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- # DreamBooth training
- # XXX dropped option: fine_tune
- import gc
- import time
- import argparse
- import itertools
- import math
- import os
- import toml
- from multiprocessing import Value
- from tqdm import tqdm
- import torch
- from accelerate.utils import set_seed
- import diffusers
- from diffusers import DDPMScheduler
- import library.train_util as train_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, get_weighted_text_embeddings, pyramid_noise_like
- def train(args):
- train_util.verify_training_args(args)
- train_util.prepare_dataset_args(args, False)
- cache_latents = args.cache_latents
- if args.seed is not None:
- set_seed(args.seed) # 乱数系列を初期化する
- tokenizer = train_util.load_tokenizer(args)
- blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, True))
- if args.dataset_config is not None:
- print(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"]
- if any(getattr(args, attr) is not None for attr in ignored):
- print(
- "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:
- user_config = {
- "datasets": [
- {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
- ]
- }
- blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
- train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
- current_epoch = Value("i", 0)
- current_step = Value("i", 0)
- ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
- collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
- if args.no_token_padding:
- train_dataset_group.disable_token_padding()
- if args.debug_dataset:
- train_util.debug_dataset(train_dataset_group)
- 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"
- # acceleratorを準備する
- print("prepare accelerator")
- if args.gradient_accumulation_steps > 1:
- print(
- f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong"
- )
- print(
- f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル(U-NetおよびText Encoder)の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です"
- )
- accelerator, unwrap_model = 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, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype, accelerator)
- # verify load/save model formats
- if load_stable_diffusion_format:
- src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
- src_diffusers_model_path = None
- else:
- src_stable_diffusion_ckpt = None
- src_diffusers_model_path = args.pretrained_model_name_or_path
- if args.save_model_as is None:
- save_stable_diffusion_format = load_stable_diffusion_format
- use_safetensors = args.use_safetensors
- else:
- save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
- use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
- # 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)
- # 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")
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- gc.collect()
- accelerator.wait_for_everyone()
- # 学習を準備する:モデルを適切な状態にする
- train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
- unet.requires_grad_(True) # 念のため追加
- text_encoder.requires_grad_(train_text_encoder)
- if not train_text_encoder:
- print("Text Encoder is not trained.")
- if args.gradient_checkpointing:
- unet.enable_gradient_checkpointing()
- text_encoder.gradient_checkpointing_enable()
- if not cache_latents:
- vae.requires_grad_(False)
- vae.eval()
- vae.to(accelerator.device, dtype=weight_dtype)
- # 学習に必要なクラスを準備する
- print("prepare optimizer, data loader etc.")
- if train_text_encoder:
- trainable_params = itertools.chain(unet.parameters(), text_encoder.parameters())
- else:
- trainable_params = unet.parameters()
- _, _, optimizer = train_util.get_optimizer(args, trainable_params)
- # dataloaderを準備する
- # DataLoaderのプロセス数:0はメインプロセスになる
- n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
- train_dataloader = torch.utils.data.DataLoader(
- train_dataset_group,
- batch_size=1,
- shuffle=True,
- collate_fn=collater,
- 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
- )
- print(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)
- if args.stop_text_encoder_training is None:
- args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end
- # lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
- lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
- # 実験的機能:勾配も含めたfp16学習を行う モデル全体をfp16にする
- if args.full_fp16:
- assert (
- args.mixed_precision == "fp16"
- ), "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。"
- print("enable full fp16 training.")
