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Updated Workflows + Added new MultiView nodes

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Trellis2SparseMultiViewGenerator
Trellis2ImageCondMultiViewGenerator
Trellis2ShapeMultiViewGenerator
Trellis2ShapeCascadeMultiViewGenerator
Trellis2TexSlatMultiViewGenerator
This commit is contained in:
Bruno Fargnoli
2026-04-20 23:04:20 +02:00
parent 450c4c58e4
commit f0bc251544
34 changed files with 33203 additions and 89390 deletions
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README.md
+2
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@@ -14,6 +14,8 @@
| Date | Description |
| --- | --- |
| **2026-04-20** | Recreated all Workflows |
| **2026-04-20** | Added node "Sparse MultiView Generator"<br>Added node "ImageCond MultiView Generator"<br>Added node "Shape MultiView Generator"<br>Added node "Shape Cascade MultiView Generator"<br>Added node "Tex Slat MultiView Generator" |
| **2026-04-20** | Added node "Fill Holes Nicely with Meshlib"<br>Fixed DinoV3 Features Extractor |
| **2026-04-06** | Added new "DINO-Lock" functionality<br>New fill_holes in Sparse Generator<br>Thanks to Easymode on Discord |
| **2026-04-05** | Added node "Extract Images from Video"<br>Can be used with "Sparse Generator with ReconViaGen" |
example_workflows/Advanced.json
-769
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@@ -1,769 +0,0 @@
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example_workflows/Better_Texture.json
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example_workflows/LowPoly.json
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File diff suppressed because it is too large Load Diff
example_workflows/Watertight_Mesh.json → example_workflows/MeshOnly.json
+886 -914
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File diff suppressed because it is too large Load Diff
example_workflows/Advanced_CustomSteps.json → example_workflows/MeshOnly_LowPoly.json
+1068 -895
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File diff suppressed because it is too large Load Diff
example_workflows/Advanced_CustomSteps_MeshOnly.json → example_workflows/MeshOnly_MultiView.json
+1123 -821
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File diff suppressed because it is too large Load Diff
example_workflows/MeshRefiner.json
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View File
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example_workflows/TextureMesh.json → example_workflows/MeshTexturing.json
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View File
@@ -1,173 +1,279 @@
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View File
File diff suppressed because it is too large Load Diff
example_workflows/Max_Quality.json → example_workflows/MeshWithTexturing.json
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View File
File diff suppressed because it is too large Load Diff
example_workflows/Watertight_No_Holes.json → example_workflows/MeshWithTexturing_LowPoly.json
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View File
File diff suppressed because it is too large Load Diff
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View File
File diff suppressed because it is too large Load Diff
example_workflows/MultiViews.json
-1109
View File
File diff suppressed because it is too large Load Diff
example_workflows/MultiViews_MeshOnly.json
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View File
File diff suppressed because it is too large Load Diff
example_workflows/Only_Mesh_Advanced.json
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example_workflows/Only_Mesh_Simple.json
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example_workflows/Projection_6Views_Hy20.json
-8578
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example_workflows/Projection_MultiView_Hy2.0_Qwen_2Views_LowPoly_FullWorkflow.json → example_workflows/Projection_Hy20_Qwen_2Views.json
+3194 -4079
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example_workflows/Projection_MultiView_Hy2.0_Qwen_4Views_Upscaled.json → example_workflows/Projection_Hy20_Qwen_4Views.json
+8032 -8125
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example_workflows/Projection_MultiView_Hy2.0_Qwen_4Views.json → example_workflows/Projection_Hy20_Qwen_6Views.json
+12942 -6218
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example_workflows/Projection_MultiView_Hy2.0_Qwen_2Views.json
-8504
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example_workflows/Projection_MultiView_Hy2.0_Qwen_2Views_LowPoly.json
-9546
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example_workflows/Projection_MultiView_Hy2.0_Qwen_2Views_LowPoly_FullWorkflow_Fast.json
-9296
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example_workflows/Projection_MultiView_Hy2.0_Qwen_2Views_Upscaled.json
-10347
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example_workflows/Projection_MultiView_with_Hunyuan3D2.0.json
-2740
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example_workflows/ReconViaGen_MeshOnly.json
-1019
