# training with captions import argparse import copy import math import os from multiprocessing import Value from typing import List 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, sd3_models, sd3_train_utils, sd3_utils, strategy_base, strategy_sd3 from library.sdxl_train_util import match_mixed_precision # , sdxl_model_util import library.train_util as train_util from library.utils import setup_logging, add_logging_arguments setup_logging() import logging logger = logging.getLogger(__name__) import library.config_util as config_util # import library.sdxl_train_util as sdxl_train_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, # prepare_scheduler_for_custom_training, # scale_v_prediction_loss_like_noise_prediction, # add_v_prediction_like_loss, # apply_debiased_estimation, # apply_masked_loss, # ) def train(args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) # sdxl_train_util.verify_sdxl_training_args(args) deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) # temporary: backward compatibility for deprecated options. remove in the future if not args.skip_cache_check: args.skip_cache_check = args.skip_latents_validity_check assert ( not args.weighted_captions ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません" # assert ( # not args.train_text_encoder or not args.cache_text_encoder_outputs # ), "cache_text_encoder_outputs is not supported when training text encoder / text encoderを学習するときはcache_text_encoder_outputsはサポートされていません" # # training text encoder is not supported # assert ( # not args.train_text_encoder # ), "training text encoder is not supported currently / text encoderの学習は現在サポートされていません" # # training without text encoder cache is not supported: because T5XXL must be cached # assert ( # args.cache_text_encoder_outputs # ), "training without text encoder cache is not supported currently / text encoderのキャッシュなしの学習は現在サポートされていません" assert not args.train_text_encoder or (args.use_t5xxl_cache_only or not args.cache_text_encoder_outputs), ( "when training text encoder, text encoder outputs must not be cached (except for T5XXL)" + " / text encoderの学習時はtext encoderの出力はキャッシュできません（t5xxlのみキャッシュすることは可能です）" ) if args.use_t5xxl_cache_only and not args.cache_text_encoder_outputs: logger.warning( "use_t5xxl_cache_only is enabled, so cache_text_encoder_outputs is automatically enabled." + " / use_t5xxl_cache_onlyが有効なため、cache_text_encoder_outputsも自動的に有効になります" ) args.cache_text_encoder_outputs = True # if args.block_lr: # block_lrs = [float(lr) for lr in args.block_lr.split(",")] # assert ( # len(block_lrs) == UNET_NUM_BLOCKS_FOR_BLOCK_LR # ), f"block_lr must have {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / block_lrは{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値を指定してください" # else: # block_lrs = None cache_latents = args.cache_latents use_dreambooth_method = args.in_json is None if args.seed is not None: set_seed(args.seed) # 乱数系列を初期化する # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization. if args.cache_latents: latents_caching_strategy = strategy_sd3.Sd3LatentsCachingStrategy( args.cache_latents_to_disk, args.vae_batch_size, args.skip_cache_check ) strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy) # load tokenizer and prepare tokenize strategy sd3_tokenizer = sd3_models.SD3Tokenizer(t5xxl_max_length=args.t5xxl_max_token_length) sd3_tokenize_strategy = strategy_sd3.Sd3TokenizeStrategy(args.t5xxl_max_token_length) strategy_base.TokenizeStrategy.set_strategy(sd3_tokenize_strategy) # 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 args.dataset_config is not None: logger.info(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) ignored = ["train_data_dir", "in_json"] if any(getattr(args, attr) is not None for attr in ignored): logger.warning( "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: 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=[sd3_tokenizer]) train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) else: train_dataset_group = train_util.load_arbitrary_dataset(args, [sd3_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) train_dataset_group.verify_bucket_reso_steps(8) # TODO これでいいか確認 if args.debug_dataset: if args.cache_text_encoder_outputs: strategy_base.TextEncoderOutputsCachingStrategy.set_strategy( strategy_sd3.Sd3TextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, False, False, ) ) train_dataset_group.set_current_strategies() train_util.debug_dataset(train_dataset_group, True) return