, + sh_degree=0, + campos=campos, + prefiltered=False, + debug=False, + ) + + rasterizer = GaussianRasterizer(raster_settings=raster_settings) + + # Rasterize visible Gaussians to image, obtain their radii (on screen). + rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( + means3D=means3D, + means2D=torch.zeros_like(means3D, dtype=torch.float32, device=device), + shs=None, + colors_precomp=rgbs, + opacities=opacity, + scales=scales, + rotations=rotations, + cov3D_precomp=None, + ) + + rendered_image = rendered_image.clamp(0, 1) + + images.append(rendered_image) + alphas.append(rendered_alpha) + + images = torch.stack(images, dim=0).view(B, V, 3, self.opt.output_size, self.opt.output_size) + alphas = torch.stack(alphas, dim=0).view(B, V, 1, self.opt.output_size, self.opt.output_size) + + return { + "image": images, # [B, V, 3, H, W] + "alpha": alphas, # [B, V, 1, H, W] + } + + + def to_ply(self, gaussians, compatible=True): + # gaussians: [B, N, 14] + # compatible: save pre-activated gaussians as in the original paper + + assert gaussians.shape[0] == 1, 'only support batch size 1' + + from plyfile import PlyData, PlyElement + + means3D = gaussians[0, :, 0:3].contiguous().float() + opacity = gaussians[0, :, 3:4].contiguous().float() + scales = gaussians[0, :, 4:7].contiguous().float() + rotations = gaussians[0, :, 7:11].contiguous().float() + shs = gaussians[0, :, 11:].unsqueeze(1).contiguous().float() # [N, 1, 3] + + # prune by opacity + mask = opacity.squeeze(-1) >= 0.005 + means3D = means3D[mask] + opacity = opacity[mask] + scales = scales[mask] + rotations = rotations[mask] + shs = shs[mask] + + # invert activation to make it compatible with the original ply format + if compatible: + opacity = kiui.op.inverse_sigmoid(opacity) + scales = torch.log(scales + 1e-8) + shs = (shs - 0.5) / 0.28209479177387814 + + xyzs = means3D.detach().cpu().numpy() + f_dc = shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() + opacities = opacity.detach().cpu().numpy() + scales = scales.detach().cpu().numpy() + rotations = rotations.detach().cpu().numpy() + + l = ['x', 'y', 'z'] + # All channels except the 3 DC + for i in range(f_dc.shape[1]): + l.append('f_dc_{}'.format(i)) + l.append('opacity') + for i in range(scales.shape[1]): + l.append('scale_{}'.format(i)) + for i in range(rotations.shape[1]): + l.append('rot_{}'.format(i)) + + dtype_full = [(attribute, 'f4') for attribute in l] + + elements = np.empty(xyzs.shape[0], dtype=dtype_full) + attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1) + elements[:] = list(map(tuple, attributes)) + el = PlyElement.describe(elements, 'vertex') + + return PlyData([el]) + + def create_from_ply(self, plydata, compatible=True): + + xyz = np.stack((np.asarray(plydata.elements[0]["x"]), + np.asarray(plydata.elements[0]["y"]), + np.asarray(plydata.elements[0]["z"])), axis=1) + print("Number of points at loading : ", xyz.shape[0]) + + opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] + + shs = np.zeros((xyz.shape[0], 3)) + shs[:, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) + shs[:, 1] = np.asarray(plydata.elements[0]["f_dc_1"]) + shs[:, 2] = np.asarray(plydata.elements[0]["f_dc_2"]) + + scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] + scales = np.zeros((xyz.shape[0], len(scale_names))) + for idx, attr_name in enumerate(scale_names): + scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot_")] + rots = np.zeros((xyz.shape[0], len(rot_names))) + for idx, attr_name in enumerate(rot_names): + rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) + + gaussians = np.concatenate([xyz, opacities, scales, rots, shs], axis=1) + gaussians = torch.from_numpy(gaussians).float() # cpu + + if compatible: + gaussians[..., 3:4] = torch.sigmoid(gaussians[..., 3:4]) + gaussians[..., 4:7] = torch.exp(gaussians[..., 4:7]) + gaussians[..., 11:] = 0.28209479177387814 * gaussians[..., 11:] + 0.5 + + return gaussians \ No newline at end of file diff --git a/gen_3d_modules/LGM/core/options.py b/gen_3d_modules/LGM/core/options.py new file mode 100644 index 0000000..ba6e8f1 --- /dev/null +++ b/gen_3d_modules/LGM/core/options.py @@ -0,0 +1,117 @@ +from dataclasses import dataclass +from typing import Tuple, Literal, Dict, Optional + + +@dataclass +class Options: + ### model + # Unet image input size + input_size: int = 256 + # Unet definition + down_channels: Tuple[int, ...] = (64, 128, 256, 512, 1024, 1024) + down_attention: Tuple[bool, ...] = (False, False, False, True, True, True) + mid_attention: bool = True + up_channels: Tuple[int, ...] = (1024, 1024, 512, 256) + up_attention: Tuple[bool, ...] = (True, True, True, False) + # Unet output size, dependent on the input_size and U-Net structure! + splat_size: int = 64 + # gaussian render size + output_size: int = 256 + + ### dataset + # data mode (only support s3 now) + data_mode: Literal['s3'] = 's3' + # fovy of the dataset + fovy: float = 49.1 + # camera near plane + znear: float = 0.5 + # camera far plane + zfar: float = 2.5 + # number of all views (input + output) + num_views: int = 12 + # number of views + num_input_views: int = 4 + # camera radius + cam_radius: float = 1.5 # to better use [-1, 1]^3 space + # num workers + num_workers: int = 8 + + ### training + # workspace + workspace: str = './workspace' + # resume + resume: Optional[str] = None + # batch size (per-GPU) + batch_size: int = 8 + # gradient accumulation + gradient_accumulation_steps: int = 1 + # training epochs + num_epochs: int = 30 + # lpips loss weight + lambda_lpips: float = 1.0 + # gradient clip + gradient_clip: float = 1.0 + # mixed precision + mixed_precision: str = 'bf16' + # learning rate + lr: float = 4e-4 + # augmentation prob for grid distortion + prob_grid_distortion: float = 0.5 + # augmentation prob for camera jitter + prob_cam_jitter: float = 0.5 + + ### testing + # test image path + test_path: Optional[str] = None + + ### misc + # nvdiffrast backend setting + force_cuda_rast: bool = False + # render fancy video with gaussian scaling effect + fancy_video: bool = False + + +# all the default settings +config_defaults: Dict[str, Options] = {} +config_doc: Dict[str, str] = {} + +config_doc['default'] = 'the default settings for LGM' +config_defaults['default'] = Options() + +config_doc['small'] = 'small model with lower resolution Gaussians' +config_defaults['small'] = Options( + input_size=256, + splat_size=64, + output_size=256, + batch_size=8, + gradient_accumulation_steps=1, + mixed_precision='bf16', +) + +config_doc['big'] = 'big model with higher resolution Gaussians' +config_defaults['big'] = Options( + input_size=256, + up_channels=(1024, 1024, 512, 256, 128), # one more decoder + up_attention=(True, True, True, False, False), + splat_size=128, + output_size=512, # render & supervise Gaussians at a higher resolution. + batch_size=8, + num_views=8, + gradient_accumulation_steps=1, + mixed_precision='bf16', +) + +config_doc['tiny'] = 'tiny model for ablation' +config_defaults['tiny'] = Options( + input_size=256, + down_channels=(32, 64, 128, 256, 512), + down_attention=(False, False, False, False, True), + up_channels=(512, 256, 128), + up_attention=(True, False, False, False), + splat_size=64, + output_size=256, + batch_size=16, + num_views=8, + gradient_accumulation_steps=1, + mixed_precision='bf16', +) diff --git a/gen_3d_modules/LGM/core/provider_objaverse.py b/gen_3d_modules/LGM/core/provider_objaverse.py new file mode 100644 index 0000000..310eaee --- /dev/null +++ b/gen_3d_modules/LGM/core/provider_objaverse.py @@ -0,0 +1,172 @@ +import os +import cv2 +import random +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms.functional as TF +from torch.utils.data import Dataset + +import kiui +from LGM.core.options import Options +from LGM.core.utils import get_rays, grid_distortion, orbit_camera_jitter + +IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) +IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) + + +class ObjaverseDataset(Dataset): + + def _warn(self): + raise NotImplementedError('this dataset is just an example and cannot be used directly, you should modify it to your own setting! (search keyword TODO)') + + def __init__(self, opt: Options, training=True): + + self.opt = opt + self.training = training + + # TODO: remove this barrier + self._warn() + + # TODO: load the list of objects for training + self.items = [] + with open('TODO: file containing the list', 'r') as f: + for line in f.readlines(): + self.items.append(line.strip()) + + # naive split + if self.training: + self.items = self.items[:-self.opt.batch_size] + else: + self.items = self.items[-self.opt.batch_size:] + + # default camera intrinsics + self.tan_half_fov = np.tan(0.5 * np.deg2rad(self.opt.fovy)) + self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32) + self.proj_matrix[0, 0] = 1 / self.tan_half_fov + self.proj_matrix[1, 1] = 1 / self.tan_half_fov + self.proj_matrix[2, 2] = (self.opt.zfar + self.opt.znear) / (self.opt.zfar - self.opt.znear) + self.proj_matrix[3, 2] = - (self.opt.zfar * self.opt.znear) / (self.opt.zfar - self.opt.znear) + self.proj_matrix[2, 3] = 1 + + + def __len__(self): + return len(self.items) + + def __getitem__(self, idx): + + uid = self.items[idx] + results = {} + + # load num_views images + images = [] + masks = [] + cam_poses = [] + + vid_cnt = 0 + + # TODO: choose views, based on your rendering settings + if self.training: + # input views are in (36, 72), other views are randomly selected + vids = np.random.permutation(np.arange(36, 73))[:self.opt.num_input_views].tolist() + np.random.permutation(100).tolist() + else: + # fixed views + vids = np.arange(36, 73, 4).tolist() + np.arange(100).tolist() + + for vid in vids: + + image_path = os.path.join(uid, 'rgb', f'{vid:03d}.png') + camera_path = os.path.join(uid, 'pose', f'{vid:03d}.txt') + + try: + # TODO: load data (modify self.client here) + image = np.frombuffer(self.client.get(image_path), np.uint8) + image = torch.from_numpy(cv2.imdecode(image, cv2.IMREAD_UNCHANGED).astype(np.float32) / 255) # [512, 512, 4] in [0, 1] + c2w = [float(t) for t in self.client.get(camera_path).decode().strip().split(' ')] + c2w = torch.tensor(c2w, dtype=torch.float32).reshape(4, 4) + except Exception as e: + # print(f'[WARN] dataset {uid} {vid}: {e}') + continue + + # TODO: you may have a different camera system + # blender world + opencv cam --> opengl world & cam + c2w[1] *= -1 + c2w[[1, 2]] = c2w[[2, 1]] + c2w[:3, 1:3] *= -1 # invert up and forward direction + + # scale up radius to fully use the [-1, 1]^3 space! + c2w[:3, 3] *= self.opt.cam_radius / 1.5 # 1.5 is the default scale + + image = image.permute(2, 0, 1) # [4, 512, 512] + mask = image[3:4] # [1, 512, 512] + image = image[:3] * mask + (1 - mask) # [3, 512, 512], to white bg + image = image[[2,1,0]].contiguous() # bgr to rgb + + images.append(image) + masks.append(mask.squeeze(0)) + cam_poses.append(c2w) + + vid_cnt += 1 + if vid_cnt == self.opt.num_views: + break + + if vid_cnt < self.opt.num_views: + print(f'[WARN] dataset {uid}: not enough valid views, only {vid_cnt} views found!') + n = self.opt.num_views - vid_cnt + images = images + [images[-1]] * n + masks = masks + [masks[-1]] * n + cam_poses = cam_poses + [cam_poses[-1]] * n + + images = torch.stack(images, dim=0) # [V, C, H, W] + masks = torch.stack(masks, dim=0) # [V, H, W] + cam_poses = torch.stack(cam_poses, dim=0) # [V, 4, 4] + + # normalized camera feats as in paper (transform the first pose to a fixed position) + transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32) @ torch.inverse(cam_poses[0]) + cam_poses = transform.unsqueeze(0) @ cam_poses # [V, 4, 4] + + images_input = F.interpolate(images[:self.opt.num_input_views].clone(), size=(self.opt.input_size, self.opt.input_size), mode='bilinear', align_corners=False) # [V, C, H, W] + cam_poses_input = cam_poses[:self.opt.num_input_views].clone() + + # data augmentation + if self.training: + # apply random grid distortion to simulate 3D inconsistency + if random.random() < self.opt.prob_grid_distortion: + images_input[1:] = grid_distortion(images_input[1:]) + # apply camera jittering (only to input!) + if random.random() < self.opt.prob_cam_jitter: + cam_poses_input[1:] = orbit_camera_jitter(cam_poses_input[1:]) + + images_input = TF.normalize(images_input, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD) + + # resize render ground-truth images, range still in [0, 1] + results['images_output'] = F.interpolate(images, size=(self.opt.output_size, self.opt.output_size), mode='bilinear', align_corners=False) # [V, C, output_size, output_size] + results['masks_output'] = F.interpolate(masks.unsqueeze(1), size=(self.opt.output_size, self.opt.output_size), mode='bilinear', align_corners=False) # [V, 1, output_size, output_size] + + # build rays for input views + rays_embeddings = [] + for i in range(self.opt.num_input_views): + rays_o, rays_d = get_rays(cam_poses_input[i], self.opt.input_size, self.opt.input_size, self.opt.fovy) # [h, w, 3] + rays_plucker = torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d], dim=-1) # [h, w, 6] + rays_embeddings.append(rays_plucker) + + + rays_embeddings = torch.stack(rays_embeddings, dim=0).permute(0, 3, 1, 2).contiguous() # [V, 6, h, w] + final_input = torch.cat([images_input, rays_embeddings], dim=1) # [V=4, 9, H, W] + results['input'] = final_input + + # opengl to colmap camera for gaussian renderer + cam_poses[:, :3, 1:3] *= -1 # invert up & forward direction + + # cameras needed by gaussian rasterizer + cam_view = torch.inverse(cam_poses).transpose(1, 2) # [V, 4, 4] + cam_view_proj = cam_view @ self.proj_matrix # [V, 4, 4] + cam_pos = - cam_poses[:, :3, 3] # [V, 3] + + results['cam_view'] = cam_view + results['cam_view_proj'] = cam_view_proj + results['cam_pos'] = cam_pos + + return results \ No newline at end of file diff --git a/gen_3d_modules/LGM/core/unet.py b/gen_3d_modules/LGM/core/unet.py new file mode 100644 index 0000000..a1d6673 --- /dev/null +++ b/gen_3d_modules/LGM/core/unet.py @@ -0,0 +1,319 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import numpy as np +from typing import Tuple, Literal +from functools import partial + +from LGM.core.attention import MemEffAttention + +class MVAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + groups: int = 32, + eps: float = 1e-5, + residual: bool = True, + skip_scale: float = 1, + num_frames: int = 4, # WARN: hardcoded! + ): + super().__init__() + + self.residual = residual + self.skip_scale = skip_scale + self.num_frames = num_frames + + self.norm = nn.GroupNorm(num_groups=groups, num_channels=dim, eps=eps, affine=True) + self.attn = MemEffAttention(dim, num_heads, qkv_bias, proj_bias, attn_drop, proj_drop) + + def forward(self, x): + # x: [B*V, C, H, W] + BV, C, H, W = x.shape + B = BV // self.num_frames # assert BV % self.num_frames == 0 + + res = x + x = self.norm(x) + + x = x.reshape(B, self.num_frames, C, H, W).permute(0, 1, 3, 4, 2).reshape(B, -1, C) + x = self.attn(x) + x = x.reshape(B, self.num_frames, H, W, C).permute(0, 1, 4, 2, 3).reshape(BV, C, H, W) + + if self.residual: + x = (x + res) * self.skip_scale + return x + +class ResnetBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + resample: Literal['default', 'up', 'down'] = 'default', + groups: int = 32, + eps: float = 1e-5, + skip_scale: float = 1, # multiplied to output + ): + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.skip_scale = skip_scale + + self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.norm2 = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.act = F.silu + + self.resample = None + if resample == 'up': + self.resample = partial(F.interpolate, scale_factor=2.0, mode="nearest") + elif resample == 'down': + self.resample = nn.AvgPool2d(kernel_size=2, stride=2) + + self.shortcut = nn.Identity() + if self.in_channels != self.out_channels: + self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=True) + + + def forward(self, x): + res = x + + x = self.norm1(x) + x = self.act(x) + + if self.resample: + res = self.resample(res) + x = self.resample(x) + + x = self.conv1(x) + x = self.norm2(x) + x = self.act(x) + x = self.conv2(x) + + x = (x + self.shortcut(res)) * self.skip_scale + + return x + +class DownBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_layers: int = 1, + downsample: bool = True, + attention: bool = True, + attention_heads: int = 16, + skip_scale: float = 1, + ): + super().__init__() + + nets = [] + attns = [] + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + nets.append(ResnetBlock(in_channels, out_channels, skip_scale=skip_scale)) + if attention: + attns.append(MVAttention(out_channels, attention_heads, skip_scale=skip_scale)) + else: + attns.append(None) + self.nets = nn.ModuleList(nets) + self.attns = nn.ModuleList(attns) + + self.downsample = None + if downsample: + self.downsample = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1) + + def forward(self, x): + xs = [] + + for attn, net in zip(self.attns, self.nets): + x = net(x) + if attn: + x = attn(x) + xs.append(x) + + if self.downsample: + x = self.downsample(x) + xs.append(x) + + return x, xs + + +class MidBlock(nn.Module): + def __init__( + self, + in_channels: int, + num_layers: int = 1, + attention: bool = True, + attention_heads: int = 16, + skip_scale: float = 1, + ): + super().__init__() + + nets = [] + attns = [] + # first layer + nets.append(ResnetBlock(in_channels, in_channels, skip_scale=skip_scale)) + # more layers + for i in range(num_layers): + nets.append(ResnetBlock(in_channels, in_channels, skip_scale=skip_scale)) + if attention: + attns.append(MVAttention(in_channels, attention_heads, skip_scale=skip_scale)) + else: + attns.append(None) + self.nets = nn.ModuleList(nets) + self.attns = nn.ModuleList(attns) + + def forward(self, x): + x = self.nets[0](x) + for attn, net in zip(self.attns, self.nets[1:]): + if attn: + x = attn(x) + x = net(x) + return x + + +class UpBlock(nn.Module): + def __init__( + self, + in_channels: int, + prev_out_channels: int, + out_channels: int, + num_layers: int = 1, + upsample: bool = True, + attention: bool = True, + attention_heads: int = 16, + skip_scale: float = 1, + ): + super().__init__() + + nets = [] + attns = [] + for i in range(num_layers): + cin = in_channels if i == 0 else out_channels + cskip = prev_out_channels if (i == num_layers - 1) else out_channels + + nets.append(ResnetBlock(cin + cskip, out_channels, skip_scale=skip_scale)) + if attention: + attns.append(MVAttention(out_channels, attention_heads, skip_scale=skip_scale)) + else: + attns.append(None) + self.nets = nn.ModuleList(nets) + self.attns = nn.ModuleList(attns) + + self.upsample = None + if upsample: + self.upsample = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x, xs): + + for attn, net in zip(self.attns, self.nets): + res_x = xs[-1] + xs = xs[:-1] + x = torch.cat([x, res_x], dim=1) + x = net(x) + if attn: + x = attn(x) + + if self.upsample: + x = F.interpolate(x, scale_factor=2.0, mode='nearest') + x = self.upsample(x) + + return x + + +# it could be asymmetric! +class UNet(nn.Module): + def __init__( + self, + in_channels: int = 3, + out_channels: int = 3, + down_channels: Tuple[int, ...] = (64, 128, 256, 512, 1024), + down_attention: Tuple[bool, ...] = (False, False, False, True, True), + mid_attention: bool = True, + up_channels: Tuple[int, ...] = (1024, 512, 256), + up_attention: Tuple[bool, ...] = (True, True, False), + layers_per_block: int = 2, + skip_scale: float = np.sqrt(0.5), + ): + super().__init__() + + # first + self.conv_in = nn.Conv2d(in_channels, down_channels[0], kernel_size=3, stride=1, padding=1) + + # down + down_blocks = [] + cout = down_channels[0] + for i in range(len(down_channels)): + cin = cout + cout = down_channels[i] + + down_blocks.append(DownBlock( + cin, cout, + num_layers=layers_per_block, + downsample=(i != len(down_channels) - 1), # not final layer + attention=down_attention[i], + skip_scale=skip_scale, + )) + self.down_blocks = nn.ModuleList(down_blocks) + + # mid + self.mid_block = MidBlock(down_channels[-1], attention=mid_attention, skip_scale=skip_scale) + + # up + up_blocks = [] + cout = up_channels[0] + for i in range(len(up_channels)): + cin = cout + cout = up_channels[i] + cskip = down_channels[max(-2 - i, -len(down_channels))] # for assymetric + + up_blocks.append(UpBlock( + cin, cskip, cout, + num_layers=layers_per_block + 1, # one more layer for up + upsample=(i != len(up_channels) - 1), # not final layer + attention=up_attention[i], + skip_scale=skip_scale, + )) + self.up_blocks = nn.ModuleList(up_blocks) + + # last + self.norm_out = nn.GroupNorm(num_channels=up_channels[-1], num_groups=32, eps=1e-5) + self.conv_out = nn.Conv2d(up_channels[-1], out_channels, kernel_size=3, stride=1, padding=1) + + + def forward(self, x): + # x: [B, Cin, H, W] + + # first + x = self.conv_in(x) + + # down + xss = [x] + for block in self.down_blocks: + x, xs = block(x) + xss.extend(xs) + + # mid + x = self.mid_block(x) + + # up + for block in self.up_blocks: + xs = xss[-len(block.nets):] + xss = xss[:-len(block.nets)] + x = block(x, xs) + + # last + x = self.norm_out(x) + x = F.silu(x) + x = self.conv_out(x) # [B, Cout, H', W'] + + return x diff --git a/gen_3d_modules/LGM/core/utils.py b/gen_3d_modules/LGM/core/utils.py new file mode 100644 index 0000000..655f921 --- /dev/null +++ b/gen_3d_modules/LGM/core/utils.py @@ -0,0 +1,134 @@ +import numpy as np +import logging +import sys + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import roma +from kiui.op import safe_normalize + +def get_rays(pose, h, w, fovy, opengl=True): + + x, y = torch.meshgrid( + torch.arange(w, device=pose.device), + torch.arange(h, device=pose.device), + indexing="xy", + ) + x = x.flatten() + y = y.flatten() + + cx = w * 0.5 + cy = h * 0.5 + + focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy)) + + camera_dirs = F.pad( + torch.stack( + [ + (x - cx + 0.5) / focal, + (y - cy + 0.5) / focal * (-1.0 if opengl else 1.0), + ], + dim=-1, + ), + (0, 1), + value=(-1.0 if opengl else 1.0), + ) # [hw, 3] + + rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1) # [hw, 3] + rays_o = pose[:3, 3].unsqueeze(0).expand_as(rays_d) # [hw, 3] + + rays_o = rays_o.view(h, w, 3) + rays_d = safe_normalize(rays_d).view(h, w, 3) + + return rays_o, rays_d + +def orbit_camera_jitter(poses, strength=0.1): + # poses: [B, 4, 4], assume orbit camera in opengl format + # random orbital rotate + + B = poses.shape[0] + rotvec_x = poses[:, :3, 1] * strength * np.pi * (torch.rand(B, 1, device=poses.device) * 2 - 1) + rotvec_y = poses[:, :3, 0] * strength * np.pi / 2 * (torch.rand(B, 1, device=poses.device) * 2 - 1) + + rot = roma.rotvec_to_rotmat(rotvec_x) @ roma.rotvec_to_rotmat(rotvec_y) + R = rot @ poses[:, :3, :3] + T = rot @ poses[:, :3, 3:] + + new_poses = poses.clone() + new_poses[:, :3, :3] = R + new_poses[:, :3, 3:] = T + + return new_poses + +def grid_distortion(images, strength=0.5): + # images: [B, C, H, W] + # num_steps: int, grid resolution for distortion + # strength: float in [0, 1], strength of distortion + + B, C, H, W = images.shape + + num_steps = np.random.randint(8, 17) + grid_steps = torch.linspace(-1, 1, num_steps) + + # have to loop batch... + grids = [] + for b in range(B): + # construct displacement + x_steps = torch.linspace(0, 1, num_steps) # [num_steps], inclusive + x_steps = (x_steps + strength * (torch.rand_like(x_steps) - 0.5) / (num_steps - 1)).clamp(0, 1) # perturb + x_steps = (x_steps * W).long() # [num_steps] + x_steps[0] = 0 + x_steps[-1] = W + xs = [] + for i in range(num_steps - 1): + xs.append(torch.linspace(grid_steps[i], grid_steps[i + 1], x_steps[i + 1] - x_steps[i])) + xs = torch.cat(xs, dim=0) # [W] + + y_steps = torch.linspace(0, 1, num_steps) # [num_steps], inclusive + y_steps = (y_steps + strength * (torch.rand_like(y_steps) - 0.5) / (num_steps - 1)).clamp(0, 1) # perturb + y_steps = (y_steps * H).long() # [num_steps] + y_steps[0] = 0 + y_steps[-1] = H + ys = [] + for i in range(num_steps - 1): + ys.append(torch.linspace(grid_steps[i], grid_steps[i + 1], y_steps[i + 1] - y_steps[i])) + ys = torch.cat(ys, dim=0) # [H] + + # construct grid + grid_x, grid_y = torch.meshgrid(xs, ys, indexing='xy') # [H, W] + grid = torch.stack([grid_x, grid_y], dim=-1) # [H, W, 2] + + grids.append(grid) + + grids = torch.stack(grids, dim=0).to(images.device) # [B, H, W, 2] + + # grid sample + images = F.grid_sample(images, grids, align_corners=False) + + return images + +class WarningFilter(logging.Filter): + def filter(self, record): + if record.levelno == logging.WARNING: + record.msg = f"Warn!: {record.msg}" + return True + +def create_handler(stream, levels, formatter): + handler = logging.StreamHandler(stream) + handler.setLevel(min(levels)) + handler.addFilter(lambda record: record.levelno in levels) + handler.addFilter(WarningFilter()) # Apply the custom filter + handler.setFormatter(formatter) + + return handler +def setup_logger(logger_name, level, stdout_levels, stderr_levels, formatter): + logger = logging.getLogger(logger_name) + for handler in logger.handlers[:]: + logger.removeHandler(handler) + logger.setLevel(level) + stdout_handler = create_handler(sys.stdout, stdout_levels, formatter) + stderr_handler = create_handler(sys.stderr, stderr_levels, formatter) + logger.addHandler(stdout_handler) + logger.addHandler(stderr_handler) \ No newline at end of file diff --git a/gen_3d_modules/LGM/large_multiview_gaussian_model.py b/gen_3d_modules/LGM/large_multiview_gaussian_model.py new file mode 100644 index 0000000..55a3d68 --- /dev/null +++ b/gen_3d_modules/LGM/large_multiview_gaussian_model.py @@ -0,0 +1,169 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from kiui.lpips import LPIPS +from kiui.cam import orbit_camera + +from LGM.core.unet import UNet +from LGM.core.options import Options +from LGM.core.gs import GaussianRenderer +from LGM.core.utils import get_rays + +class LargeMultiviewGaussianModel(nn.Module): + def __init__( + self, + opt: Options, + ): + super().__init__() + + self.opt = opt + + # unet + self.unet = UNet( + 9, 14, + down_channels=self.opt.down_channels, + down_attention=self.opt.down_attention, + mid_attention=self.opt.mid_attention, + up_channels=self.opt.up_channels, + up_attention=self.opt.up_attention, + ) + + # last conv + self.conv = nn.Conv2d(14, 14, kernel_size=1) # NOTE: maybe remove it if train again + + # Gaussian Renderer + self.gs = GaussianRenderer(opt) + + # activations... + self.pos_act = lambda x: x.clamp(-1, 1) + self.scale_act = lambda x: 0.1 * F.softplus(x) + self.opacity_act = lambda x: torch.sigmoid(x) + self.rot_act = F.normalize + self.rgb_act = lambda x: 0.5 * torch.tanh(x) + 0.5 # NOTE: may use sigmoid if train again + + # LPIPS loss + if self.opt.lambda_lpips > 0: + self.lpips_loss = LPIPS(net='vgg') + self.lpips_loss.requires_grad_(False) + + + def state_dict(self, **kwargs): + # remove lpips_loss + state_dict = super().state_dict(**kwargs) + for k in list(state_dict.keys()): + if 'lpips_loss' in k: + del state_dict[k] + return state_dict + + + def prepare_default_rays(self, device, elevation=0): + cam_poses = np.stack([ + orbit_camera(elevation, 0, radius=self.opt.cam_radius), + orbit_camera(elevation, 90, radius=self.opt.cam_radius), + orbit_camera(elevation, 180, radius=self.opt.cam_radius), + orbit_camera(elevation, 270, radius=self.opt.cam_radius), + ], axis=0) # [4, 4, 4] + cam_poses = torch.from_numpy(cam_poses) + + rays_embeddings = [] + for i in range(cam_poses.shape[0]): + rays_o, rays_d = get_rays(cam_poses[i], self.opt.input_size, self.opt.input_size, self.opt.fovy) # [h, w, 3] + rays_plucker = torch.cat([torch.cross(rays_o, rays_d, dim=-1), rays_d], dim=-1) # [h, w, 6] + rays_embeddings.append(rays_plucker) + + ## visualize rays for plotting figure + # kiui.vis.plot_image(rays_d * 0.5 + 0.5, save=True) + + rays_embeddings = torch.stack(rays_embeddings, dim=0).permute(0, 3, 1, 2).contiguous().to(device) # [V, 6, h, w] + + return rays_embeddings + + + def forward_gaussians(self, images): + # images: [B, 4, 9, H, W] + # return: Gaussians: [B, dim_t] + + B, V, C, H, W = images.shape + images = images.view(B*V, C, H, W) + + x = self.unet(images) # [B*4, 14, h, w] + x = self.conv(x) # [B*4, 14, h, w] + + x = x.reshape(B, 4, 14, self.opt.splat_size, self.opt.splat_size) + + ## visualize multi-view gaussian features for plotting figure + # tmp_alpha = self.opacity_act(x[0, :, 3:4]) + # tmp_img_rgb = self.rgb_act(x[0, :, 11:]) * tmp_alpha + (1 - tmp_alpha) + # tmp_img_pos = self.pos_act(x[0, :, 0:3]) * 0.5 + 0.5 + # kiui.vis.plot_image(tmp_img_rgb, save=True) + # kiui.vis.plot_image(tmp_img_pos, save=True) + + x = x.permute(0, 1, 3, 4, 2).reshape(B, -1, 14) + + pos = self.pos_act(x[..., 0:3]) # [B, N, 3] + opacity = self.opacity_act(x[..., 3:4]) + scale = self.scale_act(x[..., 4:7]) + rotation = self.rot_act(x[..., 7:11]) + rgbs = self.rgb_act(x[..., 11:]) + + gaussians = torch.cat([pos, opacity, scale, rotation, rgbs], dim=-1) # [B, N, 14] + + return gaussians + + + def forward(self, data, step_ratio=1): + # data: output of the dataloader + # return: loss + + results = {} + loss = 0 + + images = data['input'] # [B, 4, 9, h, W], input features + + # use the first view to predict gaussians + gaussians = self.forward_gaussians(images) # [B, N, 14] + + results['gaussians'] = gaussians + + # random bg for training + if self.training: + bg_color = torch.rand(3, dtype=torch.float32, device=gaussians.device) + else: + bg_color = torch.ones(3, dtype=torch.float32, device=gaussians.device) + + # use the other views for rendering and supervision + results = self.gs.render(gaussians, data['cam_view'], data['cam_view_proj'], data['cam_pos'], bg_color=bg_color) + pred_images = results['image'] # [B, V, C, output_size, output_size] + pred_alphas = results['alpha'] # [B, V, 1, output_size, output_size] + + results['images_pred'] = pred_images + results['alphas_pred'] = pred_alphas + + gt_images = data['images_output'] # [B, V, 3, output_size, output_size], ground-truth novel views + gt_masks = data['masks_output'] # [B, V, 1, output_size, output_size], ground-truth masks + + gt_images = gt_images * gt_masks + bg_color.view(1, 1, 3, 1, 1) * (1 - gt_masks) + + loss_mse = F.mse_loss(pred_images, gt_images) + F.mse_loss(pred_alphas, gt_masks) + loss = loss + loss_mse + + if self.opt.lambda_lpips > 0: + loss_lpips = self.lpips_loss( + # gt_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, + # pred_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, + # downsampled to at most 256 to reduce memory cost + F.interpolate(gt_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, (256, 256), mode='bilinear', align_corners=False), + F.interpolate(pred_images.view(-1, 3, self.opt.output_size, self.opt.output_size) * 2 - 1, (256, 256), mode='bilinear', align_corners=False), + ).mean() + results['loss_lpips'] = loss_lpips + loss = loss + self.opt.lambda_lpips * loss_lpips + + results['loss'] = loss + + # metric + with torch.no_grad(): + psnr = -10 * torch.log10(torch.mean((pred_images.detach() - gt_images) ** 2)) + results['psnr'] = psnr + + return results \ No newline at end of file diff --git a/gen_3d_modules/LGM/mvdream/mv_unet.py b/gen_3d_modules/LGM/mvdream/mv_unet.py new file mode 100644 index 0000000..2071d2a --- /dev/null +++ b/gen_3d_modules/LGM/mvdream/mv_unet.py @@ -0,0 +1,1025 @@ +import math +import numpy as np +from inspect import isfunction +from typing import Optional, Any, List + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat + +from diffusers.configuration_utils import ConfigMixin +from diffusers.models.modeling_utils import ModelMixin + +from kiui.cam import orbit_camera + +def memory_efficient_attention(q, k, v, attn_bias=None): + """ + Implements a memory-efficient attention mechanism. + + Parameters: + - q, k, v: Query, Key, Value tensors of shape (batch_size, seq_len_q, dim) + - attn_bias: Optional bias tensor of shape (seq_len_k, seq_len_v) + + Returns: + - output tensor of shape (batch_size, seq_len_q, dim) + """ + # Calculate attention scores + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(k.size(-1)) + if attn_bias is not None: + scores += attn_bias + + # Apply softmax to get attention weights + attention_weights = torch.nn.functional.softmax(scores, dim=-1) + + # Compute the weighted sum of values + output = torch.matmul(attention_weights, v) + + return output + +def get_camera( + num_frames, elevation=0, azimuth_start=0, azimuth_span=360, blender_coord=True, extra_view=False, +): + angle_gap = azimuth_span / num_frames + cameras = [] + for azimuth in np.arange(azimuth_start, azimuth_span + azimuth_start, angle_gap): + + pose = orbit_camera(elevation, azimuth, radius=1) # [4, 4] + + # opengl to blender + if blender_coord: + pose[2] *= -1 + pose[[1, 2]] = pose[[2, 1]] + + cameras.append(pose.flatten()) + + if extra_view: + cameras.append(np.zeros_like(cameras[0])) + + return torch.from_numpy(np.stack(cameras, axis=0)).float() # [num_frames, 16] + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps[:, None] * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + # import pdb; pdb.set_trace() + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def default(val, d): + if val is not None: + return val + return d() if isfunction(d) else d + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__( + self, + query_dim, + context_dim=None, + heads=8, + dim_head=64, + dropout=0.0, + ip_dim=0, + ip_weight=1, + ): + super().__init__() + + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.ip_dim = ip_dim + self.ip_weight = ip_weight + + if self.ip_dim > 0: + self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None): + q = self.to_q(x) + context = default(context, x) + + if self.ip_dim > 0: + # context： [B, 77 + 16(ip), 1024] + token_len = context.shape[1] + context_ip = context[:, -self.ip_dim :, :] + k_ip = self.to_k_ip(context_ip) + v_ip = self.to_v_ip(context_ip) + context = context[:, : (token_len - self.ip_dim), :] + + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + out = memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + if self.ip_dim > 0: + k_ip, v_ip = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (k_ip, v_ip), + ) + # actually compute the attention, what we cannot get enough of + out_ip = memory_efficient_attention( + q, k_ip, v_ip, attn_bias=None, op=self.attention_op + ) + out = out + self.ip_weight * out_ip + + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + + +class BasicTransformerBlock3D(nn.Module): + + def __init__( + self, + dim, + n_heads, + d_head, + context_dim, + dropout=0.0, + gated_ff=True, + ip_dim=0, + ip_weight=1, + ): + super().__init__() + + self.attn1 = MemoryEfficientCrossAttention( + query_dim=dim, + context_dim=None, # self-attention + heads=n_heads, + dim_head=d_head, + dropout=dropout, + ) + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = MemoryEfficientCrossAttention( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + # ip only applies to cross-attention + ip_dim=ip_dim, + ip_weight=ip_weight, + ) + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + + def forward(self, x, context=None, num_frames=1): + x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous() + x = self.attn1(self.norm1(x), context=None) + x + x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous() + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer3D(nn.Module): + + def __init__( + self, + in_channels, + n_heads, + d_head, + context_dim, # cross attention input dim + depth=1, + dropout=0.0, + ip_dim=0, + ip_weight=1, + ): + super().__init__() + + if not isinstance(context_dim, list): + context_dim = [context_dim] + + self.in_channels = in_channels + + inner_dim = n_heads * d_head + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock3D( + inner_dim, + n_heads, + d_head, + context_dim=context_dim[d], + dropout=dropout, + ip_dim=ip_dim, + ip_weight=ip_weight, + ) + for d in range(depth) + ] + ) + + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + + + def forward(self, x, context=None, num_frames=1): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i], num_frames=num_frames) + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + + return x + x_in + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q, k, v = map( + lambda t: t.reshape(b, t.shape[1], self.heads, -1) + .transpose(1, 2) + .reshape(b, self.heads, t.shape[1], -1) + .contiguous(), + (q, k, v), + ) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + ): + super().__init__() + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5) + self.proj_in = nn.Linear(embedding_dim, dim) + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, dim * ff_mult, bias=False), + nn.GELU(), + nn.Linear(dim * ff_mult, dim, bias=False), + ) + ] + ) + ) + + def forward(self, x): + latents = self.latents.repeat(x.size(0), 1, 1) + x = self.proj_in(x) + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) + + +class CondSequential(nn.Sequential): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, num_frames=1): + for layer in self: + if isinstance(layer, ResBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer3D): + x = layer(x, context, num_frames=num_frames) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd( + dims, self.channels, self.out_channels, 3, padding=padding + ) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, + self.channels, + self.out_channels, + 3, + stride=stride, + padding=padding, + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(nn.Module): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + nn.GroupNorm(32, channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + nn.GroupNorm(32, self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class MultiViewUNetModel(ModelMixin, ConfigMixin): + """ + The full multi-view UNet model with attention, timestep embedding and camera embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + :param camera_dim: dimensionality of camera input. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + transformer_depth=1, + context_dim=None, + n_embed=None, + num_attention_blocks=None, + adm_in_channels=None, + camera_dim=None, + ip_dim=0, # imagedream uses ip_dim > 0 + ip_weight=1.0, + **kwargs, + ): + super().__init__() + assert context_dim is not None + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert ( + num_head_channels != -1 + ), "Either num_heads or num_head_channels has to be set" + + if num_head_channels == -1: + assert ( + num_heads != -1 + ), "Either num_heads or num_head_channels has to be set" + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError( + "provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult" + ) + self.num_res_blocks = num_res_blocks + + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all( + map( + lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], + range(len(num_attention_blocks)), + ) + ) + print( + f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set." + ) + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + self.ip_dim = ip_dim + self.ip_weight = ip_weight + + if self.ip_dim > 0: + self.image_embed = Resampler( + dim=context_dim, + depth=4, + dim_head=64, + heads=12, + num_queries=ip_dim, # num token + embedding_dim=1280, + output_dim=context_dim, + ff_mult=4, + ) + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + nn.Linear(model_channels, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + if camera_dim is not None: + time_embed_dim = model_channels * 4 + self.camera_embed = nn.Sequential( + nn.Linear(camera_dim, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(self.num_classes, time_embed_dim) + elif self.num_classes == "continuous": + # print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + nn.Linear(adm_in_channels, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + CondSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers: List[Any] = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + + if num_attention_blocks is None or nr < num_attention_blocks[level]: + layers.append( + SpatialTransformer3D( + ch, + num_heads, + dim_head, + context_dim=context_dim, + depth=transformer_depth, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight, + ) + ) + self.input_blocks.append(CondSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + CondSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + + self.middle_block = CondSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ), + SpatialTransformer3D( + ch, + num_heads, + dim_head, + context_dim=context_dim, + depth=transformer_depth, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + + if num_attention_blocks is None or i < num_attention_blocks[level]: + layers.append( + SpatialTransformer3D( + ch, + num_heads, + dim_head, + context_dim=context_dim, + depth=transformer_depth, + ip_dim=self.ip_dim, + ip_weight=self.ip_weight, + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(CondSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + nn.GroupNorm(32, ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + nn.GroupNorm(32, ch), + conv_nd(dims, model_channels, n_embed, 1), + # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward( + self, + x, + timesteps=None, + context=None, + y=None, + camera=None, + num_frames=1, + ip=None, + ip_img=None, + **kwargs, + ): + """ + Apply the model to an input batch. + :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views). + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :param num_frames: a integer indicating number of frames for tensor reshaping. + :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views). + """ + assert ( + x.shape[0] % num_frames == 0 + ), "input batch size must be dividable by num_frames!" + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + + hs = [] + + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y is not None + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + # Add camera embeddings + if camera is not None: + emb = emb + self.camera_embed(camera) + + # imagedream variant + if self.ip_dim > 0: + x[(num_frames - 1) :: num_frames, :, :, :] = ip_img # place at [4, 9] + ip_emb = self.image_embed(ip) + context = torch.cat((context, ip_emb), 1) + + h = x + for module in self.input_blocks: + h = module(h, emb, context, num_frames=num_frames) + hs.append(h) + h = self.middle_block(h, emb, context, num_frames=num_frames) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context, num_frames=num_frames) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/gen_3d_modules/LGM/mvdream/pipeline_mvdream.py b/gen_3d_modules/LGM/mvdream/pipeline_mvdream.py new file mode 100644 index 0000000..ee59371 --- /dev/null +++ b/gen_3d_modules/LGM/mvdream/pipeline_mvdream.py @@ -0,0 +1,566 @@ +import torch +import torch.nn.functional as F +import inspect +import numpy as np +from typing import Callable, List, Optional, Union +from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModel, CLIPImageProcessor +from diffusers import AutoencoderKL, DiffusionPipeline +from diffusers.utils import ( + deprecate, + is_accelerate_available, + is_accelerate_version, + logging, +) +from diffusers.configuration_utils import FrozenDict +from diffusers.schedulers import DDIMScheduler +from diffusers.utils.torch_utils import randn_tensor + +import comfy.utils + +from LGM.mvdream.mv_unet import MultiViewUNetModel, get_camera + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class MVDreamPipeline(DiffusionPipeline): + + _optional_components = ["feature_extractor", "image_encoder"] + + def __init__( + self, + vae: AutoencoderKL, + unet: MultiViewUNetModel, + tokenizer: CLIPTokenizer, + text_encoder: CLIPTextModel, + scheduler: DDIMScheduler, + # imagedream variant + feature_extractor: CLIPImageProcessor, + image_encoder: CLIPVisionModel, + requires_safety_checker: bool = False, + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: # type: ignore + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " # type: ignore + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate( + "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: # type: ignore + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate( + "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + unet=unet, + scheduler=scheduler, + tokenizer=tokenizer, + text_encoder=text_encoder, + feature_extractor=feature_extractor, + image_encoder=image_encoder, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. + + When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in + several steps. This is useful to save a large amount of memory and to allow the processing of larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + Note that offloading happens on a submodule basis. Memory savings are higher than with + `enable_model_cpu_offload`, but performance is lower. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): + from accelerate import cpu_offload + else: + raise ImportError( + "`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher" + ) + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + def enable_model_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError( + "`enable_model_offload` requires `accelerate v0.17.0` or higher." + ) + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + hook = None + for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]: + _, hook = cpu_offload_with_hook( + cpu_offloaded_model, device, prev_module_hook=hook + ) + + # We'll offload the last model manually. + self.final_offload_hook = hook + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance: bool, + negative_prompt=None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + raise ValueError( + f"`prompt` should be either a string or a list of strings, but got {type(prompt)}." + ) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + prompt_embeds = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view( + bs_embed * num_images_per_prompt, seq_len, -1 + ) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to( + dtype=self.text_encoder.dtype, device=device + ) + + negative_prompt_embeds = negative_prompt_embeds.repeat( + 1, num_images_per_prompt, 1 + ) + negative_prompt_embeds = negative_prompt_embeds.view( + batch_size * num_images_per_prompt, seq_len, -1 + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + return prompt_embeds + + def decode_latents(self, latents): + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents).sample + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_channels_latents, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def encode_image(self, image, device, num_images_per_prompt): + dtype = next(self.image_encoder.parameters()).dtype + + if image.dtype == np.float32: + image = (image * 255).astype(np.uint8) + + image = self.feature_extractor(image, return_tensors="pt").pixel_values + image = image.to(device=device, dtype=dtype) + + image_embeds = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] + image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) + + return torch.zeros_like(image_embeds), image_embeds + + def encode_image_latents(self, image, device, num_images_per_prompt): + + dtype = next(self.image_encoder.parameters()).dtype + + image = torch.from_numpy(image).unsqueeze(0).permute(0, 3, 1, 2).to(device=device) # [1, 3, H, W] + image = 2 * image - 1 + image = F.interpolate(image, (256, 256), mode='bilinear', align_corners=False) + image = image.to(dtype=dtype) + + posterior = self.vae.encode(image).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor # [B, C, H, W] + latents = latents.repeat_interleave(num_images_per_prompt, dim=0) + + return torch.zeros_like(latents), latents + + @torch.no_grad() + def __call__( + self, + prompt: str = "", + image: Optional[np.ndarray] = None, + height: int = 256, + width: int = 256, + elevation: float = 0, + num_inference_steps: int = 50, + guidance_scale: float = 7.0, + negative_prompt: str = "", + num_images_per_prompt: int = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + output_type: Optional[str] = "numpy", # pil, numpy, latents + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + num_frames: int = 4, + device=torch.device("cuda:0"), + ): + self.unet = self.unet.to(device=device) + self.vae = self.vae.to(device=device) + self.text_encoder = self.text_encoder.to(device=device) + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # imagedream variant + if image is not None: + assert isinstance(image, np.ndarray) and image.dtype == np.float32 + self.image_encoder = self.image_encoder.to(device=device) + image_embeds_neg, image_embeds_pos = self.encode_image(image, device, num_images_per_prompt) + image_latents_neg, image_latents_pos = self.encode_image_latents(image, device, num_images_per_prompt) + + _prompt_embeds = self._encode_prompt( + prompt=prompt, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + ) # type: ignore + prompt_embeds_neg, prompt_embeds_pos = _prompt_embeds.chunk(2) + + # Prepare latent variables + actual_num_frames = num_frames if image is None else num_frames + 1 + latents: torch.Tensor = self.prepare_latents( + actual_num_frames * num_images_per_prompt, + 4, + height, + width, + prompt_embeds_pos.dtype, + device, + generator, + None, + ) + + if image is not None: + camera = get_camera(num_frames, elevation=elevation, extra_view=True).to(dtype=latents.dtype, device=device) + else: + camera = get_camera(num_frames, elevation=elevation, extra_view=False).to(dtype=latents.dtype, device=device) + camera = camera.repeat_interleave(num_images_per_prompt, dim=0) + + # Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Denoising loop + timesteps_len = len(timesteps) + comfy_pbar = comfy.utils.ProgressBar(timesteps_len) + + num_warmup_steps = timesteps_len - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + multiplier = 2 if do_classifier_free_guidance else 1 + latent_model_input = torch.cat([latents] * multiplier) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + unet_inputs = { + 'x': latent_model_input, + 'timesteps': torch.tensor([t] * actual_num_frames * multiplier, dtype=latent_model_input.dtype, device=device), + 'context': torch.cat([prompt_embeds_neg] * actual_num_frames + [prompt_embeds_pos] * actual_num_frames), + 'num_frames': actual_num_frames, + 'camera': torch.cat([camera] * multiplier), + } + + if image is not None: + unet_inputs['ip'] = torch.cat([image_embeds_neg] * actual_num_frames + [image_embeds_pos] * actual_num_frames) + unet_inputs['ip_img'] = torch.cat([image_latents_neg] + [image_latents_pos]) # no repeat + + # predict the noise residual + noise_pred = self.unet.forward(**unet_inputs) + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents: torch.Tensor = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs, return_dict=False + )[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) # type: ignore + + comfy_pbar.update_absolute(i + 1) + + # Post-processing + if output_type == "latent": + image = latents + elif output_type == "pil": + image = self.decode_latents(latents) + image = self.numpy_to_pil(image) + else: # numpy + image = self.decode_latents(latents) + + # Offload last model to CPU + if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: + self.final_offload_hook.offload() + + return image \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/__init__.py b/gen_3d_modules/TriplaneGaussian/__init__.py new file mode 100644 index 0000000..889caab --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/__init__.py @@ -0,0 +1,11 @@ +import importlib +from .utils.typing import * + +def find(cls_string) -> Type: + cls_full_name = cls_string.split(".") + module_string = ".".join(cls_full_name[:-1]) + cls_name = cls_full_name[-1] + print(module_string) + module = importlib.import_module(module_string, package=__name__) + cls = getattr(module, cls_name) + return cls \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/data.py b/gen_3d_modules/TriplaneGaussian/data.py new file mode 100644 index 0000000..153dd41 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/data.py @@ -0,0 +1,237 @@ +import json +import math +from dataclasses import dataclass, field + +import os +import imageio +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +from torch.utils.data import Dataset + +from .utils.config import parse_structured +from .utils.ops import get_intrinsic_from_fov, get_ray_directions, get_rays +from .utils.typing import * + +@dataclass +class CustomImageDataModuleConfig: + background_color: Tuple[float, float, float] = field( + default_factory=lambda: (1.0, 1.0, 1.0) + ) + + relative_pose: bool = False + cond_height: int = 512 + cond_width: int = 512 + cond_camera_distance: float = 1.6 + cond_fovy_deg: float = 40.0 + cond_elevation_deg: float = 0.0 + cond_azimuth_deg: float = 0.0 + num_workers: int = 16 + + eval_height: int = 512 + eval_width: int = 512 + eval_batch_size: int = 1 + eval_elevation_deg: float = 0.0 + eval_camera_distance: float = 1.6 + eval_fovy_deg: float = 40.0 + n_test_views: int = 120 + num_views_output: int = 120 + only_3dgs: bool = False + +class CustomImageOrbitDataset(Dataset): + def __init__(self, reference_image, reference_mask, cfg: Any) -> None: + super().