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example_workflows/ReconViaGen_MeshOnly_FromVideo.json
-1053
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example_workflows/RefineMesh.json
-810
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@@ -1,810 +0,0 @@
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File diff suppressed because it is too large Load Diff
example_workflows/Simple.json
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"flags": {}
}
],
"config": {},
"extra": {
"workflowRendererVersion": "LG",
"ue_links": [],
"ds": {
"scale": 0.5644739300537773,
"offset": [
683.341395587175,
735.5254552429528
]
},
"links_added_by_ue": [],
"frontendVersion": "1.42.8",
"VHS_latentpreview": false,
"VHS_latentpreviewrate": 0,
"VHS_MetadataImage": true,
"VHS_KeepIntermediate": true
},
"version": 0.4
}
example_workflows/Using_Qwen_Rembg.json
-4504
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File diff suppressed because it is too large Load Diff
nodes.py
+821 -1
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@@ -1,5 +1,6 @@
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image, ImageSequence, ImageOps
@@ -5609,7 +5610,816 @@ class Trellis2MaxTokensCalculator:
print(f"Error, cannot get VRAM size : {e}")
max_tokens = default_max_tokens
return (max_tokens,)
return (max_tokens,)
class Trellis2ImageCondMultiViewGenerator:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("TRELLIS2PIPELINE",),
"front_image": ("IMAGE",)
},
"optional": {
"back_image": ("IMAGE",),
"left_image": ("IMAGE",),
"right_image": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE_CONDS", "IMAGE_CONDS", "VIEWS_LIST", "TRELLIS2PIPELINE",)
RETURN_NAMES = ("conds_512", "conds_1024", "views_list", "pipeline",)
FUNCTION = "process"
CATEGORY = "Trellis2Wrapper"
OUTPUT_NODE = True
def process(self,
pipeline,
front_image,
back_image = None,
left_image = None,
right_image = None):
front_pil = tensor2pil(front_image)
# Convert optional view image tensors to PIL
back_pil = tensor2pil(back_image) if back_image is not None else None
left_pil = tensor2pil(left_image) if left_image is not None else None
right_pil = tensor2pil(right_image) if right_image is not None else None
# Collect views
views_dict = {'front': front_pil}
if back_pil is not None: views_dict['back'] = back_pil
if left_pil is not None: views_dict['left'] = left_pil
if right_pil is not None: views_dict['right'] = right_pil
views_list = list(views_dict.keys())
# Calculate conditioning per view
conds_512 = {}
conds_1024 = {}
pipeline.load_image_cond_model()
for v, img in views_dict.items():
c512 = pipeline.get_cond([img], 512)
c1024 = pipeline.get_cond([img], 1024)
conds_512[v] = c512
conds_1024[v] = c1024
if not pipeline.keep_models_loaded:
pipeline.unload_image_cond_model()
return (conds_512, conds_1024, views_list, pipeline,)
class Trellis2SparseMultiViewGenerator:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("TRELLIS2PIPELINE",),
"image_conds": ("IMAGE_CONDS",),
"views_list": ("VIEWS_LIST",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0x7fffffff}),
"sparse_structure_steps": ("INT",{"default":12, "min":1, "max":100},),
"sparse_structure_guidance_strength": ("FLOAT",{"default":6.50,"min":0.00,"max":99.99,"step":0.01}),
"sparse_structure_guidance_rescale": ("FLOAT",{"default":0.05,"min":0.00,"max":1.00,"step":0.01}),
"sparse_structure_rescale_t": ("FLOAT",{"default":4.00,"min":0.00,"max":9.99,"step":0.01}),
"sparse_structure_sampler": (["euler", "heun", "rk4", "rk5"], {"default": "euler"}),
"sparse_structure_resolution": ("INT", {"default":32,"min":32,"max":128,"step":4}),
"sparse_structure_guidance_interval_start": ("FLOAT",{"default":0.10,"min":0.00,"max":1.00,"step":0.01}),
"sparse_structure_guidance_interval_end": ("FLOAT",{"default":1.00,"min":0.00,"max":1.00,"step":0.01}),
"fill_holes":("BOOLEAN",{"default":True}),
"hole_iterations": ("INT",{"default":1,"min":1,"max":9,"step":1}),
"verbose": ("BOOLEAN",{"default":False}),
"dino_lock": ("FLOAT",{"default":0.00,"min":0.00,"max":1.00,"step":0.01}),
"dino_substeps": ("INT",{"default":4,"min":1,"max":99,"step":1}),
"hole_fill_algorithm": (["morphological_closing","flood_fill","remove_small_holes"],{"default":"flood_fill"}),
"dino_foundation_cap": ("FLOAT",{"default":1.00,"min":0.01,"max":1.00,"step":0.01}),
"keep_only_shell": ("BOOLEAN",{"default":True}),
"front_axis": (["z", "x"], {"default": "z"}),
"blend_temperature": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
},
}
RETURN_TYPES = ("COORDS", "INT", "VIEWS_LIST", "TRELLIS2PIPELINE",)
RETURN_NAMES = ("coords", "sparse_structure_resolution", "views_list", "pipeline",)
FUNCTION = "process"
CATEGORY = "Trellis2Wrapper"
OUTPUT_NODE = True
def process(self, pipeline, image_conds, views_list, seed,
# sparse
sparse_structure_steps,
sparse_structure_guidance_strength,
sparse_structure_guidance_rescale,
sparse_structure_rescale_t,
sparse_structure_sampler,
sparse_structure_resolution,
sparse_structure_guidance_interval_start,
sparse_structure_guidance_interval_end,
fill_holes,
hole_iterations,
verbose,
dino_lock,
dino_substeps,
hole_fill_algorithm,
dino_foundation_cap,
keep_only_shell,
front_axis,
