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Merge branch 'comfyanonymous:master' into chroma-support

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Silver 2025-04-01 16:42:40 +02:00 committed by GitHub
commit cb3e388e8f
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4 changed files with 12 additions and 6 deletions
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4
comfy/model_base.py
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@ -1001,7 +1001,6 @@ class WAN21(BaseModel):
device = kwargs["device"] device = kwargs["device"]
if image is None: if image is None:
image = torch.zeros_like(noise)
shape_image = list(noise.shape) shape_image = list(noise.shape)
shape_image[1] = extra_channels shape_image[1] = extra_channels
image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
@ -1014,6 +1013,9 @@ class WAN21(BaseModel):
if not self.image_to_video or extra_channels == image.shape[1]: if not self.image_to_video or extra_channels == image.shape[1]:
return image return image
if image.shape[1] > (extra_channels - 4):
image = image[:, :(extra_channels - 4)]
mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
if mask is None: if mask is None:
mask = torch.zeros_like(noise)[:, :4] mask = torch.zeros_like(noise)[:, :4]

1
comfy_extras/nodes_lt.py
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@ -446,7 +446,6 @@ class LTXVPreprocess:
CATEGORY = "image" CATEGORY = "image"
def preprocess(self, image, img_compression): def preprocess(self, image, img_compression):
if img_compression > 0:
output_images = [] output_images = []
for i in range(image.shape[0]): for i in range(image.shape[0]):
output_images.append(preprocess(image[i], img_compression)) output_images.append(preprocess(image[i], img_compression))

5
comfy_extras/nodes_mask.py
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@ -87,6 +87,11 @@ class ImageCompositeMasked:
CATEGORY = "image" CATEGORY = "image"
def composite(self, destination, source, x, y, resize_source, mask = None): def composite(self, destination, source, x, y, resize_source, mask = None):
if destination.shape[-1] < source.shape[-1]:
source = source[...,:destination.shape[-1]]
elif destination.shape[-1] > source.shape[-1]:
destination = torch.nn.functional.pad(destination, (0, 1))
destination[..., -1] = source[..., -1]
destination = destination.clone().movedim(-1, 1) destination = destination.clone().movedim(-1, 1)
output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
return (output,) return (output,)

2
requirements.txt
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@ -1,4 +1,4 @@
comfyui-frontend-package==1.14.5 comfyui-frontend-package==1.14.6
torch torch
torchsde torchsde
torchvision torchvision