# extract approximating LoRA by svd from two FLUX 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 safetensors import safe_open from tqdm import tqdm from library import flux_utils, sai_model_spec, model_util, sdxl_model_util import lora from library.utils import MemoryEfficientSafeOpen from library.utils import setup_logging from networks import lora_flux setup_logging() import logging logger = logging.getLogger(__name__) # CLAMP_QUANTILE = 0.99 # MIN_DIFF = 1e-1 def save_to_file(file_name, state_dict, metadata, 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) save_file(state_dict, file_name, metadata=metadata) def svd( model_org=None, model_tuned=None, save_to=None, dim=4, device=None, save_precision=None, clamp_quantile=0.99, min_diff=0.01, no_metadata=False, mem_eff_safe_open=False, ): 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 calc_dtype = torch.float save_dtype = str_to_dtype(save_precision) store_device = "cpu" # open models lora_weights = {} if not mem_eff_safe_open: # use original safetensors.safe_open open_fn = lambda fn: safe_open(fn, framework="pt") else: logger.info("Using memory efficient safe_open") open_fn = lambda fn: MemoryEfficientSafeOpen(fn) with open_fn(model_org) as f_org: # filter keys keys = [] for key in f_org.keys(): if not ("single_block" in key or "double_block" in key): continue if ".bias" in key: continue if "norm" in key: continue keys.append(key) with open_fn(model_tuned) as f_tuned: for key in tqdm(keys): # get tensors and calculate difference value_o = f_org.get_tensor(key) value_t = f_tuned.get_tensor(key) mat = value_t.to(calc_dtype) - value_o.to(calc_dtype) del value_o, value_t # extract LoRA weights if device: mat = mat.to(device) out_dim, in_dim = mat.size()[0:2] rank = min(dim, in_dim, out_dim) # LoRA rank cannot exceed the original dim 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) U = U.to(store_device, dtype=save_dtype).contiguous() Vh = Vh.to(store_device, dtype=save_dtype).contiguous() # print(f"key: {key}, U: {U.size()}, Vh: {Vh.size()}") lora_weights[key] = (U, Vh) del mat, U, S, Vh # make state dict for LoRA lora_sd = {} for key, (up_weight, down_weight) in lora_weights.items(): lora_name = key.replace(".weight", "").replace(".", "_") lora_name = lora_flux.LoRANetwork.LORA_PREFIX_FLUX + "_" + lora_name 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]) # same as rank # minimum metadata net_kwargs = {} metadata = { "ss_v2": str(False), "ss_base_model_version": flux_utils.MODEL_VERSION_FLUX_V1, "ss_network_module": "networks.lora_flux", "ss_network_dim": str(dim), "ss_network_alpha": str(float(dim)), "ss_network_args": json.dumps(net_kwargs), } if not no_metadata: title = os.path.splitext(os.path.basename(save_to))[0] sai_metadata = sai_model_spec.build_metadata(lora_sd, False, False, False, True, False, time.time(), title, flux="dev") metadata.update(sai_metadata) save_to_file(save_to, lora_sd, metadata, save_dtype) logger.info(f"LoRA weights saved to {save_to}") 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float", ) parser.add_argument( "--model_org", type=str, default=None, required=True, help="Original model: safetensors file / 元モデル、safetensors", ) parser.add_argument( "--model_tuned", type=str, default=None, required=True, help="Tuned model, LoRA is difference of `original to tuned`: safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors", ) parser.add_argument( "--mem_eff_safe_open", action="store_true", help="use memory efficient safe_open. This is an experimental feature, use only when memory is not enough." " / メモリ効率の良いsafe_openを使用する。実装は実験的なものなので、メモリが足りない場合のみ使用してください。", ) parser.add_argument( "--save_to", type=str, default=None, required=True, help="destination file name: safetensors file / 保存先のファイル名、safetensors", ) parser.add_argument( "--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）" ) parser.add_argument( "--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う" ) parser.add_argument( "--clamp_quantile", type=float, default=0.99, help="Quantile clamping value, float, (0-1). Default = 0.99 / 値をクランプするための分位点、float、(0-1)。デフォルトは0.99", ) # parser.add_argument( # "--min_diff", # type=float, # default=0.01, # help="Minimum difference between finetuned model and base to consider them different enough to extract, float, (0-1). Default = 0.01 /" # + "LoRAを抽出するために元モデルと派生モデルの差分の最小値、float、(0-1)。デフォルトは0.01", # ) 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）", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() svd(**vars(args))