blend_temperature
):
self.seed_all(seed)
sparse_structure_guidance_interval = [sparse_structure_guidance_interval_start,sparse_structure_guidance_interval_end]
sparse_structure_sampler_params = {"steps":sparse_structure_steps,"guidance_strength":sparse_structure_guidance_strength,"guidance_rescale":sparse_structure_guidance_rescale,"guidance_interval":sparse_structure_guidance_interval,"rescale_t":sparse_structure_rescale_t}
sparse_sampler_prefix = pipeline.GetSamplerName(sparse_structure_sampler)
pipeline.load_sparse_structure_model()
if pipeline.low_vram:
for v in image_conds:
image_conds[v] = pipeline._cond_to(image_conds[v], pipeline.device)
# Sample sparse structure latent
flow_model = pipeline.models['sparse_structure_flow_model']
reso = flow_model.resolution
in_channels = flow_model.in_channels
noise = torch.randn(1, in_channels, reso, reso, reso).to(pipeline.device)
sampler_class = getattr(samplers, f"Flow{sparse_sampler_prefix}MultiViewGuidanceIntervalSampler", samplers.FlowEulerMultiViewGuidanceIntervalSampler)
sampler = sampler_class(
sigma_min=1e-5,
resolution=flow_model.resolution if hasattr(flow_model, 'resolution') else flow_model[0].resolution
)
sparse_structure_sampler_params = {**pipeline.sparse_structure_sampler_params, **sparse_structure_sampler_params}
if pipeline.low_vram:
flow_model.to(pipeline.device)
z_s = sampler.sample(
flow_model,
noise,
conds=image_conds,
**sparse_structure_sampler_params,
views=views_list,
front_axis=front_axis,
blend_temperature=blend_temperature,
verbose=verbose,
dino_lock=dino_lock,
dino_substeps=dino_substeps,
dino_foundation_cap=dino_foundation_cap,
tqdm_desc="Sampling sparse structure (MultiView)",
).samples
if pipeline.low_vram:
flow_model.cpu()
pipeline._cleanup_cuda()
# Decode sparse structure latent
decoder = pipeline.models['sparse_structure_decoder']
if pipeline.low_vram:
decoder.to(pipeline.device)
# Standard decoding logic from sample_sparse_structure
decoded = decoder(z_s) > 0
if pipeline.low_vram:
decoder.cpu()
pipeline._cleanup_cuda()
# if resolution != decoded.shape[2]:
# ratio = decoded.shape[2] // resolution
# decoded = torch.nn.functional.max_pool3d(decoded.float(), ratio, ratio, 0) > 0.5
if sparse_structure_resolution != decoded.shape[2]:
if sparse_structure_resolution < decoded.shape[2]:
ratio = decoded.shape[2] // sparse_structure_resolution
decoded = torch.nn.functional.max_pool3d(decoded.float(), ratio, ratio, 0) > 0.5
else:
decoded = torch.nn.functional.interpolate(decoded.float(), size=(sparse_structure_resolution, sparse_structure_resolution, sparse_structure_resolution), mode='nearest') > 0.5
# Optionally fill holes in the sparse voxel grid using the selected algorithm
if fill_holes:
hole_structure = 1
try:
from scipy.ndimage import binary_closing, label, binary_fill_holes
arr = decoded.cpu().numpy()
if arr.ndim == 5:
arr = arr[:, 0]
closed = np.zeros_like(arr)
for b in range(arr.shape[0]):
filled = arr[b].astype(np.bool_)
inv = ~filled
labeled, num_features = label(inv)
border_mask = np.zeros_like(inv)
border_mask[0,:,:] = border_mask[-1,:,:] = 1
border_mask[:,0,:] = border_mask[:,-1,:] = 1
border_mask[:,:,0] = border_mask[:,:,-1] = 1
border_labels = np.unique(labeled[border_mask==1])
holes = np.isin(labeled, border_labels, invert=True) & (labeled > 0)
n_holes = np.unique(labeled[holes]).size
print(f"[Sparse HoleFill] Batch {b}: Found {n_holes} holes before filling.")
if hole_fill_algorithm == "morphological_closing":
closed[b] = binary_closing(arr[b], structure=np.ones((hole_structure,)*3), iterations=hole_iterations)
elif hole_fill_algorithm == "flood_fill":
# Robust structure-preserving hole filling:
# 1. Morphological closing to connect small gaps
# 2. Fill internal holes
# 3. Keep only the largest connected component
from scipy.ndimage import binary_closing, label, binary_fill_holes
# Step 1: Morphological closing (small structure, 1 iter)
closed1 = binary_closing(arr[b], structure=np.ones((hole_structure,)*3), iterations=hole_iterations)
# Step 2: Fill internal holes
filled = binary_fill_holes(closed1)
# Step 3: Keep only the largest connected component
labeled, num = label(filled)
if num > 0:
sizes = np.bincount(labeled.ravel())
sizes[0] = 0 # background
largest = sizes.argmax()
closed[b] = (labeled == largest)
else:
closed[b] = filled
elif hole_fill_algorithm == "remove_small_holes":
# Remove small holes by area (2D slices)
from skimage.morphology import remove_small_holes
# Apply per-slice (z axis)
temp = np.copy(arr[b])
for z in range(temp.shape[0]):
temp[z] = remove_small_holes(temp[z].astype(bool), area_threshold=hole_structure**2)
closed[b] = temp
else:
print(f"[Sparse HoleFill] Unknown algorithm: {hole_fill_algorithm}, skipping.")
closed[b] = arr[b]
# Count holes after filling
filled2 = closed[b].astype(np.bool_)
inv2 = ~filled2
labeled2, num_features2 = label(inv2)
border_labels2 = np.unique(labeled2[border_mask==1])
holes2 = np.isin(labeled2, border_labels2, invert=True) & (labeled2 > 0)
n_holes2 = np.unique(labeled2[holes2]).size
print(f"[Sparse HoleFill] Batch {b}: {n_holes-n_holes2} holes filled, {n_holes2} remain after filling.")
