Upload lora-scripts/sd-scripts/sdxl_train_control_net_lllite_old.py with huggingface_hub
Browse files
lora-scripts/sd-scripts/sdxl_train_control_net_lllite_old.py
ADDED
@@ -0,0 +1,586 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import json
|
3 |
+
import math
|
4 |
+
import os
|
5 |
+
import random
|
6 |
+
import time
|
7 |
+
from multiprocessing import Value
|
8 |
+
from types import SimpleNamespace
|
9 |
+
import toml
|
10 |
+
|
11 |
+
from tqdm import tqdm
|
12 |
+
|
13 |
+
import torch
|
14 |
+
from library.device_utils import init_ipex, clean_memory_on_device
|
15 |
+
init_ipex()
|
16 |
+
|
17 |
+
from torch.nn.parallel import DistributedDataParallel as DDP
|
18 |
+
from accelerate.utils import set_seed
|
19 |
+
from diffusers import DDPMScheduler, ControlNetModel
|
20 |
+
from safetensors.torch import load_file
|
21 |
+
from library import deepspeed_utils, sai_model_spec, sdxl_model_util, sdxl_original_unet, sdxl_train_util
|
22 |
+
|
23 |
+
import library.model_util as model_util
|
24 |
+
import library.train_util as train_util
|
25 |
+
import library.config_util as config_util
|
26 |
+
from library.config_util import (
|
27 |
+
ConfigSanitizer,
|
28 |
+
BlueprintGenerator,
|
29 |
+
)
|
30 |
+
import library.huggingface_util as huggingface_util
|
31 |
+
import library.custom_train_functions as custom_train_functions
|
32 |
+
from library.custom_train_functions import (
|
33 |
+
add_v_prediction_like_loss,
|
34 |
+
apply_snr_weight,
|
35 |
+
prepare_scheduler_for_custom_training,
|
36 |
+
pyramid_noise_like,
|
37 |
+
apply_noise_offset,
|
38 |
+
scale_v_prediction_loss_like_noise_prediction,
|
39 |
+
apply_debiased_estimation,
|
40 |
+
)
|
41 |
+
import networks.control_net_lllite as control_net_lllite
|
42 |
+
from library.utils import setup_logging, add_logging_arguments
|
43 |
+
|
44 |
+
setup_logging()
|
45 |
+
import logging
|
46 |
+
|
47 |
+
logger = logging.getLogger(__name__)
|
48 |
+
|
49 |
+
|
50 |
+
# TODO 他のスクリプトと共通化する
|
51 |
+
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
|
52 |
+
logs = {
|
53 |
+
"loss/current": current_loss,
|
54 |
+
"loss/average": avr_loss,
|
55 |
+
"lr": lr_scheduler.get_last_lr()[0],
|
56 |
+
}
|
57 |
+
|
58 |
+
if args.optimizer_type.lower().startswith("DAdapt".lower()):
|
59 |
+
logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
|
60 |
+
|
61 |
+
return logs
|
62 |
+
|
63 |
+
|
64 |
+
def train(args):
|
65 |
+
train_util.verify_training_args(args)
|
66 |
+
train_util.prepare_dataset_args(args, True)
|
67 |
+
sdxl_train_util.verify_sdxl_training_args(args)
|
68 |
+
setup_logging(args, reset=True)
|
69 |
+
|
70 |
+
cache_latents = args.cache_latents
|
71 |
+
use_user_config = args.dataset_config is not None
|
72 |
+
|
73 |
+
if args.seed is None:
|
74 |
+
args.seed = random.randint(0, 2**32)
|
75 |
+
set_seed(args.seed)
|
76 |
+
|
77 |
+
tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args)
|
78 |
+
|
79 |
+
# データセットを準備する
|
80 |
+
blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, True, True))
|
81 |
+
if use_user_config:
|
82 |
+
logger.info(f"Load dataset config from {args.dataset_config}")
|
83 |
+
user_config = config_util.load_user_config(args.dataset_config)
|
84 |
+