if len(train_dataset_group) == 0: logger.error( "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。" ) return if cache_latents: assert ( train_dataset_group.is_latent_cacheable() ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" if args.cache_text_encoder_outputs: assert ( train_dataset_group.is_text_encoder_output_cacheable() ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" # acceleratorを準備する logger.info("prepare accelerator") accelerator = 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) vae_dtype = weight_dtype # torch.float32 if args.no_half_vae else weight_dtype # SD3 VAE works with fp16 t5xxl_dtype = weight_dtype if args.t5xxl_dtype is not None: if args.t5xxl_dtype == "fp16": t5xxl_dtype = torch.float16 elif args.t5xxl_dtype == "bf16": t5xxl_dtype = torch.bfloat16 elif args.t5xxl_dtype == "fp32" or args.t5xxl_dtype == "float": t5xxl_dtype = torch.float32 else: raise ValueError(f"unexpected t5xxl_dtype: {args.t5xxl_dtype}") t5xxl_device = accelerator.device if args.t5xxl_device is None else args.t5xxl_device clip_dtype = weight_dtype # if not args.train_text_encoder else None # モデルを読み込む attn_mode = "xformers" if args.xformers else "torch" assert ( attn_mode == "torch" ), f"attn_mode {attn_mode} is not supported yet. Please use `--sdpa` instead of `--xformers`. / attn_mode {attn_mode} はサポートされていません。`--xformers`の代わりに`--sdpa`を使ってください。" # SD3 state dict may contain multiple models, so we need to load it and extract one by one. annoying. logger.info(f"Loading SD3 models from {args.pretrained_model_name_or_path}") device_to_load = accelerator.device if args.lowram else "cpu" sd3_state_dict = sd3_utils.load_safetensors( args.pretrained_model_name_or_path, device_to_load, args.disable_mmap_load_safetensors ) # load VAE for caching latents vae: sd3_models.SDVAE = None if cache_latents: vae = sd3_train_utils.load_target_model("vae", args, sd3_state_dict, accelerator, attn_mode, vae_dtype, device_to_load) vae.to(accelerator.device, dtype=vae_dtype) vae.requires_grad_(False) vae.eval() train_dataset_group.new_cache_latents(vae, accelerator) vae.to("cpu") # if no sampling, vae can be deleted clean_memory_on_device(accelerator.device) accelerator.wait_for_everyone() # load clip_l, clip_g, t5xxl for caching text encoder outputs # # models are usually loaded on CPU and moved to GPU later. This is to avoid OOM on GPU0. # mmdit, clip_l, clip_g, t5xxl, vae = sd3_train_utils.load_target_model( # args, accelerator, attn_mode, weight_dtype, clip_dtype, t5xxl_device, t5xxl_dtype, vae_dtype # ) clip_l = sd3_train_utils.load_target_model("clip_l", args, sd3_state_dict, accelerator, attn_mode, clip_dtype, device_to_load) clip_g = sd3_train_utils.load_target_model("clip_g", args, sd3_state_dict, accelerator, attn_mode, clip_dtype, device_to_load) assert clip_l is not None, "clip_l is required / clip_lは必須です" assert clip_g is not None, "clip_g is required / clip_gは必須です" t5xxl = sd3_train_utils.load_target_model("t5xxl", args, sd3_state_dict, accelerator, attn_mode, t5xxl_dtype, device_to_load) # logit_scale = logit_scale.to(accelerator.device, dtype=weight_dtype) # should be deleted after caching text encoder outputs when not training text encoder # this strategy should not be used other than this process text_encoding_strategy = strategy_sd3.Sd3TextEncodingStrategy() strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy) # 学習を準備する：モデルを適切な状態にする train_clip_l = False train_clip_g = False train_t5xxl = False if args.train_text_encoder: accelerator.print("enable text encoder training") if args.gradient_checkpointing: clip_l.gradient_checkpointing_enable() clip_g.gradient_checkpointing_enable() lr_te1 = args.learning_rate_te1 if args.learning_rate_te1 is not None else args.learning_rate # 0 means not train lr_te2 = args.learning_rate_te2 if args.learning_rate_te2 is not None else args.learning_rate # 0 means not train train_clip_l = lr_te1 != 0 train_clip_g = lr_te2 != 0 if not train_clip_l: clip_l.to(weight_dtype) if not train_clip_g: clip_g.to(weight_dtype) clip_l.requires_grad_(train_clip_l) clip_g.requires_grad_(train_clip_g) clip_l.train(train_clip_l) clip_g.train(train_clip_g) else: clip_l.to(weight_dtype) clip_g.to(weight_dtype) clip_l.requires_grad_(False) clip_g.requires_grad_(False) clip_l.eval() clip_g.eval() if t5xxl is not None: t5xxl.to(t5xxl_dtype) t5xxl.requires_grad_(False) t5xxl.eval() # cache