Added node "Extract Frames from Video"
This commit is contained in:
Bruno Fargnoli
@@ -14,6 +14,7 @@
|
||||
|
||||
| Date | Description |
|
||||
| --- | --- |
|
||||
| **2026-04-05** | Added node "Extract Images from Video"<br>Can be used with "Sparse Generator with ReconViaGen" |
|
||||
| **2026-04-04** | Added node "Sparse Generator with ReconViaGen" |
|
||||
| **2026-04-01** | Added node "Voxel to Mesh"<br>It replaces Remeshing to make watertight mesh |
|
||||
| **2026-03-21** | Added node "Projection HighPoly to LowPoly"<br>Added node "Render MultiView" |
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -2383,20 +2383,58 @@ class Trellis2PreProcessImage:
|
||||
CATEGORY = "Trellis2Wrapper"
|
||||
|
||||
def process(self, image, padding, remove_background, max_size):
|
||||
image = tensor2pil(image)
|
||||
|
||||
if remove_background:
|
||||
from rembg import remove
|
||||
image = remove(image)
|
||||
|
||||
image = self.preprocess_image(image, max_size)
|
||||
|
||||
if padding>0:
|
||||
border = (int(padding), int(padding), int(padding), int(padding))
|
||||
fill_color = self.parse_fill_for_image("0,0,0,255", image)
|
||||
image = ImageOps.expand(image,border=border,fill=fill_color)
|
||||
|
||||
image = pil2tensor(image)
|
||||
if image.ndim == 3:
|
||||
image = tensor2pil(image)
|
||||
|
||||
if remove_background:
|
||||
from rembg import remove
|
||||
image = remove(image)
|
||||
|
||||
image = self.preprocess_image(image, max_size)
|
||||
|
||||
if padding>0:
|
||||
border = (int(padding), int(padding), int(padding), int(padding))
|
||||
fill_color = self.parse_fill_for_image("0,0,0,255", image)
|
||||
image = ImageOps.expand(image,border=border,fill=fill_color)
|
||||
|
||||
image = pil2tensor(image)
|
||||
elif image.ndim == 4:
|
||||
images = convert_tensor_images_to_pil(image)
|
||||
tensor_list = []
|
||||
for img in images:
|
||||
if remove_background:
|
||||
from rembg import remove
|
||||
img = remove(img)
|
||||
|
||||
img = self.preprocess_image(img, max_size)
|
||||
|
||||
if padding>0:
|
||||
border = (int(padding), int(padding), int(padding), int(padding))
|
||||
fill_color = self.parse_fill_for_image("0,0,0,255", img)
|
||||
img = ImageOps.expand(img,border=border,fill=fill_color)
|
||||
|
||||
tensor_list.append(pil2tensor(img))
|
||||
|
||||
max_h = max(t.shape[-3] for t in tensor_list)
|
||||
max_w = max(t.shape[-2] for t in tensor_list)
|
||||
|
||||
resized_tensors = []
|
||||
|
||||
for t in tensor_list:
|
||||
# Ensure tensor is [C, H, W] for PyTorch's interpolate function
|
||||
# Current shape is likely [H, W, C] or [1, H, W, C]
|
||||
temp_t = t.squeeze() # Get to [H, W, C]
|
||||
temp_t = temp_t.permute(2, 0, 1).unsqueeze(0) # Becomes [1, C, H, W]
|
||||
|
||||
# 2. Resize to the max dimensions
|
||||
# Using 'bicubic' or 'bilinear' for better quality than 'nearest'
|
||||
temp_t = F.interpolate(temp_t, size=(max_h, max_w), mode='bicubic', align_corners=False)
|
||||
|
||||
# 3. Convert back to ComfyUI format [H, W, C]
|
||||
temp_t = temp_t.squeeze(0).permute(1, 2, 0)
|
||||
resized_tensors.append(temp_t)
|
||||
|
||||
image = torch.stack(resized_tensors)
|
||||
|
||||
return (image,)
|
||||
|
||||
@@ -3527,7 +3565,7 @@ class Trellis2ImageCondGenerator:
|
||||
"required": {
|
||||
"pipeline": ("TRELLIS2PIPELINE",),
|
||||
"image": ("IMAGE",),
|
||||
"max_views": ("INT", {"default": 4, "min": 1, "max": 16}),
|
||||
"max_views": ("INT", {"default": 1, "min": 1, "max": 999}),
|
||||
},
|
||||
}
|
||||
|
||||
@@ -3779,6 +3817,9 @@ class Trellis2ShapeCascadeGenerator:
|
||||
print(f"Num Tokens: {num_tokens}")
|
||||
hr_resolution = 512
|
||||
break
|
||||
|
||||
if pipeline.low_vram:
|
||||
cond = pipeline._cond_to(cond, pipeline.device)
|
||||
|
||||
