441 lines
18 KiB
Python
441 lines
18 KiB
Python
import torch
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import os
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import comfy.utils
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import folder_paths
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import numpy as np
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import math
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import cv2
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import PIL.Image
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from comfy.ldm.modules.attention import optimized_attention
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from .resampler import Resampler
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from insightface.app import FaceAnalysis
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import torchvision.transforms.v2 as T
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import torch.nn.functional as F
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MODELS_DIR = os.path.join(folder_paths.models_dir, "instantid")
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if "instantid" not in folder_paths.folder_names_and_paths:
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current_paths = [MODELS_DIR]
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else:
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current_paths, _ = folder_paths.folder_names_and_paths["instantid"]
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folder_paths.folder_names_and_paths["instantid"] = (current_paths, folder_paths.supported_pt_extensions)
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INSIGHTFACE_DIR = os.path.join(folder_paths.models_dir, "insightface")
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def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
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stickwidth = 4
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
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kps = np.array(kps)
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h, w, _ = image_pil.shape
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out_img = np.zeros([h, w, 3])
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for i in range(len(limbSeq)):
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index = limbSeq[i]
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color = color_list[index[0]]
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x = kps[index][:, 0]
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y = kps[index][:, 1]
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
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polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
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out_img = (out_img * 0.6).astype(np.uint8)
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for idx_kp, kp in enumerate(kps):
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color = color_list[idx_kp]
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x, y = kp
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
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out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
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return out_img_pil
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class CrossAttentionPatchIID:
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# forward for patching
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def __init__(self, weight, instantid, number, cond, uncond, mask=None, sigma_start=0.0, sigma_end=1.0):
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self.weights = [weight]
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self.instantid = [instantid]
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self.conds = [cond]
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self.unconds = [uncond]
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self.number = number
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self.masks = [mask]
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self.sigma_start = [sigma_start]
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self.sigma_end = [sigma_end]
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self.k_key = str(self.number*2+1) + "_to_k_ip"
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self.v_key = str(self.number*2+1) + "_to_v_ip"
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def set_new_condition(self, weight, instantid, number, cond, uncond, mask=None, sigma_start=0.0, sigma_end=1.0):
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self.weights.append(weight)
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self.instantid.append(instantid)
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self.conds.append(cond)
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self.unconds.append(uncond)
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self.masks.append(mask)
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self.sigma_start.append(sigma_start)
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self.sigma_end.append(sigma_end)
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def __call__(self, n, context_attn2, value_attn2, extra_options):
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org_dtype = n.dtype
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cond_or_uncond = extra_options["cond_or_uncond"]
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sigma = extra_options["sigmas"][0] if 'sigmas' in extra_options else None
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sigma = sigma.item() if sigma else 999999999.9
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q = n
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k = context_attn2
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v = value_attn2
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b = q.shape[0]
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qs = q.shape[1]
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batch_prompt = b // len(cond_or_uncond)
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out = optimized_attention(q, k, v, extra_options["n_heads"])
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_, _, lh, lw = extra_options["original_shape"]
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for weight, cond, uncond, instantid, mask, sigma_start, sigma_end in zip(self.weights, self.conds, self.unconds, self.instantid, self.masks, self.sigma_start, self.sigma_end):
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if sigma < sigma_start and sigma > sigma_end:
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k_cond = instantid.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
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k_uncond = instantid.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
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v_cond = instantid.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
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v_uncond = instantid.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)
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iid_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
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iid_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
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out_iid = optimized_attention(q, iid_k, iid_v, extra_options["n_heads"])
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out_iid = out_iid * weight
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if mask is not None:
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# TODO: needs checking
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mask_h = lh / math.sqrt(lh * lw / qs)
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mask_h = int(mask_h) + int((qs % int(mask_h)) != 0)
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mask_w = qs // mask_h
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mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
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# if we don't have enough masks repeat the last one until we reach the right size
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if mask_downsample.shape[0] < batch_prompt:
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mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:, :, :].repeat((batch_prompt-mask_downsample.shape[0], 1, 1))), dim=0)
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# if we have too many remove the exceeding
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elif mask_downsample.shape[0] > batch_prompt:
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mask_downsample = mask_downsample[:batch_prompt, :, :]
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# repeat the masks
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mask_downsample = mask_downsample.repeat(len(cond_or_uncond), 1, 1)
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mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1, 1).repeat(1, 1, out.shape[2])
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out_iid = out_iid * mask_downsample
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out = out + out_iid
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return out.to(dtype=org_dtype)
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class InstantID(torch.nn.Module):
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def __init__(self, instantid_model, cross_attention_dim=1280, output_cross_attention_dim=1024, clip_embeddings_dim=512, clip_extra_context_tokens=16):
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super().__init__()
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self.clip_embeddings_dim = clip_embeddings_dim
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self.cross_attention_dim = cross_attention_dim
