Upload lora-scripts/sd-scripts/train_network.py with huggingface_hub
Browse files
lora-scripts/sd-scripts/train_network.py
ADDED
@@ -0,0 +1,1117 @@
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|
1 |
+
import importlib
|
2 |
+
import argparse
|
3 |
+
import math
|
4 |
+
import os
|
5 |
+
import sys
|
6 |
+
import random
|
7 |
+
import time
|
8 |
+
import json
|
9 |
+
from multiprocessing import Value
|
10 |
+
import toml
|
11 |
+
|
12 |
+
from tqdm import tqdm
|
13 |
+
|
14 |
+
import torch
|
15 |
+
from library.device_utils import init_ipex, clean_memory_on_device
|
16 |
+
|
17 |
+
init_ipex()
|
18 |
+
|
19 |
+
from accelerate.utils import set_seed
|
20 |
+
from diffusers import DDPMScheduler
|
21 |
+
from library import deepspeed_utils, model_util
|
22 |
+
|
23 |
+
import library.train_util as train_util
|
24 |
+
from library.train_util import DreamBoothDataset
|
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 |
+
apply_snr_weight,
|
34 |
+
get_weighted_text_embeddings,
|
35 |
+
prepare_scheduler_for_custom_training,
|
36 |
+
scale_v_prediction_loss_like_noise_prediction,
|
37 |
+
add_v_prediction_like_loss,
|
38 |
+
apply_debiased_estimation,
|
39 |
+
apply_masked_loss,
|
40 |
+
)
|
41 |
+
from library.utils import setup_logging, add_logging_arguments
|
42 |
+
|
43 |
+
setup_logging()
|
44 |
+
import logging
|
45 |
+
|
46 |
+
logger = logging.getLogger(__name__)
|
47 |
+
|
48 |
+
|
49 |
+
class NetworkTrainer:
|
50 |
+
def __init__(self):
|
51 |
+
self.vae_scale_factor = 0.18215
|
52 |
+
self.is_sdxl = False
|
53 |
+
|
54 |
+
# TODO 他のスクリプトと共通化する
|
55 |
+
def generate_step_logs(
|
56 |
+
self, args: argparse.Namespace, current_loss, avr_loss, lr_scheduler, keys_scaled=None, mean_norm=None, maximum_norm=None
|
57 |
+
):
|
58 |
+
logs = {"loss/current": current_loss, "loss/average": avr_loss}
|
59 |
+
|
60 |
+
if keys_scaled is not None:
|
61 |
+
logs["max_norm/keys_scaled"] = keys_scaled
|
62 |
+
logs["max_norm/average_key_norm"] = mean_norm
|
63 |
+
logs["max_norm/max_key_norm"] = maximum_norm
|
64 |
+
|
65 |
+
lrs = lr_scheduler.get_last_lr()
|
66 |
+
|
67 |
+
if args.network_train_text_encoder_only or len(lrs) <= 2: # not block lr (or single block)
|
68 |
+
if args.network_train_unet_only:
|
69 |
+
logs["lr/unet"] = float(lrs[0])
|
70 |
+
elif args.network_train_text_encoder_only:
|
71 |
+
logs["lr/textencoder"] = float(lrs[0])
|
72 |
+
else:
|
73 |
+
logs["lr/textencoder"] = float(lrs[0])
|
74 |
+
logs["lr/unet"] = float(lrs[-1]) # may be same to textencoder
|
75 |
+
|
76 |
+
if (
|
77 |
+
args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
|
78 |
+
): # tracking d*lr value of unet.
|
79 |
+
logs["lr/d*lr"] = (
|
80 |
+
lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
|
81 |
+
)
|
82 |
+
else:
|
83 |
+
idx = 0
|
84 |
+
if not args.network_train_unet_only:
|
85 |
+
logs["lr/textencoder"] = float(lrs[0])
|
86 |
+
idx = 1
|
87 |
+
|
88 |
+
for i in range(idx, len(lrs)):
|
89 |
+
logs[f"lr/group{i}"] = float(lrs[i])
|
90 |
+
if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
|
91 |
+
logs[f"lr/d*lr/group{i}"] = (
|
92 |
+
lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
|
93 |
+
)
|
94 |
+
|
95 |
+
return logs
|
96 |
+
|
97 |
+
def assert_extra_args(self, args, train_dataset_group):
|
98 |
+
pass
|
99 |
+
|
100 |
+
def load_target_model(self, args, weight_dtype, accelerator):
|
101 |
+
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
|
102 |
+
return model_util.get_model_version_str_for_sd1_sd2(args.v2, args.v_parameterization), text_encoder, vae, unet
|
103 |
+
|
104 |
+
def load_tokenizer(self, args):
|
105 |
+
tokenizer = train_util.load_tokenizer(args)
|
106 |
+
return tokenizer
|
107 |
+
|
108 |
+
def is_text_encoder_outputs_cached(self, args):
|
109 |
+
return False
|
110 |
+
|
111 |
+
def is_train_text_encoder(self, args):
|
112 |
+
return not args.network_train_unet_only and not self.is_text_encoder_outputs_cached(args)
|
113 |
+
|
114 |
+
def cache_text_encoder_outputs_if_needed(
|
115 |
+
self, args, accelerator, unet, vae, tokenizers, text_encoders, data_loader, weight_dtype
|
116 |
+
):
|
117 |
+
for t_enc in text_encoders:
|
118 |
+
t_enc.to(accelerator.device, dtype=weight_dtype)
|
119 |
+
|
120 |
+
def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype):
|
121 |
+
input_ids = batch["input_ids"].to(accelerator.device)
|
122 |
+
encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizers[0], text_encoders[0], weight_dtype)
|
123 |
+
return encoder_hidden_states
|
124 |
+
|
125 |
+
def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype):
|
126 |
+
noise_pred = unet(noisy_latents, timesteps, text_conds).sample
|
127 |
+
return noise_pred
|
128 |
+
|
129 |
+
def all_reduce_network(self, accelerator, network):
|
130 |
+
for param in network.parameters():
|
131 |
+
if param.grad is not None:
|
132 |
+
param.grad = accelerator.reduce(param.grad, reduction="mean")
|
133 |
+
|
134 |
+
def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet):
|
135 |
+
train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet)
|
136 |
+
|
137 |
+
def train(self, args):
|
138 |
+
session_id = random.randint(0, 2**32)
|
139 |
+
training_started_at = time.time()
|
140 |
+
train_util.verify_training_args(args)
|
141 |
+
train_util.prepare_dataset_args(args, True)
|
142 |
+
deepspeed_utils.prepare_deepspeed_args(args)
|
143 |
+
setup_logging(args, reset=True)
|
144 |
+
|
145 |
+
cache_latents = args.cache_latents
|
146 |
+
use_dreambooth_method = args.in_json is None
|
147 |
+
use_user_config = args.dataset_config is not None
|
148 |
+
|
149 |
+
if args.seed is None:
|
150 |
+
args.seed = random.randint(0, 2**32)
|
151 |
+
set_seed(args.seed)
|
152 |
+
|
153 |
+
# tokenizerは単体またはリスト、tokenizersは必ずリスト:既存のコードとの互換性のため
|
154 |
+
tokenizer = self.load_tokenizer(args)
|
155 |
+
tokenizers = tokenizer if isinstance(tokenizer, list) else [tokenizer]
|
156 |
+
|
157 |
+
# データセットを準備する
|
158 |
+
if args.dataset_class is None:
|
159 |
+
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True))
|
160 |
+
if use_user_config:
|
161 |
+
logger.info(f"Loading dataset config from {args.dataset_config}")
|
162 |
+
user_config = config_util.load_user_config(args.dataset_config)
|
163 |
+
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
|
164 |
+
if any(getattr(args, attr) is not None for attr in ignored):
|
165 |
+
logger.warning(
|
166 |
+
"ignoring the 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(
|
167 |
+
", ".join(ignored)
|
168 |
+
)
|
169 |
+
)
|
170 |
+
else:
|
171 |
+
if use_dreambooth_method:
|
172 |
+
logger.info("Using DreamBooth method.")
