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Kohya S
2023-04-01 09:13:47 +09:00
parent c93cbbc373
commit 1cd07770a4
2 changed files with 1067 additions and 1069 deletions
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1775
library/model_util.py
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361
networks/merge_lora.py
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@@ -1,4 +1,3 @@
import math import math
import argparse import argparse
import os import os
@@ -9,216 +8,234 @@ import lora
def load_state_dict(file_name, dtype): def load_state_dict(file_name, dtype):
if os.path.splitext(file_name)[1] == '.safetensors': if os.path.splitext(file_name)[1] == ".safetensors":
sd = load_file(file_name) sd = load_file(file_name)
else: else:
sd = torch.load(file_name, map_location='cpu') sd = torch.load(file_name, map_location="cpu")
for key in list(sd.keys()): for key in list(sd.keys()):
if type(sd[key]) == torch.Tensor: if type(sd[key]) == torch.Tensor:
sd[key] = sd[key].to(dtype) sd[key] = sd[key].to(dtype)
return sd return sd
def save_to_file(file_name, model, state_dict, dtype): def save_to_file(file_name, model, state_dict, dtype):
if dtype is not None: if dtype is not None:
for key in list(state_dict.keys()): for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor: if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype) state_dict[key] = state_dict[key].to(dtype)
if os.path.splitext(file_name)[1] == '.safetensors': if os.path.splitext(file_name)[1] == ".safetensors":
save_file(model, file_name) save_file(model, file_name)
else: else:
torch.save(model, file_name) torch.save(model, file_name)
def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype): def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
text_encoder.to(merge_dtype) text_encoder.to(merge_dtype)
unet.to(merge_dtype) unet.to(merge_dtype)
# create module map # create module map
name_to_module = {} name_to_module = {}
for i, root_module in enumerate([text_encoder, unet]): for i, root_module in enumerate([text_encoder, unet]):
if i == 0: if i == 0:
prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE 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":
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):
print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
print(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:
print(f"no module found for LoRA weight: {key}")
continue
module = name_to_module[module_name]
# print(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
# print(module_name, down_weight.size(), up_weight.size())
if len(weight.size()) == 2:
# linear
weight = weight + ratio * (up_weight @ down_weight) * scale
elif down_weight.size()[2:4] == (1, 1):
# conv2d 1x1
weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
).unsqueeze(2).unsqueeze(3) * scale
else: else:
# conv2d 3x3 prefix = lora.LoRANetwork.LORA_PREFIX_UNET
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3) target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
# print(conved.size(), weight.size(), module.stride, module.padding)
weight = weight + ratio * conved * scale
module.weight = torch.nn.Parameter(weight) 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":
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):
print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
print(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:
print(f"no module found for LoRA weight: {key}")
continue
module = name_to_module[module_name]
# print(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
# print(module_name, down_weight.size(), up_weight.size())
if len(weight.size()) == 2:
# linear
weight = weight + ratio * (up_weight @ down_weight) * scale
elif down_weight.size()[2:4] == (1, 1):
# conv2d 1x1
weight = (
weight
+ ratio
* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
* scale
)
else:
# conv2d 3x3
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
# print(conved.size(), weight.size(), module.stride, module.padding)
weight = weight + ratio * conved * scale
module.weight = torch.nn.Parameter(weight)
def merge_lora_models(models, ratios, merge_dtype): def merge_lora_models(models, ratios, merge_dtype):
base_alphas = {} # alpha for merged model base_alphas = {} # alpha for merged model
base_dims = {} base_dims = {}
merged_sd = {} merged_sd = {}
for model, ratio in zip(models, ratios): for model, ratio in zip(models, ratios):
print(f"loading: {model}") print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype) lora_sd = load_state_dict(model, merge_dtype)
# get alpha and dim # get alpha and dim
alphas = {} # alpha for current model alphas = {} # alpha for current model
dims = {} # dims for current model dims = {} # dims for current model
for key in lora_sd.keys(): for key in lora_sd.keys():
if 'alpha' in key: if "alpha" in key:
lora_module_name = key[:key.rfind(".alpha")] lora_module_name = key[: key.rfind(".alpha")]
alpha = float(lora_sd[key].detach().numpy()) alpha = float(lora_sd[key].detach().numpy())
alphas[lora_module_name] = alpha alphas[lora_module_name] = alpha
if lora_module_name not in base_alphas: if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha base_alphas[lora_module_name] = alpha
elif "lora_down" in key: elif "lora_down" in key:
lora_module_name = key[:key.rfind(".lora_down")] lora_module_name = key[: key.rfind(".lora_down")]
dim = lora_sd[key].size()[0] dim = lora_sd[key].size()[0]
dims[lora_module_name] = dim dims[lora_module_name] = dim
if lora_module_name not in base_dims: if lora_module_name not in base_dims:
base_dims[lora_module_name] = dim base_dims[lora_module_name] = dim
for lora_module_name in dims.keys(): for lora_module_name in dims.keys():
if lora_module_name not in alphas: if lora_module_name not in alphas:
alpha = dims[lora_module_name] alpha = dims[lora_module_name]
alphas[lora_module_name] = alpha alphas[lora_module_name] = alpha
if lora_module_name not in base_alphas: if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha base_alphas[lora_module_name] = alpha
print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}") print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
# merge # merge
print(f"merging...") print(f"merging...")