ignored = ["train_data_dir", "conditioning_data_dir"]
|
85 |
+
if any(getattr(args, attr) is not None for attr in ignored):
|
86 |
+
logger.warning(
|
87 |
+
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
|
88 |
+
", ".join(ignored)
|
89 |
+
)
|
90 |
+
)
|
91 |
+
else:
|
92 |
+
user_config = {
|
93 |
+
"datasets": [
|
94 |
+
{
|
95 |
+
"subsets": config_util.generate_controlnet_subsets_config_by_subdirs(
|
96 |
+
args.train_data_dir,
|
97 |
+
args.conditioning_data_dir,
|
98 |
+
args.caption_extension,
|
99 |
+
)
|
100 |
+
}
|
101 |
+
]
|
102 |
+
}
|
103 |
+
|
104 |
+
blueprint = blueprint_generator.generate(user_config, args, tokenizer=[tokenizer1, tokenizer2])
|
105 |
+
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
106 |
+
|
107 |
+
current_epoch = Value("i", 0)
|
108 |
+
current_step = Value("i", 0)
|
109 |
+
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
110 |
+
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
111 |
+
|
112 |
+
train_dataset_group.verify_bucket_reso_steps(32)
|
113 |
+
|
114 |
+
if args.debug_dataset:
|
115 |
+
train_util.debug_dataset(train_dataset_group)
|
116 |
+
return
|
117 |
+
if len(train_dataset_group) == 0:
|
118 |
+
logger.error(
|
119 |
+
"No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください(train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります)"
|
120 |
+
)
|
121 |
+
return
|
122 |
+
|
123 |
+
if cache_latents:
|
124 |
+
assert (
|
125 |
+
train_dataset_group.is_latent_cacheable()
|
126 |
+
), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
|
127 |
+
else:
|
128 |
+
logger.warning(
|
129 |
+
"WARNING: random_crop is not supported yet for ControlNet training / ControlNetの学習ではrandom_cropはまだサポートされていません"
|
130 |
+
)
|
131 |
+
|
132 |
+
if args.cache_text_encoder_outputs:
|
133 |
+
assert (
|
134 |
+
train_dataset_group.is_text_encoder_output_cacheable()
|
135 |
+
), "when caching Text Encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / Text Encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"
|
136 |
+
|
137 |
+
# acceleratorを準備する
|
138 |
+
logger.info("prepare accelerator")
|
139 |
+
accelerator = train_util.prepare_accelerator(args)
|
140 |
+
is_main_process = accelerator.is_main_process
|
141 |
+
|
142 |
+
# mixed precisionに対応した型を用意しておき適宜castする
|
143 |
+
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
144 |
+
vae_dtype = torch.float32 if args.no_half_vae else weight_dtype
|
145 |
+
|
146 |
+
# モデルを読み込む
|
147 |
+
(
|
148 |
+
load_stable_diffusion_format,
|
149 |
+
text_encoder1,
|
150 |
+
text_encoder2,
|
151 |
+
vae,
|
152 |
+
unet,
|
153 |
+
logit_scale,
|
154 |
+
ckpt_info,
|
155 |
+
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)
|
156 |
+
|
157 |
+
# モデルに xformers とか memory efficient attention を組み込む
|
158 |
+
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa)
|
159 |
+
|
160 |
+
# 学習を準備する
|
161 |
+
if cache_latents:
|
162 |
+
vae.to(accelerator.device, dtype=vae_dtype)
|
163 |
+
vae.requires_grad_(False)
|
164 |
+
vae.eval()
|
165 |
+
with torch.no_grad():
|
166 |
+
train_dataset_group.cache_latents(
|
167 |
+
vae,
|
168 |
+
args.vae_batch_size,
|
169 |
+
args.cache_latents_to_disk,
|
170 |
+
accelerator.is_main_process,
|
171 |
+
)
|
172 |
