219 lines
8.4 KiB
Python
219 lines
8.4 KiB
Python
|
||
import math
|
||
import argparse
|
||
import os
|
||
import torch
|
||
from safetensors.torch import load_file, save_file
|
||
import library.model_util as model_util
|
||
import lora
|
||
|
||
|
||
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":
|
||
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):
|
||
base_alphas = {} # alpha for merged model
|
||
base_dims = {}
|
||
|
||
merged_sd = {}
|
||
for model, ratio in zip(models, ratios):
|
||
print(f"loading: {model}")
|
||
lora_sd = load_state_dict(model, merge_dtype)
|
||
|
||
# get alpha and dim
|
||
alphas = {} # alpha for current model
|
||
dims = {} # dims for current model
|
||
for key in lora_sd.keys():
|
||
if 'alpha' in key:
|
||
lora_module_name = key[:key.rfind(".alpha")]
|
||
alpha = float(lora_sd[key].detach().numpy())
|
||
alphas[lora_module_name] = alpha
|
||
if lora_module_name not in base_alphas:
|
||
base_alphas[lora_module_name] = alpha
|
||
elif "lora_down" in key:
|
||
lora_module_name = key[:key.rfind(".lora_down")]
|
||
dim = lora_sd[key].size()[0]
|
||
dims[lora_module_name] = dim
|
||
if lora_module_name not in base_dims:
|
||
base_dims[lora_module_name] = dim
|
||
|
||
for lora_module_name in dims.keys():
|
||
if lora_module_name not in alphas:
|
||
alpha = dims[lora_module_name]
|
||
alphas[lora_module_name] = alpha
|
||
if lora_module_name not in base_alphas:
|
||
base_alphas[lora_module_name] = alpha
|
||
|
||
print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
|
||
|
||
# merge
|
||
print(f"merging...")
|
||
for key in lora_sd.keys():
|
||
if 'alpha' in key:
|
||
continue
|
||
|
||
lora_module_name = key[:key.rfind(".lora_")]
|
||
|
||
base_alpha = base_alphas[lora_module_name]
|
||
alpha = alphas[lora_module_name]
|
||
|
||
scale = math.sqrt(alpha / base_alpha) * ratio
|
||
|
||
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] * scale
|
||
else:
|
||
merged_sd[key] = lora_sd[key] * scale
|
||
|
||
# set alpha to sd
|
||
for lora_module_name, alpha in base_alphas.items():
|
||
key = lora_module_name + ".alpha"
|
||
merged_sd[key] = torch.tensor(alpha)
|
||
|
||
print("merged model")
|
||
print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
|
||
|
||
return merged_sd
|
||
|
||
|
||
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:
|
||
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)
|
||
|
||
merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
|
||
|
||
print(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)
|
||
|
||
print(f"saving model to: {args.save_to}")
|
||
save_to_file(args.save_to, state_dict, state_dict, save_dtype)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
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")
|
||
|
||
args = parser.parse_args()
|
||
merge(args)
|