support masked loss in sdxl_train ref #589

README.md

@@ -251,7 +251,9 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum
 
 ### Masked loss
 
-`train_network.py` and `sdxl_train_network.py` now support the masked loss. `--masked_loss` option is added. 
+`train_network.py`, `sdxl_train_network.py` and `sdxl_train.py` now support the masked loss. `--masked_loss` option is added. 
+
+NOTE: `train_network.py` and `sdxl_train.py` are not tested yet.
 
 ControlNet dataset is used to specify the mask. The mask images should be the RGB images. The pixel value 255 in R channel is treated as the mask (the loss is calculated only for the pixels with the mask), and 0 is treated as the non-mask. See details for the dataset specification in the [LLLite documentation](./docs/train_lllite_README.md#preparing-the-dataset).
 

sdxl_train.py

@@ -11,6 +11,7 @@
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from accelerate.utils import set_seed
@@ -124,7 +125,7 @@ def train(args):
 
     # データセットを準備する
     if args.dataset_class is None:
-        blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, True))
+        blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True))
         if args.dataset_config is not None:
             logger.info(f"Load dataset config from {args.dataset_config}")
             user_config = config_util.load_user_config(args.dataset_config)
@@ -579,6 +580,16 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
                 ):
                     # do not mean over batch dimension for snr weight or scale v-pred loss
                     loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+
+                    if args.masked_loss:
+                        # mask image is -1 to 1. we need to convert it to 0 to 1
+                        mask_image = batch["conditioning_images"].to(dtype=weight_dtype)[:, 0].unsqueeze(1)  # use R channel
+
+                        # resize to the same size as the loss
+                        mask_image = torch.nn.functional.interpolate(mask_image, size=loss.shape[2:], mode="area")
+                        mask_image = mask_image / 2 + 0.5
+                        loss = loss * mask_image
+
                     loss = loss.mean([1, 2, 3])
 
                     if args.min_snr_gamma:
@@ -780,6 +791,13 @@ def setup_parser() -> argparse.ArgumentParser:
         + f"U-Netの各ブロックの学習率、カンマ区切り、{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値",
     )
 
+    # TODO common masked_loss argument
+    parser.add_argument(
+        "--masked_loss",
+        action="store_true",
+        help="apply mask for calculating loss. conditioning_data_dir is required for dataset. / 損失計算時にマスクを適用する。datasetにはconditioning_data_dirが必要",
+    )
+
     return parser