- unet.to(weight_dtype)
- text_encoder.to(weight_dtype)
- # acceleratorがなんかよろしくやってくれるらしい
- if train_text_encoder:
- unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- unet, text_encoder, optimizer, train_dataloader, lr_scheduler
- )
- else:
- unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
- # transform DDP after prepare
- text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
- if not train_text_encoder:
- text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error
- # 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)
- # 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
- print("running training / 学習開始")
- print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
- print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
- print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
- print(f" num epochs / epoch数: {num_train_epochs}")
- print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
- 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}")
- print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
- print(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
- )
- if accelerator.is_main_process:
- accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name)
- loss_list = []
- loss_total = 0.0
- for epoch in range(num_train_epochs):
- print(f"epoch {epoch+1}/{num_train_epochs}")
- current_epoch.value = epoch + 1
- # 指定したステップ数までText Encoderを学習する:epoch最初の状態
- unet.train()
- # train==True is required to enable gradient_checkpointing
- if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
- text_encoder.train()
- for step, batch in enumerate(train_dataloader):
- current_step.value = global_step
- # 指定したステップ数でText Encoderの学習を止める
- if global_step == args.stop_text_encoder_training:
- print(f"stop text encoder training at step {global_step}")
- if not args.gradient_checkpointing:
- text_encoder.train(False)
- text_encoder.requires_grad_(False)
- with accelerator.accumulate(unet):
- with torch.no_grad():
- # latentに変換
- if cache_latents:
- latents = batch["latents"].to(accelerator.device)
- else:
- latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
- latents = latents * 0.18215
- b_size = latents.shape[0]
- # Sample noise that we'll add to the latents
- noise = torch.randn_like(latents, device=latents.device)
- if args.noise_offset:
- # https://www.crosslabs.org//blog/diffusion-with-offset-noise
- noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
- elif args.multires_noise_iterations:
- noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
- # Get the text embedding for conditioning
- with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
- if args.weighted_captions:
- encoder_hidden_states = get_weighted_text_embeddings(
- tokenizer,
- text_encoder,
- batch["captions"],
- accelerator.device,
- args.max_token_length // 75 if args.max_token_length else 1,
- clip_skip=args.clip_skip,
- )
- else:
- input_ids = batch["input_ids"].to(accelerator.device)
- encoder_hidden_states = train_util.get_hidden_states(
- args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype
- )
- # Sample a random timestep for each image
- timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
- timesteps = timesteps.long()
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
- # Predict the noise residual
- with accelerator.autocast():
- noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
- if args.v_parameterization:
- # v-parameterization training
- target = noise_scheduler.get_velocity(latents, noise, timesteps)
- else:
- target = noise
- loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
- 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)
- 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:
- if train_text_encoder:
- params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
- else:
- params_to_clip = unet.parameters()
- accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad(set_to_none=True)
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- progress_bar.update(1)
- global_step += 1
- train_util.sample_images(
- accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
- )
- # 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:
- src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
- train_util.save_sd_model_on_epoch_end_or_stepwise(
- args,
- False,
- accelerator,
- src_path,
- save_stable_diffusion_format,
- use_safetensors,
- save_dtype,
- epoch,
- num_train_epochs,
- global_step,
- unwrap_model(text_encoder),
- unwrap_model(unet),
- vae,
- )
- current_loss = loss.detach().item()
- if args.logging_dir is not None:
- logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
- if args.optimizer_type.lower() == "DAdaptation".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)
- if epoch == 0:
- loss_list.append(current_loss)
- else:
- loss_total -= loss_list[step]
- loss_list[step] = current_loss
- loss_total += current_loss
- avr_loss = loss_total / len(loss_list)
- 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 args.logging_dir is not None:
- logs = {"loss/epoch": loss_total / len(loss_list)}
- accelerator.log(logs, step=epoch + 1)
- accelerator.wait_for_everyone()
- if args.save_every_n_epochs is not None:
- if accelerator.is_main_process:
- # checking for saving is in util
- src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
- train_util.save_sd_model_on_epoch_end_or_stepwise(
- args,
- True,
- accelerator,
- src_path,
- save_stable_diffusion_format,
- use_safetensors,
- save_dtype,
- epoch,
- num_train_epochs,
- global_step,
- unwrap_model(text_encoder),
- unwrap_model(unet),
- vae,
- )
- train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
- is_main_process = accelerator.is_main_process
- if is_main_process:
- unet = unwrap_model(unet)
- text_encoder = unwrap_model(text_encoder)
- accelerator.end_training()
- if args.save_state and is_main_process:
- train_util.save_state_on_train_end(args, accelerator)
- del accelerator # この後メモリを使うのでこれは消す
- if is_main_process:
- src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
- train_util.save_sd_model_on_train_end(
- args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae
- )
- print("model saved.")
- def setup_parser() -> argparse.ArgumentParser:
- parser = argparse.ArgumentParser()
- train_util.add_sd_models_arguments(parser)
- train_util.add_dataset_arguments(parser, True, False, True)
- train_util.add_training_arguments(parser, True)
- train_util.add_sd_saving_arguments(parser)
- train_util.add_optimizer_arguments(parser)
- config_util.add_config_arguments(parser)
- custom_train_functions.add_custom_train_arguments(parser)
- parser.add_argument(
- "--no_token_padding",
- action="store_true",
- help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする(Diffusers版DreamBoothと同じ動作)",
- )
- parser.add_argument(
- "--stop_text_encoder_training",
- type=int,
- default=None,
- help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない",
- )
- return parser
- if __name__ == "__main__":
- parser = setup_parser()
- args = parser.parse_args()
- args = train_util.read_config_from_file(args, parser)
- train(args)
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