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Kohya_ss/sdxl_merge_lora.py

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+'''
+Replace kohya_ss/sd-scripts/networks/sdxl_merge_lora.py
+'''
+
+import math
+import argparse
+import os
+import time
+import torch
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from library import sai_model_spec, sdxl_model_util, train_util
+import library.model_util as model_util
+import lora
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
+
+
+def load_state_dict(file_name, dtype):
+    if os.path.splitext(file_name)[1] == ".safetensors":
+        sd = load_file(file_name)
+        metadata = train_util.load_metadata_from_safetensors(file_name)
+    else:
+        sd = torch.load(file_name, map_location="cuda")
+        metadata = {}
+
+    for key in list(sd.keys()):
+        if type(sd[key]) == torch.Tensor:
+            sd[key] = sd[key].to(dtype)
+
+    return sd, metadata
+
+
+def save_to_file(file_name, model, state_dict, dtype, metadata):
+    if dtype is not None:
+        for key in list(state_dict.keys()):
+            if type(state_dict[key]) == torch.Tensor:
+                state_dict[key] = state_dict[key].to(dtype)
+
+    if os.path.splitext(file_name)[1] == ".safetensors":
+        save_file(model, file_name, metadata=metadata)
+    else:
+        torch.save(model, file_name)
+
+
+def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_dtype):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    text_encoder1.to(device)
+    text_encoder2.to(device)
+    unet.to(device)
+
+    # create module map
+    name_to_module = {}
+    for i, root_module in enumerate([text_encoder1, text_encoder2, unet]):
+        if i <= 1:
+            if i == 0:
+                prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER1
+            else:
+                prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER2
+            target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
+        else:
+            prefix = lora.LoRANetwork.LORA_PREFIX_UNET
+            target_replace_modules = (
+                lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE + lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
+            )
+
+        for name, module in root_module.named_modules():
+            if module.__class__.__name__ in target_replace_modules:
+                for child_name, child_module in module.named_modules():
+                    if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
+                        lora_name = prefix + "." + name + "." + child_name
+                        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)
+        
+        # Move lora weights to CUDA
+        for key in lora_sd.keys():
+            if isinstance(lora_sd[key], torch.Tensor):
+                lora_sd[key] = lora_sd[key].to(device)
+
+        logger.info(f"merging...")
+        for key in tqdm(lora_sd.keys()):
+            if "lora_down" in key:
+                up_key = key.replace("lora_down", "lora_up")
+                alpha_key = key[: key.index("lora_down")] + "alpha"
+
+                # find original module for this lora
+                module_name = ".".join(key.split(".")[:-2])  # remove trailing ".lora_down.weight"
+                if module_name not in name_to_module:
+                    logger.info(f"no module found for LoRA weight: {key}")
+                    continue
+                module = name_to_module[module_name]
+                # logger.info(f"apply {key} to {module}")
+
+                down_weight = lora_sd[key]
+                up_weight = lora_sd[up_key]
+
+                dim = down_weight.size()[0]
+                alpha = lora_sd.get(alpha_key, dim)
+                scale = alpha / dim
+
+                # W <- W + U * D
+                weight = module.weight
+                # logger.info(module_name, down_weight.size(), up_weight.size())
+                if len(weight.size()) == 2:
+                    # linear
+                    weight = weight + ratio * (up_weight @ down_weight) * scale
+                elif down_weight.size()[2:4] == (1, 1):
+                    # conv2d 1x1
+                    weight = (
+                        weight
+                        + ratio
+                        * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+                        * scale
+                    )
+                else:
+                    # conv2d 3x3
+                    conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
+                    # logger.info(conved.size(), weight.size(), module.stride, module.padding)