|
173 |
+
user_config = {
|
174 |
+
"datasets": [
|
175 |
+
{
|
176 |
+
"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(
|
177 |
+
args.train_data_dir, args.reg_data_dir
|
178 |
+
)
|
179 |
+
}
|
180 |
+
]
|
181 |
+
}
|
182 |
+
else:
|
183 |
+
logger.info("Training with captions.")
|
184 |
+
user_config = {
|
185 |
+
"datasets": [
|
186 |
+
{
|
187 |
+
"subsets": [
|
188 |
+
{
|
189 |
+
"image_dir": args.train_data_dir,
|
190 |
+
"metadata_file": args.in_json,
|
191 |
+
}
|
192 |
+
]
|
193 |
+
}
|
194 |
+
]
|
195 |
+
}
|
196 |
+
|
197 |
+
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
|
198 |
+
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
199 |
+
else:
|
200 |
+
# use arbitrary dataset class
|
201 |
+
train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer)
|
202 |
+
|
203 |
+
current_epoch = Value("i", 0)
|
204 |
+
current_step = Value("i", 0)
|
205 |
+
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
206 |
+
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
207 |
+
|
208 |
+
if args.debug_dataset:
|
209 |
+
train_util.debug_dataset(train_dataset_group)
|
210 |
+
return
|
211 |
+
if len(train_dataset_group) == 0:
|
212 |
+
logger.error(
|
213 |
+
"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)"
|
214 |
+
)
|
215 |
+
return
|
216 |
+
|
217 |
+
if cache_latents:
|
218 |
+
assert (
|
219 |
+
train_dataset_group.is_latent_cacheable()
|
220 |
+
), "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"
|
221 |
+
|
222 |
+
self.assert_extra_args(args, train_dataset_group)
|
223 |
+
|
224 |
+
# acceleratorを準備する
|
225 |
+
logger.info("preparing accelerator")
|
226 |
+
accelerator = train_util.prepare_accelerator(args)
|
227 |
+
is_main_process = accelerator.is_main_process
|
228 |
+
|
229 |
+
# mixed precisionに対応した型を用意しておき適宜castする
|
230 |
+
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
231 |
+
vae_dtype = torch.float32 if args.no_half_vae else weight_dtype
|
232 |
+
|
233 |
+
# モデルを読み込む
|
234 |
+
model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator)
|
235 |
+
|
236 |
+
# text_encoder is List[CLIPTextModel] or CLIPTextModel
|
237 |
+
text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder]
|
238 |
+
|
239 |
+
# モデルに xformers とか memory efficient attention を組み込む
|
240 |
+
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa)
|
241 |
+
if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える
|
242 |
+
vae.set_use_memory_efficient_attention_xformers(args.xformers)
|
243 |
+
|
244 |
+
# 差分追加学習のためにモデルを読み込む
|
245 |
+
sys.path.append(os.path.dirname(__file__))
|
246 |
+
accelerator.print("import network module:", args.network_module)
|
247 |
+
network_module = importlib.import_module(args.network_module)
|
248 |
+
|
249 |
+
if args.base_weights is not None:
|
250 |
+
# base_weights が指定されている場合は、指定された重みを読み込みマージする
|
251 |
+
for i, weight_path in enumerate(args.base_weights):
|
252 |
+
if args.base_weights_multiplier is None or len(args.base_weights_multiplier) <= i:
|
253 |
+
multiplier = 1.0
|
254 |
+
else:
|
255 |
+
multiplier = args.base_weights_multiplier[i]
|
256 |
+
|
257 |
+
accelerator.print(f"merging module: {weight_path} with multiplier {multiplier}")
|
258 |
+
|
259 |
+
module, weights_sd = network_module.create_network_from_weights(
|
260 |
+
multiplier, weight_path, vae, text_encoder, unet, for_inference=True
|
261 |
+
)
|
262 |
+
module.merge_to(text_encoder, unet, weights_sd, weight_dtype, accelerator.device if args.lowram else "cpu")
|
263 |
+
|
264 |
+
accelerator.print(f"all weights merged: {', '.join(args.base_weights)}")
|
265 |
+
|
266 |
+
# 学習を準備する
|
267 |
+
if cache_latents:
|
268 |
+
vae.to(accelerator.device, dtype=vae_dtype)
|
269 |
+
vae.requires_grad_(False)
|
270 |
+
vae.eval()
|
271 |
+
with torch.no_grad():
|
272 |
+
train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
|
273 |
+
vae.to("cpu")
|
274 |
+
clean_memory_on_device(accelerator.device)
|
275 |
+
|
276 |
+
accelerator.wait_for_everyone()
|
277 |
+
|
278 |
+
# 必要ならテキストエンコーダーの出力をキャッシュする: Text Encoderはcpuまたはgpuへ移される
|
279 |
+
# cache text encoder outputs if needed: Text Encoder is moved to cpu or gpu
|
280 |
+
self.cache_text_encoder_outputs_if_needed(
|
281 |
+
args, accelerator, unet, vae, tokenizers, text_encoders, train_dataset_group, weight_dtype
|
282 |
+
)
|
283 |
+
|
284 |
+
# prepare network
|
285 |
+
net_kwargs = {}
|
286 |
+
if args.network_args is not None:
|
287 |
+
for net_arg in args.network_args:
|
288 |
+
key, value = net_arg.split("=")
|
289 |
+
net_kwargs[key] = value
