Move freeze_blocks to sd3_train because it's only for sd3

README.md

@@ -309,6 +309,9 @@ resolution = [512, 512]
 
 SD3 training is done with `sd3_train.py`. 
 
+__Sep 1, 2024__:
+- `--num_last_block_to_freeze` is added to `sd3_train.py`. This option is to freeze the last n blocks of the MMDiT. See [#1417](https://github.com/kohya-ss/sd-scripts/pull/1417) for details. Thanks to sdbds!
+
 __Jul  27, 2024__: 
 - Latents and text encoder outputs caching mechanism is refactored significantly. 
   - Existing cache files for SD3 need to be recreated. Please delete the previous cache files. 

library/train_util.py

@@ -3246,12 +3246,6 @@ 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):
@@ -5764,21 +5758,6 @@ 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

@@ -373,7 +373,20 @@ def train(args):
         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)
+        # freeze last n blocks of MM-DIT
+        block_name = "x_block"
+        filtered_blocks = [(name, param) for name, param in mmdit.named_parameters() if block_name in name]
+        accelerator.print(f"filtered_blocks: {len(filtered_blocks)}")
+
+        num_blocks_to_freeze = min(len(filtered_blocks), args.num_last_block_to_freeze)
+
+        accelerator.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
 
     training_models = []
     params_to_optimize = []
@@ -1033,12 +1046,17 @@ def setup_parser() -> argparse.ArgumentParser:
         default=None,
         help="number of optimizers for fused backward pass and optimizer step / fused backward passとoptimizer stepのためのoptimizer数",
     )
-
     parser.add_argument(
         "--skip_latents_validity_check",
         action="store_true",
         help="skip latents validity check / latentsの正当性チェックをスキップする",
     )
+    parser.add_argument(
+        "--num_last_block_to_freeze",
+        type=int,
+        default=None,
+        help="freeze last n blocks of MM-DIT / MM-DITの最後のnブロックを凍結する",
+    )
     return parser