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self.output_cross_attention_dim = output_cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.image_proj_model = self.init_proj()
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self.image_proj_model.load_state_dict(instantid_model["image_proj"])
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self.ip_layers = To_KV(instantid_model["ip_adapter"])
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def init_proj(self):
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image_proj_model = Resampler(
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dim=self.cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=20,
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num_queries=self.clip_extra_context_tokens,
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embedding_dim=self.clip_embeddings_dim,
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output_dim=self.output_cross_attention_dim,
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ff_mult=4
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)
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return image_proj_model
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@torch.inference_mode()
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def get_image_embeds(self, clip_embed, clip_embed_zeroed):
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#image_prompt_embeds = clip_embed.clone().detach()
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image_prompt_embeds = self.image_proj_model(clip_embed)
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#uncond_image_prompt_embeds = clip_embed_zeroed.clone().detach()
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uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed)
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return image_prompt_embeds, uncond_image_prompt_embeds
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class ImageProjModel(torch.nn.Module):
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, image_embeds):
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embeds = image_embeds
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clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class To_KV(torch.nn.Module):
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def __init__(self, state_dict):
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super().__init__()
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self.to_kvs = torch.nn.ModuleDict()
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for key, value in state_dict.items():
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k = key.replace(".weight", "").replace(".", "_")
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self.to_kvs[k] = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)
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self.to_kvs[k].weight.data = value
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def _set_model_patch_replace(model, patch_kwargs, key):
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to = model.model_options["transformer_options"]
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if "patches_replace" not in to:
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to["patches_replace"] = {}
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if "attn2" not in to["patches_replace"]:
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to["patches_replace"]["attn2"] = {}
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if key not in to["patches_replace"]["attn2"] or not isinstance(to["patches_replace"]["attn2"][key], CrossAttentionPatchIID):
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patch = CrossAttentionPatchIID(**patch_kwargs)
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to["patches_replace"]["attn2"][key] = patch
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else:
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to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs)
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class InstantIDModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "instantid_file": (folder_paths.get_filename_list("instantid"), )}}
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RETURN_TYPES = ("INSTANTID",)
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FUNCTION = "load_model"
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CATEGORY = "InstantID"
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def load_model(self, instantid_file):
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ckpt_path = folder_paths.get_full_path("instantid", instantid_file)
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model = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
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if ckpt_path.lower().endswith(".safetensors"):
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st_model = {"image_proj": {}, "ip_adapter": {}}
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for key in model.keys():
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if key.startswith("image_proj."):
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st_model["image_proj"][key.replace("image_proj.", "")] = model[key]
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elif key.startswith("ip_adapter."):
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st_model["ip_adapter"][key.replace("ip_adapter.", "")] = model[key]
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model = st_model
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return (model,)
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def tensorToNP(image):
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out = torch.clamp(255. * image.detach().cpu(), 0, 255).to(torch.uint8)
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out = out[..., [2, 1, 0]]
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out = out.numpy()
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return out
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def extractFeatures(insightface, image, extract_kps=False):
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face_img = tensorToNP(image)
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out = []
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insightface.det_model.input_size = (640,640) # reset the detection size
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for i in range(face_img.shape[0]):
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for size in [(size, size) for size in range(640, 128, -64)]:
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insightface.det_model.input_size = size # TODO: hacky but seems to be working
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face = insightface.get(face_img[i])
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if face:
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face = sorted(face, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1]
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if extract_kps:
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out.append(draw_kps(face_img[i], face['kps']))
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else:
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out.append(torch.from_numpy(face['embedding']).unsqueeze(0))
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if 640 not in size:
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print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
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break
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if out:
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if extract_kps:
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out = torch.stack(T.ToTensor()(out), dim=0).permute([0,2,3,1])
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else:
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out = torch.stack(out, dim=0)
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else:
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out = None
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return out
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class InstantIDFaceAnalysis:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"provider": (["CPU", "CUDA", "ROCM"], ),
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},
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}
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RETURN_TYPES = ("FACEANALYSIS",)
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FUNCTION = "load_insight_face"
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CATEGORY = "InstantID"
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def load_insight_face(self, provider):
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model = FaceAnalysis(name="antelopev2", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',]) # buffalo_l
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model.prepare(ctx_id=0, det_size=(640, 640))
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return (model,)
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class FaceKeypointsPreprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"faceanalysis": ("FACEANALYSIS", ),
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"image": ("IMAGE", ),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "preprocess_image"
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CATEGORY = "InstantID"
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def preprocess_image(self, faceanalysis, image):
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face_kps = extractFeatures(faceanalysis, image, extract_kps=True)
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if face_kps is None:
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face_kps = torch.zeros_like(image)
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print(f"\033[33mWARNING: no face detected, unable to extract the keypoints!\033[0m")
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#raise Exception('Face Keypoints Image: No face detected.')