+                    weight = weight + ratio * conved * scale
+
+                module.weight = torch.nn.Parameter(weight)
+
+
+def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
+    base_alphas = {}  # alpha for merged model
+    base_dims = {}
+
+    merged_sd = {}
+    v2 = None
+    base_model = None
+    for model, ratio in zip(models, ratios):
+        logger.info(f"loading: {model}")
+        lora_sd, lora_metadata = load_state_dict(model, merge_dtype)
+
+        if lora_metadata is not None:
+            if v2 is None:
+                v2 = lora_metadata.get(train_util.SS_METADATA_KEY_V2, None)  # returns string, SDXLはv2がないのでFalseのはず
+            if base_model is None:
+                base_model = lora_metadata.get(train_util.SS_METADATA_KEY_BASE_MODEL_VERSION, None)
+
+        # get alpha and dim
+        alphas = {}  # alpha for current model
+        dims = {}  # dims for current model
+        for key in lora_sd.keys():
+            if "alpha" in key:
+                lora_module_name = key[: key.rfind(".alpha")]
+                alpha = float(lora_sd[key].detach().numpy())
+                alphas[lora_module_name] = alpha
+                if lora_module_name not in base_alphas:
+                    base_alphas[lora_module_name] = alpha
+            elif "lora_down" in key:
+                lora_module_name = key[: key.rfind(".lora_down")]
+                dim = lora_sd[key].size()[0]
+                dims[lora_module_name] = dim
+                if lora_module_name not in base_dims:
+                    base_dims[lora_module_name] = dim
+
+        for lora_module_name in dims.keys():
+            if lora_module_name not in alphas:
+                alpha = dims[lora_module_name]
+                alphas[lora_module_name] = alpha
+                if lora_module_name not in base_alphas:
+                    base_alphas[lora_module_name] = alpha
+
+        logger.info(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
+
+        # merge
+        logger.info(f"merging...")
+        for key in tqdm(lora_sd.keys()):
+            if "alpha" in key:
+                continue
+            
+            if "lora_up" in key and concat:
+                concat_dim = 1
+            elif "lora_down" in key and concat:
+                concat_dim = 0
+            else:
+                concat_dim = None
+
+            lora_module_name = key[: key.rfind(".lora_")]
+
+            base_alpha = base_alphas[lora_module_name]
+            alpha = alphas[lora_module_name]
+
+            scale = math.sqrt(alpha / base_alpha) * ratio
+            scale = abs(scale) if "lora_up" in key else scale # マイナスの重みに対応する。
+            
+            if key in merged_sd:
+                assert (
+                    merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None
+                ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
+                if concat_dim is not None:
+                    merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim)
+                else:
+                    merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
+            else:
+                merged_sd[key] = lora_sd[key] * scale
+
+    # set alpha to sd
+    for lora_module_name, alpha in base_alphas.items():
+        key = lora_module_name + ".alpha"
+        merged_sd[key] = torch.tensor(alpha)
+        if shuffle:
+            key_down = lora_module_name + ".lora_down.weight"
+            key_up = lora_module_name + ".lora_up.weight"
+            dim = merged_sd[key_down].shape[0]
+            perm = torch.randperm(dim)
+            merged_sd[key_down] = merged_sd[key_down][perm]
+            merged_sd[key_up] = merged_sd[key_up][:,perm]
+
+    logger.info("merged model")
+    logger.info(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
+
+    # check all dims are same
+    dims_list = list(set(base_dims.values()))
+    alphas_list = list(set(base_alphas.values()))
+    all_same_dims = True
+    all_same_alphas = True
+    for dims in dims_list:
+        if dims != dims_list[0]:
+            all_same_dims = False
+            break
+    for alphas in alphas_list:
+        if alphas != alphas_list[0]:
+            all_same_alphas = False
+            break
+
+    # build minimum metadata
+    dims = f"{dims_list[0]}" if all_same_dims else "Dynamic"
+    alphas = f"{alphas_list[0]}" if all_same_alphas else "Dynamic"
+    metadata = train_util.build_minimum_network_metadata(v2, base_model, "networks.lora", dims, alphas, None)
+
+    return merged_sd, metadata
+
+
