formatting, update README

README.md

@@ -137,6 +137,12 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ## Change History
 
+### Sep 13, 2024 / 2024-09-13: 
+
+- `sdxl_merge_lora.py` now supports OFT. Thanks to Maru-mee for the PR [#1580](https://github.com/kohya-ss/sd-scripts/pull/1580). Will be included in the next release.
+
+- `sdxl_merge_lora.py` が OFT をサポートしました。PR [#1580](https://github.com/kohya-ss/sd-scripts/pull/1580) Maru-mee 氏に感謝します。次のリリースに含まれます。
+
 ### Jun 23, 2024 / 2024-06-23: 
 
 - Fixed `cache_latents.py` and `cache_text_encoder_outputs.py` not working. (Will be included in the next release.)

networks/sdxl_merge_lora.py

@@ -10,11 +10,14 @@ import library.model_util as model_util
 import lora
 import oft
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 import concurrent.futures
 
+
 def load_state_dict(file_name, dtype):
     if os.path.splitext(file_name)[1] == ".safetensors":
         sd = load_file(file_name)
@@ -41,14 +44,16 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
     else:
         torch.save(model, file_name)
 
+
 def detect_method_from_training_model(models, dtype):
     for model in models:
         lora_sd, _ = load_state_dict(model, dtype)
         for key in tqdm(lora_sd.keys()):
-            if 'lora_up' in key or 'lora_down' in key:
-                return 'LoRA'
+            if "lora_up" in key or "lora_down" in key:
+                return "LoRA"
             elif "oft_blocks" in key:
-                return 'OFT'
+                return "OFT"
+
 
 def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_dtype):
     text_encoder1.to(merge_dtype)
@@ -62,7 +67,7 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
     # create module map
     name_to_module = {}
     for i, root_module in enumerate([text_encoder1, text_encoder2, unet]):
-        if method == 'LoRA':
+        if method == "LoRA":
             if i <= 1:
                 if i == 0:
                     prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER1
@@ -74,7 +79,7 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
                 target_replace_modules = (
                     lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE + lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
                 )
-        elif method == 'OFT':
+        elif method == "OFT":
             prefix = oft.OFTNetwork.OFT_PREFIX_UNET
             # ALL_LINEAR includes ATTN_ONLY, so we don't need to specify ATTN_ONLY
             target_replace_modules = (
@@ -89,14 +94,13 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
                         lora_name = lora_name.replace(".", "_")
                         name_to_module[lora_name] = child_module
 
-                            
     for model, ratio in zip(models, ratios):
         logger.info(f"loading: {model}")
         lora_sd, _ = load_state_dict(model, merge_dtype)
 
         logger.info(f"merging...")
 
-        if method == 'LoRA':
+        if method == "LoRA":
             for key in tqdm(lora_sd.keys()):
                 if "lora_down" in key:
                     up_key = key.replace("lora_down", "lora_up")
@@ -139,11 +143,10 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
 
                     module.weight = torch.nn.Parameter(weight)
 
-       
-        elif method == 'OFT':
+        elif method == "OFT":
 
             multiplier = 1.0
-            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
             for key in tqdm(lora_sd.keys()):
                 if "oft_blocks" in key:
@@ -323,7 +326,9 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
 
 
 def merge(args):
-    assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
+    assert len(args.models) == len(
+        args.ratios
+    ), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
 
     def str_to_dtype(p):
         if p == "float":
@@ -410,10 +415,16 @@ def setup_parser() -> argparse.ArgumentParser:
         help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする",
     )
     parser.add_argument(
-        "--save_to", type=str, default=None, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
+        "--save_to",
+        type=str,
+        default=None,
+        help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors",
     )
     parser.add_argument(
-        "--models", type=str, nargs="*", help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors"
+        "--models",
+        type=str,
+        nargs="*",
+        help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors",
     )
     parser.add_argument("--ratios", type=float, nargs="*", help="ratios for each model / それぞれのLoRAモデルの比率")
     parser.add_argument(
@@ -431,8 +442,7 @@ def setup_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--shuffle",
         action="store_true",
-        help="shuffle lora weight./ "
-        + "LoRAの重みをシャッフルする",
+        help="shuffle lora weight./ " + "LoRAの重みをシャッフルする",
     )
 
     return parser