fix alpha mask without disk cache closes #1351, ref #1339

library/train_util.py

@@ -1265,7 +1265,8 @@ def __getitem__(self, index):
                 if subset.alpha_mask:
                     if img.shape[2] == 4:
                         alpha_mask = img[:, :, 3]  # [H,W]
-                        alpha_mask = transforms.ToTensor()(alpha_mask)  # 0-255 -> 0-1
+                        alpha_mask = alpha_mask.astype(np.float32) / 255.0  # 0.0~1.0
+                        alpha_mask = torch.FloatTensor(alpha_mask)
                     else:
                         alpha_mask = torch.ones((img.shape[0], img.shape[1]), dtype=torch.float32)
                 else:
@@ -2211,7 +2212,7 @@ def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool, alph
 # 戻り値は、latents_tensor, (original_size width, original_size height), (crop left, crop top)
 def load_latents_from_disk(
     npz_path,
-) -> Tuple[Optional[torch.Tensor], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
+) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
     npz = np.load(npz_path)
     if "latents" not in npz:
         raise ValueError(f"error: npz is old format. please re-generate {npz_path}")
@@ -2229,7 +2230,7 @@ def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_ltrb, fli
     if flipped_latents_tensor is not None:
         kwargs["latents_flipped"] = flipped_latents_tensor.float().cpu().numpy()
     if alpha_mask is not None:
-        kwargs["alpha_mask"] = alpha_mask  # ndarray
+        kwargs["alpha_mask"] = alpha_mask.float().cpu().numpy()
     np.savez(
         npz_path,
         latents=latents_tensor.float().cpu().numpy(),
@@ -2496,8 +2497,9 @@ def cache_batch_latents(
             if image.shape[2] == 4:
                 alpha_mask = image[:, :, 3]  # [H,W]
                 alpha_mask = alpha_mask.astype(np.float32) / 255.0
+                alpha_mask = torch.FloatTensor(alpha_mask)  # [H,W]
             else:
-                alpha_mask = np.ones_like(image[:, :, 0], dtype=np.float32)
+                alpha_mask = torch.ones_like(image[:, :, 0], dtype=torch.float32)  # [H,W]
         else:
             alpha_mask = None
         alpha_masks.append(alpha_mask)