+
vae.to("cpu")
|
173 |
+
clean_memory_on_device(accelerator.device)
|
174 |
+
|
175 |
+
accelerator.wait_for_everyone()
|
176 |
+
|
177 |
+
# TextEncoderの出力をキャッシュする
|
178 |
+
if args.cache_text_encoder_outputs:
|
179 |
+
# Text Encodes are eval and no grad
|
180 |
+
with torch.no_grad():
|
181 |
+
train_dataset_group.cache_text_encoder_outputs(
|
182 |
+
(tokenizer1, tokenizer2),
|
183 |
+
(text_encoder1, text_encoder2),
|
184 |
+
accelerator.device,
|
185 |
+
None,
|
186 |
+
args.cache_text_encoder_outputs_to_disk,
|
187 |
+
accelerator.is_main_process,
|
188 |
+
)
|
189 |
+
accelerator.wait_for_everyone()
|
190 |
+
|
191 |
+
# prepare ControlNet
|
192 |
+
network = control_net_lllite.ControlNetLLLite(unet, args.cond_emb_dim, args.network_dim, args.network_dropout)
|
193 |
+
network.apply_to()
|
194 |
+
|
195 |
+
if args.network_weights is not None:
|
196 |
+
info = network.load_weights(args.network_weights)
|
197 |
+
accelerator.print(f"load ControlNet weights from {args.network_weights}: {info}")
|
198 |
+
|
199 |
+
if args.gradient_checkpointing:
|
200 |
+
unet.enable_gradient_checkpointing()
|
201 |
+
network.enable_gradient_checkpointing() # may have no effect
|
202 |
+
|
203 |
+
# 学習に必要なクラスを準備する
|
204 |
+
accelerator.print("prepare optimizer, data loader etc.")
|
205 |
+
|
206 |
+
trainable_params = list(network.prepare_optimizer_params())
|
207 |
+
logger.info(f"trainable params count: {len(trainable_params)}")
|
208 |
+
logger.info(f"number of trainable parameters: {sum(p.numel() for p in trainable_params if p.requires_grad)}")
|
209 |
+
|
210 |
+
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
211 |
+
|
212 |
+
# dataloaderを準備する
|
213 |
+
# DataLoaderのプロセス数:0 は persistent_workers が使えないので注意
|
214 |
+
n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers
|
215 |
+
|
216 |
+
train_dataloader = torch.utils.data.DataLoader(
|
217 |
+
train_dataset_group,
|
218 |
+
batch_size=1,
|
219 |
+
shuffle=True,
|
220 |
+
collate_fn=collator,
|
221 |
+
num_workers=n_workers,
|
222 |
+
persistent_workers=args.persistent_data_loader_workers,
|
223 |
+
)
|
224 |
+
|
225 |
+
# 学習ステップ数を計算する
|
226 |
+
if args.max_train_epochs is not None:
|
227 |
+
args.max_train_steps = args.max_train_epochs * math.ceil(
|
228 |
+
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
|
229 |
+
)
|
230 |
+
accelerator.print(
|
231 |
+
f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}"
|
232 |
+
)
|
233 |
+
|
234 |
+
# データセット側にも学習ステップを送信
|
235 |
+
train_dataset_group.set_max_train_steps(args.max_train_steps)
|
236 |
+
|
237 |
+
# lr schedulerを用意する
|
238 |
+
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
|
239 |
+
|
240 |
+
# 実験的機能:勾配も含めたfp16/bf16学習を行う モデル全体をfp16/bf16にする
|
241 |
+
if args.full_fp16:
|
242 |
+
assert (
|
243 |
+
args.mixed_precision == "fp16"
|
244 |
+
), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
245 |
+
accelerator.print("enable full fp16 training.")
|
246 |
+
unet.to(weight_dtype)
|
247 |
+
network.to(weight_dtype)
|
248 |
+
elif args.full_bf16:
|
249 |
+
assert (
|
250 |
+
args.mixed_precision == "bf16"
|
251 |
+
), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。"
|
252 |
+
accelerator.print("enable full bf16 training.")
