diff --git a/train_db - Copy.py b/train_db - Copy.py deleted file mode 100644 index f441d5d..0000000 --- a/train_db - Copy.py +++ /dev/null @@ -1,426 +0,0 @@ -# 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 - -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) - collater = train_util.collater_class(current_epoch,current_step) - - 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) - - # 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) - 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 - 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) - - 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 - 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 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") - - 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) - - # Get the text embedding for conditioning - with torch.set_grad_enabled(global_step < args.stop_text_encoder_training): - 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 - 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 - ) - - 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: - 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, 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) diff --git a/train_network - Copy.py b/train_network - Copy.py deleted file mode 100644 index 20ad2c4..0000000 --- a/train_network - Copy.py +++ /dev/null @@ -1,710 +0,0 @@ -from torch.nn.parallel import DistributedDataParallel as DDP -import importlib -import argparse -import gc -import math -import os -import random -import time -import json -import toml -from multiprocessing import Value - -from tqdm import tqdm -import torch -from accelerate.utils import set_seed -from diffusers import DDPMScheduler - -import library.train_util as train_util -from library.train_util import ( - DreamBoothDataset, -) -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 - - -# TODO 他のスクリプトと共通化する -def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler): - logs = {"loss/current": current_loss, "loss/average": avr_loss} - - if args.network_train_unet_only: - logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0]) - elif args.network_train_text_encoder_only: - logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0]) - else: - logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0]) - logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1]) # may be same to textencoder - - if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value of unet. - logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"] - - return logs - - -def train(args): - session_id = random.randint(0, 2**32) - training_started_at = time.time() - train_util.verify_training_args(args) - train_util.prepare_dataset_args(args, True) - - cache_latents = args.cache_latents - use_dreambooth_method = args.in_json is None - use_user_config = args.dataset_config is not None - - if args.seed is not None: - set_seed(args.seed) - - tokenizer = train_util.load_tokenizer(args) - - # データセットを準備する - blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, True)) - if use_user_config: - 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", "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: - if use_dreambooth_method: - print("Use DreamBooth method.") - user_config = { - "datasets": [ - {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)} - ] - } - else: - print("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 = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) - - current_epoch = Value('i',0) - current_step = Value('i',0) - collater = train_util.collater_class(current_epoch,current_step) - - if args.debug_dataset: - train_util.debug_dataset(train_dataset_group) - return - if len(train_dataset_group) == 0: - print( - "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）" - ) - 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) - is_main_process = accelerator.is_main_process - - # 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) - - # work on low-ram device - if args.lowram: - text_encoder.to("cuda") - unet.to("cuda") - - # 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) - vae.to("cpu") - if torch.cuda.is_available(): - torch.cuda.empty_cache() - gc.collect() - - # prepare network - import sys - - sys.path.append(os.path.dirname(__file__)) - print("import network module:", args.network_module) - network_module = importlib.import_module(args.network_module) - - net_kwargs = {} - if args.network_args is not None: - for net_arg in args.network_args: - key, value = net_arg.split("=") - net_kwargs[key] = value - - # if a new network is added in future, add if ~ then blocks for each network (;'∀') - network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs) - if network is None: - return - - if args.network_weights is not None: - print("load network weights from:", args.network_weights) - network.load_weights(args.network_weights) - - train_unet = not args.network_train_text_encoder_only - train_text_encoder = not args.network_train_unet_only - network.apply_to(text_encoder, unet, train_text_encoder, train_unet) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - text_encoder.gradient_checkpointing_enable() - network.enable_gradient_checkpointing() # may have no effect - - # 学習に必要なクラスを準備する - print("prepare optimizer, data loader etc.") - - trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr) - optimizer_name, optimizer_args, 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) - if is_main_process: - 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.") - network.to(weight_dtype) - - # acceleratorがなんかよろしくやってくれるらしい - if train_unet and train_text_encoder: - unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler - ) - elif train_unet: - unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, network, optimizer, train_dataloader, lr_scheduler - ) - elif train_text_encoder: - text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - text_encoder, network, optimizer, train_dataloader, lr_scheduler - ) - else: - network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler) - - unet.requires_grad_(False) - unet.to(accelerator.device, dtype=weight_dtype) - text_encoder.requires_grad_(False) - text_encoder.to(accelerator.device) - if args.gradient_checkpointing: # according to TI example in Diffusers, train is required - unet.train() - text_encoder.train() - - # set top parameter requires_grad = True for gradient checkpointing works - if type(text_encoder) == DDP: - text_encoder.module.text_model.embeddings.requires_grad_(True) - else: - text_encoder.text_model.embeddings.requires_grad_(True) - else: - unet.eval() - text_encoder.eval() - - # support DistributedDataParallel - if type(text_encoder) == DDP: - text_encoder = text_encoder.module - unet = unet.module - network = network.module - - network.prepare_grad_etc(text_encoder, unet) - - 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) - - # 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 - # TODO: find a way to handle total batch size when there are multiple datasets - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - if is_main_process: - 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: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}") - # 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}") - - # TODO refactor metadata creation and move to util - metadata = { - "ss_session_id": session_id, # random integer indicating which group of epochs the model came from - "ss_training_started_at": training_started_at, # unix timestamp - "ss_output_name": args.output_name, - "ss_learning_rate": args.learning_rate, - "ss_text_encoder_lr": args.text_encoder_lr, - "ss_unet_lr": args.unet_lr, - "ss_num_train_images": train_dataset_group.num_train_images, - "ss_num_reg_images": train_dataset_group.num_reg_images, - "ss_num_batches_per_epoch": len(train_dataloader), - "ss_num_epochs": num_train_epochs, - "ss_gradient_checkpointing": args.gradient_checkpointing, - "ss_gradient_accumulation_steps": args.gradient_accumulation_steps, - "ss_max_train_steps": args.max_train_steps, - "ss_lr_warmup_steps": args.lr_warmup_steps, - "ss_lr_scheduler": args.lr_scheduler, - "ss_network_module": args.network_module, - "ss_network_dim": args.network_dim, # None means default because another network than LoRA may have another default dim - "ss_network_alpha": args.network_alpha, # some networks may not use this value - "ss_mixed_precision": args.mixed_precision, - "ss_full_fp16": bool(args.full_fp16), - "ss_v2": bool(args.v2), - "ss_clip_skip": args.clip_skip, - "ss_max_token_length": args.max_token_length, - "ss_cache_latents": bool(args.cache_latents), - "ss_seed": args.seed, - "ss_lowram": args.lowram, - "ss_noise_offset": args.noise_offset, - "ss_training_comment": args.training_comment, # will not be updated after training - "ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(), - "ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""), - "ss_max_grad_norm": args.max_grad_norm, - "ss_caption_dropout_rate": args.caption_dropout_rate, - "ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs, - "ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate, - "ss_face_crop_aug_range": args.face_crop_aug_range, - "ss_prior_loss_weight": args.prior_loss_weight, - } - - if use_user_config: - # save metadata of multiple datasets - # NOTE: pack "ss_datasets" value as json one time - # or should also pack nested collections as json? - datasets_metadata = [] - tag_frequency = {} # merge tag frequency for metadata editor - dataset_dirs_info = {} # merge subset dirs for metadata editor - - for dataset in train_dataset_group.datasets: - is_dreambooth_dataset = isinstance(dataset, DreamBoothDataset) - dataset_metadata = { - "is_dreambooth": is_dreambooth_dataset, - "batch_size_per_device": dataset.batch_size, - "num_train_images": dataset.num_train_images, # includes repeating - "num_reg_images": dataset.num_reg_images, - "resolution": (dataset.width, dataset.height), - "enable_bucket": bool(dataset.enable_bucket), - "min_bucket_reso": dataset.min_bucket_reso, - "max_bucket_reso": dataset.max_bucket_reso, - "tag_frequency": dataset.tag_frequency, - "bucket_info": dataset.bucket_info, - } - - subsets_metadata = [] - for subset in dataset.subsets: - subset_metadata = { - "img_count": subset.img_count, - "num_repeats": subset.num_repeats, - "color_aug": bool(subset.color_aug), - "flip_aug": bool(subset.flip_aug), - "random_crop": bool(subset.random_crop), - "shuffle_caption": bool(subset.shuffle_caption), - "keep_tokens": subset.keep_tokens, - } - - image_dir_or_metadata_file = None - if subset.image_dir: - image_dir = os.path.basename(subset.image_dir) - subset_metadata["image_dir"] = image_dir - image_dir_or_metadata_file = image_dir - - if is_dreambooth_dataset: - subset_metadata["class_tokens"] = subset.class_tokens - subset_metadata["is_reg"] = subset.is_reg - if subset.is_reg: - image_dir_or_metadata_file = None # not merging reg dataset - else: - metadata_file = os.path.basename(subset.metadata_file) - subset_metadata["metadata_file"] = metadata_file - image_dir_or_metadata_file = metadata_file # may overwrite - - subsets_metadata.append(subset_metadata) - - # merge dataset dir: not reg subset only - # TODO update additional-network extension to show detailed dataset config from metadata - if image_dir_or_metadata_file is not None: - # datasets may have a certain dir multiple times - v = image_dir_or_metadata_file - i = 2 - while v in dataset_dirs_info: - v = image_dir_or_metadata_file + f" ({i})" - i += 1 - image_dir_or_metadata_file = v - - dataset_dirs_info[image_dir_or_metadata_file] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count} - - dataset_metadata["subsets"] = subsets_metadata - datasets_metadata.append(dataset_metadata) - - # merge tag frequency: - for ds_dir_name, ds_freq_for_dir in dataset.tag_frequency.items(): - # 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 - # 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 - # 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 - if ds_dir_name in tag_frequency: - continue - tag_frequency[ds_dir_name] = ds_freq_for_dir - - metadata["ss_datasets"] = json.dumps(datasets_metadata) - metadata["ss_tag_frequency"] = json.dumps(tag_frequency) - metadata["ss_dataset_dirs"] = json.dumps(dataset_dirs_info) - else: - # conserving backward compatibility when using train_dataset_dir and reg_dataset_dir - assert ( - len(train_dataset_group.datasets) == 1 - ), 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。" - - dataset = train_dataset_group.datasets[0] - - dataset_dirs_info = {} - reg_dataset_dirs_info = {} - if use_dreambooth_method: - for subset in dataset.subsets: - info = reg_dataset_dirs_info if subset.is_reg else dataset_dirs_info - info[os.path.basename(subset.image_dir)] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count} - else: - for subset in dataset.subsets: - dataset_dirs_info[os.path.basename(subset.metadata_file)] = { - "n_repeats": subset.num_repeats, - "img_count": subset.img_count, - } - - metadata.update( - { - "ss_batch_size_per_device": args.train_batch_size, - "ss_total_batch_size": total_batch_size, - "ss_resolution": args.resolution, - "ss_color_aug": bool(args.color_aug), - "ss_flip_aug": bool(args.flip_aug), - "ss_random_crop": bool(args.random_crop), - "ss_shuffle_caption": bool(args.shuffle_caption), - "ss_enable_bucket": bool(dataset.enable_bucket), - "ss_bucket_no_upscale": bool(dataset.bucket_no_upscale), - "ss_min_bucket_reso": dataset.min_bucket_reso, - "ss_max_bucket_reso": dataset.max_bucket_reso, - "ss_keep_tokens": args.keep_tokens, - "ss_dataset_dirs": json.dumps(dataset_dirs_info), - "ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info), - "ss_tag_frequency": json.dumps(dataset.tag_frequency), - "ss_bucket_info": json.dumps(dataset.bucket_info), - } - ) - - # add extra args - if args.network_args: - metadata["ss_network_args"] = json.dumps(net_kwargs) - # for key, value in net_kwargs.items(): - # metadata["ss_arg_" + key] = value - - # model name and hash - if args.pretrained_model_name_or_path is not None: - sd_model_name = args.pretrained_model_name_or_path - if os.path.exists(sd_model_name): - metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name) - metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(sd_model_name) - sd_model_name = os.path.basename(sd_model_name) - metadata["ss_sd_model_name"] = sd_model_name - - if args.vae is not None: - vae_name = args.vae - if os.path.exists(vae_name): - metadata["ss_vae_hash"] = train_util.model_hash(vae_name) - metadata["ss_new_vae_hash"] = train_util.calculate_sha256(vae_name) - vae_name = os.path.basename(vae_name) - metadata["ss_vae_name"] = vae_name - - metadata = {k: str(v) for k, v in metadata.items()} - - # make minimum metadata for filtering - minimum_keys = ["ss_network_module", "ss_network_dim", "ss_network_alpha", "ss_network_args"] - minimum_metadata = {} - for key in minimum_keys: - if key in metadata: - minimum_metadata[key] = metadata[key] - - 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("network_train") - - loss_list = [] - loss_total = 0.0 - del train_dataset_group - for epoch in range(num_train_epochs): - if is_main_process: - print(f"epoch {epoch+1}/{num_train_epochs}") - current_epoch.value = epoch+1 - - metadata["ss_epoch"] = str(epoch + 1) - - network.on_epoch_start(text_encoder, unet) - - for step, batch in enumerate(train_dataloader): - current_step.value = global_step - with accelerator.accumulate(network): - 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(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, 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 - 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: - params_to_clip = network.get_trainable_params() - 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() - 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 args.logging_dir is not None: - logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler) - accelerator.log(logs, step=global_step) - - 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: - model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name - - def save_func(): - ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as - ckpt_file = os.path.join(args.output_dir, ckpt_name) - metadata["ss_training_finished_at"] = str(time.time()) - print(f"saving checkpoint: {ckpt_file}") - unwrap_model(network).save_weights(ckpt_file, save_dtype, minimum_metadata if args.no_metadata else metadata) - - def remove_old_func(old_epoch_no): - old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as - old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name) - if os.path.exists(old_ckpt_file): - print(f"removing old checkpoint: {old_ckpt_file}") - os.remove(old_ckpt_file) - - if is_main_process: - saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs) - if saving and args.save_state: - train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1) - - train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet) - - # end of epoch - - metadata["ss_epoch"] = str(num_train_epochs) - metadata["ss_training_finished_at"] = str(time.time()) - - if is_main_process: - network = unwrap_model(network) - - 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: - os.makedirs(args.output_dir, exist_ok=True) - - model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name - ckpt_name = model_name + "." + args.save_model_as - ckpt_file = os.path.join(args.output_dir, ckpt_name) - - print(f"save trained model to {ckpt_file}") - network.save_weights(ckpt_file, save_dtype, minimum_metadata if args.no_metadata else metadata) - 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, True, True) - train_util.add_training_arguments(parser, True) - train_util.add_optimizer_arguments(parser) - config_util.add_config_arguments(parser) - custom_train_functions.add_custom_train_arguments(parser) - - parser.add_argument("--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") - parser.add_argument( - "--save_model_as", - type=str, - default="safetensors", - choices=[None, "ckpt", "pt", "safetensors"], - 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）", - ) - - parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率") - parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率") - - parser.add_argument("--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") - parser.add_argument("--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") - parser.add_argument( - "--network_dim", type=int, default=None, 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）" - ) - parser.add_argument( - "--network_alpha", - type=float, - default=1, - 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）", - ) - parser.add_argument( - "--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数" - ) - parser.add_argument("--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") - parser.add_argument( - "--network_train_text_encoder_only", action="store_true", help="only training Text Encoder part / Text Encoder関連部分のみ学習する" - ) - parser.add_argument( - "--training_comment", type=str, default=None, 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" - ) - - return parser - - -if __name__ == "__main__": - parser = setup_parser() - - args = parser.parse_args() - args = train_util.read_config_from_file(args, parser) - - train(args) diff --git a/train_textual_inversion - Copy.py b/train_textual_inversion - Copy.py deleted file mode 100644 index 681bc62..0000000 --- a/train_textual_inversion - Copy.py +++ /dev/null @@ -1,589 +0,0 @@ -import importlib -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 - -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 - - 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) - - tokenizer = train_util.load_tokenizer(args) - - # 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, _ = train_util.load_target_model(args, weight_dtype) - - # 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: - print( - 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) - print(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)}" - - # 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): - token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]] - # print(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)}" - # print(token_ids, embeddings.size()) - for