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import argparse
import os
import torch
from safetensors.torch import load_file, save_file
import library.model_util as model_util
import lora
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
def load_state_dict(file_name, dtype):
if os.path.splitext(file_name)[1] == '.safetensors':
sd = load_file(file_name)
else:
sd = torch.load(file_name, map_location='cpu')
for key in list(sd.keys()):
if type(sd[key]) == torch.Tensor:
sd[key] = sd[key].to(dtype)
return sd
def save_to_file(file_name, model, state_dict, dtype):
if dtype is not None:
for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype)
if os.path.splitext(file_name)[1] == '.safetensors':
save_file(model, file_name)
else:
torch.save(model, file_name)
def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
text_encoder.to(merge_dtype)
unet.to(merge_dtype)
# create module map
name_to_module = {}
for i, root_module in enumerate([text_encoder, unet]):
if i == 0:
prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
else:
prefix = lora.LoRANetwork.LORA_PREFIX_UNET
target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)):
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
name_to_module[lora_name] = child_module
for model, ratio in zip(models, ratios):
logger.info(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
logger.info(f"merging...")
for key in lora_sd.keys():
if "lora_down" in key:
up_key = key.replace("lora_down", "lora_up")
alpha_key = key[:key.index("lora_down")] + 'alpha'
# find original module for this lora
module_name = '.'.join(key.split('.')[:-2]) # remove trailing ".lora_down.weight"
if module_name not in name_to_module:
logger.info(f"no module found for LoRA weight: {key}")
continue
module = name_to_module[module_name]
# logger.info(f"apply {key} to {module}")
down_weight = lora_sd[key]
up_weight = lora_sd[up_key]
dim = down_weight.size()[0]
alpha = lora_sd.get(alpha_key, dim)
scale = alpha / dim
# W <- W + U * D
weight = module.weight
if len(weight.size()) == 2:
# linear
weight = weight + ratio * (up_weight @ down_weight) * scale
else:
# conv2d
weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * scale
module.weight = torch.nn.Parameter(weight)
def merge_lora_models(models, ratios, merge_dtype):
merged_sd = {}
alpha = None
dim = None
for model, ratio in zip(models, ratios):
logger.info(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
logger.info(f"merging...")
for key in lora_sd.keys():
if 'alpha' in key:
if key in merged_sd:
assert merged_sd[key] == lora_sd[key], f"alpha mismatch / alphaが異なる場合、現時点ではマージできません"
else:
alpha = lora_sd[key].detach().numpy()
merged_sd[key] = lora_sd[key]
else:
if key in merged_sd:
assert merged_sd[key].size() == lora_sd[key].size(
), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio
else:
if "lora_down" in key:
dim = lora_sd[key].size()[0]
merged_sd[key] = lora_sd[key] * ratio
logger.info(f"dim (rank): {dim}, alpha: {alpha}")
if alpha is None:
alpha = dim
return merged_sd, dim, alpha
def merge(args):
assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
def str_to_dtype(p):
if p == 'float':
return torch.float
if p == 'fp16':
return torch.float16
if p == 'bf16':
return torch.bfloat16
return None
merge_dtype = str_to_dtype(args.precision)
save_dtype = str_to_dtype(args.save_precision)
if save_dtype is None:
save_dtype = merge_dtype
if args.sd_model is not None:
logger.info(f"loading SD model: {args.sd_model}")
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
logger.info("")
logger.info(f"saving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
args.sd_model, 0, 0, save_dtype, vae)
else:
state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype)
logger.info(f"")
logger.info(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype)
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("--save_precision", type=str, default=None,
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、省略時はマージ時の精度と同じ")
parser.add_argument("--precision", type=str, default="float",
choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
parser.add_argument("--sd_model", type=str, default=None,
help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
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")
parser.add_argument("--models", type=str, nargs='*',
help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
parser.add_argument("--ratios", type=float, nargs='*',
help="ratios for each model / それぞれのLoRAモデルの比率")
return parser
if __name__ == '__main__':
parser = setup_parser()
args = parser.parse_args()
merge(args)
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