# Optionally remove deeply interior voxels, keeping surface and near-surface structure
if keep_only_shell:
from scipy.ndimage import binary_erosion
filled = closed[b].astype(np.bool_)
before_count = int(filled.sum())
struct = np.ones((3, 3, 3), dtype=bool)
# Erode twice: only voxels surviving 2 erosion passes are >=2 layers deep
# This preserves thin structures (e.g. necks with 3x3 cross-section)
eroded = binary_erosion(filled, structure=struct, border_value=0)
eroded = binary_erosion(eroded, structure=struct, border_value=0)
# Remove only deeply interior voxels (>=2 layers from any surface)
shell = filled & ~eroded
closed[b] = shell
after_count = int(shell.sum())
if verbose:
print(f"[Sparse Shell] Batch {b}: {before_count} -> {after_count} voxels (removed {before_count - after_count} deeply interior)")
decoded = torch.from_numpy(closed).to(decoded.device)
# Debug: print tensor info before extracting coordinates
if verbose:
print(f"[Sparse HoleFill] decoded shape: {decoded.shape}, dtype: {decoded.dtype}, device: {decoded.device}")
print(f"[Sparse HoleFill] decoded min: {decoded.min().item()}, max: {decoded.max().item()}, unique: {torch.unique(decoded)}")
# Safety: ensure tensor is contiguous and on CPU for argwhere
decoded = decoded.contiguous().cpu()
except ImportError:
print("[Warning] scipy or skimage not installed, skipping hole filling.")
except Exception as e:
print(f"[Warning] Hole filling failed: {e}")
try:
coords = torch.argwhere(decoded)[:, [0, 1, 2, 3]].int()
except Exception as e:
print(f"[Sparse HoleFill] Error in torch.argwhere: {e}")
raise
if verbose:
print(f"[Sparse HoleFill] coords shape: {coords.shape}, min: {coords.min(dim=0).values.tolist() if coords.numel()>0 else 'empty'}, max: {coords.max(dim=0).values.tolist() if coords.numel()>0 else 'empty'}")
if coords.numel() == 0:
raise RuntimeError("No voxels remain after hole filling/shell extraction. The mask is empty. Adjust your input, mask, or hole filling parameters.")
else:
coords = torch.argwhere(decoded)[:, [0, 2, 3, 4]].int()
coords = coords.cpu()
del decoded
del z_s
if pipeline.low_vram:
for v in image_conds:
image_conds[v] = pipeline._cond_cpu(image_conds[v])
pipeline._cleanup_cuda()
return (coords, sparse_structure_resolution, views_list, pipeline,)
def seed_all(self, seed: int = 0):
import random
"""
Set random seeds of all components.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
class Trellis2ShapeMultiViewGenerator:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("TRELLIS2PIPELINE",),
"image_conds": ("IMAGE_CONDS",),
"views_list": ("VIEWS_LIST",),
"coords": ("COORDS",),
"resolution": ([512,1024],{"default":1024}),
"shape_steps": ("INT",{"default":12, "min":1, "max":100},),
"shape_guidance_strength": ("FLOAT",{"default":6.50,"min":0.00,"max":99.99,"step":0.01}),
"shape_guidance_rescale": ("FLOAT",{"default":0.05,"min":0.00,"max":1.00,"step":0.01}),
"shape_rescale_t": ("FLOAT",{"default":4.00,"min":0.00,"max":9.99,"step":0.01}),
"shape_sampler": (["euler", "heun", "rk4", "rk5"], {"default": "euler"}),
"shape_guidance_interval_start": ("FLOAT",{"default":0.10,"min":0.00,"max":1.00,"step":0.01}),
"shape_guidance_interval_end": ("FLOAT",{"default":1.00,"min":0.00,"max":1.00,"step":0.01}),
"verbose": ("BOOLEAN",{"default":False}),
"dino_lock": ("FLOAT",{"default":0.00,"min":0.00,"max":1.00,"step":0.01}),
"dino_substeps": ("INT",{"default":4,"min":1,"max":99,"step":1}),
"dino_foundation_cap": ("FLOAT",{"default":1.00,"min":0.01,"max":1.00,"step":0.01}),
"front_axis": (["z", "x"], {"default": "z"}),
"blend_temperature": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
},
}
RETURN_TYPES = ("SHAPE_SLAT", "INT", "VIEWS_LIST", "TRELLIS2PIPELINE",)
RETURN_NAMES = ("shape_slat", "resolution", "views_list", "pipeline",)
FUNCTION = "process"
CATEGORY = "Trellis2Wrapper"
OUTPUT_NODE = True
def process(self, pipeline, image_conds, views_list, coords, resolution,