__init__() + self.cfg: CustomImageDataModuleConfig = parse_structured(CustomImageDataModuleConfig, cfg) + + self.n_views = self.cfg.n_test_views + assert self.n_views % self.cfg.num_views_output == 0 + + self.all_images = reference_image + self.all_masks = reference_mask + + azimuth_deg: Float[Tensor, "B"] = torch.linspace(0, 360.0, self.n_views + 1)[ + : self.n_views + ] + elevation_deg: Float[Tensor, "B"] = torch.full_like( + azimuth_deg, self.cfg.eval_elevation_deg + ) + camera_distances: Float[Tensor, "B"] = torch.full_like( + elevation_deg, self.cfg.eval_camera_distance + ) + + elevation = elevation_deg * math.pi / 180 + azimuth = azimuth_deg * math.pi / 180 + + # convert spherical coordinates to cartesian coordinates + # right hand coordinate system, x back, y right, z up + # elevation in (-90, 90), azimuth from +x to +y in (-180, 180) + camera_positions: Float[Tensor, "B 3"] = torch.stack( + [ + camera_distances * torch.cos(elevation) * torch.cos(azimuth), + camera_distances * torch.cos(elevation) * torch.sin(azimuth), + camera_distances * torch.sin(elevation), + ], + dim=-1, + ) + + # default scene center at origin + center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions) + # default camera up direction as +z + up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[ + None, : + ].repeat(self.n_views, 1) + + fovy_deg: Float[Tensor, "B"] = torch.full_like( + elevation_deg, self.cfg.eval_fovy_deg + ) + fovy = fovy_deg * math.pi / 180 + + lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1) + right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1) + up = F.normalize(torch.cross(right, lookat), dim=-1) + c2w3x4: Float[Tensor, "B 3 4"] = torch.cat( + [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]], + dim=-1, + ) + c2w: Float[Tensor, "B 4 4"] = torch.cat( + [c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1 + ) + c2w[:, 3, 3] = 1.0 + + # get directions by dividing directions_unit_focal by focal length + focal_length: Float[Tensor, "B"] = ( + 0.5 * self.cfg.eval_height / torch.tan(0.5 * fovy) + ) + directions_unit_focal = get_ray_directions( + H=self.cfg.eval_height, + W=self.cfg.eval_width, + focal=1.0, + ) + directions: Float[Tensor, "B H W 3"] = directions_unit_focal[ + None, :, :, : + ].repeat(self.n_views, 1, 1, 1) + directions[:, :, :, :2] = ( + directions[:, :, :, :2] / focal_length[:, None, None, None] + ) + # must use normalize=True to normalize directions here + rays_o, rays_d = get_rays(directions, c2w, keepdim=True) + + intrinsic: Float[Tensor, "B 3 3"] = get_intrinsic_from_fov( + self.cfg.eval_fovy_deg * math.pi / 180, + H=self.cfg.eval_height, + W=self.cfg.eval_width, + bs=self.n_views, + ) + intrinsic_normed: Float[Tensor, "B 3 3"] = intrinsic.clone() + intrinsic_normed[..., 0, 2] /= self.cfg.eval_width + intrinsic_normed[..., 1, 2] /= self.cfg.eval_height + intrinsic_normed[..., 0, 0] /= self.cfg.eval_width + intrinsic_normed[..., 1, 1] /= self.cfg.eval_height + + self.rays_o, self.rays_d = rays_o, rays_d + self.intrinsic = intrinsic + self.intrinsic_normed = intrinsic_normed + self.c2w = c2w + self.camera_positions = camera_positions + + self.background_color = torch.as_tensor(self.cfg.background_color) + + # condition + self.intrinsic_cond = get_intrinsic_from_fov( + np.deg2rad(self.cfg.cond_fovy_deg), + H=self.cfg.cond_height, + W=self.cfg.cond_width, + ) + self.intrinsic_normed_cond = self.intrinsic_cond.clone() + self.intrinsic_normed_cond[..., 0, 2] /= self.cfg.cond_width + self.intrinsic_normed_cond[..., 1, 2] /= self.cfg.cond_height + self.intrinsic_normed_cond[..., 0, 0] /= self.cfg.cond_width + self.intrinsic_normed_cond[..., 1, 1] /= self.cfg.cond_height + + + if self.cfg.relative_pose: + self.c2w_cond = torch.as_tensor( + [ + [0, 0, 1, self.cfg.cond_camera_distance], + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 0, 1], + ] + ).float() + else: + cond_elevation = self.cfg.cond_elevation_deg * math.pi / 180 + cond_azimuth = self.cfg.cond_azimuth_deg * math.pi / 180 + cond_camera_position: Float[Tensor, "3"] = torch.as_tensor( + [ + self.cfg.cond_camera_distance * np.cos(cond_elevation) * np.cos(cond_azimuth), + self.cfg.cond_camera_distance * np.cos(cond_elevation) * np.sin(cond_azimuth), + self.cfg.cond_camera_distance * np.sin(cond_elevation), + ], dtype=torch.float32 + ) + + cond_center: Float[Tensor, "3"] = torch.zeros_like(cond_camera_position) + cond_up: Float[Tensor, "3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32) + cond_lookat: Float[Tensor, "3"] = F.normalize(cond_center - cond_camera_position, dim=-1) + cond_right: Float[Tensor, "3"] = F.normalize(torch.cross(cond_lookat, cond_up), dim=-1) + cond_up = F.normalize(torch.cross(cond_right, cond_lookat), dim=-1) + cond_c2w3x4: Float[Tensor, "3 4"] = torch.cat( + [torch.stack([cond_right, cond_up, -cond_lookat], dim=-1), cond_camera_position[:, None]], + dim=-1, + ) + cond_c2w: Float[Tensor, "4 4"] = torch.cat( + [cond_c2w3x4, torch.zeros_like(cond_c2w3x4[:1])], dim=0 + ) + cond_c2w[3, 3] = 1.0 + self.c2w_cond = cond_c2w + + def __len__(self): + if self.cfg.only_3dgs: + return len(self.all_images) + else: + return len(self.all_images) * self.n_views // self.cfg.num_views_output + + def __getitem__(self, index): + if self.cfg.only_3dgs: + scene_index = index + view_index = [0] + else: + scene_index = index * self.cfg.num_views_output // self.n_views + view_start = index % (self.n_views // self.cfg.num_views_output) + view_index = list(range(self.n_views))[view_start * self.cfg.num_views_output : + (view_start + 1) * self.cfg.num_views_output] + + mask_cond: Float[Tensor, "Hc Wc 1"] = self.resize_image(self.all_masks[scene_index].unsqueeze(2)) + rgb_cond: Float[Tensor, "Hc Wc 3"] = self.resize_image(self.all_images[scene_index]) * mask_cond + self.background_color[None, None, :] * (1 - mask_cond) + + out = { + "rgb_cond": rgb_cond, + "c2w_cond": self.c2w_cond.unsqueeze(0), + "mask_cond": mask_cond, + "intrinsic_cond": self.intrinsic_cond.unsqueeze(0), + "intrinsic_normed_cond": self.intrinsic_normed_cond.unsqueeze(0), + "view_index": torch.as_tensor(view_index), + "rays_o": self.rays_o[view_index], + "rays_d": self.rays_d[view_index], + "intrinsic": self.intrinsic[view_index], + "intrinsic_normed": self.intrinsic_normed[view_index], + "c2w": self.c2w[view_index], + "camera_positions": self.camera_positions[view_index], + } + out["c2w"][..., :3, 1:3] *= -1 + out["c2w_cond"][..., :3, 1:3] *= -1 + out["index"] = torch.as_tensor(scene_index) + out["background_color"] = self.background_color + instance_id = str(scene_index) + out["instance_id"] = instance_id + return out + + def resize_image(self, img): + img_new = img.permute(2, 0, 1).unsqueeze(0) + img_new = F.interpolate(img_new, (self.cfg.cond_width, self.cfg.cond_height), mode="bilinear", align_corners=False).contiguous() + return img_new.permute(0, 2, 3, 1) + + def collate(self, batch): + batch = torch.utils.data.default_collate(batch) + batch.update({"height": self.cfg.eval_height, "width": self.cfg.eval_width}) + return batch \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/__init__.py b/gen_3d_modules/TriplaneGaussian/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/TriplaneGaussian/models/image_feature.py b/gen_3d_modules/TriplaneGaussian/models/image_feature.py new file mode 100644 index 0000000..88d3424 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/image_feature.py @@ -0,0 +1,48 @@ +from dataclasses import dataclass +import torch +import torch.nn.functional as F +from einops import rearrange + +from ..utils.base import BaseModule +from ..utils.ops import compute_distance_transform +from ..utils.typing import * + +class ImageFeature(BaseModule): + @dataclass + class Config(BaseModule.Config): + use_rgb: bool = True + use_feature: bool = True + use_mask: bool = True + feature_dim: int = 128 + out_dim: int = 133 + backbone: str = "default" + freeze_backbone_params: bool = True + + cfg: Config + + def forward(self, rgb, mask=None, feature=None): + B, Nv, H, W = rgb.shape[:4] + rgb = rearrange(rgb, "B Nv H W C -> (B Nv) C H W") + if mask is not None: + mask = rearrange(mask, "B Nv H W C -> (B Nv) C H W") + + assert feature is not None + # reshape dino tokens to image-like size + feature = rearrange(feature, "B (Nv Nt) C -> (B Nv) Nt C", Nv=Nv) + feature = feature[:, 1:].reshape(B * Nv, H // 14, W // 14, -1).permute(0, 3, 1, 2).contiguous() + feature = F.interpolate(feature, size=(H, W), mode='bilinear', align_corners=False) + + if mask is not None and mask.is_floating_point(): + mask = mask > 0.5 + + image_features = [] + if self.cfg.use_rgb: + image_features.append(rgb) + if self.cfg.use_feature: + image_features.append(feature) + if self.cfg.use_mask: + image_features += [mask, compute_distance_transform(mask)] + + # detach features, occur error when with grad + image_features = torch.cat(image_features, dim=1)#.detach() + return rearrange(image_features, "(B Nv) C H W -> B Nv C H W", B=B, Nv=Nv).squeeze(1) \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/networks.py b/gen_3d_modules/TriplaneGaussian/models/networks.py new file mode 100644 index 0000000..082671e --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/networks.py @@ -0,0 +1,204 @@ +from dataclasses import dataclass + +import torch +import torch.nn as nn +from einops import rearrange +import numpy as np + +from ..utils.base import BaseModule +from ..utils.ops import get_activation +from ..utils.typing import * + +class PointOutLayer(BaseModule): + @dataclass + class Config(BaseModule.Config): + in_channels: int = 1024 + out_channels: int = 3 + cfg: Config + def configure(self) -> None: + super().configure() + self.point_layer = nn.Linear(self.cfg.in_channels, self.cfg.out_channels) + self.initialize_weights() + + def initialize_weights(self): + nn.init.constant_(self.point_layer.weight, 0) + nn.init.constant_(self.point_layer.bias, 0) + + def forward(self, x): + return self.point_layer(x) + +class TriplaneUpsampleNetwork(BaseModule): + @dataclass + class Config(BaseModule.Config): + in_channels: int = 1024 + out_channels: int = 80 + + cfg: Config + + def configure(self) -> None: + super().configure() + self.upsample = nn.ConvTranspose2d( + self.cfg.in_channels, self.cfg.out_channels, kernel_size=2, stride=2 + ) + + def forward( + self, triplanes: Float[Tensor, "B 3 Ci Hp Wp"] + ) -> Float[Tensor, "B 3 Co Hp2 Wp2"]: + triplanes_up = rearrange( + self.upsample( + rearrange(triplanes, "B Np Ci Hp Wp -> (B Np) Ci Hp Wp", Np=3) + ), + "(B Np) Co Hp Wp -> B Np Co Hp Wp", + Np=3, + ) + return triplanes_up + + +class MLP(nn.Module): + def __init__( + self, + dim_in: int, + dim_out: int, + n_neurons: int, + n_hidden_layers: int, + activation: str = "relu", + output_activation: Optional[str] = None, + bias: bool = True, + ): + super().__init__() + layers = [ + self.make_linear( + dim_in, n_neurons, is_first=True, is_last=False, bias=bias + ), + self.make_activation(activation), + ] + for i in range(n_hidden_layers - 1): + layers += [ + self.make_linear( + n_neurons, n_neurons, is_first=False, is_last=False, bias=bias + ), + self.make_activation(activation), + ] + layers += [ + self.make_linear( + n_neurons, dim_out, is_first=False, is_last=True, bias=bias + ) + ] + self.layers = nn.Sequential(*layers) + self.output_activation = get_activation(output_activation) + + def forward(self, x): + x = self.layers(x) + x = self.output_activation(x) + return x + + def make_linear(self, dim_in, dim_out, is_first, is_last, bias=True): + layer = nn.Linear(dim_in, dim_out, bias=bias) + return layer + + def make_activation(self, activation): + if activation == "relu": + return nn.ReLU(inplace=True) + elif activation == "silu": + return nn.SiLU(inplace=True) + else: + raise NotImplementedError + +class GSProjection(nn.Module): + def __init__(self, + in_channels: int = 80, + sh_degree: int = 3, + init_scaling: float = -5.0, + init_density: float = 0.1) -> None: + super().__init__() + + self.out_keys = GS_KEYS + ["shs"] + self.out_channels = GS_CHANNELS + [(sh_degree + 1) ** 2 * 3] + + self.out_layers = nn.ModuleList() + for key, ch in zip(self.out_keys, self.out_channels): + layer = nn.Linear(in_channels, ch) + # initialize + nn.init.constant_(layer.weight, 0) + nn.init.constant_(layer.bias, 0) + + if key == "scaling": + nn.init.constant_(layer.bias, init_scaling) + elif key == "rotation": + nn.init.constant_(layer.bias, 0) + nn.init.constant_(layer.bias[0], 1.0) + elif key == "opacity": + inverse_sigmoid = lambda x: np.log(x / (1 - x)) + nn.init.constant_(layer.bias, inverse_sigmoid(init_density)) + + self.out_layers.append(layer) + + def forward(self, x): + ret = [] + for k, layer in zip(self.out_keys, self.out_layers): + v = layer(x) + if k == "rotation": + v = torch.nn.functional.normalize(v) + elif k == "scaling": + v = torch.exp(v) + # v = v.detach() # FIXME: for DEBUG + elif k == "opacity": + v = torch.sigmoid(v) + # elif k == "shs": + # v = torch.reshape(v, (v.shape[0], -1, 3)) + ret.append(v) + ret = torch.cat(ret, dim=-1) + return ret + +def get_encoding(n_input_dims: int, config) -> nn.Module: + raise NotImplementedError + + +def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module: + raise NotImplementedError + + +# Resnet Blocks for pointnet +class ResnetBlockFC(nn.Module): + ''' Fully connected ResNet Block class. + + Args: + size_in (int): input dimension + size_out (int): output dimension + size_h (int): hidden dimension + ''' + + def __init__(self, size_in, size_out=None, size_h=None): + super().__init__() + # Attributes + if size_out is None: + size_out = size_in + + if size_h is None: + size_h = min(size_in, size_out) + + self.size_in = size_in + self.size_h = size_h + self.size_out = size_out + # Submodules + self.fc_0 = nn.Linear(size_in, size_h) + self.fc_1 = nn.Linear(size_h, size_out) + self.actvn = nn.ReLU() + + if size_in == size_out: + self.shortcut = None + else: + self.shortcut = nn.Linear(size_in, size_out, bias=False) + # Initialization + nn.init.zeros_(self.fc_1.weight) + + def forward(self, x): + net = self.fc_0(self.actvn(x)) + dx = self.fc_1(self.actvn(net)) + + if self.shortcut is not None: + x_s = self.shortcut(x) + else: + x_s = x + + return x_s + dx \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/pointclouds/LICENSE_POINTNET b/gen_3d_modules/TriplaneGaussian/models/pointclouds/LICENSE_POINTNET new file mode 100644 index 0000000..5358ea9 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/pointclouds/LICENSE_POINTNET @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, Andreas Geiger + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/pointclouds/pointnet.py b/gen_3d_modules/TriplaneGaussian/models/pointclouds/pointnet.py new file mode 100644 index 0000000..bc77ce1 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/pointclouds/pointnet.py @@ -0,0 +1,121 @@ +# modified from https://github.com/autonomousvision/convolutional_occupancy_networks/blob/master/src/encoder/pointnet.py +from dataclasses import dataclass +import torch +import torch.nn as nn +from torch_scatter import scatter_mean, scatter_max + +from TriplaneGaussian.utils.base import BaseModule +from TriplaneGaussian.models.networks import ResnetBlockFC +from TriplaneGaussian.utils.ops import scale_tensor + +class LocalPoolPointnet(BaseModule): + ''' PointNet-based encoder network with ResNet blocks for each point. + Number of input points are fixed. + + Args: + c_dim (int): dimension of latent code c + dim (int): input points dimension + hidden_dim (int): hidden dimension of the network + scatter_type (str): feature aggregation when doing local pooling + plane_resolution (int): defined resolution for plane feature + padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55] + n_blocks (int): number of blocks ResNetBlockFC layers + ''' + + @dataclass + class Config(BaseModule.Config): + input_channels: int = 3 + c_dim: int = 128 + hidden_dim: int = 128 + scatter_type: str = "max" + plane_size: int = 32 + n_blocks: int = 5 + radius: float = 1. + + cfg: Config + + def configure(self) -> None: + super().configure() + self.fc_pos = nn.Linear(self.cfg.input_channels, 2 * self.cfg.hidden_dim) + self.blocks = nn.ModuleList([ + ResnetBlockFC(2 * self.cfg.hidden_dim, self.cfg.hidden_dim) for i in range(self.cfg.n_blocks) + ]) + self.fc_c = nn.Linear(self.cfg.hidden_dim, self.cfg.c_dim) + + self.actvn = nn.ReLU() + + if self.cfg.scatter_type == 'max': + self.scatter = scatter_max + elif self.cfg.scatter_type == 'mean': + self.scatter = scatter_mean + else: + raise ValueError('incorrect scatter type') + + + def generate_plane_features(self, index, c): + # acquire indices of features in plane + # xy = normalize_coordinate(p.clone(), plane=plane, padding=self.padding) # normalize to the range of (0, 1) + # index = self.coordinate2index(x, self.cfg.plane_size) + + # scatter plane features from points + fea_plane = c.new_zeros(index.shape[0], self.cfg.c_dim, self.cfg.plane_size ** 2) + c = c.permute(0, 2, 1) # B x 512 x T + fea_plane = scatter_mean(c, index, out=fea_plane) # B x 512 x reso^2 + fea_plane = fea_plane.reshape(index.shape[0], self.cfg.c_dim, self.cfg.plane_size, self.cfg.plane_size) # sparce matrix (B x 512 x reso x reso) + + return fea_plane + + def pool_local(self, xy, index, c): + bs, fea_dim = c.shape[0], c.shape[2] + keys = xy.keys() + + c_out = 0 + for key in keys: + # scatter plane features from points + fea = self.scatter(c.permute(0, 2, 1), index[key], dim_size=self.cfg.plane_size ** 2) + if self.scatter == scatter_max: + fea = fea[0] + # gather feature back to points + fea = fea.gather(dim=2, index=index[key].expand(-1, fea_dim, -1)) + c_out += fea + return c_out.permute(0, 2, 1) + + def coordinate2index(self, x): + x = (x * self.cfg.plane_size).long() + index = x[..., 0] + self.cfg.plane_size * x[..., 1] + assert index.max() < self.cfg.plane_size ** 2 + return index[:, None, :] + + def forward(self, p): + batch_size, T, D = p.shape + + # acquire the index for each point + coord = {} + index = {} + + position = torch.clamp(p[..., :3], -self.cfg.radius + 1e-6, self.cfg.radius - 1e-6) + position_norm = scale_tensor(position, (-self.cfg.radius, self.cfg.radius), (0, 1)) + coord["xy"] = position_norm[..., [0, 1]] + coord["xz"] = position_norm[..., [0, 2]] + coord["yz"] = position_norm[..., [1, 2]] + index["xy"] = self.coordinate2index(coord["xy"]) + index["xz"] = self.coordinate2index(coord["xz"]) + index["yz"] = self.coordinate2index(coord["yz"]) + + net = self.fc_pos(p) + + net = self.blocks[0](net) + for block in self.blocks[1:]: + pooled = self.pool_local(coord, index, net) + net = torch.cat([net, pooled], dim=2) + net = block(net) + + c = self.fc_c(net) + + features = torch.stack([ + self.generate_plane_features(index["xy"], c), + self.generate_plane_features(index["xz"], c), + self.generate_plane_features(index["yz"], c) + ], dim=1) + + return features diff --git a/gen_3d_modules/TriplaneGaussian/models/pointclouds/simplepoint.py b/gen_3d_modules/TriplaneGaussian/models/pointclouds/simplepoint.py new file mode 100644 index 0000000..72c8a26 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/pointclouds/simplepoint.py @@ -0,0 +1,110 @@ +from dataclasses import dataclass, field +import torch +from einops import rearrange + +import TriplaneGaussian +from TriplaneGaussian.utils.base import BaseModule +from TriplaneGaussian.utils.typing import * + +class SimplePointGenerator(BaseModule): + @dataclass + class Config(BaseModule.Config): + camera_embedder_cls: str = "" + camera_embedder: dict = field(default_factory=dict) + + image_tokenizer_cls: str = "" + image_tokenizer: dict = field(default_factory=dict) + + tokenizer_cls: str = "" + tokenizer: dict = field(default_factory=dict) + + backbone_cls: str = "" + backbone: dict = field(default_factory=dict) + + post_processor_cls: str = "" + post_processor: dict = field(default_factory=dict) + + pointcloud_upsampling_cls: str = "" + pointcloud_upsampling: dict = field(default_factory=dict) + + flip_c2w_cond: bool = True + + cfg: Config + + def configure(self) -> None: + super().configure() + + self.image_tokenizer = TriplaneGaussian.find(self.cfg.image_tokenizer_cls)( + self.cfg.image_tokenizer + ) + + assert self.cfg.camera_embedder_cls == 'TriplaneGaussian.models.networks.MLP' + weights = self.cfg.camera_embedder.pop("weights") if "weights" in self.cfg.camera_embedder else None + self.camera_embedder = TriplaneGaussian.find(self.cfg.camera_embedder_cls)(**self.cfg.camera_embedder) + if weights: + from TriplaneGaussian.utils.misc import load_module_weights + weights_path, module_name = weights.split(":") + state_dict = load_module_weights( + weights_path, module_name=module_name, map_location="cpu" + ) + self.camera_embedder.load_state_dict(state_dict) + + self.tokenizer = TriplaneGaussian.find(self.cfg.tokenizer_cls)(self.cfg.tokenizer) + + self.backbone = TriplaneGaussian.find(self.cfg.backbone_cls)(self.cfg.backbone) + + self.post_processor = TriplaneGaussian.find(self.cfg.post_processor_cls)( + self.cfg.post_processor + ) + + self.pointcloud_upsampling = TriplaneGaussian.find(self.cfg.pointcloud_upsampling_cls)(self.cfg.pointcloud_upsampling) + + def forward(self, batch, encoder_hidden_states=None, **kwargs): + batch_size, n_input_views = batch["rgb_cond"].shape[:2] + + if encoder_hidden_states is None: + # Camera modulation + c2w_cond = batch["c2w_cond"].clone() + if self.cfg.flip_c2w_cond: + c2w_cond[..., :3, 1:3] *= -1 + camera_extri = c2w_cond.view(*c2w_cond.shape[:-2], -1) + camera_intri = batch["intrinsic_normed_cond"].view( + *batch["intrinsic_normed_cond"].shape[:-2], -1) + camera_feats = torch.cat([camera_intri, camera_extri], dim=-1) + # camera_feats = rearrange(camera_feats, 'B Nv C -> (B Nv) C') + + camera_feats = self.camera_embedder(camera_feats) + + encoder_hidden_states: Float[Tensor, "B Cit Nit"] = self.image_tokenizer( + rearrange(batch["rgb_cond"], 'B Nv H W C -> B Nv C H W'), + modulation_cond=camera_feats, + ) + encoder_hidden_states = rearrange( + encoder_hidden_states, 'B Nv C Nt -> B (Nv Nt) C', Nv=n_input_views) + + tokens: Float[Tensor, "B Ct Nt"] = self.tokenizer(batch_size) + + tokens = self.backbone( + tokens, + encoder_hidden_states=encoder_hidden_states, + modulation_cond=None, + ) + pointclouds = self.post_processor(self.tokenizer.detokenize(tokens)) + + upsampling_input = { + "input_image_tokens": encoder_hidden_states.permute(0, 2, 1), + "input_image_tokens_global": encoder_hidden_states[:, :1], + "c2w_cond": c2w_cond, + "rgb_cond": batch["rgb_cond"], + "intrinsic_cond": batch["intrinsic_cond"], + "intrinsic_normed_cond": batch["intrinsic_normed_cond"], + "points": pointclouds.float() + } + up_results = self.pointcloud_upsampling(upsampling_input) + up_results.insert(0, pointclouds) + pointclouds = up_results[-1] + out = { + "points": pointclouds, + "up_results": up_results + } + return out \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/renderer.py b/gen_3d_modules/TriplaneGaussian/models/renderer.py new file mode 100644 index 0000000..256b025 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/renderer.py @@ -0,0 +1,408 @@ +from dataclasses import dataclass, field +from collections import defaultdict +from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianRasterizer +from plyfile import PlyData, PlyElement +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import math + +from ..utils.typing import * +from ..utils.base import BaseModule +from ..utils.ops import trunc_exp +from ..utils.ops import scale_tensor +from .networks import MLP +from einops import rearrange, reduce + +from mesh_processer.mesh_utils import construct_list_of_gs_attributes, write_gs_ply + +inverse_sigmoid = lambda x: np.log(x / (1 - x)) + +def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0): + Rt = np.zeros((4, 4)) + Rt[:3, :3] = R.transpose() + Rt[:3, 3] = t + Rt[3, 3] = 1.0 + + C2W = np.linalg.inv(Rt) + cam_center = C2W[:3, 3] + cam_center = (cam_center + translate) * scale + C2W[:3, 3] = cam_center + Rt = np.linalg.inv(C2W) + return np.float32(Rt) + +def getProjectionMatrix(znear, zfar, fovX, fovY): + tanHalfFovY = math.tan((fovY / 2)) + tanHalfFovX = math.tan((fovX / 2)) + + top = tanHalfFovY * znear + bottom = -top + right = tanHalfFovX * znear + left = -right + + P = torch.zeros(4, 4) + + z_sign = 1.0 + + P[0, 0] = 2.0 * znear / (right - left) + P[1, 1] = 2.0 * znear / (top - bottom) + P[0, 2] = (right + left) / (right - left) + P[1, 2] = (top + bottom) / (top - bottom) + P[3, 2] = z_sign + P[2, 2] = z_sign * zfar / (zfar - znear) + P[2, 3] = -(zfar * znear) / (zfar - znear) + return P + +def intrinsic_to_fov(intrinsic, w, h): + fx, fy = intrinsic[0, 0], intrinsic[1, 1] + fov_x = 2 * torch.arctan2(w, 2 * fx) + fov_y = 2 * torch.arctan2(h, 2 * fy) + return fov_x, fov_y + + +class Camera: + def __init__(self, w2c, intrinsic, FoVx, FoVy, height, width, trans=np.array([0.0, 0.0, 0.0]), scale=1.0) -> None: + self.FoVx = FoVx + self.FoVy = FoVy + self.height = height + self.width = width + self.world_view_transform = w2c.transpose(0, 1) + + self.zfar = 100.0 + self.znear = 0.01 + + self.trans = trans + self.scale = scale + + self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).to(w2c.device) + self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0) + self.camera_center = self.world_view_transform.inverse()[3, :3] + + @staticmethod + def from_c2w(c2w, intrinsic, height, width): + w2c = torch.inverse(c2w) + FoVx, FoVy = intrinsic_to_fov(intrinsic, w=torch.tensor(width, device=w2c.device), h=torch.tensor(height, device=w2c.device)) + return Camera(w2c=w2c, intrinsic=intrinsic, FoVx=FoVx, FoVy=FoVy, height=height, width=width) + +class GaussianModel(NamedTuple): + xyz: Tensor + opacity: Tensor + rotation: Tensor + scaling: Tensor + shs: Tensor + + def to_ply(self): + + xyz = self.xyz.detach().cpu().numpy() + normals = np.zeros_like(xyz) + features_dc = self.shs[:, :1] + features_rest = self.shs[:, 1:] + f_dc = features_dc.detach().flatten(start_dim=1).contiguous().cpu().numpy() + f_rest = features_rest.detach().flatten(start_dim=1).contiguous().cpu().numpy() + opacities = inverse_sigmoid(torch.clamp(self.opacity, 1e-3, 1 - 1e-3).detach().cpu().numpy()) + scale = np.log(self.scaling.detach().cpu().numpy()) + rotation = self.rotation.detach().cpu().numpy() + + return write_gs_ply(xyz, normals, f_dc, f_rest, opacities, scale, rotation, construct_list_of_gs_attributes(features_dc, features_rest, self.scaling, self.rotation)) + +class GSLayer(BaseModule): + @dataclass + class Config(BaseModule.Config): + in_channels: int = 128 + feature_channels: dict = field(default_factory=dict) + xyz_offset: bool = True + restrict_offset: bool = False + use_rgb: bool = False + clip_scaling: Optional[float] = None + init_scaling: float = -5.0 + init_density: float = 0.1 + + cfg: Config + + def configure(self, *args, **kwargs) -> None: + self.out_layers = nn.ModuleList() + for key, out_ch in self.cfg.feature_channels.items(): + if key == "shs" and self.cfg.use_rgb: + out_ch = 3 + layer = nn.Linear(self.cfg.in_channels, out_ch) + + # initialize + if not (key == "shs" and self.cfg.use_rgb): + nn.init.constant_(layer.weight, 0) + nn.init.constant_(layer.bias, 0) + if key == "scaling": + nn.init.constant_(layer.bias, self.cfg.init_scaling) + elif key == "rotation": + nn.init.constant_(layer.bias, 0) + nn.init.constant_(layer.bias[0], 1.0) + elif key == "opacity": + nn.init.constant_(layer.bias, inverse_sigmoid(self.cfg.init_density)) + + self.out_layers.append(layer) + + def forward(self, x, pts): + ret = {} + for k, layer in zip(self.cfg.feature_channels.keys(), self.out_layers): + v = layer(x) + if k == "rotation": + v = torch.nn.functional.normalize(v) + elif k == "scaling": + v = trunc_exp(v) + if self.cfg.clip_scaling is not None: + v = torch.clamp(v, min=0, max=self.cfg.clip_scaling) + elif k == "opacity": + v = torch.sigmoid(v) + elif k == "shs": + if self.cfg.use_rgb: + v = torch.sigmoid(v) + v = torch.reshape(v, (v.shape[0], -1, 3)) + elif k == "xyz": + if self.cfg.restrict_offset: + max_step = 1.2 / 32 + v = (torch.sigmoid(v) - 0.5) * max_step + v = v + pts if self.cfg.xyz_offset else pts + ret[k] = v + + return GaussianModel(**ret) + +class GS3DRenderer(BaseModule): + @dataclass + class Config(BaseModule.Config): + mlp_network_config: Optional[dict] = None + gs_out: dict = field(default_factory=dict) + sh_degree: int = 3 + scaling_modifier: float = 1.0 + random_background: bool = False + radius: float = 1.0 + feature_reduction: str = "concat" + projection_feature_dim: int = 773 + background_color: Tuple[float, float, float] = field( + default_factory=lambda: (1.0, 1.0, 1.0) + ) + + cfg: Config + + def configure(self, *args, **kwargs) -> None: + if self.cfg.feature_reduction == "mean": + mlp_in = 80 + elif self.cfg.feature_reduction == "concat": + mlp_in = 80 * 3 + else: + raise NotImplementedError + mlp_in = mlp_in + self.cfg.projection_feature_dim + if self.cfg.mlp_network_config is not None: + self.mlp_net = MLP(mlp_in, self.cfg.gs_out.in_channels, **self.cfg.mlp_network_config) + else: + self.cfg.gs_out.in_channels = mlp_in + self.gs_net = GSLayer(self.cfg.gs_out) + + def forward_gs(self, x, p): + if self.cfg.mlp_network_config is not None: + x = self.mlp_net(x) + return self.gs_net(x, p) + + def forward_single_view(self, + gs: GaussianModel, + viewpoint_camera: Camera, + background_color: Optional[Float[Tensor, "3"]], + ret_mask: bool = True, + ): + # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means + screenspace_points = torch.zeros_like(gs.xyz, dtype=gs.xyz.dtype, requires_grad=True, device=self.device) + 0 + try: + screenspace_points.retain_grad() + except: + pass + + bg_color = background_color + # Set up rasterization configuration + tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) + tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) + + raster_settings = GaussianRasterizationSettings( + image_height=int(viewpoint_camera.height), + image_width=int(viewpoint_camera.width), + tanfovx=tanfovx, + tanfovy=tanfovy, + bg=bg_color, + scale_modifier=self.cfg.scaling_modifier, + viewmatrix=viewpoint_camera.world_view_transform, + projmatrix=viewpoint_camera.full_proj_transform.float(), + sh_degree=self.cfg.sh_degree, + campos=viewpoint_camera.camera_center, + prefiltered=False, + debug=False + ) + + rasterizer = GaussianRasterizer(raster_settings=raster_settings) + + means3D = gs.xyz + means2D = screenspace_points + opacity = gs.opacity + + # If precomputed 3d covariance is provided, use it. If not, then it will be computed from + # scaling / rotation by the rasterizer. + scales = None + rotations = None + cov3D_precomp = None + scales = gs.scaling + rotations = gs.rotation + + # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors + # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. + shs = None + colors_precomp = None + if self.gs_net.cfg.use_rgb: + colors_precomp = gs.shs.squeeze(1) + else: + shs = gs.shs + + # Rasterize visible Gaussians to image, obtain their radii (on screen). + with torch.autocast(device_type=self.device.type, dtype=torch.float32): + rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( + means3D = means3D, + means2D = means2D, + shs = shs, + colors_precomp = colors_precomp, + opacities = opacity, + scales = scales, + rotations = rotations, + cov3D_precomp = cov3D_precomp) + + ret = { + "comp_rgb": rendered_image.permute(1, 2, 0), + "comp_rgb_bg": bg_color + } + + if ret_mask: + mask_bg_color = torch.zeros(3, dtype=torch.float32, device=self.device) + raster_settings = GaussianRasterizationSettings( + image_height=int(viewpoint_camera.height), + image_width=int(viewpoint_camera.width), + tanfovx=tanfovx, + tanfovy=tanfovy, + bg=mask_bg_color, + scale_modifier=self.cfg.scaling_modifier, + viewmatrix=viewpoint_camera.world_view_transform, + projmatrix=viewpoint_camera.full_proj_transform.float(), + sh_degree=0, + campos=viewpoint_camera.camera_center, + prefiltered=False, + debug=False + ) + rasterizer = GaussianRasterizer(raster_settings=raster_settings) + + with torch.autocast(device_type=self.device.type, dtype=torch.float32): + rendered_mask, radii, rendered_depth, rendered_alpha = rasterizer( + means3D = means3D, + means2D = means2D, + # shs = , + colors_precomp = torch.ones_like(means3D), + opacities = opacity, + scales = scales, + rotations = rotations, + cov3D_precomp = cov3D_precomp) + ret["comp_mask"] = rendered_mask.permute(1, 2, 0) + + return ret + + def query_triplane( + self, + positions: Float[Tensor, "*B N 3"], + triplanes: Float[Tensor, "*B 3 Cp Hp Wp"], + ) -> Dict[str, Tensor]: + batched = positions.ndim == 3 + if not batched: + # no batch dimension + triplanes = triplanes[None, ...] + positions = positions[None, ...] + + positions = scale_tensor(positions, (-self.cfg.radius, self.cfg.radius), (-1, 1)) + indices2D: Float[Tensor, "B 3 N 2"] = torch.stack( + (positions[..., [0, 1]], positions[..., [0, 2]], positions[..., [1, 2]]), + dim=-3, + ) + out: Float[Tensor, "B3 Cp 1 N"] = F.grid_sample( + rearrange(triplanes, "B Np Cp Hp Wp -> (B Np) Cp Hp Wp", Np=3), + rearrange(indices2D, "B Np N Nd -> (B Np) () N Nd", Np=3), + align_corners=False, + mode="bilinear", + ) + if self.cfg.feature_reduction == "concat": + out = rearrange(out, "(B Np) Cp () N -> B N (Np Cp)", Np=3) + elif self.cfg.feature_reduction == "mean": + out = reduce(out, "(B Np) Cp () N -> B N Cp", Np=3, reduction="mean") + else: + raise NotImplementedError + + if not batched: + out = out.squeeze(0) + + return out + + def forward_single_batch( + self, + gs_hidden_features: Float[Tensor, "Np Cp"], + query_points: Float[Tensor, "Np 3"], + c2ws: Float[Tensor, "Nv 4 4"], + intrinsics: Float[Tensor, "Nv 4 4"], + height: int, + width: int, + background_color: Optional[Float[Tensor, "3"]], + ): + gs: GaussianModel = self.forward_gs(gs_hidden_features, query_points) + out_list = [] + + for c2w, intrinsic in zip(c2ws, intrinsics): + out_list.append(self.forward_single_view( + gs, + Camera.from_c2w(c2w, intrinsic, height, width), + background_color + )) + + out = defaultdict(list) + for out_ in out_list: + for k, v in out_.items(): + out[k].append(v) + out = {k: torch.stack(v, dim=0) for k, v in out.items()} + out["3dgs"] = gs + + return out + + def forward(self, + gs_hidden_features: Float[Tensor, "B Np Cp"], + query_points: Float[Tensor, "B Np 3"], + c2w: Float[Tensor, "B Nv 4 4"], + intrinsic: Float[Tensor, "B Nv 4 4"], + height, + width, + additional_features: Optional[Float[Tensor, "B C H W"]] = None, + background_color: Optional[Float[Tensor, "B 3"]] = None, + **kwargs): + batch_size = gs_hidden_features.shape[0] + out_list = [] + gs_hidden_features = self.query_triplane(query_points, gs_hidden_features) + if additional_features is not None: + gs_hidden_features = torch.cat([gs_hidden_features, additional_features], dim=-1) + + for b in range(batch_size): + out_list.append(self.forward_single_batch( + gs_hidden_features[b], + query_points[b], + c2w[b], + intrinsic[b], + height, width, + background_color[b] if background_color is not None else None)) + + out = defaultdict(list) + for out_ in out_list: + for k, v in out_.items(): + out[k].append(v) + for k, v in out.items(): + if isinstance(v[0], torch.Tensor): + out[k] = torch.stack(v, dim=0) + else: + out[k] = v + return out + \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/LICENSE b/gen_3d_modules/TriplaneGaussian/models/snowflake/LICENSE new file mode 100644 index 0000000..01c2b54 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 AllenXiang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD.py new file mode 100644 index 0000000..eae592c --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD.py @@ -0,0 +1,68 @@ +# -*- coding: utf-8 -*- +# @Author: Peng Xiang + +import torch +import torch.nn as nn +from .utils import MLP_Res, MLP_CONV +from .skip_transformer import SkipTransformer + + +class SPD(nn.Module): + def __init__(self, dim_feat=512, up_factor=2, i=0, radius=1, bounding=True, global_feat=True): + """Snowflake Point Deconvolution""" + super(SPD, self).__init__() + self.i = i + self.up_factor = up_factor + + self.bounding = bounding + self.radius = radius + + self.global_feat = global_feat + self.ps_dim = 32 if global_feat else 64 + + self.mlp_1 = MLP_CONV(in_channel=3, layer_dims=[64, 128]) + self.mlp_2 = MLP_CONV(in_channel=128 * 2 + dim_feat if self.global_feat else 128, layer_dims=[256, 128]) + + self.skip_transformer = SkipTransformer(in_channel=128, dim=64) + + self.mlp_ps = MLP_CONV(in_channel=128, layer_dims=[64, self.ps_dim]) + self.ps = nn.ConvTranspose1d(self.ps_dim, 128, up_factor, up_factor, bias=False) # point-wise splitting + + self.up_sampler = nn.Upsample(scale_factor=up_factor) + self.mlp_delta_feature = MLP_Res(in_dim=256, hidden_dim=128, out_dim=128) + + self.mlp_delta = MLP_CONV(in_channel=128, layer_dims=[64, 3]) + + def forward(self, pcd_prev, feat_global=None, K_prev=None): + """ + Args: + pcd_prev: Tensor, (B, 3, N_prev) + feat_global: Tensor, (B, dim_feat, 1) + K_prev: Tensor, (B, 128, N_prev) + + Returns: + pcd_child: Tensor, up sampled point cloud, (B, 3, N_prev * up_factor) + K_curr: Tensor, displacement feature of current step, (B, 128, N_prev * up_factor) + """ + b, _, n_prev = pcd_prev.shape + feat_1 = self.mlp_1(pcd_prev) + feat_1 = torch.cat([feat_1, + torch.max(feat_1, 2, keepdim=True)[0].repeat((1, 1, feat_1.size(2))), + feat_global.repeat(1, 1, feat_1.size(2))], 1) if self.global_feat else feat_1 + Q = self.mlp_2(feat_1) + + H = self.skip_transformer(pcd_prev, K_prev if K_prev is not None else Q, Q) + + feat_child = self.mlp_ps(H) + feat_child = self.ps(feat_child) # (B, 128, N_prev * up_factor) + H_up = self.up_sampler(H) + K_curr = self.mlp_delta_feature(torch.cat([feat_child, H_up], 1)) + + delta = self.mlp_delta(torch.relu(K_curr)) + if self.bounding: + delta = torch.tanh(delta) / self.radius**self.i # (B, 3, N_prev * up_factor) + + pcd_child = self.up_sampler(pcd_prev) + pcd_child = pcd_child + delta + + return pcd_child, K_curr \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD_crossattn.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD_crossattn.py new file mode 100644 index 0000000..90273e9 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD_crossattn.py @@ -0,0 +1,81 @@ +# -*- coding: utf-8 -*- +# @Author: Peng Xiang + +import torch +import torch.nn as nn +from .utils import MLP_Res, MLP_CONV +from .skip_transformer import SkipTransformer +from .attention import ResidualTransformerBlock + +class SPD_crossattn(nn.Module): + def __init__(self, dim_feat=512, up_factor=2, i=0, radius=1, bounding=True, global_feat=True): + """Snowflake Point Deconvolution""" + super().__init__() + self.i = i + self.up_factor = up_factor + + self.bounding = bounding + self.radius = radius + + self.global_feat = global_feat + self.ps_dim = 32 if global_feat else 64 + + self.mlp_1 = MLP_CONV(in_channel=3, layer_dims=[64, 128]) + self.pcd_image_attn = ResidualTransformerBlock( + device=torch.device('cuda'), + dtype=torch.float32, + n_data=128, + width=128, + heads=8, + init_scale=1.0, + ) + + self.mlp_2 = MLP_CONV(in_channel=128 * 2 + dim_feat if self.global_feat else 128, layer_dims=[256, 128]) + + self.skip_transformer = SkipTransformer(in_channel=128, dim=64) + + self.mlp_ps = MLP_CONV(in_channel=128, layer_dims=[64, self.ps_dim]) + self.ps = nn.ConvTranspose1d(self.ps_dim, 128, up_factor, up_factor, bias=False) # point-wise splitting + + self.up_sampler = nn.Upsample(scale_factor=up_factor) + self.mlp_delta_feature = MLP_Res(in_dim=256, hidden_dim=128, out_dim=128) + + self.mlp_delta = MLP_CONV(in_channel=128, layer_dims=[64, 3]) + + def forward(self, pcd_prev, feat_global=None, K_prev=None): + """ + Args: + pcd_prev: Tensor, (B, 3, N_prev) + feat_global: Tensor, (B, dim_feat, 1) + K_prev: Tensor, (B, 128, N_prev) + + Returns: + pcd_child: Tensor, up sampled point cloud, (B, 3, N_prev * up_factor) + K_curr: Tensor, displacement feature of current step, (B, 128, N_prev * up_factor) + """ + b, _, n_prev = pcd_prev.shape + feat_1 = self.mlp_1(pcd_prev) + # feat_1 = torch.cat([feat_1, + # torch.max(feat_1, 2, keepdim=True)[0].repeat((1, 1, feat_1.size(2))), + # feat_global.repeat(1, 1, feat_1.size(2))], 1) if self.global_feat else feat_1 + feat_1 = torch.permute(feat_1, (0, 2, 1)) + feat_global = torch.permute(feat_global, (0, 2, 1)) + feat_1 = self.pcd_image_attn(feat_1, feat_global) + Q = torch.permute(feat_1, (0, 2, 1)) + # Q = self.mlp_2(feat_1) + + H = self.skip_transformer(pcd_prev, K_prev if K_prev is not None else Q, Q) + + feat_child = self.mlp_ps(H) + feat_child = self.ps(feat_child) # (B, 128, N_prev * up_factor) + H_up = self.up_sampler(H) + K_curr = self.mlp_delta_feature(torch.cat([feat_child, H_up], 1)) + + delta = self.mlp_delta(torch.relu(K_curr)) + if self.bounding: + delta = torch.tanh(delta) / self.radius**self.i # (B, 3, N_prev * up_factor) + + pcd_child = self.up_sampler(pcd_prev) + pcd_child = pcd_child + delta + + return pcd_child, K_curr \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD_pp.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD_pp.py new file mode 100644 index 0000000..886444e --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/SPD_pp.py @@ -0,0 +1,71 @@ + +import torch +import torch.nn as nn +from .utils import MLP_Res, MLP_CONV +from .skip_transformer import SkipTransformer + +class SPD_pp(nn.Module): + def __init__(self, dim_feat=512, up_factor=2, i=0, radius=1, bounding=True, global_feat=True): + """Snowflake Point Deconvolution""" + super(SPD_pp, self).__init__() + self.i = i + self.up_factor = up_factor + + self.bounding = bounding + self.radius = radius + + self.global_feat = global_feat + self.ps_dim = 32 if global_feat else 64 + + self.mlp_1 = MLP_CONV(in_channel=3, layer_dims=[64, 128]) + self.mlp_2 = MLP_CONV( + in_channel=128 * 2 + dim_feat if self.global_feat else 128, layer_dims=[256, 128]) + + self.skip_transformer = SkipTransformer(in_channel=128, dim=64) + + self.mlp_ps = MLP_CONV(in_channel=128, layer_dims=[64, self.ps_dim]) + self.ps = nn.ConvTranspose1d( + self.ps_dim, 128, up_factor, up_factor, bias=False) # point-wise splitting + + self.up_sampler = nn.Upsample(scale_factor=up_factor) + self.mlp_delta_feature = MLP_Res( + in_dim=256, hidden_dim=128, out_dim=128) + + self.mlp_delta = MLP_CONV(in_channel=128, layer_dims=[64, 3]) + + def forward(self, pcd_prev, feat_cond=None, K_prev=None): + """ + Args: + pcd_prev: Tensor, (B, 3, N_prev) + feat_cond: Tensor, (B, dim_feat, N_prev) + K_prev: Tensor, (B, 128, N_prev) + + Returns: + pcd_child: Tensor, up sampled point cloud, (B, 3, N_prev * up_factor) + K_curr: Tensor, displacement feature of current step, (B, 128, N_prev * up_factor) + """ + b, _, n_prev = pcd_prev.shape + feat_1 = self.mlp_1(pcd_prev) + feat_1 = torch.cat([feat_1, + torch.max(feat_1, 2, keepdim=True)[ + 0].repeat((1, 1, feat_1.size(2))), + feat_cond], 1) if self.global_feat else feat_1 + Q = self.mlp_2(feat_1) + + H = self.skip_transformer( + pcd_prev, K_prev if K_prev is not None else Q, Q) + + feat_child = self.mlp_ps(H) + feat_child = self.ps(feat_child) # (B, 128, N_prev * up_factor) + H_up = self.up_sampler(H) + K_curr = self.mlp_delta_feature(torch.cat([feat_child, H_up], 1)) + + delta = self.mlp_delta(torch.relu(K_curr)) + if self.bounding: + # (B, 3, N_prev * up_factor) + delta = torch.tanh(delta) / self.radius**self.i + + pcd_child = self.up_sampler(pcd_prev) + pcd_child = pcd_child + delta + + return pcd_child, K_curr diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/attention.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/attention.py new file mode 100644 index 0000000..51d4d80 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/attention.py @@ -0,0 +1,239 @@ +import torch +import torch.nn as nn +import math +import math +from typing import Optional +from typing import Callable, Iterable, Sequence, Union + +import torch + +def checkpoint( + func: Callable[..., Union[torch.Tensor, Sequence[torch.Tensor]]], + inputs: Sequence[torch.Tensor], + params: Iterable[torch.Tensor], + flag: bool, +): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) + for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def init_linear(l, stddev): + nn.init.normal_(l.weight, std=stddev) + if l.bias is not None: + nn.init.constant_(l.bias, 0.0) + +class MLP(nn.Module): + def __init__(self, *, device: torch.device, dtype: torch.dtype, width: int, init_scale: float): + super().__init__() + self.width = width + self.c_fc = nn.Linear(width, width * 4, device=device, dtype=dtype) + self.c_proj = nn.Linear(width * 4, width, device=device, dtype=dtype) + self.gelu = nn.GELU() + init_linear(self.c_fc, init_scale) + init_linear(self.c_proj, init_scale) + + def forward(self, x): + return self.c_proj(self.gelu(self.c_fc(x))) + +class QKVMultiheadCrossAttention(nn.Module): + def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_data: int): + super().__init__() + self.device = device + self.dtype = dtype + self.heads = heads + self.n_data = n_data + + def forward(self, q, kv): + _, n_ctx, _ = q.shape + bs, n_data, width = kv.shape + attn_ch = width // self.heads // 2 + scale = 1 / math.sqrt(math.sqrt(attn_ch)) + q = q.view(bs, n_ctx, self.heads, -1) + kv = kv.view(bs, n_data, self.heads, -1) + k, v = torch.split(kv, attn_ch, dim=-1) + weight = torch.einsum( + "bthc,bshc->bhts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + wdtype = weight.dtype + weight = torch.softmax(weight.float(), dim=-1).type(wdtype) + return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1) + + + +class QKVMultiheadAttention(nn.Module): + def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int): + super().__init__() + self.device = device + self.dtype = dtype + self.heads = heads + self.n_ctx = n_ctx + + def forward(self, qkv): + bs, n_ctx, width = qkv.shape + attn_ch = width // self.heads // 3 + scale = 1 / math.sqrt(math.sqrt(attn_ch)) + qkv = qkv.view(bs, n_ctx, self.heads, -1) + q, k, v = torch.split(qkv, attn_ch, dim=-1) + weight = torch.einsum( + "bthc,bshc->bhts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + wdtype = weight.dtype + weight = torch.softmax(weight.float(), dim=-1).type(wdtype) + return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1) + + + +class MultiheadCrossAttention(nn.Module): + def __init__( + self, + *, + device: torch.device, + dtype: torch.dtype, + n_data: int, + width: int, + heads: int, + init_scale: float, + data_width: Optional[int] = None, + ): + super().__init__() + self.n_data = n_data + self.width = width + self.heads = heads + self.data_width = width if data_width is None else data_width + self.c_q = nn.Linear(width, width, device=device, dtype=dtype) + self.c_kv = nn.Linear(self.data_width, width * 2, + device=device, dtype=dtype) + self.c_proj = nn.Linear(width, width, device=device, dtype=dtype) + self.attention = QKVMultiheadCrossAttention( + device=device, dtype=dtype, heads=heads, n_data=n_data + ) + init_linear(self.c_q, init_scale) + init_linear(self.c_kv, init_scale) + init_linear(self.c_proj, init_scale) + + def forward(self, x, data): + x = self.c_q(x) + data = self.c_kv(data) + x = checkpoint(self.attention, (x, data), (), True) + x = self.c_proj(x) + return x + + +class MultiheadAttention(nn.Module): + def __init__( + self, + *, + device: torch.device, + dtype: torch.dtype, + n_ctx: int, + width: int, + heads: int, + init_scale: float, + ): + super().__init__() + self.n_ctx = n_ctx + self.width = width + self.heads = heads + self.c_qkv = nn.Linear(width, width * 3, device=device, dtype=dtype) + self.c_proj = nn.Linear(width, width, device=device, dtype=dtype) + self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx) + init_linear(self.c_qkv, init_scale) + init_linear(self.c_proj, init_scale) + + def forward(self, x): + x = self.c_qkv(x) + x = checkpoint(self.attention, (x,), (), True) + x = self.c_proj(x) + return x + + +class ResidualTransformerBlock(nn.Module): + def __init__( + self, + *, + device: torch.device, + dtype: torch.dtype, + n_data: int, + width: int, + heads: int, + data_width: Optional[int] = None, + init_scale: float = 1.0, + ): + super().__init__() + + if data_width is None: + data_width = width + + self.attn_cross = MultiheadCrossAttention( + device=device, + dtype=dtype, + n_data=n_data, + width=width, + heads=heads, + data_width=data_width, + init_scale=init_scale, + ) + self.attn_self = MultiheadAttention( + device=device, + dtype=dtype, + n_ctx=n_data, + width=width, + heads=heads, + init_scale=init_scale, + ) + self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype) + self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype) + self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype) + self.mlp = MLP(device=device, dtype=dtype, + width=width, init_scale=init_scale) + self.ln_4 = nn.LayerNorm(width, device=device, dtype=dtype) + + def forward(self, x: torch.Tensor, data: torch.Tensor): + x = x + self.attn_cross(self.ln_1(x), self.ln_2(data)) + x = x + self.attn_self(self.ln_3(x)) + x = x + self.mlp(self.ln_4(x)) + return x \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/model_spdpp.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/model_spdpp.py new file mode 100644 index 0000000..839a971 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/model_spdpp.py @@ -0,0 +1,188 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from TriplaneGaussian.utils.base import BaseModule +from TriplaneGaussian.utils.typing import * +from dataclasses import dataclass, field + +from pytorch3d.renderer import ( + AlphaCompositor, + NormWeightedCompositor, + PointsRasterizationSettings, + PointsRasterizer, + PointsRenderer) +from pytorch3d.renderer.cameras import CamerasBase +from pytorch3d.structures import Pointclouds +from pytorch3d.utils.camera_conversions import cameras_from_opencv_projection + +from .utils import fps_subsample +from einops import rearrange + +from .utils import MLP_CONV +from .SPD import SPD +from .SPD_crossattn import SPD_crossattn +from .SPD_pp import SPD_pp + +SPD_BLOCK = { + 'SPD': SPD, + 'SPD_crossattn': SPD_crossattn, + 'SPD_PP': SPD_pp, +} + + +def points_projection(points: Float[Tensor, "B Np 3"], + c2ws: Float[Tensor, "B 4 4"], + intrinsics: Float[Tensor, "B 3 3"], + local_features: Float[Tensor, "B C H W"], + raster_point_radius: float = 0.0075, # point size + raster_points_per_pixel: int = 1, # a single point per pixel, for now + bin_size: int = 0): + """ + points: (B, Np, 3) + """ + + B, C, H, W = local_features.shape + device = local_features.device + raster_settings = PointsRasterizationSettings( + image_size=(H, W), + radius=raster_point_radius, + points_per_pixel=raster_points_per_pixel, + bin_size=bin_size, + ) + Np = points.shape[1] + c2ws = c2ws.transpose(0, 1).flatten(0, 1) + intrinsics = intrinsics.transpose(0, 1).flatten(0, 1) + R = raster_settings.points_per_pixel + w2cs = torch.inverse(c2ws) + image_size = torch.as_tensor([H, W]).view( + 1, 2).expand(w2cs.shape[0], -1).to(device) + cameras = cameras_from_opencv_projection( + w2cs[:, :3, :3], w2cs[:, :3, 3], intrinsics, image_size) + rasterize = PointsRasterizer( + cameras=cameras, raster_settings=raster_settings) + fragments = rasterize(Pointclouds(points)) + fragments_idx: Tensor = fragments.idx.long() + visible_pixels = (fragments_idx > -1) # (B, H, W, R) + points_to_visible_pixels = fragments_idx[visible_pixels] + # Reshape local features to (B, H, W, R, C) + local_features = local_features.permute( + 0, 2, 3, 1).unsqueeze(-2).expand(-1, -1, -1, R, -1) # (B, H, W, R, C) + # Get local features corresponding to visible points + local_features_proj = torch.zeros(B * Np, C, device=device) + local_features_proj[points_to_visible_pixels] = local_features[visible_pixels] + local_features_proj = local_features_proj.reshape(B, Np, C) + return local_features_proj + + +class Decoder(nn.Module): + def __init__(self, input_channels=1152, dim_feat=512, num_p0=512, + radius=1, bounding=True, up_factors=None, + SPD_type='SPD', + token_type='image_token' + ): + super(Decoder, self).__init__() + # self.decoder_coarse = SeedGenerator(dim_feat=dim_feat, num_pc=num_p0) + if up_factors is None: + up_factors = [1] + else: + up_factors = up_factors + uppers = [] + self.num_p0 = num_p0 + self.mlp_feat_cond = MLP_CONV(in_channel=input_channels, + layer_dims=[dim_feat*2, dim_feat]) + + for i, factor in enumerate(up_factors): + uppers.append( + SPD_BLOCK[SPD_type](dim_feat=dim_feat, up_factor=factor, + i=i, bounding=bounding, radius=radius)) + self.uppers = nn.ModuleList(uppers) + self.token_type = token_type + + def calculate_pcl_token(self, pcl_token, up_factor): + up_token = F.interpolate(pcl_token, scale_factor=up_factor, mode='nearest') + return up_token + + def calculate_image_token(self, pcd, input_image_tokens, batch): + """ + Args: + """ + batch_size, n_input_views = batch["rgb_cond"].shape[:2] + h_cond, w_cond = batch["rgb_cond"].shape[2:4] + input_image_tokens = rearrange( + input_image_tokens, '(B Nv) C Nt -> B (Nv Nt) C', Nv=n_input_views) + local_features = input_image_tokens[:, 1:].reshape( + batch_size, h_cond // 14, w_cond // 14, -1).permute(0, 3, 1, 2) + local_features = F.interpolate(local_features, size=( + h_cond, w_cond), mode='bilinear', align_corners=False) + batch['c2w_cond'][..., :3, 1:3] *= -1 + local_features_proj = points_projection( + pcd, + batch['c2w_cond'], + batch['intrinsic_cond'], + local_features, + ) + local_features_proj = local_features_proj.permute(0, 2, 1).contiguous() + return local_features_proj + + def forward(self, x): + """ + Args: + points: Tensor, (b, num_p0, 3) + feat_cond: Tensor, (b, dim_feat) dinov2: 325x768 + # partial_coarse: Tensor, (b, n_coarse, 3) + """ + points = x['points'] + if self.token_type == 'pcl_token': + feat_cond = x['pcl_token'] + elif self.token_type == 'image_token': + feat_cond = x['input_image_tokens'] + feat_cond = self.mlp_feat_cond(feat_cond) + arr_pcd = [] + feat_prev = None + + pcd = torch.permute(points, (0, 2, 1)).contiguous() + pcl_up_scale = 1 + for upper in self.uppers: + if self.token_type == 'pcl_token': + up_cond = self.calculate_pcl_token( + feat_cond, pcl_up_scale) + pcl_up_scale *= upper.up_factor + elif self.token_type == 'image_token': + up_cond = self.calculate_image_token(points, feat_cond, x) + pcd, feat_prev = upper(pcd, up_cond, feat_prev) + points = torch.permute(pcd, (0, 2, 1)).contiguous() + arr_pcd.append(points) + return arr_pcd + + +class SnowflakeModelSPDPP(BaseModule): + """ + apply PC^2 / PCL token to decoder + """ + @dataclass + class Config(BaseModule.Config): + input_channels: int = 1152 + dim_feat: int = 128 + num_p0: int = 512 + radius: float = 1 + bounding: bool = True + use_fps: bool = True + up_factors: List[int] = field(default_factory=lambda: [2, 2]) + image_full_token_cond: bool = False + SPD_type: str = 'SPD_PP' + token_type: str = 'pcl_token' + cfg: Config + + def configure(self) -> None: + super().configure() + self.decoder = Decoder(input_channels=self.cfg.input_channels, + dim_feat=self.cfg.dim_feat, num_p0=self.cfg.num_p0, + radius=self.cfg.radius, up_factors=self.cfg.up_factors, bounding=self.cfg.bounding, + SPD_type=self.cfg.SPD_type, + token_type=self.cfg.token_type + ) + + def forward(self, x): + results = self.decoder(x) + return results \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/__init__.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/__init__.py new file mode 100644 index 0000000..5fd361f --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/__init__.py @@ -0,0 +1,3 @@ +import pointnet2_ops.pointnet2_modules +import pointnet2_ops.pointnet2_utils +from pointnet2_ops._version import __version__ diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/ball_query.h b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/ball_query.h new file mode 100644 index 0000000..1bbc638 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/ball_query.h @@ -0,0 +1,5 @@ +#pragma once +#include + +at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, + const int nsample); diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/cuda_utils.h b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/cuda_utils.h new file mode 100644 index 0000000..0fd5b6e --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/cuda_utils.h @@ -0,0 +1,41 @@ +#ifndef _CUDA_UTILS_H +#define _CUDA_UTILS_H + +#include +#include +#include + +#include +#include + +#include + +#define TOTAL_THREADS 512 + +inline int opt_n_threads(int work_size) { + const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); + + return max(min(1 << pow_2, TOTAL_THREADS), 1); +} + +inline dim3 opt_block_config(int x, int y) { + const int x_threads = opt_n_threads(x); + const int y_threads = + max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1); + dim3 block_config(x_threads, y_threads, 1); + + return block_config; +} + +#define CUDA_CHECK_ERRORS() \ + do { \ + cudaError_t err = cudaGetLastError(); \ + if (cudaSuccess != err) { \ + fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n", \ + cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__, \ + __FILE__); \ + exit(-1); \ + } \ + } while (0) + +#endif diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/group_points.h b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/group_points.h new file mode 100644 index 0000000..ad20cda --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/group_points.h @@ -0,0 +1,5 @@ +#pragma once +#include + +at::Tensor group_points(at::Tensor points, at::Tensor idx); +at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n); diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/interpolate.h b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/interpolate.h new file mode 100644 index 0000000..26b3464 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/interpolate.h @@ -0,0 +1,10 @@ +#pragma once + +#include +#include + +std::vector three_nn(at::Tensor unknowns, at::Tensor knows); +at::Tensor three_interpolate(at::Tensor points, at::Tensor idx, + at::Tensor weight); +at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx, + at::Tensor weight, const int m); diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/sampling.h b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/sampling.h new file mode 100644 index 0000000..d795271 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/sampling.h @@ -0,0 +1,6 @@ +#pragma once +#include + +at::Tensor gather_points(at::Tensor points, at::Tensor idx); +at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, const int n); +at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples); diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/utils.h b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/utils.h new file mode 100644 index 0000000..5f080ed --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/utils.h @@ -0,0 +1,25 @@ +#pragma once +#include +#include + +#define CHECK_CUDA(x) \ + do { \ + AT_ASSERT(x.is_cuda(), #x " must be a CUDA tensor"); \ + } while (0) + +#define CHECK_CONTIGUOUS(x) \ + do { \ + AT_ASSERT(x.is_contiguous(), #x " must be a contiguous tensor"); \ + } while (0) + +#define CHECK_IS_INT(x) \ + do { \ + AT_ASSERT(x.scalar_type() == at::ScalarType::Int, \ + #x " must be an int tensor"); \ + } while (0) + +#define CHECK_IS_FLOAT(x) \ + do { \ + AT_ASSERT(x.scalar_type() == at::ScalarType::Float, \ + #x " must be a float tensor"); \ + } while (0) diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query.cpp b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query.cpp new file mode 100644 index 0000000..b1797c1 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query.cpp @@ -0,0 +1,32 @@ +#include "ball_query.h" +#include "utils.h" + +void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, + int nsample, const float *new_xyz, + const float *xyz, int *idx); + +at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, + const int nsample) { + CHECK_CONTIGUOUS(new_xyz); + CHECK_CONTIGUOUS(xyz); + CHECK_IS_FLOAT(new_xyz); + CHECK_IS_FLOAT(xyz); + + if (new_xyz.is_cuda()) { + CHECK_CUDA(xyz); + } + + at::Tensor idx = + torch::zeros({new_xyz.size(0), new_xyz.size(1), nsample}, + at::device(new_xyz.device()).dtype(at::ScalarType::Int)); + + if (new_xyz.is_cuda()) { + query_ball_point_kernel_wrapper(xyz.size(0), xyz.size(1), new_xyz.size(1), + radius, nsample, new_xyz.data_ptr(), + xyz.data_ptr(), idx.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return idx; +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query_gpu.cu b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query_gpu.cu new file mode 100644 index 0000000..559aef9 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query_gpu.cu @@ -0,0 +1,54 @@ +#include +#include +#include + +#include "cuda_utils.h" + +// input: new_xyz(b, m, 3) xyz(b, n, 3) +// output: idx(b, m, nsample) +__global__ void query_ball_point_kernel(int b, int n, int m, float radius, + int nsample, + const float *__restrict__ new_xyz, + const float *__restrict__ xyz, + int *__restrict__ idx) { + int batch_index = blockIdx.x; + xyz += batch_index * n * 3; + new_xyz += batch_index * m * 3; + idx += m * nsample * batch_index; + + int index = threadIdx.x; + int stride = blockDim.x; + + float radius2 = radius * radius; + for (int j = index; j < m; j += stride) { + float new_x = new_xyz[j * 3 + 0]; + float new_y = new_xyz[j * 3 + 1]; + float new_z = new_xyz[j * 3 + 2]; + for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k) { + float x = xyz[k * 3 + 0]; + float y = xyz[k * 3 + 1]; + float z = xyz[k * 3 + 2]; + float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + + (new_z - z) * (new_z - z); + if (d2 < radius2) { + if (cnt == 0) { + for (int l = 0; l < nsample; ++l) { + idx[j * nsample + l] = k; + } + } + idx[j * nsample + cnt] = k; + ++cnt; + } + } + } +} + +void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, + int nsample, const float *new_xyz, + const float *xyz, int *idx) { + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + query_ball_point_kernel<<>>( + b, n, m, radius, nsample, new_xyz, xyz, idx); + + CUDA_CHECK_ERRORS(); +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/bindings.cpp b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/bindings.cpp new file mode 100644 index 0000000..d1916ce --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/bindings.cpp @@ -0,0 +1,19 @@ +#include "ball_query.h" +#include "group_points.h" +#include "interpolate.h" +#include "sampling.