File: //opt/kohya_ss/sd-scripts/train_textual_inversion_XTI.py
import importlib
import argparse
import math
import os
import toml
from multiprocessing import Value
from tqdm import tqdm
import torch
from library import deepspeed_utils
from library.device_utils import init_ipex, clean_memory_on_device
init_ipex()
from accelerate.utils import set_seed
import diffusers
from diffusers import DDPMScheduler
import library
import library.train_util as train_util
import library.huggingface_util as huggingface_util
import library.config_util as config_util
from library.config_util import (
ConfigSanitizer,
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import (
apply_snr_weight,
prepare_scheduler_for_custom_training,
pyramid_noise_like,
apply_noise_offset,
scale_v_prediction_loss_like_noise_prediction,
apply_debiased_estimation,
apply_masked_loss,
)
import library.original_unet as original_unet
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
from library.utils import setup_logging, add_logging_arguments
setup_logging()
import logging
logger = logging.getLogger(__name__)
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
def train(args):
if args.output_name is None:
args.output_name = args.token_string
use_template = args.use_object_template or args.use_style_template
setup_logging(args, reset=True)
train_util.verify_training_args(args)
train_util.prepare_dataset_args(args, True)
if args.sample_every_n_steps is not None or args.sample_every_n_epochs is not None:
logger.warning(
"sample_every_n_steps and sample_every_n_epochs are not supported in this script currently / sample_every_n_stepsとsample_every_n_epochsは現在このスクリプトではサポートされていません"
)
assert (
args.dataset_class is None
), "dataset_class is not supported in this script currently / dataset_classは現在このスクリプトではサポートされていません"
cache_latents = args.cache_latents
if args.seed is not None:
set_seed(args.seed)
tokenizer = train_util.load_tokenizer(args)
# acceleratorを準備する
logger.info("prepare accelerator")
accelerator = train_util.prepare_accelerator(args)
# mixed precisionに対応した型を用意しておき適宜castする
weight_dtype, save_dtype = train_util.prepare_dtype(args)
# モデルを読み込む
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
# Convert the init_word to token_id
if args.init_word is not None:
init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
logger.warning(
f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
)
else:
init_token_ids = None
# add new word to tokenizer, count is num_vectors_per_token
token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
num_added_tokens = tokenizer.add_tokens(token_strings)
assert (
num_added_tokens == args.num_vectors_per_token
), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
logger.info(f"tokens are added: {token_ids}")
assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
token_strings_XTI = []
XTI_layers = [
"IN01",
"IN02",
"IN04",
"IN05",
"IN07",
"IN08",
"MID",
"OUT03",
"OUT04",
"OUT05",
"OUT06",
"OUT07",
"OUT08",
"OUT09",
"OUT10",
"OUT11",
]
for layer_name in XTI_layers:
token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings]
tokenizer.add_tokens(token_strings_XTI)
token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI)
logger.info(f"tokens are added (XTI): {token_ids_XTI}")
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
if init_token_ids is not None:
for i, token_id in enumerate(token_ids_XTI):
token_embeds[token_id] = token_embeds[init_token_ids[(i // 16) % len(init_token_ids)]]
# logger.info(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
# load weights
if args.weights is not None:
embeddings = load_weights(args.weights)
assert len(token_ids) == len(
embeddings
), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
# logger.info(token_ids, embeddings.size())
for token_id, embedding in zip(token_ids_XTI, embeddings):
token_embeds[token_id] = embedding
# logger.info(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
logger.info(f"weighs loaded")
logger.info(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
# データセットを準備する
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, False))
if args.dataset_config is not None:
logger.info(f"Load dataset config from {args.dataset_config}")
user_config = config_util.load_user_config(args.dataset_config)
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
if any(getattr(args, attr) is not None for attr in ignored):
logger.info(
"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(
", ".join(ignored)
)
)
else:
use_dreambooth_method = args.in_json is None
if use_dreambooth_method:
logger.info("Use DreamBooth method.")
user_config = {
"datasets": [
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
]
}
else:
logger.info("Train with captions.")
