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fix(lanczos): correct dimension transposition for single-channel tensors

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movedim(-1, 0) on a 2D [H, W] tensor (grayscale/mask case) was swapping
spatial dimensions instead of adding a channel axis, causing output
masks to have width and height inverted when using the lanczos scale
method.
This commit is contained in:
Edoardo Carmignani 2026-02-27 17:46:26 +01:00
parent 35e9fce775
commit cdbb3155b7
1 changed files with 3 additions and 2 deletions
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5
comfy/utils.py
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@ -993,10 +993,11 @@ def bislerp(samples, width, height):
def lanczos(samples, width, height):
#the below API is strict and expects grayscale to be squeezed
samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)
if samples.ndim == 4:
samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)
images = [Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
images = [torch.from_numpy(t).movedim(-1, 0) if (t := np.array(image).astype(np.float32) / 255.0).ndim == 3 else torch.from_numpy(t) for image in images]
result = torch.stack(images)
return result.to(samples.device, samples.dtype)