coords_dev = coords.to(pipeline.device)
|
||||
# Sample structured latent
|
||||
@@ -4804,7 +4845,7 @@ class Trellis2SparseGeneratorWithReconViaGen:
|
||||
pipeline.unload_sparse_structure_vggt_cond()
|
||||
self.unload_vggt_model(pipeline)
|
||||
|
||||
return (coords, sparse_structure_resolution, pipeline,)
|
||||
return (coords, sparse_structure_resolution, pipeline)
|
||||
|
||||
def load_vggt_model(self, pipeline):
|
||||
if pipeline.VGGT_model is None:
|
||||
@@ -4900,7 +4941,7 @@ class Trellis2SparseGeneratorWithReconViaGen:
|
||||
ss_cond = pipeline._cond_cpu(ss_cond)
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return coords
|
||||
return coords
|
||||
|
||||
@torch.no_grad()
|
||||
def _run_ss_stage(
|
||||
@@ -4986,6 +5027,7 @@ class Trellis2SparseGeneratorWithReconViaGen:
|
||||
Returns:
|
||||
dict: The conditioning information
|
||||
"""
|
||||
pipeline.models['sparse_structure_vggt_cond'].to(pipeline.device)
|
||||
cond = pipeline.models['sparse_structure_vggt_cond'](aggregated_tokens_list, image_cond)
|
||||
neg_cond = torch.zeros_like(cond)
|
||||
return {
|
||||
@@ -5059,7 +5101,46 @@ class Trellis2SparseGeneratorWithReconViaGen:
|
||||
transform = transforms.Compose([
|
||||
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
||||
])
|
||||
pipeline.image_cond_model_transform = transform
|
||||
pipeline.image_cond_model_transform = transform
|
||||
|
||||
class Trellis2ExtractImagesFromVideo:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"video_file": ("STRING",),
|
||||
"frames_per_second": ("INT",{"default":1,"min":1,"max":50,"step":1}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("images",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "Trellis2Wrapper"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
def process(self, video_file, frames_per_second):
|
||||
import imageio
|
||||
|
||||
vid = imageio.get_reader(video_file, 'ffmpeg')
|
||||
fps = vid.get_meta_data()['fps']
|
||||
frames = []
|
||||
for i, frame in enumerate(vid):
|
||||
if i % max(int(fps/frames_per_second), 1) == 0:
|
||||
img = Image.fromarray(frame)
|
||||
W, H = img.size
|
||||
img = img.resize((int(W / H * 1024), 1024))
|
||||
frames.append(img)
|
||||
vid.close()
|
||||
|
||||
tensor_list = [torch.from_numpy(np.array(img).astype(np.float32) / 255.0) for img in frames]
|
||||
|
||||
print(f"{len(frames)} frames extracted")
|
||||
|
||||
tensor_frames = torch.stack(tensor_list)
|
||||
#tensor_frames = tensor_frames.permute(0, 2, 3, 1)
|
||||
|
||||
return (tensor_frames,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"Trellis2LoadModel": Trellis2LoadModel,
|
||||
@@ -5118,6 +5199,7 @@ NODE_CLASS_MAPPINGS = {
|
||||
"Trellis2VoxelToMesh": Trellis2VoxelToMesh,
|
||||
"Trellis2UnloadAllModels": Trellis2UnloadAllModels,
|
||||
"Trellis2SparseGeneratorWithReconViaGen": Trellis2SparseGeneratorWithReconViaGen,
|
||||
"Trellis2ExtractImagesFromVideo": Trellis2ExtractImagesFromVideo,
|
||||
}
|
||||
|
||||
|
||||
@@ -5178,4 +5260,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"Trellis2VoxelToMesh": "Trellis2 - Voxel to Mesh",
|
||||
"Trellis2UnloadAllModels": "Trellis2 - Unload All ComfyUI Models",
|
||||
"Trellis2SparseGeneratorWithReconViaGen": "Trellis2 - Sparse Generator with ReconViaGen",
|
||||
"Trellis2ExtractImagesFromVideo": "Trellis 2 - Extract Images from Video",
|
||||
}
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "trellis2"
|
||||
description = "ComfyUI Wrapper for Microsoft Trellis.2 - Native and Compact Structured Latents for 3D Generation"
|
||||
version = "1.0.20"
|
||||
version = "1.0.21"
|
||||
license = {file = "LICENSE"}
|
||||
# classifiers = [
|
||||
# # For OS-independent nodes (works on all operating systems)
|
||||
|
||||
Reference in New Issue
Block a user