user_config = {
"datasets": [
{
"subsets": [
{
"image_dir": args.train_data_dir,
"metadata_file": args.in_json,
}
]
}
]
}
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
train_dataset_group, val_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
train_dataset_group.enable_XTI(XTI_layers, token_strings=token_strings)
current_epoch = Value("i", 0)
current_step = Value("i", 0)
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
if use_template:
logger.info(f"use template for training captions. is object: {args.use_object_template}")
templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
replace_to = " ".join(token_strings)
captions = []
for tmpl in templates:
captions.append(tmpl.format(replace_to))
train_dataset_group.add_replacement("", captions)
if args.num_vectors_per_token > 1:
prompt_replacement = (args.token_string, replace_to)
else:
prompt_replacement = None
else:
if args.num_vectors_per_token > 1:
replace_to = " ".join(token_strings)
train_dataset_group.add_replacement(args.token_string, replace_to)
prompt_replacement = (args.token_string, replace_to)
else:
prompt_replacement = None
if args.debug_dataset:
train_util.debug_dataset(train_dataset_group, show_input_ids=True)
return
if len(train_dataset_group) == 0:
logger.error("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
return
if cache_latents:
assert (
train_dataset_group.is_latent_cacheable()
), "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"
# モデルに xformers とか memory efficient attention を組み込む
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa)
original_unet.UNet2DConditionModel.forward = unet_forward_XTI
original_unet.CrossAttnDownBlock2D.forward = downblock_forward_XTI
original_unet.CrossAttnUpBlock2D.forward = upblock_forward_XTI
# 学習を準備する
if cache_latents:
vae.to(accelerator.device, dtype=weight_dtype)
vae.requires_grad_(False)
vae.eval()
with torch.no_grad():
train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
vae.to("cpu")
clean_memory_on_device(accelerator.device)
accelerator.wait_for_everyone()
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
text_encoder.gradient_checkpointing_enable()
# 学習に必要なクラスを準備する
logger.info("prepare optimizer, data loader etc.")
trainable_params = text_encoder.get_input_embeddings().parameters()
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
# dataloaderを準備する
# DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers
train_dataloader = torch.utils.data.DataLoader(
train_dataset_group,
batch_size=1,
shuffle=True,
collate_fn=collator,
num_workers=n_workers,
persistent_workers=args.persistent_data_loader_workers,
)
# 学習ステップ数を計算する
if args.max_train_epochs is not None:
args.max_train_steps = args.max_train_epochs * math.ceil(
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
)
logger.info(
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}"
)
# データセット側にも学習ステップを送信
train_dataset_group.set_max_train_steps(args.max_train_steps)
# lr schedulerを用意する
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
# acceleratorがなんかよろしくやってくれるらしい
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, lr_scheduler
)
index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
# logger.info(len(index_no_updates), torch.sum(index_no_updates))
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
# Freeze all parameters except for the token embeddings in text encoder
text_encoder.requires_grad_(True)
text_encoder.text_model.encoder.requires_grad_(False)
text_encoder.text_model.final_layer_norm.requires_grad_(False)
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
# text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
unet.requires_grad_(False)
unet.to(accelerator.device, dtype=weight_dtype)
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
unet.train()
else:
unet.eval()
if not cache_latents:
vae.requires_grad_(False)
vae.eval()
vae.to(accelerator.device, dtype=weight_dtype)
# 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
if args.full_fp16:
train_util.patch_accelerator_for_fp16_training(accelerator)
text_encoder.to(weight_dtype)
# resumeする
train_util.resume_from_local_or_hf_if_specified(accelerator, args)
# epoch数を計算する
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
# 学習する
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("running training / 学習開始")
logger.info(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
logger.info(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
logger.info(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
logger.info(f" num epochs / epoch数: {num_train_epochs}")
logger.info(f" batch size per device / バッチサイズ: {args.train_batch_size}")
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}"
)
logger.info(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
logger.info(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
global_step = 0
noise_scheduler = DDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
)
prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
if args.zero_terminal_snr:
custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
if accelerator.is_main_process:
init_kwargs = {}
if args.wandb_run_name:
init_kwargs["wandb"] = {"name": args.wandb_run_name}
if args.log_tracker_config is not None:
init_kwargs = toml.load(args.log_tracker_config)
accelerator.init_trackers(
"textual_inversion" if args.log_tracker_name is None else args.log_tracker_name,
config=train_util.get_sanitized_config_or_none(args),
init_kwargs=init_kwargs,
)
# function for saving/removing
def save_model(ckpt_name, embs, steps, epoch_no, force_sync_upload=False):
os.makedirs(args.output_dir, exist_ok=True)
ckpt_file = os.path.join(args.output_dir, ckpt_name)
logger.info("")
logger.info(f"saving checkpoint: {ckpt_file}")
save_weights(ckpt_file, embs, save_dtype)
if args.huggingface_repo_id is not None:
huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
def remove_model(old_ckpt_name):
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
if os.path.exists(old_ckpt_file):
logger.info(f"removing old checkpoint: {old_ckpt_file}")
os.remove(old_ckpt_file)
# training loop
for epoch in range(num_train_epochs):
logger.info("")
logger.info(f"epoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1
text_encoder.train()
loss_total = 0
for step, batch in enumerate(train_dataloader):
current_step.value = global_step
with accelerator.accumulate(text_encoder):
with torch.no_grad():
if "latents" in batch and batch["latents"] is not None:
latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
else:
# latentに変換
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
b_size = latents.shape[0]
# Get the text embedding for conditioning
input_ids = batch["input_ids"].to(accelerator.device)
