fix to work cond mask and alpha mask

library/config_util.py

@@ -78,7 +78,6 @@ class BaseSubsetParams:
     caption_tag_dropout_rate: float = 0.0
     token_warmup_min: int = 1
     token_warmup_step: float = 0
-    alpha_mask: bool = False
 
 
 @dataclass
@@ -87,11 +86,13 @@ class DreamBoothSubsetParams(BaseSubsetParams):
     class_tokens: Optional[str] = None
     caption_extension: str = ".caption"
     cache_info: bool = False
+    alpha_mask: bool = False
 
 
 @dataclass
 class FineTuningSubsetParams(BaseSubsetParams):
     metadata_file: Optional[str] = None
+    alpha_mask: bool = False
 
 
 @dataclass

library/custom_train_functions.py

@@ -484,9 +484,11 @@ def apply_masked_loss(loss, batch):
         # conditioning image is -1 to 1. we need to convert it to 0 to 1
         mask_image = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1)  # use R channel
         mask_image = mask_image / 2 + 0.5
+        # print(f"conditioning_image: {mask_image.shape}")
     elif "alpha_masks" in batch and batch["alpha_masks"] is not None:
         # alpha mask is 0 to 1
-        mask_image = batch["alpha_masks"].to(dtype=loss.dtype)
+        mask_image = batch["alpha_masks"].to(dtype=loss.dtype).unsqueeze(1) # add channel dimension
+        # print(f"mask_image: {mask_image.shape}, {mask_image.mean()}")
     else:
         return loss
 

library/train_util.py

@@ -561,6 +561,7 @@ class ControlNetSubset(BaseSubset):
 
         super().__init__(
             image_dir,
+            False,  # alpha_mask
             num_repeats,
             shuffle_caption,
             caption_separator,
@@ -1947,6 +1948,7 @@ class ControlNetDataset(BaseDataset):
                 None,
                 subset.caption_extension,
                 subset.cache_info,
+                False,
                 subset.num_repeats,
                 subset.shuffle_caption,
                 subset.caption_separator,
@@ -2196,6 +2198,9 @@ def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool, alph
                 return False
             if npz["alpha_mask"].shape[0:2] != reso:  # HxW
                 return False
+        else:
+            if "alpha_mask" in npz:
+                return False
     except Exception as e:
         logger.error(f"Error loading file: {npz_path}")
         raise e
@@ -2296,6 +2301,13 @@ def debug_dataset(train_dataset, show_input_ids=False):
                         if os.name == "nt":
                             cv2.imshow("cond_img", cond_img)
 
+                    if "alpha_masks" in example and example["alpha_masks"] is not None:
+                        alpha_mask = example["alpha_masks"][j]
+                        logger.info(f"alpha mask size: {alpha_mask.size()}")
+                        alpha_mask = (alpha_mask[0].numpy() * 255.0).astype(np.uint8)
+                        if os.name == "nt":
+                            cv2.imshow("alpha_mask", alpha_mask)
+
                     if os.name == "nt":  # only windows
                         cv2.imshow("img", im)
                         k = cv2.waitKey()