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("gather_points", &gather_points); + m.def("gather_points_grad", &gather_points_grad); + m.def("furthest_point_sampling", &furthest_point_sampling); + + m.def("three_nn", &three_nn); + m.def("three_interpolate", &three_interpolate); + m.def("three_interpolate_grad", &three_interpolate_grad); + + m.def("ball_query", &ball_query); + + m.def("group_points", &group_points); + m.def("group_points_grad", &group_points_grad); +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points.cpp b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points.cpp new file mode 100644 index 0000000..285a4bd --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points.cpp @@ -0,0 +1,62 @@ +#include "group_points.h" +#include "utils.h" + +void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, + const float *points, const int *idx, + float *out); + +void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, + int nsample, const float *grad_out, + const int *idx, float *grad_points); + +at::Tensor group_points(at::Tensor points, at::Tensor idx) { + CHECK_CONTIGUOUS(points); + CHECK_CONTIGUOUS(idx); + CHECK_IS_FLOAT(points); + CHECK_IS_INT(idx); + + if (points.is_cuda()) { + CHECK_CUDA(idx); + } + + at::Tensor output = + torch::zeros({points.size(0), points.size(1), idx.size(1), idx.size(2)}, + at::device(points.device()).dtype(at::ScalarType::Float)); + + if (points.is_cuda()) { + group_points_kernel_wrapper(points.size(0), points.size(1), points.size(2), + idx.size(1), idx.size(2), + points.data_ptr(), idx.data_ptr(), + output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} + +at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) { + CHECK_CONTIGUOUS(grad_out); + CHECK_CONTIGUOUS(idx); + CHECK_IS_FLOAT(grad_out); + CHECK_IS_INT(idx); + + if (grad_out.is_cuda()) { + CHECK_CUDA(idx); + } + + at::Tensor output = + torch::zeros({grad_out.size(0), grad_out.size(1), n}, + at::device(grad_out.device()).dtype(at::ScalarType::Float)); + + if (grad_out.is_cuda()) { + group_points_grad_kernel_wrapper( + grad_out.size(0), grad_out.size(1), n, idx.size(1), idx.size(2), + grad_out.data_ptr(), idx.data_ptr(), + output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points_gpu.cu b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points_gpu.cu new file mode 100644 index 0000000..57c2b1b --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points_gpu.cu @@ -0,0 +1,75 @@ +#include +#include + +#include "cuda_utils.h" + +// input: points(b, c, n) idx(b, npoints, nsample) +// output: out(b, c, npoints, nsample) +__global__ void group_points_kernel(int b, int c, int n, int npoints, + int nsample, + const float *__restrict__ points, + const int *__restrict__ idx, + float *__restrict__ out) { + int batch_index = blockIdx.x; + points += batch_index * n * c; + idx += batch_index * npoints * nsample; + out += batch_index * npoints * nsample * c; + + const int index = threadIdx.y * blockDim.x + threadIdx.x; + const int stride = blockDim.y * blockDim.x; + for (int i = index; i < c * npoints; i += stride) { + const int l = i / npoints; + const int j = i % npoints; + for (int k = 0; k < nsample; ++k) { + int ii = idx[j * nsample + k]; + out[(l * npoints + j) * nsample + k] = points[l * n + ii]; + } + } +} + +void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, + const float *points, const int *idx, + float *out) { + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + group_points_kernel<<>>( + b, c, n, npoints, nsample, points, idx, out); + + CUDA_CHECK_ERRORS(); +} + +// input: grad_out(b, c, npoints, nsample), idx(b, npoints, nsample) +// output: grad_points(b, c, n) +__global__ void group_points_grad_kernel(int b, int c, int n, int npoints, + int nsample, + const float *__restrict__ grad_out, + const int *__restrict__ idx, + float *__restrict__ grad_points) { + int batch_index = blockIdx.x; + grad_out += batch_index * npoints * nsample * c; + idx += batch_index * npoints * nsample; + grad_points += batch_index * n * c; + + const int index = threadIdx.y * blockDim.x + threadIdx.x; + const int stride = blockDim.y * blockDim.x; + for (int i = index; i < c * npoints; i += stride) { + const int l = i / npoints; + const int j = i % npoints; + for (int k = 0; k < nsample; ++k) { + int ii = idx[j * nsample + k]; + atomicAdd(grad_points + l * n + ii, + grad_out[(l * npoints + j) * nsample + k]); + } + } +} + +void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, + int nsample, const float *grad_out, + const int *idx, float *grad_points) { + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + group_points_grad_kernel<<>>( + b, c, n, npoints, nsample, grad_out, idx, grad_points); + + CUDA_CHECK_ERRORS(); +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate.cpp b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate.cpp new file mode 100644 index 0000000..cdee31c --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate.cpp @@ -0,0 +1,99 @@ +#include "interpolate.h" +#include "utils.h" + +void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, + const float *known, float *dist2, int *idx); +void three_interpolate_kernel_wrapper(int b, int c, int m, int n, + const float *points, const int *idx, + const float *weight, float *out); +void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, + const float *grad_out, + const int *idx, const float *weight, + float *grad_points); + +std::vector three_nn(at::Tensor unknowns, at::Tensor knows) { + CHECK_CONTIGUOUS(unknowns); + CHECK_CONTIGUOUS(knows); + CHECK_IS_FLOAT(unknowns); + CHECK_IS_FLOAT(knows); + + if (unknowns.is_cuda()) { + CHECK_CUDA(knows); + } + + at::Tensor idx = + torch::zeros({unknowns.size(0), unknowns.size(1), 3}, + at::device(unknowns.device()).dtype(at::ScalarType::Int)); + at::Tensor dist2 = + torch::zeros({unknowns.size(0), unknowns.size(1), 3}, + at::device(unknowns.device()).dtype(at::ScalarType::Float)); + + if (unknowns.is_cuda()) { + three_nn_kernel_wrapper(unknowns.size(0), unknowns.size(1), knows.size(1), + unknowns.data_ptr(), knows.data_ptr(), + dist2.data_ptr(), idx.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return {dist2, idx}; +} + +at::Tensor three_interpolate(at::Tensor points, at::Tensor idx, + at::Tensor weight) { + CHECK_CONTIGUOUS(points); + CHECK_CONTIGUOUS(idx); + CHECK_CONTIGUOUS(weight); + CHECK_IS_FLOAT(points); + CHECK_IS_INT(idx); + CHECK_IS_FLOAT(weight); + + if (points.is_cuda()) { + CHECK_CUDA(idx); + CHECK_CUDA(weight); + } + + at::Tensor output = + torch::zeros({points.size(0), points.size(1), idx.size(1)}, + at::device(points.device()).dtype(at::ScalarType::Float)); + + if (points.is_cuda()) { + three_interpolate_kernel_wrapper( + points.size(0), points.size(1), points.size(2), idx.size(1), + points.data_ptr(), idx.data_ptr(), weight.data_ptr(), + output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} +at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx, + at::Tensor weight, const int m) { + CHECK_CONTIGUOUS(grad_out); + CHECK_CONTIGUOUS(idx); + CHECK_CONTIGUOUS(weight); + CHECK_IS_FLOAT(grad_out); + CHECK_IS_INT(idx); + CHECK_IS_FLOAT(weight); + + if (grad_out.is_cuda()) { + CHECK_CUDA(idx); + CHECK_CUDA(weight); + } + + at::Tensor output = + torch::zeros({grad_out.size(0), grad_out.size(1), m}, + at::device(grad_out.device()).dtype(at::ScalarType::Float)); + + if (grad_out.is_cuda()) { + three_interpolate_grad_kernel_wrapper( + grad_out.size(0), grad_out.size(1), grad_out.size(2), m, + grad_out.data_ptr(), idx.data_ptr(), + weight.data_ptr(), output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate_gpu.cu b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate_gpu.cu new file mode 100644 index 0000000..81c5548 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate_gpu.cu @@ -0,0 +1,154 @@ +#include +#include +#include + +#include "cuda_utils.h" + +// input: unknown(b, n, 3) known(b, m, 3) +// output: dist2(b, n, 3), idx(b, n, 3) +__global__ void three_nn_kernel(int b, int n, int m, + const float *__restrict__ unknown, + const float *__restrict__ known, + float *__restrict__ dist2, + int *__restrict__ idx) { + int batch_index = blockIdx.x; + unknown += batch_index * n * 3; + known += batch_index * m * 3; + dist2 += batch_index * n * 3; + idx += batch_index * n * 3; + + int index = threadIdx.x; + int stride = blockDim.x; + for (int j = index; j < n; j += stride) { + float ux = unknown[j * 3 + 0]; + float uy = unknown[j * 3 + 1]; + float uz = unknown[j * 3 + 2]; + + double best1 = 1e40, best2 = 1e40, best3 = 1e40; + int besti1 = 0, besti2 = 0, besti3 = 0; + for (int k = 0; k < m; ++k) { + float x = known[k * 3 + 0]; + float y = known[k * 3 + 1]; + float z = known[k * 3 + 2]; + float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); + if (d < best1) { + best3 = best2; + besti3 = besti2; + best2 = best1; + besti2 = besti1; + best1 = d; + besti1 = k; + } else if (d < best2) { + best3 = best2; + besti3 = besti2; + best2 = d; + besti2 = k; + } else if (d < best3) { + best3 = d; + besti3 = k; + } + } + dist2[j * 3 + 0] = best1; + dist2[j * 3 + 1] = best2; + dist2[j * 3 + 2] = best3; + + idx[j * 3 + 0] = besti1; + idx[j * 3 + 1] = besti2; + idx[j * 3 + 2] = besti3; + } +} + +void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, + const float *known, float *dist2, int *idx) { + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + three_nn_kernel<<>>(b, n, m, unknown, known, + dist2, idx); + + CUDA_CHECK_ERRORS(); +} + +// input: points(b, c, m), idx(b, n, 3), weight(b, n, 3) +// output: out(b, c, n) +__global__ void three_interpolate_kernel(int b, int c, int m, int n, + const float *__restrict__ points, + const int *__restrict__ idx, + const float *__restrict__ weight, + float *__restrict__ out) { + int batch_index = blockIdx.x; + points += batch_index * m * c; + + idx += batch_index * n * 3; + weight += batch_index * n * 3; + + out += batch_index * n * c; + + const int index = threadIdx.y * blockDim.x + threadIdx.x; + const int stride = blockDim.y * blockDim.x; + for (int i = index; i < c * n; i += stride) { + const int l = i / n; + const int j = i % n; + float w1 = weight[j * 3 + 0]; + float w2 = weight[j * 3 + 1]; + float w3 = weight[j * 3 + 2]; + + int i1 = idx[j * 3 + 0]; + int i2 = idx[j * 3 + 1]; + int i3 = idx[j * 3 + 2]; + + out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 + + points[l * m + i3] * w3; + } +} + +void three_interpolate_kernel_wrapper(int b, int c, int m, int n, + const float *points, const int *idx, + const float *weight, float *out) { + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + three_interpolate_kernel<<>>( + b, c, m, n, points, idx, weight, out); + + CUDA_CHECK_ERRORS(); +} + +// input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3) +// output: grad_points(b, c, m) + +__global__ void three_interpolate_grad_kernel( + int b, int c, int n, int m, const float *__restrict__ grad_out, + const int *__restrict__ idx, const float *__restrict__ weight, + float *__restrict__ grad_points) { + int batch_index = blockIdx.x; + grad_out += batch_index * n * c; + idx += batch_index * n * 3; + weight += batch_index * n * 3; + grad_points += batch_index * m * c; + + const int index = threadIdx.y * blockDim.x + threadIdx.x; + const int stride = blockDim.y * blockDim.x; + for (int i = index; i < c * n; i += stride) { + const int l = i / n; + const int j = i % n; + float w1 = weight[j * 3 + 0]; + float w2 = weight[j * 3 + 1]; + float w3 = weight[j * 3 + 2]; + + int i1 = idx[j * 3 + 0]; + int i2 = idx[j * 3 + 1]; + int i3 = idx[j * 3 + 2]; + + atomicAdd(grad_points + l * m + i1, grad_out[i] * w1); + atomicAdd(grad_points + l * m + i2, grad_out[i] * w2); + atomicAdd(grad_points + l * m + i3, grad_out[i] * w3); + } +} + +void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, + const float *grad_out, + const int *idx, const float *weight, + float *grad_points) { + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + three_interpolate_grad_kernel<<>>( + b, c, n, m, grad_out, idx, weight, grad_points); + + CUDA_CHECK_ERRORS(); +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling.cpp b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling.cpp new file mode 100644 index 0000000..ddbdc11 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling.cpp @@ -0,0 +1,87 @@ +#include "sampling.h" +#include "utils.h" + +void gather_points_kernel_wrapper(int b, int c, int n, int npoints, + const float *points, const int *idx, + float *out); +void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, + const float *grad_out, const int *idx, + float *grad_points); + +void furthest_point_sampling_kernel_wrapper(int b, int n, int m, + const float *dataset, float *temp, + int *idxs); + +at::Tensor gather_points(at::Tensor points, at::Tensor idx) { + CHECK_CONTIGUOUS(points); + CHECK_CONTIGUOUS(idx); + CHECK_IS_FLOAT(points); + CHECK_IS_INT(idx); + + if (points.is_cuda()) { + CHECK_CUDA(idx); + } + + at::Tensor output = + torch::zeros({points.size(0), points.size(1), idx.size(1)}, + at::device(points.device()).dtype(at::ScalarType::Float)); + + if (points.is_cuda()) { + gather_points_kernel_wrapper(points.size(0), points.size(1), points.size(2), + idx.size(1), points.data_ptr(), + idx.data_ptr(), output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} + +at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, + const int n) { + CHECK_CONTIGUOUS(grad_out); + CHECK_CONTIGUOUS(idx); + CHECK_IS_FLOAT(grad_out); + CHECK_IS_INT(idx); + + if (grad_out.is_cuda()) { + CHECK_CUDA(idx); + } + + at::Tensor output = + torch::zeros({grad_out.size(0), grad_out.size(1), n}, + at::device(grad_out.device()).dtype(at::ScalarType::Float)); + + if (grad_out.is_cuda()) { + gather_points_grad_kernel_wrapper(grad_out.size(0), grad_out.size(1), n, + idx.size(1), grad_out.data_ptr(), + idx.data_ptr(), + output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} +at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples) { + CHECK_CONTIGUOUS(points); + CHECK_IS_FLOAT(points); + + at::Tensor output = + torch::zeros({points.size(0), nsamples}, + at::device(points.device()).dtype(at::ScalarType::Int)); + + at::Tensor tmp = + torch::full({points.size(0), points.size(1)}, 1e10, + at::device(points.device()).dtype(at::ScalarType::Float)); + + if (points.is_cuda()) { + furthest_point_sampling_kernel_wrapper( + points.size(0), points.size(1), nsamples, points.data_ptr(), + tmp.data_ptr(), output.data_ptr()); + } else { + AT_ASSERT(false, "CPU not supported"); + } + + return output; +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling_gpu.cu b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling_gpu.cu new file mode 100644 index 0000000..fc573f0 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling_gpu.cu @@ -0,0 +1,229 @@ +#include +#include + +#include "cuda_utils.h" + +// input: points(b, c, n) idx(b, m) +// output: out(b, c, m) +__global__ void gather_points_kernel(int b, int c, int n, int m, + const float *__restrict__ points, + const int *__restrict__ idx, + float *__restrict__ out) { + for (int i = blockIdx.x; i < b; i += gridDim.x) { + for (int l = blockIdx.y; l < c; l += gridDim.y) { + for (int j = threadIdx.x; j < m; j += blockDim.x) { + int a = idx[i * m + j]; + out[(i * c + l) * m + j] = points[(i * c + l) * n + a]; + } + } + } +} + +void gather_points_kernel_wrapper(int b, int c, int n, int npoints, + const float *points, const int *idx, + float *out) { + gather_points_kernel<<>>(b, c, n, npoints, + points, idx, out); + + CUDA_CHECK_ERRORS(); +} + +// input: grad_out(b, c, m) idx(b, m) +// output: grad_points(b, c, n) +__global__ void gather_points_grad_kernel(int b, int c, int n, int m, + const float *__restrict__ grad_out, + const int *__restrict__ idx, + float *__restrict__ grad_points) { + for (int i = blockIdx.x; i < b; i += gridDim.x) { + for (int l = blockIdx.y; l < c; l += gridDim.y) { + for (int j = threadIdx.x; j < m; j += blockDim.x) { + int a = idx[i * m + j]; + atomicAdd(grad_points + (i * c + l) * n + a, + grad_out[(i * c + l) * m + j]); + } + } + } +} + +void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, + const float *grad_out, const int *idx, + float *grad_points) { + gather_points_grad_kernel<<>>( + b, c, n, npoints, grad_out, idx, grad_points); + + CUDA_CHECK_ERRORS(); +} + +__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, + int idx1, int idx2) { + const float v1 = dists[idx1], v2 = dists[idx2]; + const int i1 = dists_i[idx1], i2 = dists_i[idx2]; + dists[idx1] = max(v1, v2); + dists_i[idx1] = v2 > v1 ? i2 : i1; +} + +// Input dataset: (b, n, 3), tmp: (b, n) +// Ouput idxs (b, m) +template +__global__ void furthest_point_sampling_kernel( + int b, int n, int m, const float *__restrict__ dataset, + float *__restrict__ temp, int *__restrict__ idxs) { + if (m <= 0) return; + __shared__ float dists[block_size]; + __shared__ int dists_i[block_size]; + + int batch_index = blockIdx.x; + dataset += batch_index * n * 3; + temp += batch_index * n; + idxs += batch_index * m; + + int tid = threadIdx.x; + const int stride = block_size; + + int old = 0; + if (threadIdx.x == 0) idxs[0] = old; + + __syncthreads(); + for (int j = 1; j < m; j++) { + int besti = 0; + float best = -1; + float x1 = dataset[old * 3 + 0]; + float y1 = dataset[old * 3 + 1]; + float z1 = dataset[old * 3 + 2]; + for (int k = tid; k < n; k += stride) { + float x2, y2, z2; + x2 = dataset[k * 3 + 0]; + y2 = dataset[k * 3 + 1]; + z2 = dataset[k * 3 + 2]; + float mag = (x2 * x2) + (y2 * y2) + (z2 * z2); + if (mag <= 1e-3) continue; + + float d = + (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); + + float d2 = min(d, temp[k]); + temp[k] = d2; + besti = d2 > best ? k : besti; + best = d2 > best ? d2 : best; + } + dists[tid] = best; + dists_i[tid] = besti; + __syncthreads(); + + if (block_size >= 512) { + if (tid < 256) { + __update(dists, dists_i, tid, tid + 256); + } + __syncthreads(); + } + if (block_size >= 256) { + if (tid < 128) { + __update(dists, dists_i, tid, tid + 128); + } + __syncthreads(); + } + if (block_size >= 128) { + if (tid < 64) { + __update(dists, dists_i, tid, tid + 64); + } + __syncthreads(); + } + if (block_size >= 64) { + if (tid < 32) { + __update(dists, dists_i, tid, tid + 32); + } + __syncthreads(); + } + if (block_size >= 32) { + if (tid < 16) { + __update(dists, dists_i, tid, tid + 16); + } + __syncthreads(); + } + if (block_size >= 16) { + if (tid < 8) { + __update(dists, dists_i, tid, tid + 8); + } + __syncthreads(); + } + if (block_size >= 8) { + if (tid < 4) { + __update(dists, dists_i, tid, tid + 4); + } + __syncthreads(); + } + if (block_size >= 4) { + if (tid < 2) { + __update(dists, dists_i, tid, tid + 2); + } + __syncthreads(); + } + if (block_size >= 2) { + if (tid < 1) { + __update(dists, dists_i, tid, tid + 1); + } + __syncthreads(); + } + + old = dists_i[0]; + if (tid == 0) idxs[j] = old; + } +} + +void furthest_point_sampling_kernel_wrapper(int b, int n, int m, + const float *dataset, float *temp, + int *idxs) { + unsigned int n_threads = opt_n_threads(n); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + switch (n_threads) { + case 512: + furthest_point_sampling_kernel<512> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 256: + furthest_point_sampling_kernel<256> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 128: + furthest_point_sampling_kernel<128> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 64: + furthest_point_sampling_kernel<64> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 32: + furthest_point_sampling_kernel<32> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 16: + furthest_point_sampling_kernel<16> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 8: + furthest_point_sampling_kernel<8> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 4: + furthest_point_sampling_kernel<4> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 2: + furthest_point_sampling_kernel<2> + <<>>(b, n, m, dataset, temp, idxs); + break; + case 1: + furthest_point_sampling_kernel<1> + <<>>(b, n, m, dataset, temp, idxs); + break; + default: + furthest_point_sampling_kernel<512> + <<>>(b, n, m, dataset, temp, idxs); + } + + CUDA_CHECK_ERRORS(); +} diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_version.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_version.py new file mode 100644 index 0000000..528787c --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/_version.py @@ -0,0 +1 @@ +__version__ = "3.0.0" diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/pointnet2_modules.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/pointnet2_modules.py new file mode 100644 index 0000000..a0ad4f6 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/pointnet2_modules.py @@ -0,0 +1,209 @@ +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from pointnet2_ops import pointnet2_utils + + +def build_shared_mlp(mlp_spec: List[int], bn: bool = True): + layers = [] + for i in range(1, len(mlp_spec)): + layers.append( + nn.Conv2d(mlp_spec[i - 1], mlp_spec[i], kernel_size=1, bias=not bn) + ) + if bn: + layers.append(nn.BatchNorm2d(mlp_spec[i])) + layers.append(nn.ReLU(True)) + + return nn.Sequential(*layers) + + +class _PointnetSAModuleBase(nn.Module): + def __init__(self): + super(_PointnetSAModuleBase, self).__init__() + self.npoint = None + self.groupers = None + self.mlps = None + + def forward( + self, xyz: torch.Tensor, features: Optional[torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + r""" + Parameters + ---------- + xyz : torch.Tensor + (B, N, 3) tensor of the xyz coordinates of the features + features : torch.Tensor + (B, C, N) tensor of the descriptors of the the features + + Returns + ------- + new_xyz : torch.Tensor + (B, npoint, 3) tensor of the new features' xyz + new_features : torch.Tensor + (B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors + """ + + new_features_list = [] + + xyz_flipped = xyz.transpose(1, 2).contiguous() + new_xyz = ( + pointnet2_utils.gather_operation( + xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint) + ) + .transpose(1, 2) + .contiguous() + if self.npoint is not None + else None + ) + + for i in range(len(self.groupers)): + new_features = self.groupers[i]( + xyz, new_xyz, features + ) # (B, C, npoint, nsample) + + new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample) + new_features = F.max_pool2d( + new_features, kernel_size=[1, new_features.size(3)] + ) # (B, mlp[-1], npoint, 1) + new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint) + + new_features_list.append(new_features) + + return new_xyz, torch.cat(new_features_list, dim=1) + + +class PointnetSAModuleMSG(_PointnetSAModuleBase): + r"""Pointnet set abstrction layer with multiscale grouping + + Parameters + ---------- + npoint : int + Number of features + radii : list of float32 + list of radii to group with + nsamples : list of int32 + Number of samples in each ball query + mlps : list of list of int32 + Spec of the pointnet before the global max_pool for each scale + bn : bool + Use batchnorm + """ + + def __init__(self, npoint, radii, nsamples, mlps, bn=True, use_xyz=True): + # type: (PointnetSAModuleMSG, int, List[float], List[int], List[List[int]], bool, bool) -> None + super(PointnetSAModuleMSG, self).__init__() + + assert len(radii) == len(nsamples) == len(mlps) + + self.npoint = npoint + self.groupers = nn.ModuleList() + self.mlps = nn.ModuleList() + for i in range(len(radii)): + radius = radii[i] + nsample = nsamples[i] + self.groupers.append( + pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) + if npoint is not None + else pointnet2_utils.GroupAll(use_xyz) + ) + mlp_spec = mlps[i] + if use_xyz: + mlp_spec[0] += 3 + + self.mlps.append(build_shared_mlp(mlp_spec, bn)) + + +class PointnetSAModule(PointnetSAModuleMSG): + r"""Pointnet set abstrction layer + + Parameters + ---------- + npoint : int + Number of features + radius : float + Radius of ball + nsample : int + Number of samples in the ball query + mlp : list + Spec of the pointnet before the global max_pool + bn : bool + Use batchnorm + """ + + def __init__( + self, mlp, npoint=None, radius=None, nsample=None, bn=True, use_xyz=True + ): + # type: (PointnetSAModule, List[int], int, float, int, bool, bool) -> None + super(PointnetSAModule, self).__init__( + mlps=[mlp], + npoint=npoint, + radii=[radius], + nsamples=[nsample], + bn=bn, + use_xyz=use_xyz, + ) + + +class PointnetFPModule(nn.Module): + r"""Propigates the features of one set to another + + Parameters + ---------- + mlp : list + Pointnet module parameters + bn : bool + Use batchnorm + """ + + def __init__(self, mlp, bn=True): + # type: (PointnetFPModule, List[int], bool) -> None + super(PointnetFPModule, self).__init__() + self.mlp = build_shared_mlp(mlp, bn=bn) + + def forward(self, unknown, known, unknow_feats, known_feats): + # type: (PointnetFPModule, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor + r""" + Parameters + ---------- + unknown : torch.Tensor + (B, n, 3) tensor of the xyz positions of the unknown features + known : torch.Tensor + (B, m, 3) tensor of the xyz positions of the known features + unknow_feats : torch.Tensor + (B, C1, n) tensor of the features to be propigated to + known_feats : torch.Tensor + (B, C2, m) tensor of features to be propigated + + Returns + ------- + new_features : torch.Tensor + (B, mlp[-1], n) tensor of the features of the unknown features + """ + + if known is not None: + dist, idx = pointnet2_utils.three_nn(unknown, known) + dist_recip = 1.0 / (dist + 1e-8) + norm = torch.sum(dist_recip, dim=2, keepdim=True) + weight = dist_recip / norm + + interpolated_feats = pointnet2_utils.three_interpolate( + known_feats, idx, weight + ) + else: + interpolated_feats = known_feats.expand( + *(known_feats.size()[0:2] + [unknown.size(1)]) + ) + + if unknow_feats is not None: + new_features = torch.cat( + [interpolated_feats, unknow_feats], dim=1 + ) # (B, C2 + C1, n) + else: + new_features = interpolated_feats + + new_features = new_features.unsqueeze(-1) + new_features = self.mlp(new_features) + + return new_features.squeeze(-1) diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/pointnet2_utils.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/pointnet2_utils.py new file mode 100644 index 0000000..150fccc --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/pointnet2_ops/pointnet2_utils.py @@ -0,0 +1,379 @@ +import torch +import torch.nn as nn +import warnings +from torch.autograd import Function +from typing import * + +try: + import pointnet2_ops._ext as _ext +except ImportError: + from torch.utils.cpp_extension import load + import glob + import os.path as osp + import os + + warnings.warn("Unable to load pointnet2_ops cpp extension. JIT Compiling.") + + _ext_src_root = osp.join(osp.dirname(__file__), "_ext-src") + _ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob( + osp.join(_ext_src_root, "src", "*.cu") + ) + _ext_headers = glob.glob(osp.join(_ext_src_root, "include", "*")) + + os.environ["TORCH_CUDA_ARCH_LIST"] = "3.7+PTX;5.0;6.0;6.1;6.2;7.0;7.5" + _ext = load( + "_ext", + sources=_ext_sources, + extra_include_paths=[osp.join(_ext_src_root, "include")], + extra_cflags=["-O3"], + extra_cuda_cflags=["-O3", "-Xfatbin", "-compress-all"], + with_cuda=True, + ) + + +class FurthestPointSampling(Function): + @staticmethod + def forward(ctx, xyz, npoint): + # type: (Any, torch.Tensor, int) -> torch.Tensor + r""" + Uses iterative furthest point sampling to select a set of npoint features that have the largest + minimum distance + + Parameters + ---------- + xyz : torch.Tensor + (B, N, 3) tensor where N > npoint + npoint : int32 + number of features in the sampled set + + Returns + ------- + torch.Tensor + (B, npoint) tensor containing the set + """ + out = _ext.furthest_point_sampling(xyz, npoint) + + ctx.mark_non_differentiable(out) + + return out + + @staticmethod + def backward(ctx, grad_out): + return () + + +furthest_point_sample = FurthestPointSampling.apply + + +class GatherOperation(Function): + @staticmethod + def forward(ctx, features, idx): + # type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor + r""" + + Parameters + ---------- + features : torch.Tensor + (B, C, N) tensor + + idx : torch.Tensor + (B, npoint) tensor of the features to gather + + Returns + ------- + torch.Tensor + (B, C, npoint) tensor + """ + + ctx.save_for_backward(idx, features) + + return _ext.gather_points(features, idx) + + @staticmethod + def backward(ctx, grad_out): + idx, features = ctx.saved_tensors + N = features.size(2) + + grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N) + return grad_features, None + + +gather_operation = GatherOperation.apply + + +class ThreeNN(Function): + @staticmethod + def forward(ctx, unknown, known): + # type: (Any, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor] + r""" + Find the three nearest neighbors of unknown in known + Parameters + ---------- + unknown : torch.Tensor + (B, n, 3) tensor of known features + known : torch.Tensor + (B, m, 3) tensor of unknown features + + Returns + ------- + dist : torch.Tensor + (B, n, 3) l2 distance to the three nearest neighbors + idx : torch.Tensor + (B, n, 3) index of 3 nearest neighbors + """ + dist2, idx = _ext.three_nn(unknown, known) + dist = torch.sqrt(dist2) + + ctx.mark_non_differentiable(dist, idx) + + return dist, idx + + @staticmethod + def backward(ctx, grad_dist, grad_idx): + return () + + +three_nn = ThreeNN.apply + + +class ThreeInterpolate(Function): + @staticmethod + def forward(ctx, features, idx, weight): + # type(Any, torch.Tensor, torch.Tensor, torch.Tensor) -> Torch.Tensor + r""" + Performs weight linear interpolation on 3 features + Parameters + ---------- + features : torch.Tensor + (B, c, m) Features descriptors to be interpolated from + idx : torch.Tensor + (B, n, 3) three nearest neighbors of the target features in features + weight : torch.Tensor + (B, n, 3) weights + + Returns + ------- + torch.Tensor + (B, c, n) tensor of the interpolated features + """ + ctx.save_for_backward(idx, weight, features) + + return _ext.three_interpolate(features, idx, weight) + + @staticmethod + def backward(ctx, grad_out): + # type: (Any, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor] + r""" + Parameters + ---------- + grad_out : torch.Tensor + (B, c, n) tensor with gradients of ouputs + + Returns + ------- + grad_features : torch.Tensor + (B, c, m) tensor with gradients of features + + None + + None + """ + idx, weight, features = ctx.saved_tensors + m = features.size(2) + + grad_features = _ext.three_interpolate_grad( + grad_out.contiguous(), idx, weight, m + ) + + return grad_features, torch.zeros_like(idx), torch.zeros_like(weight) + + +three_interpolate = ThreeInterpolate.apply + + +class GroupingOperation(Function): + @staticmethod + def forward(ctx, features, idx): + # type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor + r""" + + Parameters + ---------- + features : torch.Tensor + (B, C, N) tensor of features to group + idx : torch.Tensor + (B, npoint, nsample) tensor containing the indicies of features to group with + + Returns + ------- + torch.Tensor + (B, C, npoint, nsample) tensor + """ + ctx.save_for_backward(idx, features) + + return _ext.group_points(features, idx) + + @staticmethod + def backward(ctx, grad_out): + # type: (Any, torch.tensor) -> Tuple[torch.Tensor, torch.Tensor] + r""" + + Parameters + ---------- + grad_out : torch.Tensor + (B, C, npoint, nsample) tensor of the gradients of the output from forward + + Returns + ------- + torch.Tensor + (B, C, N) gradient of the features + None + """ + idx, features = ctx.saved_tensors + N = features.size(2) + + grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N) + + return grad_features, torch.zeros_like(idx) + + +grouping_operation = GroupingOperation.apply + + +class BallQuery(Function): + @staticmethod + def forward(ctx, radius, nsample, xyz, new_xyz): + # type: (Any, float, int, torch.Tensor, torch.Tensor) -> torch.Tensor + r""" + + Parameters + ---------- + radius : float + radius of the balls + nsample : int + maximum number of features in the balls + xyz : torch.Tensor + (B, N, 3) xyz coordinates of the features + new_xyz : torch.Tensor + (B, npoint, 3) centers of the ball query + + Returns + ------- + torch.Tensor + (B, npoint, nsample) tensor with the indicies of the features that form the query balls + """ + output = _ext.ball_query(new_xyz, xyz, radius, nsample) + + ctx.mark_non_differentiable(output) + + return output + + @staticmethod + def backward(ctx, grad_out): + return () + + +ball_query = BallQuery.apply + + +class QueryAndGroup(nn.Module): + r""" + Groups with a ball query of radius + + Parameters + --------- + radius : float32 + Radius of ball + nsample : int32 + Maximum number of features to gather in the ball + """ + + def __init__(self, radius, nsample, use_xyz=True): + # type: (QueryAndGroup, float, int, bool) -> None + super(QueryAndGroup, self).__init__() + self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz + + def forward(self, xyz, new_xyz, features=None): + # type: (QueryAndGroup, torch.Tensor. torch.Tensor, torch.Tensor) -> Tuple[Torch.Tensor] + r""" + Parameters + ---------- + xyz : torch.Tensor + xyz coordinates of the features (B, N, 3) + new_xyz : torch.Tensor + centriods (B, npoint, 3) + features : torch.Tensor + Descriptors of the features (B, C, N) + + Returns + ------- + new_features : torch.Tensor + (B, 3 + C, npoint, nsample) tensor + """ + + idx = ball_query(self.radius, self.nsample, xyz, new_xyz) + xyz_trans = xyz.transpose(1, 2).contiguous() + grouped_xyz = grouping_operation(xyz_trans, idx) # (B, 3, npoint, nsample) + grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1) + + if features is not None: + grouped_features = grouping_operation(features, idx) + if self.use_xyz: + new_features = torch.cat( + [grouped_xyz, grouped_features], dim=1 + ) # (B, C + 3, npoint, nsample) + else: + new_features = grouped_features + else: + assert ( + self.use_xyz + ), "Cannot have not features and not use xyz as a feature!" + new_features = grouped_xyz + + return new_features + + +class GroupAll(nn.Module): + r""" + Groups all features + + Parameters + --------- + """ + + def __init__(self, use_xyz=True): + # type: (GroupAll, bool) -> None + super(GroupAll, self).__init__() + self.use_xyz = use_xyz + + def forward(self, xyz, new_xyz, features=None): + # type: (GroupAll, torch.Tensor, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor] + r""" + Parameters + ---------- + xyz : torch.Tensor + xyz coordinates of the features (B, N, 3) + new_xyz : torch.Tensor + Ignored + features : torch.Tensor + Descriptors of the features (B, C, N) + + Returns + ------- + new_features : torch.Tensor + (B, C + 3, 1, N) tensor + """ + + grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) + if features is not None: + grouped_features = features.unsqueeze(2) + if self.use_xyz: + new_features = torch.cat( + [grouped_xyz, grouped_features], dim=1 + ) # (B, 3 + C, 1, N) + else: + new_features = grouped_features + else: + new_features = grouped_xyz + + return new_features diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/setup.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/setup.py new file mode 100644 index 0000000..fc39d02 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib/setup.py @@ -0,0 +1,41 @@ +import glob +import os +import os.path as osp + +from setuptools import find_packages, setup +import torch +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +this_dir = osp.dirname(osp.abspath(__file__)) +_ext_src_root = osp.join("pointnet2_ops", "_ext-src") +_ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob( + osp.join(_ext_src_root, "src", "*.cu") +) +_ext_headers = glob.glob(osp.join(_ext_src_root, "include", "*")) + +requirements = [] + +exec(open(osp.join("pointnet2_ops", "_version.py")).read()) + +os.environ["TORCH_CUDA_ARCH_LIST"] = ".".join(map(str, torch.cuda.get_device_capability())) +# os.environ["TORCH_CUDA_ARCH_LIST"] = "5.0;6.0;6.1;6.2;7.0;7.5;8.0;8.6" +setup( + name="pointnet2_ops", + version=__version__, + author="Erik Wijmans", + packages=find_packages(), + install_requires=requirements, + ext_modules=[ + CUDAExtension( + name="pointnet2_ops._ext", + sources=_ext_sources, + extra_compile_args={ + "cxx": ["-O3"], + "nvcc": ["-O3", "-Xfatbin", "-compress-all"], + }, + include_dirs=[osp.join(this_dir, _ext_src_root, "include")], + ) + ], + cmdclass={"build_ext": BuildExtension}, + include_package_data=True, +) diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/skip_transformer.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/skip_transformer.py new file mode 100644 index 0000000..36462f7 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/skip_transformer.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +# @Author: Peng Xiang + +import torch +from torch import nn, einsum +from .utils import MLP_Res, grouping_operation, query_knn + + +class SkipTransformer(nn.Module): + def __init__(self, in_channel, dim=256, n_knn=16, pos_hidden_dim=64, attn_hidden_multiplier=4): + super(SkipTransformer, self).__init__() + self.mlp_v = MLP_Res(in_dim=in_channel*2, hidden_dim=in_channel, out_dim=in_channel) + self.n_knn = n_knn + self.conv_key = nn.Conv1d(in_channel, dim, 1) + self.conv_query = nn.Conv1d(in_channel, dim, 1) + self.conv_value = nn.Conv1d(in_channel, dim, 1) + + self.pos_mlp = nn.Sequential( + nn.Conv2d(3, pos_hidden_dim, 1), + nn.BatchNorm2d(pos_hidden_dim), + nn.ReLU(), + nn.Conv2d(pos_hidden_dim, dim, 1) + ) + + self.attn_mlp = nn.Sequential( + nn.Conv2d(dim, dim * attn_hidden_multiplier, 1), + nn.BatchNorm2d(dim * attn_hidden_multiplier), + nn.ReLU(), + nn.Conv2d(dim * attn_hidden_multiplier, dim, 1) + ) + + self.conv_end = nn.Conv1d(dim, in_channel, 1) + + def forward(self, pos, key, query, include_self=True): + """ + Args: + pos: (B, 3, N) + key: (B, in_channel, N) + query: (B, in_channel, N) + include_self: boolean + + Returns: + Tensor: (B, in_channel, N), shape context feature + """ + value = self.mlp_v(torch.cat([key, query], 1)) + identity = value + key = self.conv_key(key) + query = self.conv_query(query) + value = self.conv_value(value) + b, dim, n = value.shape + + pos_flipped = pos.permute(0, 2, 1).contiguous() + idx_knn = query_knn(self.n_knn, pos_flipped, pos_flipped, include_self=include_self) + + key = grouping_operation(key, idx_knn) # b, dim, n, n_knn + qk_rel = query.reshape((b, -1, n, 1)) - key + + pos_rel = pos.reshape((b, -1, n, 1)) - grouping_operation(pos, idx_knn) # b, 3, n, n_knn + pos_embedding = self.pos_mlp(pos_rel) + + attention = self.attn_mlp(qk_rel + pos_embedding) # b, dim, n, n_knn + attention = torch.softmax(attention, -1) + + value = value.reshape((b, -1, n, 1)) + pos_embedding # + + agg = einsum('b c i j, b c i j -> b c i', attention, value) # b, dim, n + y = self.conv_end(agg) + + return y + identity diff --git a/gen_3d_modules/TriplaneGaussian/models/snowflake/utils.py b/gen_3d_modules/TriplaneGaussian/models/snowflake/utils.py new file mode 100644 index 0000000..6661e38 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/snowflake/utils.py @@ -0,0 +1,741 @@ +# -*- coding: utf-8 -*- +# @Author: Peng Xiang + +import types +import torch +import torch.nn.functional as F +import numpy as np +from torch import nn, einsum +from pointnet2_ops.pointnet2_utils import furthest_point_sample, \ + gather_operation, ball_query, three_nn, three_interpolate, grouping_operation + +class Conv1d(nn.Module): + def __init__(self, in_channel, out_channel, kernel_size=1, stride=1, if_bn=True, activation_fn=torch.relu): + super(Conv1d, self).__init__() + self.conv = nn.Conv1d(in_channel, out_channel, kernel_size, stride=stride) + self.if_bn = if_bn + self.bn = nn.BatchNorm1d(out_channel) + self.activation_fn = activation_fn + + def forward(self, input): + out = self.conv(input) + if self.if_bn: + out = self.bn(out) + + if self.activation_fn is not None: + out = self.activation_fn(out) + + return out + +class Conv2d(nn.Module): + def __init__(self, in_channel, out_channel, kernel_size=(1, 1), stride=(1, 1), if_bn=True, activation_fn=torch.relu): + super(Conv2d, self).__init__() + self.conv = nn.Conv2d(in_channel, out_channel, kernel_size, stride=stride) + self.if_bn = if_bn + self.bn = nn.BatchNorm2d(out_channel) + self.activation_fn = activation_fn + + def forward(self, input): + out = self.conv(input) + if self.if_bn: + out = self.bn(out) + + if self.activation_fn is not None: + out = self.activation_fn(out) + + return out + +class MLP(nn.Module): + def __init__(self, in_channel, layer_dims, bn=None): + super(MLP, self).__init__() + layers = [] + last_channel = in_channel + for out_channel in layer_dims[:-1]: + layers.append(nn.Linear(last_channel, out_channel)) + if bn: + layers.append(nn.BatchNorm1d(out_channel)) + layers.append(nn.ReLU()) + last_channel = out_channel + layers.append(nn.Linear(last_channel, layer_dims[-1])) + self.mlp = nn.Sequential(*layers) + + def forward(self, inputs): + return self.mlp(inputs) + +class MLP_CONV(nn.Module): + def __init__(self, in_channel, layer_dims, bn=None): + super(MLP_CONV, self).__init__() + layers = [] + last_channel = in_channel + for out_channel in layer_dims[:-1]: + layers.append(nn.Conv1d(last_channel, out_channel, 1)) + if bn: + layers.append(nn.BatchNorm1d(out_channel)) + layers.append(nn.ReLU()) + last_channel = out_channel + layers.append(nn.Conv1d(last_channel, layer_dims[-1], 1)) + self.mlp = nn.Sequential(*layers) + + def forward(self, inputs): + return self.mlp(inputs) + +class MLP_Res(nn.Module): + def __init__(self, in_dim=128, hidden_dim=None, out_dim=128): + super(MLP_Res, self).__init__() + if hidden_dim is None: + hidden_dim = in_dim + self.conv_1 = nn.Conv1d(in_dim, hidden_dim, 1) + self.conv_2 = nn.Conv1d(hidden_dim, out_dim, 1) + self.conv_shortcut = nn.Conv1d(in_dim, out_dim, 1) + + def forward(self, x): + """ + Args: + x: (B, out_dim, n) + """ + shortcut = self.conv_shortcut(x) + out = self.conv_2(torch.relu(self.conv_1(x))) + shortcut + return out + + +def sample_and_group(xyz, points, npoint, nsample, radius, use_xyz=True): + """ + Args: + xyz: Tensor, (B, 3, N) + points: Tensor, (B, f, N) + npoint: int + nsample: int + radius: float + use_xyz: boolean + + Returns: + new_xyz: Tensor, (B, 3, npoint) + new_points: Tensor, (B, 3 | f+3 | f, npoint, nsample) + idx_local: Tensor, (B, npoint, nsample) + grouped_xyz: Tensor, (B, 3, npoint, nsample) + + """ + xyz_flipped = xyz.permute(0, 2, 1).contiguous() # (B, N, 3) + new_xyz = gather_operation(xyz, furthest_point_sample(xyz_flipped, npoint)) # (B, 3, npoint) + + idx = ball_query(radius, nsample, xyz_flipped, new_xyz.permute(0, 2, 1).contiguous()) # (B, npoint, nsample) + grouped_xyz = grouping_operation(xyz, idx) # (B, 3, npoint, nsample) + grouped_xyz -= new_xyz.unsqueeze(3).repeat(1, 1, 1, nsample) + + if points is not None: + grouped_points = grouping_operation(points, idx) # (B, f, npoint, nsample) + if use_xyz: + new_points = torch.cat([grouped_xyz, grouped_points], 1) + else: + new_points = grouped_points + else: + new_points = grouped_xyz + + return new_xyz, new_points, idx, grouped_xyz + + +def sample_and_group_all(xyz, points, use_xyz=True): + """ + Args: + xyz: Tensor, (B, 3, nsample) + points: Tensor, (B, f, nsample) + use_xyz: boolean + + Returns: + new_xyz: Tensor, (B, 3, 1) + new_points: Tensor, (B, f|f+3|3, 1, nsample) + idx: Tensor, (B, 1, nsample) + grouped_xyz: Tensor, (B, 3, 1, nsample) + """ + b, _, nsample = xyz.shape + device = xyz.device + new_xyz = torch.zeros((1, 3, 1), dtype=torch.float, device=device).repeat(b, 1, 1) + grouped_xyz = xyz.reshape((b, 3, 1, nsample)) + idx = torch.arange(nsample, device=device).reshape(1, 1, nsample).repeat(b, 1, 1) + if points is not None: + if use_xyz: + new_points = torch.cat([xyz, points], 1) + else: + new_points = points + new_points = new_points.unsqueeze(2) + else: + new_points = grouped_xyz + + return new_xyz, new_points, idx, grouped_xyz + + +class PointNet_SA_Module(nn.Module): + def __init__(self, npoint, nsample, radius, in_channel, mlp, if_bn=True, group_all=False, use_xyz=True): + """ + Args: + npoint: int, number of points to sample + nsample: int, number of points in each local region + radius: float + in_channel: int, input channel of features(points) + mlp: list of int, + """ + super(PointNet_SA_Module, self).__init__() + self.npoint = npoint + self.nsample = nsample + self.radius = radius + self.mlp = mlp + self.group_all = group_all + self.use_xyz = use_xyz + if use_xyz: + in_channel += 3 + + last_channel = in_channel + self.mlp_conv = [] + for out_channel in mlp: + self.mlp_conv.append(Conv2d(last_channel, out_channel, if_bn=if_bn)) + last_channel = out_channel + + self.mlp_conv = nn.Sequential(*self.mlp_conv) + + def forward(self, xyz, points): + """ + Args: + xyz: Tensor, (B, 3, N) + points: Tensor, (B, f, N) + + Returns: + new_xyz: Tensor, (B, 3, npoint) + new_points: Tensor, (B, mlp[-1], npoint) + """ + if self.group_all: + new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(xyz, points, self.use_xyz) + else: + new_xyz, new_points, idx, grouped_xyz = sample_and_group(xyz, points, self.npoint, self.nsample, self.radius, self.use_xyz) + + new_points = self.mlp_conv(new_points) + new_points = torch.max(new_points, 3)[0] + + return new_xyz, new_points + + +class PointNet_FP_Module(nn.Module): + def __init__(self, in_channel, mlp, use_points1=False, in_channel_points1=None, if_bn=True): + """ + Args: + in_channel: int, input channel of points2 + mlp: list of int + use_points1: boolean, if use points + in_channel_points1: int, input channel of points1 + """ + super(PointNet_FP_Module, self).__init__() + self.use_points1 = use_points1 + + if use_points1: + in_channel += in_channel_points1 + + last_channel = in_channel + self.mlp_conv = [] + for out_channel in mlp: + self.mlp_conv.append(Conv1d(last_channel, out_channel, if_bn=if_bn)) + last_channel = out_channel + + self.mlp_conv = nn.Sequential(*self.mlp_conv) + + def forward(self, xyz1, xyz2, points1, points2): + """ + Args: + xyz1: Tensor, (B, 3, N) + xyz2: Tensor, (B, 3, M) + points1: Tensor, (B, in_channel, N) + points2: Tensor, (B, in_channel, M) + + Returns:MLP_CONV + new_points: Tensor, (B, mlp[-1], N) + """ + dist, idx = three_nn(xyz1.permute(0, 2, 1).contiguous(), xyz2.permute(0, 2, 1).contiguous()) + dist = torch.clamp_min(dist, 1e-10) # (B, N, 3) + recip_dist = 1.0/dist + norm = torch.sum(recip_dist, 2, keepdim=True).repeat((1, 1, 3)) + weight = recip_dist / norm + interpolated_points = three_interpolate(points2, idx, weight) # B, in_channel, N + + if self.use_points1: + new_points = torch.cat([interpolated_points, points1], 1) + else: + new_points = interpolated_points + + new_points = self.mlp_conv(new_points) + return new_points + + +def square_distance(src, dst): + """ + Calculate Euclid distance between each two points. + + src^T * dst = xn * xm + yn * ym + zn * zm； + sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; + sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; + dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 + = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst + + Input: + src: source points, [B, N, C] + dst: target points, [B, M, C] + Output: + dist: per-point square distance, [B, N, M] + """ + B, N, _ = src.shape + _, M, _ = dst.shape + dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) # B, N, M + dist += torch.sum(src ** 2, -1).view(B, N, 1) + dist += torch.sum(dst ** 2, -1).view(B, 1, M) + return dist + + +def query_knn(nsample, xyz, new_xyz, include_self=True): + """Find k-NN of new_xyz in xyz""" + pad = 0 if include_self else 1 + sqrdists = square_distance(new_xyz, xyz) # B, S, N + idx = torch.argsort(sqrdists, dim=-1, descending=False)[:, :, pad: nsample+pad] + return idx.int() + + +def sample_and_group_knn(xyz, points, npoint, k, use_xyz=True, idx=None): + """ + Args: + xyz: Tensor, (B, 3, N) + points: Tensor, (B, f, N) + npoint: int + nsample: int + radius: float + use_xyz: boolean + + Returns: + new_xyz: Tensor, (B, 3, npoint) + new_points: Tensor, (B, 3 | f+3 | f, npoint, nsample) + idx_local: Tensor, (B, npoint, nsample) + grouped_xyz: Tensor, (B, 3, npoint, nsample) + + """ + xyz_flipped = xyz.permute(0, 2, 1).contiguous() # (B, N, 3) + new_xyz = gather_operation(xyz, furthest_point_sample(xyz_flipped, npoint)) # (B, 3, npoint) + if idx is None: + idx = query_knn(k, xyz_flipped, new_xyz.permute(0, 2, 1).contiguous()) + grouped_xyz = grouping_operation(xyz, idx) # (B, 3, npoint, nsample) + grouped_xyz -= new_xyz.unsqueeze(3).repeat(1, 1, 1, k) + + if points is not None: + grouped_points = grouping_operation(points, idx) # (B, f, npoint, nsample) + if use_xyz: + new_points = torch.cat([grouped_xyz, grouped_points], 1) + else: + new_points = grouped_points + else: + new_points = grouped_xyz + + return new_xyz, new_points, idx, grouped_xyz + + +class PointNet_SA_Module_KNN(nn.Module): + def __init__(self, npoint, nsample, in_channel, mlp, if_bn=True, group_all=False, use_xyz=True, if_idx=False): + """ + Args: + npoint: int, number of points to sample + nsample: int, number of points in each local region + radius: float + in_channel: int, input channel of features(points) + mlp: list of int, + """ + super(PointNet_SA_Module_KNN, self).__init__() + self.npoint = npoint + self.nsample = nsample + self.mlp = mlp + self.group_all = group_all + self.use_xyz = use_xyz + self.if_idx = if_idx + if use_xyz: + in_channel += 3 + + last_channel = in_channel + self.mlp_conv = [] + for out_channel in mlp[:-1]: + self.mlp_conv.append(Conv2d(last_channel, out_channel, if_bn=if_bn)) + last_channel = out_channel + self.mlp_conv.append(Conv2d(last_channel, mlp[-1], if_bn=False, activation_fn=None)) + self.mlp_conv = nn.Sequential(*self.mlp_conv) + + def forward(self, xyz, points, idx=None): + """ + Args: + xyz: Tensor, (B, 3, N) + points: Tensor, (B, f, N) + + Returns: + new_xyz: Tensor, (B, 3, npoint) + new_points: Tensor, (B, mlp[-1], npoint) + """ + if self.group_all: + new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(xyz, points, self.use_xyz) + else: + new_xyz, new_points, idx, grouped_xyz = sample_and_group_knn(xyz, points, self.npoint, self.nsample, self.use_xyz, idx=idx) + + new_points = self.mlp_conv(new_points) + new_points = torch.max(new_points, 3)[0] + + if self.if_idx: + return new_xyz, new_points, idx + else: + return new_xyz, new_points + + +def fps_subsample(pcd, n_points=2048): + """ + Args + pcd: (b, 16384, 3) + + returns + new_pcd: (b, n_points, 3) + """ + new_pcd = gather_operation(pcd.permute(0, 2, 1).contiguous(), furthest_point_sample(pcd, n_points)) + new_pcd = new_pcd.permute(0, 2, 1).contiguous() + return new_pcd + + +class Transformer(nn.Module): + def __init__(self, in_channel, dim=256, n_knn=16, pos_hidden_dim=64, attn_hidden_multiplier=4): + super(Transformer, self).__init__() + self.n_knn = n_knn + self.conv_key = nn.Conv1d(dim, dim, 1) + self.conv_query = nn.Conv1d(dim, dim, 1) + self.conv_value = nn.Conv1d(dim, dim, 1) + + self.pos_mlp = nn.Sequential( + nn.Conv2d(3, pos_hidden_dim, 1), + nn.BatchNorm2d(pos_hidden_dim), + nn.ReLU(), + nn.Conv2d(pos_hidden_dim, dim, 1) + ) + + self.attn_mlp = nn.Sequential( + nn.Conv2d(dim, dim * attn_hidden_multiplier, 1), + nn.BatchNorm2d(dim * attn_hidden_multiplier), + nn.ReLU(), + nn.Conv2d(dim * attn_hidden_multiplier, dim, 1) + ) + + self.linear_start = nn.Conv1d(in_channel, dim, 1) + self.linear_end = nn.Conv1d(dim, in_channel, 1) + + def forward(self, x, pos): + """feed forward of transformer + Args: + x: Tensor of features, (B, in_channel, n) + pos: Tensor of positions, (B, 3, n) + + Returns: + y: Tensor of features with attention, (B, in_channel, n) + """ + + identity = x + + x = self.linear_start(x) + b, dim, n = x.shape + + pos_flipped = pos.permute(0, 2, 1).contiguous() + idx_knn = query_knn(self.n_knn, pos_flipped, pos_flipped) + key = self.conv_key(x) + value = self.conv_value(x) + query = self.conv_query(x) + + key = grouping_operation(key, idx_knn) # b, dim, n, n_knn + qk_rel = query.reshape((b, -1, n, 1)) - key + + pos_rel = pos.reshape((b, -1, n, 1)) - grouping_operation(pos, idx_knn) # b, 3, n, n_knn + pos_embedding = self.pos_mlp(pos_rel) # b, dim, n, n_knn + + attention = self.attn_mlp(qk_rel + pos_embedding) + attention = torch.softmax(attention, -1) + + value = value.reshape((b, -1, n, 1)) + pos_embedding + + agg = einsum('b c i j, b c i j -> b c i', attention, value) # b, dim, n + y = self.linear_end(agg) + + return y+identity + + +class CouplingLayer(nn.Module): + + def __init__(self, d, intermediate_dim, swap=False): + nn.Module.__init__(self) + self.d = d - (d // 2) + self.swap = swap + self.net_s_t = nn.Sequential( + nn.Linear(self.d, intermediate_dim), + nn.ReLU(inplace=True), + nn.Linear(intermediate_dim, intermediate_dim), + nn.ReLU(inplace=True), + nn.Linear(intermediate_dim, (d - self.d) * 2), + ) + + def forward(self, x, logpx=None, reverse=False): + + if self.swap: + x = torch.cat([x[:, self.d:], x[:, :self.d]], 1) + + in_dim = self.d + out_dim = x.shape[1] - self.d + + s_t = self.net_s_t(x[:, :in_dim]) + scale = torch.sigmoid(s_t[:, :out_dim] + 2.) + shift = s_t[:, out_dim:] + + logdetjac = torch.sum(torch.log(scale).view(scale.shape[0], -1), 1, keepdim=True) + + if not reverse: + y1 = x[:, self.d:] * scale + shift + delta_logp = -logdetjac + else: + y1 = (x[:, self.d:] - shift) / scale + delta_logp = logdetjac + + y = torch.cat([x[:, :self.d], y1], 1) if not self.swap else torch.cat([y1, x[:, :self.d]], 1) + + if logpx is None: + return y + else: + return y, logpx + delta_logp + + +class SequentialFlow(nn.Module): + """A generalized nn.Sequential container for normalizing flows. + """ + + def __init__(self, layersList): + super(SequentialFlow, self).