import importlib import argparse import gc import math import os import sys import random import time import json from multiprocessing import Value import toml from tqdm import tqdm import torch try: import intel_extension_for_pytorch as ipex if torch.xpu.is_available(): from library.ipex import ipex_init ipex_init() except Exception: pass from accelerate.utils import set_seed from diffusers import DDPMScheduler from library import model_util 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.huggingface_util as huggingface_util import library.custom_train_functions as custom_train_functions from library.custom_train_functions import ( apply_snr_weight, get_weighted_text_embeddings, prepare_scheduler_for_custom_training, scale_v_prediction_loss_like_noise_prediction, add_v_prediction_like_loss, apply_debiased_estimation, ) class NetworkTrainer: def __init__(self): self.vae_scale_factor = 0.18215 self.is_sdxl = False # TODO 他のスクリプトと共通化する def generate_step_logs( self, args: argparse.Namespace, current_loss, avr_loss, lr_scheduler, keys_scaled=None, mean_norm=None, maximum_norm=None ): logs = {"loss/current": current_loss, "loss/average": avr_loss} if keys_scaled is not None: logs["max_norm/keys_scaled"] = keys_scaled logs["max_norm/average_key_norm"] = mean_norm logs["max_norm/max_key_norm"] = maximum_norm lrs = lr_scheduler.get_last_lr() if args.network_train_text_encoder_only or len(lrs) <= 2: # not block lr (or single block) if args.network_train_unet_only: logs["lr/unet"] = float(lrs[0]) elif args.network_train_text_encoder_only: logs["lr/textencoder"] = float(lrs[0]) else: logs["lr/textencoder"] = float(lrs[0]) logs["lr/unet"] = float(lrs[-1]) # may be same to textencoder if ( args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".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"] ) else: idx = 0 if not args.network_train_unet_only: logs["lr/textencoder"] = float(lrs[0]) idx = 1 for i in range(idx, len(lrs)): logs[f"lr/group{i}"] = float(lrs[i]) if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower(): logs[f"lr/d*lr/group{i}"] = ( lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"] ) return logs def assert_extra_args(self, args, train_dataset_group): pass def load_target_model(self, args, weight_dtype, accelerator): text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator) return model_util.get_model_version_str_for_sd1_sd2(args.v2, args.v_parameterization), text_encoder, vae, unet def load_tokenizer(self, args): tokenizer = train_util.load_tokenizer(args) return tokenizer def is_text_encoder_outputs_cached(self, args): return False def is_train_text_encoder(self, args): return not args.network_train_unet_only and not self.is_text_encoder_outputs_cached(args) def cache_text_encoder_outputs_if_needed( self, args, accelerator, unet, vae, tokenizers, text_encoders, data_loader, weight_dtype ): for t_enc in text_encoders: t_enc.to(accelerator.device) def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype): input_ids = batch["input_ids"].to(accelerator.device) encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizers[0], text_encoders[0], weight_dtype) return encoder_hidden_states def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype): noise_pred = unet(noisy_latents, timesteps, text_conds).sample return noise_pred def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet): train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet) def train(self, 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 None: args.seed = random.randint(0, 2**32) set_seed(args.seed) # tokenizerは単体またはリスト、tokenizersは必ずリスト：既存のコードとの互換性のため tokenizer = self.load_tokenizer(args) tokenizers = tokenizer if isinstance(tokenizer, list) else [tokenizer] # データセットを準備する if args.dataset_class is None: blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, True)) if use_user_config: print(f"Loading 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( "ignoring the following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format( ", ".join(ignored) ) ) else: if use_dreambooth_method: print("Using DreamBooth method.") user_config = { "datasets": [ { "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( args.train_data_dir, args.reg_data_dir ) } ] } else: print("Training 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) else: # use arbitrary dataset class train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer) current_epoch = Value("i", 0) current_step = Value("i", 0) ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None collator = train_util.collator_class(current_epoch, current_step, ds_for_collator) 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" self.assert_extra_args(args, train_dataset_group) # acceleratorを準備する print("preparing accelerator") accelerator = 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) vae_dtype = torch.float32 if