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return (face_kps,)
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class ApplyInstantID:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"instantid": ("INSTANTID", ),
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"insightface": ("FACEANALYSIS", ),
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"image_features": ("IMAGE", ),
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"model": ("MODEL", ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01,}),
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"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001,}),
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"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001,}),
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},
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"optional": {
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"attn_mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("MODEL", "POSITIVE", "NEGATIVE", )
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FUNCTION = "apply_instantid"
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CATEGORY = "InstantID"
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def apply_instantid(self, instantid, insightface, image_features, model, positive, negative, weight, start_at, end_at, attn_mask=None):
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self.dtype = torch.float16 if comfy.model_management.should_use_fp16() else torch.float32
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self.device = comfy.model_management.get_torch_device()
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self.weight = weight
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output_cross_attention_dim = instantid["ip_adapter"]["1.to_k_ip.weight"].shape[1]
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is_sdxl = output_cross_attention_dim == 2048
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cross_attention_dim = 1280
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clip_extra_context_tokens = 16
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face_embed = extractFeatures(insightface, image_features)
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if face_embed is None:
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raise Exception('Feature Extractor: No face detected.')
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clip_embed = face_embed
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clip_embed_zeroed = torch.zeros_like(clip_embed)
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clip_embeddings_dim = face_embed.shape[-1]
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self.instantid = InstantID(
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instantid,
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cross_attention_dim=cross_attention_dim,
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output_cross_attention_dim=output_cross_attention_dim,
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clip_embeddings_dim=clip_embeddings_dim,
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clip_extra_context_tokens=clip_extra_context_tokens,
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)
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self.instantid.to(self.device, dtype=self.dtype)
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image_prompt_embeds, uncond_image_prompt_embeds = self.instantid.get_image_embeds(clip_embed.to(self.device, dtype=self.dtype), clip_embed_zeroed.to(self.device, dtype=self.dtype))
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image_prompt_embeds = image_prompt_embeds.to(self.device, dtype=self.dtype)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device, dtype=self.dtype)
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work_model = model.clone()
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sigma_start = work_model.model.model_sampling.percent_to_sigma(start_at)
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sigma_end = work_model.model.model_sampling.percent_to_sigma(end_at)
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if attn_mask is not None:
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attn_mask = attn_mask.to(self.device)
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patch_kwargs = {
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"number": 0,
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"weight": self.weight,
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"instantid": self.instantid,
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"cond": image_prompt_embeds,
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"uncond": uncond_image_prompt_embeds,
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"mask": attn_mask,
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"sigma_start": sigma_start,
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"sigma_end": sigma_end,
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}
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if not is_sdxl:
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for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention
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_set_model_patch_replace(work_model, patch_kwargs, ("input", id))
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patch_kwargs["number"] += 1
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for id in [3,4,5,6,7,8,9,10,11]: # id of output_blocks that have cross attention
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_set_model_patch_replace(work_model, patch_kwargs, ("output", id))
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patch_kwargs["number"] += 1
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_set_model_patch_replace(work_model, patch_kwargs, ("middle", 0))
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else:
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for id in [4,5,7,8]: # id of input_blocks that have cross attention
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block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
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for index in block_indices:
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_set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
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patch_kwargs["number"] += 1
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for id in range(6): # id of output_blocks that have cross attention
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block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth
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for index in block_indices:
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_set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
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patch_kwargs["number"] += 1
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for index in range(10):
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_set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index))
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patch_kwargs["number"] += 1
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pos = []
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for t in positive:
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n = [t[0], t[1].copy()]
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n[1]['cross_attn_controlnet'] = image_prompt_embeds.cpu()
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pos.append(n)
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#pos[0][1]['cross_attn_controlnet'] = image_prompt_embeds.cpu()
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neg = []
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for t in negative:
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n = [t[0], t[1].copy()]
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n[1]['cross_attn_controlnet'] = uncond_image_prompt_embeds.cpu()
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neg.append(n)
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|
#neg[0][1]['cross_attn_controlnet'] = uncond_image_prompt_embeds.cpu()
|
|
|
|
return(work_model, pos, neg, )
|
|
|
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NODE_CLASS_MAPPINGS = {
|
|
"InstantIDModelLoader": InstantIDModelLoader,
|
|
"InstantIDFaceAnalysis": InstantIDFaceAnalysis,
|
|
"ApplyInstantID": ApplyInstantID,
|
|
"FaceKeypointsPreprocessor": FaceKeypointsPreprocessor,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"InstantIDModelLoader": "Load InstantID Model",
|
|
"InstantIDFaceAnalysis": "InstantID Face Analysis",
|
|
"ApplyInstantID": "Apply InstantID",
|
|
"FaceKeypointsPreprocessor": "Face Keypoints Preprocessor",
|
|
}
|