|
253 |
+
unet.to(weight_dtype)
|
254 |
+
network.to(weight_dtype)
|
255 |
+
|
256 |
+
# acceleratorがなんかよろしくやってくれるらしい
|
257 |
+
unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
258 |
+
unet, network, optimizer, train_dataloader, lr_scheduler
|
259 |
+
)
|
260 |
+
network: control_net_lllite.ControlNetLLLite
|
261 |
+
|
262 |
+
if args.gradient_checkpointing:
|
263 |
+
unet.train() # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる
|
264 |
+
else:
|
265 |
+
unet.eval()
|
266 |
+
|
267 |
+
network.prepare_grad_etc()
|
268 |
+
|
269 |
+
# TextEncoderの出力をキャッシュするときにはCPUへ移動する
|
270 |
+
if args.cache_text_encoder_outputs:
|
271 |
+
# move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
|
272 |
+
text_encoder1.to("cpu", dtype=torch.float32)
|
273 |
+
text_encoder2.to("cpu", dtype=torch.float32)
|
274 |
+
clean_memory_on_device(accelerator.device)
|
275 |
+
else:
|
276 |
+
# make sure Text Encoders are on GPU
|
277 |
+
text_encoder1.to(accelerator.device)
|
278 |
+
text_encoder2.to(accelerator.device)
|
279 |
+
|
280 |
+
if not cache_latents:
|
281 |
+
vae.requires_grad_(False)
|
282 |
+
vae.eval()
|
283 |
+
vae.to(accelerator.device, dtype=vae_dtype)
|
284 |
+
|
285 |
+
# 実験的機能:勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
286 |
+
if args.full_fp16:
|
287 |
+
train_util.patch_accelerator_for_fp16_training(accelerator)
|
288 |
+
|
289 |
+
# resumeする
|
290 |
+
train_util.resume_from_local_or_hf_if_specified(accelerator, args)
|
291 |
+
|
292 |
+
# epoch数を計算する
|
293 |
+
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
294 |
+
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
295 |
+
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
296 |
+
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
297 |
+
|
298 |
+
# 学習する
|
299 |
+
# TODO: find a way to handle total batch size when there are multiple datasets
|
300 |
+
accelerator.print("running training / 学習開始")
|
301 |
+
accelerator.print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
|
302 |
+
accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
303 |
+
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
304 |
+
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
|
305 |
+
accelerator.print(
|
306 |
+
f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
|
307 |
+
)
|
308 |
+
# logger.info(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}")
|
309 |
+
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
310 |
+
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
311 |
+
|
312 |
+
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
313 |
+
global_step = 0
|
314 |
+
|
315 |
+
noise_scheduler = DDPMScheduler(
|
316 |
+
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
|
317 |
+
)
|
318 |
+
prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
|
319 |
+
if args.zero_terminal_snr:
|
320 |
+
custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
|
321 |
+
|
322 |
+
if accelerator.is_main_process:
|
323 |
+
init_kwargs = {}
|
324 |
+
if args.log_tracker_config is not None:
|
325 |
+
init_kwargs = toml.load(args.log_tracker_config)
|
326 |
+
accelerator.init_trackers(
|
327 |
+
"lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
|
328 |
+
)
|
329 |
+
|
330 |
+
loss_recorder = train_util.LossRecorder()
|
331 |
+
del train_dataset_group
|
332 |
+
|
333 |
+
# function for saving/removing
|
334 |
+
def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False):
|
335 |
+
os.makedirs(args.output_dir, exist_ok=True)
|
336 |
+
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
337 |
+
|
338 |
+
accelerator.print(f"\nsaving checkpoint: {ckpt_file}")
|
339 |
+
sai_metadata = train_util.get_sai_model_spec(None, args, True, True, False)
|
340 |
+
sai_metadata["modelspec.architecture"] = sai_model_spec.ARCH_SD_XL_V1_BASE + "/control-net-lllite"
|
341 |
+
|
342 |
+
unwrapped_nw.save_weights(ckpt_file, save_dtype, sai_metadata)
|
343 |
+
if args.huggingface_repo_id is not None:
