# convert Diffusers v1.x/v2.0 model to original Stable Diffusion import argparse import os import torch from diffusers import StableDiffusionPipeline import library.model_util as model_util from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) def convert(args): # 引数を確認する load_dtype = torch.float16 if args.fp16 else None save_dtype = None if args.fp16 or args.save_precision_as == "fp16": save_dtype = torch.float16 elif args.bf16 or args.save_precision_as == "bf16": save_dtype = torch.bfloat16 elif args.float or args.save_precision_as == "float": save_dtype = torch.float is_load_ckpt = os.path.isfile(args.model_to_load) is_save_ckpt = len(os.path.splitext(args.model_to_save)[1]) > 0 assert not is_load_ckpt or args.v1 != args.v2, "v1 or v2 is required to load checkpoint / checkpointの読み込みにはv1/v2指定が必要です" # assert ( # is_save_ckpt or args.reference_model is not None # ), f"reference model is required to save as Diffusers / Diffusers形式での保存には参照モデルが必要です" # モデルを読み込む msg = "checkpoint" if is_load_ckpt else ("Diffusers" + (" as fp16" if args.fp16 else "")) logger.info(f"loading {msg}: {args.model_to_load}") if is_load_ckpt: v2_model = args.v2 text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint( v2_model, args.model_to_load, unet_use_linear_projection_in_v2=args.unet_use_linear_projection ) else: pipe = StableDiffusionPipeline.from_pretrained( args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None, variant=args.variant ) text_encoder = pipe.text_encoder vae = pipe.vae unet = pipe.unet if args.v1 == args.v2: # 自動判定する v2_model = unet.config.cross_attention_dim == 1024 logger.info("checking model version: model is " + ("v2" if v2_model else "v1")) else: v2_model = not args.v1 # 変換して保存する msg = ("checkpoint" + ("" if save_dtype is None else f" in {save_dtype}")) if is_save_ckpt else "Diffusers" logger.info(f"converting and saving as {msg}: {args.model_to_save}") if is_save_ckpt: original_model = args.model_to_load if is_load_ckpt else None key_count = model_util.save_stable_diffusion_checkpoint( v2_model, args.model_to_save, text_encoder, unet, original_model, args.epoch, args.global_step, None if args.metadata is None else eval(args.metadata), save_dtype=save_dtype, vae=vae, ) logger.info(f"model saved. total converted state_dict keys: {key_count}") else: logger.info( f"copy scheduler/tokenizer config from: {args.reference_model if args.reference_model is not None else 'default model'}" ) model_util.save_diffusers_checkpoint( v2_model, args.model_to_save, text_encoder, unet, args.reference_model, vae, args.use_safetensors ) logger.info("model saved.") def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument( "--v1", action="store_true", help="load v1.x model (v1 or v2 is required to load checkpoint) / 1.xのモデルを読み込む" ) parser.add_argument( "--v2", action="store_true", help="load v2.0 model (v1 or v2 is required to load checkpoint) / 2.0のモデルを読み込む" ) parser.add_argument( "--unet_use_linear_projection", action="store_true", help="When saving v2 model as Diffusers, set U-Net config to `use_linear_projection=true` (to match stabilityai's model) / Diffusers形式でv2モデルを保存するときにU-Netの設定を`use_linear_projection=true`にする（stabilityaiのモデルと合わせる）", ) parser.add_argument( "--fp16", action="store_true", help="load as fp16 (Diffusers only) and save as fp16 (checkpoint only) / fp16形式で読み込み（Diffusers形式のみ対応）、保存する（checkpointのみ対応）", ) parser.add_argument("--bf16", action="store_true", help="save as bf16 (checkpoint only) / bf16形式で保存する（checkpointのみ対応）") parser.add_argument( "--float", action="store_true", help="save as float (checkpoint only) / float(float32)形式で保存する（checkpointのみ対応）" ) parser.add_argument( "--save_precision_as", type=str, default="no", choices=["fp16", "bf16", "float"], help="save precision, do not specify with --fp16/--bf16/--float / 保存する精度、--fp16/--bf16/--floatと併用しないでください", ) parser.add_argument("--epoch", type=int, default=0, help="epoch to write to checkpoint / checkpointに記録するepoch数の値") parser.add_argument( "--global_step", type=int, default=0, help="global_step to write to checkpoint / checkpointに記録するglobal_stepの値" ) parser.add_argument( "--metadata", type=str, default=None, help='モデルに保存されるメタデータ、Pythonの辞書形式で指定 / metadata: metadata written in to the model in Python Dictionary. Example metadata: \'{"name": "model_name", "resolution": "512x512"}\'', ) parser.add_argument( "--variant", type=str, default=None, help="読む込むDiffusersのvariantを指定する、例: fp16 / variant: Diffusers variant to load. Example: fp16", ) parser.add_argument( "--reference_model", type=str, default=None, help="scheduler/tokenizerのコピー元Diffusersモデル、Diffusers形式で保存するときに使用される、省略時は`runwayml/stable-diffusion-v1-5` または `stabilityai/stable-diffusion-2-1` / reference Diffusers model to copy scheduler/tokenizer config from, used when saving as Diffusers format, default is `runwayml/stable-diffusion-v1-5` or `stabilityai/stable-diffusion-2-1`", ) parser.add_argument( "--use_safetensors", action="store_true", help="use safetensors format to save Diffusers model (checkpoint depends on the file extension) / Duffusersモデルをsafetensors形式で保存する（checkpointは拡張子で自動判定）", ) parser.add_argument( "model_to_load", type=str, default=None, help="model to load: checkpoint file or Diffusers model's directory / 読み込むモデル、checkpointかDiffusers形式モデルのディレクトリ", ) parser.add_argument( "model_to_save", type=str, default=None, help="model to save: checkpoint (with extension) or Diffusers model's directory (without extension) / 変換後のモデル、拡張子がある場合はcheckpoint、ない場合はDiffusesモデルとして保存", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() convert(args)