Sd3 freeze x_block (kohya-ss#1417)

* Update sd3_train.py

* add freeze block lr

* Update train_util.py

* update

Author: sdbds

library/train_util.py

@@ -3246,6 +3246,12 @@ def add_sd_models_arguments(parser: argparse.ArgumentParser):
         default=None,
         help="directory for caching Tokenizer (for offline training) / Tokenizerをキャッシュするディレクトリ（ネット接続なしでの学習のため）",
     )
+    parser.add_argument(
+        "--num_last_block_to_freeze",
+        type=int,
+        default=None,
+        help="num_last_block_to_freeze",
+    )
 
 
 def add_optimizer_arguments(parser: argparse.ArgumentParser):
@@ -5758,6 +5764,21 @@ def sample_image_inference(
         pass
 
 
+def freeze_blocks(model, num_last_block_to_freeze, block_name="x_block"):
+
+    filtered_blocks = [(name, param) for name, param in model.named_parameters() if block_name in name]
+    print(f"filtered_blocks: {len(filtered_blocks)}")
+
+    num_blocks_to_freeze = min(len(filtered_blocks), num_last_block_to_freeze)
+
+    print(f"freeze_blocks: {num_blocks_to_freeze}")
+    
+    start_freezing_from = max(0, len(filtered_blocks) - num_blocks_to_freeze)
+
+    for i in range(start_freezing_from, len(filtered_blocks)):
+        _, param = filtered_blocks[i]
+        param.requires_grad = False
+
 # endregion
 
 

sd3_train.py

@@ -368,12 +368,19 @@ def train(args):
         vae.eval()
         vae.to(accelerator.device, dtype=vae_dtype)
 
+    mmdit.requires_grad_(train_mmdit)
+    if not train_mmdit:
+        mmdit.to(accelerator.device, dtype=weight_dtype)  # because of unet is not prepared
+
+    if args.num_last_block_to_freeze:
+        train_util.freeze_blocks(mmdit,num_last_block_to_freeze=args.num_last_block_to_freeze)
+
     training_models = []
     params_to_optimize = []
     # if train_unet:
     training_models.append(mmdit)
     # if block_lrs is None:
-    params_to_optimize.append({"params": list(mmdit.parameters()), "lr": args.learning_rate})
+    params_to_optimize.append({"params": list(filter(lambda p: p.requires_grad, mmdit.parameters())), "lr": args.learning_rate})
     # else:
     #     params_to_optimize.extend(get_block_params_to_optimize(mmdit, block_lrs))