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Nicolas Stepien 2026-01-30 03:42:53 -05:00 committed by GitHub
commit 4361f56e1a
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2 changed files with 18 additions and 5 deletions

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@ -644,9 +644,13 @@ class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
if w < h:
new_w = shorter_edge
new_h = int(h * (shorter_edge / w))
else:
elif h < w:
new_h = shorter_edge
new_w = int(w * (shorter_edge / h))
else:
# Square image: set both dimensions directly to avoid precision loss from float operations
new_w = shorter_edge
new_h = shorter_edge
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
return pil_to_tensor(img)
@ -674,9 +678,13 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
if w > h:
new_w = longer_edge
new_h = int(h * (longer_edge / w))
else:
elif h > w:
new_h = longer_edge
new_w = int(w * (longer_edge / h))
else:
# Square image: set both dimensions directly to avoid precision loss from float operations
new_w = longer_edge
new_h = longer_edge
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
resized_images.append(pil_to_tensor(img))
return torch.cat(resized_images, dim=0)

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@ -395,10 +395,15 @@ class ResizeAndPadImage(IO.ComfyNode):
scale_w = target_width / orig_width
scale_h = target_height / orig_height
scale = min(scale_w, scale_h)
new_width = int(orig_width * scale)
new_height = int(orig_height * scale)
# When aspect ratios match, scale directly to target to avoid precision loss from float multiplication
if scale_w == scale_h:
new_width = target_width
new_height = target_height
else:
scale = min(scale_w, scale_h)
new_width = int(orig_width * scale)
new_height = int(orig_height * scale)
image_permuted = image.permute(0, 3, 1, 2)