|
344 |
+
huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
|
345 |
+
|
346 |
+
def remove_model(old_ckpt_name):
|
347 |
+
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
|
348 |
+
if os.path.exists(old_ckpt_file):
|
349 |
+
accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
|
350 |
+
os.remove(old_ckpt_file)
|
351 |
+
|
352 |
+
# training loop
|
353 |
+
for epoch in range(num_train_epochs):
|
354 |
+
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
355 |
+
current_epoch.value = epoch + 1
|
356 |
+
|
357 |
+
network.on_epoch_start() # train()
|
358 |
+
|
359 |
+
for step, batch in enumerate(train_dataloader):
|
360 |
+
current_step.value = global_step
|
361 |
+
with accelerator.accumulate(network):
|
362 |
+
with torch.no_grad():
|
363 |
+
if "latents" in batch and batch["latents"] is not None:
|
364 |
+
latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
|
365 |
+
else:
|
366 |
+
# latentに変換
|
367 |
+
latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype)
|
368 |
+
|
369 |
+
# NaNが含まれていれば警告を表示し0に置き換える
|
370 |
+
if torch.any(torch.isnan(latents)):
|
371 |
+
accelerator.print("NaN found in latents, replacing with zeros")
|
372 |
+
latents = torch.nan_to_num(latents, 0, out=latents)
|
373 |
+
latents = latents * sdxl_model_util.VAE_SCALE_FACTOR
|
374 |
+
|
375 |
+
if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
|
376 |
+
input_ids1 = batch["input_ids"]
|
377 |
+
input_ids2 = batch["input_ids2"]
|
378 |
+
with torch.no_grad():
|
379 |
+
# Get the text embedding for conditioning
|
380 |
+
input_ids1 = input_ids1.to(accelerator.device)
|
381 |
+
input_ids2 = input_ids2.to(accelerator.device)
|
382 |
+
encoder_hidden_states1, encoder_hidden_states2, pool2 = train_util.get_hidden_states_sdxl(
|
383 |
+
args.max_token_length,
|
384 |
+
input_ids1,
|
385 |
+
input_ids2,
|
386 |
+
tokenizer1,
|
387 |
+
tokenizer2,
|
388 |
+
text_encoder1,
|
389 |
+
text_encoder2,
|
390 |
+
None if not args.full_fp16 else weight_dtype,
|
391 |
+
)
|
392 |
+
else:
|
393 |
+
encoder_hidden_states1 = batch["text_encoder_outputs1_list"].to(accelerator.device).to(weight_dtype)
|
394 |
+
encoder_hidden_states2 = batch["text_encoder_outputs2_list"].to(accelerator.device).to(weight_dtype)
|
395 |
+
pool2 = batch["text_encoder_pool2_list"].to(accelerator.device).to(weight_dtype)
|
396 |
+
|
397 |
+
# get size embeddings
|
398 |
+
orig_size = batch["original_sizes_hw"]
|
399 |
+
crop_size = batch["crop_top_lefts"]
|
400 |
+
target_size = batch["target_sizes_hw"]
|
401 |
+
embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, accelerator.device).to(weight_dtype)
|
402 |
+
|
403 |
+
# concat embeddings
|
404 |
+
vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype)
|
405 |
+
text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype)
|
406 |
+
|
407 |
+
# Sample noise, sample a random timestep for each image, and add noise to the latents,
|
408 |
+
# with noise offset and/or multires noise if specified
|
409 |
+
noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
|
410 |
+
|
411 |
+
noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype
|
412 |
+
|
413 |
+
controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)
|
414 |
+
|
415 |
+
with accelerator.autocast():
|
416 |
+
# conditioning imageをControlNetに渡す / pass conditioning image to ControlNet
|
417 |
+
# 内部でcond_embに変換される / it will be converted to cond_emb inside
|
418 |
+
network.set_cond_image(controlnet_image)
|
419 |
+
|
420 |
+
# それらの値を使いつつ、U-Netでノイズを予測する / predict noise with U-Net using those values
|
421 |
+
noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)
|
422 |
+
|
423 |
+
if args.v_parameterization:
|
424 |
+
# v-parameterization training
|
425 |
+
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
426 |
+
else:
|
427 |
+
target = noise
|
428 |
+
|
429 |
+
loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
|
430 |
+
loss = loss.mean([1, 2, 3])
|
431 |
+
|
432 |