# weight_dtype) use float instead of fp16/bf16 because text encoder is float
encoder_hidden_states = torch.stack(
[
train_util.get_hidden_states(args, s, tokenizer, text_encoder, weight_dtype)
for s in torch.split(input_ids, 1, dim=1)
]
)
# Sample noise, sample a random timestep for each image, and add noise to the latents,
# with noise offset and/or multires noise if specified
noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
# Predict the noise residual
with accelerator.autocast():
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
if args.v_parameterization:
# v-parameterization training
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
target = noise
huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
loss = apply_masked_loss(loss, batch)
loss = loss.mean([1, 2, 3])
loss_weights = batch["loss_weights"] # 各sampleごとのweight
loss = loss * loss_weights
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.debiased_estimation_loss:
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
accelerator.backward(loss)
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
params_to_clip = text_encoder.get_input_embeddings().parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
# Let's make sure we don't update any embedding weights besides the newly added token
with torch.no_grad():
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[
index_no_updates
]
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
# TODO: fix sample_images
# train_util.sample_images(
# accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
# )
# 指定ステップごとにモデルを保存
if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
updated_embs = (
accelerator.unwrap_model(text_encoder)
.get_input_embeddings()
.weight[token_ids_XTI]
.data.detach()
.clone()
)
ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
save_model(ckpt_name, updated_embs, global_step, epoch)
if args.save_state:
train_util.save_and_remove_state_stepwise(args, accelerator, global_step)
remove_step_no = train_util.get_remove_step_no(args, global_step)
if remove_step_no is not None:
remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)
remove_model(remove_ckpt_name)
current_loss = loss.detach().item()
if len(accelerator.trackers) > 0:
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
if (
args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
): # tracking d*lr value
logs["lr/d*lr"] = (
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
)
accelerator.log(logs, step=global_step)
loss_total += current_loss
avr_loss = loss_total / (step + 1)
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if len(accelerator.trackers) > 0:
logs = {"loss/epoch": loss_total / len(train_dataloader)}
accelerator.log(logs, step=epoch + 1)
accelerator.wait_for_everyone()
updated_embs = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[token_ids_XTI].data.detach().clone()
if args.save_every_n_epochs is not None:
saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs
if accelerator.is_main_process and saving:
ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
save_model(ckpt_name, updated_embs, epoch + 1, global_step)
remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)
if remove_epoch_no is not None:
remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
remove_model(remove_ckpt_name)
if args.save_state:
train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)
# TODO: fix sample_images
# train_util.sample_images(
# accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
# )
# end of epoch
is_main_process = accelerator.is_main_process
if is_main_process:
text_encoder = accelerator.unwrap_model(text_encoder)
accelerator.end_training()
if is_main_process and (args.save_state or args.save_state_on_train_end):
train_util.save_state_on_train_end(args, accelerator)
updated_embs = text_encoder.get_input_embeddings().weight[token_ids_XTI].data.detach().clone()
del accelerator # この後メモリを使うのでこれは消す
if is_main_process:
ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
save_model(ckpt_name, updated_embs, global_step, num_train_epochs, force_sync_upload=True)
logger.info("model saved.")
def save_weights(file, updated_embs, save_dtype):
updated_embs = updated_embs.reshape(16, -1, updated_embs.shape[-1])
updated_embs = updated_embs.chunk(16)
XTI_layers = [
"IN01",
"IN02",
"IN04",
"IN05",
"IN07",
"IN08",
"MID",
"OUT03",
"OUT04",
"OUT05",
"OUT06",
"OUT07",
"OUT08",
"OUT09",
"OUT10",
"OUT11",
]
state_dict = {}
for i, layer_name in enumerate(XTI_layers):
state_dict[layer_name] = updated_embs[i].squeeze(0).detach().clone().to("cpu").to(save_dtype)
# if save_dtype is not None:
# for key in list(state_dict.keys()):
# v = state_dict[key]
# v = v.detach().clone().to("cpu").to(save_dtype)
# state_dict[key] = v
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import save_file
save_file(state_dict, file)
else:
torch.save(state_dict, file) # can be loaded in Web UI
def load_weights(file):
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import load_file
data = load_file(file)
else:
raise ValueError(f"NOT XTI: {file}")
if len(data.values()) != 16:
raise ValueError(f"NOT XTI: {file}")
emb = torch.concat([x for x in data.values()])
return emb
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
add_logging_arguments(parser)
train_util.add_sd_models_arguments(parser)
train_util.add_dataset_arguments(parser, True, True, False)
train_util.add_training_arguments(parser, True)
train_util.add_masked_loss_arguments(parser)
deepspeed_utils.add_deepspeed_arguments(parser)
train_util.add_optimizer_arguments(parser)
config_util.add_config_arguments(parser)
custom_train_functions.add_custom_train_arguments(parser, False)
parser.add_argument(
"--save_model_as",
type=str,
default="pt",
choices=[None, "ckpt", "pt", "safetensors"],
help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）",
)
parser.add_argument(
"--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み"
)
parser.add_argument(
"--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
)
parser.add_argument(
"--token_string",
type=str,
default=None,
help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
)
parser.add_argument(
"--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可"
)
parser.add_argument(
"--use_object_template",
action="store_true",
help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
)
parser.add_argument(
"--use_style_template",
action="store_true",
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
)
return parser
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
train_util.verify_command_line_training_args(args)
args = train_util.read_config_from_file(args, parser)
train(args)