__init__() + self.chain = nn.ModuleList(layersList) + + def forward(self, x, logpx=None, reverse=False, inds=None): + if inds is None: + if reverse: + inds = range(len(self.chain) - 1, -1, -1) + else: + inds = range(len(self.chain)) + + if logpx is None: + for i in inds: + x = self.chain[i](x, reverse=reverse) + return x + else: + for i in inds: + x, logpx = self.chain[i](x, logpx, reverse=reverse) + return x, logpx + + +def build_latent_flow(args): + chain = [] + for i in range(args.latent_flow_depth): + chain.append(CouplingLayer(args.latent_dim, args.latent_flow_hidden_dim, swap=(i % 2 == 0))) + return SequentialFlow(chain) + + +################## +## SpectralNorm ## +################## + +POWER_ITERATION_FN = "spectral_norm_power_iteration" + + +class SpectralNorm(object): + def __init__(self, name='weight', dim=0, eps=1e-12): + self.name = name + self.dim = dim + self.eps = eps + + def compute_weight(self, module, n_power_iterations): + if n_power_iterations < 0: + raise ValueError( + 'Expected n_power_iterations to be non-negative, but ' + 'got n_power_iterations={}'.format(n_power_iterations) + ) + + weight = getattr(module, self.name + '_orig') + u = getattr(module, self.name + '_u') + v = getattr(module, self.name + '_v') + weight_mat = weight + if self.dim != 0: + # permute dim to front + weight_mat = weight_mat.permute(self.dim, * [d for d in range(weight_mat.dim()) if d != self.dim]) + height = weight_mat.size(0) + weight_mat = weight_mat.reshape(height, -1) + with torch.no_grad(): + for _ in range(n_power_iterations): + # Spectral norm of weight equals to `u^T W v`, where `u` and `v` + # are the first left and right singular vectors. + # This power iteration produces approximations of `u` and `v`. + v = F.normalize(torch.matmul(weight_mat.t(), u), dim=0, eps=self.eps) + u = F.normalize(torch.matmul(weight_mat, v), dim=0, eps=self.eps) + setattr(module, self.name + '_u', u) + setattr(module, self.name + '_v', v) + + sigma = torch.dot(u, torch.matmul(weight_mat, v)) + weight = weight / sigma + setattr(module, self.name, weight) + + def remove(self, module): + weight = getattr(module, self.name) + delattr(module, self.name) + delattr(module, self.name + '_u') + delattr(module, self.name + '_orig') + module.register_parameter(self.name, torch.nn.Parameter(weight)) + + def get_update_method(self, module): + def update_fn(module, n_power_iterations): + self.compute_weight(module, n_power_iterations) + + return update_fn + + def __call__(self, module, unused_inputs): + del unused_inputs + self.compute_weight(module, n_power_iterations=0) + + # requires_grad might be either True or False during inference. + if not module.training: + r_g = getattr(module, self.name + '_orig').requires_grad + setattr(module, self.name, getattr(module, self.name).detach().requires_grad_(r_g)) + + @staticmethod + def apply(module, name, dim, eps): + fn = SpectralNorm(name, dim, eps) + weight = module._parameters[name] + height = weight.size(dim) + + u = F.normalize(weight.new_empty(height).normal_(0, 1), dim=0, eps=fn.eps) + v = F.normalize(weight.new_empty(int(weight.numel() / height)).normal_(0, 1), dim=0, eps=fn.eps) + delattr(module, fn.name) + module.register_parameter(fn.name + "_orig", weight) + # We still need to assign weight back as fn.name because all sorts of + # things may assume that it exists, e.g., when initializing weights. + # However, we can't directly assign as it could be an nn.Parameter and + # gets added as a parameter. Instead, we register weight.data as a + # buffer, which will cause weight to be included in the state dict + # and also supports nn.init due to shared storage. + module.register_buffer(fn.name, weight.data) + module.register_buffer(fn.name + "_u", u) + module.register_buffer(fn.name + "_v", v) + + setattr(module, POWER_ITERATION_FN, types.MethodType(fn.get_update_method(module), module)) + + module.register_forward_pre_hook(fn) + return fn + + +def inplace_spectral_norm(module, name='weight', dim=None, eps=1e-12): + r"""Applies spectral normalization to a parameter in the given module. + .. math:: + \mathbf{W} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})} \\ + \sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2} + Spectral normalization stabilizes the training of discriminators (critics) + in Generaive Adversarial Networks (GANs) by rescaling the weight tensor + with spectral norm :math:`\sigma` of the weight matrix calculated using + power iteration method. If the dimension of the weight tensor is greater + than 2, it is reshaped to 2D in power iteration method to get spectral + norm. This is implemented via a hook that calculates spectral norm and + rescales weight before every :meth:`~Module.forward` call. + See `Spectral Normalization for Generative Adversarial Networks`_ . + .. _`Spectral Normalization for Generative Adversarial Networks`: https://arxiv.org/abs/1802.05957 + Args: + module (nn.Module): containing module + name (str, optional): name of weight parameter + n_power_iterations (int, optional): number of power iterations to + calculate spectal norm + dim (int, optional): dimension corresponding to number of outputs, + the default is 0, except for modules that are instances of + ConvTranspose1/2/3d, when it is 1 + eps (float, optional): epsilon for numerical stability in + calculating norms + Returns: + The original module with the spectal norm hook + Example:: + >>> m = spectral_norm(nn.Linear(20, 40)) + Linear (20 -> 40) + >>> m.weight_u.size() + torch.Size([20]) + """ + if dim is None: + if isinstance(module, (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d)): + dim = 1 + else: + dim = 0 + SpectralNorm.apply(module, name, dim=dim, eps=eps) + return module + + +def remove_spectral_norm(module, name='weight'): + r"""Removes the spectral normalization reparameterization from a module. + Args: + module (nn.Module): containing module + name (str, optional): name of weight parameter + Example: + >>> m = spectral_norm(nn.Linear(40, 10)) + >>> remove_spectral_norm(m) + """ + for k, hook in module._forward_pre_hooks.items(): + if isinstance(hook, SpectralNorm) and hook.name == name: + hook.remove(module) + del module._forward_pre_hooks[k] + return module + + raise ValueError("spectral_norm of '{}' not found in {}".format(name, module)) + + +def add_spectral_norm(model, logger=None): + """Applies spectral norm to all modules within the scope of a CNF.""" + + def apply_spectral_norm(module): + if 'weight' in module._parameters: + if logger: logger.info("Adding spectral norm to {}".format(module)) + inplace_spectral_norm(module, 'weight') + + def find_coupling_layer(module): + if isinstance(module, CouplingLayer): + module.apply(apply_spectral_norm) + else: + for child in module.children(): + find_coupling_layer(child) + + find_coupling_layer(model) + + +def spectral_norm_power_iteration(model, n_power_iterations=1): + + def recursive_power_iteration(module): + if hasattr(module, POWER_ITERATION_FN): + getattr(module, POWER_ITERATION_FN)(n_power_iterations) + + model.apply(recursive_power_iteration) + +def reparameterize_gaussian(mean, logvar): + std = torch.exp(0.5 * logvar) + eps = torch.randn(std.size()).to(mean) + return mean + std * eps + + + +def gaussian_entropy(logvar): + const = 0.5 * float(logvar.size(1)) * (1. + np.log(np.pi * 2)) + ent = 0.5 * logvar.sum(dim=1, keepdim=False) + const + return ent + + +def standard_normal_logprob(z): + dim = z.size(-1) + log_z = -0.5 * dim * np.log(2 * np.pi) + return log_z - z.pow(2) / 2 + +def truncated_normal_(tensor, mean=0, std=1, trunc_std=2): + """ + Taken from https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/15 + """ + size = tensor.shape + tmp = tensor.new_empty(size + (4,)).normal_() + valid = (tmp < trunc_std) & (tmp > -trunc_std) + ind = valid.max(-1, keepdim=True)[1] + tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1)) + tensor.data.mul_(std).add_(mean) + return tensor \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/tokenizers/dinov2.py b/gen_3d_modules/TriplaneGaussian/models/tokenizers/dinov2.py new file mode 100644 index 0000000..e342c76 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/tokenizers/dinov2.py @@ -0,0 +1,1175 @@ +# coding=utf-8 +# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch DINOv2 model.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Set, Tuple, Union +from dataclasses import dataclass + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BackboneOutput, + BaseModelOutput, + BaseModelOutputWithPooling, + ImageClassifierOutput, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import ( + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from transformers.utils.backbone_utils import BackboneMixin +from transformers.models.dinov2.configuration_dinov2 import Dinov2Config + +from TriplaneGaussian.models.transformers import MemoryEfficientAttentionMixin +from TriplaneGaussian.utils.typing import * + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "Dinov2Config" + +# Base docstring +_CHECKPOINT_FOR_DOC = "facebook/dinov2-base" +_EXPECTED_OUTPUT_SHAPE = [1, 257, 768] + +# Image classification docstring +_IMAGE_CLASS_CHECKPOINT = "facebook/dinov2-base" + + +DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "facebook/dinov2-base", + # See all DINOv2 models at https://huggingface.co/models?filter=dinov2 +] + + +class Dinov2Embeddings(nn.Module): + """ + Construct the CLS token, mask token, position and patch embeddings. + """ + + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + # register as mask token as it's not used in optimization + # to avoid the use of find_unused_parameters_true + # self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size)) + self.register_buffer("mask_token", torch.zeros(1, config.hidden_size)) + self.patch_embeddings = Dinov2PatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter( + torch.randn(1, num_patches + 1, config.hidden_size) + ) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def interpolate_pos_encoding( + self, embeddings: torch.Tensor, height: int, width: int + ) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + height = height // self.config.patch_size + width = width // self.config.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + height, width = height + 0.1, width + 0.1 + patch_pos_embed = patch_pos_embed.reshape( + 1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim + ) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=( + height / math.sqrt(num_positions), + width / math.sqrt(num_positions), + ), + mode="bicubic", + align_corners=False, + ) + if ( + int(height) != patch_pos_embed.shape[-2] + or int(width) != patch_pos_embed.shape[-1] + ): + raise ValueError( + "Width or height does not match with the interpolated position embeddings" + ) + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward( + self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + patch_embeddings = self.patch_embeddings(pixel_values) + embeddings = patch_embeddings + + if bool_masked_pos is not None: + embeddings = torch.where( + bool_masked_pos.unsqueeze(-1), + self.mask_token.to(embeddings.dtype).unsqueeze(0), + embeddings, + ) + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + embeddings = embeddings + self.interpolate_pos_encoding( + embeddings, height, width + ) + + embeddings = self.dropout(embeddings) + + return embeddings + + +class Dinov2PatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = ( + image_size + if isinstance(image_size, collections.abc.Iterable) + else (image_size, image_size) + ) + patch_size = ( + patch_size + if isinstance(patch_size, collections.abc.Iterable) + else (patch_size, patch_size) + ) + num_patches = (image_size[1] // patch_size[1]) * ( + image_size[0] // patch_size[0] + ) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d( + num_channels, hidden_size, kernel_size=patch_size, stride=patch_size + ) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2 +class Dinov2SelfAttention(nn.Module): + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.attention_probs_dropout_prob = config.attention_probs_dropout_prob + + self.query = nn.Linear( + config.hidden_size, self.all_head_size, bias=config.qkv_bias + ) + self.key = nn.Linear( + config.hidden_size, self.all_head_size, bias=config.qkv_bias + ) + self.value = nn.Linear( + config.hidden_size, self.all_head_size, bias=config.qkv_bias + ) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.use_memory_efficient_attention_xformers: bool = False + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + if self.use_memory_efficient_attention_xformers: + import xformers + assert head_mask is None and not output_attentions + new_size = hidden_states.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + key_layer = self.key(hidden_states).view(new_size) + value_layer = self.value(hidden_states).view(new_size) + query_layer = mixed_query_layer.view(new_size) + context_layer = xformers.ops.memory_efficient_attention( + query_layer, key_layer, value_layer, p=self.attention_probs_dropout_prob + ) + context_layer = context_layer.view(*hidden_states.size()[:-1], -1) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + return outputs + + def set_use_memory_efficient_attention_xformers( + self, valid: bool, attention_op: Optional[Callable] = None + ): + self.use_memory_efficient_attention_xformers = valid + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2 +class Dinov2SelfOutput(nn.Module): + """ + The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2 +class Dinov2Attention(nn.Module): + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + self.attention = Dinov2SelfAttention(config) + self.output = Dinov2SelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.attention.num_attention_heads, + self.attention.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len( + heads + ) + self.attention.all_head_size = ( + self.attention.attention_head_size * self.attention.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +class Dinov2LayerScale(nn.Module): + def __init__(self, config) -> None: + super().__init__() + self.lambda1 = nn.Parameter( + config.layerscale_value * torch.ones(config.hidden_size) + ) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + return hidden_state * self.lambda1 + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path( + input: torch.Tensor, drop_prob: float = 0.0, training: bool = False +) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, + however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the + layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the + argument. + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * ( + input.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand( + shape, dtype=input.dtype, device=input.device + ) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.beit.modeling_beit.BeitDropPath +class Dinov2DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: Optional[float] = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Dinov2MLP(nn.Module): + def __init__(self, config) -> None: + super().__init__() + in_features = out_features = config.hidden_size + hidden_features = int(config.hidden_size * config.mlp_ratio) + self.fc1 = nn.Linear(in_features, hidden_features, bias=True) + if isinstance(config.hidden_act, str): + self.activation = ACT2FN[config.hidden_act] + else: + self.activation = config.hidden_act + self.fc2 = nn.Linear(hidden_features, out_features, bias=True) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + hidden_state = self.fc1(hidden_state) + hidden_state = self.activation(hidden_state) + hidden_state = self.fc2(hidden_state) + return hidden_state + + +class Dinov2SwiGLUFFN(nn.Module): + def __init__(self, config) -> None: + super().__init__() + in_features = out_features = config.hidden_size + hidden_features = int(config.hidden_size * config.mlp_ratio) + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + + self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True) + self.weights_out = nn.Linear(hidden_features, out_features, bias=True) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + hidden_state = self.weights_in(hidden_state) + x1, x2 = hidden_state.chunk(2, dim=-1) + hidden = nn.functional.silu(x1) * x2 + return self.weights_out(hidden) + + +class Dinov2Layer(nn.Module, MemoryEfficientAttentionMixin): + """This corresponds to the Block class in the original implementation.""" + + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + + self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.norm1_modulation = None + self.attention = Dinov2Attention(config) + self.layer_scale1 = Dinov2LayerScale(config) + self.drop_path1 = ( + Dinov2DropPath(config.drop_path_rate) + if config.drop_path_rate > 0.0 + else nn.Identity() + ) + + self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.norm2_modulation = None + + if config.use_swiglu_ffn: + self.mlp = Dinov2SwiGLUFFN(config) + else: + self.mlp = Dinov2MLP(config) + self.layer_scale2 = Dinov2LayerScale(config) + self.drop_path2 = ( + Dinov2DropPath(config.drop_path_rate) + if config.drop_path_rate > 0.0 + else nn.Identity() + ) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + modulation_cond: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + hidden_states_norm = self.norm1(hidden_states) + if self.norm1_modulation is not None: + assert modulation_cond is not None + hidden_states_norm = self.norm1_modulation( + hidden_states_norm, modulation_cond + ) + self_attention_outputs = self.attention( + hidden_states_norm, # in Dinov2, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + + attention_output = self.layer_scale1(attention_output) + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states + + # in Dinov2, layernorm is also applied after self-attention + layer_output = self.norm2(hidden_states) + if self.norm2_modulation is not None: + assert modulation_cond is not None + layer_output = self.norm2_modulation(layer_output, modulation_cond) + layer_output = self.mlp(layer_output) + layer_output = self.layer_scale2(layer_output) + + # second residual connection + layer_output = layer_output + hidden_states + + outputs = (layer_output,) + outputs + + return outputs + + def register_ada_norm_modulation(self, norm1_mod: nn.Module, norm2_mod: nn.Module): + self.norm1_modulation = norm1_mod + self.norm2_modulation = norm2_mod + + +# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->Dinov2 +class Dinov2Encoder(nn.Module, MemoryEfficientAttentionMixin): + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [Dinov2Layer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + modulation_cond: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + layer_head_mask, + modulation_cond, + ) + else: + layer_outputs = layer_module( + hidden_states, layer_head_mask, modulation_cond, output_attentions + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, all_hidden_states, all_self_attentions] + if v is not None + ) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class Dinov2PreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Dinov2Config + base_model_prefix = "dinov2" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid + # `trunc_normal_cpu` not implemented in `half` issues + module.weight.data = nn.init.trunc_normal_( + module.weight.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.weight.dtype) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, Dinov2Embeddings): + module.position_embeddings.data = nn.init.trunc_normal_( + module.position_embeddings.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.position_embeddings.dtype) + + module.cls_token.data = nn.init.trunc_normal_( + module.cls_token.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.cls_token.dtype) + + def _set_gradient_checkpointing( + self, module: Dinov2Encoder, value: bool = False + ) -> None: + if isinstance(module, Dinov2Encoder): + module.gradient_checkpointing = value + + +DINOV2_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`Dinov2Config`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +DINOV2_BASE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`BitImageProcessor.preprocess`] for details. + + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for + pre-training. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +DINOV2_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`BitImageProcessor.preprocess`] for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +@dataclass +class CustomBaseModelOutputWithPooling(BaseModelOutputWithPooling): + patch_embeddings: Optional[torch.FloatTensor] = None + + +@add_start_docstrings( + "The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.", + DINOV2_START_DOCSTRING, +) +class Dinov2Model(Dinov2PreTrainedModel, MemoryEfficientAttentionMixin): + def __init__(self, config: Dinov2Config): + super().__init__(config) + self.config = config + + self.embeddings = Dinov2Embeddings(config) + self.encoder = Dinov2Encoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> Dinov2PatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(DINOV2_BASE_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPooling, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + modulation_cond: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + pixel_values, bool_masked_pos=bool_masked_pos + ) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + modulation_cond=modulation_cond, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = sequence_output[:, 0, :] + + if not return_dict: + head_outputs = (sequence_output, pooled_output) + return head_outputs + encoder_outputs[1:] + + return CustomBaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + patch_embeddings=embedding_output + ) + + def set_gradient_checkpointing(self, value: bool = False) -> None: + self._set_gradient_checkpointing(self.encoder, value) + + +@add_start_docstrings( + """ + Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state + of the [CLS] token) e.g. for ImageNet. + """, + DINOV2_START_DOCSTRING, +) +class Dinov2ForImageClassification(Dinov2PreTrainedModel): + def __init__(self, config: Dinov2Config) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.dinov2 = Dinov2Model(config) + + # Classifier head + self.classifier = ( + nn.Linear(config.hidden_size * 2, config.num_labels) + if config.num_labels > 0 + else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_IMAGE_CLASS_CHECKPOINT, + output_type=ImageClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + outputs = self.dinov2( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] # batch_size, sequence_length, hidden_size + + cls_token = sequence_output[:, 0] + patch_tokens = sequence_output[:, 1:] + + linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1) + + logits = self.classifier(linear_input) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and ( + labels.dtype == torch.long or labels.dtype == torch.int + ): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Dinov2 backbone, to be used with frameworks like DETR and MaskFormer. + """, + DINOV2_START_DOCSTRING, +) +class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin): + def __init__(self, config): + super().__init__(config) + super()._init_backbone(config) + + self.num_features = [ + config.hidden_size for _ in range(config.num_hidden_layers + 1) + ] + self.embeddings = Dinov2Embeddings(config) + self.encoder = Dinov2Encoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> Dinov2PatchEmbeddings: + return self.embeddings.patch_embeddings + + @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.Tensor, + output_hidden_states: Optional[bool] = None, + output_attentions: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> BackboneOutput: + """ + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoBackbone + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base") + >>> model = AutoBackbone.from_pretrained( + ... "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"] + ... ) + + >>> inputs = processor(image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 768, 16, 16] + ```""" + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + + embedding_output = self.embeddings(pixel_values) + + outputs = self.encoder( + embedding_output, + output_hidden_states=True, + output_attentions=output_attentions, + return_dict=return_dict, + ) + + hidden_states = outputs.hidden_states if return_dict else outputs[1] + + feature_maps = () + for stage, hidden_state in zip(self.stage_names, hidden_states): + if stage in self.out_features: + if self.config.apply_layernorm: + hidden_state = self.layernorm(hidden_state) + if self.config.reshape_hidden_states: + batch_size, _, height, width = pixel_values.shape + patch_size = self.config.patch_size + hidden_state = hidden_state[:, 1:, :].reshape( + batch_size, width // patch_size, height // patch_size, -1 + ) + hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() + feature_maps += (hidden_state,) + + if not return_dict: + if output_hidden_states: + output = (feature_maps,) + outputs[1:] + else: + output = (feature_maps,) + outputs[2:] + return output + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=outputs.attentions if output_attentions else None, + ) + + + +class CustomPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, image_size: int, patch_size: int, num_channels: int, hidden_size: int): + super().__init__() + + image_size = ( + image_size + if isinstance(image_size, collections.abc.Iterable) + else (image_size, image_size) + ) + patch_size = ( + patch_size + if isinstance(patch_size, collections.abc.Iterable) + else (patch_size, patch_size) + ) + num_patches = (image_size[1] // patch_size[1]) * ( + image_size[0] // patch_size[0] + ) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d( + num_channels, hidden_size, kernel_size=patch_size, stride=patch_size + ) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +class CustomEmbeddings(nn.Module): + """ + Construct the CLS token, mask token, position and patch embeddings. + """ + + def __init__(self, image_size: int, patch_size: int, num_channels: int, hidden_size: int) -> None: + super().__init__() + + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.hidden_size = hidden_size + + self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size)) + + self.patch_embeddings = CustomPatchEmbeddings(image_size, patch_size, num_channels, hidden_size) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter( + torch.randn(1, num_patches + 1, self.hidden_size) + ) + + def interpolate_pos_encoding( + self, embeddings: torch.Tensor, height: int, width: int + ) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + height = height // self.patch_size + width = width // self.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + height, width = height + 0.1, width + 0.1 + patch_pos_embed = patch_pos_embed.reshape( + 1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim + ) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=( + height / math.sqrt(num_positions), + width / math.sqrt(num_positions), + ), + mode="bicubic", + align_corners=False, + ) + if ( + int(height) != patch_pos_embed.shape[-2] + or int(width) != patch_pos_embed.shape[-1] + ): + raise ValueError( + "Width or height does not match with the interpolated position embeddings" + ) + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward( + self, pixel_values: torch.Tensor, + ) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + patch_embeddings = self.patch_embeddings(pixel_values) + embeddings = patch_embeddings + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + embeddings = embeddings + self.interpolate_pos_encoding( + embeddings, height, width + ) + + return embeddings \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/tokenizers/image.py b/gen_3d_modules/TriplaneGaussian/models/tokenizers/image.py new file mode 100644 index 0000000..09bf081 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/tokenizers/image.py @@ -0,0 +1,135 @@ +from dataclasses import dataclass + +import torch +import torch.nn as nn +from einops import rearrange + +from TriplaneGaussian.utils.base import BaseModule +from TriplaneGaussian.models.tokenizers.dinov2 import Dinov2Model +from TriplaneGaussian.models.transformers import Modulation +from TriplaneGaussian.utils.typing import * + +class DINOV2SingleImageTokenizer(BaseModule): + @dataclass + class Config(BaseModule.Config): + pretrained_model_name_or_path: str = "facebook/dinov2-base" + width: int = 224 + height: int = 224 + modulation: bool = False + modulation_zero_init: bool = False + modulation_single_layer: bool = False + modulation_cond_dim: int = 16 + freeze_backbone_params: bool = True + enable_memory_efficient_attention: bool = False + enable_gradient_checkpointing: bool = False + use_patch_embeddings: bool = False + patch_embeddings_aggr_method: str = 'concat' + + cfg: Config + + def configure(self) -> None: + super().configure() + model: Dinov2Model + + if self.cfg.freeze_backbone_params: + # freeze dino backbone parameters + self.register_non_module( + "model", + Dinov2Model.from_pretrained(self.cfg.pretrained_model_name_or_path).to( + self.device + ), + ) + + model = self.non_module("model") + for p in model.parameters(): + p.requires_grad_(False) + model.eval() + else: + self.model = Dinov2Model.from_pretrained( + self.cfg.pretrained_model_name_or_path + ).to(self.device) + model = self.model + + model.set_use_memory_efficient_attention_xformers( + self.cfg.enable_memory_efficient_attention + ) + model.set_gradient_checkpointing(self.cfg.enable_gradient_checkpointing) + + # add modulation + if self.cfg.modulation: + modulations = [] + for layer in model.encoder.layer: + norm1_modulation = Modulation( + model.config.hidden_size, + self.cfg.modulation_cond_dim, + zero_init=self.cfg.modulation_zero_init, + single_layer=self.cfg.modulation_single_layer, + ) + norm2_modulation = Modulation( + model.config.hidden_size, + self.cfg.modulation_cond_dim, + zero_init=self.cfg.modulation_zero_init, + single_layer=self.cfg.modulation_single_layer, + ) + layer.register_ada_norm_modulation(norm1_modulation, norm2_modulation) + modulations += [norm1_modulation, norm2_modulation] + self.modulations = nn.ModuleList(modulations) + + self.register_buffer( + "image_mean", + torch.as_tensor([0.485, 0.456, 0.406]).reshape(1, 1, 3, 1, 1), + persistent=False, + ) + self.register_buffer( + "image_std", + torch.as_tensor([0.229, 0.224, 0.225]).reshape(1, 1, 3, 1, 1), + persistent=False, + ) + + def forward( + self, + images: Float[Tensor, "B *N C H W"], + modulation_cond: Optional[Float[Tensor, "B *N Cc"]], + ) -> Float[Tensor, "B *N Ct Nt"]: + model: Dinov2Model + if self.cfg.freeze_backbone_params: + model = self.non_module("model") + else: + model = self.model + + packed = False + if images.ndim == 4: + packed = True + images = images.unsqueeze(1) + if modulation_cond is not None: + assert modulation_cond.ndim == 2 + modulation_cond = modulation_cond.unsqueeze(1) + + batch_size, n_input_views = images.shape[:2] + images = (images - self.image_mean) / self.image_std + out = model( + rearrange(images, "B N C H W -> (B N) C H W"), + modulation_cond=rearrange(modulation_cond, "B N Cc -> (B N) Cc") + if modulation_cond is not None + else None, + ) + local_features, global_features = out.last_hidden_state, out.pooler_output + if self.cfg.use_patch_embeddings: + patch_embeddings = out.patch_embeddings + if self.cfg.patch_embeddings_aggr_method == 'concat': + local_features = torch.cat([local_features, patch_embeddings], dim=1) + elif self.cfg.patch_embeddings_aggr_method == 'add': + local_features = local_features + patch_embeddings + else: + raise NotImplementedError + local_features = local_features.permute(0, 2, 1) + local_features = rearrange( + local_features, "(B N) Ct Nt -> B N Ct Nt", B=batch_size + ) + if packed: + local_features = local_features.squeeze(1) + + return local_features + + def detokenize(self, *args, **kwargs): + raise NotImplementedError \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/tokenizers/point.py b/gen_3d_modules/TriplaneGaussian/models/tokenizers/point.py new file mode 100644 index 0000000..5d52454 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/tokenizers/point.py @@ -0,0 +1,29 @@ +from dataclasses import dataclass +import torch.nn as nn +from TriplaneGaussian.utils.base import BaseModule +from TriplaneGaussian.utils.typing import * +import torch + +class PointLearnablePositionalEmbedding(BaseModule): + @dataclass + class Config(BaseModule.Config): + num_pcl: int = 2048 + num_channels: int = 512 + + cfg: Config + + def configure(self) -> None: + super().configure() + self.pcl_embeddings = nn.Embedding( + self.cfg.num_pcl , self.cfg.num_channels + ) + + def forward(self, batch_size: int) -> Float[Tensor, "B Ct Nt"]: + range_ = torch.arange(self.cfg.num_pcl, device=self.device) + embeddings = self.pcl_embeddings(range_).unsqueeze(0).repeat((batch_size,1,1)) + return torch.permute(embeddings, (0,2,1)) + + def detokenize( + self, tokens: Float[Tensor, "B Ct Nt"] + ) -> Float[Tensor, "B 3 Ct Hp Wp"]: + return torch.permute(tokens, (0,2,1)) \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/models/tokenizers/triplane.py b/gen_3d_modules/TriplaneGaussian/models/tokenizers/triplane.py new file mode 100644 index 0000000..bee93a0 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/tokenizers/triplane.py @@ -0,0 +1,52 @@ +from dataclasses import dataclass +import math + +import torch +import torch.nn as nn +from einops import rearrange, repeat + +from TriplaneGaussian.utils.base import BaseModule +from TriplaneGaussian.utils.typing import * + + +class TriplaneLearnablePositionalEmbedding(BaseModule): + @dataclass + class Config(BaseModule.Config): + plane_size: int = 32 + num_channels: int = 1024 + + cfg: Config + + def configure(self) -> None: + super().configure() + self.embeddings = nn.Parameter( + torch.randn( + (3, self.cfg.num_channels, self.cfg.plane_size, self.cfg.plane_size), + dtype=torch.float32, + ) + * 1 + / math.sqrt(self.cfg.num_channels) + ) + + def forward(self, batch_size: int, cond_embeddings: Float[Tensor, "B Ct"] = None) -> Float[Tensor, "B Ct Nt"]: + embeddings = repeat(self.embeddings, "Np Ct Hp Wp -> B Np Ct Hp Wp", B=batch_size) + if cond_embeddings is not None: + embeddings = embeddings + cond_embeddings + return rearrange( + embeddings, + "B Np Ct Hp Wp -> B Ct (Np Hp Wp)", + ) + + def detokenize( + self, tokens: Float[Tensor, "B Ct Nt"] + ) -> Float[Tensor, "B 3 Ct Hp Wp"]: + batch_size, Ct, Nt = tokens.shape + assert Nt == self.cfg.plane_size**2 * 3 + assert Ct == self.cfg.num_channels + return rearrange( + tokens, + "B Ct (Np Hp Wp) -> B Np Ct Hp Wp", + Np=3, + Hp=self.cfg.plane_size, + Wp=self.cfg.plane_size, + ) diff --git a/gen_3d_modules/TriplaneGaussian/models/transformers.py b/gen_3d_modules/TriplaneGaussian/models/transformers.py new file mode 100644 index 0000000..82ccb14 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/models/transformers.py @@ -0,0 +1,908 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.utils.torch_utils import maybe_allow_in_graph +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import Attention +from diffusers.models.embeddings import CombinedTimestepLabelEmbeddings + +from dataclasses import dataclass +from ..utils.base import BaseModule +from ..utils.typing import * + + +class MemoryEfficientAttentionMixin: + def enable_xformers_memory_efficient_attention( + self, attention_op: Optional[Callable] = None + ): + r""" + Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this + option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed + up during training is not guaranteed. + + + + ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes + precedent. + + + + Parameters: + attention_op (`Callable`, *optional*): + Override the default `None` operator for use as `op` argument to the + [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention) + function of xFormers. + + Examples: + + ```py + >>> import torch + >>> from diffusers import DiffusionPipeline + >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp + + >>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) + >>> pipe = pipe.to("cuda") + >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) + >>> # Workaround for not accepting attention shape using VAE for Flash Attention + >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None) + ``` + """ + self.set_use_memory_efficient_attention_xformers(True, attention_op) + + def disable_xformers_memory_efficient_attention(self): + r""" + Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). + """ + self.set_use_memory_efficient_attention_xformers(False) + + def set_use_memory_efficient_attention_xformers( + self, valid: bool, attention_op: Optional[Callable] = None + ) -> None: + # Recursively walk through all the children. + # Any children which exposes the set_use_memory_efficient_attention_xformers method + # gets the message + def fn_recursive_set_mem_eff(module: torch.nn.Module): + if hasattr(module, "set_use_memory_efficient_attention_xformers"): + module.set_use_memory_efficient_attention_xformers(valid, attention_op) + + for child in module.children(): + fn_recursive_set_mem_eff(child) + + for module in self.children(): + if isinstance(module, torch.nn.Module): + fn_recursive_set_mem_eff(module) + + +@maybe_allow_in_graph +class GatedSelfAttentionDense(nn.Module): + r""" + A gated self-attention dense layer that combines visual features and object features. + + Parameters: + query_dim (`int`): The number of channels in the query. + context_dim (`int`): The number of channels in the context. + n_heads (`int`): The number of heads to use for attention. + d_head (`int`): The number of channels in each head. + """ + + def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj feature + self.linear = nn.Linear(context_dim, query_dim) + + self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head) + self.ff = FeedForward(query_dim, activation_fn="geglu") + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0))) + self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0))) + + self.enabled = True + + def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor: + if not self.enabled: + return x + + n_visual = x.shape[1] + objs = self.linear(objs) + + x = ( + x + + self.alpha_attn.tanh() + * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :] + ) + x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x)) + + return x + + +@maybe_allow_in_graph +class BasicTransformerBlock(nn.Module, MemoryEfficientAttentionMixin): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the attention computation to float32. This is useful for mixed precision training. + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + attention_type (`str`, *optional*, defaults to `"default"`): + The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + cond_dim_ada_norm_continuous: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + attention_type: str = "default", + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = ( + num_embeds_ada_norm is not None + ) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = ( + num_embeds_ada_norm is not None + ) and norm_type == "ada_norm" + self.use_ada_layer_norm_continuous = ( + cond_dim_ada_norm_continuous is not None + ) and norm_type == "ada_norm_continuous" + + assert ( + int(self.use_ada_layer_norm) + + int(self.use_ada_layer_norm_continuous) + + int(self.use_ada_layer_norm_zero) + <= 1 + ) + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_continuous: + self.norm1 = AdaLayerNormContinuous(dim, cond_dim_ada_norm_continuous) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + if self.use_ada_layer_norm: + self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_continuous: + self.norm2 = AdaLayerNormContinuous(dim, cond_dim_ada_norm_continuous) + else: + self.norm2 = nn.LayerNorm( + dim, elementwise_affine=norm_elementwise_affine + ) + + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim + if not double_self_attention + else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + if self.use_ada_layer_norm_continuous: + self.norm3 = AdaLayerNormContinuous(dim, cond_dim_ada_norm_continuous) + else: + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward( + dim, + dropout=dropout, + activation_fn=activation_fn, + final_dropout=final_dropout, + ) + + # 4. Fuser + if attention_type == "gated" or attention_type == "gated-text-image": + self.fuser = GatedSelfAttentionDense( + dim, cross_attention_dim, num_attention_heads, attention_head_dim + ) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + modulation_cond: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ) -> torch.FloatTensor: + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_continuous: + norm_hidden_states = self.norm1(hidden_states, modulation_cond) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + # 1. Retrieve lora scale. + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + # 2. Prepare GLIGEN inputs + cross_attention_kwargs = ( + cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + ) + gligen_kwargs = cross_attention_kwargs.pop("gligen", None) + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if self.only_cross_attention + else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # 2.5 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + # 2.5 ends + + # 3. Cross-Attention + if self.attn2 is not None: + if self.use_ada_layer_norm: + norm_hidden_states = self.norm2(hidden_states, timestep) + elif self.use_ada_layer_norm_continuous: + norm_hidden_states = self.norm2(hidden_states, modulation_cond) + else: + norm_hidden_states = self.norm2(hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + if self.use_ada_layer_norm_continuous: + norm_hidden_states = self.norm3(hidden_states, modulation_cond) + else: + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = ( + norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ) + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [ + self.ff(hid_slice, scale=lora_scale) + for hid_slice in norm_hidden_states.chunk( + num_chunks, dim=self._chunk_dim + ) + ], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states, scale=lora_scale) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states + + +class FeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + dim (`int`): The number of channels in the input. + dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. + mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. + """ + + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + activation_fn: str = "geglu", + final_dropout: bool = False, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + linear_cls = nn.Linear + + if activation_fn == "gelu": + act_fn = GELU(dim, inner_dim) + if activation_fn == "gelu-approximate": + act_fn = GELU(dim, inner_dim, approximate="tanh") + elif activation_fn == "geglu": + act_fn = GEGLU(dim, inner_dim) + elif activation_fn == "geglu-approximate": + act_fn = ApproximateGELU(dim, inner_dim) + + self.net = nn.ModuleList([]) + # project in + self.net.append(act_fn) + # project dropout + self.net.append(nn.Dropout(dropout)) + # project out + self.net.append(linear_cls(inner_dim, dim_out)) + # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout + if final_dropout: + self.net.append(nn.Dropout(dropout)) + + def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +class GELU(nn.Module): + r""" + GELU activation function with tanh approximation support with `approximate="tanh"`. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation. + """ + + def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out) + self.approximate = approximate + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + if gate.device.type != "mps": + return F.gelu(gate, approximate=self.approximate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to( + dtype=gate.dtype + ) + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = self.gelu(hidden_states) + return hidden_states + + +class GEGLU(nn.Module): + r""" + A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + linear_cls = nn.Linear + + self.proj = linear_cls(dim_in, dim_out * 2) + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + if gate.device.type != "mps": + return F.gelu(gate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype) + + def forward(self, hidden_states, scale: float = 1.0): + args = () + hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1) + return hidden_states * self.gelu(gate) + + +class ApproximateGELU(nn.Module): + r""" + The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2: + https://arxiv.org/abs/1606.08415. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + return x * torch.sigmoid(1.702 * x) + + +class AdaLayerNorm(nn.Module): + r""" + Norm layer modified to incorporate timestep embeddings. + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + num_embeddings (`int`): The size of the dictionary of embeddings. + """ + + def __init__(self, embedding_dim: int, num_embeddings: int): + super().__init__() + self.emb = nn.Embedding(num_embeddings, embedding_dim) + self.silu = nn.SiLU() + self.linear = nn.Linear(embedding_dim, embedding_dim * 2) + self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False) + + def forward(self, x: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor: + emb = self.linear(self.silu(self.emb(timestep))) + scale, shift = torch.chunk(emb, 2, dim=1) + x = self.norm(x) * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + return x + + +class AdaLayerNormContinuous(nn.Module): + r""" + Norm layer modified to incorporate arbitrary continuous embeddings. + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + """ + + def __init__(self, embedding_dim: int, condition_dim: int): + super().__init__() + self.silu = nn.SiLU() + self.linear1 = nn.Linear(condition_dim, condition_dim) + self.linear2 = nn.Linear(condition_dim, embedding_dim * 2) + self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False) + + def forward(self, x: torch.Tensor, condition: torch.Tensor) -> torch.Tensor: + emb = self.linear2(self.silu(self.linear1(condition))) + scale, shift = torch.chunk(emb, 2, dim=1) + x = self.norm(x) * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + return x + + +class Modulation(nn.Module): + def __init__(self, embedding_dim: int, condition_dim: int, zero_init: bool = False, single_layer: bool = False): + super().__init__() + self.silu = nn.SiLU() + if single_layer: + self.linear1 = nn.Identity() + else: + self.linear1 = nn.Linear(condition_dim, condition_dim) + + self.linear2 = nn.Linear(condition_dim, embedding_dim * 2) + + # Only zero init the last linear layer + if zero_init: + nn.init.zeros_(self.linear2.weight) + nn.init.zeros_(self.linear2.bias) + + def forward(self, x: torch.Tensor, condition: torch.Tensor) -> torch.Tensor: + emb = self.linear2(self.silu(self.linear1(condition))) + scale, shift = torch.chunk(emb, 2, dim=1) + x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + return x + + +class AdaLayerNormZero(nn.Module): + r""" + Norm layer adaptive layer norm zero (adaLN-Zero). + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + num_embeddings (`int`): The size of the dictionary of embeddings. + """ + + def __init__(self, embedding_dim: int, num_embeddings: int): + super().__init__() + + self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim) + + self.silu = nn.SiLU() + self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) + self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) + + def forward( + self, + x: torch.Tensor, + timestep: torch.Tensor, + class_labels: torch.LongTensor, + hidden_dtype: Optional[torch.dtype] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + emb = self.linear( + self.silu(self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)) + ) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk( + 6, dim=1 + ) + x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return x, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +class AdaGroupNorm(nn.Module): + r""" + GroupNorm layer modified to incorporate timestep embeddings. + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + num_embeddings (`int`): The size of the dictionary of embeddings. + num_groups (`int`): The number of groups to separate the channels into. + act_fn (`str`, *optional*, defaults to `None`): The activation function to use. + eps (`float`, *optional*, defaults to `1e-5`): The epsilon value to use for numerical stability. + """ + + def __init__( + self, + embedding_dim: int, + out_dim: int, + num_groups: int, + act_fn: Optional[str] = None, + eps: float = 1e-5, + ): + super().__init__() + self.num_groups = num_groups + self.eps = eps + + if act_fn is None: + self.act = None + else: + self.act = get_activation(act_fn) + + self.linear = nn.Linear(embedding_dim, out_dim * 2) + + def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: + if self.act: + emb = self.act(emb) + emb = self.linear(emb) + emb = emb[:, :, None, None] + scale, shift = emb.chunk(2, dim=1) + + x = F.group_norm(x, self.num_groups, eps=self.eps) + x = x * (1 + scale) + shift + return x + +class Transformer1D(BaseModule, MemoryEfficientAttentionMixin): + """ + A 1D Transformer model for sequence data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + @dataclass + class Config(BaseModule.Config): + num_attention_heads: int = 16 + attention_head_dim: int = 88 + in_channels: Optional[int] = None + out_channels: Optional[int] = None + num_layers: int = 1 + dropout: float = 0.0 + norm_num_groups: int = 32 + cross_attention_dim: Optional[int] = None + attention_bias: bool = False + activation_fn: str = "geglu" + num_embeds_ada_norm: Optional[int] = None + cond_dim_ada_norm_continuous: Optional[int] = None + only_cross_attention: bool = False + double_self_attention: bool = False + upcast_attention: bool = False + norm_type: str = "layer_norm" + norm_elementwise_affine: bool = True + attention_type: str = "default" + enable_memory_efficient_attention: bool = False + gradient_checkpointing: bool = False + + cfg: Config + + def configure(self) -> None: + super().configure() + + self.num_attention_heads = self.cfg.num_attention_heads + self.attention_head_dim = self.cfg.attention_head_dim + inner_dim = self.num_attention_heads * self.attention_head_dim + + linear_cls = nn.Linear + + if self.cfg.norm_type == "layer_norm" and ( + self.cfg.num_embeds_ada_norm is not None + or self.cfg.cond_dim_ada_norm_continuous is not None + ): + raise ValueError("Incorrect norm_type.") + + # 2. Define input layers + self.in_channels = self.cfg.in_channels + + self.norm = torch.nn.GroupNorm( + num_groups=self.cfg.norm_num_groups, + num_channels=self.cfg.in_channels, + eps=1e-6, + affine=True, + ) + self.proj_in = linear_cls(self.cfg.in_channels, inner_dim) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + self.num_attention_heads, + self.attention_head_dim, + dropout=self.cfg.dropout, + cross_attention_dim=self.cfg.cross_attention_dim, + activation_fn=self.cfg.activation_fn, + num_embeds_ada_norm=self.cfg.num_embeds_ada_norm, + cond_dim_ada_norm_continuous=self.cfg.cond_dim_ada_norm_continuous, + attention_bias=self.cfg.attention_bias, + only_cross_attention=self.cfg.only_cross_attention, + double_self_attention=self.cfg.double_self_attention, + upcast_attention=self.cfg.upcast_attention, + norm_type=self.cfg.norm_type, + norm_elementwise_affine=self.cfg.norm_elementwise_affine, + attention_type=self.cfg.attention_type, + ) + for d in range(self.cfg.num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = ( + self.cfg.in_channels + if self.cfg.out_channels is None + else self.cfg.out_channels + ) + + self.proj_out = linear_cls(inner_dim, self.cfg.in_channels) + + self.gradient_checkpointing = self.cfg.gradient_checkpointing + + self.set_use_memory_efficient_attention_xformers( + self.cfg.enable_memory_efficient_attention + ) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + modulation_cond: Optional[torch.FloatTensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + ): + """ + The [`Transformer1DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + cross_attention_kwargs ( `Dict[str, Any]`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + attention_mask ( `torch.Tensor`, *optional*): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = ( + 1 - encoder_attention_mask.to(hidden_states.dtype) + ) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + batch, _, seq_len = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 1).reshape( + batch, seq_len, inner_dim + ) + hidden_states = self.proj_in(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + if self.training and self.gradient_checkpointing: + hidden_states = torch.utils.checkpoint.checkpoint( + block, + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + timestep, + modulation_cond, + cross_attention_kwargs, + class_labels, + use_reentrant=False, + ) + else: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + modulation_cond=modulation_cond, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, seq_len, inner_dim) + .permute(0, 2, 1) + .contiguous() + ) + + output = hidden_states + residual + + return output diff --git a/gen_3d_modules/TriplaneGaussian/triplane_gaussian_transformers.py b/gen_3d_modules/TriplaneGaussian/triplane_gaussian_transformers.py new file mode 100644 index 0000000..cdb8483 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/triplane_gaussian_transformers.py @@ -0,0 +1,167 @@ +import torch +from dataclasses import dataclass, field +from einops import rearrange + +from TriplaneGaussian import find +from TriplaneGaussian.models.image_feature import ImageFeature +from TriplaneGaussian.utils.saving import SaverMixin +from TriplaneGaussian.utils.config import parse_structured +from TriplaneGaussian.utils.ops import points_projection +from TriplaneGaussian.utils.misc import load_module_weights +from TriplaneGaussian.utils.typing import * + +class TGS(torch.nn.Module, SaverMixin): + @dataclass + class Config: + weights: Optional[str] = None + weights_ignore_modules: Optional[List[str]] = None + + camera_embedder_cls: str = "" + camera_embedder: dict = field(default_factory=dict) + + image_feature: dict = field(default_factory=dict) + + image_tokenizer_cls: str = "" + image_tokenizer: dict = field(default_factory=dict) + + tokenizer_cls: str = "" + tokenizer: dict = field(default_factory=dict) + + backbone_cls: str = "" + backbone: dict = field(default_factory=dict) + + post_processor_cls: str = "" + post_processor: dict = field(default_factory=dict) + + renderer_cls: str = "" + renderer: dict = field(default_factory=dict) + + pointcloud_generator_cls: str = "" + pointcloud_generator: dict = field(default_factory=dict) + + pointcloud_encoder_cls: str = "" + pointcloud_encoder: dict = field(default_factory=dict) + + cfg: Config + + def load_weights(self, weights: str, ignore_modules: Optional[List[str]] = None): + state_dict = load_module_weights( + weights, ignore_modules=ignore_modules, map_location="cpu" + ) + self.load_state_dict(state_dict, strict=False) + + def __init__(self, cfg): + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + + self.image_tokenizer = find(self.cfg.image_tokenizer_cls)( + self.cfg.image_tokenizer + ) + + assert self.cfg.camera_embedder_cls == 'TriplaneGaussian.models.networks.MLP' + weights = self.cfg.camera_embedder.pop("weights") if "weights" in self.cfg.camera_embedder else None + self.camera_embedder = find(self.cfg.camera_embedder_cls)(**self.cfg.camera_embedder) + if weights: + from TriplaneGaussian.utils.misc import load_module_weights + weights_path, module_name = weights.split(":") + state_dict = load_module_weights( + weights_path, module_name=module_name, map_location="cpu" + ) + self.camera_embedder.load_state_dict(state_dict) + + self.image_feature = ImageFeature(self.cfg.image_feature) + + self.tokenizer = find(self.cfg.tokenizer_cls)(self.cfg.tokenizer) + + self.backbone = find(self.cfg.backbone_cls)(self.cfg.backbone) + + self.post_processor = find(self.cfg.post_processor_cls)( + self.cfg.post_processor + ) + + self.renderer = find(self.cfg.renderer_cls)(self.cfg.renderer) + + # pointcloud generator + self.pointcloud_generator = find(self.cfg.pointcloud_generator_cls)(self.cfg.pointcloud_generator) + + self.point_encoder = find(self.cfg.pointcloud_encoder_cls)(self.cfg.pointcloud_encoder) + + # load checkpoint + if self.cfg.weights is not None: + self.load_weights(self.cfg.weights, self.cfg.weights_ignore_modules) + + def _forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + # generate point cloud + out = self.pointcloud_generator(batch) + pointclouds = out["points"] + + batch_size, n_input_views = batch["rgb_cond"].shape[:2] + + # Camera modulation + camera_extri = batch["c2w_cond"].view(*batch["c2w_cond"].shape[:-2], -1) + camera_intri = batch["intrinsic_normed_cond"].view(*batch["intrinsic_normed_cond"].shape[:-2], -1) + camera_feats = torch.cat([camera_intri, camera_extri], dim=-1) + + camera_feats = self.camera_embedder(camera_feats) + + input_image_tokens: Float[Tensor, "B Cit Nit"] = self.image_tokenizer( + rearrange(batch["rgb_cond"], 'B Nv H W C -> B Nv C H W'), + modulation_cond=camera_feats, + ) + input_image_tokens = rearrange(input_image_tokens, 'B Nv C Nt -> B (Nv Nt) C', Nv=n_input_views) + + # get image features for projection + image_features = self.image_feature( + rgb = batch["rgb_cond"], + mask = batch.get("mask_cond", None), + feature = input_image_tokens + ) + + # only support number of input view is one + c2w_cond = batch["c2w_cond"].squeeze(1) + intrinsic_cond = batch["intrinsic_cond"].squeeze(1) + proj_feats = points_projection(pointclouds, c2w_cond, intrinsic_cond, image_features) + + point_cond_embeddings = self.point_encoder(torch.cat([pointclouds, proj_feats], dim=-1)) + tokens: Float[Tensor, "B Ct Nt"] = self.tokenizer(batch_size, cond_embeddings=point_cond_embeddings) + + tokens = self.backbone( + tokens, + encoder_hidden_states=input_image_tokens, + modulation_cond=None, + ) + + scene_codes = self.post_processor(self.tokenizer.detokenize(tokens)) + rend_out = self.renderer(scene_codes, + query_points=pointclouds, + additional_features=proj_feats, + **batch) + + return {**out, **rend_out} + + def forward(self, batch): + plys = [] + + out = self._forward(batch) + batch_size = batch["index"].shape[0] + for b in range(batch_size): + if batch["view_index"][b, 0] == 0: + plys.append(out["3dgs"][b].to_ply()) + + #self.render_test_video(out, batch, b) + + return plys + + def render_test_video(self, out, batch, b): + for index, render_image in enumerate(out["comp_rgb"][b]): + view_index = batch["view_index"][b, index] + self.save_image_grid( + f"video/{batch['instance_id'][b]}/{view_index}.png", + [ + { + "type": "rgb", + "img": render_image, + "kwargs": {"data_format": "HWC"}, + } + ] + ) \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/utils/__init__.py b/gen_3d_modules/TriplaneGaussian/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gen_3d_modules/TriplaneGaussian/utils/base.py b/gen_3d_modules/TriplaneGaussian/utils/base.py new file mode 100644 index 0000000..e98f63c --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/utils/base.py @@ -0,0 +1,129 @@ +from dataclasses import dataclass + +import torch.nn as nn + +from .config import parse_structured +from .misc import get_device, load_module_weights +from .typing import * + + +class Configurable: + @dataclass + class Config: + pass + + def __init__(self, cfg: Optional[dict] = None) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + + +class Updateable: + def do_update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ): + for attr in self.__dir__(): + if attr.startswith("_"): + continue + try: + module = getattr(self, attr) + except: + continue # ignore attributes like property, which can't be retrived using getattr? + if isinstance(module, Updateable): + module.do_update_step( + epoch, global_step, on_load_weights=on_load_weights + ) + self.update_step(epoch, global_step, on_load_weights=on_load_weights) + + def do_update_step_end(self, epoch: int, global_step: int): + for attr in self.