+
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
433 |
+
loss = loss * loss_weights
|
434 |
+
|
435 |
+
if args.min_snr_gamma:
|
436 |
+
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
437 |
+
if args.scale_v_pred_loss_like_noise_pred:
|
438 |
+
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
439 |
+
if args.v_pred_like_loss:
|
440 |
+
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
441 |
+
if args.debiased_estimation_loss:
|
442 |
+
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
443 |
+
|
444 |
+
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
445 |
+
|
446 |
+
accelerator.backward(loss)
|
447 |
+
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
448 |
+
params_to_clip = network.get_trainable_params()
|
449 |
+
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
450 |
+
|
451 |
+
optimizer.step()
|
452 |
+
lr_scheduler.step()
|
453 |
+
optimizer.zero_grad(set_to_none=True)
|
454 |
+
|
455 |
+
# Checks if the accelerator has performed an optimization step behind the scenes
|
456 |
+
if accelerator.sync_gradients:
|
457 |
+
progress_bar.update(1)
|
458 |
+
global_step += 1
|
459 |
+
|
460 |
+
# sdxl_train_util.sample_images(accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
461 |
+
|
462 |
+
# 指定ステップごとにモデルを保存
|
463 |
+
if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
|
464 |
+
accelerator.wait_for_everyone()
|
465 |
+
if accelerator.is_main_process:
|
466 |
+
ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
|
467 |
+
save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch)
|
468 |
+
|
469 |
+
if args.save_state:
|
470 |
+
train_util.save_and_remove_state_stepwise(args, accelerator, global_step)
|
471 |
+
|
472 |
+
remove_step_no = train_util.get_remove_step_no(args, global_step)
|
473 |
+
if remove_step_no is not None:
|
474 |
+
remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)
|
475 |
+
remove_model(remove_ckpt_name)
|
476 |
+
|
477 |
+
current_loss = loss.detach().item()
|
478 |
+
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
479 |
+
avr_loss: float = loss_recorder.moving_average
|
480 |
+
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
481 |
+
progress_bar.set_postfix(**logs)
|
482 |
+
|
483 |
+
if args.logging_dir is not None:
|
484 |
+
logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
|
485 |
+
accelerator.log(logs, step=global_step)
|
486 |
+
|
487 |
+
if global_step >= args.max_train_steps:
|
488 |
+
break
|
489 |
+
|
490 |
+
if args.logging_dir is not None:
|
491 |
+
logs = {"loss/epoch": loss_recorder.moving_average}
|
492 |
+
accelerator.log(logs, step=epoch + 1)
|
493 |
+
|
494 |
+
accelerator.wait_for_everyone()
|
495 |
+
|
496 |
+
# 指定エポックごとにモデルを保存
|
497 |
+
if args.save_every_n_epochs is not None:
|
498 |
+
saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs
|
499 |
+
if is_main_process and saving:
|
500 |
+
ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
|
501 |
+
save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1)
|
502 |
+
|
503 |
+
remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)
|
504 |
+
if remove_epoch_no is not None:
|
505 |
+
remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
|
506 |
+
remove_model(remove_ckpt_name)
|
507 |
+
|
508 |
+
if args.save_state:
|
509 |
+
train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)
|
510 |
+
|
511 |
+
# self.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
512 |
+
|
513 |
+
# end of epoch
|
514 |
+
|
515 |
+
if is_main_process:
|
516 |
+
network = accelerator.unwrap_model(network)
|
517 |
+
|
518 |
+
accelerator.end_training()
|
519 |
+
|
520 |
+
if is_main_process and args.save_state:
|
521 |
+
train_util.save_state_on_train_end(args, accelerator)
|
522 |
+
|
523 |
+
if is_main_process:
|
524 |
+
ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
|
525 |
+
save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True)
|
526 |
+
|
527 |
+
logger.info("model saved.")