for key in lora_sd.keys(): for key in lora_sd.keys():
if 'alpha' in key: if "alpha" in key:
continue continue
lora_module_name = key[:key.rfind(".lora_")] lora_module_name = key[: key.rfind(".lora_")]
base_alpha = base_alphas[lora_module_name] base_alpha = base_alphas[lora_module_name]
alpha = alphas[lora_module_name] alpha = alphas[lora_module_name]
scale = math.sqrt(alpha / base_alpha) * ratio scale = math.sqrt(alpha / base_alpha) * ratio
if key in merged_sd: if key in merged_sd:
assert merged_sd[key].size() == lora_sd[key].size( assert (
), 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].size() == lora_sd[key].size()
merged_sd[key] = merged_sd[key] + lora_sd[key] * scale ), 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"
else: merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
merged_sd[key] = lora_sd[key] * scale else:
merged_sd[key] = lora_sd[key] * scale
# set alpha to sd # set alpha to sd
for lora_module_name, alpha in base_alphas.items(): for lora_module_name, alpha in base_alphas.items():
key = lora_module_name + ".alpha" key = lora_module_name + ".alpha"
merged_sd[key] = torch.tensor(alpha) merged_sd[key] = torch.tensor(alpha)
print("merged model") print("merged model")
print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}") print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
return merged_sd return merged_sd
def merge(args): 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" 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): def str_to_dtype(p):
if p == 'float': if p == "float":
return torch.float return torch.float
if p == 'fp16': if p == "fp16":
return torch.float16 return torch.float16
if p == 'bf16': if p == "bf16":
return torch.bfloat16 return torch.bfloat16
return None return None
merge_dtype = str_to_dtype(args.precision) merge_dtype = str_to_dtype(args.precision)
save_dtype = str_to_dtype(args.save_precision) save_dtype = str_to_dtype(args.save_precision)
if save_dtype is None: if save_dtype is None:
save_dtype = merge_dtype save_dtype = merge_dtype
if args.sd_model is not None: if args.sd_model is not None:
print(f"loading SD model: {args.sd_model}") print(f"loading SD model: {args.sd_model}")
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, 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) merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
print(f"saving SD model to: {args.save_to}") print(f"saving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae)
args.sd_model, 0, 0, save_dtype, vae) else:
else: state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
print(f"saving model to: {args.save_to}") print(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype) save_to_file(args.save_to, state_dict, state_dict, save_dtype)
def setup_parser() -> argparse.ArgumentParser: def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("--v2", action='store_true', 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")
help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む') parser.add_argument(
parser.add_argument("--save_precision", type=str, default=None, "--save_precision",
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") type=str,
parser.add_argument("--precision", type=str, default="float", default=None,
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）") choices=[None, "float", "fp16", "bf16"],
parser.add_argument("--sd_model", type=str, default=None, 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",
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, parser.add_argument(
help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors") "--precision",
parser.add_argument("--models", type=str, nargs='*', type=str,
help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors") default="float",
parser.add_argument("--ratios", type=float, nargs='*', choices=["float", "fp16", "bf16"],
help="ratios for each model / それぞれのLoRAモデルの比率") 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 return parser
if __name__ == '__main__': if __name__ == "__main__":
parser = setup_parser() parser = setup_parser()
args = parser.parse_args() args = parser.parse_args()
merge(args) merge(args)