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use **kwargs and change svd() calling convention to make svd() reusable
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 * add required attributes to model_org, model_tuned, save_to
 * set "*_alpha" using str(float(foo))
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@wkpark
wkpark committed Nov 9, 2023
1 parent 6231aa9 commit e20e9f6
Showing 1 changed file with 41 additions and 39 deletions.
80 changes: 41 additions & 39 deletions networks/extract_lora_from_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ def save_to_file(file_name, model, state_dict, dtype):
torch.save(model, file_name)


def svd(args):
def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=None, conv_dim=None, v_parameterization=None, device=None, save_precision=None, no_metadata=False):
def str_to_dtype(p):
if p == "float":
return torch.float
Expand All @@ -39,44 +39,44 @@ def str_to_dtype(p):
return torch.bfloat16
return None

assert args.v2 != args.sdxl or (
not args.v2 and not args.sdxl
assert v2 != sdxl or (
not v2 and not sdxl
), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません"
if args.v_parameterization is None:
args.v_parameterization = args.v2
if v_parameterization is None:
v_parameterization = v2

save_dtype = str_to_dtype(args.save_precision)
save_dtype = str_to_dtype(save_precision)

# load models
if not args.sdxl:
print(f"loading original SD model : {args.model_org}")
text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_org)
if not sdxl:
print(f"loading original SD model : {model_org}")
text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_org)
text_encoders_o = [text_encoder_o]
print(f"loading tuned SD model : {args.model_tuned}")
text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned)
print(f"loading tuned SD model : {model_tuned}")
text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_tuned)
text_encoders_t = [text_encoder_t]
model_version = model_util.get_model_version_str_for_sd1_sd2(args.v2, args.v_parameterization)
model_version = model_util.get_model_version_str_for_sd1_sd2(v2, v_parameterization)
else:
print(f"loading original SDXL model : {args.model_org}")
print(f"loading original SDXL model : {model_org}")
text_encoder_o1, text_encoder_o2, _, unet_o, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_org, "cpu"
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_org, "cpu"
)
text_encoders_o = [text_encoder_o1, text_encoder_o2]
print(f"loading original SDXL model : {args.model_tuned}")
print(f"loading original SDXL model : {model_tuned}")
text_encoder_t1, text_encoder_t2, _, unet_t, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_tuned, "cpu"
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_tuned, "cpu"
)
text_encoders_t = [text_encoder_t1, text_encoder_t2]
model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0

# create LoRA network to extract weights: Use dim (rank) as alpha
if args.conv_dim is None:
if conv_dim is None:
kwargs = {}
else:
kwargs = {"conv_dim": args.conv_dim, "conv_alpha": args.conv_dim}
kwargs = {"conv_dim": conv_dim, "conv_alpha": conv_dim}

lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoders_o, unet_o, **kwargs)
lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoders_t, unet_t, **kwargs)
lora_network_o = lora.create_network(1.0, dim, dim, None, text_encoders_o, unet_o, **kwargs)
lora_network_t = lora.create_network(1.0, dim, dim, None, text_encoders_t, unet_t, **kwargs)
assert len(lora_network_o.text_encoder_loras) == len(
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） "
Expand Down Expand Up @@ -120,16 +120,16 @@ def str_to_dtype(p):
lora_weights = {}
with torch.no_grad():
for lora_name, mat in tqdm(list(diffs.items())):
# if args.conv_dim is None, diffs do not include LoRAs for conv2d-3x3
# if conv_dim is None, diffs do not include LoRAs for conv2d-3x3
conv2d = len(mat.size()) == 4
kernel_size = None if not conv2d else mat.size()[2:4]
conv2d_3x3 = conv2d and kernel_size != (1, 1)

rank = args.dim if not conv2d_3x3 or args.conv_dim is None else args.conv_dim
rank = dim if not conv2d_3x3 or conv_dim is None else conv_dim
out_dim, in_dim = mat.size()[0:2]

if args.device:
mat = mat.to(args.device)
if device:
mat = mat.to(device)

# print(lora_name, mat.size(), mat.device, rank, in_dim, out_dim)
rank = min(rank, in_dim, out_dim) # LoRA rank cannot exceed the original dim
Expand Down Expand Up @@ -178,42 +178,42 @@ def str_to_dtype(p):
info = lora_network_save.load_state_dict(lora_sd)
print(f"Loading extracted LoRA weights: {info}")

dir_name = os.path.dirname(args.save_to)
dir_name = os.path.dirname(save_to)
if dir_name and not os.path.exists(dir_name):
os.makedirs(dir_name, exist_ok=True)

# minimum metadata
net_kwargs = {}
if args.conv_dim is not None:
net_kwargs["conv_dim"] = args.conv_dim
net_kwargs["conv_alpha"] = args.conv_dim
if conv_dim is not None:
net_kwargs["conv_dim"] = str(conv_dim)
net_kwargs["conv_alpha"] = str(float(conv_dim))

metadata = {
"ss_v2": str(args.v2),
"ss_v2": str(v2),
"ss_base_model_version": model_version,
"ss_network_module": "networks.lora",
"ss_network_dim": str(args.dim),
"ss_network_alpha": str(args.dim),
"ss_network_dim": str(dim),
"ss_network_alpha": str(float(dim)),
"ss_network_args": json.dumps(net_kwargs),
}

if not args.no_metadata:
title = os.path.splitext(os.path.basename(args.save_to))[0]
if not no_metadata:
title = os.path.splitext(os.path.basename(save_to))[0]
sai_metadata = sai_model_spec.build_metadata(
None, args.v2, args.v_parameterization, args.sdxl, True, False, time.time(), title=title
None, v2, v_parameterization, sdxl, True, False, time.time(), title=title
)
metadata.update(sai_metadata)

lora_network_save.save_weights(args.save_to, save_dtype, metadata)
print(f"LoRA weights are saved to: {args.save_to}")
lora_network_save.save_weights(save_to, save_dtype, metadata)
print(f"LoRA weights are saved to: {save_to}")


def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument("--v2", action="store_true", help="load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む")
parser.add_argument(
"--v_parameterization",
type=bool,
action="store_true",
default=None,
help="make LoRA metadata for v-parameterization (default is same to v2) / 作成するLoRAのメタデータにv-parameterization用と設定する（省略時はv2と同じ）",
)
Expand All @@ -231,16 +231,18 @@ def setup_parser() -> argparse.ArgumentParser:
"--model_org",
type=str,
default=None,
required=True,
help="Stable Diffusion original model: ckpt or safetensors file / 元モデル、ckptまたはsafetensors",
)
parser.add_argument(
"--model_tuned",
type=str,
default=None,
required=True,
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",
)
parser.add_argument(
"--save_to", type=str, default=None, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
"--save_to", type=str, default=None, required=True, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたは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(
Expand All @@ -264,4 +266,4 @@ def setup_parser() -> argparse.ArgumentParser:
parser = setup_parser()

args = parser.parse_args()
svd(args)
svd(**vars(args))

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