# training with captions # XXX dropped option: hypernetwork training import argparse import gc 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 def train(args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) 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(False, True, 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", "in_json"] 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": [ { "image_dir": args.train_data_dir, "metadata_file": args.in_json, } ] } ] } 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.debug_dataset: train_util.debug_dataset(train_dataset_group) return if len(train_dataset_group) == 0: print( "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプション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" # acceleratorを準備する print("prepare accelerator") 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) # 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()) # Diffusers版のxformers使用フラグを設定する関数 def set_diffusers_xformers_flag(model, valid): # model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう # pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀) # U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか # 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^) # Recursively walk through all the children. # Any children which exposes the set_use_memory_efficient_attention_xformers method # gets the message def fn_recursive_set_mem_eff(module: torch.nn.Module): if hasattr(module, "set_use_memory_efficient_attention_xformers"): module.set_use_memory_efficient_attention_xformers(valid) for child in module.children(): fn_recursive_set_mem_eff(child) fn_recursive_set_mem_eff(model) # モデルに xformers とか memory efficient attention を組み込む if args.diffusers_xformers: print("Use xformers by Diffusers") set_diffusers_xformers_flag(unet, True) else: # Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある print("Disable Diffusers' xformers") set_diffusers_xformers_flag(unet, False) 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) vae.to("cpu") if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() # 学習を準備する：モデルを適切な状態にする training_models = [] if args.gradient_checkpointing: unet.enable_gradient_checkpointing() training_models.append(unet) if args.train_text_encoder: print("enable text encoder training") if args.gradient_checkpointing: text_encoder.gradient_checkpointing_enable() training_models.append(text_encoder) else: text_encoder.to(accelerator.device, dtype=weight_dtype) text_encoder.requires_grad_(False) # text encoderは学習しない if args.gradient_checkpointing: text_encoder.gradient_checkpointing_enable() text_encoder.train() # required for gradient_checkpointing else: text_encoder.eval() if not cache_latents: vae.requires_grad_(False) vae.eval() vae.to(accelerator.device, dtype=weight_dtype) for m in training_models: m.requires_grad_(True) params = [] for m in training_models: params.extend(m.parameters()) params_to_optimize = params # 学習に必要なクラスを準備する print("prepare optimizer, data loader etc.") _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) # 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) # lr schedulerを用意する 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 args.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) # 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 if args.resume is not None: print(f"resume training from state: {args.resume}") accelerator.load_state(args.resume) # 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 examples / サンプル数: {train_dataset_group.num_train_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 accumulation 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("finetuning") for epoch in range(num_train_epochs): print(f"epoch {epoch+1}/{num_train_epochs}") current_epoch.value = epoch + 1 for m in training_models: m.train() loss_total = 0 for step, batch in enumerate(train_dataloader): current_step.value = global_step with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device) 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] with torch.set_grad_enabled(args.train_text_encoder): # Get the text embedding for conditioning 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 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) # 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 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 if args.min_snr_gamma: # do not mean over batch dimension for snr weight loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") loss = loss.mean([1, 2, 3]) loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma) loss = loss.mean() # mean over batch dimension else: loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean") accelerator.backward(loss) if accelerator.sync_gradients and args.max_grad_norm != 0.0: params_to_clip = [] for m in training_models: params_to_clip.extend(m.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 ) current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず 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) # TODO moving averageにする 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 args.logging_dir is not None: logs = {"loss/epoch": loss_total / len(train_dataloader)} accelerator.log(logs, step=epoch + 1) accelerator.wait_for_everyone() if args.save_every_n_epochs is not None: src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path train_util.save_sd_model_on_epoch_end( args, 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: 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, False, True, True) train_util.add_training_arguments(parser, False) 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("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する") parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する") return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() args = train_util.read_config_from_file(args, parser) train(args)