Spaces:
Build error
Build error
File size: 5,093 Bytes
e69a9f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# 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
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:
save_dtype = torch.float16
elif args.bf16:
save_dtype = torch.bfloat16
elif args.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, f"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 ""))
print(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)
else:
pipe = StableDiffusionPipeline.from_pretrained(args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None)
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
print("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"
print(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, save_dtype, vae)
print(f"model saved. total converted state_dict keys: {key_count}")
else:
print(f"copy scheduler/tokenizer config from: {args.reference_model}")
model_util.save_diffusers_checkpoint(v2_model, args.model_to_save, text_encoder, unet, args.reference_model, vae, args.use_safetensors)
print(f"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("--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("--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("--reference_model", type=str, default=None,
help="reference model for schduler/tokenizer, required in saving Diffusers, copy schduler/tokenizer from this / scheduler/tokenizerのコピー元のDiffusersモデル、Diffusers形式で保存するときに必要")
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)
|