mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-06 13:47:06 +00:00
5f6bf29e52
* Add get_my_logger() * Use logger instead of print * Fix log level * Removed line-breaks for readability * Use setup_logging() * Add rich to requirements.txt * Make simple * Use logger instead of print --------- Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
361 lines
14 KiB
Python
361 lines
14 KiB
Python
import math
|
||
import argparse
|
||
import os
|
||
import time
|
||
import torch
|
||
from safetensors.torch import load_file, save_file
|
||
from library import sai_model_spec, train_util
|
||
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)
|
||
metadata = train_util.load_metadata_from_safetensors(file_name)
|
||
else:
|
||
sd = torch.load(file_name, map_location="cpu")
|
||
metadata = {}
|
||
|
||
for key in list(sd.keys()):
|
||
if type(sd[key]) == torch.Tensor:
|
||
sd[key] = sd[key].to(dtype)
|
||
|
||
return sd, metadata
|
||
|
||
|
||
def save_to_file(file_name, model, state_dict, dtype, metadata):
|
||
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, metadata=metadata)
|
||
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 + lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
|
||
)
|
||
|
||
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):
|
||
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
|
||
if len(up_weight.size()) == 4: # use linear projection mismatch
|
||
up_weight = up_weight.squeeze(3).squeeze(2)
|
||
down_weight = down_weight.squeeze(3).squeeze(2)
|
||
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)
|
||
# logger.info(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, concat=False, shuffle=False):
|
||
base_alphas = {} # alpha for merged model
|
||
base_dims = {}
|
||
|
||
merged_sd = {}
|
||
v2 = None
|
||
base_model = None
|
||
for model, ratio in zip(models, ratios):
|
||
logger.info(f"loading: {model}")
|
||
lora_sd, lora_metadata = load_state_dict(model, merge_dtype)
|
||
|
||
if lora_metadata is not None:
|
||
if v2 is None:
|
||
v2 = lora_metadata.get(train_util.SS_METADATA_KEY_V2, None) # return string
|
||
if base_model is None:
|
||
base_model = lora_metadata.get(train_util.SS_METADATA_KEY_BASE_MODEL_VERSION, None)
|
||
|
||
# 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
|
||
|
||
logger.info(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
|
||
|
||
# merge
|
||
logger.info(f"merging...")
|
||
for key in lora_sd.keys():
|
||
if "alpha" in key:
|
||
continue
|
||
if "lora_up" in key and concat:
|
||
concat_dim = 1
|
||
elif "lora_down" in key and concat:
|
||
concat_dim = 0
|
||
else:
|
||
concat_dim = None
|
||
|
||
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
|
||
scale = abs(scale) if "lora_up" in key else scale # マイナスの重みに対応する。
|
||
|
||
if key in merged_sd:
|
||
assert (
|
||
merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None
|
||
), 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"
|
||
if concat_dim is not None:
|
||
merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim)
|
||
else:
|
||
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)
|
||
if shuffle:
|
||
key_down = lora_module_name + ".lora_down.weight"
|
||
key_up = lora_module_name + ".lora_up.weight"
|
||
dim = merged_sd[key_down].shape[0]
|
||
perm = torch.randperm(dim)
|
||
merged_sd[key_down] = merged_sd[key_down][perm]
|
||
merged_sd[key_up] = merged_sd[key_up][:,perm]
|
||
|
||
logger.info("merged model")
|
||
logger.info(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
|
||
|
||
# check all dims are same
|
||
dims_list = list(set(base_dims.values()))
|
||
alphas_list = list(set(base_alphas.values()))
|
||
all_same_dims = True
|
||
all_same_alphas = True
|
||
for dims in dims_list:
|
||
if dims != dims_list[0]:
|
||
all_same_dims = False
|
||
break
|
||
for alphas in alphas_list:
|
||
if alphas != alphas_list[0]:
|
||
all_same_alphas = False
|
||
break
|
||
|
||
# build minimum metadata
|
||
dims = f"{dims_list[0]}" if all_same_dims else "Dynamic"
|
||
alphas = f"{alphas_list[0]}" if all_same_alphas else "Dynamic"
|
||
metadata = train_util.build_minimum_network_metadata(v2, base_model, "networks.lora", dims, alphas, None)
|
||
|
||
return merged_sd, metadata, v2 == "True"
|
||
|
||
|
||
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)
|
||
|
||
if args.no_metadata:
|
||
sai_metadata = None
|
||
else:
|
||
merged_from = sai_model_spec.build_merged_from([args.sd_model] + args.models)
|
||
title = os.path.splitext(os.path.basename(args.save_to))[0]
|
||
sai_metadata = sai_model_spec.build_metadata(
|
||
None,
|
||
args.v2,
|
||
args.v2,
|
||
False,
|
||
False,
|
||
False,
|
||
time.time(),
|
||
title=title,
|
||
merged_from=merged_from,
|
||
is_stable_diffusion_ckpt=True,
|
||
)
|
||
if args.v2:
|
||
# TODO read sai modelspec
|
||
logger.warning(
|
||
"Cannot determine if model is for v-prediction, so save metadata as v-prediction / modelがv-prediction用か否か不明なため、仮にv-prediction用としてmetadataを保存します"
|
||
)
|
||
|
||
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, sai_metadata, save_dtype, vae
|
||
)
|
||
else:
|
||
state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle)
|
||
|
||
logger.info(f"calculating hashes and creating metadata...")
|
||
|
||
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
|
||
metadata["sshs_model_hash"] = model_hash
|
||
metadata["sshs_legacy_hash"] = legacy_hash
|
||
|
||
if not args.no_metadata:
|
||
merged_from = sai_model_spec.build_merged_from(args.models)
|
||
title = os.path.splitext(os.path.basename(args.save_to))[0]
|
||
sai_metadata = sai_model_spec.build_metadata(
|
||
state_dict, v2, v2, False, True, False, time.time(), title=title, merged_from=merged_from
|
||
)
|
||
if v2:
|
||
# TODO read sai modelspec
|
||
logger.warning(
|
||
"Cannot determine if LoRA is for v-prediction, so save metadata as v-prediction / LoRAがv-prediction用か否か不明なため、仮にv-prediction用としてmetadataを保存します"
|
||
)
|
||
metadata.update(sai_metadata)
|
||
|
||
logger.info(f"saving model to: {args.save_to}")
|
||
save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
|
||
|
||
|
||
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")
|
||
parser.add_argument(
|
||
"--no_metadata",
|
||
action="store_true",
|
||
help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / "
|
||
+ "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）",
|
||
)
|
||
parser.add_argument(
|
||
"--concat",
|
||
action="store_true",
|
||
help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / "
|
||
+ "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）",
|
||
)
|
||
parser.add_argument(
|
||
"--shuffle",
|
||
action="store_true",
|
||
help="shuffle lora weight./ "
|
||
+ "LoRAの重みをシャッフルする",
|
||
)
|
||
|
||
return parser
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = setup_parser()
|
||
|
||
args = parser.parse_args()
|
||
merge(args)
|