__dir__(): + if attr.startswith("_"): + continue + try: + module = getattr(self, attr) + except: + continue # ignore attributes like property, which can't be retrived using getattr? + if isinstance(module, Updateable): + module.do_update_step_end(epoch, global_step) + self.update_step_end(epoch, global_step) + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # override this method to implement custom update logic + # if on_load_weights is True, you should be careful doing things related to model evaluations, + # as the models and tensors are not guarenteed to be on the same device + pass + + def update_step_end(self, epoch: int, global_step: int): + pass + + +def update_if_possible(module: Any, epoch: int, global_step: int) -> None: + if isinstance(module, Updateable): + module.do_update_step(epoch, global_step) + + +def update_end_if_possible(module: Any, epoch: int, global_step: int) -> None: + if isinstance(module, Updateable): + module.do_update_step_end(epoch, global_step) + + +class BaseObject(Updateable): + @dataclass + class Config: + pass + + cfg: Config # add this to every subclass of BaseObject to enable static type checking + + def __init__( + self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs + ) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + self.device = get_device() + self.configure(*args, **kwargs) + + def configure(self, *args, **kwargs) -> None: + pass + + +class BaseModule(nn.Module, Updateable): + @dataclass + class Config: + weights: Optional[str] = None + freeze: Optional[bool] = False + + cfg: Config # add this to every subclass of BaseModule to enable static type checking + + def __init__( + self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs + ) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + self.device = get_device() + self._non_modules = {} + self.configure(*args, **kwargs) + if self.cfg.weights is not None: + # format: path/to/weights:module_name + weights_path, module_name = self.cfg.weights.split(":") + state_dict = load_module_weights( + weights_path, module_name=module_name, map_location="cpu" + ) + self.load_state_dict(state_dict, strict=False) + # self.do_update_step( + # epoch, global_step, on_load_weights=True + # ) # restore states + + if self.cfg.freeze: + for params in self.parameters(): + params.requires_grad = False + + def configure(self, *args, **kwargs) -> None: + pass + + def register_non_module(self, name: str, module: nn.Module) -> None: + # non-modules won't be treated as model parameters + if name in self._non_modules: + raise ValueError(f"Non-module {name} already exists!") + self._non_modules[name] = module + + def non_module(self, name: str): + return self._non_modules.get(name, None) diff --git a/gen_3d_modules/TriplaneGaussian/utils/config.py b/gen_3d_modules/TriplaneGaussian/utils/config.py new file mode 100644 index 0000000..869bc39 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/utils/config.py @@ -0,0 +1,72 @@ +import os +from dataclasses import dataclass, field + +from omegaconf import OmegaConf + +from .typing import * + +# ============ Register OmegaConf Recolvers ============= # +OmegaConf.register_new_resolver("calc_exp_lr_decay_rate", lambda factor, n: factor ** (1.0 / n), replace=True) +OmegaConf.register_new_resolver("add", lambda a, b: a + b, replace=True) +OmegaConf.register_new_resolver("sub", lambda a, b: a - b, replace=True) +OmegaConf.register_new_resolver("mul", lambda a, b: a * b, replace=True) +OmegaConf.register_new_resolver("div", lambda a, b: a / b, replace=True) +OmegaConf.register_new_resolver("idiv", lambda a, b: a // b, replace=True) +OmegaConf.register_new_resolver("basename", lambda p: os.path.basename(p), replace=True) +OmegaConf.register_new_resolver("rmspace", lambda s, sub: s.replace(" ", sub), replace=True) +OmegaConf.register_new_resolver("tuple2", lambda s: [float(s), float(s)], replace=True) +OmegaConf.register_new_resolver("gt0", lambda s: s > 0, replace=True) +OmegaConf.register_new_resolver("not", lambda s: not s, replace=True) +OmegaConf.register_new_resolver("shsdim", lambda sh_degree: (sh_degree + 1) ** 2 * 3, replace=True) +# ======================================================= # + +# ============== Automatic Name Resolvers =============== # +def get_naming_convention(cfg): + # TODO + name = f"tgs_{cfg.system.backbone.num_layers}" + return name + +# ======================================================= # + +@dataclass +class ExperimentConfig: + n_gpus: int = 1 + data: dict = field(default_factory=dict) + system: dict = field(default_factory=dict) + +def load_config( + *yamls: str, cli_args: list = [], from_string=False, makedirs=True, **kwargs +) -> Any: + if from_string: + parse_func = OmegaConf.create + else: + parse_func = OmegaConf.load + yaml_confs = [] + for y in yamls: + conf = parse_func(y) + extends = conf.pop("extends", None) + if extends: + assert os.path.exists(extends), f"File {extends} does not exist." + yaml_confs.append(OmegaConf.load(extends)) + yaml_confs.append(conf) + cli_conf = OmegaConf.from_cli(cli_args) + cfg = OmegaConf.merge(*yaml_confs, cli_conf, kwargs) + OmegaConf.resolve(cfg) + assert isinstance(cfg, DictConfig) + scfg: ExperimentConfig = parse_structured(ExperimentConfig, cfg) + + return scfg + + +def config_to_primitive(config, resolve: bool = True) -> Any: + return OmegaConf.to_container(config, resolve=resolve) + + +def dump_config(path: str, config) -> None: + with open(path, "w") as fp: + OmegaConf.save(config=config, f=fp) + + +def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any: + scfg = OmegaConf.structured(fields(**cfg)) + return scfg diff --git a/gen_3d_modules/TriplaneGaussian/utils/misc.py b/gen_3d_modules/TriplaneGaussian/utils/misc.py new file mode 100644 index 0000000..7daf634 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/utils/misc.py @@ -0,0 +1,88 @@ +import os +import re + +import torch +from packaging import version + +from .typing import * + + +def parse_version(ver: str): + return version.parse(ver) + + +def get_rank(): + # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing, + # therefore LOCAL_RANK needs to be checked first + rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK") + for key in rank_keys: + rank = os.environ.get(key) + if rank is not None: + return int(rank) + return 0 + + +def get_device(): + return torch.device(f"cuda:{get_rank()}") + + +def load_module_weights( + path, module_name=None, ignore_modules=None, map_location=None +) -> Tuple[dict, int, int]: + if module_name is not None and ignore_modules is not None: + raise ValueError("module_name and ignore_modules cannot be both set") + if map_location is None: + map_location = get_device() + + ckpt = torch.load(path, map_location=map_location) + state_dict = ckpt["state_dict"] + state_dict_to_load = state_dict + + if ignore_modules is not None: + state_dict_to_load = {} + for k, v in state_dict.items(): + ignore = any( + [k.startswith(ignore_module + ".") for ignore_module in ignore_modules] + ) + if ignore: + continue + state_dict_to_load[k] = v + + if module_name is not None: + state_dict_to_load = {} + for k, v in state_dict.items(): + m = re.match(rf"^{module_name}\.(.*)$", k) + if m is None: + continue + state_dict_to_load[m.group(1)] = v + + return state_dict_to_load + +# convert a function into recursive style to handle nested dict/list/tuple variables +def make_recursive_func(func): + def wrapper(vars, *args, **kwargs): + if isinstance(vars, list): + return [wrapper(x, *args, **kwargs) for x in vars] + elif isinstance(vars, tuple): + return tuple([wrapper(x, *args, **kwargs) for x in vars]) + elif isinstance(vars, dict): + return {k: wrapper(v, *args, **kwargs) for k, v in vars.items()} + else: + return func(vars, *args, **kwargs) + + return wrapper + +@make_recursive_func +def todevice(vars, device="cuda"): + if isinstance(vars, torch.Tensor): + return vars.to(device) + elif isinstance(vars, str): + return vars + elif isinstance(vars, bool): + return vars + elif isinstance(vars, float): + return vars + elif isinstance(vars, int): + return vars + else: + raise NotImplementedError("invalid input type {} for tensor2numpy".format(type(vars))) diff --git a/gen_3d_modules/TriplaneGaussian/utils/ops.py b/gen_3d_modules/TriplaneGaussian/utils/ops.py new file mode 100644 index 0000000..29f0fd4 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/utils/ops.py @@ -0,0 +1,279 @@ +import math + +import numpy as np +import torch +import torch.nn.functional as F +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd +from pytorch3d import io +from pytorch3d.renderer import ( + PointsRasterizationSettings, + PointsRasterizer) +from pytorch3d.structures import Pointclouds +from pytorch3d.utils.camera_conversions import cameras_from_opencv_projection +import cv2 + +from .typing import * + +ValidScale = Union[Tuple[float, float], Num[Tensor, "2 D"]] + +def scale_tensor( + dat: Num[Tensor, "... D"], inp_scale: ValidScale, tgt_scale: ValidScale +): + if inp_scale is None: + inp_scale = (0, 1) + if tgt_scale is None: + tgt_scale = (0, 1) + if isinstance(tgt_scale, Tensor): + assert dat.shape[-1] == tgt_scale.shape[-1] + dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0]) + dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0] + return dat + + +class _TruncExp(Function): # pylint: disable=abstract-method + # Implementation from torch-ngp: + # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, x): # pylint: disable=arguments-differ + ctx.save_for_backward(x) + return torch.exp(x) + + @staticmethod + @custom_bwd + def backward(ctx, g): # pylint: disable=arguments-differ + x = ctx.saved_tensors[0] + return g * torch.exp(torch.clamp(x, max=15)) + + +trunc_exp = _TruncExp.apply + + +def get_activation(name) -> Callable: + if name is None: + return lambda x: x + name = name.lower() + if name == "none": + return lambda x: x + elif name == "lin2srgb": + return lambda x: torch.where( + x > 0.0031308, + torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055, + 12.92 * x, + ).clamp(0.0, 1.0) + elif name == "exp": + return lambda x: torch.exp(x) + elif name == "shifted_exp": + return lambda x: torch.exp(x - 1.0) + elif name == "trunc_exp": + return trunc_exp + elif name == "shifted_trunc_exp": + return lambda x: trunc_exp(x - 1.0) + elif name == "sigmoid": + return lambda x: torch.sigmoid(x) + elif name == "tanh": + return lambda x: torch.tanh(x) + elif name == "shifted_softplus": + return lambda x: F.softplus(x - 1.0) + elif name == "scale_-11_01": + return lambda x: x * 0.5 + 0.5 + else: + try: + return getattr(F, name) + except AttributeError: + raise ValueError(f"Unknown activation function: {name}") + +def get_ray_directions( + H: int, + W: int, + focal: Union[float, Tuple[float, float]], + principal: Optional[Tuple[float, float]] = None, + use_pixel_centers: bool = True, +) -> Float[Tensor, "H W 3"]: + """ + Get ray directions for all pixels in camera coordinate. + Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/ + ray-tracing-generating-camera-rays/standard-coordinate-systems + + Inputs: + H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers + Outputs: + directions: (H, W, 3), the direction of the rays in camera coordinate + """ + pixel_center = 0.5 if use_pixel_centers else 0 + + if isinstance(focal, float): + fx, fy = focal, focal + cx, cy = W / 2, H / 2 + else: + fx, fy = focal + assert principal is not None + cx, cy = principal + + i, j = torch.meshgrid( + torch.arange(W, dtype=torch.float32) + pixel_center, + torch.arange(H, dtype=torch.float32) + pixel_center, + indexing="xy", + ) + + directions: Float[Tensor, "H W 3"] = torch.stack( + [(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1 + ) + + return directions + + +def get_rays( + directions: Float[Tensor, "... 3"], + c2w: Float[Tensor, "... 4 4"], + keepdim=False, + noise_scale=0.0, +) -> Tuple[Float[Tensor, "... 3"], Float[Tensor, "... 3"]]: + # Rotate ray directions from camera coordinate to the world coordinate + assert directions.shape[-1] == 3 + + if directions.ndim == 2: # (N_rays, 3) + if c2w.ndim == 2: # (4, 4) + c2w = c2w[None, :, :] + assert c2w.ndim == 3 # (N_rays, 4, 4) or (1, 4, 4) + rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1) # (N_rays, 3) + rays_o = c2w[:, :3, 3].expand(rays_d.shape) + elif directions.ndim == 3: # (H, W, 3) + assert c2w.ndim in [2, 3] + if c2w.ndim == 2: # (4, 4) + rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum( + -1 + ) # (H, W, 3) + rays_o = c2w[None, None, :3, 3].expand(rays_d.shape) + elif c2w.ndim == 3: # (B, 4, 4) + rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum( + -1 + ) # (B, H, W, 3) + rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape) + elif directions.ndim == 4: # (B, H, W, 3) + assert c2w.ndim == 3 # (B, 4, 4) + rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum( + -1 + ) # (B, H, W, 3) + rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape) + + # add camera noise to avoid grid-like artifect + # https://github.com/ashawkey/stable-dreamfusion/blob/49c3d4fa01d68a4f027755acf94e1ff6020458cc/nerf/utils.py#L373 + if noise_scale > 0: + rays_o = rays_o + torch.randn(3, device=rays_o.device) * noise_scale + rays_d = rays_d + torch.randn(3, device=rays_d.device) * noise_scale + + rays_d = F.normalize(rays_d, dim=-1) + if not keepdim: + rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3) + + return rays_o, rays_d + + +def get_projection_matrix( + fovy: Union[float, Float[Tensor, "B"]], aspect_wh: float, near: float, far: float +) -> Float[Tensor, "*B 4 4"]: + if isinstance(fovy, float): + proj_mtx = torch.zeros(4, 4, dtype=torch.float32) + proj_mtx[0, 0] = 1.0 / (math.tan(fovy / 2.0) * aspect_wh) + proj_mtx[1, 1] = -1.0 / math.tan( + fovy / 2.0 + ) # add a negative sign here as the y axis is flipped in nvdiffrast output + proj_mtx[2, 2] = -(far + near) / (far - near) + proj_mtx[2, 3] = -2.0 * far * near / (far - near) + proj_mtx[3, 2] = -1.0 + else: + batch_size = fovy.shape[0] + proj_mtx = torch.zeros(batch_size, 4, 4, dtype=torch.float32) + proj_mtx[:, 0, 0] = 1.0 / (torch.tan(fovy / 2.0) * aspect_wh) + proj_mtx[:, 1, 1] = -1.0 / torch.tan( + fovy / 2.0 + ) # add a negative sign here as the y axis is flipped in nvdiffrast output + proj_mtx[:, 2, 2] = -(far + near) / (far - near) + proj_mtx[:, 2, 3] = -2.0 * far * near / (far - near) + proj_mtx[:, 3, 2] = -1.0 + return proj_mtx + + +def get_mvp_matrix( + c2w: Float[Tensor, "*B 4 4"], proj_mtx: Float[Tensor, "*B 4 4"] +) -> Float[Tensor, "*B 4 4"]: + # calculate w2c from c2w: R' = Rt, t' = -Rt * t + # mathematically equivalent to (c2w)^-1 + if c2w.ndim == 2: + assert proj_mtx.ndim == 2 + w2c: Float[Tensor, "4 4"] = torch.zeros(4, 4).to(c2w) + w2c[:3, :3] = c2w[:3, :3].permute(1, 0) + w2c[:3, 3:] = -c2w[:3, :3].permute(1, 0) @ c2w[:3, 3:] + w2c[3, 3] = 1.0 + else: + w2c: Float[Tensor, "B 4 4"] = torch.zeros(c2w.shape[0], 4, 4).to(c2w) + w2c[:, :3, :3] = c2w[:, :3, :3].permute(0, 2, 1) + w2c[:, :3, 3:] = -c2w[:, :3, :3].permute(0, 2, 1) @ c2w[:, :3, 3:] + w2c[:, 3, 3] = 1.0 + # calculate mvp matrix by proj_mtx @ w2c (mv_mtx) + mvp_mtx = proj_mtx @ w2c + return mvp_mtx + +def get_intrinsic_from_fov(fov, H, W, bs=-1): + focal_length = 0.5 * H / np.tan(0.5 * fov) + intrinsic = np.identity(3, dtype=np.float32) + intrinsic[0, 0] = focal_length + intrinsic[1, 1] = focal_length + intrinsic[0, 2] = W / 2.0 + intrinsic[1, 2] = H / 2.0 + + if bs > 0: + intrinsic = intrinsic[None].repeat(bs, axis=0) + + return torch.from_numpy(intrinsic) + +def points_projection(points: Float[Tensor, "B Np 3"], + c2ws: Float[Tensor, "B 4 4"], + intrinsics: Float[Tensor, "B 3 3"], + local_features: Float[Tensor, "B C H W"], + # Rasterization settings + raster_point_radius: float = 0.0075, # point size + raster_points_per_pixel: int = 1, # a single point per pixel, for now + bin_size: int = 0): + B, C, H, W = local_features.shape + device = local_features.device + raster_settings = PointsRasterizationSettings( + image_size=(H, W), + radius=raster_point_radius, + points_per_pixel=raster_points_per_pixel, + bin_size=bin_size, + ) + Np = points.shape[1] + R = raster_settings.points_per_pixel + + w2cs = torch.inverse(c2ws) + image_size = torch.as_tensor([H, W]).view(1, 2).expand(w2cs.shape[0], -1).to(device) + cameras = cameras_from_opencv_projection(w2cs[:, :3, :3], w2cs[:, :3, 3], intrinsics, image_size) + + rasterize = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) + fragments = rasterize(Pointclouds(points)) + fragments_idx: Tensor = fragments.idx.long() + visible_pixels = (fragments_idx > -1) # (B, H, W, R) + points_to_visible_pixels = fragments_idx[visible_pixels] + + # Reshape local features to (B, H, W, R, C) + local_features = local_features.permute(0, 2, 3, 1).unsqueeze(-2).expand(-1, -1, -1, R, -1) # (B, H, W, R, C) + + # Get local features corresponding to visible points + local_features_proj = torch.zeros(B * Np, C, device=device) + local_features_proj[points_to_visible_pixels] = local_features[visible_pixels] + local_features_proj = local_features_proj.reshape(B, Np, C) + + return local_features_proj + +def compute_distance_transform(mask: torch.Tensor): + image_size = mask.shape[-1] + distance_transform = torch.stack([ + torch.from_numpy(cv2.distanceTransform( + (1 - m), distanceType=cv2.DIST_L2, maskSize=cv2.DIST_MASK_3 + ) / (image_size / 2)) + for m in mask.squeeze(1).detach().cpu().numpy().astype(np.uint8) + ]).unsqueeze(1).clip(0, 1).to(mask.device) + return distance_transform \ No newline at end of file diff --git a/gen_3d_modules/TriplaneGaussian/utils/saving.py b/gen_3d_modules/TriplaneGaussian/utils/saving.py new file mode 100644 index 0000000..f8be1bc --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/utils/saving.py @@ -0,0 +1,314 @@ +import os +import re +import shutil + +import cv2 +import imageio +import matplotlib.pyplot as plt +import numpy as np +import torch +from matplotlib import cm +from matplotlib.colors import LinearSegmentedColormap +from PIL import Image, ImageDraw + +from .typing import * + +class SaverMixin: + _save_dir: Optional[str] = None + + def set_save_dir(self, save_dir: str): + self._save_dir = save_dir + + def get_save_dir(self): + if self._save_dir is None: + raise ValueError("Save dir is not set") + return self._save_dir + + def convert_data(self, data): + if data is None: + return None + elif isinstance(data, np.ndarray): + return data + elif isinstance(data, torch.Tensor): + return data.detach().cpu().numpy() + elif isinstance(data, list): + return [self.convert_data(d) for d in data] + elif isinstance(data, dict): + return {k: self.convert_data(v) for k, v in data.items()} + else: + raise TypeError( + "Data must be in type numpy.ndarray, torch.Tensor, list or dict, getting", + type(data), + ) + + def get_save_path(self, filename): + save_path = os.path.join(self.get_save_dir(), filename) + os.makedirs(os.path.dirname(save_path), exist_ok=True) + return save_path + + DEFAULT_RGB_KWARGS = {"data_format": "HWC", "data_range": (0, 1)} + DEFAULT_UV_KWARGS = { + "data_format": "HWC", + "data_range": (0, 1), + "cmap": "checkerboard", + } + DEFAULT_GRAYSCALE_KWARGS = {"data_range": None, "cmap": "jet"} + DEFAULT_GRID_KWARGS = {"align": "max"} + + def get_rgb_image_(self, img, data_format, data_range, rgba=False): + img = self.convert_data(img) + assert data_format in ["CHW", "HWC"] + if data_format == "CHW": + img = img.transpose(1, 2, 0) + if img.dtype != np.uint8: + img = img.clip(min=data_range[0], max=data_range[1]) + img = ( + (img - data_range[0]) / (data_range[1] - data_range[0]) * 255.0 + ).astype(np.uint8) + nc = 4 if rgba else 3 + imgs = [img[..., start : start + nc] for start in range(0, img.shape[-1], nc)] + imgs = [ + img_ + if img_.shape[-1] == nc + else np.concatenate( + [ + img_, + np.zeros( + (img_.shape[0], img_.shape[1], nc - img_.shape[2]), + dtype=img_.dtype, + ), + ], + axis=-1, + ) + for img_ in imgs + ] + img = np.concatenate(imgs, axis=1) + if rgba: + img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA) + else: + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + def _save_rgb_image( + self, + filename, + img, + data_format, + data_range + ): + img = self.get_rgb_image_(img, data_format, data_range) + cv2.imwrite(filename, img) + + def save_rgb_image( + self, + filename, + img, + data_format=DEFAULT_RGB_KWARGS["data_format"], + data_range=DEFAULT_RGB_KWARGS["data_range"], + ) -> str: + save_path = self.get_save_path(filename) + self._save_rgb_image(save_path, img, data_format, data_range) + return save_path + + def get_grayscale_image_(self, img, data_range, cmap): + img = self.convert_data(img) + img = np.nan_to_num(img) + if data_range is None: + img = (img - img.min()) / (img.max() - img.min()) + else: + img = img.clip(data_range[0], data_range[1]) + img = (img - data_range[0]) / (data_range[1] - data_range[0]) + assert cmap in [None, "jet", "magma", "spectral"] + if cmap == None: + img = (img * 255.0).astype(np.uint8) + img = np.repeat(img[..., None], 3, axis=2) + elif cmap == "jet": + img = (img * 255.0).astype(np.uint8) + img = cv2.applyColorMap(img, cv2.COLORMAP_JET) + elif cmap == "magma": + img = 1.0 - img + base = cm.get_cmap("magma") + num_bins = 256 + colormap = LinearSegmentedColormap.from_list( + f"{base.name}{num_bins}", base(np.linspace(0, 1, num_bins)), num_bins + )(np.linspace(0, 1, num_bins))[:, :3] + a = np.floor(img * 255.0) + b = (a + 1).clip(max=255.0) + f = img * 255.0 - a + a = a.astype(np.uint16).clip(0, 255) + b = b.astype(np.uint16).clip(0, 255) + img = colormap[a] + (colormap[b] - colormap[a]) * f[..., None] + img = (img * 255.0).astype(np.uint8) + elif cmap == "spectral": + colormap = plt.get_cmap("Spectral") + + def blend_rgba(image): + image = image[..., :3] * image[..., -1:] + ( + 1.0 - image[..., -1:] + ) # blend A to RGB + return image + + img = colormap(img) + img = blend_rgba(img) + img = (img * 255).astype(np.uint8) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + def _save_grayscale_image( + self, + filename, + img, + data_range, + cmap, + ): + img = self.get_grayscale_image_(img, data_range, cmap) + cv2.imwrite(filename, img) + + def save_grayscale_image( + self, + filename, + img, + data_range=DEFAULT_GRAYSCALE_KWARGS["data_range"], + cmap=DEFAULT_GRAYSCALE_KWARGS["cmap"], + ) -> str: + save_path = self.get_save_path(filename) + self._save_grayscale_image(save_path, img, data_range, cmap) + return save_path + + def get_image_grid_(self, imgs, align): + if isinstance(imgs[0], list): + return np.concatenate( + [self.get_image_grid_(row, align) for row in imgs], axis=0 + ) + cols = [] + for col in imgs: + assert col["type"] in ["rgb", "uv", "grayscale"] + if col["type"] == "rgb": + rgb_kwargs = self.DEFAULT_RGB_KWARGS.copy() + rgb_kwargs.update(col["kwargs"]) + cols.append(self.get_rgb_image_(col["img"], **rgb_kwargs)) + elif col["type"] == "uv": + uv_kwargs = self.DEFAULT_UV_KWARGS.copy() + uv_kwargs.update(col["kwargs"]) + cols.append(self.get_uv_image_(col["img"], **uv_kwargs)) + elif col["type"] == "grayscale": + grayscale_kwargs = self.DEFAULT_GRAYSCALE_KWARGS.copy() + grayscale_kwargs.update(col["kwargs"]) + cols.append(self.get_grayscale_image_(col["img"], **grayscale_kwargs)) + + if align == "max": + h = max([col.shape[0] for col in cols]) + w = max([col.shape[1] for col in cols]) + elif align == "min": + h = min([col.shape[0] for col in cols]) + w = min([col.shape[1] for col in cols]) + elif isinstance(align, int): + h = align + w = align + elif ( + isinstance(align, tuple) + and isinstance(align[0], int) + and isinstance(align[1], int) + ): + h, w = align + else: + raise ValueError( + f"Unsupported image grid align: {align}, should be min, max, int or (int, int)" + ) + + for i in range(len(cols)): + if cols[i].shape[0] != h or cols[i].shape[1] != w: + cols[i] = cv2.resize(cols[i], (w, h), interpolation=cv2.INTER_LINEAR) + return np.concatenate(cols, axis=1) + + def save_image_grid( + self, + filename, + imgs, + align=DEFAULT_GRID_KWARGS["align"], + texts: Optional[List[float]] = None, + ): + save_path = self.get_save_path(filename) + img = self.get_image_grid_(imgs, align=align) + + if texts is not None: + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + black, white = (0, 0, 0), (255, 255, 255) + for i, text in enumerate(texts): + draw.text((2, (img.size[1] // len(texts)) * i + 1), f"{text}", white) + draw.text((0, (img.size[1] // len(texts)) * i + 1), f"{text}", white) + draw.text((2, (img.size[1] // len(texts)) * i - 1), f"{text}", white) + draw.text((0, (img.size[1] // len(texts)) * i - 1), f"{text}", white) + draw.text((1, (img.size[1] // len(texts)) * i), f"{text}", black) + img = np.asarray(img) + + cv2.imwrite(save_path, img) + return save_path + + def save_image(self, filename, img) -> str: + save_path = self.get_save_path(filename) + img = self.convert_data(img) + assert img.dtype == np.uint8 or img.dtype == np.uint16 + if img.ndim == 3 and img.shape[-1] == 3: + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + elif img.ndim == 3 and img.shape[-1] == 4: + img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA) + cv2.imwrite(save_path, img) + return save_path + + def save_img_sequence( + self, + filename, + img_dir, + matcher, + save_format="mp4", + fps=30, + ) -> str: + assert save_format in ["gif", "mp4"] + if not filename.endswith(save_format): + filename += f".{save_format}" + save_path = self.get_save_path(filename) + matcher = re.compile(matcher) + img_dir = os.path.join(self.get_save_dir(), img_dir) + imgs = [] + for f in os.listdir(img_dir): + if matcher.search(f): + imgs.append(f) + imgs = sorted(imgs, key=lambda f: int(matcher.search(f).groups()[0])) + imgs = [cv2.imread(os.path.join(img_dir, f)) for f in imgs] + + if save_format == "gif": + imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs] + imageio.mimsave(save_path, imgs, fps=fps, palettesize=256) + elif save_format == "mp4": + imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs] + imageio.mimsave(save_path, imgs, fps=fps) + return save_path + + def save_img_sequences( + self, + seq_dir, + matcher, + save_format="mp4", + fps=30, + delete=True + ): + seq_dir_ = os.path.join(self.get_save_dir(), seq_dir) + for f in os.listdir(seq_dir_): + img_dir_ = os.path.join(seq_dir_, f) + if not os.path.isdir(img_dir_): + continue + try: + self.save_img_sequence( + os.path.join(seq_dir, f), + os.path.join(seq_dir, f), + matcher, + save_format=save_format, + fps=fps + ) + except: + raise ValueError(f"Video saving for directory {seq_dir_} failed!") + + if delete: + shutil.rmtree(img_dir_) diff --git a/gen_3d_modules/TriplaneGaussian/utils/typing.py b/gen_3d_modules/TriplaneGaussian/utils/typing.py new file mode 100644 index 0000000..dee9f96 --- /dev/null +++ b/gen_3d_modules/TriplaneGaussian/utils/typing.py @@ -0,0 +1,40 @@ +""" +This module contains type annotations for the project, using +1. Python type hints (https://docs.python.org/3/library/typing.html) for Python objects +2. jaxtyping (https://github.com/google/jaxtyping/blob/main/API.md) for PyTorch tensors + +Two types of typing checking can be used: +1. Static type checking with mypy (install with pip and enabled as the default linter in VSCode) +2. Runtime type checking with typeguard (install with pip and triggered at runtime, mainly for tensor dtype and shape checking) +""" + +# Basic types +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Literal, + NamedTuple, + NewType, + Optional, + Sized, + Tuple, + Type, + TypeVar, + Union, +) + +# Tensor dtype +# for jaxtyping usage, see https://github.com/google/jaxtyping/blob/main/API.md +from jaxtyping import Bool, Complex, Float, Inexact, Int, Integer, Num, Shaped, UInt + +# Config type +from omegaconf import DictConfig + +# PyTorch Tensor type +from torch import Tensor + +# Runtime type checking decorator +from typeguard import typechecked as typechecker diff --git a/gen_3d_modules/TripoSR/models/isosurface.py b/gen_3d_modules/TripoSR/models/isosurface.py new file mode 100644 index 0000000..39c8389 --- /dev/null +++ b/gen_3d_modules/TripoSR/models/isosurface.py @@ -0,0 +1,48 @@ +from typing import Callable, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +from torchmcubes import marching_cubes + + +class IsosurfaceHelper(nn.Module): + points_range: Tuple[float, float] = (0, 1) + + @property + def grid_vertices(self) -> torch.FloatTensor: + raise NotImplementedError + + +class MarchingCubeHelper(IsosurfaceHelper): + def __init__(self, resolution: int) -> None: + super().__init__() + self.resolution = resolution + self.mc_func: Callable = marching_cubes + self._grid_vertices: Optional[torch.FloatTensor] = None + + @property + def grid_vertices(self) -> torch.FloatTensor: + if self._grid_vertices is None: + # keep the vertices on CPU so that we can support very large resolution + x, y, z = ( + torch.linspace(*self.points_range, self.resolution), + torch.linspace(*self.points_range, self.resolution), + torch.linspace(*self.points_range, self.resolution), + ) + x, y, z = torch.meshgrid(x, y, z, indexing="ij") + verts = torch.cat( + [x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1 + ).reshape(-1, 3) + self._grid_vertices = verts + return self._grid_vertices + + def forward( + self, + level: torch.FloatTensor, + ) -> Tuple[torch.FloatTensor, torch.LongTensor]: + level = -level.view(self.resolution, self.resolution, self.resolution) + v_pos, t_pos_idx = self.mc_func(level.detach(), 0.0) + v_pos = v_pos[..., [2, 1, 0]] + v_pos = v_pos / (self.resolution - 1.0) + return v_pos, t_pos_idx diff --git a/gen_3d_modules/TripoSR/models/nerf_renderer.py b/gen_3d_modules/TripoSR/models/nerf_renderer.py new file mode 100644 index 0000000..0cf3ab6 --- /dev/null +++ b/gen_3d_modules/TripoSR/models/nerf_renderer.py @@ -0,0 +1,180 @@ +from dataclasses import dataclass +from typing import Dict + +import torch +import torch.nn.functional as F +from einops import rearrange, reduce + +from ..utils import ( + BaseModule, + chunk_batch, + get_activation, + rays_intersect_bbox, + scale_tensor, +) + + +class TriplaneNeRFRenderer(BaseModule): + @dataclass + class Config(BaseModule.Config): + radius: float + + feature_reduction: str = "concat" + density_activation: str = "trunc_exp" + density_bias: float = -1.0 + color_activation: str = "sigmoid" + num_samples_per_ray: int = 128 + randomized: bool = False + + cfg: Config + + def configure(self) -> None: + assert self.cfg.feature_reduction in ["concat", "mean"] + self.chunk_size = 0 + + def set_chunk_size(self, chunk_size: int): + assert ( + chunk_size >= 0 + ), "chunk_size must be a non-negative integer (0 for no chunking)." + self.chunk_size = chunk_size + + def query_triplane( + self, + decoder: torch.nn.Module, + positions: torch.Tensor, + triplane: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + input_shape = positions.shape[:-1] + positions = positions.view(-1, 3) + + # positions in (-radius, radius) + # normalized to (-1, 1) for grid sample + positions = scale_tensor( + positions, (-self.cfg.radius, self.cfg.radius), (-1, 1) + ) + + def _query_chunk(x): + indices2D: torch.Tensor = torch.stack( + (x[..., [0, 1]], x[..., [0, 2]], x[..., [1, 2]]), + dim=-3, + ) + out: torch.Tensor = F.grid_sample( + rearrange(triplane, "Np Cp Hp Wp -> Np Cp Hp Wp", Np=3), + rearrange(indices2D, "Np N Nd -> Np () N Nd", Np=3), + align_corners=False, + mode="bilinear", + ) + if self.cfg.feature_reduction == "concat": + out = rearrange(out, "Np Cp () N -> N (Np Cp)", Np=3) + elif self.cfg.feature_reduction == "mean": + out = reduce(out, "Np Cp () N -> N Cp", Np=3, reduction="mean") + else: + raise NotImplementedError + + net_out: Dict[str, torch.Tensor] = decoder(out) + return net_out + + if self.chunk_size > 0: + net_out = chunk_batch(_query_chunk, self.chunk_size, positions) + else: + net_out = _query_chunk(positions) + + net_out["density_act"] = get_activation(self.cfg.density_activation)( + net_out["density"] + self.cfg.density_bias + ) + net_out["color"] = get_activation(self.cfg.color_activation)( + net_out["features"] + ) + + net_out = {k: v.view(*input_shape, -1) for k, v in net_out.items()} + + return net_out + + def _forward( + self, + decoder: torch.nn.Module, + triplane: torch.Tensor, + rays_o: torch.Tensor, + rays_d: torch.Tensor, + **kwargs, + ): + rays_shape = rays_o.shape[:-1] + rays_o = rays_o.view(-1, 3) + rays_d = rays_d.view(-1, 3) + n_rays = rays_o.shape[0] + + t_near, t_far, rays_valid = rays_intersect_bbox(rays_o, rays_d, self.cfg.radius) + t_near, t_far = t_near[rays_valid], t_far[rays_valid] + + t_vals = torch.linspace( + 0, 1, self.cfg.num_samples_per_ray + 1, device=triplane.device + ) + t_mid = (t_vals[:-1] + t_vals[1:]) / 2.0 + z_vals = t_near * (1 - t_mid[None]) + t_far * t_mid[None] # (N_rays, N_samples) + + xyz = ( + rays_o[:, None, :] + z_vals[..., None] * rays_d[..., None, :] + ) # (N_rays, N_sample, 3) + + mlp_out = self.query_triplane( + decoder=decoder, + positions=xyz, + triplane=triplane, + ) + + eps = 1e-10 + # deltas = z_vals[:, 1:] - z_vals[:, :-1] # (N_rays, N_samples) + deltas = t_vals[1:] - t_vals[:-1] # (N_rays, N_samples) + alpha = 1 - torch.exp( + -deltas * mlp_out["density_act"][..., 0] + ) # (N_rays, N_samples) + accum_prod = torch.cat( + [ + torch.ones_like(alpha[:, :1]), + torch.cumprod(1 - alpha[:, :-1] + eps, dim=-1), + ], + dim=-1, + ) + weights = alpha * accum_prod # (N_rays, N_samples) + comp_rgb_ = (weights[..., None] * mlp_out["color"]).sum(dim=-2) # (N_rays, 3) + opacity_ = weights.sum(dim=-1) # (N_rays) + + comp_rgb = torch.zeros( + n_rays, 3, dtype=comp_rgb_.dtype, device=comp_rgb_.device + ) + opacity = torch.zeros(n_rays, dtype=opacity_.dtype, device=opacity_.device) + comp_rgb[rays_valid] = comp_rgb_ + opacity[rays_valid] = opacity_ + + comp_rgb += 1 - opacity[..., None] + comp_rgb = comp_rgb.view(*rays_shape, 3) + + return comp_rgb + + def forward( + self, + decoder: torch.nn.Module, + triplane: torch.Tensor, + rays_o: torch.Tensor, + rays_d: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + if triplane.ndim == 4: + comp_rgb = self._forward(decoder, triplane, rays_o, rays_d) + else: + comp_rgb = torch.stack( + [ + self._forward(decoder, triplane[i], rays_o[i], rays_d[i]) + for i in range(triplane.shape[0]) + ], + dim=0, + ) + + return comp_rgb + + def train(self, mode=True): + self.randomized = mode and self.cfg.randomized + return super().train(mode=mode) + + def eval(self): + self.randomized = False + return super().eval() diff --git a/gen_3d_modules/TripoSR/models/network_utils.py b/gen_3d_modules/TripoSR/models/network_utils.py new file mode 100644 index 0000000..470c92b --- /dev/null +++ b/gen_3d_modules/TripoSR/models/network_utils.py @@ -0,0 +1,124 @@ +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn as nn +from einops import rearrange + +from ..utils import BaseModule + + +class TriplaneUpsampleNetwork(BaseModule): + @dataclass + class Config(BaseModule.Config): + in_channels: int + out_channels: int + + cfg: Config + + def configure(self) -> None: + self.upsample = nn.ConvTranspose2d( + self.cfg.in_channels, self.cfg.out_channels, kernel_size=2, stride=2 + ) + + def forward(self, triplanes: torch.Tensor) -> torch.Tensor: + triplanes_up = rearrange( + self.upsample( + rearrange(triplanes, "B Np Ci Hp Wp -> (B Np) Ci Hp Wp", Np=3) + ), + "(B Np) Co Hp Wp -> B Np Co Hp Wp", + Np=3, + ) + return triplanes_up + + +class NeRFMLP(BaseModule): + @dataclass + class Config(BaseModule.Config): + in_channels: int + n_neurons: int + n_hidden_layers: int + activation: str = "relu" + bias: bool = True + weight_init: Optional[str] = "kaiming_uniform" + bias_init: Optional[str] = None + + cfg: Config + + def configure(self) -> None: + layers = [ + self.make_linear( + self.cfg.in_channels, + self.cfg.n_neurons, + bias=self.cfg.bias, + weight_init=self.cfg.weight_init, + bias_init=self.cfg.bias_init, + ), + self.make_activation(self.cfg.activation), + ] + for i in range(self.cfg.n_hidden_layers - 1): + layers += [ + self.make_linear( + self.cfg.n_neurons, + self.cfg.n_neurons, + bias=self.cfg.bias, + weight_init=self.cfg.weight_init, + bias_init=self.cfg.bias_init, + ), + self.make_activation(self.cfg.activation), + ] + layers += [ + self.make_linear( + self.cfg.n_neurons, + 4, # density 1 + features 3 + bias=self.cfg.bias, + weight_init=self.cfg.weight_init, + bias_init=self.cfg.bias_init, + ) + ] + self.layers = nn.Sequential(*layers) + + def make_linear( + self, + dim_in, + dim_out, + bias=True, + weight_init=None, + bias_init=None, + ): + layer = nn.Linear(dim_in, dim_out, bias=bias) + + if weight_init is None: + pass + elif weight_init == "kaiming_uniform": + torch.nn.init.kaiming_uniform_(layer.weight, nonlinearity="relu") + else: + raise NotImplementedError + + if bias: + if bias_init is None: + pass + elif bias_init == "zero": + torch.nn.init.zeros_(layer.bias) + else: + raise NotImplementedError + + return layer + + def make_activation(self, activation): + if activation == "relu": + return nn.ReLU(inplace=True) + elif activation == "silu": + return nn.SiLU(inplace=True) + else: + raise NotImplementedError + + def forward(self, x): + inp_shape = x.shape[:-1] + x = x.reshape(-1, x.shape[-1]) + + features = self.layers(x) + features = features.reshape(*inp_shape, -1) + out = {"density": features[..., 0:1], "features": features[..., 1:4]} + + return out diff --git a/gen_3d_modules/TripoSR/models/tokenizers/image.py b/gen_3d_modules/TripoSR/models/tokenizers/image.py new file mode 100644 index 0000000..d4034b2 --- /dev/null +++ b/gen_3d_modules/TripoSR/models/tokenizers/image.py @@ -0,0 +1,66 @@ +from dataclasses import dataclass + +import torch +import torch.nn as nn +from einops import rearrange +from huggingface_hub import hf_hub_download +from transformers.models.vit.modeling_vit import ViTModel + +from ...utils import BaseModule + + +class DINOSingleImageTokenizer(BaseModule): + @dataclass + class Config(BaseModule.Config): + pretrained_model_name_or_path: str = "facebook/dino-vitb16" + enable_gradient_checkpointing: bool = False + + cfg: Config + + def configure(self) -> None: + self.model: ViTModel = ViTModel( + ViTModel.config_class.from_pretrained( + hf_hub_download( + repo_id=self.cfg.pretrained_model_name_or_path, + filename="config.json", + ) + ) + ) + + if self.cfg.enable_gradient_checkpointing: + self.model.encoder.gradient_checkpointing = True + + self.register_buffer( + "image_mean", + torch.as_tensor([0.485, 0.456, 0.406]).reshape(1, 1, 3, 1, 1), + persistent=False, + ) + self.register_buffer( + "image_std", + torch.as_tensor([0.229, 0.224, 0.225]).reshape(1, 1, 3, 1, 1), + persistent=False, + ) + + def forward(self, images: torch.FloatTensor, **kwargs) -> torch.FloatTensor: + packed = False + if images.ndim == 4: + packed = True + images = images.unsqueeze(1) + + batch_size, n_input_views = images.shape[:2] + images = (images - self.image_mean) / self.image_std + out = self.model( + rearrange(images, "B N C H W -> (B N) C H W"), interpolate_pos_encoding=True + ) + local_features, global_features = out.last_hidden_state, out.pooler_output + local_features = local_features.permute(0, 2, 1) + local_features = rearrange( + local_features, "(B N) Ct Nt -> B N Ct Nt", B=batch_size + ) + if packed: + local_features = local_features.squeeze(1) + + return local_features + + def detokenize(self, *args, **kwargs): + raise NotImplementedError diff --git a/gen_3d_modules/TripoSR/models/tokenizers/triplane.py b/gen_3d_modules/TripoSR/models/tokenizers/triplane.py new file mode 100644 index 0000000..ecdd7fd --- /dev/null +++ b/gen_3d_modules/TripoSR/models/tokenizers/triplane.py @@ -0,0 +1,45 @@ +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +from einops import rearrange, repeat + +from ...utils import BaseModule + + +class Triplane1DTokenizer(BaseModule): + @dataclass + class Config(BaseModule.Config): + plane_size: int + num_channels: int + + cfg: Config + + def configure(self) -> None: + self.embeddings = nn.Parameter( + torch.randn( + (3, self.cfg.num_channels, self.cfg.plane_size, self.cfg.plane_size), + dtype=torch.float32, + ) + * 1 + / math.sqrt(self.cfg.num_channels) + ) + + def forward(self, batch_size: int) -> torch.Tensor: + return rearrange( + repeat(self.embeddings, "Np Ct Hp Wp -> B Np Ct Hp Wp", B=batch_size), + "B Np Ct Hp Wp -> B Ct (Np Hp Wp)", + ) + + def detokenize(self, tokens: torch.Tensor) -> torch.Tensor: + batch_size, Ct, Nt = tokens.shape + assert Nt == self.cfg.plane_size**2 * 3 + assert Ct == self.cfg.num_channels + return rearrange( + tokens, + "B Ct (Np Hp Wp) -> B Np Ct Hp Wp", + Np=3, + Hp=self.cfg.plane_size, + Wp=self.cfg.plane_size, + ) diff --git a/gen_3d_modules/TripoSR/models/transformer/attention.py b/gen_3d_modules/TripoSR/models/transformer/attention.py new file mode 100644 index 0000000..34bd997 --- /dev/null +++ b/gen_3d_modules/TripoSR/models/transformer/attention.py @@ -0,0 +1,653 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# -------- +# +# Modified 2024 by the Tripo AI and Stability AI Team. +# +# Copyright (c) 2024 Tripo AI & Stability AI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + + +class Attention(nn.Module): + r""" + A cross attention layer. + + Parameters: + query_dim (`int`): + The number of channels in the query. + cross_attention_dim (`int`, *optional*): + The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`. + heads (`int`, *optional*, defaults to 8): + The number of heads to use for multi-head attention. + dim_head (`int`, *optional*, defaults to 64): + The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): + The dropout probability to use. + bias (`bool`, *optional*, defaults to False): + Set to `True` for the query, key, and value linear layers to contain a bias parameter. + upcast_attention (`bool`, *optional*, defaults to False): + Set to `True` to upcast the attention computation to `float32`. + upcast_softmax (`bool`, *optional*, defaults to False): + Set to `True` to upcast the softmax computation to `float32`. + cross_attention_norm (`str`, *optional*, defaults to `None`): + The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. + cross_attention_norm_num_groups (`int`, *optional*, defaults to 32): + The number of groups to use for the group norm in the cross attention. + added_kv_proj_dim (`int`, *optional*, defaults to `None`): + The number of channels to use for the added key and value projections. If `None`, no projection is used. + norm_num_groups (`int`, *optional*, defaults to `None`): + The number of groups to use for the group norm in the attention. + spatial_norm_dim (`int`, *optional*, defaults to `None`): + The number of channels to use for the spatial normalization. + out_bias (`bool`, *optional*, defaults to `True`): + Set to `True` to use a bias in the output linear layer. + scale_qk (`bool`, *optional*, defaults to `True`): + Set to `True` to scale the query and key by `1 / sqrt(dim_head)`. + only_cross_attention (`bool`, *optional*, defaults to `False`): + Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if + `added_kv_proj_dim` is not `None`. + eps (`float`, *optional*, defaults to 1e-5): + An additional value added to the denominator in group normalization that is used for numerical stability. + rescale_output_factor (`float`, *optional*, defaults to 1.0): + A factor to rescale the output by dividing it with this value. + residual_connection (`bool`, *optional*, defaults to `False`): + Set to `True` to add the residual connection to the output. + _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`): + Set to `True` if the attention block is loaded from a deprecated state dict. + processor (`AttnProcessor`, *optional*, defaults to `None`): + The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and + `AttnProcessor` otherwise. + """ + + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + bias: bool = False, + upcast_attention: bool = False, + upcast_softmax: bool = False, + cross_attention_norm: Optional[str] = None, + cross_attention_norm_num_groups: int = 32, + added_kv_proj_dim: Optional[int] = None, + norm_num_groups: Optional[int] = None, + out_bias: bool = True, + scale_qk: bool = True, + only_cross_attention: bool = False, + eps: float = 1e-5, + rescale_output_factor: float = 1.0, + residual_connection: bool = False, + _from_deprecated_attn_block: bool = False, + processor: Optional["AttnProcessor"] = None, + out_dim: int = None, + ): + super().__init__() + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.query_dim = query_dim + self.cross_attention_dim = ( + cross_attention_dim if cross_attention_dim is not None else query_dim + ) + self.upcast_attention = upcast_attention + self.upcast_softmax = upcast_softmax + self.rescale_output_factor = rescale_output_factor + self.residual_connection = residual_connection + self.dropout = dropout + self.fused_projections = False + self.out_dim = out_dim if out_dim is not None else query_dim + + # we make use of this private variable to know whether this class is loaded + # with an deprecated state dict so that we can convert it on the fly + self._from_deprecated_attn_block = _from_deprecated_attn_block + + self.scale_qk = scale_qk + self.scale = dim_head**-0.5 if self.scale_qk else 1.0 + + self.heads = out_dim // dim_head if out_dim is not None else heads + # for slice_size > 0 the attention score computation + # is split across the batch axis to save memory + # You can set slice_size with `set_attention_slice` + self.sliceable_head_dim = heads + + self.added_kv_proj_dim = added_kv_proj_dim + self.only_cross_attention = only_cross_attention + + if self.added_kv_proj_dim is None and self.only_cross_attention: + raise ValueError( + "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." + ) + + if norm_num_groups is not None: + self.group_norm = nn.GroupNorm( + num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True + ) + else: + self.group_norm = None + + self.spatial_norm = None + + if cross_attention_norm is None: + self.norm_cross = None + elif cross_attention_norm == "layer_norm": + self.norm_cross = nn.LayerNorm(self.cross_attention_dim) + elif cross_attention_norm == "group_norm": + if self.added_kv_proj_dim is not None: + # The given `encoder_hidden_states` are initially of shape + # (batch_size, seq_len, added_kv_proj_dim) before being projected + # to (batch_size, seq_len, cross_attention_dim). The norm is applied + # before the projection, so we need to use `added_kv_proj_dim` as + # the number of channels for the group norm. + norm_cross_num_channels = added_kv_proj_dim + else: + norm_cross_num_channels = self.cross_attention_dim + + self.norm_cross = nn.GroupNorm( + num_channels=norm_cross_num_channels, + num_groups=cross_attention_norm_num_groups, + eps=1e-5, + affine=True, + ) + else: + raise ValueError( + f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'" + ) + + linear_cls = nn.Linear + + self.linear_cls = linear_cls + self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias) + + if not self.only_cross_attention: + # only relevant for the `AddedKVProcessor` classes + self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) + self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) + else: + self.to_k = None + self.to_v = None + + if self.added_kv_proj_dim is not None: + self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim) + self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim) + + self.to_out = nn.ModuleList([]) + self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias)) + self.to_out.append(nn.Dropout(dropout)) + + # set attention processor + # We use the AttnProcessor2_0 by default when torch 2.x is used which uses + # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention + # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 + if processor is None: + processor = ( + AttnProcessor2_0() + if hasattr(F, "scaled_dot_product_attention") and self.scale_qk + else AttnProcessor() + ) + self.set_processor(processor) + + def set_processor(self, processor: "AttnProcessor") -> None: + self.processor = processor + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + **cross_attention_kwargs, + ) -> torch.Tensor: + r""" + The forward method of the `Attention` class. + + Args: + hidden_states (`torch.Tensor`): + The hidden states of the query. + encoder_hidden_states (`torch.Tensor`, *optional*): + The hidden states of the encoder. + attention_mask (`torch.Tensor`, *optional*): + The attention mask to use. If `None`, no mask is applied. + **cross_attention_kwargs: + Additional keyword arguments to pass along to the cross attention. + + Returns: + `torch.Tensor`: The output of the attention layer. + """ + # The `Attention` class can call different attention processors / attention functions + # here we simply pass along all tensors to the selected processor class + # For standard processors that are defined here, `**cross_attention_kwargs` is empty + return self.processor( + self, + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor: + r""" + Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads` + is the number of heads initialized while constructing the `Attention` class. + + Args: + tensor (`torch.Tensor`): The tensor to reshape. + + Returns: + `torch.Tensor`: The reshaped tensor. + """ + head_size = self.heads + batch_size, seq_len, dim = tensor.shape + tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) + tensor = tensor.permute(0, 2, 1, 3).reshape( + batch_size // head_size, seq_len, dim * head_size + ) + return tensor + + def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor: + r""" + Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is + the number of heads initialized while constructing the `Attention` class. + + Args: + tensor (`torch.Tensor`): The tensor to reshape. + out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is + reshaped to `[batch_size * heads, seq_len, dim // heads]`. + + Returns: + `torch.Tensor`: The reshaped tensor. + """ + head_size = self.heads + batch_size, seq_len, dim = tensor.shape + tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size) + tensor = tensor.permute(0, 2, 1, 3) + + if out_dim == 3: + tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size) + + return tensor + + def get_attention_scores( + self, + query: torch.Tensor, + key: torch.Tensor, + attention_mask: torch.Tensor = None, + ) -> torch.Tensor: + r""" + Compute the attention scores. + + Args: + query (`torch.Tensor`): The query tensor. + key (`torch.Tensor`): The key tensor. + attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied. + + Returns: + `torch.Tensor`: The attention probabilities/scores. + """ + dtype = query.dtype + if self.upcast_attention: + query = query.float() + key = key.float() + + if attention_mask is None: + baddbmm_input = torch.empty( + query.shape[0], + query.shape[1], + key.shape[1], + dtype=query.dtype, + device=query.device, + ) + beta = 0 + else: + baddbmm_input = attention_mask + beta = 1 + + attention_scores = torch.baddbmm( + baddbmm_input, + query, + key.transpose(-1, -2), + beta=beta, + alpha=self.scale, + ) + del baddbmm_input + + if self.upcast_softmax: + attention_scores = attention_scores.float() + + attention_probs = attention_scores.softmax(dim=-1) + del attention_scores + + attention_probs = attention_probs.to(dtype) + + return attention_probs + + def prepare_attention_mask( + self, + attention_mask: torch.Tensor, + target_length: int, + batch_size: int, + out_dim: int = 3, + ) -> torch.Tensor: + r""" + Prepare the attention mask for the attention computation. + + Args: + attention_mask (`torch.Tensor`): + The attention mask to prepare. + target_length (`int`): + The target length of the attention mask. This is the length of the attention mask after padding. + batch_size (`int`): + The batch size, which is used to repeat the attention mask. + out_dim (`int`, *optional*, defaults to `3`): + The output dimension of the attention mask. Can be either `3` or `4`. + + Returns: + `torch.Tensor`: The prepared attention mask. + """ + head_size = self.heads + if attention_mask is None: + return attention_mask + + current_length: int = attention_mask.shape[-1] + if current_length != target_length: + if attention_mask.device.type == "mps": + # HACK: MPS: Does not support padding by greater than dimension of input tensor. + # Instead, we can manually construct the padding tensor. + padding_shape = ( + attention_mask.shape[0], + attention_mask.shape[1], + target_length, + ) + padding = torch.zeros( + padding_shape, + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + attention_mask = torch.cat([attention_mask, padding], dim=2) + else: + # TODO: for pipelines such as stable-diffusion, padding cross-attn mask: + # we want to instead pad by (0, remaining_length), where remaining_length is: + # remaining_length: int = target_length - current_length + # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + + if out_dim == 3: + if attention_mask.shape[0] < batch_size * head_size: + attention_mask = attention_mask.repeat_interleave(head_size, dim=0) + elif out_dim == 4: + attention_mask = attention_mask.unsqueeze(1) + attention_mask = attention_mask.repeat_interleave(head_size, dim=1) + + return attention_mask + + def norm_encoder_hidden_states( + self, encoder_hidden_states: torch.Tensor + ) -> torch.Tensor: + r""" + Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the + `Attention` class. + + Args: + encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder. + + Returns: + `torch.Tensor`: The normalized encoder hidden states. + """ + assert ( + self.norm_cross is not None + ), "self.norm_cross must be defined to call self.norm_encoder_hidden_states" + + if isinstance(self.norm_cross, nn.LayerNorm): + encoder_hidden_states = self.norm_cross(encoder_hidden_states) + elif isinstance(self.norm_cross, nn.GroupNorm): + # Group norm norms along the channels dimension and expects + # input to be in the shape of (N, C, *). In this case, we want + # to norm along the hidden dimension, so we need to move + # (batch_size, sequence_length, hidden_size) -> + # (batch_size, hidden_size, sequence_length) + encoder_hidden_states = encoder_hidden_states.transpose(1, 2) + encoder_hidden_states = self.norm_cross(encoder_hidden_states) + encoder_hidden_states = encoder_hidden_states.transpose(1, 2) + else: + assert False + + return encoder_hidden_states + + @torch.no_grad() + def fuse_projections(self, fuse=True): + is_cross_attention = self.cross_attention_dim != self.query_dim + device = self.to_q.weight.data.device + dtype = self.to_q.weight.data.dtype + + if not is_cross_attention: + # fetch weight matrices. + concatenated_weights = torch.cat( + [self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data] + ) + in_features = concatenated_weights.shape[1] + out_features = concatenated_weights.shape[0] + + # create a new single projection layer and copy over the weights. + self.to_qkv = self.linear_cls( + in_features, out_features, bias=False, device=device, dtype=dtype + ) + self.to_qkv.weight.copy_(concatenated_weights) + + else: + concatenated_weights = torch.cat( + [self.to_k.weight.data, self.to_v.weight.data] + ) + in_features = concatenated_weights.shape[1] + out_features = concatenated_weights.shape[0] + + self.to_kv = self.linear_cls( + in_features, out_features, bias=False, device=device, dtype=dtype + ) + self.to_kv.weight.copy_(concatenated_weights) + + self.fused_projections = fuse + + +class AttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.Tensor: + residual = hidden_states + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view( + batch_size, channel, height * width + ).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( + 1, 2 + ) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states( + encoder_hidden_states + ) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape( + batch_size, channel, height, width + ) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class AttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). + """ + + def __init__(self): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError( + "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." + ) + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + residual = hidden_states + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view( + batch_size, channel, height * width + ).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape + if encoder_hidden_states is None + else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask( + attention_mask, sequence_length, batch_size + ) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view( + batch_size, attn.heads, -1, attention_mask.shape[-1] + ) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose( + 1, 2 + ) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states( + encoder_hidden_states + ) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape( + batch_size, -1, attn.heads * head_dim + ) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape( + batch_size, channel, height, width + ) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states diff --git a/gen_3d_modules/TripoSR/models/transformer/basic_transformer_block.py b/gen_3d_modules/TripoSR/models/transformer/basic_transformer_block.py new file mode 100644 index 0000000..1ea41b0 --- /dev/null +++ b/gen_3d_modules/TripoSR/models/transformer/basic_transformer_block.py @@ -0,0 +1,334 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# -------- +# +# Modified 2024 by the Tripo AI and Stability AI Team. +# +# Copyright (c) 2024 Tripo AI & Stability AI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from .attention import Attention + + +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the attention computation to float32. This is useful for mixed precision training. + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + assert norm_type == "layer_norm" + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=( + cross_attention_dim if not double_self_attention else None + ), + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward( + dim, + dropout=dropout, + activation_fn=activation_fn, + final_dropout=final_dropout, + ) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + norm_hidden_states = self.norm1(hidden_states) + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=( + encoder_hidden_states if self.only_cross_attention else None + ), + attention_mask=attention_mask, + ) + + hidden_states = attn_output + hidden_states + + # 3. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = self.norm2(hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + ) + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [ + self.ff(hid_slice) + for hid_slice in norm_hidden_states.chunk( + num_chunks, dim=self._chunk_dim + ) + ], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + hidden_states = ff_output + hidden_states + + return hidden_states + + +class FeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + dim (`int`): The number of channels in the input. + dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. + mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. + """ + + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + activation_fn: str = "geglu", + final_dropout: bool = False, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + linear_cls = nn.Linear + + if activation_fn == "gelu": + act_fn = GELU(dim, inner_dim) + if activation_fn == "gelu-approximate": + act_fn = GELU(dim, inner_dim, approximate="tanh") + elif activation_fn == "geglu": + act_fn = GEGLU(dim, inner_dim) + elif activation_fn == "geglu-approximate": + act_fn = ApproximateGELU(dim, inner_dim) + + self.net = nn.ModuleList([]) + # project in + self.net.append(act_fn) + # project dropout + self.net.append(nn.Dropout(dropout)) + # project out + self.net.append(linear_cls(inner_dim, dim_out)) + # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout + if final_dropout: + self.net.append(nn.Dropout(dropout)) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +class GELU(nn.Module): + r""" + GELU activation function with tanh approximation support with `approximate="tanh"`. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation. + """ + + def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out) + self.approximate = approximate + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + if gate.device.type != "mps": + return F.gelu(gate, approximate=self.approximate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to( + dtype=gate.dtype + ) + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = self.gelu(hidden_states) + return hidden_states + + +class GEGLU(nn.Module): + r""" + A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + linear_cls = nn.Linear + + self.proj = linear_cls(dim_in, dim_out * 2) + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + if gate.device.type != "mps": + return F.gelu(gate) + # mps: gelu is not implemented for float16 + return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype) + + def forward(self, hidden_states, scale: float = 1.0): + args = () + hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1) + return hidden_states * self.gelu(gate) + + +class ApproximateGELU(nn.Module): + r""" + The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2: + https://arxiv.org/abs/1606.08415. + + Parameters: + dim_in (`int`): The number of channels in the input. + dim_out (`int`): The number of channels in the output. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + return x * torch.sigmoid(1.702 * x) diff --git a/gen_3d_modules/TripoSR/models/transformer/transformer_1d.py b/gen_3d_modules/TripoSR/models/transformer/transformer_1d.py new file mode 100644 index 0000000..a98d0fb --- /dev/null +++ b/gen_3d_modules/TripoSR/models/transformer/transformer_1d.py @@ -0,0 +1,219 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# -------- +# +# Modified 2024 by the Tripo AI and Stability AI Team. +# +# Copyright (c) 2024 Tripo AI & Stability AI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from ...utils import BaseModule +from .basic_transformer_block import BasicTransformerBlock + + +class Transformer1D(BaseModule): + @dataclass + class Config(BaseModule.Config): + num_attention_heads: int = 16 + attention_head_dim: int = 88 + in_channels: Optional[int] = None + out_channels: Optional[int] = None + num_layers: int = 1 + dropout: float = 0.0 + norm_num_groups: int = 32 + cross_attention_dim: Optional[int] = None + attention_bias: bool = False + activation_fn: str = "geglu" + only_cross_attention: bool = False + double_self_attention: bool = False + upcast_attention: bool = False + norm_type: str = "layer_norm" + norm_elementwise_affine: bool = True + gradient_checkpointing: bool = False + + cfg: Config + + def configure(self) -> None: + self.num_attention_heads = self.cfg.num_attention_heads + self.attention_head_dim = self.cfg.attention_head_dim + inner_dim = self.num_attention_heads * self.attention_head_dim + + linear_cls = nn.Linear + + # 2. Define input layers + self.in_channels = self.cfg.in_channels + + self.norm = torch.nn.GroupNorm( + num_groups=self.cfg.norm_num_groups, + num_channels=self.cfg.in_channels, + eps=1e-6, + affine=True, + ) + self.proj_in = linear_cls(self.cfg.in_channels, inner_dim) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + self.num_attention_heads, + self.attention_head_dim, + dropout=self.cfg.dropout, + cross_attention_dim=self.cfg.cross_attention_dim, + activation_fn=self.cfg.activation_fn, + attention_bias=self.cfg.attention_bias, + only_cross_attention=self.cfg.only_cross_attention, + double_self_attention=self.cfg.double_self_attention, + upcast_attention=self.cfg.upcast_attention, + norm_type=self.cfg.norm_type, + norm_elementwise_affine=self.cfg.norm_elementwise_affine, + ) + for d in range(self.cfg.num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = ( + self.cfg.in_channels + if self.cfg.out_channels is None + else self.cfg.out_channels + ) + + self.proj_out = linear_cls(inner_dim, self.cfg.in_channels) + + self.gradient_checkpointing = self.cfg.gradient_checkpointing + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + ): + """ + The [`Transformer1DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + attention_mask ( `torch.Tensor`, *optional*): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + + Returns: + torch.FloatTensor + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = ( + 1 - encoder_attention_mask.to(hidden_states.dtype) + ) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + batch, _, seq_len = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 1).reshape( + batch, seq_len, inner_dim + ) + hidden_states = self.proj_in(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + if self.training and self.gradient_checkpointing: + hidden_states = torch.utils.checkpoint.checkpoint( + block, + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + use_reentrant=False, + ) + else: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + ) + + # 3. Output + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, seq_len, inner_dim) + .permute(0, 2, 1) + .contiguous() + ) + + output = hidden_states + residual + + return output diff --git a/gen_3d_modules/TripoSR/system.py b/gen_3d_modules/TripoSR/system.py new file mode 100644 index 0000000..c6d42f9 --- /dev/null +++ b/gen_3d_modules/TripoSR/system.py @@ -0,0 +1,192 @@ +import math +import os +from dataclasses import dataclass, field +from typing import List, Union + +import numpy as np +import PIL.Image +import torch +import torch.nn.functional as F +import trimesh +from einops import rearrange +from huggingface_hub import hf_hub_download +from omegaconf import OmegaConf +from PIL import Image + +from .models.isosurface import MarchingCubeHelper +from .utils import ( + BaseModule, + ImagePreprocessor, + find_class, + get_spherical_cameras, + scale_tensor, +) + + +class TSR(BaseModule): + @dataclass + class Config(BaseModule.Config): + cond_image_size: int + + image_tokenizer_cls: str + image_tokenizer: dict + + tokenizer_cls: str + tokenizer: dict + + backbone_cls: str + backbone: dict + + post_processor_cls: str + post_processor: dict + + decoder_cls: str + decoder: dict + + renderer_cls: str + renderer: dict + + cfg: Config + + @classmethod + def from_pretrained( + cls, weight_path: str, config_path: str + ): + cfg = OmegaConf.load(config_path) + OmegaConf.resolve(cfg) + model = cls(cfg) + ckpt = torch.load(weight_path, map_location="cpu") + model.load_state_dict(ckpt) + return model + + def configure(self): + self.image_tokenizer = find_class(self.cfg.image_tokenizer_cls)( + self.cfg.image_tokenizer + ) + self.tokenizer = find_class(self.cfg.tokenizer_cls)(self.cfg.tokenizer) + self.backbone = find_class(self.cfg.backbone_cls)(self.cfg.backbone) + self.post_processor = find_class(self.cfg.post_processor_cls)( + self.cfg.post_processor + ) + self.decoder = find_class(self.cfg.decoder_cls)(self.cfg.decoder) + self.renderer = find_class(self.cfg.renderer_cls)(self.cfg.renderer) + self.image_processor = ImagePreprocessor() + self.isosurface_helper = None + + def forward( + self, + image: Union[ + PIL.Image.Image, + np.ndarray, + torch.FloatTensor, + List[PIL.Image.Image], + List[np.ndarray], + List[torch.FloatTensor], + ], + device: str, + ) -> torch.FloatTensor: + rgb_cond = self.image_processor(image, self.cfg.cond_image_size)[:, None].to( + device + ) + batch_size = rgb_cond.shape[0] + + input_image_tokens: torch.Tensor = self.image_tokenizer( + rearrange(rgb_cond, "B Nv H W C -> B Nv C H W", Nv=1), + ) + + input_image_tokens = rearrange( + input_image_tokens, "B Nv C Nt -> B (Nv Nt) C", Nv=1 + ) + + tokens: torch.Tensor = self.tokenizer(batch_size) + + tokens = self.backbone( + tokens, + encoder_hidden_states=input_image_tokens, + ) + + scene_codes = self.post_processor(self.tokenizer.detokenize(tokens)) + return scene_codes + + def render( + self, + scene_codes, + n_views: int, + elevation_deg: float = 0.0, + camera_distance: float = 1.9, + fovy_deg: float = 40.0, + height: int = 256, + width: int = 256, + return_type: str = "pil", + ): + rays_o, rays_d = get_spherical_cameras( + n_views, elevation_deg, camera_distance, fovy_deg, height, width + ) + rays_o, rays_d = rays_o.to(scene_codes.device), rays_d.to(scene_codes.device) + + def process_output(image: torch.FloatTensor): + if return_type == "pt": + return image + elif return_type == "np": + return image.detach().cpu().numpy() + elif return_type == "pil": + return Image.fromarray( + (image.detach().cpu().numpy() * 255.0).astype(np.uint8) + ) + else: + raise NotImplementedError + + images = [] + for scene_code in scene_codes: + images_ = [] + for i in range(n_views): + with torch.no_grad(): + image = self.renderer( + self.decoder, scene_code, rays_o[i], rays_d[i] + ) + images_.append(process_output(image)) + images.append(images_) + + return images + + def set_marching_cubes_resolution(self, resolution: int): + if ( + self.isosurface_helper is not None + and self.isosurface_helper.resolution == resolution + ): + return + self.isosurface_helper = MarchingCubeHelper(resolution) + + def extract_mesh(self, scene_codes, resolution: int = 256, threshold: float = 25.0): + self.set_marching_cubes_resolution(resolution) + meshes = [] + for scene_code in scene_codes: + with torch.no_grad(): + density = self.renderer.query_triplane( + self.decoder, + scale_tensor( + self.isosurface_helper.grid_vertices.to(scene_codes.device), + self.isosurface_helper.points_range, + (-self.renderer.cfg.radius, self.renderer.cfg.radius), + ), + scene_code, + )["density_act"] + v_pos, t_pos_idx = self.isosurface_helper(-(density - threshold)) + v_pos = scale_tensor( + v_pos, + self.isosurface_helper.points_range, + (-self.renderer.cfg.radius, self.renderer.cfg.radius), + ) + with torch.no_grad(): + color = self.renderer.query_triplane( + self.decoder, + v_pos, + scene_code, + )["color"] + mesh = trimesh.Trimesh( + vertices=v_pos.cpu().numpy(), + faces=t_pos_idx.cpu().numpy(), + vertex_colors=color.cpu().numpy(), + ) + meshes.append(mesh) + return meshes diff --git a/gen_3d_modules/TripoSR/utils.py b/gen_3d_modules/TripoSR/utils.py new file mode 100644 index 0000000..3b07fa6 --- /dev/null +++ b/gen_3d_modules/TripoSR/utils.py @@ -0,0 +1,474 @@ +import importlib +import math +from collections import defaultdict +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import imageio +import numpy as np +import PIL.Image +import torch +import torch.nn as nn +import torch.nn.functional as F +import trimesh +from omegaconf import DictConfig, OmegaConf +from PIL import Image + +def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any: + scfg = OmegaConf.merge(OmegaConf.structured(fields), cfg) + return scfg + + +def find_class(cls_string): + module_string = ".".join(cls_string.split(".")[:-1]) + cls_name = cls_string.split(".")[-1] + module = importlib.import_module(module_string, package=None) + cls = getattr(module, cls_name) + return cls + + +def get_intrinsic_from_fov(fov, H, W, bs=-1): + focal_length = 0.5 * H / np.tan(0.5 * fov) + intrinsic = np.identity(3, dtype=np.float32) + intrinsic[0, 0] = focal_length + intrinsic[1, 1] = focal_length + intrinsic[0, 2] = W / 2.0 + intrinsic[1, 2] = H / 2.0 + + if bs > 0: + intrinsic = intrinsic[None].repeat(bs, axis=0) + + return torch.from_numpy(intrinsic) + + +class BaseModule(nn.Module): + @dataclass + class Config: + pass + + cfg: Config # add this to every subclass of BaseModule to enable static type checking + + def __init__( + self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs + ) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + self.configure(*args, **kwargs) + + def configure(self, *args, **kwargs) -> None: + raise NotImplementedError + + +class ImagePreprocessor: + def convert_and_resize( + self, + image: Union[PIL.Image.Image, np.ndarray, torch.Tensor], + size: int, + ): + if isinstance(image, PIL.Image.Image): + image = torch.from_numpy(np.array(image).astype(np.float32) / 255.0) + elif isinstance(image, np.ndarray): + if image.dtype == np.uint8: + image = torch.from_numpy(image.astype(np.float32) / 255.0) + else: + image = torch.from_numpy(image) + elif isinstance(image, torch.Tensor): + pass + + batched = image.ndim == 4 + + if not batched: + image = image[None, ...] + image = F.interpolate( + image.permute(0, 3, 1, 2), + (size, size), + mode="bilinear", + align_corners=False, + antialias=True, + ).permute(0, 2, 3, 1) + if not batched: + image = image[0] + return image + + def __call__( + self, + image: Union[ + PIL.Image.Image, + np.ndarray, + torch.FloatTensor, + List[PIL.Image.Image], + List[np.ndarray], + List[torch.FloatTensor], + ], + size: int, + ) -> Any: + if isinstance(image, (np.ndarray, torch.FloatTensor)) and image.ndim == 4: + image = self.convert_and_resize(image, size) + else: + if not isinstance(image, list): + image = [image] + image = [self.convert_and_resize(im, size) for im in image] + image = torch.stack(image, dim=0) + return image + + +def rays_intersect_bbox( + rays_o: torch.Tensor, + rays_d: torch.Tensor, + radius: float, + near: float = 0.0, + valid_thresh: float = 0.01, +): + input_shape = rays_o.shape[:-1] + rays_o, rays_d = rays_o.view(-1, 3), rays_d.view(-1, 3) + rays_d_valid = torch.where( + rays_d.abs() < 1e-6, torch.full_like(rays_d, 1e-6), rays_d + ) + if type(radius) in [int, float]: + radius = torch.FloatTensor( + [[-radius, radius], [-radius, radius], [-radius, radius]] + ).to(rays_o.device) + radius = ( + 1.0 - 1.0e-3 + ) * radius # tighten the radius to make sure the intersection point lies in the bounding box + interx0 = (radius[..., 1] - rays_o) / rays_d_valid + interx1 = (radius[..., 0] - rays_o) / rays_d_valid + t_near = torch.minimum(interx0, interx1).amax(dim=-1).clamp_min(near) + t_far = torch.maximum(interx0, interx1).amin(dim=-1) + + # check wheter a ray intersects the bbox or not + rays_valid = t_far - t_near > valid_thresh + + t_near[torch.where(~rays_valid)] = 0.0 + t_far[torch.where(~rays_valid)] = 0.0 + + t_near = t_near.view(*input_shape, 1) + t_far = t_far.view(*input_shape, 1) + rays_valid = rays_valid.view(*input_shape) + + return t_near, t_far, rays_valid + + +def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any: + if chunk_size <= 0: + return func(*args, **kwargs) + B = None + for arg in list(args) + list(kwargs.values()): + if isinstance(arg, torch.Tensor): + B = arg.shape[0] + break + assert ( + B is not None + ), "No tensor found in args or kwargs, cannot determine batch size." + out = defaultdict(list) + out_type = None + # max(1, B) to support B == 0 + for i in range(0, max(1, B), chunk_size): + out_chunk = func( + *[ + arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg + for arg in args + ], + **{ + k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg + for k, arg in kwargs.items() + }, + ) + if out_chunk is None: + continue + out_type = type(out_chunk) + if isinstance(out_chunk, torch.Tensor): + out_chunk = {0: out_chunk} + elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list): + chunk_length = len(out_chunk) + out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)} + elif isinstance(out_chunk, dict): + pass + else: + print( + f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}." + ) + exit(1) + for k, v in out_chunk.items(): + v = v if torch.is_grad_enabled() else v.detach() + out[k].append(v) + + if out_type is None: + return None + + out_merged: Dict[Any, Optional[torch.Tensor]] = {} + for k, v in out.items(): + if all([vv is None for vv in v]): + # allow None in return value + out_merged[k] = None + elif all([isinstance(vv, torch.Tensor) for vv in v]): + out_merged[k] = torch.cat(v, dim=0) + else: + raise TypeError( + f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}" + ) + + if out_type is torch.Tensor: + return out_merged[0] + elif out_type in [tuple, list]: + return out_type([out_merged[i] for i in range(chunk_length)]) + elif out_type is dict: + return out_merged + + +ValidScale = Union[Tuple[float, float], torch.FloatTensor] + + +def scale_tensor(dat: torch.FloatTensor, inp_scale: ValidScale, tgt_scale: ValidScale): + if inp_scale is None: + inp_scale = (0, 1) + if tgt_scale is None: + tgt_scale = (0, 1) + if isinstance(tgt_scale, torch.FloatTensor): + assert dat.shape[-1] == tgt_scale.shape[-1] + dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0]) + dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0] + return dat + + +def get_activation(name) -> Callable: + if name is None: + return lambda x: x + name = name.lower() + if name == "none": + return lambda x: x + elif name == "exp": + return lambda x: torch.exp(x) + elif name == "sigmoid": + return lambda x: torch.sigmoid(x) + elif name == "tanh": + return lambda x: torch.tanh(x) + elif name == "softplus": + return lambda x: F.softplus(x) + else: + try: + return getattr(F, name) + except AttributeError: + raise ValueError(f"Unknown activation function: {name}") + + +def get_ray_directions( + H: int, + W: int, + focal: Union[float, Tuple[float, float]], + principal: Optional[Tuple[float, float]] = None, + use_pixel_centers: bool = True, + normalize: bool = True, +) -> torch.FloatTensor: + """ + Get ray directions for all pixels in camera coordinate. + Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/ + ray-tracing-generating-camera-rays/standard-coordinate-systems + + Inputs: + H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers + Outputs: + directions: (H, W, 3), the direction of the rays in camera coordinate + """ + pixel_center = 0.5 if use_pixel_centers else 0 + + if isinstance(focal, float): + fx, fy = focal, focal + cx, cy = W / 2, H / 2 + else: + fx, fy = focal + assert principal is not None + cx, cy = principal + + i, j = torch.meshgrid( + torch.arange(W, dtype=torch.float32) + pixel_center, + torch.arange(H, dtype=torch.float32) + pixel_center, + indexing="xy", + ) + + directions = torch.stack([(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1) + + if normalize: + directions = F.normalize(directions, dim=-1) + + return directions + + +def get_rays( + directions, + c2w, + keepdim=False, + normalize=False, +) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + # Rotate ray directions from camera coordinate to the world coordinate + assert directions.shape[-1] == 3 + + if directions.ndim == 2: # (N_rays, 3) + if c2w.ndim == 2: # (4, 4) + c2w = c2w[None, :, :] + assert c2w.ndim == 3 # (N_rays, 4, 4) or (1, 4, 4) + rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1) # (N_rays, 3) + rays_o = c2w[:, :3, 3].expand(rays_d.shape) + elif directions.ndim == 3: # (H, W, 3) + assert c2w.ndim in [2, 3] + if c2w.ndim == 2: # (4, 4) + rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum( + -1 + ) # (H, W, 3) + rays_o = c2w[None, None, :3, 3].expand(rays_d.shape) + elif c2w.ndim == 3: # (B, 4, 4) + rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum( + -1 + ) # (B, H, W, 3) + rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape) + elif directions.ndim == 4: # (B, H, W, 3) + assert c2w.ndim == 3 # (B, 4, 4) + rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum( + -1 + ) # (B, H, W, 3) + rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape) + + if normalize: + rays_d = F.normalize(rays_d, dim=-1) + if not keepdim: + rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3) + + return rays_o, rays_d + + +def get_spherical_cameras( + n_views: int, + elevation_deg: float, + camera_distance: float, + fovy_deg: float, + height: int, + width: int, +): + azimuth_deg = torch.linspace(0, 360.0, n_views + 1)[:n_views] + elevation_deg = torch.full_like(azimuth_deg, elevation_deg) + camera_distances = torch.full_like(elevation_deg, camera_distance) + + elevation = elevation_deg * math.pi / 180 + azimuth = azimuth_deg * math.pi / 180 + + # convert spherical coordinates to cartesian coordinates + # right hand coordinate system, x back, y right, z up + # elevation in (-90, 90), azimuth from +x to +y in (-180, 180) + camera_positions = torch.stack( + [ + camera_distances * torch.cos(elevation) * torch.cos(azimuth), + camera_distances * torch.cos(elevation) * torch.sin(azimuth), + camera_distances * torch.sin(elevation), + ], + dim=-1, + ) + + # default scene center at origin + center = torch.zeros_like(camera_positions) + # default camera up direction as +z + up = torch.as_tensor([0, 0, 1], dtype=torch.float32)[None, :].repeat(n_views, 1) + + fovy = torch.full_like(elevation_deg, fovy_deg) * math.pi / 180 + + lookat = F.normalize(center - camera_positions, dim=-1) + right = F.normalize(torch.cross(lookat, up), dim=-1) + up = F.normalize(torch.cross(right, lookat), dim=-1) + c2w3x4 = torch.cat( + [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]], + dim=-1, + ) + c2w = torch.cat([c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1) + c2w[:, 3, 3] = 1.0 + + # get directions by dividing directions_unit_focal by focal length + focal_length = 0.5 * height / torch.tan(0.5 * fovy) + directions_unit_focal = get_ray_directions( + H=height, + W=width, + focal=1.0, + ) + directions = directions_unit_focal[None, :, :, :].repeat(n_views, 1, 1, 1) + directions[:, :, :, :2] = ( + directions[:, :, :, :2] / focal_length[:, None, None, None] + ) + # must use normalize=True to normalize directions here + rays_o, rays_d = get_rays(directions, c2w, keepdim=True, normalize=True) + + return rays_o, rays_d + + +def remove_background( + image: PIL.Image.Image, + rembg_session: Any = None, + force: bool = False, + **rembg_kwargs, +) -> PIL.Image.Image: + import rembg + do_remove = True + if image.mode == "RGBA" and image.getextrema()[3][0] < 255: + do_remove = False + do_remove = do_remove or force + if do_remove: + image = rembg.remove(image, session=rembg_session, **rembg_kwargs) + return image + + +def resize_foreground( + image: PIL.Image.Image, + ratio: float, +) -> PIL.Image.Image: + image = np.array(image) + assert image.shape[-1] == 4 + alpha = np.where(image[..., 3] > 0) + y1, y2, x1, x2 = ( + alpha[0].min(), + alpha[0].max(), + alpha[1].min(), + alpha[1].max(), + ) + # crop the foreground + fg = image[y1:y2, x1:x2] + # pad to square + size = max(fg.shape[0], fg.shape[1]) + ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2 + ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0 + new_image = np.pad( + fg, + ((ph0, ph1), (pw0, pw1), (0, 0)), + mode="constant", + constant_values=((0, 0), (0, 0), (0, 0)), + ) + + # compute padding according to the ratio + new_size = int(new_image.shape[0] / ratio) + # pad to size, double side + ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2 + ph1, pw1 = new_size - size - ph0, new_size - size - pw0 + new_image = np.pad( + new_image, + ((ph0, ph1), (pw0, pw1), (0, 0)), + mode="constant", + constant_values=((0, 0), (0, 0), (0, 0)), + ) + new_image = PIL.Image.fromarray(new_image) + return new_image + + +def save_video( + frames: List[PIL.Image.Image], + output_path: str, + fps: int = 30, +): + # use imageio to save video + frames = [np.array(frame) for frame in frames] + writer = imageio.get_writer(output_path, fps=fps) + for frame in frames: + writer.append_data(frame) + writer.close() + + +def to_gradio_3d_orientation(mesh): + mesh.apply_transform(trimesh.transformations.rotation_matrix(-np.pi/2, [1, 0, 0])) + mesh.apply_scale([1, 1, -1]) + mesh.apply_transform(trimesh.transformations.rotation_matrix(np.pi/2, [0, 1, 0])) + return mesh diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_RT.txt new file mode 100644 index 0000000..7ef2610 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_RT.txt @@ -0,0 +1,3 @@ +-5.266582965850830078e-01 7.410295009613037109e-01 -4.165407419204711914e-01 -5.960464477539062500e-08 +5.865638996738198330e-08 4.900035560131072998e-01 8.717204332351684570e-01 -9.462351613365171943e-08 +8.500770330429077148e-01 4.590988159179687500e-01 -2.580644786357879639e-01 -1.300000071525573730e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_left_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_left_RT.txt new file mode 100644 index 0000000..7db25bb --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_left_RT.txt @@ -0,0 +1,3 @@ +-9.734988808631896973e-01 1.993551850318908691e-01 -1.120596975088119507e-01 -1.713633537292480469e-07 +3.790224578636980368e-09 4.900034964084625244e-01 8.717204928398132324e-01 1.772203575001185527e-07 +2.286916375160217285e-01 8.486189246177673340e-01 -4.770178496837615967e-01 -1.838477611541748047e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_right_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_right_RT.txt new file mode 100644 index 0000000..be45ed8 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_back_right_RT.txt @@ -0,0 +1,3 @@ +2.286914736032485962e-01 8.486190438270568848e-01 -4.770178198814392090e-01 1.564621925354003906e-07 +-3.417914484771245043e-08 4.900034070014953613e-01 8.717205524444580078e-01 -7.293811421504869941e-08 +9.734990000724792480e-01 -1.993550658226013184e-01 1.120596155524253845e-01 -1.838477969169616699e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_RT.txt new file mode 100644 index 0000000..8278639 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_RT.txt @@ -0,0 +1,3 @@ +5.266583561897277832e-01 -7.410295009613037109e-01 4.165407419204711914e-01 0.000000000000000000e+00 +5.865638996738198330e-08 4.900035560131072998e-01 8.717204332351684570e-01 9.462351613365171943e-08 +-8.500770330429077148e-01 -4.590988159179687500e-01 2.580645382404327393e-01 -1.300000071525573730e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_left_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_left_RT.txt new file mode 100644 index 0000000..6255b9f --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_left_RT.txt @@ -0,0 +1,3 @@ +-2.286916971206665039e-01 -8.486189842224121094e-01 4.770179092884063721e-01 -2.458691596984863281e-07 +9.085837859856837895e-09 4.900034666061401367e-01 8.717205524444580078e-01 1.205695667749751010e-07 +-9.734990000724792480e-01 1.993551701307296753e-01 -1.120597645640373230e-01 -1.838477969169616699e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_right_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_right_RT.txt new file mode 100644 index 0000000..e1d76c8 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_front_right_RT.txt @@ -0,0 +1,3 @@ +9.734989404678344727e-01 -1.993551850318908691e-01 1.120596975088119507e-01 -1.415610313415527344e-07 +3.790224578636980368e-09 4.900034964084625244e-01 8.717204928398132324e-01 -1.772203575001185527e-07 +-2.286916375160217285e-01 -8.486189246177673340e-01 4.770178794860839844e-01 -1.838477611541748047e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_left_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_left_RT.txt new file mode 100644 index 0000000..bd42197 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_left_RT.txt @@ -0,0 +1,3 @@ +-8.500771522521972656e-01 -4.590989053249359131e-01 2.580644488334655762e-01 0.000000000000000000e+00 +-4.257411134744870651e-08 4.900034964084625244e-01 8.717204928398132324e-01 9.006067358541258727e-08 +-5.266583561897277832e-01 7.410295605659484863e-01 -4.165408313274383545e-01 -1.300000071525573730e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_right_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_right_RT.txt new file mode 100644 index 0000000..2d37c02 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_right_RT.txt @@ -0,0 +1,3 @@ +8.500770330429077148e-01 4.590989053249359131e-01 -2.580644488334655762e-01 5.960464477539062500e-08 +-4.257411134744870651e-08 4.900034964084625244e-01 8.717204928398132324e-01 -9.006067358541258727e-08 +5.266583561897277832e-01 -7.410295605659484863e-01 4.165407419204711914e-01 -1.300000071525573730e+00 diff --git a/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_top_RT.txt b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_top_RT.txt new file mode 100644 index 0000000..1d71f22 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/fixed_poses/nine_views/000_top_RT.txt @@ -0,0 +1,3 @@ +9.958608150482177734e-01 7.923202216625213623e-02 -4.453715682029724121e-02 -3.098167056236889039e-09 +-9.089154005050659180e-02 8.681122064590454102e-01 -4.879753291606903076e-01 5.784738377201392723e-08 +-2.028124157504862524e-08 4.900035560131072998e-01 8.717204332351684570e-01 -1.300000071525573730e+00 diff --git a/gen_3d_modules/Wonder3D/data/normal_utils.py b/gen_3d_modules/Wonder3D/data/normal_utils.py new file mode 100644 index 0000000..dff3730 --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/normal_utils.py @@ -0,0 +1,45 @@ +import numpy as np + +def camNormal2worldNormal(rot_c2w, camNormal): + H,W,_ = camNormal.shape + normal_img = np.matmul(rot_c2w[None, :, :], camNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3]) + + return normal_img + +def worldNormal2camNormal(rot_w2c, normal_map_world): + H,W,_ = normal_map_world.shape + # normal_img = np.matmul(rot_w2c[None, :, :], worldNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3]) + + # faster version + # Reshape the normal map into a 2D array where each row represents a normal vector + normal_map_flat = normal_map_world.reshape(-1, 3) + + # Transform the normal vectors using the transformation matrix + normal_map_camera_flat = np.dot(normal_map_flat, rot_w2c.T) + + # Reshape the transformed normal map back to its original shape + normal_map_camera = normal_map_camera_flat.reshape(normal_map_world.shape) + + return normal_map_camera + +def trans_normal(normal, RT_w2c, RT_w2c_target): + + # normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal) + # normal_target_cam = worldNormal2camNormal(RT_w2c_target[:3,:3], normal_world) + + relative_RT = np.matmul(RT_w2c_target[:3,:3], np.linalg.inv(RT_w2c[:3,:3])) + normal_target_cam = worldNormal2camNormal(relative_RT[:3,:3], normal) + + return normal_target_cam + +def img2normal(img): + return (img/255.)*2-1 + +def normal2img(normal): + return np.uint8((normal*0.5+0.5)*255) + +def norm_normalize(normal, dim=-1): + + normal = normal/(np.linalg.norm(normal, axis=dim, keepdims=True)+1e-6) + + return normal \ No newline at end of file diff --git a/gen_3d_modules/Wonder3D/data/objaverse_dataset.py b/gen_3d_modules/Wonder3D/data/objaverse_dataset.py new file mode 100644 index 0000000..f0bed0b --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/objaverse_dataset.py @@ -0,0 +1,510 @@ +from typing import Dict +import numpy as np +from omegaconf import DictConfig, ListConfig +import torch +from torch.utils.data import Dataset +from pathlib import Path +import json +from PIL import Image +from torchvision import transforms +from einops import rearrange +from typing import Literal, Tuple, Optional, Any +import random + +import json +import os, sys +import math + +from .normal_utils import trans_normal, normal2img, img2normal + + +class ObjaverseDataset(Dataset): + def __init__(self, + root_dir: str, + num_views: int, + bg_color: Any, + img_wh: Tuple[int, int], + object_list: str, + groups_num: int=1, + validation: bool = False, + data_view_num: int = 6, + num_validation_samples: int = 64, + num_samples: Optional[int] = None, + invalid_list: Optional[str] = None, + trans_norm_system: bool = True, # if True, transform all normals map into the cam system of front view + augment_data: bool = False, + read_normal: bool = True, + read_color: bool = False, + read_depth: bool = False, + read_mask: bool = True, + mix_color_normal: bool = False, + suffix: str = 'png', + subscene_tag: int = 3, + backup_scene: str = "9438abf986c7453a9f4df7c34aa2e65b" + ) -> None: + """Create a dataset from a folder of images. + If you pass in a root directory it will be searched for images + ending in ext (ext can be a list) + """ + self.root_dir = Path(root_dir) + self.num_views = num_views + self.bg_color = bg_color + self.validation = validation + self.num_samples = num_samples + self.trans_norm_system = trans_norm_system + self.augment_data = augment_data + self.invalid_list = invalid_list + self.groups_num = groups_num + print("augment data: ", self.augment_data) + self.img_wh = img_wh + self.read_normal = read_normal + self.read_color = read_color + self.read_depth = read_depth + self.read_mask = read_mask + self.mix_color_normal = mix_color_normal # mix load color and normal maps + self.suffix = suffix + self.subscene_tag = subscene_tag + + self.view_types = ['front', 'front_right', 'right', 'back', 'left', 'front_left'] + self.fix_cam_pose_dir = "./Gen_3D_Modules/MVDream/data/fixed_poses/nine_views" + + self.fix_cam_poses = self.load_fixed_poses() # world2cam matrix + + if object_list is not None: + with open(object_list) as f: + self.objects = json.load(f) + self.objects = [os.path.basename(o).replace(".glb", "") for o in self.objects] + else: + self.objects = os.listdir(self.root_dir) + self.objects = sorted(self.objects) + + if self.invalid_list is not None: + with open(self.invalid_list) as f: + self.invalid_objects = json.load(f) + self.invalid_objects = [os.path.basename(o).replace(".glb", "") for o in self.invalid_objects] + else: + self.invalid_objects = [] + + + self.all_objects = set(self.objects) - (set(self.invalid_objects) & set(self.objects)) + self.all_objects = list(self.all_objects) + + if not validation: + self.all_objects = self.all_objects[:-num_validation_samples] + else: + self.all_objects = self.all_objects[-num_validation_samples:] + if num_samples is not None: + self.all_objects = self.all_objects[:num_samples] + + print("loading ", len(self.all_objects), " objects in the dataset") + + if self.mix_color_normal: + self.backup_data = self.__getitem_mix__(0, backup_scene) + else: + self.backup_data = self.__getitem_joint__(0, backup_scene) + + def __len__(self): + return len(self.objects)*self.total_view + + def load_fixed_poses(self): + poses = {} + for face in self.view_types: + RT = np.loadtxt(os.path.join(self.fix_cam_pose_dir,'%03d_%s_RT.txt'%(0, face))) + poses[face] = RT + + return poses + + def cartesian_to_spherical(self, xyz): + ptsnew = np.hstack((xyz, np.zeros(xyz.shape))) + xy = xyz[:,0]**2 + xyz[:,1]**2 + z = np.sqrt(xy + xyz[:,2]**2) + theta = np.arctan2(np.sqrt(xy), xyz[:,2]) # for elevation angle defined from Z-axis down + #ptsnew[:,4] = np.arctan2(xyz[:,2], np.sqrt(xy)) # for elevation angle defined from XY-plane up + azimuth = np.arctan2(xyz[:,1], xyz[:,0]) + return np.array([theta, azimuth, z]) + + def get_T(self, target_RT, cond_RT): + R, T = target_RT[:3, :3], target_RT[:, -1] + T_target = -R.T @ T # change to cam2world + + R, T = cond_RT[:3, :3], cond_RT[:, -1] + T_cond = -R.T @ T + + theta_cond, azimuth_cond, z_cond = self.cartesian_to_spherical(T_cond[None, :]) + theta_target, azimuth_target, z_target = self.cartesian_to_spherical(T_target[None, :]) + + d_theta = theta_target - theta_cond + d_azimuth = (azimuth_target - azimuth_cond) % (2 * math.pi) + d_z = z_target - z_cond + + # d_T = torch.tensor([d_theta.item(), math.sin(d_azimuth.item()), math.cos(d_azimuth.item()), d_z.item()]) + return d_theta, d_azimuth + + def get_bg_color(self): + if self.bg_color == 'white': + bg_color = np.array([1., 1., 1.], dtype=np.float32) + elif self.bg_color == 'black': + bg_color = np.array([0., 0., 0.], dtype=np.float32) + elif self.bg_color == 'gray': + bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32) + elif self.bg_color == 'random': + bg_color = np.random.rand(3) + elif self.bg_color == 'three_choices': + white = np.array([1., 1., 1.], dtype=np.float32) + black = np.array([0., 0., 0.], dtype=np.float32) + gray = np.array([0.5, 0.5, 0.5], dtype=np.float32) + bg_color = random.choice([white, black, gray]) + elif isinstance(self.bg_color, float): + bg_color = np.array([self.bg_color] * 3, dtype=np.float32) + else: + raise NotImplementedError + return bg_color + + + + def load_mask(self, img_path, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + img = np.array(Image.open(img_path).resize(self.img_wh)) + img = np.float32(img > 0) + + assert len(np.shape(img)) == 2 + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img + + def load_image(self, img_path, bg_color, alpha, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + img = np.array(Image.open(img_path).resize(self.img_wh)) + img = img.astype(np.float32) / 255. # [0, 1] + assert img.shape[-1] == 3 or img.shape[-1] == 4 # RGB or RGBA + + if alpha is None and img.shape[-1] == 4: + alpha = img[:, :, 3:] + img = img[:, :, :3] + + if alpha.shape[-1] != 1: + alpha = alpha[:, :, None] + + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img + + def load_depth(self, img_path, bg_color, alpha, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + img = np.array(Image.open(img_path).resize(self.img_wh)) + img = img.astype(np.float32) / 65535. # [0, 1] + + img[img > 0.4] = 0 + img = img / 0.4 + + assert img.ndim == 2 # depth + img = np.stack([img]*3, axis=-1) + + if alpha.shape[-1] != 1: + alpha = alpha[:, :, None] + + # print(np.max(img[:, :, 0])) + + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img + + def load_normal(self, img_path, bg_color, alpha, RT_w2c=None, RT_w2c_cond=None, return_type='np'): + # not using cv2 as may load in uint16 format + # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255] + # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC) + # pil always returns uint8 + normal = np.array(Image.open(img_path).resize(self.img_wh)) + + assert normal.shape[-1] == 3 or normal.shape[-1] == 4 # RGB or RGBA + + if alpha is None and normal.shape[-1] == 4: + alpha = normal[:, :, 3:] / 255. + normal = normal[:, :, :3] + + normal = trans_normal(img2normal(normal), RT_w2c, RT_w2c_cond) + + img = (normal*0.5 + 0.5).astype(np.float32) # [0, 1] + + if alpha.shape[-1] != 1: + alpha = alpha[:, :, None] + + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + else: + raise NotImplementedError + + return img + + def __len__(self): + return len(self.all_objects) + + def __getitem_mix__(self, index, debug_object=None): + if debug_object is not None: + object_name = debug_object # + set_idx = random.sample(range(0, self.groups_num), 1)[0] # without replacement + else: + object_name = self.all_objects[index%len(self.all_objects)] + set_idx = 0 + + if self.augment_data: + cond_view = random.sample(self.view_types, k=1)[0] + else: + cond_view = 'front' + + + # ! if you would like predict depth; modify here + if random.random() < 0.5: + read_color, read_normal, read_depth = True, False, False + else: + read_color, read_normal, read_depth = False, True, False + + read_normal = read_normal & self.read_normal + read_depth = read_depth & self.read_depth + + assert (read_color and (read_normal or read_depth)) is False + + view_types = self.view_types + + cond_w2c = self.fix_cam_poses[cond_view] + + tgt_w2cs = [self.fix_cam_poses[view] for view in view_types] + + elevations = [] + azimuths = [] + + # get the bg color + bg_color = self.get_bg_color() + + if self.read_mask: + cond_alpha = self.load_mask(os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), return_type='np') + else: + cond_alpha = None + img_tensors_in = [ + self.load_image(os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), bg_color, cond_alpha, return_type='pt').permute(2, 0, 1) + ] * self.num_views + img_tensors_out = [] + + for view, tgt_w2c in zip(view_types, tgt_w2cs): + img_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, view, self.suffix)) + mask_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, view, self.suffix)) + normal_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "normals_%03d_%s.%s" % (set_idx, view, self.suffix)) + depth_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "depth_%03d_%s.%s" % (set_idx, view, self.suffix)) + if self.read_mask: + alpha = self.load_mask(mask_path, return_type='np') + else: + alpha = None + + if read_color: + img_tensor = self.load_image(img_path, bg_color, alpha, return_type="pt") + img_tensor = img_tensor.permute(2, 0, 1) + img_tensors_out.append(img_tensor) + + if read_normal: + normal_tensor = self.load_normal(normal_path, bg_color, alpha, RT_w2c=tgt_w2c, RT_w2c_cond=cond_w2c, return_type="pt").permute(2, 0, 1) + img_tensors_out.append(normal_tensor) + if read_depth: + depth_tensor = self.load_depth(depth_path, bg_color, alpha, return_type="pt").permute(2, 0, 1) + img_tensors_out.append(depth_tensor) + + # evelations, azimuths + elevation, azimuth = self.get_T(tgt_w2c, cond_w2c) + elevations.append(elevation) + azimuths.append(azimuth) + + img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W) + img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W) + + + elevations = torch.as_tensor(elevations).float().squeeze(1) + azimuths = torch.as_tensor(azimuths).float().squeeze(1) + elevations_cond = torch.as_tensor([0] * self.num_views).float() # fixed only use 4 views to train + camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3) + + normal_class = torch.tensor([1, 0]).float() + normal_task_embeddings = torch.stack([normal_class]*self.num_views, dim=0) # (Nv, 2) + color_class = torch.tensor([0, 1]).float() + color_task_embeddings = torch.stack([color_class]*self.num_views, dim=0) # (Nv, 2) + if read_normal or read_depth: + task_embeddings = normal_task_embeddings + if read_color: + task_embeddings = color_task_embeddings + # print(elevations) + # print(azimuths) + return { + 'elevations_cond': elevations_cond, + 'elevations_cond_deg': torch.rad2deg(elevations_cond), + 'elevations': elevations, + 'azimuths': azimuths, + 'elevations_deg': torch.rad2deg(elevations), + 'azimuths_deg': torch.rad2deg(azimuths), + 'imgs_in': img_tensors_in, + 'imgs_out': img_tensors_out, + 'camera_embeddings': camera_embeddings, + 'task_embeddings': task_embeddings + } + + + def __getitem_joint__(self, index, debug_object=None): + if debug_object is not None: + object_name = debug_object # + set_idx = random.sample(range(0, self.groups_num), 1)[0] # without replacement + else: + object_name = self.all_objects[index%len(self.all_objects)] + set_idx = 0 + + if self.augment_data: + cond_view = random.sample(self.view_types, k=1)[0] + else: + cond_view = 'front' + + view_types = self.view_types + + cond_w2c = self.fix_cam_poses[cond_view] + + tgt_w2cs = [self.fix_cam_poses[view] for view in view_types] + + elevations = [] + azimuths = [] + + # get the bg color + bg_color = self.get_bg_color() + + if self.read_mask: + cond_alpha = self.load_mask(os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), return_type='np') + else: + cond_alpha = None + img_tensors_in = [ + self.load_image(os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), bg_color, cond_alpha, return_type='pt').permute(2, 0, 1) + ] * self.num_views + img_tensors_out = [] + normal_tensors_out = [] + for view, tgt_w2c in zip(view_types, tgt_w2cs): + img_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, view, self.suffix)) + mask_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, view, self.suffix)) + if self.read_mask: + alpha = self.load_mask(mask_path, return_type='np') + else: + alpha = None + + if self.read_color: + img_tensor = self.load_image(img_path, bg_color, alpha, return_type="pt") + img_tensor = img_tensor.permute(2, 0, 1) + img_tensors_out.append(img_tensor) + + if self.read_normal: + normal_path = os.path.join(self.root_dir, object_name[:self.subscene_tag], object_name, "normals_%03d_%s.%s" % (set_idx, view, self.suffix)) + normal_tensor = self.load_normal(normal_path, bg_color, alpha, RT_w2c=tgt_w2c, RT_w2c_cond=cond_w2c, return_type="pt").permute(2, 0, 1) + normal_tensors_out.append(normal_tensor) + + # evelations, azimuths + elevation, azimuth = self.get_T(tgt_w2c, cond_w2c) + elevations.append(elevation) + azimuths.append(azimuth) + + img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W) + if self.read_color: + img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W) + if self.read_normal: + normal_tensors_out = torch.stack(normal_tensors_out, dim=0).float() # (Nv, 3, H, W) + + elevations = torch.as_tensor(elevations).float().squeeze(1) + azimuths = torch.as_tensor(azimuths).float().squeeze(1) + elevations_cond = torch.as_tensor([0] * self.num_views).float() # fixed only use 4 views to train + + camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3) + + normal_class = torch.tensor([1, 0]).float() + normal_task_embeddings = torch.stack([normal_class]*self.num_views, dim=0) # (Nv, 2) + color_class = torch.tensor([0, 1]).float() + color_task_embeddings = torch.stack([color_class]*self.num_views, dim=0) # (Nv, 2) + + return { + 'elevations_cond': elevations_cond, + 'elevations_cond_deg': torch.rad2deg(elevations_cond), + 'elevations': elevations, + 'azimuths': azimuths, + 'elevations_deg': torch.rad2deg(elevations), + 'azimuths_deg': torch.rad2deg(azimuths), + 'imgs_in': img_tensors_in, + 'imgs_out': img_tensors_out, + 'normals_out': normal_tensors_out, + 'camera_embeddings': camera_embeddings, + 'normal_task_embeddings': normal_task_embeddings, + 'color_task_embeddings': color_task_embeddings + } + + def __getitem__(self, index): + try: + if self.mix_color_normal: + data = self.__getitem_mix__(index) + else: + data = self.__getitem_joint__(index) + return data + except: + print("load error ", self.all_objects[index%len(self.all_objects)] ) + return self.backup_data + + +class ConcatDataset(torch.utils.data.Dataset): + def __init__(self, datasets, weights): + self.datasets = datasets + self.weights = weights + self.num_datasets = len(datasets) + + def __getitem__(self, i): + + chosen = random.choices(self.datasets, self.weights, k=1)[0] + return chosen[i] + + def __len__(self): + return max(len(d) for d in self.datasets) + +if __name__ == "__main__": + train_dataset = ObjaverseDataset( + root_dir="/ghome/l5/xxlong/.objaverse/hf-objaverse-v1/renderings", + size=(128, 128), + ext="hdf5", + default_trans=torch.zeros(3), + return_paths=False, + total_view=8, + validation=False, + object_list=None, + views_mode='fourviews' + ) + data0 = train_dataset[0] + data1 = train_dataset[50] + # print(data) diff --git a/gen_3d_modules/Wonder3D/data/single_image_dataset.py b/gen_3d_modules/Wonder3D/data/single_image_dataset.py new file mode 100644 index 0000000..09f57cb --- /dev/null +++ b/gen_3d_modules/Wonder3D/data/single_image_dataset.py @@ -0,0 +1,292 @@ +import numpy as np +import torch +from torch.utils.data import Dataset +from PIL import Image +from typing import Literal, Tuple, Optional, Any +import cv2 + +import os +import math + + +import cv2 +import numpy as np + +def add_margin(pil_img, color=0, size=256): + width, height = pil_img.size + result = Image.new(pil_img.mode, (size, size), color) + result.paste(pil_img, ((size - width) // 2, (size - height) // 2)) + return result + +def scale_and_place_object(image, scale_factor): + assert np.shape(image)[-1]==4 # RGBA + + # Extract the alpha channel (transparency) and the object (RGB channels) + alpha_channel = image[:, :, 3] + + # Find the bounding box coordinates of the object + coords = cv2.findNonZero(alpha_channel) + x, y, width, height = cv2.boundingRect(coords) + + # Calculate the scale factor for resizing + original_height, original_width = image.shape[:2] + + if width > height: + size = width + original_size = original_width + else: + size = height + original_size = original_height + + scale_factor = min(scale_factor, size / (original_size+0.0)) + + new_size = scale_factor * original_size + scale_factor = new_size / size + + # Calculate the new size based on the scale factor + new_width = int(width * scale_factor) + new_height = int(height * scale_factor) + + center_x = original_width // 2 + center_y = original_height // 2 + + paste_x = center_x - (new_width // 2) + paste_y = center_y - (new_height // 2) + + # Resize the object (RGB channels) to the new size + rescaled_object = cv2.resize(image[y:y+height, x:x+width], (new_width, new_height)) + + # Create a new RGBA image with the resized image + new_image = np.zeros((original_height, original_width, 4), dtype=np.uint8) + + new_image[paste_y:paste_y + new_height, paste_x:paste_x + new_width] = rescaled_object + + return new_image + +class SingleImageDataset(Dataset): + def __init__(self, + fix_cam_pose_dir: str, + num_views: int, + img_wh: Tuple[int, int], + bg_color: str, + crop_size: int = 224, + single_image: Optional[Image.Image] = None, + num_validation_samples: Optional[int] = None, + root_dir: Optional[str] = '', + filepaths: Optional[list] = None, + cond_type: Optional[str] = None + ) -> None: + """Create a dataset from a folder of images. + If you pass in a root directory it will be searched for images + ending in ext (ext can be a list) + """ + self.root_dir = root_dir + self.num_views = num_views + self.img_wh = img_wh + self.crop_size = crop_size + self.bg_color = bg_color + self.cond_type = cond_type + + if self.num_views == 4: + self.view_types = ['front', 'right', 'back', 'left'] + elif self.num_views == 5: + self.view_types = ['front', 'front_right', 'right', 'back', 'left'] + elif self.num_views == 6: + self.view_types = ['front', 'front_right', 'right', 'back', 'left', 'front_left'] + + self.fix_cam_pose_dir = fix_cam_pose_dir + + self.fix_cam_poses = self.load_fixed_poses() # world2cam matrix + + if single_image is None: + if filepaths is None: + # Get a list of all files in the directory + file_list = os.listdir(self.root_dir) + else: + file_list = filepaths + + # Filter the files that end with .png or .jpg + self.file_list = [file for file in file_list if file.endswith(('.png', '.jpg'))] + else: + self.file_list = None + + # load all images + self.all_images = [] + self.all_alphas = [] + bg_color = self.get_bg_color() + + if single_image is not None: + image, alpha = self.load_image(None, bg_color, return_type='pt', Imagefile=single_image) + self.all_images.append(image) + self.all_alphas.append(alpha) + else: + for file in self.file_list: + print(os.path.join(self.root_dir, file)) + image, alpha = self.load_image(os.path.join(self.root_dir, file), bg_color, return_type='pt') + self.all_images.append(image) + self.all_alphas.append(alpha) + + self.all_images = self.all_images[:num_validation_samples] + self.all_alphas = self.all_alphas[:num_validation_samples] + + + def __len__(self): + return len(self.all_images) + + def load_fixed_poses(self): + poses = {} + for face in self.view_types: + RT = np.loadtxt(os.path.join(self.fix_cam_pose_dir,'%03d_%s_RT.txt'%(0, face))) + poses[face] = RT + + return poses + + def cartesian_to_spherical(self, xyz): + ptsnew = np.hstack((xyz, np.zeros(xyz.shape))) + xy = xyz[:,0]**2 + xyz[:,1]**2 + z = np.sqrt(xy + xyz[:,2]**2) + theta = np.arctan2(np.sqrt(xy), xyz[:,2]) # for elevation angle defined from Z-axis down + #ptsnew[:,4] = np.arctan2(xyz[:,2], np.sqrt(xy)) # for elevation angle defined from XY-plane up + azimuth = np.arctan2(xyz[:,1], xyz[:,0]) + return np.array([theta, azimuth, z]) + + def get_T(self, target_RT, cond_RT): + R, T = target_RT[:3, :3], target_RT[:, -1] + T_target = -R.T @ T # change to cam2world + + R, T = cond_RT[:3, :3], cond_RT[:, -1] + T_cond = -R.T @ T + + theta_cond, azimuth_cond, z_cond = self.cartesian_to_spherical(T_cond[None, :]) + theta_target, azimuth_target, z_target = self.cartesian_to_spherical(T_target[None, :]) + + d_theta = theta_target - theta_cond + d_azimuth = (azimuth_target - azimuth_cond) % (2 * math.pi) + d_z = z_target - z_cond + + # d_T = torch.tensor([d_theta.item(), math.sin(d_azimuth.item()), math.cos(d_azimuth.item()), d_z.item()]) + return d_theta, d_azimuth + + def get_bg_color(self): + if self.bg_color == 'white': + bg_color = np.array([1., 1., 1.], dtype=np.float32) + elif self.bg_color == 'black': + bg_color = np.array([0., 0., 0.], dtype=np.float32) + elif self.bg_color == 'gray': + bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32) + elif self.bg_color == 'random': + bg_color = np.random.rand(3) + elif isinstance(self.bg_color, float): + bg_color = np.array([self.bg_color] * 3, dtype=np.float32) + else: + raise NotImplementedError + return bg_color + + + def load_image(self, img_path, bg_color, return_type='np', Imagefile=None): + # pil always returns uint8 + if Imagefile is None: + image_input = Image.open(img_path) + else: + image_input = Imagefile + image_size = self.img_wh[0] + + if self.crop_size!