|
290 |
+
|
291 |
+
# if a new network is added in future, add if ~ then blocks for each network (;'∀')
|
292 |
+
if args.dim_from_weights:
|
293 |
+
network, _ = network_module.create_network_from_weights(1, args.network_weights, vae, text_encoder, unet, **net_kwargs)
|
294 |
+
else:
|
295 |
+
if "dropout" not in net_kwargs:
|
296 |
+
# workaround for LyCORIS (;^ω^)
|
297 |
+
net_kwargs["dropout"] = args.network_dropout
|
298 |
+
|
299 |
+
network = network_module.create_network(
|
300 |
+
1.0,
|
301 |
+
args.network_dim,
|
302 |
+
args.network_alpha,
|
303 |
+
vae,
|
304 |
+
text_encoder,
|
305 |
+
unet,
|
306 |
+
neuron_dropout=args.network_dropout,
|
307 |
+
**net_kwargs,
|
308 |
+
)
|
309 |
+
if network is None:
|
310 |
+
return
|
311 |
+
network_has_multiplier = hasattr(network, "set_multiplier")
|
312 |
+
|
313 |
+
if hasattr(network, "prepare_network"):
|
314 |
+
network.prepare_network(args)
|
315 |
+
if args.scale_weight_norms and not hasattr(network, "apply_max_norm_regularization"):
|
316 |
+
logger.warning(
|
317 |
+
"warning: scale_weight_norms is specified but the network does not support it / scale_weight_normsが指定されていますが、ネットワークが対応していません"
|
318 |
+
)
|
319 |
+
args.scale_weight_norms = False
|
320 |
+
|
321 |
+
train_unet = not args.network_train_text_encoder_only
|
322 |
+
train_text_encoder = self.is_train_text_encoder(args)
|
323 |
+
network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
|
324 |
+
|
325 |
+
if args.network_weights is not None:
|
326 |
+
info = network.load_weights(args.network_weights)
|
327 |
+
accelerator.print(f"load network weights from {args.network_weights}: {info}")
|
328 |
+
|
329 |
+
if args.gradient_checkpointing:
|
330 |
+
unet.enable_gradient_checkpointing()
|
331 |
+
for t_enc in text_encoders:
|
332 |
+
t_enc.gradient_checkpointing_enable()
|
333 |
+
del t_enc
|
334 |
+
network.enable_gradient_checkpointing() # may have no effect
|
335 |
+
|
336 |
+
# 学習に必要なクラスを準備する
|
337 |
+
accelerator.print("prepare optimizer, data loader etc.")
|
338 |
+
|
339 |
+
# 後方互換性を確保するよ
|
340 |
+
try:
|
341 |
+
trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
|
342 |
+
except TypeError:
|
343 |
+
accelerator.print(
|
344 |
+
"Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
|
345 |
+
)
|
346 |
+
trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
|
347 |
+
|
348 |
+
optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
|
349 |
+
|
350 |
+
# dataloaderを準備する
|
351 |
+
# DataLoaderのプロセス数:0 は persistent_workers が使えないので注意
|
352 |
+
n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers
|
353 |
+
|
354 |
+
train_dataloader = torch.utils.data.DataLoader(
|
355 |
+
train_dataset_group,
|
356 |
+
batch_size=1,
|
357 |
+
shuffle=True,
|
358 |
+
collate_fn=collator,
|
359 |
+
num_workers=n_workers,
|
360 |
+
persistent_workers=args.persistent_data_loader_workers,
|
361 |
+
)
|
362 |
+
|
363 |
+
# 学習ステップ数を計算する
|
364 |
+
if args.max_train_epochs is not None:
|
365 |
+
args.max_train_steps = args.max_train_epochs * math.ceil(
|
366 |
+
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
|
367 |
+
)
|
368 |
+
accelerator.print(
|
369 |
+
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}"
|
370 |
+
)
|
371 |
+
|
372 |
+
# データセット側にも学習ステップを送信
|
373 |
+
train_dataset_group.set_max_train_steps(args.max_train_steps)
|
374 |
+
|
375 |
+
# lr schedulerを用意する
|
376 |
+
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
|
377 |
+
|
378 |
+
# 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
|
379 |
+
if args.full_fp16:
|
380 |
+
assert (
|
381 |
+
args.mixed_precision == "fp16"
|
382 |
+
), "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。"
|
383 |
+
accelerator.print("enable full fp16 training.")
|
384 |
+
network.to(weight_dtype)
|
385 |
+
elif args.full_bf16:
|
386 |
+
assert (
|
387 |
+
args.mixed_precision == "bf16"
|
388 |
+
), "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。"
|
389 |
+
accelerator.print("enable full bf16 training.")
|
390 |
+
network.to(weight_dtype)
|
391 |
+
|
392 |
+
unet_weight_dtype = te_weight_dtype = weight_dtype
|
393 |
+
# Experimental Feature: Put base model into fp8 to save vram
|
394 |
+
if args.fp8_base:
|
395 |
+
assert torch.__version__ >= "2.1.0", "fp8_base requires torch>=2.1.0 / fp8を使う場合はtorch>=2.1.0が必要です。"
|
396 |
+
assert (
|
397 |
+
args.mixed_precision != "no"
|
398 |
+
), "fp8_base requires mixed precision='fp16' or 'bf16' / fp8を使う場合はmixed_precision='fp16'または'bf16'が必要です。"
|
399 |
+
accelerator.print("enable fp8 training.")
