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fix: PIL I-mode int32 scaling and alpha normalization preservation

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P3: normalize int32 PIL I-mode values by dtype max instead of raw passthrough
P4: clamp alpha to [0,1] instead of min-max stretching

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
CoCo
2026-03-04 21:55:24 +00:00
co-authored by Claude Opus 4.6
parent f159fe313c
commit d9f9fb58a3
1 changed files with 7 additions and 2 deletions
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modules/image_loader.py
+7 -2
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@@ -86,7 +86,7 @@ class ImageLoader:
# Apply min-max range normalization if requested
if normalize:
rgb_tensor = self.normalize_image(rgb_tensor)
alpha_tensor = self.normalize_image(alpha_tensor)
alpha_tensor = alpha_tensor.clamp(0, 1)
# Prepare metadata
metadata = {
@@ -164,7 +164,12 @@ class ImageLoader:
if bit_depth == 16:
image_tensor = torch.from_numpy(image_np.astype(np.float32) / 65535.0)
elif bit_depth == 32:
image_tensor = torch.from_numpy(image_np.astype(np.float32))
# PIL "I" mode stores int32 values; scale by dtype max to get 0-1 range
if np.issubdtype(image_np.dtype, np.integer):
max_val = np.iinfo(image_np.dtype).max
image_tensor = torch.from_numpy(image_np.astype(np.float32) / max_val)
else:
image_tensor = torch.from_numpy(image_np.astype(np.float32))
else:
image_tensor = torch.from_numpy(image_np.astype(np.float32) / 255.0)