=-1: + alpha_np = np.asarray(image_input)[:, :, 3] + coords = np.stack(np.nonzero(alpha_np), 1)[:, (1, 0)] + min_x, min_y = np.min(coords, 0) + max_x, max_y = np.max(coords, 0) + ref_img_ = image_input.crop((min_x, min_y, max_x, max_y)) + h, w = ref_img_.height, ref_img_.width + scale = self.crop_size / max(h, w) + h_, w_ = int(scale * h), int(scale * w) + ref_img_ = ref_img_.resize((w_, h_)) + image_input = add_margin(ref_img_, size=image_size) + else: + image_input = add_margin(image_input, size=max(image_input.height, image_input.width)) + image_input = image_input.resize((image_size, image_size)) + + # img = scale_and_place_object(img, self.scale_ratio) + img = np.array(image_input) + img = img.astype(np.float32) / 255. # [0, 1] + assert img.shape[-1] == 4 # RGBA + + alpha = img[...,3:4] + img = img[...,:3] * alpha + bg_color * (1 - alpha) + + if return_type == "np": + pass + elif return_type == "pt": + img = torch.from_numpy(img) + alpha = torch.from_numpy(alpha) + else: + raise NotImplementedError + + return img, alpha + + + def __len__(self): + return len(self.all_images) + + def __getitem__(self, index): + + image = self.all_images[index%len(self.all_images)] + alpha = self.all_alphas[index%len(self.all_images)] + if self.file_list is not None: + filename = self.file_list[index%len(self.all_images)].replace(".png", "") + else: + filename = 'null' + + cond_w2c = self.fix_cam_poses['front'] + + tgt_w2cs = [self.fix_cam_poses[view] for view in self.view_types] + + elevations = [] + azimuths = [] + + img_tensors_in = [ + image.permute(2, 0, 1) + ] * self.num_views + + alpha_tensors_in = [ + alpha.permute(2, 0, 1) + ] * self.num_views + + for view, tgt_w2c in zip(self.view_types, tgt_w2cs): + # evelations, azimuths + elevation, azimuth = self.get_T(tgt_w2c, cond_w2c) + elevations.append(elevation) + azimuths.append(azimuth) + + img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W) + alpha_tensors_in = torch.stack(alpha_tensors_in, dim=0).float() # (Nv, 3, H, W) + + elevations = torch.as_tensor(elevations).float().squeeze(1) + azimuths = torch.as_tensor(azimuths).float().squeeze(1) + elevations_cond = torch.as_tensor([0] * self.num_views).float() + + normal_class = torch.tensor([1, 0]).float() + normal_task_embeddings = torch.stack([normal_class]*self.num_views, dim=0) # (Nv, 2) + color_class = torch.tensor([0, 1]).float() + color_task_embeddings = torch.stack([color_class]*self.num_views, dim=0) # (Nv, 2) + + camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3) + + out = { + 'elevations_cond': elevations_cond, + 'elevations_cond_deg': torch.rad2deg(elevations_cond), + 'elevations': elevations, + 'azimuths': azimuths, + 'elevations_deg': torch.rad2deg(elevations), + 'azimuths_deg': torch.rad2deg(azimuths), + 'imgs_in': img_tensors_in, + 'alphas': alpha_tensors_in, + 'camera_embeddings': camera_embeddings, + 'normal_task_embeddings': normal_task_embeddings, + 'color_task_embeddings': color_task_embeddings, + 'filename': filename, + } + + return out + + diff --git a/gen_3d_modules/Wonder3D/models/transformer_mv2d.py b/gen_3d_modules/Wonder3D/models/transformer_mv2d.py new file mode 100644 index 0000000..cdd6438 --- /dev/null +++ b/gen_3d_modules/Wonder3D/models/transformer_mv2d.py @@ -0,0 +1,985 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import ImagePositionalEmbeddings +from diffusers.utils import BaseOutput, deprecate +from diffusers.utils.torch_utils import maybe_allow_in_graph +from diffusers.models.attention import FeedForward, AdaLayerNorm, AdaLayerNormZero, Attention +from diffusers.models.embeddings import PatchEmbed +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils.import_utils import is_xformers_available + +from einops import rearrange, repeat + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + +def my_repeat(tensor, num_repeats): + """ + Repeat a tensor along a given dimension + """ + if len(tensor.shape) == 3: + return repeat(tensor, "b d c -> (b v) d c", v=num_repeats) + elif len(tensor.shape) == 4: + return repeat(tensor, "a b d c -> (a v) b d c", v=num_repeats) + + +@dataclass +class TransformerMV2DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + + +class TransformerMV2DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + num_views: int = 1, + cd_attention_last: bool=False, + cd_attention_mid: bool=False, + multiview_attention: bool=True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + if self.is_input_continuous: + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = LoRACompatibleLinear(in_channels, inner_dim) + else: + self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" + assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" + + self.height = sample_size + self.width = sample_size + self.num_vector_embeds = num_vector_embeds + self.num_latent_pixels = self.height * self.width + + self.latent_image_embedding = ImagePositionalEmbeddings( + num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width + ) + elif self.is_input_patches: + assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" + + self.height = sample_size + self.width = sample_size + + self.patch_size = patch_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicMVTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + if self.is_input_continuous: + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = LoRACompatibleLinear(inner_dim, in_channels) + else: + self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + elif self.is_input_vectorized: + self.norm_out = nn.LayerNorm(inner_dim) + self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) + elif self.is_input_patches: + self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) + self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 1. Input + if self.is_input_continuous: + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + elif self.is_input_vectorized: + hidden_states = self.latent_image_embedding(hidden_states) + elif self.is_input_patches: + hidden_states = self.pos_embed(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + elif self.is_input_vectorized: + hidden_states = self.norm_out(hidden_states) + logits = self.out(hidden_states) + # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) + logits = logits.permute(0, 2, 1) + + # log(p(x_0)) + output = F.log_softmax(logits.double(), dim=1).float() + elif self.is_input_patches: + # TODO: cleanup! + conditioning = self.transformer_blocks[0].norm1.emb( + timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) + hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] + hidden_states = self.proj_out_2(hidden_states) + + # unpatchify + height = width = int(hidden_states.shape[1] ** 0.5) + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + if not return_dict: + return (output,) + + return TransformerMV2DModelOutput(sample=output) + + +@maybe_allow_in_graph +class BasicMVTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool = False + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + self.multiview_attention = multiview_attention + self.sparse_mv_attention = sparse_mv_attention + self.mvcd_attention = mvcd_attention + + self.attn1 = CustomAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=MVAttnProcessor() + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + self.num_views = num_views + + self.cd_attention_last = cd_attention_last + + if self.cd_attention_last: + # Joint task -Attn + self.attn_joint_last = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + nn.init.zeros_(self.attn_joint_last.to_out[0].weight.data) + self.norm_joint_last = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + + self.cd_attention_mid = cd_attention_mid + + if self.cd_attention_mid: + # print("cross-domain attn in the middle") + # Joint task -Attn + self.attn_joint_mid = CustomJointAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + processor=JointAttnProcessor() + ) + nn.init.zeros_(self.attn_joint_mid.to_out[0].weight.data) + self.norm_joint_mid = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ): + assert attention_mask is None # not supported yet + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + num_views=self.num_views, + multiview_attention=self.multiview_attention, + sparse_mv_attention=self.sparse_mv_attention, + mvcd_attention=self.mvcd_attention, + **cross_attention_kwargs, + ) + + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # joint attention twice + if self.cd_attention_mid: + norm_hidden_states = ( + self.norm_joint_mid(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_mid(hidden_states) + ) + hidden_states = self.attn_joint_mid(norm_hidden_states) + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + if self.cd_attention_last: + norm_hidden_states = ( + self.norm_joint_last(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_joint_last(hidden_states) + ) + hidden_states = self.attn_joint_last(norm_hidden_states) + hidden_states + + return hidden_states + + +class CustomAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersMVAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + + +class CustomJointAttention(Attention): + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, *args, **kwargs + ): + processor = XFormersJointAttnProcessor() + self.set_processor(processor) + # print("using xformers attention processor") + +class MVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1, + multiview_attention=True + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320]) + # pdb.set_trace() + # multi-view self-attention + if multiview_attention: + key = rearrange(key, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0) + value = rearrange(value, "(b t) d c -> b (t d) c", t=num_views).repeat_interleave(num_views, dim=0) + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersMVAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_views=1., + multiview_attention=True, + sparse_mv_attention=False, + mvcd_attention=False, + ): + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key_raw = attn.to_k(encoder_hidden_states) + value_raw = attn.to_v(encoder_hidden_states) + + # print('query', query.shape, 'key', key.shape, 'value', value.shape) + #([bx4, 1024, 320]) key torch.Size([bx4, 1024, 320]) value torch.Size([bx4, 1024, 320]) + # pdb.set_trace() + # multi-view self-attention + if multiview_attention: + if not sparse_mv_attention: + key = my_repeat(rearrange(key_raw, "(b t) d c -> b (t d) c", t=num_views), num_views) + value = my_repeat(rearrange(value_raw, "(b t) d c -> b (t d) c", t=num_views), num_views) + else: + key_front = my_repeat(rearrange(key_raw, "(b t) d c -> b t d c", t=num_views)[:, 0, :, :], num_views) # [(b t), d, c] + value_front = my_repeat(rearrange(value_raw, "(b t) d c -> b t d c", t=num_views)[:, 0, :, :], num_views) + key = torch.cat([key_front, key_raw], dim=1) # shape (b t) (2 d) c + value = torch.cat([value_front, value_raw], dim=1) + + else: + # print("don't use multiview attention.") + key = key_raw + value = value_raw + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + + +class XFormersJointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + # from yuancheng; here attention_mask is None + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value, dim=0, chunks=2) + key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c + value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c + key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c + value = torch.cat([value]*2, dim=0) # (2 b t) 2d c + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class JointAttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + num_tasks=2 + ): + + residual = hidden_states + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + assert num_tasks == 2 # only support two tasks now + + key_0, key_1 = torch.chunk(key, dim=0, chunks=2) # keys shape (b t) d c + value_0, value_1 = torch.chunk(value, dim=0, chunks=2) + key = torch.cat([key_0, key_1], dim=1) # (b t) 2d c + value = torch.cat([value_0, value_1], dim=1) # (b t) 2d c + key = torch.cat([key]*2, dim=0) # ( 2 b t) 2d c + value = torch.cat([value]*2, dim=0) # (2 b t) 2d c + + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states \ No newline at end of file diff --git a/gen_3d_modules/Wonder3D/models/unet_mv2d_blocks.py b/gen_3d_modules/Wonder3D/models/unet_mv2d_blocks.py new file mode 100644 index 0000000..76085dd --- /dev/null +++ b/gen_3d_modules/Wonder3D/models/unet_mv2d_blocks.py @@ -0,0 +1,922 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.utils import is_torch_version, logging +from diffusers.models.normalization import AdaGroupNorm +from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 +from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel +from diffusers.models.resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D +from .transformer_mv2d import TransformerMV2DModel + +from diffusers.models.unets.unet_2d_blocks import DownBlock2D, ResnetDownsampleBlock2D, AttnDownBlock2D, CrossAttnDownBlock2D, SimpleCrossAttnDownBlock2D, SkipDownBlock2D, AttnSkipDownBlock2D, DownEncoderBlock2D, AttnDownEncoderBlock2D, KDownBlock2D, KCrossAttnDownBlock2D +from diffusers.models.unets.unet_2d_blocks import UpBlock2D, ResnetUpsampleBlock2D, CrossAttnUpBlock2D, SimpleCrossAttnUpBlock2D, AttnUpBlock2D, SkipUpBlock2D, AttnSkipUpBlock2D, UpDecoderBlock2D, AttnUpDecoderBlock2D, KUpBlock2D, KCrossAttnUpBlock2D + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + transformer_layers_per_block=1, + num_attention_heads=None, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + resnet_skip_time_act=False, + resnet_out_scale_factor=1.0, + cross_attention_norm=None, + attention_head_dim=None, + downsample_type=None, + num_views=1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock2D": + return DownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "ResnetDownsampleBlock2D": + return ResnetDownsampleBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + ) + elif down_block_type == "AttnDownBlock2D": + if add_downsample is False: + downsample_type = None + else: + downsample_type = downsample_type or "conv" # default to 'conv' + return AttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + downsample_type=downsample_type, + ) + elif down_block_type == "CrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") + return CrossAttnDownBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + # custom MV2D attention block + elif down_block_type == "CrossAttnDownBlockMV2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMV2D") + return CrossAttnDownBlockMV2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + elif down_block_type == "SimpleCrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") + return SimpleCrossAttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif down_block_type == "SkipDownBlock2D": + return SkipDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "AttnSkipDownBlock2D": + return AttnSkipDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "DownEncoderBlock2D": + return DownEncoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "AttnDownEncoderBlock2D": + return AttnDownEncoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "KDownBlock2D": + return KDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + ) + elif down_block_type == "KCrossAttnDownBlock2D": + return KCrossAttnDownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + add_self_attention=True if not add_downsample else False, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + transformer_layers_per_block=1, + num_attention_heads=None, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + resnet_skip_time_act=False, + resnet_out_scale_factor=1.0, + cross_attention_norm=None, + attention_head_dim=None, + upsample_type=None, + num_views=1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock2D": + return UpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "ResnetUpsampleBlock2D": + return ResnetUpsampleBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + ) + elif up_block_type == "CrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") + return CrossAttnUpBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + # custom MV2D attention block + elif up_block_type == "CrossAttnUpBlockMV2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMV2D") + return CrossAttnUpBlockMV2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + elif up_block_type == "SimpleCrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") + return SimpleCrossAttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + output_scale_factor=resnet_out_scale_factor, + only_cross_attention=only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif up_block_type == "AttnUpBlock2D": + if add_upsample is False: + upsample_type = None + else: + upsample_type = upsample_type or "conv" # default to 'conv' + + return AttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + upsample_type=upsample_type, + ) + elif up_block_type == "SkipUpBlock2D": + return SkipUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "AttnSkipUpBlock2D": + return AttnSkipUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "UpDecoderBlock2D": + return UpDecoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + elif up_block_type == "AttnUpDecoderBlock2D": + return AttnUpDecoderBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + attention_head_dim=attention_head_dim, + resnet_time_scale_shift=resnet_time_scale_shift, + temb_channels=temb_channels, + ) + elif up_block_type == "KUpBlock2D": + return KUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + ) + elif up_block_type == "KCrossAttnUpBlock2D": + return KCrossAttnUpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + cross_attention_dim=cross_attention_dim, + attention_head_dim=attention_head_dim, + ) + + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlockMV2DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for _ in range(num_layers): + if not dual_cross_attention: + attentions.append( + TransformerMV2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + ) + else: + raise NotImplementedError + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnUpBlockMV2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + TransformerMV2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + ) + else: + raise NotImplementedError + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ): + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + None, # timestep + None, # class_labels + cross_attention_kwargs, + attention_mask, + encoder_attention_mask, + **ckpt_kwargs, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class CrossAttnDownBlockMV2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool=False + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + TransformerMV2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + ) + else: + raise NotImplementedError + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + additional_residuals=None, + ): + output_states = () + + blocks = list(zip(self.resnets, self.attentions)) + + for i, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + None, # timestep + None, # class_labels + cross_attention_kwargs, + attention_mask, + encoder_attention_mask, + **ckpt_kwargs, + )[0] + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + hidden_states = hidden_states + additional_residuals + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + diff --git a/gen_3d_modules/Wonder3D/models/unet_mv2d_condition.py b/gen_3d_modules/Wonder3D/models/unet_mv2d_condition.py new file mode 100644 index 0000000..0511953 --- /dev/null +++ b/gen_3d_modules/Wonder3D/models/unet_mv2d_condition.py @@ -0,0 +1,1485 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union +import os + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor +from diffusers.models.embeddings import ( + GaussianFourierProjection, + ImageHintTimeEmbedding, + ImageProjection, + ImageTimeEmbedding, + TextImageProjection, + TextImageTimeEmbedding, + TextTimeEmbedding, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.modeling_utils import ModelMixin, load_state_dict, _load_state_dict_into_model +from diffusers.models.unets.unet_2d_blocks import ( + CrossAttnDownBlock2D, + CrossAttnUpBlock2D, + DownBlock2D, + UNetMidBlock2DCrossAttn, + UNetMidBlock2DSimpleCrossAttn, + UpBlock2D, +) +from huggingface_hub.constants import HF_HUB_OFFLINE, HUGGINGFACE_HUB_CACHE +from diffusers.utils import ( + CONFIG_NAME, + FLAX_WEIGHTS_NAME, + SAFETENSORS_WEIGHTS_NAME, + WEIGHTS_NAME, + _add_variant, + _get_model_file, + deprecate, + is_accelerate_available, + is_torch_version, + logging, +) +from diffusers import __version__ +from .unet_mv2d_blocks import ( + CrossAttnDownBlockMV2D, + CrossAttnUpBlockMV2D, + UNetMidBlockMV2DCrossAttn, + get_down_block, + get_up_block, +) + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNetMV2DConditionOutput(BaseOutput): + """ + The output of [`UNet2DConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class UNetMV2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. + flip_sin_to_cos (`bool`, *optional*, defaults to `False`): + Whether to flip the sin to cos in the time embedding. + freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): + The tuple of downsample blocks to use. + mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): + Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or + `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): + The tuple of upsample blocks to use. + only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): + Whether to include self-attention in the basic transformer blocks, see + [`~models.attention.BasicTransformerBlock`]. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. + mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. + act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. + norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. + If `None`, normalization and activation layers is skipped in post-processing. + norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + encoder_hid_dim (`int`, *optional*, defaults to None): + If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` + dimension to `cross_attention_dim`. + encoder_hid_dim_type (`str`, *optional*, defaults to `None`): + If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text + embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. + attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. + num_attention_heads (`int`, *optional*): + The number of attention heads. If not defined, defaults to `attention_head_dim` + resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config + for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. + class_embed_type (`str`, *optional*, defaults to `None`): + The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, + `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. + addition_embed_type (`str`, *optional*, defaults to `None`): + Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or + "text". "text" will use the `TextTimeEmbedding` layer. + addition_time_embed_dim: (`int`, *optional*, defaults to `None`): + Dimension for the timestep embeddings. + num_class_embeds (`int`, *optional*, defaults to `None`): + Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing + class conditioning with `class_embed_type` equal to `None`. + time_embedding_type (`str`, *optional*, defaults to `positional`): + The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. + time_embedding_dim (`int`, *optional*, defaults to `None`): + An optional override for the dimension of the projected time embedding. + time_embedding_act_fn (`str`, *optional*, defaults to `None`): + Optional activation function to use only once on the time embeddings before they are passed to the rest of + the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. + timestep_post_act (`str`, *optional*, defaults to `None`): + The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. + time_cond_proj_dim (`int`, *optional*, defaults to `None`): + The dimension of `cond_proj` layer in the timestep embedding. + conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. + conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. + projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when + `class_embed_type="projection"`. Required when `class_embed_type="projection"`. + class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time + embeddings with the class embeddings. + mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): + Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If + `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the + `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` + otherwise. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlockMV2DCrossAttn", + up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D"), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: Union[int, Tuple[int]] = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: Union[int, Tuple[int]] = 1280, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int]]] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: int = 1.0, + time_embedding_type: str = "positional", + time_embedding_dim: Optional[int] = None, + time_embedding_act_fn: Optional[str] = None, + timestep_post_act: Optional[str] = None, + time_cond_proj_dim: Optional[int] = None, + conv_in_kernel: int = 3, + conv_out_kernel: int = 3, + projection_class_embeddings_input_dim: Optional[int] = None, + class_embeddings_concat: bool = False, + mid_block_only_cross_attention: Optional[bool] = None, + cross_attention_norm: Optional[str] = None, + addition_embed_type_num_heads=64, + num_views: int = 1, + cd_attention_last: bool = False, + cd_attention_mid: bool = False, + multiview_attention: bool = True, + sparse_mv_attention: bool = False, + mvcd_attention: bool = False + ): + super().__init__() + + self.sample_size = sample_size + + if num_attention_heads is not None: + raise ValueError( + "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." + ) + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + + # input + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in = nn.Conv2d( + in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding + ) + + # time + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 + if time_embed_dim % 2 != 0: + raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") + self.time_proj = GaussianFourierProjection( + time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + post_act_fn=timestep_post_act, + cond_proj_dim=time_cond_proj_dim, + ) + + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 + self.encoder_hid_proj = ImageProjection( + image_embed_dim=encoder_hid_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + elif class_embed_type == "simple_projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" + ) + self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) + else: + self.class_embedding = None + + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + elif addition_embed_type == "image": + # Kandinsky 2.2 + self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) + elif addition_embed_type == "image_hint": + # Kandinsky 2.2 ControlNet + self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) + elif addition_embed_type is not None: + raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") + + if time_embedding_act_fn is None: + self.time_embed_act = None + else: + self.time_embed_act = get_activation(time_embedding_act_fn) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = only_cross_attention + + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = False + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + if class_embeddings_concat: + # The time embeddings are concatenated with the class embeddings. The dimension of the + # time embeddings passed to the down, middle, and up blocks is twice the dimension of the + # regular time embeddings + blocks_time_embed_dim = time_embed_dim * 2 + else: + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock2DCrossAttn": + self.mid_block = UNetMidBlock2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + # custom MV2D attention block + elif mid_block_type == "UNetMidBlockMV2DCrossAttn": + self.mid_block = UNetMidBlockMV2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": + self.mid_block = UNetMidBlock2DSimpleCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + cross_attention_dim=cross_attention_dim[-1], + attention_head_dim=attention_head_dim[-1], + resnet_groups=norm_num_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + skip_time_act=resnet_skip_time_act, + only_cross_attention=mid_block_only_cross_attention, + cross_attention_norm=cross_attention_norm, + ) + elif mid_block_type is None: + self.mid_block = None + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, + num_views=num_views, + cd_attention_last=cd_attention_last, + cd_attention_mid=cd_attention_mid, + multiview_attention=multiview_attention, + sparse_mv_attention=sparse_mv_attention, + mvcd_attention=mvcd_attention + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_num_groups is not None: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + + self.conv_act = get_activation(act_fn) + + else: + self.conv_norm_out = None + self.conv_act = None + + conv_out_padding = (conv_out_kernel - 1) // 2 + self.conv_out = nn.Conv2d( + block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock2D, CrossAttnDownBlockMV2D, DownBlock2D, CrossAttnUpBlock2D, CrossAttnUpBlockMV2D, UpBlock2D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNetMV2DConditionOutput, Tuple]: + r""" + The [`UNet2DConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. + encoder_attention_mask (`torch.Tensor`): + A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If + `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, + which adds large negative values to the attention scores corresponding to "discard" tokens. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None: + encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 0. center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # there might be better ways to encapsulate this. + class_labels = class_labels.to(dtype=sample.dtype) + + class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) + + if self.config.class_embeddings_concat: + emb = torch.cat([emb, class_emb], dim=-1) + else: + emb = emb + class_emb + + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + elif self.config.addition_embed_type == "text_image": + # Kandinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + + image_embs = added_cond_kwargs.get("image_embeds") + text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) + aug_emb = self.add_embedding(text_embs, image_embs) + elif self.config.addition_embed_type == "text_time": + # SDXL - style + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + elif self.config.addition_embed_type == "image": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + aug_emb = self.add_embedding(image_embs) + elif self.config.addition_embed_type == "image_hint": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + hint = added_cond_kwargs.get("hint") + aug_emb, hint = self.add_embedding(image_embs, hint) + sample = torch.cat([sample, hint], dim=1) + + emb = emb + aug_emb if aug_emb is not None else emb + + if self.time_embed_act is not None: + emb = self.time_embed_act(emb) + + if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": + # Kadinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) + elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(image_embeds) + # 2. pre-process + sample = self.conv_in(sample) + + # 3. down + + is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None + is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None + + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # For t2i-adapter CrossAttnDownBlock2D + additional_residuals = {} + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + **additional_residuals, + ) + else: + sample, res_samples = downsample_block(hidden_states=sample, temb=emb) + + if is_adapter and len(down_block_additional_residuals) > 0: + sample += down_block_additional_residuals.pop(0) + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + if self.mid_block is not None: + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size + ) + + # 6. post-process + if self.conv_norm_out: + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNetMV2DConditionOutput(sample=sample) + + @classmethod + def from_pretrained_2d( + cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], + camera_embedding_type: str, num_views: int, sample_size: int, + zero_init_conv_in: bool = True, zero_init_camera_projection: bool = False, + projection_class_embeddings_input_dim: int=6, cd_attention_last: bool = False, + cd_attention_mid: bool = False, multiview_attention: bool = True, + sparse_mv_attention: bool = False, mvcd_attention: bool = False, + in_channels: int = 8, out_channels: int = 4, + **kwargs + ): + r""" + Instantiate a pretrained PyTorch model from a pretrained model configuration. + + The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To + train the model, set it back in training mode with `model.train()`. + + Parameters: + pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): + Can be either: + + - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on + the Hub. + - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved + with [`~ModelMixin.save_pretrained`]. + + cache_dir (`Union[str, os.PathLike]`, *optional*): + Path to a directory where a downloaded pretrained model configuration is cached if the standard cache + is not used. + torch_dtype (`str` or `torch.dtype`, *optional*): + Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the + dtype is automatically derived from the model's weights. + force_download (`bool`, *optional*, defaults to `False`): + Whether or not to force the (re-)download of the model weights and configuration files, overriding the + cached versions if they exist. + resume_download (`bool`, *optional*, defaults to `False`): + Whether or not to resume downloading the model weights and configuration files. If set to `False`, any + incompletely downloaded files are deleted. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. + output_loading_info (`bool`, *optional*, defaults to `False`): + Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. + local_files_only(`bool`, *optional*, defaults to `False`): + Whether to only load local model weights and configuration files or not. If set to `True`, the model + won't be downloaded from the Hub. + use_auth_token (`str` or *bool*, *optional*): + The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from + `diffusers-cli login` (stored in `~/.huggingface`) is used. + revision (`str`, *optional*, defaults to `"main"`): + The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier + allowed by Git. + from_flax (`bool`, *optional*, defaults to `False`): + Load the model weights from a Flax checkpoint save file. + subfolder (`str`, *optional*, defaults to `""`): + The subfolder location of a model file within a larger model repository on the Hub or locally. + mirror (`str`, *optional*): + Mirror source to resolve accessibility issues if you're downloading a model in China. We do not + guarantee the timeliness or safety of the source, and you should refer to the mirror site for more + information. + device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): + A map that specifies where each submodule should go. It doesn't need to be defined for each + parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the + same device. + + Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For + more information about each option see [designing a device + map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). + max_memory (`Dict`, *optional*): + A dictionary device identifier for the maximum memory. Will default to the maximum memory available for + each GPU and the available CPU RAM if unset. + offload_folder (`str` or `os.PathLike`, *optional*): + The path to offload weights if `device_map` contains the value `"disk"`. + offload_state_dict (`bool`, *optional*): + If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if + the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` + when there is some disk offload. + low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): + Speed up model loading only loading the pretrained weights and not initializing the weights. This also + tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. + Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this + argument to `True` will raise an error. + variant (`str`, *optional*): + Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when + loading `from_flax`. + use_safetensors (`bool`, *optional*, defaults to `None`): + If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the + `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors` + weights. If set to `False`, `safetensors` weights are not loaded. + + + + To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with + `huggingface-cli login`. You can also activate the special + ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a + firewalled environment. + + + + Example: + + ```py + from diffusers import UNet2DConditionModel + + unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + ``` + + If you get the error message below, you need to finetune the weights for your downstream task: + + ```bash + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated + You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. + ``` + """ + cache_dir = kwargs.pop("cache_dir", HUGGINGFACE_HUB_CACHE) + ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) + force_download = kwargs.pop("force_download", False) + from_flax = kwargs.pop("from_flax", False) + resume_download = kwargs.pop("resume_download", False) + proxies = kwargs.pop("proxies", None) + output_loading_info = kwargs.pop("output_loading_info", False) + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) + use_auth_token = kwargs.pop("use_auth_token", None) + revision = kwargs.pop("revision", None) + torch_dtype = kwargs.pop("torch_dtype", None) + subfolder = kwargs.pop("subfolder", None) + device_map = kwargs.pop("device_map", None) + max_memory = kwargs.pop("max_memory", None) + offload_folder = kwargs.pop("offload_folder", None) + offload_state_dict = kwargs.pop("offload_state_dict", False) + variant = kwargs.pop("variant", None) + use_safetensors = kwargs.pop("use_safetensors", None) + + allow_pickle = False + if use_safetensors is None: + use_safetensors = True + allow_pickle = True + + if device_map is not None and not is_accelerate_available(): + raise NotImplementedError( + "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" + " `device_map=None`. You can install accelerate with `pip install accelerate`." + ) + + # Check if we can handle device_map and dispatching the weights + if device_map is not None and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `device_map=None`." + ) + + # Load config if we don't provide a configuration + config_path = pretrained_model_name_or_path + + user_agent = { + "diffusers": __version__, + "file_type": "model", + "framework": "pytorch", + } + + # load config + config, unused_kwargs, commit_hash = cls.load_config( + config_path, + cache_dir=cache_dir, + return_unused_kwargs=True, + return_commit_hash=True, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + device_map=device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + user_agent=user_agent, + **kwargs, + ) + + # modify config + config["_class_name"] = cls.__name__ + config['in_channels'] = in_channels + config['out_channels'] = out_channels + config['sample_size'] = sample_size # training resolution + config['num_views'] = num_views + config['cd_attention_last'] = cd_attention_last + config['cd_attention_mid'] = cd_attention_mid + config['multiview_attention'] = multiview_attention + config['sparse_mv_attention'] = sparse_mv_attention + config['mvcd_attention'] = mvcd_attention + config["down_block_types"] = [ + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "CrossAttnDownBlockMV2D", + "DownBlock2D" + ] + config['mid_block_type'] = "UNetMidBlockMV2DCrossAttn" + config["up_block_types"] = [ + "UpBlock2D", + "CrossAttnUpBlockMV2D", + "CrossAttnUpBlockMV2D", + "CrossAttnUpBlockMV2D" + ] + config['class_embed_type'] = 'projection' + if camera_embedding_type == 'e_de_da_sincos': + config['projection_class_embeddings_input_dim'] = projection_class_embeddings_input_dim # default 6 + else: + raise NotImplementedError + + # load model + model_file = None + if from_flax: + raise NotImplementedError + else: + if use_safetensors: + try: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + except IOError as e: + if not allow_pickle: + raise e + pass + if model_file is None: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + + model = cls.from_config(config, **unused_kwargs) + import copy + state_dict_v0 = load_state_dict(model_file, variant=variant) + state_dict = copy.deepcopy(state_dict_v0) + # attn_joint -> attn_joint_last; norm_joint -> norm_joint_last + # attn_joint_twice -> attn_joint_mid; norm_joint_twice -> norm_joint_mid + for key in state_dict_v0: + if 'attn_joint.' in key: + tmp = copy.deepcopy(key) + state_dict[key.replace("attn_joint.", "attn_joint_last.")] = state_dict.pop(tmp) + if 'norm_joint.' in key: + tmp = copy.deepcopy(key) + state_dict[key.replace("norm_joint.", "norm_joint_last.")] = state_dict.pop(tmp) + if 'attn_joint_twice.' in key: + tmp = copy.deepcopy(key) + state_dict[key.replace("attn_joint_twice.", "attn_joint_mid.")] = state_dict.pop(tmp) + if 'norm_joint_twice.' in key: + tmp = copy.deepcopy(key) + state_dict[key.replace("norm_joint_twice.", "norm_joint_mid.")] = state_dict.pop(tmp) + + model._convert_deprecated_attention_blocks(state_dict) + + conv_in_weight = state_dict['conv_in.weight'] + conv_out_weight = state_dict['conv_out.weight'] + model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model_2d( + model, + state_dict, + model_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=True, + ) + if any([key == 'conv_in.weight' for key, _, _ in mismatched_keys]): + # initialize from the original SD structure + model.conv_in.weight.data[:,:4] = conv_in_weight + + # whether to place all zero to new layers? + if zero_init_conv_in: + model.conv_in.weight.data[:,4:] = 0. + + if any([key == 'conv_out.weight' for key, _, _ in mismatched_keys]): + # initialize from the original SD structure + model.conv_out.weight.data[:,:4] = conv_out_weight + if out_channels == 8: # copy for the last 4 channels + model.conv_out.weight.data[:, 4:] = conv_out_weight + + if zero_init_camera_projection: + for p in model.class_embedding.parameters(): + torch.nn.init.zeros_(p) + + loading_info = { + "missing_keys": missing_keys, + "unexpected_keys": unexpected_keys, + "mismatched_keys": mismatched_keys, + "error_msgs": error_msgs, + } + + if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." + ) + elif torch_dtype is not None: + model = model.to(torch_dtype) + + model.register_to_config(_name_or_path=pretrained_model_name_or_path) + + # Set model in evaluation mode to deactivate DropOut modules by default + model.eval() + if output_loading_info: + return model, loading_info + + return model + + @classmethod + def _load_pretrained_model_2d( + cls, + model, + state_dict, + resolved_archive_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=False, + ): + # Retrieve missing & unexpected_keys + model_state_dict = model.state_dict() + loaded_keys = list(state_dict.keys()) + + expected_keys = list(model_state_dict.keys()) + + original_loaded_keys = loaded_keys + + missing_keys = list(set(expected_keys) - set(loaded_keys)) + unexpected_keys = list(set(loaded_keys) - set(expected_keys)) + + # Make sure we are able to load base models as well as derived models (with heads) + model_to_load = model + + def _find_mismatched_keys( + state_dict, + model_state_dict, + loaded_keys, + ignore_mismatched_sizes, + ): + mismatched_keys = [] + if ignore_mismatched_sizes: + for checkpoint_key in loaded_keys: + model_key = checkpoint_key + + if ( + model_key in model_state_dict + and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape + ): + mismatched_keys.append( + (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) + ) + del state_dict[checkpoint_key] + return mismatched_keys + + if state_dict is not None: + # Whole checkpoint + mismatched_keys = _find_mismatched_keys( + state_dict, + model_state_dict, + original_loaded_keys, + ignore_mismatched_sizes, + ) + error_msgs = _load_state_dict_into_model(model_to_load, state_dict) + + if len(error_msgs) > 0: + error_msg = "\n\t".join(error_msgs) + if "size mismatch" in error_msg: + error_msg += ( + "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method." + ) + raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}") + + if len(unexpected_keys) > 0: + logger.warning( + f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when" + f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are" + f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task" + " or with another architecture (e.g. initializing a BertForSequenceClassification model from a" + " BertForPreTraining model).\n- This IS NOT expected if you are initializing" + f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly" + " identical (initializing a BertForSequenceClassification model from a" + " BertForSequenceClassification model)." + ) + else: + logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n") + if len(missing_keys) > 0: + logger.warning( + f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" + f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably" + " TRAIN this model on a down-stream task to be able to use it for predictions and inference." + ) + elif len(mismatched_keys) == 0: + logger.info( + f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at" + f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the" + f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions" + " without further training." + ) + if len(mismatched_keys) > 0: + mismatched_warning = "\n".join( + [ + f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" + for key, shape1, shape2 in mismatched_keys + ] + ) + logger.warning( + f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" + f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not" + f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be" + " able to use it for predictions and inference." + ) + + return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs + diff --git a/gen_3d_modules/Wonder3D/pipelines/pipeline_mvdiffusion_image.py b/gen_3d_modules/Wonder3D/pipelines/pipeline_mvdiffusion_image.py new file mode 100644 index 0000000..84a5b3c --- /dev/null +++ b/gen_3d_modules/Wonder3D/pipelines/pipeline_mvdiffusion_image.py @@ -0,0 +1,517 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import warnings +from typing import Callable, List, Optional, Union + +import PIL +import torch +import torchvision.transforms.functional as TF +from packaging import version +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection + +from diffusers.configuration_utils import FrozenDict +from diffusers.image_processor import VaeImageProcessor +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import deprecate, logging +from diffusers.utils.torch_utils import randn_tensor +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker +from einops import rearrange, repeat + +import comfy.utils + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class MVDiffusionImagePipeline(DiffusionPipeline): + r""" + Pipeline to generate image variations from an input image using Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. + image_encoder ([`~transformers.CLIPVisionModelWithProjection`]): + Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). + text_encoder ([`~transformers.CLIPTextModel`]): + Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). + tokenizer ([`~transformers.CLIPTokenizer`]): + A `CLIPTokenizer` to tokenize text. + unet ([`UNet2DConditionModel`]): + A `UNet2DConditionModel` to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details + about a model's potential harms. + feature_extractor ([`~transformers.CLIPImageProcessor`]): + A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. + """ + # TODO: feature_extractor is required to encode images (if they are in PIL format), + # we should give a descriptive message if the pipeline doesn't have one. + _optional_components = ["safety_checker"] + + def __init__( + self, + vae: AutoencoderKL, + image_encoder: CLIPVisionModelWithProjection, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPImageProcessor, + requires_safety_checker: bool = False, + camera_embedding_type: str = 'e_de_da_sincos', + num_views: int = 6 + ): + super().__init__() + + if safety_checker is None and requires_safety_checker: + logger.warn( + f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" + " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" + " results in services or applications open to the public. Both the diffusers team and Hugging Face" + " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" + " it only for use-cases that involve analyzing network behavior or auditing its results. For more" + " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." + ) + + if safety_checker is not None and feature_extractor is None: + raise ValueError( + "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" + " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." + ) + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + image_encoder=image_encoder, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + self.camera_embedding_type: str = camera_embedding_type + self.num_views: int = num_views + + self.camera_embedding = torch.tensor( + [[ 0.0000, 0.0000, 0.0000, 1.0000, 0.0000], + [ 0.0000, -0.2362, 0.8125, 1.0000, 0.0000], + [ 0.0000, -0.1686, 1.6934, 1.0000, 0.0000], + [ 0.0000, 0.5220, 3.1406, 1.0000, 0.0000], + [ 0.0000, 0.6904, 4.8359, 1.0000, 0.0000], + [ 0.0000, 0.3733, 5.5859, 1.0000, 0.0000], + [ 0.0000, 0.0000, 0.0000, 0.0000, 1.0000], + [ 0.0000, -0.2362, 0.8125, 0.0000, 1.0000], + [ 0.0000, -0.1686, 1.6934, 0.0000, 1.0000], + [ 0.0000, 0.5220, 3.1406, 0.0000, 1.0000], + [ 0.0000, 0.6904, 4.8359, 0.0000, 1.0000], + [ 0.0000, 0.3733, 5.5859, 0.0000, 1.0000]], dtype=torch.float16) + + def _encode_image(self, image_pil, device, num_images_per_prompt, do_classifier_free_guidance): + dtype = next(self.image_encoder.parameters()).dtype + + image_pt = self.feature_extractor(images=image_pil, return_tensors="pt").pixel_values + image_pt = image_pt.to(device=device, dtype=dtype) + image_embeddings = self.image_encoder(image_pt).image_embeds + image_embeddings = image_embeddings.unsqueeze(1) + + # duplicate image embeddings for each generation per prompt, using mps friendly method + # Note: repeat differently from official pipelines + # B1B2B3B4 -> B1B2B3B4B1B2B3B4 + bs_embed, seq_len, _ = image_embeddings.shape + image_embeddings = image_embeddings.repeat(num_images_per_prompt, 1, 1) + + if do_classifier_free_guidance: + negative_prompt_embeds = torch.zeros_like(image_embeddings) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings]) + + image_pt = torch.stack([TF.to_tensor(img) for img in image_pil], dim=0).to(device).to(dtype) + image_pt = image_pt * 2.0 - 1.0 + image_latents = self.vae.encode(image_pt).latent_dist.mode() * self.vae.config.scaling_factor + # Note: repeat differently from official pipelines + # B1B2B3B4 -> B1B2B3B4B1B2B3B4 + image_latents = image_latents.repeat(num_images_per_prompt, 1, 1, 1) + + if do_classifier_free_guidance: + image_latents = torch.cat([torch.zeros_like(image_latents), image_latents]) + + return image_embeddings, image_latents + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is None: + has_nsfw_concept = None + else: + if torch.is_tensor(image): + feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") + else: + feature_extractor_input = self.image_processor.numpy_to_pil(image) + safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + ) + return image, has_nsfw_concept + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents + def decode_latents(self, latents): + warnings.warn( + "The decode_latents method is deprecated and will be removed in a future version. Please" + " use VaeImageProcessor instead", + FutureWarning, + ) + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents, return_dict=False)[0] + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs(self, image, height, width, callback_steps): + if ( + not isinstance(image, torch.Tensor) + and not isinstance(image, PIL.Image.Image) + and not isinstance(image, list) + ): + raise ValueError( + "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" + f" {type(image)}" + ) + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_camera_embedding(self, camera_embedding: Union[float, torch.Tensor], do_classifier_free_guidance, num_images_per_prompt=1): + # (B, 3) + camera_embedding = camera_embedding.to(dtype=self.unet.dtype, device=self.unet.device) + + if self.camera_embedding_type == 'e_de_da_sincos': + # (B, 6) + camera_embedding = torch.cat([ + torch.sin(camera_embedding), + torch.cos(camera_embedding) + ], dim=-1) + assert self.unet.config.class_embed_type == 'projection' + assert self.unet.config.projection_class_embeddings_input_dim == 6 or self.unet.config.projection_class_embeddings_input_dim == 10 + else: + raise NotImplementedError + + # Note: repeat differently from official pipelines + # B1B2B3B4 -> B1B2B3B4B1B2B3B4 + camera_embedding = camera_embedding.repeat(num_images_per_prompt, 1) + + if do_classifier_free_guidance: + camera_embedding = torch.cat([ + camera_embedding, + camera_embedding + ], dim=0) + + return camera_embedding + + @torch.no_grad() + def __call__( + self, + image: Union[List[PIL.Image.Image], torch.FloatTensor], + # elevation_cond: torch.FloatTensor, + # elevation: torch.FloatTensor, + # azimuth: torch.FloatTensor, + camera_embedding: Optional[torch.FloatTensor]=None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + normal_cond: Optional[Union[List[PIL.Image.Image], torch.FloatTensor]] = None, + ): + r""" + The call function to the pipeline for generation. + + Args: + image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): + Image or images to guide image generation. If you provide a tensor, it needs to be compatible with + [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json). + height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. This parameter is modulated by `strength`. + guidance_scale (`float`, *optional*, defaults to 7.5): + A higher guidance scale value encourages the model to generate images closely linked to the text + `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies + to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that calls every `callback_steps` steps during inference. The function is called with the + following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function is called. If not specified, the callback is called at + every step. + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, + otherwise a `tuple` is returned where the first element is a list with the generated images and the + second element is a list of `bool`s indicating whether the corresponding generated image contains + "not-safe-for-work" (nsfw) content. + + Examples: + + ```py + from diffusers import StableDiffusionImageVariationPipeline + from PIL import Image + from io import BytesIO + import requests + + pipe = StableDiffusionImageVariationPipeline.from_pretrained( + "lambdalabs/sd-image-variations-diffusers", revision="v2.0" + ) + pipe = pipe.to("cuda") + + url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200" + + response = requests.get(url) + image = Image.open(BytesIO(response.content)).convert("RGB") + + out = pipe(image, num_images_per_prompt=3, guidance_scale=15) + out["images"][0].save("result.jpg") + ``` + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs(image, height, width, callback_steps) + + + # 2. Define call parameters + if isinstance(image, list): + batch_size = len(image) + elif isinstance(image, torch.Tensor): + batch_size = image.shape[0] + assert batch_size >= self.num_views and batch_size % self.num_views == 0 + elif isinstance(image, PIL.Image.Image): + image = [image]*self.num_views*2 + batch_size = self.num_views*2 + + device = self._execution_device + dtype = self.vae.dtype + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale != 1.0 + + # 3. Encode input image + if isinstance(image, list): + image_pil = image + elif isinstance(image, torch.Tensor): + image_pil = [TF.to_pil_image(image[i]) for i in range(image.shape[0])] + image_embeddings, image_latents = self._encode_image(image_pil, device, num_images_per_prompt, do_classifier_free_guidance) + + if normal_cond is not None: + if isinstance(normal_cond, list): + normal_cond_pil = normal_cond + elif isinstance(normal_cond, torch.Tensor): + normal_cond_pil = [TF.to_pil_image(normal_cond[i]) for i in range(normal_cond.shape[0])] + _, image_latents = self._encode_image(normal_cond_pil, device, num_images_per_prompt, do_classifier_free_guidance) + + + # assert len(elevation_cond) == batch_size and len(elevation) == batch_size and len(azimuth) == batch_size + # camera_embeddings = self.prepare_camera_condition(elevation_cond, elevation, azimuth, do_classifier_free_guidance=do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt) + + if camera_embedding is not None: + assert len(camera_embedding) == batch_size + else: + camera_embedding = self.camera_embedding.to(dtype) + camera_embedding = repeat(camera_embedding, "Nv Nce -> (B Nv) Nce", B=batch_size//len(camera_embedding)) + camera_embeddings = self.prepare_camera_embedding(camera_embedding, do_classifier_free_guidance=do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.out_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + image_embeddings.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Denoising loop + timesteps_len = len(timesteps) + comfy_pbar = comfy.utils.ProgressBar(timesteps_len) + + num_warmup_steps = timesteps_len - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = torch.cat([ + latent_model_input, image_latents + ], dim=1) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings, class_labels=camera_embeddings).