|
400 |
+
unet_weight_dtype = torch.float8_e4m3fn
|
401 |
+
te_weight_dtype = torch.float8_e4m3fn
|
402 |
+
|
403 |
+
unet.requires_grad_(False)
|
404 |
+
unet.to(dtype=unet_weight_dtype)
|
405 |
+
for t_enc in text_encoders:
|
406 |
+
t_enc.requires_grad_(False)
|
407 |
+
|
408 |
+
# in case of cpu, dtype is already set to fp32 because cpu does not support fp8/fp16/bf16
|
409 |
+
if t_enc.device.type != "cpu":
|
410 |
+
t_enc.to(dtype=te_weight_dtype)
|
411 |
+
# nn.Embedding not support FP8
|
412 |
+
t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype))
|
413 |
+
|
414 |
+
# acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good
|
415 |
+
if args.deepspeed:
|
416 |
+
ds_model = deepspeed_utils.prepare_deepspeed_model(
|
417 |
+
args,
|
418 |
+
unet=unet if train_unet else None,
|
419 |
+
text_encoder1=text_encoders[0] if train_text_encoder else None,
|
420 |
+
text_encoder2=text_encoders[1] if train_text_encoder and len(text_encoders) > 1 else None,
|
421 |
+
network=network,
|
422 |
+
)
|
423 |
+
ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
424 |
+
ds_model, optimizer, train_dataloader, lr_scheduler
|
425 |
+
)
|
426 |
+
training_model = ds_model
|
427 |
+
else:
|
428 |
+
if train_unet:
|
429 |
+
unet = accelerator.prepare(unet)
|
430 |
+
else:
|
431 |
+
unet.to(accelerator.device, dtype=unet_weight_dtype) # move to device because unet is not prepared by accelerator
|
432 |
+
if train_text_encoder:
|
433 |
+
if len(text_encoders) > 1:
|
434 |
+
text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders]
|
435 |
+
else:
|
436 |
+
text_encoder = accelerator.prepare(text_encoder)
|
437 |
+
text_encoders = [text_encoder]
|
438 |
+
else:
|
439 |
+
pass # if text_encoder is not trained, no need to prepare. and device and dtype are already set
|
440 |
+
|
441 |
+
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
442 |
+
network, optimizer, train_dataloader, lr_scheduler
|
443 |
+
)
|
444 |
+
training_model = network
|
445 |
+
|
446 |
+
if args.gradient_checkpointing:
|
447 |
+
# according to TI example in Diffusers, train is required
|
448 |
+
unet.train()
|
449 |
+
for t_enc in text_encoders:
|
450 |
+
t_enc.train()
|
451 |
+
|
452 |
+
# set top parameter requires_grad = True for gradient checkpointing works
|
453 |
+
if train_text_encoder:
|
454 |
+
t_enc.text_model.embeddings.requires_grad_(True)
|
455 |
+
|
456 |
+
else:
|
457 |
+
unet.eval()
|
458 |
+
for t_enc in text_encoders:
|
459 |
+
t_enc.eval()
|
460 |
+
|
461 |
+
del t_enc
|
462 |
+
|
463 |
+
accelerator.unwrap_model(network).prepare_grad_etc(text_encoder, unet)
|
464 |
+
|
465 |
+
if not cache_latents: # キャッシュしない場合はVAEを使うのでVAEを準備する
|
466 |
+
vae.requires_grad_(False)
|
467 |
+
vae.eval()
|
468 |
+
vae.to(accelerator.device, dtype=vae_dtype)
|
469 |
+
|
470 |
+
# 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
|
471 |
+
if args.full_fp16:
|
472 |
+
train_util.patch_accelerator_for_fp16_training(accelerator)
|
473 |
+
|
474 |
+
# before resuming make hook for saving/loading to save/load the network weights only
|
475 |
+
def save_model_hook(models, weights, output_dir):
|
476 |
+
# pop weights of other models than network to save only network weights
|
477 |
+
# only main process or deepspeed https://github.com/huggingface/diffusers/issues/2606
|
478 |
+
if accelerator.is_main_process or args.deepspeed:
|
479 |
+
remove_indices = []
|
480 |
+
for i, model in enumerate(models):
|
481 |
+
if not isinstance(model, type(accelerator.unwrap_model(network))):
|
482 |
+
remove_indices.append(i)
|
483 |
+
for i in reversed(remove_indices):
|
484 |
+
if len(weights) > i:
|
485 |
+
weights.pop(i)
|
486 |
+
# print(f"save model hook: {len(weights)} weights will be saved")
|
487 |
+
|
488 |
+
def load_model_hook(models, input_dir):
|
489 |
+
# remove models except network
|
490 |
+
remove_indices = []
|
491 |
+
for i, model in enumerate(models):
|
492 |
+
if not isinstance(model, type(accelerator.unwrap_model(network))):
|
493 |
+
remove_indices.append(i)
|
494 |
+
for i in reversed(remove_indices):
|
495 |
+
models.pop(i)
|
496 |
+
# print(f"load model hook: {len(models)} models will be loaded")
|
497 |
+
|
498 |
+
accelerator.register_save_state_pre_hook(save_model_hook)
|
499 |
+
accelerator.register_load_state_pre_hook(load_model_hook)
|
500 |
+
|
501 |
+
# resumeする
|
502 |
+
train_util.resume_from_local_or_hf_if_specified(accelerator, args)
|
503 |
+
|
504 |
+
# epoch数を計算する
|
505 |
+
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
506 |
+
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
507 |
+
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
508 |
+
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
509 |
+
|
510 |
+
# 学習する
|
511 |
+
# TODO: find a way to handle total batch size when there are multiple datasets
|
512 |
+
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
513 |
+
|
514 |
+
accelerator.print("running training / 学習開始")
|
515 |
+
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}")
|
516 |
+
accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
517 |
+
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
518 |
+
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
|
519 |
+
accelerator.print(
|
520 |
+
f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
|
521 |
+
)
|
522 |
+
# accelerator.print(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}")
|
523 |
+
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
524 |
+
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
525 |
+
|
526 |
+
# TODO refactor metadata creation and move to util
|
527 |
+
metadata = {
|
528 |
+
"ss_session_id": session_id, # random integer indicating which group of epochs the model came from
|
529 |
+
"ss_training_started_at": training_started_at, # unix timestamp
|
530 |
+
"ss_output_name": args.output_name,
|
531 |
+
"ss_learning_rate": args.learning_rate,
|
532 |
+
"ss_text_encoder_lr": args.text_encoder_lr,
|
533 |
+
"ss_unet_lr": args.unet_lr,
|
534 |
+
"ss_num_train_images": train_dataset_group.num_train_images,
|
535 |
+
"ss_num_reg_images": train_dataset_group.num_reg_images,
|
536 |
+
"ss_num_batches_per_epoch": len(train_dataloader),
|
537 |
+
"ss_num_epochs": num_train_epochs,
|
538 |
+
"ss_gradient_checkpointing": args.gradient_checkpointing,
|
539 |
+
"ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
|
540 |
+
"ss_max_train_steps": args.max_train_steps,
|
541 |
+
"ss_lr_warmup_steps": args.lr_warmup_steps,
|
542 |
+
"ss_lr_scheduler": args.lr_scheduler,
|
543 |
+
"ss_network_module": args.network_module,
|
544 |
+
"ss_network_dim": args.network_dim, # None means default because another network than LoRA may have another default dim
|
545 |
+
"ss_network_alpha": args.network_alpha, # some networks may not have alpha
|
546 |
+
"ss_network_dropout": args.network_dropout, # some networks may not have dropout
|
547 |
+
"ss_mixed_precision": args.mixed_precision,
|
548 |
+
"ss_full_fp16": bool(args.full_fp16),
|
549 |
+
"ss_v2": bool(args.v2),
|
550 |
+
"ss_base_model_version": model_version,
|
551 |
+
"ss_clip_skip": args.clip_skip,
|
552 |
+
"ss_max_token_length": args.max_token_length,
|
553 |
+
"ss_cache_latents": bool(args.cache_latents),
|
554 |
+
"ss_seed": args.seed,
|
555 |
+
"ss_lowram": args.lowram,
|
556 |
+
"ss_noise_offset": args.noise_offset,
|
557 |
+
"ss_multires_noise_iterations": args.multires_noise_iterations,
|
558 |
+
"ss_multires_noise_discount": args.multires_noise_discount,
|
559 |
+
"ss_adaptive_noise_scale": args.adaptive_noise_scale,
|
560 |
+
"ss_zero_terminal_snr": args.zero_terminal_snr,
|
561 |
+
"ss_training_comment": args.training_comment, # will not be updated after training
|
562 |
+
"ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
|
563 |
+
"ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
|
564 |
+
"ss_max_grad_norm": args.max_grad_norm,
|
565 |
+
"ss_caption_dropout_rate": args.caption_dropout_rate,
|
566 |
+
"ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs,
|
567 |
+
"ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,
|
568 |
+
"ss_face_crop_aug_range": args.face_crop_aug_range,
|
569 |
+
"ss_prior_loss_weight": args.prior_loss_weight,
|
570 |
+
"ss_min_snr_gamma": args.min_snr_gamma,
|
571 |
+
"ss_scale_weight_norms": args.scale_weight_norms,
|
572 |
+
"ss_ip_noise_gamma": args.ip_noise_gamma,
|
573 |
+
"ss_debiased_estimation": bool(args.debiased_estimation_loss),
|
574 |
+
"ss_noise_offset_random_strength": args.noise_offset_random_strength,
|
575 |
+
"ss_ip_noise_gamma_random_strength": args.ip_noise_gamma_random_strength,
|
576 |
+
"ss_loss_type": args.loss_type,
|
577 |
+
"ss_huber_schedule": args.huber_schedule,
|
578 |
+
"ss_huber_c": args.huber_c,
|
579 |
+
}
|
580 |
+
|
581 |
+
if use_user_config:
|
582 |
+
# save metadata of multiple datasets
|
583 |
+
# NOTE: pack "ss_datasets" value as json one time
|
584 |
+
# or should also pack nested collections as json?
|
585 |
+
datasets_metadata = []
|
586 |
+
tag_frequency = {} # merge tag frequency for metadata editor
|
587 |
+
dataset_dirs_info = {} # merge subset dirs for metadata editor
|
588 |
+
|
589 |
+
for dataset in train_dataset_group.datasets:
|
590 |
+
is_dreambooth_dataset = isinstance(dataset, DreamBoothDataset)
|
591 |
+
dataset_metadata = {
|
592 |
+
"is_dreambooth": is_dreambooth_dataset,
|
593 |
+
"batch_size_per_device": dataset.batch_size,
|
594 |
+
"num_train_images": dataset.num_train_images, # includes repeating
|
595 |
+
"num_reg_images": dataset.num_reg_images,
|
596 |
+
"resolution": (dataset.width, dataset.height),
|
597 |
+
"enable_bucket": bool(dataset.enable_bucket),
|
598 |
+
"min_bucket_reso": dataset.min_bucket_reso,
|
599 |
+
"max_bucket_reso": dataset.max_bucket_reso,
|
600 |
+
"tag_frequency": dataset.tag_frequency,
|
601 |
+
"bucket_info": dataset.bucket_info,
|
602 |
+
}
|
603 |
+
|
604 |
+
subsets_metadata = []
|
605 |
+
for subset in dataset.subsets:
|
606 |
+
subset_metadata = {
|
607 |
+
"img_count": subset.img_count,
|
608 |
+
"num_repeats": subset.num_repeats,
|
609 |
+
"color_aug": bool(subset.color_aug),
|
610 |
+
"flip_aug": bool(subset.flip_aug),
|
611 |
+
"random_crop": bool(subset.random_crop),
|
612 |
+
"shuffle_caption": bool(subset.shuffle_caption),
|
613 |
+
"keep_tokens": subset.keep_tokens,
|
614 |
+
"keep_tokens_separator": subset.keep_tokens_separator,
|
615 |
+
"secondary_separator": subset.secondary_separator,
|
616 |
+
"enable_wildcard": bool(subset.enable_wildcard),
|
617 |
+
"caption_prefix": subset.caption_prefix,
|
618 |
+
"caption_suffix": subset.caption_suffix,
|
619 |
+
}
|
620 |
+
|
621 |
+
image_dir_or_metadata_file = None
|
622 |
+
if subset.image_dir:
|
623 |
+
image_dir = os.path.basename(subset.image_dir)
|
624 |
+
subset_metadata["image_dir"] = image_dir
|
625 |
+
image_dir_or_metadata_file = image_dir
|
626 |
+
|
627 |
+
if is_dreambooth_dataset:
|
628 |
+
subset_metadata["class_tokens"] = subset.class_tokens
|
629 |
+
subset_metadata["is_reg"] = subset.is_reg
|
630 |
+
if subset.is_reg:
|
631 |
+
image_dir_or_metadata_file = None # not merging reg dataset
|
632 |
+
else:
|
633 |
+
metadata_file = os.path.basename(subset.metadata_file)
|
634 |
+
subset_metadata["metadata_file"] = metadata_file
|
635 |
+
image_dir_or_metadata_file = metadata_file # may overwrite
|
636 |
+
|
637 |
+
subsets_metadata.append(subset_metadata)
|
638 |
+
|
639 |
+
# merge dataset dir: not reg subset only
|
640 |
+
# TODO update additional-network extension to show detailed dataset config from metadata
|
641 |
+
if image_dir_or_metadata_file is not None:
|
642 |
+
# datasets may have a certain dir multiple times
|
643 |
+
v = image_dir_or_metadata_file
|
644 |
+
i = 2
|
645 |
+
while v in dataset_dirs_info:
|
646 |
+
v = image_dir_or_metadata_file + f" ({i})"
|
647 |
+
i += 1
|
648 |
+
image_dir_or_metadata_file = v
|
649 |
+
|
650 |
+
dataset_dirs_info[image_dir_or_metadata_file] = {
|
651 |
+
"n_repeats": subset.num_repeats,
|
652 |
+
"img_count": subset.img_count,
|
653 |
+
}
|
654 |
+
|
655 |
+
dataset_metadata["subsets"] = subsets_metadata
|
656 |
+
datasets_metadata.append(dataset_metadata)
|
657 |
+
|
658 |
+
# merge tag frequency:
|
659 |
+
for ds_dir_name, ds_freq_for_dir in dataset.tag_frequency.items():
|
660 |
+
# あるディレクトリが複数のdatasetで使用されている場合、一度だけ数える
|
661 |
+
# もともと繰り返し回数を指定しているので、キャプション内でのタグの出現回数と、それが学習で何度使われるかは一致しない
|
662 |
+
# なので、ここで複数datasetの回数を合算してもあまり意味はない
|
663 |
+
if ds_dir_name in tag_frequency:
|
664 |
+
continue
|
665 |
+
tag_frequency[ds_dir_name] = ds_freq_for_dir
|
666 |
+
|
667 |
+
metadata["ss_datasets"] = json.dumps(datasets_metadata)
|
668 |
+
metadata["ss_tag_frequency"] = json.dumps(tag_frequency)
|
669 |
+
metadata["ss_dataset_dirs"] = json.dumps(dataset_dirs_info)
|
670 |
+
else:
|
671 |
+
# conserving backward compatibility when using train_dataset_dir and reg_dataset_dir
|
672 |
+
assert (
|
673 |
+
len(train_dataset_group.datasets) == 1
|
674 |
+
), f"There should be a single dataset but {len(train_dataset_group.datasets)} found. This seems to be a bug. / データセットは1個だけ存在するはずですが、実際には{len(train_dataset_group.datasets)}個でした。プログラムのバグかもしれません。"
|
675 |
+
|
676 |
+
dataset = train_dataset_group.datasets[0]
|
677 |
+
|
678 |
+
dataset_dirs_info = {}
|
679 |
+
reg_dataset_dirs_info = {}
|
680 |
+
if use_dreambooth_method:
|
681 |
+
for subset in dataset.subsets:
|
682 |
+
info = reg_dataset_dirs_info if subset.is_reg else dataset_dirs_info
|
683 |
+
info[os.path.basename(subset.image_dir)] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}
|
684 |
+
else:
|
685 |
+
for subset in dataset.subsets:
|
686 |
+
dataset_dirs_info[os.path.basename(subset.metadata_file)] = {
|
687 |
+
"n_repeats": subset.num_repeats,
|
688 |
+
"img_count": subset.img_count,
|
689 |
+
}
|
690 |
+
|
691 |
+
metadata.update(
|
692 |
+
{
|
693 |
+
"ss_batch_size_per_device": args.train_batch_size,
|
694 |
+
"ss_total_batch_size": total_batch_size,
|
695 |
+
"ss_resolution": args.resolution,
|
696 |
+
"ss_color_aug": bool(args.color_aug),
|
697 |
+
"ss_flip_aug": bool(args.flip_aug),
|
698 |
+
"ss_random_crop": bool(args.random_crop),
|
699 |
+
"ss_shuffle_caption": bool(args.shuffle_caption),
|
700 |
+
"ss_enable_bucket": bool(dataset.enable_bucket),
|
701 |
+
"ss_bucket_no_upscale": bool(dataset.bucket_no_upscale),
|
702 |
+
"ss_min_bucket_reso": dataset.min_bucket_reso,
|
703 |
+
"ss_max_bucket_reso": dataset.max_bucket_reso,
|
704 |
+
"ss_keep_tokens": args.keep_tokens,
|
705 |
+
"ss_dataset_dirs": json.dumps(dataset_dirs_info),
|
706 |
+
"ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info),
|
707 |
+
"ss_tag_frequency": json.dumps(dataset.tag_frequency),
|
708 |
+
"ss_bucket_info": json.dumps(dataset.bucket_info),
|
709 |
+
}
|
710 |
+
)
|
711 |
+