text encoder outputs sample_prompts_te_outputs = None if args.cache_text_encoder_outputs: # Text Encodes are eval and no grad here clip_l.to(accelerator.device) clip_g.to(accelerator.device) if t5xxl is not None: t5xxl.to(t5xxl_device) text_encoder_caching_strategy = strategy_sd3.Sd3TextEncoderOutputsCachingStrategy( args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, args.skip_cache_check, train_clip_g or train_clip_l or args.use_t5xxl_cache_only, args.apply_lg_attn_mask, args.apply_t5_attn_mask, ) strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy) clip_l.to(accelerator.device, dtype=weight_dtype) clip_g.to(accelerator.device, dtype=weight_dtype) if t5xxl is not None: t5xxl.to(t5xxl_device, dtype=t5xxl_dtype) with accelerator.autocast(): train_dataset_group.new_cache_text_encoder_outputs([clip_l, clip_g, t5xxl], accelerator) # cache sample prompt's embeddings to free text encoder's memory if args.sample_prompts is not None: logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}") prompts = sd3_train_utils.load_prompts(args.sample_prompts) sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs with accelerator.autocast(), torch.no_grad(): for prompt_dict in prompts: for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]: if p not in sample_prompts_te_outputs: logger.info(f"cache Text Encoder outputs for prompt: {p}") tokens_list = sd3_tokenize_strategy.tokenize(p) sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens( sd3_tokenize_strategy, [clip_l, clip_g, t5xxl], tokens_list, args.apply_lg_attn_mask, args.apply_t5_attn_mask, ) accelerator.wait_for_everyone() # load MMDIT # if full_fp16/bf16, model_dtype is casted to fp16/bf16. If not, model_dtype is None (float32). # by loading with model_dtype, we can reduce memory usage. model_dtype = match_mixed_precision(args, weight_dtype) # None (default) or fp16/bf16 (full_xxxx) mmdit = sd3_train_utils.load_target_model("mmdit", args, sd3_state_dict, accelerator, attn_mode, model_dtype, device_to_load) if args.gradient_checkpointing: mmdit.enable_gradient_checkpointing() train_mmdit = args.learning_rate != 0 mmdit.requires_grad_(train_mmdit) if not train_mmdit: mmdit.to(accelerator.device, dtype=weight_dtype) # because of mmdie will not be prepared if not cache_latents: # load VAE here if not cached vae = sd3_train_utils.load_target_model("vae", args, sd3_state_dict, accelerator, attn_mode, vae_dtype, device_to_load) vae.requires_grad_(False) vae.eval() vae.to(accelerator.device, dtype=vae_dtype) mmdit.requires_grad_(train_mmdit) if not train_mmdit: mmdit.to(accelerator.device, dtype=weight_dtype) # because of unet is not prepared if args.num_last_block_to_freeze: # freeze last n blocks of MM-DIT block_name = "x_block" filtered_blocks = [(name, param) for name, param in mmdit.named_parameters() if block_name in name] accelerator.print(f"filtered_blocks: {len(filtered_blocks)}") num_blocks_to_freeze = min(len(filtered_blocks), args.num_last_block_to_freeze) accelerator.print(f"freeze_blocks: {num_blocks_to_freeze}") start_freezing_from = max(0, len(filtered_blocks) - num_blocks_to_freeze) for i in range(start_freezing_from, len(filtered_blocks)): _, param = filtered_blocks[i] param.requires_grad = False training_models = [] params_to_optimize = [] # if train_unet: training_models.append(mmdit) # if block_lrs is None: params_to_optimize.append({"params": list(filter(lambda p: p.requires_grad, mmdit.parameters())), "lr": args.learning_rate}) # else: # params_to_optimize.extend(get_block_params_to_optimize(mmdit, block_lrs)) # if train_clip_l: # training_models.append(clip_l) # params_to_optimize.append({"params": list(clip_l.parameters()), "lr": args.learning_rate_te1 or args.learning_rate}) # if train_clip_g: # training_models.append(clip_g) # params_to_optimize.append({"params": list(clip_g.parameters()), "lr": args.learning_rate_te2 or args.learning_rate}) # calculate number of trainable parameters n_params = 0 for group in params_to_optimize: for p in group["params"]: n_params += p.numel() accelerator.print(f"train mmdit: {train_mmdit}") # , clip_l: {train_clip_l}, clip_g: {train_clip_g}") accelerator.print(f"number of models: {len(training_models)}") accelerator.print(f"number of trainable parameters: {n_params}") # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") if args.fused_optimizer_groups: # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each