import importlib import argparse import math import os import sys import random import time import json from multiprocessing import Value import toml from tqdm import tqdm import torch from library.device_utils import init_ipex, clean_memory_on_device init_ipex() from accelerate.utils import set_seed from diffusers import DDPMScheduler from library import deepspeed_utils, 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, apply_masked_loss, ) from library.utils import setup_logging, add_logging_arguments setup_logging() import logging logger = logging.getLogger(__name__) 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, dtype=weight_dtype) 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 all_reduce_network(self, accelerator, network): for param in network.parameters(): if param.grad is not None: param.grad = accelerator.reduce(param.grad, reduction="mean") 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) deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=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, args.masked_loss, True)) if use_user_config: logger.info(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): logger.warning( "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: logger.info("Using DreamBooth method.") user_config = { "datasets": [ { "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( args.train_data_dir, args.reg_data_dir ) } ] } else: logger.info("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: logger.error( "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 logger.info("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") clean_memory_on_device(accelerator.device) 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 # cache text encoder outputs if needed: Text Encoder is moved to cpu or gpu 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 network_has_multiplier = hasattr(network, "set_multiplier") if hasattr(network, "prepare_network"): network.prepare_network(args) if args.scale_weight_norms and not hasattr(network, "apply_max_norm_regularization"): logger.warning( "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 persistent_workers が使えないので注意 n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers train_dataloader = torch.utils.data.DataLoader( train_dataset_group, batch_size=1, shuffle=True, collate_fn=collator, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers, ) # 学習ステップ数を計算する if args.max_train_epochs is not None: args.max_train_steps = args.max_train_epochs * math.ceil( len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps ) 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_weight_dtype = te_weight_dtype = weight_dtype # Experimental Feature: Put base model into fp8 to save vram if args.fp8_base: assert torch.__version__ >= "2.1.0", "fp8_base requires torch>=2.1.0 / fp8を使う場合はtorch>=2.1.0が必要です。" assert ( args.mixed_precision != "no" ), "fp8_base requires mixed precision='fp16' or 'bf16' / fp8を使う場合はmixed_precision='fp16'または'bf16'が必要です。" accelerator.print("enable fp8 training.") unet_weight_dtype = torch.float8_e4m3fn te_weight_dtype = torch.float8_e4m3fn unet.requires_grad_(False) unet.to(dtype=unet_weight_dtype) for t_enc in text_encoders: t_enc.requires_grad_(False) # in case of cpu, dtype is already set to fp32 because cpu does not support fp8/fp16/bf16 if t_enc.device.type != "cpu": t_enc.to(dtype=te_weight_dtype) # nn.Embedding not support FP8 t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) # acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good if args.deepspeed: ds_model = deepspeed_utils.prepare_deepspeed_model( args, unet=unet if train_unet else None, text_encoder1=text_encoders[0] if train_text_encoder else None, text_encoder2=text_encoders[1] if train_text_encoder and len(text_encoders) > 1 else None, network=network, ) ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( ds_model, optimizer, train_dataloader, lr_scheduler ) training_model = ds_model else: if train_unet: unet = accelerator.prepare(unet) else: unet.to(accelerator.device, dtype=unet_weight_dtype) # move to device because unet is not prepared by accelerator if train_text_encoder: if len(text_encoders) > 1: text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders] else: text_encoder = accelerator.prepare(text_encoder) text_encoders = [text_encoder] else: pass # if text_encoder is not trained, no need to prepare. and device and dtype are already set network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( network, optimizer, train_dataloader, lr_scheduler ) training_model = 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) else: unet.eval() for t_enc in text_encoders: t_enc.eval() del t_enc accelerator.unwrap_model(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) # before resuming make hook for saving/loading to save/load the network weights only def save_model_hook(models, weights, output_dir): # pop weights of other models than network to save only network weights # only main process or deepspeed https://github.com/huggingface/diffusers/issues/2606 if accelerator.is_main_process or args.deepspeed: remove_indices = [] for i, model in enumerate(models): if not isinstance(model, type(accelerator.unwrap_model(network))): remove_indices.append(i) for i in reversed(remove_indices): if len(weights) > i: weights.pop(i) # print(f"save model hook: {len(weights)} weights will be saved") def load_model_hook(models, input_dir): # remove models except network remove_indices = [] for i, model in enumerate(models): if not isinstance(model, type(accelerator.unwrap_model(network))): remove_indices.append(i) for i in reversed(remove_indices): models.pop(i) # print(f"load model hook: {len(models)} models will be loaded") accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) # 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), "ss_noise_offset_random_strength": args.noise_offset_random_strength, "ss_ip_noise_gamma_random_strength": args.ip_noise_gamma_random_strength, "ss_loss_type": args.loss_type, "ss_huber_schedule": args.huber_schedule, "ss_huber_c": args.huber_c, } 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, "keep_tokens_separator": subset.keep_tokens_separator, "secondary_separator": subset.secondary_separator, "enable_wildcard": bool(subset.enable_wildcard), "caption_prefix": subset.caption_prefix, "caption_suffix": subset.caption_suffix, } 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.wandb_run_name: init_kwargs["wandb"] = {"name": args.wandb_run_name} if args.log_tracker_config is not None: init_kwargs = toml.load(args.log_tracker_config) accelerator.init_trackers( "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(accelerator.unwrap_model(network), "on_step_start"): on_step_start = accelerator.unwrap_model(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) # For --sample_at_first self.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet) # 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) accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet) for step, batch in enumerate(train_dataloader): current_step.value = global_step with accelerator.accumulate(training_model): on_step_start(text_encoder, unet) if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: with torch.no_grad(): # latentに変換 latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype) # 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.nan_to_num(latents, 0, out=latents) latents = latents * self.vae_scale_factor # get multiplier for each sample if network_has_multiplier: multipliers = batch["network_multipliers"] # if all multipliers are same, use single multiplier if torch.all(multipliers == multipliers[0]): multipliers = multipliers[0].item() else: raise NotImplementedError("multipliers for each sample is not supported yet") # print(f"set multiplier: {multipliers}") accelerator.unwrap_model(network).set_multiplier(multipliers) 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, huber_c = train_util.get_noise_noisy_latents_and_timesteps( args, noise_scheduler, latents ) # ensure the hidden state will require grad if args.gradient_checkpointing: for x in noisy_latents: x.requires_grad_(True) for t in text_encoder_conds: t.requires_grad_(True) # Predict the noise residual with accelerator.autocast(): noise_pred = self.call_unet( args, accelerator, unet, noisy_latents.requires_grad_(train_unet), 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 = train_util.conditional_loss( noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c ) if args.masked_loss: loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight loss = loss * loss_weights if args.min_snr_gamma: loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization) if args.scale_v_pred_loss_like_noise_pred: loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler) if args.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: self.all_reduce_network(accelerator, network) # sync DDP grad manually if args.max_grad_norm != 0.0: params_to_clip = accelerator.unwrap_model(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 = accelerator.unwrap_model(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 or args.save_state_on_train_end): 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) logger.info("model saved.") def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() add_logging_arguments(parser) train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, True, True, True) train_util.add_training_arguments(parser, True) train_util.add_masked_loss_arguments(parser) deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser) 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() train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) trainer = NetworkTrainer() trainer.train(args)