|
712 |
+
# add extra args
|
713 |
+
if args.network_args:
|
714 |
+
metadata["ss_network_args"] = json.dumps(net_kwargs)
|
715 |
+
|
716 |
+
# model name and hash
|
717 |
+
if args.pretrained_model_name_or_path is not None:
|
718 |
+
sd_model_name = args.pretrained_model_name_or_path
|
719 |
+
if os.path.exists(sd_model_name):
|
720 |
+
metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)
|
721 |
+
metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(sd_model_name)
|
722 |
+
sd_model_name = os.path.basename(sd_model_name)
|
723 |
+
metadata["ss_sd_model_name"] = sd_model_name
|
724 |
+
|
725 |
+
if args.vae is not None:
|
726 |
+
vae_name = args.vae
|
727 |
+
if os.path.exists(vae_name):
|
728 |
+
metadata["ss_vae_hash"] = train_util.model_hash(vae_name)
|
729 |
+
metadata["ss_new_vae_hash"] = train_util.calculate_sha256(vae_name)
|
730 |
+
vae_name = os.path.basename(vae_name)
|
731 |
+
metadata["ss_vae_name"] = vae_name
|
732 |
+
|
733 |
+
metadata = {k: str(v) for k, v in metadata.items()}
|
734 |
+
|
735 |
+
# make minimum metadata for filtering
|
736 |
+
minimum_metadata = {}
|
737 |
+
for key in train_util.SS_METADATA_MINIMUM_KEYS:
|
738 |
+
if key in metadata:
|
739 |
+
minimum_metadata[key] = metadata[key]
|
740 |
+
|
741 |
+
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
742 |
+
global_step = 0
|
743 |
+
|
744 |
+
noise_scheduler = DDPMScheduler(
|
745 |
+
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
|
746 |
+
)
|
747 |
+
prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
|
748 |
+
if args.zero_terminal_snr:
|
749 |
+
custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
|
750 |
+
|
751 |
+
if accelerator.is_main_process:
|
752 |
+
init_kwargs = {}
|
753 |
+
if args.wandb_run_name:
|
754 |
+
init_kwargs["wandb"] = {"name": args.wandb_run_name}
|
755 |
+
if args.log_tracker_config is not None:
|
756 |
+
init_kwargs = toml.load(args.log_tracker_config)
|
757 |
+
accelerator.init_trackers(
|
758 |
+
"network_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
|
759 |
+
)
|
760 |
+
|
761 |
+
loss_recorder = train_util.LossRecorder()
|
762 |
+
del train_dataset_group
|
763 |
+
|
764 |
+
# callback for step start
|
765 |
+
if hasattr(accelerator.unwrap_model(network), "on_step_start"):
|
766 |
+
on_step_start = accelerator.unwrap_model(network).on_step_start
|
767 |
+
else:
|
768 |
+
on_step_start = lambda *args, **kwargs: None
|
769 |
+
|
770 |
+
# function for saving/removing
|
771 |
+
def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False):
|
772 |
+
os.makedirs(args.output_dir, exist_ok=True)
|
773 |
+
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
774 |
+
|
775 |
+
accelerator.print(f"\nsaving checkpoint: {ckpt_file}")
|
776 |
+
metadata["ss_training_finished_at"] = str(time.time())
|
777 |
+
metadata["ss_steps"] = str(steps)
|
778 |
+
metadata["ss_epoch"] = str(epoch_no)
|
779 |
+
|
780 |
+
metadata_to_save = minimum_metadata if args.no_metadata else metadata
|
781 |
+
sai_metadata = train_util.get_sai_model_spec(None, args, self.is_sdxl, True, False)
|
782 |
+
metadata_to_save.update(sai_metadata)
|
783 |
+
|
784 |
+
unwrapped_nw.save_weights(ckpt_file, save_dtype, metadata_to_save)
|
785 |
+
if args.huggingface_repo_id is not None:
|
786 |
+
huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
|
787 |
+
|
788 |
+
def remove_model(old_ckpt_name):
|
789 |
+
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
|
790 |
+
if os.path.exists(old_ckpt_file):
|
791 |
+
accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
|
792 |
+
os.remove(old_ckpt_file)
|
793 |
+
|
794 |
+
# For --sample_at_first
|
795 |
+
self.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
796 |
+
|
797 |
+
# training loop
|
798 |
+
for epoch in range(num_train_epochs):
|
799 |
+
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
800 |
+
current_epoch.value = epoch + 1
|
801 |
+
|
802 |
+
metadata["ss_epoch"] = str(epoch + 1)
|
803 |
+
|
804 |
+
accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet)
|
805 |
+
|
806 |
+
for step, batch in enumerate(train_dataloader):
|
807 |
+
current_step.value = global_step
|
808 |
+
with accelerator.accumulate(training_model):
|
809 |
+
on_step_start(text_encoder, unet)
|
810 |
+
|
811 |
+
if "latents" in batch and batch["latents"] is not None:
|
812 |
+
latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
|
813 |
+
else:
|
814 |
+
with torch.no_grad():
|
815 |
+
# latentに変換
|
816 |
+
latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype)
|
817 |
+
|
818 |
+
# NaNが含まれていれば警告を表示し0に置き換える
|
819 |
+
if torch.any(torch.isnan(latents)):
|
820 |
+
accelerator.print("NaN found in latents, replacing with zeros")
|
821 |
+
latents = torch.nan_to_num(latents, 0, out=latents)
|
822 |
+
latents = latents * self.vae_scale_factor
|
823 |
+
|
824 |
+
# get multiplier for each sample
|
825 |
+
if network_has_multiplier:
|
826 |
+
multipliers = batch["network_multipliers"]
|
827 |
+
# if all multipliers are same, use single multiplier
|
828 |
+
if torch.all(multipliers == multipliers[0]):
|
829 |
+
multipliers = multipliers[0].item()
|
830 |
+
else:
|
831 |
+
raise NotImplementedError("multipliers for each sample is not supported yet")
|
832 |
+
# print(f"set multiplier: {multipliers}")
|
833 |
+
accelerator.unwrap_model(network).set_multiplier(multipliers)
|
834 |
+
|
835 |
+
with torch.set_grad_enabled(train_text_encoder), accelerator.autocast():
|
836 |
+
# Get the text embedding for conditioning
|
837 |
+
if args.weighted_captions:
|
838 |
+
text_encoder_conds = get_weighted_text_embeddings(
|
839 |
+
tokenizer,
|
840 |
+
text_encoder,
|
841 |
+
batch["captions"],
|
842 |
+
accelerator.device,
|
843 |
+
args.max_token_length // 75 if args.max_token_length else 1,
|
844 |
+
clip_skip=args.clip_skip,
|
845 |
+
)
|
846 |
+
else:
|
847 |
+
text_encoder_conds = self.get_text_cond(
|
848 |
+
args, accelerator, batch, tokenizers, text_encoders, weight_dtype
|
849 |
+
)
|
850 |
+
|
851 |
+