token_id, embedding in zip(token_ids, embeddings): - token_embeds[token_id] = embedding - # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min()) - print(f"weighs loaded") - - print(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, False)) - 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", "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: - use_dreambooth_method = args.in_json is None - if use_dreambooth_method: - print("Use DreamBooth method.") - user_config = { - "datasets": [ - {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)} - ] - } - else: - print("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 = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) - - current_epoch = Value('i',0) - current_step = Value('i',0) - collater = train_util.collater_class(current_epoch,current_step) - - # 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: - print("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: - print("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) - - # 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() - - 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 - print("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) - - # 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[0] - # print(len(index_no_updates), torch.sum(index_no_updates)) - orig_embeds_params = 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 - 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 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("textual_inversion") - - for epoch in range(num_train_epochs): - print(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) - 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 = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float) - - # 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 - - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") - loss = loss.mean([1, 2, 3]) - - if args.min_snr_gamma: - loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma) - - loss_weights = batch["loss_weights"] # 各sampleごとのweight - loss = loss * loss_weights - - 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(): - 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 - - train_util.sample_images( - accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement - ) - - 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) - - 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() - - updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone() - - if args.save_every_n_epochs is not None: - model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name - - def save_func(): - ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as - ckpt_file = os.path.join(args.output_dir, ckpt_name) - print(f"saving checkpoint: {ckpt_file}") - save_weights(ckpt_file, updated_embs, save_dtype) - - def remove_old_func(old_epoch_no): - old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as - old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name) - if os.path.exists(old_ckpt_file): - print(f"removing old checkpoint: {old_ckpt_file}") - os.remove(old_ckpt_file) - - saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs) - if saving and args.save_state: - train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1) - - 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 = unwrap_model(text_encoder) - - accelerator.end_training() - - if args.save_state: - train_util.save_state_on_train_end(args, accelerator) - - updated_embs = text_encoder.get_input_embeddings().weight[token_ids].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: - os.makedirs(args.output_dir, exist_ok=True) - - model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name - ckpt_name = model_name + "." + args.save_model_as - ckpt_file = os.path.join(args.output_dir, ckpt_name) - - print(f"save trained model to {ckpt_file}") - save_weights(ckpt_file, updated_embs, save_dtype) - print("model saved.") - - -def save_weights(file, updated_embs, save_dtype): - state_dict = {"emb_params": updated_embs} - - 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: - # compatible to Web UI's file format - data = torch.load(file, map_location="cpu") - if type(data) != dict: - raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}") - - if "string_to_param" in data: # textual inversion embeddings - data = data["string_to_param"] - if hasattr(data, "_parameters"): # support old PyTorch? - data = getattr(data, "_parameters") - - emb = next(iter(data.values())) - if type(emb) != torch.Tensor: - raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}") - - if len(emb.size()) == 1: - emb = emb.unsqueeze(0) - - return emb - - -def setup_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser() - - 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_optimizer_arguments(parser) - config_util.add_config_arguments(parser) - custom_train_functions.add_custom_train_arguments(parser) - - 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() - args = train_util.read_config_from_file(args, parser) - - train(args)