|
528 |
+
|
529 |
+
|
530 |
+
def setup_parser() -> argparse.ArgumentParser:
|
531 |
+
parser = argparse.ArgumentParser()
|
532 |
+
|
533 |
+
add_logging_arguments(parser)
|
534 |
+
train_util.add_sd_models_arguments(parser)
|
535 |
+
train_util.add_dataset_arguments(parser, False, True, True)
|
536 |
+
train_util.add_training_arguments(parser, False)
|
537 |
+
deepspeed_utils.add_deepspeed_arguments(parser)
|
538 |
+
train_util.add_optimizer_arguments(parser)
|
539 |
+
config_util.add_config_arguments(parser)
|
540 |
+
custom_train_functions.add_custom_train_arguments(parser)
|
541 |
+
sdxl_train_util.add_sdxl_training_arguments(parser)
|
542 |
+
|
543 |
+
parser.add_argument(
|
544 |
+
"--save_model_as",
|
545 |
+
type=str,
|
546 |
+
default="safetensors",
|
547 |
+
choices=[None, "ckpt", "pt", "safetensors"],
|
548 |
+
help="format to save the model (default is .safetensors) / モデル保存時の形式(デフォルトはsafetensors)",
|
549 |
+
)
|
550 |
+
parser.add_argument(
|
551 |
+
"--cond_emb_dim", type=int, default=None, help="conditioning embedding dimension / 条件付け埋め込みの次元数"
|
552 |
+
)
|
553 |
+
parser.add_argument(
|
554 |
+
"--network_weights", type=str, default=None, help="pretrained weights for network / 学習するネットワークの初期重み"
|
555 |
+
)
|
556 |
+
parser.add_argument("--network_dim", type=int, default=None, help="network dimensions (rank) / モジュールの次元数")
|
557 |
+
parser.add_argument(
|
558 |
+
"--network_dropout",
|
559 |
+
type=float,
|
560 |
+
default=None,
|
561 |
+
help="Drops neurons out of training every step (0 or None is default behavior (no dropout), 1 would drop all neurons) / 訓練時に毎ステップでニューロンをdropする(0またはNoneはdropoutなし、1は全ニューロンをdropout)",
|
562 |
+
)
|
563 |
+
parser.add_argument(
|
564 |
+
"--conditioning_data_dir",
|
565 |
+
type=str,
|
566 |
+
default=None,
|
567 |
+
help="conditioning data directory / 条件付けデータのディレクトリ",
|
568 |
+
)
|
569 |
+
parser.add_argument(
|
570 |
+
"--no_half_vae",
|
571 |
+
action="store_true",
|
572 |
+
help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
|
573 |
+
)
|
574 |
+
return parser
|
575 |
+
|
576 |
+
|
577 |
+
if __name__ == "__main__":
|
578 |
+
# sdxl_original_unet.USE_REENTRANT = False
|
579 |
+
|
580 |
+
parser = setup_parser()
|
581 |
+
|
582 |
+
args = parser.parse_args()
|
583 |
+
train_util.verify_command_line_training_args(args)
|
584 |
+
args = train_util.read_config_from_file(args, parser)
|
585 |
+
|
586 |
+
train(args)
|