# Sample noise, sample a random timestep for each image, and add noise to the latents,
|
852 |
+
# with noise offset and/or multires noise if specified
|
853 |
+
noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
|
854 |
+
args, noise_scheduler, latents
|
855 |
+
)
|
856 |
+
|
857 |
+
# ensure the hidden state will require grad
|
858 |
+
if args.gradient_checkpointing:
|
859 |
+
for x in noisy_latents:
|
860 |
+
x.requires_grad_(True)
|
861 |
+
for t in text_encoder_conds:
|
862 |
+
t.requires_grad_(True)
|
863 |
+
|
864 |
+
# Predict the noise residual
|
865 |
+
with accelerator.autocast():
|
866 |
+
noise_pred = self.call_unet(
|
867 |
+
args,
|
868 |
+
accelerator,
|
869 |
+
unet,
|
870 |
+
noisy_latents.requires_grad_(train_unet),
|
871 |
+
timesteps,
|
872 |
+
text_encoder_conds,
|
873 |
+
batch,
|
874 |
+
weight_dtype,
|
875 |
+
)
|
876 |
+
|
877 |
+
if args.v_parameterization:
|
878 |
+
# v-parameterization training
|
879 |
+
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
880 |
+
else:
|
881 |
+
target = noise
|
882 |
+
|
883 |
+
loss = train_util.conditional_loss(
|
884 |
+
noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
|
885 |
+
)
|
886 |
+
if args.masked_loss:
|
887 |
+
loss = apply_masked_loss(loss, batch)
|
888 |
+
loss = loss.mean([1, 2, 3])
|
889 |
+
|
890 |
+
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
891 |
+
loss = loss * loss_weights
|
892 |
+
|
893 |
+
if args.min_snr_gamma:
|
894 |
+
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
895 |
+
if args.scale_v_pred_loss_like_noise_pred:
|
896 |
+
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
897 |
+
if args.v_pred_like_loss:
|
898 |
+
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
899 |
+
if args.debiased_estimation_loss:
|
900 |
+
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
901 |
+
|
902 |
+
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
903 |
+
|
904 |
+
accelerator.backward(loss)
|
905 |
+
if accelerator.sync_gradients:
|
906 |
+
self.all_reduce_network(accelerator, network) # sync DDP grad manually
|
907 |
+
if args.max_grad_norm != 0.0:
|
908 |
+
params_to_clip = accelerator.unwrap_model(network).get_trainable_params()
|
909 |
+
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
910 |
+
|
911 |
+
optimizer.step()
|
912 |
+
lr_scheduler.step()
|
913 |
+
optimizer.zero_grad(set_to_none=True)
|
914 |
+
|
915 |
+
if args.scale_weight_norms:
|
916 |
+
keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization(
|
917 |
+
args.scale_weight_norms, accelerator.device
|
918 |
+
)
|
919 |
+
max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
|
920 |
+
else:
|
921 |
+
keys_scaled, mean_norm, maximum_norm = None, None, None
|
922 |
+
|
923 |
+
# Checks if the accelerator has performed an optimization step behind the scenes
|
924 |
+
if accelerator.sync_gradients:
|
925 |
+
progress_bar.update(1)
|
926 |
+
global_step += 1
|
927 |
+
|
928 |
+
self.sample_images(accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
929 |
+
|
930 |
+
# 指定ステップごとにモデルを保存
|
931 |
+
if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
|
932 |
+
accelerator.wait_for_everyone()
|
933 |
+
if accelerator.is_main_process:
|
934 |
+
ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
|
935 |
+
save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch)
|
936 |
+
|
937 |
+
if args.save_state:
|
938 |
+
train_util.save_and_remove_state_stepwise(args, accelerator, global_step)
|
939 |
+
|
940 |
+
remove_step_no = train_util.get_remove_step_no(args, global_step)
|
941 |
+
if remove_step_no is not None:
|
942 |
+
remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)
|
943 |
+
remove_model(remove_ckpt_name)
|
944 |
+
|
945 |
+
current_loss = loss.detach().item()
|
946 |
+
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
947 |
+
avr_loss: float = loss_recorder.moving_average
|
948 |
+
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
949 |
+
progress_bar.set_postfix(**logs)
|
950 |
+
|
951 |
+
if args.scale_weight_norms:
|
952 |
+
progress_bar.set_postfix(**{**max_mean_logs, **logs})
|
953 |
+
|
954 |
+
if args.logging_dir is not None:
|
955 |
+
logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
|
956 |
+
accelerator.log(logs, step=global_step)
|
957 |
+
|
958 |
+
if global_step >= args.max_train_steps:
|
959 |
+
break
|
960 |
+
|
961 |
+
if args.logging_dir is not None:
|
962 |
+
logs = {"loss/epoch": loss_recorder.moving_average}
|
963 |
+
accelerator.log(logs, step=epoch + 1)
|
964 |
+
|
965 |
+
accelerator.wait_for_everyone()
|
966 |
+
|
967 |
+
# 指定エポックごとにモデルを保存
|
968 |
+
if args.save_every_n_epochs is not None:
|
969 |
+
saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs
|
970 |
+
if is_main_process and saving:
|
971 |
+
ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
|
972 |
+
save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1)
|
973 |
+
|
974 |
+
remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)
|
975 |
+
if remove_epoch_no is not None:
|
976 |
+
remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
|
977 |
+
remove_model(remove_ckpt_name)
|
978 |
+
|
979 |
+
if args.save_state:
|
980 |
+
train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)
|
981 |
+
|
982 |
+
self.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
983 |
+
|
984 |
+
# end of epoch
|
985 |
+
|
986 |
+
# metadata["ss_epoch"] = str(num_train_epochs)
|
987 |
+
metadata["ss_training_finished_at"] = str(time.time())
|
988 |
+
|
989 |
+
if is_main_process:
|
990 |
+
network = accelerator.unwrap_model(network)
|
991 |
+
|
992 |
+
accelerator.end_training()
|
993 |
+
|
994 |
+
if is_main_process and (args.save_state or args.save_state_on_train_end):
|
995 |
+
train_util.save_state_on_train_end(args, accelerator)
|
996 |
+
|
997 |
+
if is_main_process:
|
998 |
+
ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
|
999 |
+
save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True)
|
1000 |
+
|
1001 |
+
logger.info("model saved.")
