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Use torch.nn.functional.pad

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Jedrzej Kosinski 2025-11-20 14:32:30 -08:00
parent 8e82f219dd
commit 6c6d05233c
1 changed files with 2 additions and 2 deletions
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4
nodes.py
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@ -1854,9 +1854,9 @@ class ImageBatch:
def batch(self, image1, image2): def batch(self, image1, image2):
if image1.shape[-1] != image2.shape[-1]: if image1.shape[-1] != image2.shape[-1]:
if image1.shape[-1] > image2.shape[-1]: if image1.shape[-1] > image2.shape[-1]:
image2 = torch.cat((image2, torch.ones((image2.shape[0], image2.shape[1], image2.shape[2], 1))), dim=-1) image2 = torch.nn.functional.pad(image2, (0,1), mode='constant', value=1.0)
else: else:
image1 = torch.cat((image1, torch.ones((image1.shape[0], image1.shape[1], image1.shape[2], 1))), dim=-1) image1 = torch.nn.functional.pad(image1, (0,1), mode='constant', value=1.0)
if image1.shape[1:] != image2.shape[1:]: if image1.shape[1:] != image2.shape[1:]:
image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
s = torch.cat((image1, image2), dim=0) s = torch.cat((image1, image2), dim=0)