args.no_half_vae else weight_dtype # モデルを読み込む model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator) # text_encoder is List[CLIPTextModel] or CLIPTextModel text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] # モデルに xformers とか memory efficient attention を組み込む train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa) if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える vae.set_use_memory_efficient_attention_xformers(args.xformers) # 差分追加学習のためにモデルを読み込む sys.path.append(os.path.dirname(__file__)) accelerator.print("import network module:", args.network_module) network_module = importlib.import_module(args.network_module) if args.base_weights is not None: # base_weights が指定されている場合は、指定された重みを読み込みマージする for i, weight_path in enumerate(args.base_weights): if args.base_weights_multiplier is None or len(args.base_weights_multiplier) <= i: multiplier = 1.0 else: multiplier = args.base_weights_multiplier[i] accelerator.print(f"merging module: {weight_path} with multiplier {multiplier}") module, weights_sd = network_module.create_network_from_weights( multiplier, weight_path, vae, text_encoder, unet, for_inference=True ) module.merge_to(text_encoder, unet, weights_sd, weight_dtype, accelerator.device if args.lowram else "cpu") accelerator.print(f"all weights merged: {', '.join(args.base_weights)}") # 学習を準備する if cache_latents: vae.to(accelerator.device, dtype=vae_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() # 必要ならテキストエンコーダーの出力をキャッシュする: Text Encoderはcpuまたはgpuへ移される self.cache_text_encoder_outputs_if_needed( args, accelerator, unet, vae, tokenizers, text_encoders, train_dataset_group, weight_dtype ) # prepare network 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 (;'∀') if args.dim_from_weights: network, _ = network_module.create_network_from_weights(1, args.network_weights, vae, text_encoder, unet, **net_kwargs) else: if "dropout" not in net_kwargs: # workaround for LyCORIS (;^ω^) net_kwargs["dropout"] = args.network_dropout network = network_module.create_network( 1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, neuron_dropout=args.network_dropout, **net_kwargs, ) if network is None: return if hasattr(network, "prepare_network"): network.prepare_network(args) if args.scale_weight_norms and not hasattr(network, "apply_max_norm_regularization"): print( "warning: scale_weight_norms is specified but the network does not support it / scale_weight_normsが指定されていますが、ネットワークが対応していません" ) args.scale_weight_norms = False train_unet = not args.network_train_text_encoder_only train_text_encoder = self.is_train_text_encoder(args) network.apply_to(text_encoder, unet, train_text_encoder, train_unet) if args.network_weights is not None: info = network.load_weights(args.network_weights) accelerator.print(f"load network weights from {args.network_weights}: {info}") if args.gradient_checkpointing: unet.enable_gradient_checkpointing() for t_enc in text_encoders: t_enc.gradient_checkpointing_enable() del t_enc network.enable_gradient_checkpointing() # may have no effect # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") # 後方互換性を確保するよ try: trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate) except TypeError: accelerator.print( "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)" ) 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=collator, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers, ) # 学習ステップ数を計算する if args.max_train_epochs is not None: args.max_train_steps = args.max_train_epochs * math.ceil( len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps ) accelerator.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/bf16学習を行う　モデル全体をfp16/bf16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。" accelerator.print("enable full fp16 training.") network.to(weight_dtype) elif args.full_bf16: assert ( args.mixed_precision == "bf16" ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" accelerator.print("enable full bf16 training.") network.to(weight_dtype) unet.requires_grad_(False) unet.to(dtype=weight_dtype) for t_enc in text_encoders: t_enc.requires_grad_(False) # acceleratorがなんかよろしくやってくれるらしい # TODO めちゃくちゃ冗長なのでコードを整理する if train_unet and train_text_encoder: if len(text_encoders) > 1: unet, t_enc1, t_enc2, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, text_encoders[0], text_encoders[1], network, optimizer, train_dataloader, lr_scheduler ) text_encoder = text_encoders = [t_enc1, t_enc2] del t_enc1, t_enc2 else: unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler ) text_encoders = [text_encoder] elif train_unet: unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, network, optimizer, train_dataloader, lr_scheduler ) for t_enc in text_encoders: t_enc.to(accelerator.device, dtype=weight_dtype) elif train_text_encoder: if len(text_encoders) > 1: t_enc1, t_enc2, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( text_encoders[0], text_encoders[1], network, optimizer, train_dataloader, lr_scheduler ) text_encoder = text_encoders = [t_enc1, t_enc2] del t_enc1, t_enc2 else: text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( text_encoder, network, optimizer, train_dataloader, lr_scheduler ) text_encoders = [text_encoder] unet.to(accelerator.device, dtype=weight_dtype) # move to device because unet is not prepared by accelerator else: network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( network, optimizer, train_dataloader, lr_scheduler ) # transform DDP after prepare (train_network here only) text_encoders = train_util.transform_models_if_DDP(text_encoders) unet, network = train_util.transform_models_if_DDP([unet, network]) if args.gradient_checkpointing: # according to TI example in Diffusers, train is required unet.train() for t_enc in text_encoders: t_enc.train() # set top parameter requires_grad = True for gradient checkpointing works if train_text_encoder: t_enc.text_model.embeddings.requires_grad_(True) # set top parameter requires_grad = True for gradient checkpointing works if not train_text_encoder: # train U-Net only unet.parameters().__next__().requires_grad_(True) else: unet.eval() for t_enc in text_encoders: t_enc.eval() del t_enc network.prepare_grad_etc(text_encoder, unet) if not cache_latents: # キャッシュしない場合はVAEを使うのでVAEを準備する vae.requires_grad_(False) vae.eval() vae.to(accelerator.device, dtype=vae_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 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 # 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 accelerator.print("running training / 学習開始") accelerator.print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}") accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}") accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}") accelerator.print(f" num epochs / epoch数: {num_train_epochs}") accelerator.print( f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}" ) # accelerator.print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}") accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") accelerator.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 have alpha "ss_network_dropout": args.network_dropout, # some networks may not have dropout "ss_mixed_precision": args.mixed_precision, "ss_full_fp16": bool(args.full_fp16), "ss_v2": bool(args.v2), "ss_base_model_version": model_version, "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_multires_noise_iterations": args.multires_noise_iterations, "ss_multires_noise_discount": args.multires_noise_discount, "ss_adaptive_noise_scale": args.adaptive_noise_scale, "ss_zero_terminal_snr": args.zero_terminal_snr, "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, "ss_min_snr_gamma": args.min_snr_gamma, "ss_scale_weight_norms": args.scale_weight_norms, "ss_ip_noise_gamma": args.ip_noise_gamma, "ss_debiased_estimation": bool(args.debiased_estimation_loss), } 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) # 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_metadata = {} for key in train_util.SS_METADATA_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 ) prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device) if args.zero_terminal_snr: custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler) if accelerator.is_main_process: init_kwargs = {} if args.log_tracker_config is not None: init_kwargs = toml.load(args.log_tracker_config) accelerator.init_trackers( "network_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs ) loss_recorder = train_util.LossRecorder() del train_dataset_group # callback for step start if hasattr(network, "on_step_start"): on_step_start = network.on_step_start else: on_step_start = lambda *args, **kwargs: None # function for saving/removing def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False): os.makedirs(args.output_dir, exist_ok=True) ckpt_file = os.path.join(args.output_dir, ckpt_name) accelerator.print(f"\nsaving checkpoint: {ckpt_file}") metadata["ss_training_finished_at"] = str(time.time()) metadata["ss_steps"] = str(steps) metadata["ss_epoch"] = str(epoch_no) metadata_to_save = minimum_metadata if args.no_metadata else metadata sai_metadata = train_util.get_sai_model_spec(None, args, self.is_sdxl, True, False) metadata_to_save.update(sai_metadata) unwrapped_nw.save_weights(ckpt_file, save_dtype, metadata_to_save) if args.huggingface_repo_id is not None: huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload) def remove_model(old_ckpt_name): old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name) if os.path.exists(old_ckpt_file): accelerator.print(f"removing old checkpoint: {old_ckpt_file}") os.remove(old_ckpt_file) # training loop for epoch in range(num_train_epochs): accelerator.print(f"\nepoch {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): on_step_start(text_encoder, unet) 