|
1002 |
+
|
1003 |
+
|
1004 |
+
def setup_parser() -> argparse.ArgumentParser:
|
1005 |
+
parser = argparse.ArgumentParser()
|
1006 |
+
|
1007 |
+
add_logging_arguments(parser)
|
1008 |
+
train_util.add_sd_models_arguments(parser)
|
1009 |
+
train_util.add_dataset_arguments(parser, True, True, True)
|
1010 |
+
train_util.add_training_arguments(parser, True)
|
1011 |
+
train_util.add_masked_loss_arguments(parser)
|
1012 |
+
deepspeed_utils.add_deepspeed_arguments(parser)
|
1013 |
+
train_util.add_optimizer_arguments(parser)
|
1014 |
+
config_util.add_config_arguments(parser)
|
1015 |
+
custom_train_functions.add_custom_train_arguments(parser)
|
1016 |
+
|
1017 |
+
parser.add_argument(
|
1018 |
+
"--no_metadata", action="store_true", help="do not save metadata in output model / メタデータを出力先モデルに保存しない"
|
1019 |
+
)
|
1020 |
+
parser.add_argument(
|
1021 |
+
"--save_model_as",
|
1022 |
+
type=str,
|
1023 |
+
default="safetensors",
|
1024 |
+
choices=[None, "ckpt", "pt", "safetensors"],
|
1025 |
+
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)",
|
1026 |
+
)
|
1027 |
+
|
1028 |
+
parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率")
|
1029 |
+
parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率")
|
1030 |
+
|
1031 |
+
parser.add_argument(
|
1032 |
+
"--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"
|
1033 |
+
)
|
1034 |
+
parser.add_argument(
|
1035 |
+
"--network_module", type=str, default=None, help="network module to train / 学習対象のネットワークのモジュール"
|
1036 |
+
)
|
1037 |
+
parser.add_argument(
|
1038 |
+
"--network_dim",
|
1039 |
+
type=int,
|
1040 |
+
default=None,
|
1041 |
+
help="network dimensions (depends on each network) / モジュールの次元数(ネットワークにより定義は異なります)",
|
1042 |
+
)
|
1043 |
+
parser.add_argument(
|
1044 |
+
"--network_alpha",
|
1045 |
+
type=float,
|
1046 |
+
default=1,
|
1047 |
+
help="alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1(旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定)",
|
1048 |
+
)
|
1049 |
+
parser.add_argument(
|
1050 |
+
"--network_dropout",
|
1051 |
+
type=float,
|
1052 |
+
default=None,
|
1053 |
+
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)",
|
1054 |
+
)
|
1055 |
+
parser.add_argument(
|
1056 |
+
"--network_args",
|
1057 |
+
type=str,
|
1058 |
+
default=None,
|
1059 |
+
nargs="*",
|
1060 |
+
help="additional arguments for network (key=value) / ネットワークへの追加の引数",
|
1061 |
+
)
|
1062 |
+
parser.add_argument(
|
1063 |
+
"--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する"
|
1064 |
+
)
|
1065 |
+
parser.add_argument(
|
1066 |
+
"--network_train_text_encoder_only",
|
1067 |
+
action="store_true",
|
1068 |
+
help="only training Text Encoder part / Text Encoder関連部分のみ学習する",
|
1069 |
+
)
|
1070 |
+
parser.add_argument(
|
1071 |
+
"--training_comment",
|
1072 |
+
type=str,
|
1073 |
+
default=None,
|
1074 |
+
help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列",
|
1075 |
+
)
|
1076 |
+
parser.add_argument(
|
1077 |
+
"--dim_from_weights",
|
1078 |
+
action="store_true",
|
1079 |
+
help="automatically determine dim (rank) from network_weights / dim (rank)をnetwork_weightsで指定した重みから自動で決定する",
|
1080 |
+
)
|
1081 |
+
parser.add_argument(
|
1082 |
+
"--scale_weight_norms",
|
1083 |
+
type=float,
|
1084 |
+
default=None,
|
1085 |
+
help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point) / 重みの値をスケーリングして勾配爆発を防ぐ(1が初期値としては適当)",
|
1086 |
+
)
|
1087 |
+
parser.add_argument(
|
1088 |
+
"--base_weights",
|
1089 |
+
type=str,
|
1090 |
+
default=None,
|
1091 |
+
nargs="*",
|
1092 |
+
help="network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みファイル",
|
1093 |
+
)
|
1094 |
+
parser.add_argument(
|
1095 |
+
"--base_weights_multiplier",
|
1096 |
+
type=float,
|
1097 |
+
default=None,
|
1098 |
+
nargs="*",
|
1099 |
+
help="multiplier for network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みの倍率",
|
1100 |
+
)
|
1101 |
+
parser.add_argument(
|
1102 |
+
"--no_half_vae",
|
1103 |
+
action="store_true",
|
1104 |
+
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",
|
1105 |
+
)
|
1106 |
+
return parser
|
1107 |
+
|
1108 |
+
|
1109 |
+
if __name__ == "__main__":
|
1110 |
+
parser = setup_parser()
|
1111 |
+
|
1112 |
+
args = parser.parse_args()
|
1113 |
+
train_util.verify_command_line_training_args(args)
|
1114 |
+
args = train_util.read_config_from_file(args, parser)
|
1115 |
+
|
1116 |
+
trainer = NetworkTrainer()
|
1117 |
+
trainer.train(args)
|