Merge branch 'dev' into sd3

README.md

@@ -704,6 +704,13 @@ https://github.com/kohya-ss/sd-scripts/pull/1290) frodo821 氏に感謝します
 
 - `gen_imgs.py` のプロンプトオプションに、保存時のファイル名を指定する `--f` オプションを追加しました。また同スクリプトで Diffusers ベースのキーを持つ LoRA の重みに対応しました。
 
+
+### 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

@@ -8,10 +8,15 @@ from tqdm import tqdm
 from library import sai_model_spec, sdxl_model_util, train_util
 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":
@@ -40,14 +45,29 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
         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"
+            elif "oft_blocks" in key:
+                return "OFT"
+
+
 def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_dtype):
     text_encoder1.to(merge_dtype)
     text_encoder1.to(merge_dtype)
     unet.to(merge_dtype)
 
+    # detect the method: OFT or LoRA_module
+    method = detect_method_from_training_model(models, merge_dtype)
+    logger.info(f"method:{method}")
+
     # create module map
     name_to_module = {}
     for i, root_module in enumerate([text_encoder1, text_encoder2, unet]):
+        if method == "LoRA":
             if i <= 1:
                 if i == 0:
                     prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER1
@@ -59,6 +79,12 @@ 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":
+            prefix = oft.OFTNetwork.OFT_PREFIX_UNET
+            # ALL_LINEAR includes ATTN_ONLY, so we don't need to specify ATTN_ONLY
+            target_replace_modules = (
+                oft.OFTNetwork.UNET_TARGET_REPLACE_MODULE_ALL_LINEAR + oft.OFTNetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
+            )
 
         for name, module in root_module.named_modules():
             if module.__class__.__name__ in target_replace_modules:
@@ -73,6 +99,8 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
         lora_sd, _ = load_state_dict(model, merge_dtype)
 
         logger.info(f"merging...")
+
+        if method == "LoRA":
             for key in tqdm(lora_sd.keys()):
                 if "lora_down" in key:
                     up_key = key.replace("lora_down", "lora_up")
@@ -115,6 +143,75 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_
 
                     module.weight = torch.nn.Parameter(weight)
 
+        elif method == "OFT":
+
+            multiplier = 1.0
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+            for key in tqdm(lora_sd.keys()):
+                if "oft_blocks" in key:
+                    oft_blocks = lora_sd[key]
+                    dim = oft_blocks.shape[0]
+                    break
+            for key in tqdm(lora_sd.keys()):
+                if "alpha" in key:
+                    oft_blocks = lora_sd[key]
+                    alpha = oft_blocks.item()
+                    break
+
+            def merge_to(key):
+                if "alpha" in key:
+                    return
+
+                # find original module for this OFT
+                module_name = ".".join(key.split(".")[:-1])
+                if module_name not in name_to_module:
+                    logger.info(f"no module found for OFT weight: {key}")
+                    return
+                module = name_to_module[module_name]
+
+                # logger.info(f"apply {key} to {module}")
+
+                oft_blocks = lora_sd[key]
+
+                if isinstance(module, torch.nn.Linear):
+                    out_dim = module.out_features
+                elif isinstance(module, torch.nn.Conv2d):
+                    out_dim = module.out_channels
+
+                num_blocks = dim
+                block_size = out_dim // dim
+                constraint = (0 if alpha is None else alpha) * out_dim
+
+                block_Q = oft_blocks - oft_blocks.transpose(1, 2)
+                norm_Q = torch.norm(block_Q.flatten())
+                new_norm_Q = torch.clamp(norm_Q, max=constraint)
+                block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
+                I = torch.eye(block_size, device=oft_blocks.device).unsqueeze(0).repeat(num_blocks, 1, 1)
+                block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
+                block_R_weighted = multiplier * block_R + (1 - multiplier) * I
+                R = torch.block_diag(*block_R_weighted)
+
+                # get org weight
+                org_sd = module.state_dict()
+                org_weight = org_sd["weight"].to(device)
+
+                R = R.to(org_weight.device, dtype=org_weight.dtype)
+
+                if org_weight.dim() == 4:
+                    weight = torch.einsum("oihw, op -> pihw", org_weight, R)
+                else:
+                    weight = torch.einsum("oi, op -> pi", org_weight, R)
+
+                weight = weight.contiguous()  # Make Tensor contiguous; required due to ThreadPoolExecutor
+
+                module.weight = torch.nn.Parameter(weight)
+
+            # TODO multi-threading may cause OOM on CPU if cpu_count is too high and RAM is not enough
+            max_workers = 1 if device.type != "cpu" else None  # avoid OOM on GPU
+            with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+                list(tqdm(executor.map(merge_to, lora_sd.keys()), total=len(lora_sd.keys())))
+
 
 def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
     base_alphas = {}  # alpha for merged model
@@ -229,7 +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":
@@ -316,10 +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(
@@ -337,8 +442,7 @@ def setup_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--shuffle",
         action="store_true",
-        help="shuffle lora weight./ "
+        help="shuffle lora weight./ " + "LoRAの重みをシャッフルする",
-        + "LoRAの重みをシャッフルする",
     )
 
     return parser