# shape
shape_steps,
shape_guidance_strength,
shape_guidance_rescale,
shape_rescale_t,
shape_sampler,
shape_guidance_interval_start,
shape_guidance_interval_end,
verbose,
dino_lock,
dino_substeps,
dino_foundation_cap,
front_axis,
blend_temperature
):
shape_guidance_interval = [shape_guidance_interval_start, shape_guidance_interval_end]
shape_slat_sampler_params = {"steps":shape_steps,"guidance_strength":shape_guidance_strength,"guidance_rescale":shape_guidance_rescale,"guidance_interval":shape_guidance_interval,"rescale_t":shape_rescale_t}
if resolution == 512:
pipeline.load_shape_slat_flow_model_512()
shape_slat = self.sample(
pipeline, shape_sampler,
image_conds, views_list,
pipeline.models['shape_slat_flow_model_512'],
coords, shape_slat_sampler_params,
front_axis=front_axis,
blend_temperature=blend_temperature,
verbose=verbose,
dino_lock=dino_lock,
dino_substeps=dino_substeps,
dino_foundation_cap=dino_foundation_cap
)
if not pipeline.keep_models_loaded:
pipeline.unload_shape_slat_flow_model_512()
elif resolution == 1024:
pipeline.load_shape_slat_flow_model_1024()
shape_slat = self.sample(
pipeline, shape_sampler,
image_conds, views_list,
pipeline.models['shape_slat_flow_model_1024'],
coords, shape_slat_sampler_params,
front_axis=front_axis,
blend_temperature=blend_temperature,
verbose=verbose,
dino_lock=dino_lock,
dino_substeps=dino_substeps,
dino_foundation_cap=dino_foundation_cap
)
if not pipeline.keep_models_loaded:
pipeline.unload_shape_slat_flow_model_1024()
return (shape_slat, resolution, views_list, pipeline,)
def sample(
self,
pipeline,
shape_sampler,
conds: dict,
views: list,
flow_model,
coords: torch.Tensor,
sampler_params: dict = {},
front_axis: str = 'z',
blend_temperature: float = 2.0,
verbose: bool = False,
dino_lock: float = 0.00,
dino_substeps: int = 4,
dino_foundation_cap: float = 0.92
) -> SparseTensor:
if pipeline.low_vram:
for v in conds:
conds[v] = pipeline._cond_to(conds[v], pipeline.device)
coords_dev = coords.to(pipeline.device)
noise = SparseTensor(
feats=torch.randn(coords.shape[0], flow_model.in_channels, device=pipeline.device),
coords=coords_dev,
)
# sampler = samplers.FlowEulerMultiViewGuidanceIntervalSampler(
# sigma_min=1e-5,
# resolution=flow_model.resolution,
# )
sampler_class = getattr(samplers, f"Flow{shape_sampler}MultiViewGuidanceIntervalSampler", samplers.FlowEulerMultiViewGuidanceIntervalSampler)
sampler = sampler_class(
sigma_min=1e-5,
resolution=flow_model.resolution if hasattr(flow_model, 'resolution') else flow_model[0].resolution
)
sampler_params = {**pipeline.shape_slat_sampler_params, **sampler_params}
if pipeline.low_vram:
flow_model.to(pipeline.device)
slat = sampler.sample(
flow_model,
noise,
conds=conds,
**sampler_params,
views=views,
front_axis=front_axis,
blend_temperature=blend_temperature,
verbose=verbose,
dino_lock = dino_lock,
dino_substeps = dino_substeps,
dino_foundation_cap = dino_foundation_cap,
tqdm_desc="Sampling shape SLat (MultiView)",
).samples
if pipeline.low_vram:
flow_model.cpu()
pipeline._cleanup_cuda()
std = torch.tensor(pipeline.shape_slat_normalization['std'])[None].to(slat.device)
mean = torch.tensor(pipeline.shape_slat_normalization['mean'])[None].to(slat.device)
slat = slat * std + mean
del coords_dev
if pipeline.low_vram:
for v in conds:
conds[v] = pipeline._cond_cpu(conds[v])
pipeline._cleanup_cuda()
return slat
class Trellis2ShapeCascadeMultiViewGenerator:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("TRELLIS2PIPELINE",),
"image_conds": ("IMAGE_CONDS",),
"views_list": ("VIEWS_LIST",),
"shape_slat": ("SHAPE_SLAT",),
"from_resolution": ("INT",),
"to_resolution": ([1024,1536,2048,2560,3072,3584,4096],{"default":1024}),
"sparse_structure_resolution": ("INT", {"default":32,"min":32,"max":128,"step":4}),
"max_num_tokens": ("INT",{"default":999999,"min":0,"max":999999}),
"shape_steps": ("INT",{"default":12, "min":1, "max":100},),
