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import argparse |
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import os |
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import torch |
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from diffusers import StableDiffusionPipeline |
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import library.model_util as model_util |
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from library.utils import setup_logging |
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setup_logging() |
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import logging |
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logger = logging.getLogger(__name__) |
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def convert(args): |
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load_dtype = torch.float16 if args.fp16 else None |
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save_dtype = None |
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if args.fp16 or args.save_precision_as == "fp16": |
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save_dtype = torch.float16 |
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elif args.bf16 or args.save_precision_as == "bf16": |
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save_dtype = torch.bfloat16 |
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elif args.float or args.save_precision_as == "float": |
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save_dtype = torch.float |
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is_load_ckpt = os.path.isfile(args.model_to_load) |
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is_save_ckpt = len(os.path.splitext(args.model_to_save)[1]) > 0 |
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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" |
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msg = "checkpoint" if is_load_ckpt else ("Diffusers" + (" as fp16" if args.fp16 else "")) |
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logger.info(f"loading {msg}: {args.model_to_load}") |
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if is_load_ckpt: |
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v2_model = args.v2 |
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text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint( |
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v2_model, args.model_to_load, unet_use_linear_projection_in_v2=args.unet_use_linear_projection |
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) |
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else: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None, variant=args.variant |
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) |
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text_encoder = pipe.text_encoder |
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vae = pipe.vae |
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unet = pipe.unet |
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if args.v1 == args.v2: |
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v2_model = unet.config.cross_attention_dim == 1024 |
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logger.info("checking model version: model is " + ("v2" if v2_model else "v1")) |
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else: |
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v2_model = not args.v1 |
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msg = ("checkpoint" + ("" if save_dtype is None else f" in {save_dtype}")) if is_save_ckpt else "Diffusers" |
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logger.info(f"converting and saving as {msg}: {args.model_to_save}") |
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if is_save_ckpt: |
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original_model = args.model_to_load if is_load_ckpt else None |
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key_count = model_util.save_stable_diffusion_checkpoint( |
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v2_model, |
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args.model_to_save, |
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text_encoder, |
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unet, |
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original_model, |
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args.epoch, |
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args.global_step, |
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None if args.metadata is None else eval(args.metadata), |
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save_dtype=save_dtype, |
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vae=vae, |
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) |
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logger.info(f"model saved. total converted state_dict keys: {key_count}") |
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else: |
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logger.info( |
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f"copy scheduler/tokenizer config from: {args.reference_model if args.reference_model is not None else 'default model'}" |
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) |
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model_util.save_diffusers_checkpoint( |
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v2_model, args.model_to_save, text_encoder, unet, args.reference_model, vae, args.use_safetensors |
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) |
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logger.info("model saved.") |
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def setup_parser() -> argparse.ArgumentParser: |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--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" |
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) |
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parser.add_argument( |
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"--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" |
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) |
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parser.add_argument( |
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"--unet_use_linear_projection", |
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action="store_true", |
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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）", |
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) |
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parser.add_argument( |
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"--fp16", |
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action="store_true", |
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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）", |
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) |
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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）") |
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parser.add_argument( |
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"--float", action="store_true", help="save as float (checkpoint only) / float(float32)形式で保存する（checkpointのみ対応）" |
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) |
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parser.add_argument( |
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"--save_precision_as", |
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type=str, |
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default="no", |
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choices=["fp16", "bf16", "float"], |
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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", |
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) |
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parser.add_argument("--epoch", type=int, default=0, help="epoch to write to checkpoint / checkpointに記録するepoch数の値") |
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parser.add_argument( |
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"--global_step", type=int, default=0, help="global_step to write to checkpoint / checkpointに記録するglobal_stepの値" |
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) |
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parser.add_argument( |
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"--metadata", |
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type=str, |
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default=None, |
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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"}\'', |
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) |
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parser.add_argument( |
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"--variant", |
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type=str, |
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default=None, |
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help="読む込むDiffusersのvariantを指定する、例: fp16 / variant: Diffusers variant to load. Example: fp16", |
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) |
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parser.add_argument( |
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"--reference_model", |
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type=str, |
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default=None, |
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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`", |
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) |
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parser.add_argument( |
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"--use_safetensors", |
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action="store_true", |
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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）", |
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) |
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parser.add_argument( |
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"model_to_load", |
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type=str, |
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default=None, |
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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", |
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) |
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parser.add_argument( |
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"model_to_save", |
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type=str, |
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default=None, |
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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", |
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) |
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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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convert(args) |
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