diff --git a/networks/merge_lora.py b/networks/merge_lora.py index 1d4cb3b5..09aea7b2 100644 --- a/networks/merge_lora.py +++ b/networks/merge_lora.py @@ -1,5 +1,5 @@ - +import math import argparse import os import torch @@ -85,43 +85,76 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype): weight = weight + ratio * (up_weight @ down_weight) * scale else: # conv2d - weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * scale + weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2) + ).unsqueeze(2).unsqueeze(3) * scale module.weight = torch.nn.Parameter(weight) def merge_lora_models(models, ratios, merge_dtype): - merged_sd = {} + base_alphas = {} # alpha for merged model + base_dims = {} - alpha = None - dim = None + merged_sd = {} for model, ratio in zip(models, ratios): print(f"loading: {model}") lora_sd = load_state_dict(model, merge_dtype) + # 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 + + print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}") + + # merge print(f"merging...") for key in lora_sd.keys(): if 'alpha' in key: - if key in merged_sd: - assert merged_sd[key] == lora_sd[key], f"alpha mismatch / alphaが異なる場合、現時点ではマージできません" - else: - alpha = lora_sd[key].detach().numpy() - merged_sd[key] = lora_sd[key] + continue + + 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 + + if key in merged_sd: + assert merged_sd[key].size() == lora_sd[key].size( + ), 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" + merged_sd[key] = merged_sd[key] + lora_sd[key] * scale else: - if key in merged_sd: - assert merged_sd[key].size() == lora_sd[key].size( - ), 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" - merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio - else: - if "lora_down" in key: - dim = lora_sd[key].size()[0] - merged_sd[key] = lora_sd[key] * ratio + 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) - print(f"dim (rank): {dim}, alpha: {alpha}") - if alpha is None: - alpha = dim + print("merged model") + print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}") - return merged_sd, dim, alpha + return merged_sd def merge(args): @@ -152,7 +185,7 @@ def merge(args): model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae) else: - state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype) + state_dict = merge_lora_models(args.models, args.ratios, merge_dtype) print(f"saving model to: {args.save_to}") save_to_file(args.save_to, state_dict, state_dict, save_dtype) diff --git a/networks/merge_lora_old.py b/networks/merge_lora_old.py new file mode 100644 index 00000000..1d4cb3b5 --- /dev/null +++ b/networks/merge_lora_old.py @@ -0,0 +1,179 @@ + + +import argparse +import os +import torch +from safetensors.torch import load_file, save_file +import library.model_util as model_util +import lora + + +def load_state_dict(file_name, dtype): + if os.path.splitext(file_name)[1] == '.safetensors': + sd = load_file(file_name) + else: + sd = torch.load(file_name, map_location='cpu') + for key in list(sd.keys()): + if type(sd[key]) == torch.Tensor: + sd[key] = sd[key].to(dtype) + return sd + + +def save_to_file(file_name, model, state_dict, dtype): + 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) + else: + torch.save(model, file_name) + + +def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype): + text_encoder.to(merge_dtype) + unet.to(merge_dtype) + + # create module map + name_to_module = {} + for i, root_module in enumerate([text_encoder, unet]): + if i == 0: + prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER + 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 + + 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" and child_module.kernel_size == (1, 1)): + 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): + print(f"loading: {model}") + lora_sd = load_state_dict(model, merge_dtype) + + print(f"merging...") + for key in 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: + print(f"no module found for LoRA weight: {key}") + continue + module = name_to_module[module_name] + # print(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 + if len(weight.size()) == 2: + # linear + weight = weight + ratio * (up_weight @ down_weight) * scale + else: + # conv2d + weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * scale + + module.weight = torch.nn.Parameter(weight) + + +def merge_lora_models(models, ratios, merge_dtype): + merged_sd = {} + + alpha = None + dim = None + for model, ratio in zip(models, ratios): + print(f"loading: {model}") + lora_sd = load_state_dict(model, merge_dtype) + + print(f"merging...") + for key in lora_sd.keys(): + if 'alpha' in key: + if key in merged_sd: + assert merged_sd[key] == lora_sd[key], f"alpha mismatch / alphaが異なる場合、現時点ではマージできません" + else: + alpha = lora_sd[key].detach().numpy() + merged_sd[key] = lora_sd[key] + else: + if key in merged_sd: + assert merged_sd[key].size() == lora_sd[key].size( + ), 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" + merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio + else: + if "lora_down" in key: + dim = lora_sd[key].size()[0] + merged_sd[key] = lora_sd[key] * ratio + + print(f"dim (rank): {dim}, alpha: {alpha}") + if alpha is None: + alpha = dim + + return merged_sd, dim, alpha + + +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: + print(f"loading SD model: {args.sd_model}") + + text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model) + + merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype) + + print(f"saving SD model to: {args.save_to}") + model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, + args.sd_model, 0, 0, save_dtype, vae) + else: + state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype) + + print(f"saving model to: {args.save_to}") + save_to_file(args.save_to, state_dict, state_dict, save_dtype) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--v2", action='store_true', + help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む') + 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") + + args = parser.parse_args() + merge(args)