"shape_guidance_strength": ("FLOAT",{"default":6.50,"min":0.00,"max":99.99,"step":0.01}),
"shape_guidance_rescale": ("FLOAT",{"default":0.05,"min":0.00,"max":1.00,"step":0.01}),
"shape_rescale_t": ("FLOAT",{"default":4.00,"min":0.00,"max":9.99,"step":0.01}),
"shape_sampler": (["euler", "heun", "rk4", "rk5"], {"default": "euler"}),
"shape_guidance_interval_start": ("FLOAT",{"default":0.10,"min":0.00,"max":1.00,"step":0.01}),
"shape_guidance_interval_end": ("FLOAT",{"default":1.00,"min":0.00,"max":1.00,"step":0.01}),
"verbose": ("BOOLEAN",{"default":False}),
"dino_lock": ("FLOAT",{"default":0.00,"min":0.00,"max":1.00,"step":0.01}),
"dino_substeps": ("INT",{"default":4,"min":1,"max":99,"step":1}),
"dino_foundation_cap": ("FLOAT",{"default":1.00,"min":0.01,"max":1.00,"step":0.01}),
"front_axis": (["z", "x"], {"default": "z"}),
"blend_temperature": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
},
}
RETURN_TYPES = ("SHAPE_SLAT","INT","VIEWS_LIST", "TRELLIS2PIPELINE","INT",)
RETURN_NAMES = ("shape_slat","resolution","views_list", "pipeline","num_tokens")
FUNCTION = "process"
CATEGORY = "Trellis2Wrapper"
OUTPUT_NODE = True
def process(self, pipeline, image_conds, views_list, shape_slat, from_resolution, to_resolution, sparse_structure_resolution, max_num_tokens,
# shape
shape_steps,
shape_guidance_strength,
shape_guidance_rescale,
shape_rescale_t,
shape_sampler,
shape_guidance_interval_start,
shape_guidance_interval_end,
verbose,
dino_lock,
dino_substeps,
dino_foundation_cap,
front_axis,
blend_temperature
):
shape_guidance_interval = [shape_guidance_interval_start, shape_guidance_interval_end]
shape_slat_sampler_params = {"steps":shape_steps,"guidance_strength":shape_guidance_strength,"guidance_rescale":shape_guidance_rescale,"guidance_interval":shape_guidance_interval,"rescale_t":shape_rescale_t}
args = pipeline._pretrained_args
shape_sampler_prefix = pipeline.GetSamplerName(shape_sampler)
pipeline.load_shape_slat_flow_model_1024()
flow_model = pipeline.models['shape_slat_flow_model_1024']
sampler_class = getattr(samplers, f"Flow{shape_sampler_prefix}MultiViewGuidanceIntervalSampler", samplers.FlowEulerMultiViewGuidanceIntervalSampler)
sampler = sampler_class(
sigma_min=1e-5,
resolution=flow_model.resolution if hasattr(flow_model, 'resolution') else flow_model[0].resolution
)
pipeline.shape_slat_sampler = sampler
slat, hr_resolution, num_tokens = self.sample(pipeline, shape_slat, from_resolution, to_resolution, sparse_structure_resolution, max_num_tokens, image_conds, shape_slat_sampler_params, flow_model, verbose, dino_lock, dino_substeps, dino_foundation_cap, views_list, front_axis, blend_temperature)
if not pipeline.keep_models_loaded:
pipeline.unload_shape_slat_flow_model_1024()
return (slat, hr_resolution, views_list, pipeline, num_tokens,)
def sample(self, pipeline, slat, lr_resolution, resolution, sparse_structure_resolution, max_num_tokens, conds, sampler_params, flow_model, verbose, dino_lock, dino_substeps, dino_foundation_cap, views, front_axis, blend_temperature):
# Upsample
pipeline.load_shape_slat_decoder()
if pipeline.low_vram:
pipeline.models['shape_slat_decoder'].to(pipeline.device)
pipeline.models['shape_slat_decoder'].low_vram = True
hr_coords = pipeline.models['shape_slat_decoder'].upsample(slat, upsample_times=4)
if pipeline.low_vram:
pipeline.models['shape_slat_decoder'].cpu()
pipeline.models['shape_slat_decoder'].low_vram = False
if not pipeline.keep_models_loaded:
pipeline.unload_shape_slat_decoder()
hr_resolution = resolution
ratio = (sparse_structure_resolution / 32)
while True:
quant_coords = torch.cat([
hr_coords[:, :1],
((hr_coords[:, 1:] + 0.5) / (lr_resolution * ratio) * (hr_resolution // 16)).int(),
], dim=1)
coords = quant_coords.unique(dim=0)
num_tokens = coords.shape[0]
if num_tokens < max_num_tokens:
if hr_resolution != resolution:
print(f"Due to the limited number of tokens, the resolution is reduced to {hr_resolution}.")
