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Merge aadccd28ca into bbe2c13a70
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4361f56e1a
@ -644,9 +644,13 @@ class ResizeImagesByShorterEdgeNode(ImageProcessingNode):
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if w < h:
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new_w = shorter_edge
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new_h = int(h * (shorter_edge / w))
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else:
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elif h < w:
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new_h = shorter_edge
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new_w = int(w * (shorter_edge / h))
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else:
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# Square image: set both dimensions directly to avoid precision loss from float operations
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new_w = shorter_edge
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new_h = shorter_edge
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img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
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return pil_to_tensor(img)
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@ -674,9 +678,13 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
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if w > h:
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new_w = longer_edge
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new_h = int(h * (longer_edge / w))
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else:
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elif h > w:
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new_h = longer_edge
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new_w = int(w * (longer_edge / h))
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else:
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# Square image: set both dimensions directly to avoid precision loss from float operations
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new_w = longer_edge
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new_h = longer_edge
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img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
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resized_images.append(pil_to_tensor(img))
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return torch.cat(resized_images, dim=0)
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@ -395,10 +395,15 @@ class ResizeAndPadImage(IO.ComfyNode):
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scale_w = target_width / orig_width
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scale_h = target_height / orig_height
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scale = min(scale_w, scale_h)
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new_width = int(orig_width * scale)
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new_height = int(orig_height * scale)
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# When aspect ratios match, scale directly to target to avoid precision loss from float multiplication
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if scale_w == scale_h:
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new_width = target_width
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new_height = target_height
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else:
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scale = min(scale_w, scale_h)
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new_width = int(orig_width * scale)
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new_height = int(orig_height * scale)
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image_permuted = image.permute(0, 3, 1, 2)
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