# extract approximating LoRA by svd from two SD models # The code is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py # Thanks to cloneofsimo! import argparse import json import os import time import torch from safetensors.torch import load_file, save_file from tqdm import tqdm from scripts.kohyas import sai_model_spec,model_util,sdxl_model_util,lora CLAMP_QUANTILE = 0.99 MIN_DIFF = 1e-1 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 svd(args): 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 assert args.v2 != args.sdxl or ( not args.v2 and not args.sdxl ), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません" if args.v_parameterization is None: args.v_parameterization = args.v2 save_dtype = str_to_dtype(args.save_precision) # load models if not args.sdxl: print(f"loading original SD model : {args.model_org}") text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_org) text_encoders_o = [text_encoder_o] print(f"loading tuned SD model : {args.model_tuned}") text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned) text_encoders_t = [text_encoder_t] model_version = model_util.get_model_version_str_for_sd1_sd2(args.v2, args.v_parameterization) else: print(f"loading original SDXL model : {args.model_org}") text_encoder_o1, text_encoder_o2, _, unet_o, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint( sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_org, "cpu" ) text_encoders_o = [text_encoder_o1, text_encoder_o2] print(f"loading original SDXL model : {args.model_tuned}") text_encoder_t1, text_encoder_t2, _, unet_t, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint( sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_tuned, "cpu" ) text_encoders_t = [text_encoder_t1, text_encoder_t2] model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0 # create LoRA network to extract weights: Use dim (rank) as alpha if args.conv_dim is None: kwargs = {} else: kwargs = {"conv_dim": args.conv_dim, "conv_alpha": args.conv_dim} lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoders_o, unet_o, **kwargs) lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoders_t, unet_t, **kwargs) assert len(lora_network_o.text_encoder_loras) == len( lora_network_t.text_encoder_loras ), f"model version is different (SD1.x vs SD2.x) / それぞれのモデルのバージョンが違います（SD1.xベースとSD2.xベース） " # get diffs diffs = {} text_encoder_different = False for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.text_encoder_loras, lora_network_t.text_encoder_loras)): lora_name = lora_o.lora_name module_o = lora_o.org_module module_t = lora_t.org_module diff = args.alpha * module_t.weight - args.beta * module_o.weight # Text Encoder might be same if not text_encoder_different and torch.max(torch.abs(diff)) > MIN_DIFF: text_encoder_different = True print(f"Text encoder is different. {torch.max(torch.abs(diff))} > {MIN_DIFF}") diff = diff.float() diffs[lora_name] = diff if not text_encoder_different: print("Text encoder is same. Extract U-Net only.") lora_network_o.text_encoder_loras = [] diffs = {} for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.unet_loras, lora_network_t.unet_loras)): lora_name = lora_o.lora_name module_o = lora_o.org_module module_t = lora_t.org_module diff = args.alpha * module_t.weight - args.beta * module_o.weight diff = diff.float() if args.device: diff = diff.to(args.device) diffs[lora_name] = diff # make LoRA with svd print("calculating by svd") lora_weights = {} with torch.no_grad(): for lora_name, mat in tqdm(list(diffs.items())): # if args.conv_dim is None, diffs do not include LoRAs for conv2d-3x3 conv2d = len(mat.size()) == 4 kernel_size = None if not conv2d else mat.size()[2:4] conv2d_3x3 = conv2d and kernel_size != (1, 1) rank = args.dim if not conv2d_3x3 or args.conv_dim is None else args.conv_dim out_dim, in_dim = mat.size()[0:2] if args.device: mat = mat.to(args.device) # print(lora_name, mat.size(), mat.device, rank, in_dim, out_dim) rank = min(rank, in_dim, out_dim) # LoRA rank cannot exceed the original dim if conv2d: if conv2d_3x3: mat = mat.flatten(start_dim=1) else: mat = mat.squeeze() U, S, Vh = torch.linalg.svd(mat) U = U[:, :rank] S = S[:rank] U = U @ torch.diag(S) Vh = Vh[:rank, :] dist = torch.cat([U.flatten(), Vh.flatten()]) hi_val = torch.quantile(dist, CLAMP_QUANTILE) low_val = -hi_val U = U.clamp(low_val, hi_val) Vh = Vh.clamp(low_val, hi_val) if conv2d: U = U.reshape(out_dim, rank, 1, 1) Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) U = U.to("cpu").contiguous() Vh = Vh.to("cpu").contiguous() lora_weights[lora_name] = (U, Vh) # make state dict for LoRA lora_sd = {} for lora_name, (up_weight, down_weight) in lora_weights.items(): lora_sd[lora_name + ".lora_up.weight"] = up_weight lora_sd[lora_name + ".lora_down.weight"] = down_weight lora_sd[lora_name + ".alpha"] = torch.tensor(down_weight.size()[0]) # load state dict to LoRA and save it lora_network_save, lora_sd = lora.create_network_from_weights(1.0, None, None, text_encoders_o, unet_o, weights_sd=lora_sd) lora_network_save.apply_to(text_encoders_o, unet_o) # create internal module references for state_dict info = lora_network_save.load_state_dict(lora_sd) print(f"Loading extracted LoRA weights: {info}") dir_name = os.path.dirname(args.save_to) if dir_name and not os.path.exists(dir_name): os.makedirs(dir_name, exist_ok=True) # minimum metadata net_kwargs = {} if args.conv_dim is not None: net_kwargs["conv_dim"] = args.conv_dim net_kwargs["conv_alpha"] = args.conv_dim metadata = { "ss_v2": str(args.v2), "ss_base_model_version": model_version, "ss_network_module": "networks.lora", "ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim), "ss_network_args": json.dumps(net_kwargs), } if not args.no_metadata: title = os.path.splitext(os.path.basename(args.save_to))[0] sai_metadata = sai_model_spec.build_metadata( None, args.v2, args.v_parameterization, args.sdxl, True, False, time.time(), title=title ) metadata.update(sai_metadata) lora_network_save.save_weights(args.save_to, save_dtype, metadata) return f"LoRA weights are saved to: {args.save_to}"