print(f"Num Tokens: {num_tokens}")
break
hr_resolution -= 128
if hr_resolution < 1024 and resolution >= 1024:
print(f"Num Tokens: {num_tokens}")
hr_resolution = 1024
break
if hr_resolution < 512:
print(f"Num Tokens: {num_tokens}")
hr_resolution = 512
break
if pipeline.low_vram:
for v in conds:
conds[v] = pipeline._cond_to(conds[v], pipeline.device)
coords_dev = coords.to(pipeline.device)
# Sample structured latent
noise = SparseTensor(
feats=torch.randn(coords.shape[0], flow_model.in_channels, device=pipeline.device),
coords=coords_dev,
)
sampler_params = {**pipeline.shape_slat_sampler_params, **sampler_params}
if pipeline.low_vram:
flow_model.to(pipeline.device)
slat = pipeline.shape_slat_sampler.sample(
flow_model,
noise,
conds=conds,
**sampler_params,
views=views,
front_axis=front_axis,
blend_temperature=blend_temperature,
verbose=verbose,
dino_lock=dino_lock,
dino_substeps=dino_substeps,
dino_foundation_cap=dino_foundation_cap,
tqdm_desc="Sampling shape SLat (MultiView HR)",
).samples
if pipeline.low_vram:
flow_model.cpu()
pipeline._cleanup_cuda()
std = torch.tensor(pipeline.shape_slat_normalization['std'])[None].to(slat.device)
mean = torch.tensor(pipeline.shape_slat_normalization['mean'])[None].to(slat.device)
slat = slat * std + mean
del coords_dev
if pipeline.low_vram:
for v in conds:
conds[v] = pipeline._cond_cpu(conds[v])
pipeline._cleanup_cuda()
return slat, hr_resolution, num_tokens
class Trellis2TexSlatMultiViewGenerator:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("TRELLIS2PIPELINE",),
"image_conds": ("IMAGE_CONDS",),
"views_list": ("VIEWS_LIST",),
"shape_slat": ("SHAPE_SLAT",),
"resolution": ([512,1024],{"default":1024}),
"texture_steps": ("INT",{"default":12, "min":1, "max":100},),
"texture_guidance_strength": ("FLOAT",{"default":6.50,"min":0.00,"max":99.99,"step":0.01}),
"texture_guidance_rescale": ("FLOAT",{"default":0.05,"min":0.00,"max":1.00,"step":0.01}),
"texture_rescale_t": ("FLOAT",{"default":4.00,"min":0.00,"max":9.99,"step":0.01}),
"texture_sampler": (["euler", "heun", "rk4", "rk5"], {"default": "euler"}),
"texture_guidance_interval_start": ("FLOAT",{"default":0.00,"min":0.00,"max":1.00,"step":0.01}),
"texture_guidance_interval_end": ("FLOAT",{"default":0.90,"min":0.00,"max":1.00,"step":0.01}),
"verbose": ("BOOLEAN",{"default":False}),
"dino_lock": ("FLOAT",{"default":0.00,"min":0.00,"max":1.00,"step":0.01}),
"dino_substeps": ("INT",{"default":4,"min":1,"max":99,"step":1}),
"dino_foundation_cap": ("FLOAT",{"default":1.00,"min":0.01,"max":1.00,"step":0.01}),
"front_axis": (["z", "x"], {"default": "z"}),
"blend_temperature": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
},
}
RETURN_TYPES = ("TEXTURE_SLAT", "VIEWS_LIST", "TRELLIS2PIPELINE",)
RETURN_NAMES = ("texture_slat", "views_list", "pipeline",)
FUNCTION = "process"
CATEGORY = "Trellis2Wrapper"
OUTPUT_NODE = True
def process(self, pipeline, image_conds, views_list, shape_slat, resolution,
# shape
texture_steps,
texture_guidance_strength,
texture_guidance_rescale,
texture_rescale_t,
texture_sampler,
texture_guidance_interval_start,
texture_guidance_interval_end,
verbose,
dino_lock,
dino_substeps,
dino_foundation_cap,
front_axis,
blend_temperature
):
texture_guidance_interval = [texture_guidance_interval_start,texture_guidance_interval_end]
tex_slat_sampler_params = {"steps":texture_steps,"guidance_strength":texture_guidance_strength,"guidance_rescale":texture_guidance_rescale,"guidance_interval":texture_guidance_interval,"rescale_t":texture_rescale_t}
if resolution == 512:
pipeline.load_tex_slat_flow_model_512()
flow_model = pipeline.models['tex_slat_flow_model_512']
else:
pipeline.load_tex_slat_flow_model_1024()
flow_model = pipeline.models['tex_slat_flow_model_1024']
tex_slat = self.sample(
pipeline,
texture_sampler,
image_conds, views_list,
shape_slat=shape_slat,
flow_model=flow_model,
sampler_params=tex_slat_sampler_params,
front_axis=front_axis,
blend_temperature=blend_temperature,
verbose=verbose,
dino_lock=dino_lock,
dino_substeps=dino_substeps,
dino_foundation_cap=dino_foundation_cap
)
if not pipeline.keep_models_loaded:
if resolution == 512:
pipeline.unload_tex_slat_flow_model_512()
else:
pipeline.unload_tex_slat_flow_model_1024()
return (tex_slat, views_list, pipeline,)
def sample(
self,
pipeline,
sampler,
conds: dict,
views: list,
shape_slat: SparseTensor,
flow_model,
sampler_params: dict = {},
front_axis: str = 'z',
blend_temperature: float = 2.0,
verbose: bool = False,
dino_lock: float = 0.00,
dino_substeps: int = 4,
dino_foundation_cap: float = 0.92
) -> SparseTensor:
"""
Sample structured latent for texture with multi-view blending.