+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"
+
+    def str_to_dtype(p):
+        if p == "float":
+            return torch.float
+        if p == "fp16":
+            return torch.float16
+        if p == "bf16":
+            return torch.bfloat16
+        return None
+
+    merge_dtype = str_to_dtype(args.precision)
+    save_dtype = str_to_dtype(args.save_precision)
+    if save_dtype is None:
+        save_dtype = merge_dtype
+
+    if args.sd_model is not None:
+        logger.info(f"loading SD model: {args.sd_model}")
+
+        (
+            text_model1,
+            text_model2,
+            vae,
+            unet,
+            logit_scale,
+            ckpt_info,
+        ) = sdxl_model_util.load_models_from_sdxl_checkpoint(sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.sd_model, "cuda")
+
+        merge_to_sd_model(text_model1, text_model2, unet, args.models, args.ratios, merge_dtype)
+
+        if args.no_metadata:
+            sai_metadata = None
+        else:
+            merged_from = sai_model_spec.build_merged_from([args.sd_model] + args.models)
+            title = os.path.splitext(os.path.basename(args.save_to))[0]
+            sai_metadata = sai_model_spec.build_metadata(
+                None, False, False, True, False, False, time.time(), title=title, merged_from=merged_from
+            )
+
+        logger.info(f"saving SD model to: {args.save_to}")
+        sdxl_model_util.save_stable_diffusion_checkpoint(
+            args.save_to, text_model1, text_model2, unet, 0, 0, ckpt_info, vae, logit_scale, sai_metadata, save_dtype
+        )
+    else:
+        state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle)
+
+        logger.info(f"calculating hashes and creating metadata...")
+
+        model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
+        metadata["sshs_model_hash"] = model_hash
+        metadata["sshs_legacy_hash"] = legacy_hash
+
+        if not args.no_metadata:
+            merged_from = sai_model_spec.build_merged_from(args.models)
+            title = os.path.splitext(os.path.basename(args.save_to))[0]
+            sai_metadata = sai_model_spec.build_metadata(
+                state_dict, False, False, True, True, False, time.time(), title=title, merged_from=merged_from
+            )
+            metadata.update(sai_metadata)
+
+        logger.info(f"saving model to: {args.save_to}")
+        save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_precision",
+        type=str,
+        default=None,
+        choices=[None, "float", "fp16", "bf16"],
+        help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        default="float",
+        choices=["float", "fp16", "bf16"],
+        help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）",
+    )
+    parser.add_argument(
+        "--sd_model",
+        type=str,
+        default=None,
+        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"
+    )
+    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"
+    )
+    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(
+        "--no_metadata",
+        action="store_true",
+        help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / "
+        + "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）",
+    )
+    parser.add_argument(
+        "--concat",
+        action="store_true",
+        help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / "
+        + "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）",
+    )
+    parser.add_argument(
+        "--shuffle",
+        action="store_true",
+        help="shuffle lora weight./ "
+        + "LoRAの重みをシャッフルする",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    merge(args)

LoRA Block Weight Presets/presets.txt

@@ -0,0 +1,14 @@
+## These presets were made to alleviate overfitted/overbaked LoRAs. They prioritize strong base characteristics and significant middle-layer style transfer while minimizing excessive style bleed-through.
+
+## SINE = More dramatic drop-off in OUT blocks with strong M00 peak and lower final weight. Best for when you want subject matter and minimal style influnce.
+
+## PLUM = More balanced OUT block transition and slightly softer M00 peak. Consistent OUT blocks. Better for maintaining subjects to the highest degree of faithfulness while allowing for a tiny touch of style to come in.
+
+## PEAR = Similar results to PLUM but made only for Lycoris. 
+
+SINE:1,0.8,0.6,0.4,0.5,1,0.8,0.5,0.3,0.2,0.1,0.3
+
+PLUM:1,0.8,0.6,0.4,0.5,0.95,0.6,0.5,0.3,0.4,0.3,0.5
+
+PEAR:1,0.3,0.3,0.3,0.6,0.6,0.6,0.9,0.9,0.9,1,0.3,0.3,0.3,0.6,0.6,0.6,1,1,1
+