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + comfy_pbar.update_absolute(i + 1) + + if not output_type == "latent": + if num_channels_latents == 8: + latents = torch.cat([latents[:, :4], latents[:, 4:]], dim=0) + + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype) + else: + image = latents + has_nsfw_concept = None + + if has_nsfw_concept is None: + do_denormalize = [True] * image.shape[0] + else: + do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] + + image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) + diff --git a/gen_3d_modules/Wonder3D/utils/misc.py b/gen_3d_modules/Wonder3D/utils/misc.py new file mode 100644 index 0000000..61d93e9 --- /dev/null +++ b/gen_3d_modules/Wonder3D/utils/misc.py @@ -0,0 +1,86 @@ +import os +from omegaconf import OmegaConf +from packaging import version +from typing import Dict, Optional, Tuple, List +from dataclasses import dataclass + + +# ============ Register OmegaConf Recolvers ============= # +OmegaConf.register_new_resolver('calc_exp_lr_decay_rate', lambda factor, n: factor**(1./n), replace=True) +OmegaConf.register_new_resolver('add', lambda a, b: a + b, replace=True) +OmegaConf.register_new_resolver('sub', lambda a, b: a - b, replace=True) +OmegaConf.register_new_resolver('mul', lambda a, b: a * b, replace=True) +OmegaConf.register_new_resolver('div', lambda a, b: a / b, replace=True) +OmegaConf.register_new_resolver('idiv', lambda a, b: a // b, replace=True) +OmegaConf.register_new_resolver('basename', lambda p: os.path.basename(p), replace=True) +# ======================================================= # + + +def prompt(question): + inp = input(f"{question} (y/n)").lower().strip() + if inp and inp == 'y': + return True + if inp and inp == 'n': + return False + return prompt(question) + +@dataclass +class MVConfig: + pretrained_unet_path: str + revision: Optional[str] + validation_dataset: Dict + save_dir: str + seed: Optional[int] + validation_batch_size: int + dataloader_num_workers: int + + local_rank: int + + pipe_kwargs: Dict + pipe_validation_kwargs: Dict + unet_from_pretrained_kwargs: Dict + validation_guidance_scales: List[float] + validation_grid_nrow: int + camera_embedding_lr_mult: float + + num_views: int + camera_embedding_type: str + + pred_type: str # joint, or ablation + + enable_xformers_memory_efficient_attention: bool + + cond_on_normals: bool + cond_on_colors: bool + +def load_config(*yaml_files, cli_args=[]): + yaml_confs = [OmegaConf.load(f) for f in yaml_files] + cli_conf = OmegaConf.from_cli(cli_args) + conf = OmegaConf.merge(*yaml_confs, cli_conf) + OmegaConf.resolve(conf) + + schema = OmegaConf.structured(MVConfig) + conf = OmegaConf.merge(schema, conf) + return conf + +def config_to_primitive(config, resolve=True): + return OmegaConf.to_container(config, resolve=resolve) + + +def dump_config(path, config): + with open(path, 'w') as fp: + OmegaConf.save(config=config, f=fp) + +def get_rank(): + # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing, + # therefore LOCAL_RANK needs to be checked first + rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK") + for key in rank_keys: + rank = os.environ.get(key) + if rank is not None: + return int(rank) + return 0 + + +def parse_version(ver): + return version.parse(ver) diff --git a/gen_3d_modules/Zero123Plus/model.py b/gen_3d_modules/Zero123Plus/model.py new file mode 100644 index 0000000..cc3e771 --- /dev/null +++ b/gen_3d_modules/Zero123Plus/model.py @@ -0,0 +1,271 @@ +import os +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import pytorch_lightning as pl +from tqdm import tqdm +from torchvision.transforms import v2 +from torchvision.utils import make_grid, save_image +from einops import rearrange + +from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, DDPMScheduler, UNet2DConditionModel +from .pipeline import RefOnlyNoisedUNet + + +def scale_latents(latents): + latents = (latents - 0.22) * 0.75 + return latents + + +def unscale_latents(latents): + latents = latents / 0.75 + 0.22 + return latents + + +def scale_image(image): + image = image * 0.5 / 0.8 + return image + + +def unscale_image(image): + image = image / 0.5 * 0.8 + return image + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +class MVDiffusion(pl.LightningModule): + def __init__( + self, + stable_diffusion_config, + drop_cond_prob=0.1, + ): + super(MVDiffusion, self).__init__() + + self.drop_cond_prob = drop_cond_prob + + self.register_schedule() + + # init modules + pipeline = DiffusionPipeline.from_pretrained(**stable_diffusion_config) + pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( + pipeline.scheduler.config, timestep_spacing='trailing' + ) + self.pipeline = pipeline + + train_sched = DDPMScheduler.from_config(self.pipeline.scheduler.config) + if isinstance(self.pipeline.unet, UNet2DConditionModel): + self.pipeline.unet = RefOnlyNoisedUNet(self.pipeline.unet, train_sched, self.pipeline.scheduler) + + self.train_scheduler = train_sched # use ddpm scheduler during training + + self.unet = pipeline.unet + + # validation output buffer + self.validation_step_outputs = [] + + def register_schedule(self): + self.num_timesteps = 1000 + + # replace scaled_linear schedule with linear schedule as Zero123++ + beta_start = 0.00085 + beta_end = 0.0120 + betas = torch.linspace(beta_start, beta_end, 1000, dtype=torch.float32) + + alphas = 1. - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + alphas_cumprod_prev = torch.cat([torch.ones(1, dtype=torch.float64), alphas_cumprod[:-1]], 0) + + self.register_buffer('betas', betas.float()) + self.register_buffer('alphas_cumprod', alphas_cumprod.float()) + self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev.float()) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod).float()) + self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1 - alphas_cumprod).float()) + + self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod).float()) + self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1).float()) + + def on_fit_start(self): + device = torch.device(f'cuda:{self.global_rank}') + self.pipeline.to(device) + if self.global_rank == 0: + os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True) + os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True) + + def prepare_batch_data(self, batch): + # prepare stable diffusion input + cond_imgs = batch['cond_imgs'] # (B, C, H, W) + cond_imgs = cond_imgs.to(self.device) + + # random resize the condition image + cond_size = np.random.randint(128, 513) + cond_imgs = v2.functional.resize(cond_imgs, cond_size, interpolation=3, antialias=True).clamp(0, 1) + + target_imgs = batch['target_imgs'] # (B, 6, C, H, W) + target_imgs = v2.functional.resize(target_imgs, 320, interpolation=3, antialias=True).clamp(0, 1) + target_imgs = rearrange(target_imgs, 'b (x y) c h w -> b c (x h) (y w)', x=3, y=2) # (B, C, 3H, 2W) + target_imgs = target_imgs.to(self.device) + + return cond_imgs, target_imgs + + @torch.no_grad() + def forward_vision_encoder(self, images): + dtype = next(self.pipeline.vision_encoder.parameters()).dtype + image_pil = [v2.functional.to_pil_image(images[i]) for i in range(images.shape[0])] + image_pt = self.pipeline.feature_extractor_clip(images=image_pil, return_tensors="pt").pixel_values + image_pt = image_pt.to(device=self.device, dtype=dtype) + global_embeds = self.pipeline.vision_encoder(image_pt, output_hidden_states=False).image_embeds + global_embeds = global_embeds.unsqueeze(-2) + + encoder_hidden_states = self.pipeline._encode_prompt("", self.device, 1, False)[0] + ramp = global_embeds.new_tensor(self.pipeline.config.ramping_coefficients).unsqueeze(-1) + encoder_hidden_states = encoder_hidden_states + global_embeds * ramp + + return encoder_hidden_states + + @torch.no_grad() + def encode_condition_image(self, images): + dtype = next(self.pipeline.vae.parameters()).dtype + image_pil = [v2.functional.to_pil_image(images[i]) for i in range(images.shape[0])] + image_pt = self.pipeline.feature_extractor_vae(images=image_pil, return_tensors="pt").pixel_values + image_pt = image_pt.to(device=self.device, dtype=dtype) + latents = self.pipeline.vae.encode(image_pt).latent_dist.sample() + return latents + + @torch.no_grad() + def encode_target_images(self, images): + dtype = next(self.pipeline.vae.parameters()).dtype + # equals to scaling images to [-1, 1] first and then call scale_image + images = (images - 0.5) / 0.8 # [-0.625, 0.625] + posterior = self.pipeline.vae.encode(images.to(dtype)).latent_dist + latents = posterior.sample() * self.pipeline.vae.config.scaling_factor + latents = scale_latents(latents) + return latents + + def forward_unet(self, latents, t, prompt_embeds, cond_latents): + dtype = next(self.pipeline.unet.parameters()).dtype + latents = latents.to(dtype) + prompt_embeds = prompt_embeds.to(dtype) + cond_latents = cond_latents.to(dtype) + cross_attention_kwargs = dict(cond_lat=cond_latents) + pred_noise = self.pipeline.unet( + latents, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + return pred_noise + + def predict_start_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def get_v(self, x, noise, t): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + ) + + def training_step(self, batch, batch_idx): + # get input + cond_imgs, target_imgs = self.prepare_batch_data(batch) + + # sample random timestep + B = cond_imgs.shape[0] + + t = torch.randint(0, self.num_timesteps, size=(B,)).long().to(self.device) + + # classifier-free guidance + if np.random.rand() < self.drop_cond_prob: + prompt_embeds = self.pipeline._encode_prompt([""]*B, self.device, 1, False) + cond_latents = self.encode_condition_image(torch.zeros_like(cond_imgs)) + else: + prompt_embeds = self.forward_vision_encoder(cond_imgs) + cond_latents = self.encode_condition_image(cond_imgs) + + latents = self.encode_target_images(target_imgs) + noise = torch.randn_like(latents) + latents_noisy = self.train_scheduler.add_noise(latents, noise, t) + + v_pred = self.forward_unet(latents_noisy, t, prompt_embeds, cond_latents) + v_target = self.get_v(latents, noise, t) + + loss, loss_dict = self.compute_loss(v_pred, v_target) + + # logging + self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False) + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.global_step % 500 == 0 and self.global_rank == 0: + with torch.no_grad(): + latents_pred = self.predict_start_from_z_and_v(latents_noisy, t, v_pred) + + latents = unscale_latents(latents_pred) + images = unscale_image(self.pipeline.vae.decode(latents / self.pipeline.vae.config.scaling_factor, return_dict=False)[0]) # [-1, 1] + images = (images * 0.5 + 0.5).clamp(0, 1) + images = torch.cat([target_imgs, images], dim=-2) + + grid = make_grid(images, nrow=images.shape[0], normalize=True, value_range=(0, 1)) + save_image(grid, os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png')) + + return loss + + def compute_loss(self, noise_pred, noise_gt): + loss = F.mse_loss(noise_pred, noise_gt) + + prefix = 'train' + loss_dict = {} + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + # get input + cond_imgs, target_imgs = self.prepare_batch_data(batch) + + images_pil = [v2.functional.to_pil_image(cond_imgs[i]) for i in range(cond_imgs.shape[0])] + + outputs = [] + for cond_img in images_pil: + latent = self.pipeline(cond_img, num_inference_steps=75, output_type='latent').images + image = unscale_image(self.pipeline.vae.decode(latent / self.pipeline.vae.config.scaling_factor, return_dict=False)[0]) # [-1, 1] + image = (image * 0.5 + 0.5).clamp(0, 1) + outputs.append(image) + outputs = torch.cat(outputs, dim=0).to(self.device) + images = torch.cat([target_imgs, outputs], dim=-2) + + self.validation_step_outputs.append(images) + + @torch.no_grad() + def on_validation_epoch_end(self): + images = torch.cat(self.validation_step_outputs, dim=0) + + all_images = self.all_gather(images) + all_images = rearrange(all_images, 'r b c h w -> (r b) c h w') + + if self.global_rank == 0: + grid = make_grid(all_images, nrow=8, normalize=True, value_range=(0, 1)) + save_image(grid, os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png')) + + self.validation_step_outputs.clear() # free memory + + def configure_optimizers(self): + lr = self.learning_rate + + optimizer = torch.optim.AdamW(self.unet.parameters(), lr=lr) + scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 3000, eta_min=lr/4) + + return {'optimizer': optimizer, 'lr_scheduler': scheduler} diff --git a/gen_3d_modules/Zero123Plus/pipeline.py b/gen_3d_modules/Zero123Plus/pipeline.py new file mode 100644 index 0000000..4e8c94a --- /dev/null +++ b/gen_3d_modules/Zero123Plus/pipeline.py @@ -0,0 +1,416 @@ +from typing import Any, Dict, Optional +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.schedulers import KarrasDiffusionSchedulers + +import numpy +import torch +import torch.nn as nn +import torch.utils.checkpoint +import torch.distributed +import transformers +from collections import OrderedDict +from PIL import Image +from torchvision import transforms +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer + +import diffusers +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + DiffusionPipeline, + EulerAncestralDiscreteScheduler, + UNet2DConditionModel, + ImagePipelineOutput +) +from diffusers.image_processor import VaeImageProcessor +from diffusers.models.attention_processor import Attention, AttnProcessor, XFormersAttnProcessor, AttnProcessor2_0 +from diffusers.utils.import_utils import is_xformers_available + +import comfy.utils + +def callback_update_comfy_bar(pipe, step_index, timestep, callback_kwargs): + pipe.comfy_pbar.update_absolute(step_index + 1) + return callback_kwargs + +def to_rgb_image(maybe_rgba: Image.Image): + if maybe_rgba.mode == 'RGB': + return maybe_rgba + elif maybe_rgba.mode == 'RGBA': + rgba = maybe_rgba + img = numpy.random.randint(255, 256, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8) + img = Image.fromarray(img, 'RGB') + img.paste(rgba, mask=rgba.getchannel('A')) + return img + else: + raise ValueError("Unsupported image type.", maybe_rgba.mode) + + +class ReferenceOnlyAttnProc(torch.nn.Module): + def __init__( + self, + chained_proc, + enabled=False, + name=None + ) -> None: + super().__init__() + self.enabled = enabled + self.chained_proc = chained_proc + self.name = name + + def __call__( + self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, + mode="w", ref_dict: dict = None, is_cfg_guidance = False + ) -> Any: + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + if self.enabled and is_cfg_guidance: + res0 = self.chained_proc(attn, hidden_states[:1], encoder_hidden_states[:1], attention_mask) + hidden_states = hidden_states[1:] + encoder_hidden_states = encoder_hidden_states[1:] + if self.enabled: + if mode == 'w': + ref_dict[self.name] = encoder_hidden_states + elif mode == 'r': + encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict.pop(self.name)], dim=1) + elif mode == 'm': + encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict[self.name]], dim=1) + else: + assert False, mode + res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask) + if self.enabled and is_cfg_guidance: + res = torch.cat([res0, res]) + return res + + +class RefOnlyNoisedUNet(torch.nn.Module): + def __init__(self, unet: UNet2DConditionModel, train_sched: DDPMScheduler, val_sched: EulerAncestralDiscreteScheduler) -> None: + super().__init__() + self.unet = unet + self.train_sched = train_sched + self.val_sched = val_sched + + unet_lora_attn_procs = dict() + for name, _ in unet.attn_processors.items(): + if torch.__version__ >= '2.0': + default_attn_proc = AttnProcessor2_0() + elif is_xformers_available(): + default_attn_proc = XFormersAttnProcessor() + else: + default_attn_proc = AttnProcessor() + unet_lora_attn_procs[name] = ReferenceOnlyAttnProc( + default_attn_proc, enabled=name.endswith("attn1.processor"), name=name + ) + unet.set_attn_processor(unet_lora_attn_procs) + + def __getattr__(self, name: str): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.unet, name) + + def forward_cond(self, noisy_cond_lat, timestep, encoder_hidden_states, class_labels, ref_dict, is_cfg_guidance, **kwargs): + if is_cfg_guidance: + encoder_hidden_states = encoder_hidden_states[1:] + class_labels = class_labels[1:] + self.unet( + noisy_cond_lat, timestep, + encoder_hidden_states=encoder_hidden_states, + class_labels=class_labels, + cross_attention_kwargs=dict(mode="w", ref_dict=ref_dict), + **kwargs + ) + + def forward( + self, sample, timestep, encoder_hidden_states, class_labels=None, + *args, cross_attention_kwargs, + down_block_res_samples=None, mid_block_res_sample=None, + **kwargs + ): + cond_lat = cross_attention_kwargs['cond_lat'] + is_cfg_guidance = cross_attention_kwargs.get('is_cfg_guidance', False) + noise = torch.randn_like(cond_lat) + if self.training: + noisy_cond_lat = self.train_sched.add_noise(cond_lat, noise, timestep) + noisy_cond_lat = self.train_sched.scale_model_input(noisy_cond_lat, timestep) + else: + noisy_cond_lat = self.val_sched.add_noise(cond_lat, noise, timestep.reshape(-1)) + noisy_cond_lat = self.val_sched.scale_model_input(noisy_cond_lat, timestep.reshape(-1)) + ref_dict = {} + self.forward_cond( + noisy_cond_lat, timestep, + encoder_hidden_states, class_labels, + ref_dict, is_cfg_guidance, **kwargs + ) + weight_dtype = self.unet.dtype + return self.unet( + sample, timestep, + encoder_hidden_states, *args, + class_labels=class_labels, + cross_attention_kwargs=dict(mode="r", ref_dict=ref_dict, is_cfg_guidance=is_cfg_guidance), + down_block_additional_residuals=[ + sample.to(dtype=weight_dtype) for sample in down_block_res_samples + ] if down_block_res_samples is not None else None, + mid_block_additional_residual=( + mid_block_res_sample.to(dtype=weight_dtype) + if mid_block_res_sample is not None else None + ), + **kwargs + ) + + +def scale_latents(latents): + latents = (latents - 0.22) * 0.75 + return latents + + +def unscale_latents(latents): + latents = latents / 0.75 + 0.22 + return latents + + +def scale_image(image): + image = image * 0.5 / 0.8 + return image + + +def unscale_image(image): + image = image / 0.5 * 0.8 + return image + + +class DepthControlUNet(torch.nn.Module): + def __init__(self, unet: RefOnlyNoisedUNet, controlnet: Optional[diffusers.ControlNetModel] = None, conditioning_scale=1.0) -> None: + super().__init__() + self.unet = unet + if controlnet is None: + self.controlnet = diffusers.ControlNetModel.from_unet(unet.unet) + else: + self.controlnet = controlnet + DefaultAttnProc = AttnProcessor2_0 + if is_xformers_available(): + DefaultAttnProc = XFormersAttnProcessor + self.controlnet.set_attn_processor(DefaultAttnProc()) + self.conditioning_scale = conditioning_scale + + def __getattr__(self, name: str): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.unet, name) + + def forward(self, sample, timestep, encoder_hidden_states, class_labels=None, *args, cross_attention_kwargs: dict, **kwargs): + cross_attention_kwargs = dict(cross_attention_kwargs) + control_depth = cross_attention_kwargs.pop('control_depth') + down_block_res_samples, mid_block_res_sample = self.controlnet( + sample, + timestep, + encoder_hidden_states=encoder_hidden_states, + controlnet_cond=control_depth, + conditioning_scale=self.conditioning_scale, + return_dict=False, + ) + return self.unet( + sample, + timestep, + encoder_hidden_states=encoder_hidden_states, + down_block_res_samples=down_block_res_samples, + mid_block_res_sample=mid_block_res_sample, + cross_attention_kwargs=cross_attention_kwargs + ) + + +class ModuleListDict(torch.nn.Module): + def __init__(self, procs: dict) -> None: + super().__init__() + self.keys = sorted(procs.keys()) + self.values = torch.nn.ModuleList(procs[k] for k in self.keys) + + def __getitem__(self, key): + return self.values[self.keys.index(key)] + + +class SuperNet(torch.nn.Module): + def __init__(self, state_dict: Dict[str, torch.Tensor]): + super().__init__() + state_dict = OrderedDict((k, state_dict[k]) for k in sorted(state_dict.keys())) + self.layers = torch.nn.ModuleList(state_dict.values()) + self.mapping = dict(enumerate(state_dict.keys())) + self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())} + + # .processor for unet, .self_attn for text encoder + self.split_keys = [".processor", ".self_attn"] + + # we add a hook to state_dict() and load_state_dict() so that the + # naming fits with `unet.attn_processors` + def map_to(module, state_dict, *args, **kwargs): + new_state_dict = {} + for key, value in state_dict.items(): + num = int(key.split(".")[1]) # 0 is always "layers" + new_key = key.replace(f"layers.{num}", module.mapping[num]) + new_state_dict[new_key] = value + + return new_state_dict + + def remap_key(key, state_dict): + for k in self.split_keys: + if k in key: + return key.split(k)[0] + k + return key.split('.')[0] + + def map_from(module, state_dict, *args, **kwargs): + all_keys = list(state_dict.keys()) + for key in all_keys: + replace_key = remap_key(key, state_dict) + new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}") + state_dict[new_key] = state_dict[key] + del state_dict[key] + + self._register_state_dict_hook(map_to) + self._register_load_state_dict_pre_hook(map_from, with_module=True) + + +class Zero123PlusPipeline(diffusers.StableDiffusionPipeline): + tokenizer: transformers.CLIPTokenizer + text_encoder: transformers.CLIPTextModel + vision_encoder: transformers.CLIPVisionModelWithProjection + + feature_extractor_clip: transformers.CLIPImageProcessor + unet: UNet2DConditionModel + scheduler: diffusers.schedulers.KarrasDiffusionSchedulers + + vae: AutoencoderKL + ramping: nn.Linear + + feature_extractor_vae: transformers.CLIPImageProcessor + + depth_transforms_multi = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]) + ]) + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + vision_encoder: transformers.CLIPVisionModelWithProjection, + feature_extractor_clip: CLIPImageProcessor, + feature_extractor_vae: CLIPImageProcessor, + ramping_coefficients: Optional[list] = None, + safety_checker=None, + ): + DiffusionPipeline.__init__(self) + + self.register_modules( + vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, + unet=unet, scheduler=scheduler, safety_checker=None, + vision_encoder=vision_encoder, + feature_extractor_clip=feature_extractor_clip, + feature_extractor_vae=feature_extractor_vae + ) + self.register_to_config(ramping_coefficients=ramping_coefficients) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + + def prepare(self): + train_sched = DDPMScheduler.from_config(self.scheduler.config) + if isinstance(self.unet, UNet2DConditionModel): + self.unet = RefOnlyNoisedUNet(self.unet, train_sched, self.scheduler).eval() + + def add_controlnet(self, controlnet: Optional[diffusers.ControlNetModel] = None, conditioning_scale=1.0): + self.prepare() + self.unet = DepthControlUNet(self.unet, controlnet, conditioning_scale) + return SuperNet(OrderedDict([('controlnet', self.unet.controlnet)])) + + def encode_condition_image(self, image: torch.Tensor): + image = self.vae.encode(image).latent_dist.sample() + return image + + @torch.no_grad() + def __call__( + self, + image: Image.Image = None, + prompt = "", + *args, + num_images_per_prompt: Optional[int] = 1, + guidance_scale=4.0, + depth_image: Image.Image = None, + output_type: Optional[str] = "pil", + width=640, + height=960, + num_inference_steps=28, + return_dict=True, + **kwargs + ): + self.prepare() + if image is None: + raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.") + assert not isinstance(image, torch.Tensor) + image = to_rgb_image(image) + image_1 = self.feature_extractor_vae(images=image, return_tensors="pt").pixel_values + image_2 = self.feature_extractor_clip(images=image, return_tensors="pt").pixel_values + if depth_image is not None and hasattr(self.unet, "controlnet"): + depth_image = to_rgb_image(depth_image) + depth_image = self.depth_transforms_multi(depth_image).to( + device=self.unet.controlnet.device, dtype=self.unet.controlnet.dtype + ) + image = image_1.to(device=self.vae.device, dtype=self.vae.dtype) + image_2 = image_2.to(device=self.vae.device, dtype=self.vae.dtype) + cond_lat = self.encode_condition_image(image) + if guidance_scale > 1: + negative_lat = self.encode_condition_image(torch.zeros_like(image)) + cond_lat = torch.cat([negative_lat, cond_lat]) + encoded = self.vision_encoder(image_2, output_hidden_states=False) + global_embeds = encoded.image_embeds + global_embeds = global_embeds.unsqueeze(-2) + + if hasattr(self, "encode_prompt"): + encoder_hidden_states = self.encode_prompt( + prompt, + self.device, + num_images_per_prompt, + False + )[0] + else: + encoder_hidden_states = self._encode_prompt( + prompt, + self.device, + num_images_per_prompt, + False + ) + ramp = global_embeds.new_tensor(self.config.ramping_coefficients).unsqueeze(-1) + encoder_hidden_states = encoder_hidden_states + global_embeds * ramp + cak = dict(cond_lat=cond_lat) + if hasattr(self.unet, "controlnet"): + cak['control_depth'] = depth_image + + self.comfy_pbar = comfy.utils.ProgressBar(num_inference_steps) + + latents: torch.Tensor = super().__call__( + None, + *args, + cross_attention_kwargs=cak, + guidance_scale=guidance_scale, + num_images_per_prompt=num_images_per_prompt, + prompt_embeds=encoder_hidden_states, + num_inference_steps=num_inference_steps, + output_type='latent', + width=width, + height=height, + callback_on_step_end=callback_update_comfy_bar, + callback_on_step_end_tensor_inputs=[], + **kwargs + ).images + latents = unscale_latents(latents) + if not output_type == "latent": + image = unscale_image(self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]) + else: + image = latents + + image = self.image_processor.postprocess(image, output_type=output_type) + if not return_dict: + return (image,) + + return ImagePipelineOutput(images=image) diff --git a/mesh_processer/mesh.py b/mesh_processer/mesh.py index d9e7092..3dcc08c 100644 --- a/mesh_processer/mesh.py +++ b/mesh_processer/mesh.py @@ -366,6 +366,7 @@ class Mesh: print(f"[load_trimesh] failed to load mesh, either path or given_mesh must be given") return None + should_create_empty_albedo = False if _mesh.visual.kind == 'vertex': vertex_colors = _mesh.visual.vertex_colors vertex_colors = np.array(vertex_colors[..., :3]).astype(np.float32) / 255 @@ -383,11 +384,18 @@ class Mesh: texture = np.array(_material.to_pbr().baseColorTexture).astype(np.float32) / 255 else: raise NotImplementedError(f"material type {type(_material)} not supported!") - mesh.albedo = torch.tensor(texture[..., :3], dtype=torch.float32, device=device).contiguous() - print(f"[load_trimesh] load texture: {texture.shape}") + + if texture.ndim ==3: + mesh.albedo = torch.tensor(texture[..., :3], dtype=torch.float32, device=device).contiguous() + print(f"[load_trimesh] loaded albedo texture: {texture.shape}") + else: + should_create_empty_albedo = True else: + should_create_empty_albedo = True + + if should_create_empty_albedo: mesh.set_new_albedo(1024, 1024) - print(f"[load_trimesh] failed to load texture.") + print(f"[load_trimesh] failed to load albedo texture, create empty abbedo texture with shape: {mesh.albedo.shape}") vertices = _mesh.vertices diff --git a/nodes.py b/nodes.py index b7416af..320e72b 100644 --- a/nodes.py +++ b/nodes.py @@ -1,6 +1,4 @@ import os -import sys -import re import math import copy from enum import Enum @@ -45,35 +43,36 @@ from .mesh_processer.mesh_utils import ( marching_cubes_density_to_mesh, color_func_to_albedo, ) -from .algorithms.main_3DGS import GaussianSplatting, GaussianSplattingCameraController, GSParams -from .algorithms.main_3DGS_renderer import GaussianSplattingRenderer -from .algorithms.diff_mesh import DiffMesh, DiffMeshCameraController -from .algorithms.diff_mesh import DiffRastRenderer -from .algorithms.dmtet import DMTetMesh -from .algorithms.triplane_gaussian_transformers import TGS -from .algorithms.large_multiview_gaussian_model import LGM from .algorithms.nerf_marching_cubes_converter import GSConverterNeRFMarchingCubes +# TODO remove: NeuS, DMTet from .algorithms.NeuS_runner import NeuSParams, NeuSRunner -from .algorithms.convolutional_reconstruction_model import CRMSampler -from .algorithms.Instant_NGP import InstantNGP -from .algorithms.flexicubes_trainer import FlexiCubesTrainer +from .algorithms.dmtet import DMTetMesh -from .tgs.utils.config import ExperimentConfig, load_config as load_config_tgs -from .tgs.data import CustomImageOrbitDataset -from .tgs.utils.misc import todevice, get_device -from .lgm.core.options import config_defaults -from .lgm.mvdream.pipeline_mvdream import MVDreamPipeline -from .mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline -from .mvdiffusion.data.single_image_dataset import SingleImageDataset as MVSingleImageDataset -from .mvdiffusion.utils.misc import load_config as load_config_wonder3d -from .tsr.system import TSR -from .crm.model.crm.model import CRM -from .zero123plus.pipeline import Zero123PlusPipeline -from .instant_mesh.utils.camera_util import oribt_camera_poses_to_input_cameras -from .era3d.mvdiffusion.pipelines.pipeline_mvdiffusion_unclip import StableUnCLIPImg2ImgPipeline -from .era3d.mvdiffusion.data.single_image_dataset import SingleImageDataset as Era3DSingleImageDataset -from .era3d.utils.misc import load_config as load_config_era3d +from FlexiCubes.flexicubes_trainer import FlexiCubesTrainer +from DiffRastMesh.diff_mesh import DiffMesh, DiffMeshCameraController +from DiffRastMesh.diff_mesh import DiffRastRenderer +from GaussianSplatting.main_3DGS import GaussianSplatting3D, GaussianSplattingCameraController, GSParams +from GaussianSplatting.main_3DGS_renderer import GaussianSplattingRenderer +from NeRF.Instant_NGP import InstantNGP +from TriplaneGaussian.triplane_gaussian_transformers import TGS +from TriplaneGaussian.utils.config import ExperimentConfig, load_config as load_config_tgs +from TriplaneGaussian.data import CustomImageOrbitDataset +from TriplaneGaussian.utils.misc import todevice, get_device +from LGM.core.options import config_defaults +from LGM.mvdream.pipeline_mvdream import MVDreamPipeline +from LGM.large_multiview_gaussian_model import LargeMultiviewGaussianModel +from TripoSR.system import TSR +from InstantMesh.utils.camera_util import oribt_camera_poses_to_input_cameras +from CRM.model.crm.model import ConvolutionalReconstructionModel +from CRM.model.crm.sampler import CRMSampler +from Wonder3D.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline +from Wonder3D.data.single_image_dataset import SingleImageDataset as MVSingleImageDataset +from Wonder3D.utils.misc import load_config as load_config_wonder3d +from Zero123Plus.pipeline import Zero123PlusPipeline +from Era3D.mvdiffusion.pipelines.pipeline_mvdiffusion_unclip import StableUnCLIPImg2ImgPipeline +from Era3D.mvdiffusion.data.single_image_dataset import SingleImageDataset as Era3DSingleImageDataset +from Era3D.utils.misc import load_config as load_config_era3d from Unique3D.custum_3d_diffusion.custum_pipeline.unifield_pipeline_img2mvimg import StableDiffusionImage2MVCustomPipeline from Unique3D.custum_3d_diffusion.custum_pipeline.unifield_pipeline_img2img import StableDiffusionImageCustomPipeline from Unique3D.scripts.mesh_init import fast_geo @@ -111,6 +110,10 @@ DIFFUSERS_SCHEDULER_DICT = OrderedDict([ ]) ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack") +CKPT_ROOT_PATH = os.path.join(ROOT_PATH, "Checkpoints") +CKPT_DIFFUSERS_PATH = os.path.join(CKPT_ROOT_PATH, "Diffusers") +CONFIG_ROOT_PATH = os.path.join(ROOT_PATH, "Configs") +MODULE_ROOT_PATH = os.path.join(ROOT_PATH, "Gen_3D_Modules") MANIFEST = { "name": "ComfyUI-3D-Pack", @@ -294,7 +297,7 @@ class Save_3D_Mesh: return { "required": { "mesh": ("MESH",), - "save_path": ("STRING", {"default": 'Mesh_%Y-%m-%d-%M-%S-%f.obj', "multiline": False}), + "save_path": ("STRING", {"default": 'Mesh_%Y-%m-%d-%M-%S-%f.glb', "multiline": False}), }, } @@ -443,19 +446,21 @@ class Split_Image_Grid: CATEGORY = "Comfy3D/Preprocessor" def split_image_grid(self, image, grid_side_num, use_rows): - image_pil = torch_imgs_to_pils(image)[0] + images = [] + for image_pil in torch_imgs_to_pils(image): - if use_rows: - rows = grid_side_num - clos = None - else: - clos = grid_side_num - rows = None + if use_rows: + rows = grid_side_num + clos = None + else: + clos = grid_side_num + rows = None - image_pils = pil_split_image(image_pil, rows, clos) - - images = pils_to_torch_imgs(image_pils, image.device) + image_pils = pil_split_image(image_pil, rows, clos) + images.append(pils_to_torch_imgs(image_pils, image.device)) + + images = torch.cat(images, dim=0) return (images,) class Get_Masks_From_Normal_Maps: @@ -892,16 +897,27 @@ class Mesh_Orbit_Renderer: "render_background_color_g": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "render_background_color_b": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "force_cuda_rasterize": ("BOOLEAN", {"default": False},), + }, + + "optional": { + "render_depth": ("BOOLEAN", {"default": False},), + "render_normal": ("BOOLEAN", {"default": False},), } } RETURN_TYPES = ( "IMAGE", "MASK", + "IMAGE", + "IMAGE", + "IMAGE", ) RETURN_NAMES = ( - "rendered_mesh_images", # [Number of Poses, H, W, 3] + "rendered_mesh_images", # [Number of Poses, H, W, 3] "rendered_mesh_masks", # [Number of Poses, H, W, 1] + "all_rendered_depths", # [Number of Poses, H, W, 3] + "all_rendered_normals", # [Number of Poses, H, W, 3] + "all_rendered_viewcos", # [Number of Poses, H, W, 3] ) FUNCTION = "render_mesh" @@ -918,10 +934,18 @@ class Mesh_Orbit_Renderer: render_background_color_g, render_background_color_b, force_cuda_rasterize, + render_depth=False, + render_normal=False, ): renderer = DiffRastRenderer(mesh, force_cuda_rasterize) + optional_render_types = [] + if render_depth: + optional_render_types.append('depth') + if render_normal: + optional_render_types.append('normal') + cam_controller = DiffMeshCameraController( renderer, render_image_width, @@ -930,10 +954,22 @@ class Mesh_Orbit_Renderer: static_bg=[render_background_color_r, render_background_color_g, render_background_color_b] ) - all_rendered_images, all_rendered_masks = cam_controller.render_all_pose(render_orbit_camera_poses) + extra_kwargs = {"optional_render_types": optional_render_types} + all_rendered_images, all_rendered_masks, extra_outputs = cam_controller.render_all_pose(render_orbit_camera_poses, **extra_kwargs) all_rendered_masks = all_rendered_masks.squeeze(-1) # [N, H, W, 1] -> [N, H, W] + if 'depth' in extra_outputs: + all_rendered_depths = extra_outputs['depth'].repeat(1, 1, 1, 3) # [N, H, W, 1] -> [N, H, W, 3] + else: + all_rendered_depths = None - return (all_rendered_images, all_rendered_masks) # [N, H, W, 3], [N, H, W] + if 'normal' in extra_outputs: + all_rendered_normals = extra_outputs['normal'] + all_rendered_viewcos = extra_outputs['viewcos'].repeat(1, 1, 1, 3) + else: + all_rendered_normals = None + all_rendered_viewcos = None + + return (all_rendered_images, all_rendered_masks, all_rendered_depths, all_rendered_normals, all_rendered_viewcos) class Gaussian_Splatting_Orbit_Renderer: @@ -955,10 +991,12 @@ class Gaussian_Splatting_Orbit_Renderer: RETURN_TYPES = ( "IMAGE", "MASK", + "IMAGE", ) RETURN_NAMES = ( "rendered_gs_images", # [Number of Poses, H, W, 3] - "rendered_gs_masks", # [Number of Poses, H, W, 1] + "rendered_gs_masks", # [Number of Poses, H, W, 1] + "rendered_gs_depths", # [Number of Poses, H, W, 3] ) FUNCTION = "render_gs" @@ -988,13 +1026,18 @@ class Gaussian_Splatting_Orbit_Renderer: static_bg=[render_background_color_r, render_background_color_g, render_background_color_b] ) - all_rendered_images, all_rendered_masks = cam_controller.render_all_pose(render_orbit_camera_poses) + all_rendered_images, all_rendered_masks, extra_outputs = cam_controller.render_all_pose(render_orbit_camera_poses) all_rendered_images = all_rendered_images.permute(0, 2, 3, 1) # [N, 3, H, W] -> [N, H, W, 3] all_rendered_masks = all_rendered_masks.squeeze(1) # [N, 1, H, W] -> [N, H, W] - return (all_rendered_images, all_rendered_masks) + if 'depth' in extra_outputs: + all_rendered_depths = extra_outputs['depth'].permute(0, 2, 3, 1).repeat(1, 1, 1, 3) # [N, 1, H, W] -> [N, H, W, 3] + else: + all_rendered_depths = None + + return (all_rendered_images, all_rendered_masks, all_rendered_depths) -class Gaussian_Splatting: +class Gaussian_Splatting_3D: @classmethod def INPUT_TYPES(cls): @@ -1131,7 +1174,7 @@ class Gaussian_Splatting: else: gs_init_input = mesh_to_initialize_gaussian - gs = GaussianSplatting(gs_params, gs_init_input) + gs = GaussianSplatting3D(gs_params, gs_init_input) gs.prepare_training(reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy) gs.training() @@ -1167,6 +1210,7 @@ class Fitting_Mesh_With_Multiview_Images: "geometry_learning_rate": ("FLOAT", {"default": 0.0001, "min": 0.00001, "step": 0.00001}), "ms_ssim_loss_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "remesh_after_n_iteration": ("INT", {"default": 512, "min": 128, "max": 100000}), + "invert_background_probability": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1}), "force_cuda_rasterize": ("BOOLEAN", {"default": False},), }, } @@ -1198,7 +1242,8 @@ class Fitting_Mesh_With_Multiview_Images: geometry_learning_rate, ms_ssim_loss_weight, remesh_after_n_iteration, - force_cuda_rasterize + invert_background_probability, + force_cuda_rasterize, ): mesh.set_new_albedo(mesh_albedo_width, mesh_albedo_height) @@ -1219,7 +1264,18 @@ class Fitting_Mesh_With_Multiview_Images: with torch.inference_mode(False): - mesh_fitter = DiffMesh(mesh, training_iterations, batch_size, texture_learning_rate, train_mesh_geometry, geometry_learning_rate, ms_ssim_loss_weight, remesh_after_n_iteration, force_cuda_rasterize) + mesh_fitter = DiffMesh( + mesh, + training_iterations, + batch_size, + texture_learning_rate, + train_mesh_geometry, + geometry_learning_rate, + ms_ssim_loss_weight, + remesh_after_n_iteration, + invert_background_probability, + force_cuda_rasterize + ) mesh_fitter.prepare_training(reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy) mesh_fitter.training() @@ -1295,14 +1351,15 @@ class Deep_Marching_Tetrahedrons: class Load_Triplane_Gaussian_Transformers: - checkpoints_dir = "checkpoints/tgs" + checkpoints_dir = "TriplaneGaussian" default_ckpt_name = "model_lvis_rel.ckpt" default_repo_id = "VAST-AI/TriplaneGaussian" - tgs_config_path = "configs/tgs_config.yaml" + tgs_config_path = "TriplaneGaussian_config.yaml" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) + cls.checkpoints_dir_abs = os.path.join(CKPT_ROOT_PATH, cls.checkpoints_dir) + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.tgs_config_path) all_models_names = get_list_filenames(cls.checkpoints_dir_abs, SUPPORTED_CHECKPOINTS_EXTENSIONS) if cls.default_ckpt_name not in all_models_names: all_models_names += [cls.default_ckpt_name] @@ -1325,28 +1382,25 @@ class Load_Triplane_Gaussian_Transformers: def load_TGS(self, model_name): device = get_device() - - config_path = os.path.join(ROOT_PATH, self.tgs_config_path) - cfg: ExperimentConfig = load_config_tgs(config_path) + + cfg: ExperimentConfig = load_config_tgs(self.config_path_abs) ckpt_path = resume_or_download_model_from_hf(self.checkpoints_dir_abs, self.default_repo_id, model_name, self.__class__.__name__) cfg.system.weights=ckpt_path tgs_model = TGS(cfg=cfg.system).to(device) - save_path = os.path.join(ROOT_PATH, "outputs") - tgs_model.set_save_dir(save_path) - cstr(f"[{self.__class__.__name__}] loaded model ckpt from {ckpt_path}").msg.print() return (tgs_model, ) class Triplane_Gaussian_Transformers: - tgs_config_path = "configs/tgs_config.yaml" + tgs_config_path = "TriplaneGaussian_config.yaml" @classmethod def INPUT_TYPES(cls): + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.tgs_config_path) return { "required": { "reference_image": ("IMAGE", ), @@ -1366,11 +1420,7 @@ class Triplane_Gaussian_Transformers: CATEGORY = "Comfy3D/Algorithm" def run_TGS(self, reference_image, reference_mask, tgs_model, cam_dist): - config_path = os.path.join(ROOT_PATH, self.tgs_config_path) - cfg: ExperimentConfig = load_config_tgs(config_path) - - #save_path = os.path.join(ROOT_PATH, "outputs") - #tgs_model.set_save_dir(save_path) + cfg: ExperimentConfig = load_config_tgs(self.config_path_abs) cfg.data.cond_camera_distance = cam_dist cfg.data.eval_camera_distance = cam_dist @@ -1382,22 +1432,10 @@ class Triplane_Gaussian_Transformers: collate_fn=dataset.collate ) - gs_ply = [] for batch in dataloader: batch = todevice(batch) gs_ply.extend(tgs_model(batch)) - - """ - # Output rendered video, for testing this node only - tgs_model.save_img_sequences( - "video", - "(\d+)\.png", - save_format="mp4", - fps=30, - delete=True, - ) - """ return (gs_ply[0], ) @@ -1426,11 +1464,11 @@ class Load_Diffusers_Pipeline: def load_diffusers_pipe(self, diffusers_pipeline_name, model_name, custom_pipeline, download_from_remote): # resume pretrained checkpoint - ckpt_path = os.path.join(ROOT_PATH, "checkpoints", model_name) + ckpt_path = os.path.join(CKPT_DIFFUSERS_PATH, model_name) if download_from_remote: from huggingface_hub import snapshot_download - snapshot_download(repo_id=model_name, local_dir=ckpt_path, repo_type="model", ignore_patterns=["*.json"]) + snapshot_download(repo_id=model_name, local_dir=ckpt_path, repo_type="model", ignore_patterns=["*.json", "*.py"]) diffusers_pipeline_class = DIFFUSERS_PIPE_DICT[diffusers_pipeline_name] @@ -1477,12 +1515,9 @@ class Set_Diffusers_Pipeline_Scheduler: return (pipe, ) class Set_Diffusers_Pipeline_State_Dict: - - checkpoints_dir = "checkpoints" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_base_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) return { "required": { "pipe": ("DIFFUSERS_PIPE",), @@ -1502,7 +1537,7 @@ class Set_Diffusers_Pipeline_State_Dict: def set_pipe_state_dict(self, pipe, repo_id, model_name): - checkpoints_dir_abs = os.path.join(self.checkpoints_base_dir_abs, repo_id) + checkpoints_dir_abs = os.path.join(CKPT_DIFFUSERS_PATH, repo_id) ckpt_path = resume_or_download_model_from_hf(checkpoints_dir_abs, repo_id, model_name, self.__class__.__name__) state_dict = torch.load(ckpt_path, map_location='cpu') @@ -1514,11 +1549,13 @@ class Set_Diffusers_Pipeline_State_Dict: class Wonder3D_MVDiffusion_Model: - wonder3d_config_path = "configs/wonder3d_config.yaml" - fix_cam_pose_dir = "mvdiffusion/data/fixed_poses/nine_views" + wonder3d_config_path = "Wonder3D_config.yaml" + fix_cam_pose_dir = "Wonder3D/data/fixed_poses/nine_views" @classmethod def INPUT_TYPES(cls): + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.wonder3d_config_path) + cls.fix_cam_pose_dir_abs = os.path.join(MODULE_ROOT_PATH, cls.fix_cam_pose_dir) return { "required": { "mvdiffusion_pipe": ("DIFFUSERS_PIPE",), @@ -1551,8 +1588,7 @@ class Wonder3D_MVDiffusion_Model: num_inference_steps, ): - config_path = os.path.join(ROOT_PATH, self.wonder3d_config_path) - cfg = load_config_wonder3d(config_path) + cfg = load_config_wonder3d(self.config_path_abs) batch = self.prepare_data(reference_image, reference_mask) @@ -1595,8 +1631,7 @@ class Wonder3D_MVDiffusion_Model: def prepare_data(self, ref_image, ref_mask): single_image = torch_imgs_to_pils(ref_image, ref_mask)[0] - abs_fix_cam_pose_dir = os.path.join(ROOT_PATH, self.fix_cam_pose_dir) - dataset = MVSingleImageDataset(fix_cam_pose_dir=abs_fix_cam_pose_dir, num_views=6, img_wh=[256, 256], bg_color='white', single_image=single_image) + dataset = MVSingleImageDataset(fix_cam_pose_dir=self.fix_cam_pose_dir_abs, num_views=6, img_wh=[256, 256], bg_color='white', single_image=single_image) return dataset[0] class MVDream_Model: @@ -1672,13 +1707,13 @@ class MVDream_Model: class Load_Large_Multiview_Gaussian_Model: - checkpoints_dir = "checkpoints/lgm" + checkpoints_dir = "LGM" default_ckpt_name = "model_fp16.safetensors" default_repo_id = "ashawkey/LGM" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) + cls.checkpoints_dir_abs = os.path.join(CKPT_ROOT_PATH, cls.checkpoints_dir) all_models_names = get_list_filenames(cls.checkpoints_dir_abs, SUPPORTED_CHECKPOINTS_EXTENSIONS) if cls.default_ckpt_name not in all_models_names: all_models_names += [cls.default_ckpt_name] @@ -1701,7 +1736,7 @@ class Load_Large_Multiview_Gaussian_Model: def load_LGM(self, model_name, lgb_config): - lgm_model = LGM(config_defaults[lgb_config]) + lgm_model = LargeMultiviewGaussianModel(config_defaults[lgb_config]) ckpt_path = resume_or_download_model_from_hf(self.checkpoints_dir_abs, self.default_repo_id, model_name, self.__class__.__name__) @@ -1795,11 +1830,12 @@ class Convert_3DGS_to_Mesh_with_NeRF_and_Marching_Cubes: return(converter.get_mesh(), imgs, alphas) class NeuS: - NeuS_config_path = "configs/NeuS.conf" + NeuS_config_path = "NeuS.conf" fix_cam_pose_dir = "NeuS/models/fixed_poses" @classmethod def INPUT_TYPES(cls): + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.NeuS_config_path) return { "required": { "reference_image": ("IMAGE",), @@ -1855,7 +1891,6 @@ class NeuS: num_views = reference_image.shape[0] if num_views in [6, 5, 4]: - config_path = os.path.join(ROOT_PATH, self.NeuS_config_path) abs_fix_cam_pose_dir = os.path.join(ROOT_PATH, self.fix_cam_pose_dir) with torch.inference_mode(False): @@ -1878,7 +1913,7 @@ class NeuS: reference_image, reference_mask, reference_normals, - config_path, + self.config_path_abs, abs_fix_cam_pose_dir, num_views, NeuS_params @@ -1895,18 +1930,19 @@ class NeuS: return(mesh, ) class Load_TripoSR_Model: - checkpoints_dir = "checkpoints/tsr" + checkpoints_dir = "TripoSR" default_ckpt_name = "model.ckpt" default_repo_id = "stabilityai/TripoSR" - tsr_config_path = "configs/tsr_config.yaml" + tsr_config_path = "TripoSR_config.yaml" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) + cls.checkpoints_dir_abs = os.path.join(CKPT_ROOT_PATH, cls.checkpoints_dir) all_models_names = get_list_filenames(cls.checkpoints_dir_abs, SUPPORTED_CHECKPOINTS_EXTENSIONS) if cls.default_ckpt_name not in all_models_names: all_models_names += [cls.default_ckpt_name] + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.tsr_config_path) return { "required": { "model_name": (all_models_names, ), @@ -1926,11 +1962,10 @@ class Load_TripoSR_Model: def load_TSR(self, model_name, chunk_size): ckpt_path = resume_or_download_model_from_hf(self.checkpoints_dir_abs, self.default_repo_id, model_name, self.__class__.__name__) - config_path = os.path.join(ROOT_PATH, self.tsr_config_path) tsr_model = TSR.from_pretrained( weight_path=ckpt_path, - config_path=config_path + config_path=self.config_path_abs ) tsr_model.renderer.set_chunk_size(chunk_size) @@ -1992,19 +2027,21 @@ class TripoSR: return image class Load_CRM_MVDiffusion_Model: - checkpoints_dir = "checkpoints/crm" + checkpoints_dir = "CRM" default_ckpt_name = ["pixel-diffusion.pth", "ccm-diffusion.pth"] - default_conf_name = ["configs/crm_configs/sd_v2_base_ipmv_zero_SNR.yaml", "configs/crm_configs/sd_v2_base_ipmv_chin8_zero_snr.yaml"] + default_conf_name = ["sd_v2_base_ipmv_zero_SNR.yaml", "sd_v2_base_ipmv_chin8_zero_snr.yaml"] default_repo_id = "Zhengyi/CRM" + crm_config_path = "CRM_configs" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) + cls.checkpoints_dir_abs = os.path.join(CKPT_ROOT_PATH, cls.checkpoints_dir) all_models_names = get_list_filenames(cls.checkpoints_dir_abs, SUPPORTED_CHECKPOINTS_EXTENSIONS) for ckpt_name in cls.default_ckpt_name: if ckpt_name not in all_models_names: all_models_names += [ckpt_name] + cls.config_root_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.crm_config_path) return { "required": { "model_name": (all_models_names, ), @@ -2023,13 +2060,12 @@ class Load_CRM_MVDiffusion_Model: def load_CRM(self, model_name, crm_config_path): - from .crm.imagedream.ldm.util import ( + from CRM.imagedream.ldm.util import ( instantiate_from_config, get_obj_from_str, ) - - if not os.path.isabs(crm_config_path): - crm_config_path = os.path.join(ROOT_PATH, crm_config_path) + + crm_config_path = os.path.join(self.config_root_path_abs, crm_config_path) ckpt_path = resume_or_download_model_from_hf(self.checkpoints_dir_abs, self.default_repo_id, model_name, self.__class__.__name__) @@ -2179,18 +2215,19 @@ class CRM_CCMs_MVDiffusion_Model: return(multiview_CCMs, ) class Load_Convolutional_Reconstruction_Model: - checkpoints_dir = "checkpoints/crm" + checkpoints_dir = "CRM" default_ckpt_name = "CRM.pth" default_repo_id = "Zhengyi/CRM" - config_path = "configs/crm_configs/specs_objaverse_total.json" + config_path = "CRM_configs/specs_objaverse_total.json" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) + cls.checkpoints_dir_abs = os.path.join(CKPT_ROOT_PATH, cls.checkpoints_dir) all_models_names = get_list_filenames(cls.checkpoints_dir_abs, SUPPORTED_CHECKPOINTS_EXTENSIONS) if cls.default_ckpt_name not in all_models_names: all_models_names += [cls.default_ckpt_name] + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.config_path) return { "required": { "model_name": (all_models_names, ), @@ -2210,8 +2247,8 @@ class Load_Convolutional_Reconstruction_Model: ckpt_path = resume_or_download_model_from_hf(self.checkpoints_dir_abs, self.default_repo_id, model_name, self.__class__.__name__) - crm_conf = json.load(open(os.path.join(ROOT_PATH, self.config_path))) - crm_model = CRM(crm_conf).to(DEVICE) + crm_conf = json.load(open(self.config_path_abs)) + crm_model = ConvolutionalReconstructionModel(crm_conf).to(DEVICE) crm_model.load_state_dict(torch.load(ckpt_path, map_location="cpu"), strict=False) cstr(f"[{self.__class__.__name__}] loaded model ckpt from {ckpt_path}").msg.print() @@ -2314,19 +2351,20 @@ class Zero123Plus_Diffusion_Model: return (multiview_images, orbit_camposes) class Load_InstantMesh_Reconstruction_Model: - checkpoints_dir = "checkpoints/crm" + checkpoints_dir = "InstantMesh" default_ckpt_names = ["instant_mesh_large.ckpt", "instant_mesh_base.ckpt", "instant_nerf_large.ckpt", "instant_nerf_base.ckpt"] default_repo_id = "TencentARC/InstantMesh" - config_root_dir = "configs/InstantMesh_configs" + config_root_dir = "InstantMesh_configs" @classmethod def INPUT_TYPES(cls): - cls.checkpoints_dir_abs = os.path.join(ROOT_PATH, cls.checkpoints_dir) + cls.checkpoints_dir_abs = os.path.join(CKPT_ROOT_PATH, cls.checkpoints_dir) all_models_names = get_list_filenames(cls.checkpoints_dir_abs, SUPPORTED_CHECKPOINTS_EXTENSIONS) for ckpt_name in cls.default_ckpt_names: if ckpt_name not in all_models_names: all_models_names += [ckpt_name] - + + cls.config_root_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.config_root_dir) return { "required": { "model_name": (all_models_names, ), @@ -2344,12 +2382,12 @@ class Load_InstantMesh_Reconstruction_Model: def load_LRM(self, model_name): - from .instant_mesh.utils.train_util import instantiate_from_config + from InstantMesh.utils.train_util import instantiate_from_config is_flexicubes = True if model_name.startswith('instant_mesh') else False config_name = model_name.split(".")[0] + ".yaml" - config_path = os.path.join(ROOT_PATH, self.config_root_dir, config_name) + config_path = os.path.join(self.config_root_path_abs, config_name) config = OmegaConf.load(config_path) lrm_model = instantiate_from_config(config.model_config) @@ -2419,9 +2457,10 @@ class InstantMesh_Reconstruction_Model: class Era3D_Diffusion_Model: - era3d_config_path = "configs/era3d_config.yaml" + era3d_config_path = "Era3D_config.yaml" @classmethod def INPUT_TYPES(cls): + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.era3d_config_path) return { "required": { "era3d_pipe": ("DIFFUSERS_PIPE",), @@ -2462,8 +2501,7 @@ class Era3D_Diffusion_Model: eta, radius, ): - config_path = os.path.join(ROOT_PATH, self.era3d_config_path) - cfg = load_config_era3d(config_path) + cfg = load_config_era3d(self.config_path_abs) single_image = torch_imgs_to_pils(reference_image, reference_mask)[0] @@ -2531,6 +2569,7 @@ class Instant_NGP: "marching_cude_grids_batch_size": ("INT", {"default": 128, "min": 1, "max": 0xffffffffffffffff}), "marching_cude_threshold": ("FLOAT", {"default": 10.0, "min": 0.0, "step": 0.01}), "texture_resolution": ("INT", {"default": 1024, "min": 128, "max": 8192}), + "background_color": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), "force_cuda_rast": ("BOOLEAN", {"default": False}), }, } @@ -2556,13 +2595,14 @@ class Instant_NGP: marching_cude_grids_batch_size, marching_cude_threshold, texture_resolution, + background_color, force_cuda_rast ): with torch.inference_mode(False): ngp = InstantNGP(training_resolution).to(DEVICE) ngp.prepare_training(reference_image, reference_mask, reference_orbit_camera_poses, reference_orbit_camera_fovy) - ngp.fit_nerf(training_iterations) + ngp.fit_nerf(training_iterations, background_color) vertices, triangles = marching_cubes_density_to_mesh(ngp.get_density, marching_cude_grids_resolution, marching_cude_grids_batch_size, marching_cude_threshold) @@ -2665,11 +2705,12 @@ class FlexiCubes_MVS: return (mesh, ) class Load_Unique3D_Custom_UNet: - checkpoints_dir = "checkpoints/Wuvin/Unique3D" - config_root_dir = "configs/Unique3D_configs" + checkpoints_dir = "Wuvin/Unique3D" + config_root_dir = "Unique3D_configs" @classmethod def INPUT_TYPES(cls): + cls.config_path_abs = os.path.join(CONFIG_ROOT_PATH, cls.config_root_dir) return { "required": { "pipe": ("DIFFUSERS_PIPE",), @@ -2692,8 +2733,8 @@ class Load_Unique3D_Custom_UNet: from Unique3D.custum_3d_diffusion.custum_modules.unifield_processor import AttnConfig, ConfigurableUNet2DConditionModel from Unique3D.custum_3d_diffusion.trainings.utils import load_config - cfg_path = os.path.join(ROOT_PATH, self.config_root_dir, config_name + ".yaml") - checkpoint_dir_path = os.path.join(ROOT_PATH, self.checkpoints_dir, config_name) + cfg_path = os.path.join(self.config_path_abs, config_name + ".yaml") + checkpoint_dir_path = os.path.join(CKPT_DIFFUSERS_PATH, self.checkpoints_dir, config_name) checkpoint_path = os.path.join(checkpoint_dir_path, "unet_state_dict.pth") cfg: ExprimentConfig = load_config(ExprimentConfig, cfg_path) diff --git a/requirements_post.txt b/requirements_post.txt index 79e18fe..4f61218 100644 --- a/requirements_post.txt +++ b/requirements_post.txt @@ -1,5 +1,5 @@ # local -./tgs/models/snowflake/pointnet2_ops_lib +./Gen_3D_Modules/TriplaneGaussian/models/snowflake/pointnet2_ops_lib ./simple-knn # remote diff --git a/shared_utils/camera_utils.py b/shared_utils/camera_utils.py index a2e4f06..a272d9f 100644 --- a/shared_utils/camera_utils.py +++ b/shared_utils/camera_utils.py @@ -209,19 +209,19 @@ class BaseCameraController(ABC): self.white_bg = torch.tensor([1, 1, 1], dtype=torch.float32, device=self.device) self.static_bg = None if static_bg is None else torch.tensor(static_bg, dtype=torch.float32, device=self.device) - self.pose_init() + self.post_init() super().__init__() - def pose_init(self): + def post_init(self): # Calls after default initialize at the end of __init__() pass @abstractmethod - def get_render_result(self, render_pose, bg_color): + def get_render_result(self, render_pose, bg_color, **kwargs): pass - def render_at_pose(self, cam_pose): + def render_at_pose(self, cam_pose, **kwargs): radius, elevation, azimuth, center_X, center_Y, center_Z = cam_pose orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32) @@ -232,12 +232,13 @@ class BaseCameraController(ABC): else: bg_color = self.static_bg - return self.get_render_result(render_pose, bg_color) + return self.get_render_result(render_pose, bg_color, **kwargs) - def render_all_pose(self, all_cam_poses): + def render_all_pose(self, all_cam_poses, **kwargs): all_rendered_images, all_rendered_masks = [], [] + extra_outputs = {} for cam_pose in all_cam_poses: - out = self.render_at_pose(cam_pose) + out = self.render_at_pose(cam_pose, **kwargs) image = out["image"] # [3, H, W] in [0, 1] mask = out["alpha"] # [1, H, W] in [0, 1] @@ -245,5 +246,13 @@ class BaseCameraController(ABC): all_rendered_images.append(image) all_rendered_masks.append(mask) + for k in out: + if k not in extra_outputs: + extra_outputs[k] = [] + extra_outputs[k].append(out[k]) + + for k in extra_outputs: + extra_outputs[k] = torch.stack(extra_outputs[k], dim=0) + # [Number of Poses, 3, H, W], [Number of Poses, 1, H, W] both in [0, 1] - return torch.stack(all_rendered_images, dim=0), torch.stack(all_rendered_masks, dim=0) \ No newline at end of file + return torch.stack(all_rendered_images, dim=0), torch.stack(all_rendered_masks, dim=0), extra_outputs \ No newline at end of file diff --git a/shared_utils/log_utils.py b/shared_utils/log_utils.py new file mode 100644 index 0000000..eb6b359 --- /dev/null +++ b/shared_utils/log_utils.py @@ -0,0 +1,26 @@ +import logging +import sys + +class WarningFilter(logging.Filter): + def filter(self, record): + if record.levelno == logging.WARNING: + record.msg = f"Warn!: {record.msg}" + return True + +def create_handler(stream, levels, formatter): + handler = logging.StreamHandler(stream) + handler.setLevel(min(levels)) + handler.addFilter(lambda record: record.levelno in levels) + handler.addFilter(WarningFilter()) # Apply the custom filter + handler.setFormatter(formatter) + + return handler +def setup_logger(logger_name, level, stdout_levels, stderr_levels, formatter): + logger = logging.getLogger(logger_name) + for handler in logger.handlers[:]: + logger.removeHandler(handler) + logger.setLevel(level) + stdout_handler = create_handler(sys.stdout, stdout_levels, formatter) + stderr_handler = create_handler(sys.stderr, stderr_levels, formatter) + logger.addHandler(stdout_handler) + logger.addHandler(stderr_handler) \ No newline at end of file diff --git a/web/html/gsVisualizer.html b/web/html/gsVisualizer.html index 0699e6f..98b5d3f 100644 --- a/web/html/gsVisualizer.html +++ b/web/html/gsVisualizer.html @@ -55,6 +55,6 @@ canvas.addEventListener('click', handleClick); - + \ No newline at end of file diff --git a/web/html/threeVisualizer.html b/web/html/threeVisualizer.html index 74fb9ed..af4a99f 100644 --- a/web/html/threeVisualizer.html +++ b/web/html/threeVisualizer.html @@ -48,7 +48,7 @@ ] }); - + diff --git a/web/js/threeVisualizer.js b/web/js/threeVisualizer.js index 771be20..97e8f70 100644 --- a/web/js/threeVisualizer.js +++ b/web/js/threeVisualizer.js @@ -25,7 +25,7 @@ const pmremGenerator = new THREE.PMREMGenerator( renderer ); // scene const scene = new THREE.Scene(); -scene.background = new THREE.Color( 0xffffff ); +scene.background = new THREE.Color( 0x000000 ); scene.environment = pmremGenerator.fromScene( new RoomEnvironment( renderer ), 0.04 ).texture; const ambientLight = new THREE.AmbientLight( 0xffffff , 3.0 ); @@ -124,7 +124,6 @@ async function main(filepath="") { }, onProgress, onError ); - } else if (fileExt == "glb") { const dracoLoader = new DRACOLoader(); dracoLoader.setDecoderPath( 'https://unpkg.com/three@latest/examples/jsm/libs/draco/gltf/' ); @@ -134,8 +133,7 @@ async function main(filepath="") { loader.load( url, function ( gltf ) { const model = gltf.scene; //model.position.set( 1, 1, 0 ); - //model.scale.set( 0.01, 0.01, 0.01 ); - model.scale.set( 5, 5, 5 ); + model.scale.set( 3, 3, 3 ); scene.add( model );

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