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import argparse |
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import os |
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import torch |
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from safetensors.torch import load_file, save_file |
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from tqdm import tqdm |
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import library.model_util as model_util |
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import lora |
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CLAMP_QUANTILE = 0.99 |
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MIN_DIFF = 1e-6 |
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def save_to_file(file_name, model, state_dict, dtype): |
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if dtype is not None: |
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for key in list(state_dict.keys()): |
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if type(state_dict[key]) == torch.Tensor: |
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state_dict[key] = state_dict[key].to(dtype) |
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if os.path.splitext(file_name)[1] == '.safetensors': |
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save_file(model, file_name) |
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else: |
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torch.save(model, file_name) |
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def svd(args): |
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def str_to_dtype(p): |
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if p == 'float': |
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return torch.float |
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if p == 'fp16': |
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return torch.float16 |
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if p == 'bf16': |
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return torch.bfloat16 |
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return None |
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save_dtype = str_to_dtype(args.save_precision) |
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print(f"loading SD model : {args.model_org}") |
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text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_org) |
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print(f"loading SD model : {args.model_tuned}") |
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text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned) |
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if args.conv_dim is None: |
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kwargs = {} |
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else: |
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kwargs = {"conv_dim": args.conv_dim, "conv_alpha": args.conv_dim} |
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lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_o, unet_o, **kwargs) |
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lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_t, unet_t, **kwargs) |
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assert len(lora_network_o.text_encoder_loras) == len( |
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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) " |
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diffs = {} |
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text_encoder_different = False |
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for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.text_encoder_loras, lora_network_t.text_encoder_loras)): |
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lora_name = lora_o.lora_name |
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module_o = lora_o.org_module |
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module_t = lora_t.org_module |
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diff = module_t.weight - module_o.weight |
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if torch.max(torch.abs(diff)) > MIN_DIFF: |
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text_encoder_different = True |
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diff = diff.float() |
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diffs[lora_name] = diff |
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if not text_encoder_different: |
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print("Text encoder is same. Extract U-Net only.") |
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lora_network_o.text_encoder_loras = [] |
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diffs = {} |
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for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.unet_loras, lora_network_t.unet_loras)): |
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lora_name = lora_o.lora_name |
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module_o = lora_o.org_module |
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module_t = lora_t.org_module |
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diff = module_t.weight - module_o.weight |
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diff = diff.float() |
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if args.device: |
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diff = diff.to(args.device) |
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diffs[lora_name] = diff |
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print("calculating by svd") |
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lora_weights = {} |
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with torch.no_grad(): |
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for lora_name, mat in tqdm(list(diffs.items())): |
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conv2d = (len(mat.size()) == 4) |
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kernel_size = None if not conv2d else mat.size()[2:4] |
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conv2d_3x3 = conv2d and kernel_size != (1, 1) |
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rank = args.dim if not conv2d_3x3 or args.conv_dim is None else args.conv_dim |
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out_dim, in_dim = mat.size()[0:2] |
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if args.device: |
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mat = mat.to(args.device) |
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rank = min(rank, in_dim, out_dim) |
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if conv2d: |
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if conv2d_3x3: |
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mat = mat.flatten(start_dim=1) |
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else: |
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mat = mat.squeeze() |
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U, S, Vh = torch.linalg.svd(mat) |
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U = U[:, :rank] |
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S = S[:rank] |
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U = U @ torch.diag(S) |
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Vh = Vh[:rank, :] |
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dist = torch.cat([U.flatten(), Vh.flatten()]) |
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hi_val = torch.quantile(dist, CLAMP_QUANTILE) |
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low_val = -hi_val |
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U = U.clamp(low_val, hi_val) |
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Vh = Vh.clamp(low_val, hi_val) |
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if conv2d: |
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U = U.reshape(out_dim, rank, 1, 1) |
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Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) |
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U = U.to("cpu").contiguous() |
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Vh = Vh.to("cpu").contiguous() |
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lora_weights[lora_name] = (U, Vh) |
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lora_sd = {} |
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for lora_name, (up_weight, down_weight) in lora_weights.items(): |
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lora_sd[lora_name + '.lora_up.weight'] = up_weight |
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lora_sd[lora_name + '.lora_down.weight'] = down_weight |
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lora_sd[lora_name + '.alpha'] = torch.tensor(down_weight.size()[0]) |
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lora_network_save, lora_sd = lora.create_network_from_weights(1.0, None, None, text_encoder_o, unet_o, weights_sd=lora_sd) |
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lora_network_save.apply_to(text_encoder_o, unet_o) |
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info = lora_network_save.load_state_dict(lora_sd) |
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print(f"Loading extracted LoRA weights: {info}") |
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dir_name = os.path.dirname(args.save_to) |
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if dir_name and not os.path.exists(dir_name): |
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os.makedirs(dir_name, exist_ok=True) |
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metadata = {"ss_network_module": "networks.lora", "ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim)} |
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lora_network_save.save_weights(args.save_to, save_dtype, metadata) |
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print(f"LoRA weights are saved to: {args.save_to}") |
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def setup_parser() -> argparse.ArgumentParser: |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--v2", action='store_true', |
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help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む') |
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parser.add_argument("--save_precision", type=str, default=None, |
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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") |
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parser.add_argument("--model_org", type=str, default=None, |
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help="Stable Diffusion original model: ckpt or safetensors file / 元モデル、ckptまたはsafetensors") |
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parser.add_argument("--model_tuned", type=str, default=None, |
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help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or 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") |
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parser.add_argument("--save_to", type=str, default=None, |
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help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors") |
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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)") |
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parser.add_argument("--conv_dim", type=int, default=None, |
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help="dimension (rank) of LoRA for Conv2d-3x3 (default None, disabled) / LoRAのConv2d-3x3の次元数(rank)(デフォルトNone、適用なし)") |
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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") |
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return parser |
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if __name__ == '__main__': |
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parser = setup_parser() |
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args = parser.parse_args() |
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svd(args) |
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