"""
if pipeline.low_vram:
for v in conds:
conds[v] = pipeline._cond_to(conds[v], pipeline.device)
# Normalize shape slat for conditioning
std = torch.tensor(pipeline.shape_slat_normalization['std'])[None].to(shape_slat.device)
mean = torch.tensor(pipeline.shape_slat_normalization['mean'])[None].to(shape_slat.device)
shape_slat_normalized = (shape_slat - mean) / std
#coords = shape_slat.coords
#coords_dev = coords.to(pipeline.device)
# Calculate noise channels: total input - concat cond channels
in_channels = flow_model.in_channels if isinstance(flow_model, nn.Module) else flow_model[0].in_channels
noise_channels = in_channels - shape_slat.feats.shape[1]
# noise = SparseTensor(
# feats=torch.randn(coords.shape[0], noise_channels, device=pipeline.device),
# coords=coords_dev,
# )
noise = shape_slat.replace(feats=torch.randn(shape_slat.coords.shape[0], in_channels - shape_slat.feats.shape[1]).to(pipeline.device))
sampler_params = {**pipeline.tex_slat_sampler_params, **sampler_params}
# sampler = samplers.FlowEulerMultiViewGuidanceIntervalSampler(
# sigma_min=1e-5,
# resolution=flow_model.resolution,
# )
tex_sampler_prefix = pipeline.GetSamplerName(sampler)
sampler_class = getattr(samplers, f"Flow{tex_sampler_prefix}MultiViewGuidanceIntervalSampler", samplers.FlowEulerMultiViewGuidanceIntervalSampler)
sampler = sampler_class(
sigma_min=1e-5,
resolution=flow_model.resolution if hasattr(flow_model, 'resolution') else flow_model[0].resolution
)
if pipeline.low_vram:
flow_model.to(pipeline.device)
slat = sampler.sample(
flow_model,
noise,
conds=conds,
**sampler_params,
views=views,
front_axis=front_axis,
blend_temperature=blend_temperature,
concat_cond=shape_slat_normalized,
verbose=verbose,
dino_lock=dino_lock,
dino_substeps=dino_substeps,
dino_foundation_cap=dino_foundation_cap,
tqdm_desc="Sampling texture SLat (MultiView)",
).samples
if pipeline.low_vram:
flow_model.cpu()
pipeline._cleanup_cuda()
std = torch.tensor(pipeline.tex_slat_normalization['std'])[None].to(slat.device)
mean = torch.tensor(pipeline.tex_slat_normalization['mean'])[None].to(slat.device)
slat = slat * std + mean
#del coords_dev
if pipeline.low_vram:
for v in conds:
conds[v] = pipeline._cond_cpu(conds[v])
pipeline._cleanup_cuda()
return slat
NODE_CLASS_MAPPINGS = {
"Trellis2LoadModel": Trellis2LoadModel,
@@ -5671,6 +6481,11 @@ NODE_CLASS_MAPPINGS = {
"Trellis2ExtractImagesFromVideo": Trellis2ExtractImagesFromVideo,
"Trellis2MaxTokensCalculator": Trellis2MaxTokensCalculator,
"Trellis2FillHolesNicelyWithMeshlib": Trellis2FillHolesNicelyWithMeshlib,
"Trellis2SparseMultiViewGenerator": Trellis2SparseMultiViewGenerator,
"Trellis2ImageCondMultiViewGenerator": Trellis2ImageCondMultiViewGenerator,
"Trellis2ShapeMultiViewGenerator": Trellis2ShapeMultiViewGenerator,
"Trellis2ShapeCascadeMultiViewGenerator": Trellis2ShapeCascadeMultiViewGenerator,
"Trellis2TexSlatMultiViewGenerator": Trellis2TexSlatMultiViewGenerator,
}
@@ -5734,4 +6549,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"Trellis2ExtractImagesFromVideo": "Trellis2 - Extract Images from Video",
"Trellis2MaxTokensCalculator": "Trellis2 - Max Tokens Calculator",
"Trellis2FillHolesNicelyWithMeshlib": "Trellis2 - Fill Holes Nicely With Meshlib",
"Trellis2SparseMultiViewGenerator": "Trellis2 - Sparse MultiView Generator",
"Trellis2ImageCondMultiViewGenerator": "Trellis2 - ImageCond MultiView Generator",
"Trellis2ShapeMultiViewGenerator": "Trellis2 - Shape MultiView Generator",
"Trellis2ShapeCascadeMultiViewGenerator": "Trellis2 - Shape Cascade MultiView Generator",
"Trellis2TexSlatMultiViewGenerator": "Trellis2 - Tex Slat MultiView Generator",
}
trellis2/pipelines/samplers/flow_euler.py
+5 -2
View File
@@ -164,7 +164,7 @@ class DinoLockMixin:
tqdm_desc: str = "Sampling",
dino_lock: float = 0.0,
dino_substeps: int = 4,
dino_foundation_cap: float = 0.92,
dino_foundation_cap: float = 0.92,
**kwargs
):
# Strip keys that must not reach the model
@@ -183,7 +183,10 @@ class DinoLockMixin:
**kwargs)
if dino_lock <= 0.0:
return super().sample(model, noise, cond, steps, rescale_t, verbose,
return super().sample(model, noise, cond,
steps = steps,
rescale_t = rescale_t,
verbose = verbose,
tqdm_desc=tqdm_desc,
neg_cond=neg_cond,
guidance_strength=guidance_strength,