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=vae_dtype)).latent_dist.sample() # NaNが含まれていれば警告を表示し0に置き換える if torch.any(torch.isnan(latents)): accelerator.print("NaN found in latents, replacing with zeros") latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents) latents = latents * self.vae_scale_factor b_size = latents.shape[0] with torch.set_grad_enabled(train_text_encoder), accelerator.autocast(): # Get the text embedding for conditioning if args.weighted_captions: text_encoder_conds = 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: text_encoder_conds = self.get_text_cond( args, accelerator, batch, tokenizers, text_encoders, weight_dtype ) # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps( args, noise_scheduler, latents ) # Predict the noise residual with accelerator.autocast(): noise_pred = self.call_unet( args, accelerator, unet, noisy_latents, timesteps, text_encoder_conds, batch, weight_dtype ) 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) if args.scale_v_pred_loss_like_noise_pred: loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler) if args.v_pred_like_loss: loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss) if args.debiased_estimation_loss: loss = apply_debiased_estimation(loss, timesteps, noise_scheduler) 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) if args.scale_weight_norms: keys_scaled, mean_norm, maximum_norm = network.apply_max_norm_regularization( args.scale_weight_norms, accelerator.device ) max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm} else: keys_scaled, mean_norm, maximum_norm = None, None, None # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 self.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: ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step) save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch) if args.save_state: train_util.save_and_remove_state_stepwise(args, accelerator, global_step) remove_step_no = train_util.get_remove_step_no(args, global_step) if remove_step_no is not None: remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no) remove_model(remove_ckpt_name) current_loss = loss.detach().item() loss_recorder.add(epoch=epoch, step=step, loss=current_loss) avr_loss: float = loss_recorder.moving_average logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if args.scale_weight_norms: progress_bar.set_postfix(**{**max_mean_logs, **logs}) if args.logging_dir is not None: logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm) 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_recorder.moving_average} accelerator.log(logs, step=epoch + 1) accelerator.wait_for_everyone() # 指定エポックごとにモデルを保存 if args.save_every_n_epochs is not None: saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs if is_main_process and saving: ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1) save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1) remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1) if remove_epoch_no is not None: remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no) remove_model(remove_ckpt_name) if args.save_state: train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1) self.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 = accelerator.unwrap_model(network) accelerator.end_training() if is_main_process and args.save_state: train_util.save_state_on_train_end(args, accelerator) if is_main_process: ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as) save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True) 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_dropout", type=float, default=None, help="Drops neurons out of training every step (0 or None is default behavior (no dropout), 1 would drop all neurons) / 訓練時に毎ステップでニューロンをdropする（0またはNoneはdropoutなし、1は全ニューロンをdropout）", ) parser.add_argument( "--network_args", type=str, default=None, nargs="*", help="additional arguments 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" ) parser.add_argument( "--dim_from_weights", action="store_true", help="automatically determine dim (rank) from network_weights / dim (rank)をnetwork_weightsで指定した重みから自動で決定する", ) parser.add_argument( "--scale_weight_norms", type=float, default=None, help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point) / 重みの値をスケーリングして勾配爆発を防ぐ（1が初期値としては適当）", ) parser.add_argument( "--base_weights", type=str, default=None, nargs="*", help="network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みファイル", ) parser.add_argument( "--base_weights_multiplier", type=float, default=None, nargs="*", help="multiplier for network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みの倍率", ) parser.add_argument( "--no_half_vae", action="store_true", help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() args = train_util.read_config_from_file(args, parser) trainer = NetworkTrainer() trainer.train(args)