group of parameters. # This balances memory usage and management complexity. # calculate total number of parameters n_total_params = sum(len(params["params"]) for params in params_to_optimize) params_per_group = math.ceil(n_total_params / args.fused_optimizer_groups) # split params into groups, keeping the learning rate the same for all params in a group # this will increase the number of groups if the learning rate is different for different params (e.g. U-Net and text encoders) grouped_params = [] param_group = [] param_group_lr = -1 for group in params_to_optimize: lr = group["lr"] for p in group["params"]: # if the learning rate is different for different params, start a new group if lr != param_group_lr: if param_group: grouped_params.append({"params": param_group, "lr": param_group_lr}) param_group = [] param_group_lr = lr param_group.append(p) # if the group has enough parameters, start a new group if len(param_group) == params_per_group: grouped_params.append({"params": param_group, "lr": param_group_lr}) param_group = [] param_group_lr = -1 if param_group: grouped_params.append({"params": param_group, "lr": param_group_lr}) # prepare optimizers for each group optimizers = [] for group in grouped_params: _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group]) optimizers.append(optimizer) optimizer = optimizers[0] # avoid error in the following code logger.info(f"using {len(optimizers)} optimizers for fused optimizer groups") else: _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) # prepare dataloader # strategies are set here because they cannot be referenced in another process. Copy them with the dataset # some strategies can be None train_dataset_group.set_current_strategies() # 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 if args.fused_optimizer_groups: # prepare lr schedulers for each optimizer lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers] lr_scheduler = lr_schedulers[0] # avoid error in the following code else: 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.") mmdit.to(weight_dtype) clip_l.to(weight_dtype) clip_g.to(weight_dtype) if t5xxl is not None: t5xxl.to(weight_dtype) # TODO check works with fp16 or not 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.") mmdit.to(weight_dtype) clip_l.to(weight_dtype) clip_g.to(weight_dtype) if t5xxl is not None: t5xxl.to(weight_dtype) # TODO check if this is necessary. SD3 uses pool for clip_l and clip_g # # freeze last layer and final_layer_norm in te1 since we use the output of the penultimate layer # if train_clip_l: # clip_l.text_model.encoder.layers[-1].requires_grad_(False) # clip_l.text_model.final_layer_norm.requires_grad_(False) # TextEncoderの出力をキャッシュするときには、すでに出力を取得済みなのでCPUへ移動する if args.cache_text_encoder_outputs: # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16 clip_l.to("cpu", dtype=torch.float32) clip_g.to("cpu", dtype=torch.float32) if t5xxl is not None: t5xxl.to("cpu", dtype=torch.float32) clean_memory_on_device(accelerator.device) else: # make sure Text Encoders are on GPU # TODO support CPU for text encoders clip_l.to(accelerator.device) clip_g.to(accelerator.device) if t5xxl is not None: t5xxl.to(accelerator.device) # TODO cache sample prompt's embeddings to free text encoder's memory if args.cache_text_encoder_outputs: if not args.save_t5xxl: t5xxl = None # free memory clean_memory_on_device(accelerator.device) if args.deepspeed: ds_model = deepspeed_utils.prepare_deepspeed_model( args, mmdit=mmdit, clip_l=clip_l if train_clip_l else None, clip_g=clip_g if train_clip_g else None, ) # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007 ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( ds_model, optimizer, train_dataloader, lr_scheduler ) training_models = [ds_model] else: # acceleratorがなんかよろしくやってくれるらしい if train_mmdit: mmdit = accelerator.prepare(mmdit) if train_clip_l: clip_l = accelerator.prepare(clip_l) if train_clip_g: clip_g = accelerator.prepare(clip_g) optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする if args.full_fp16: # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do. # -> But we think it's ok to patch accelerator even if deepspeed is enabled. train_util.patch_accelerator_for_fp16_training(accelerator) # resumeする train_util.resume_from_local_or_hf_if_specified(accelerator, args) if args.fused_backward_pass: # use fused optimizer for backward pass: other optimizers will be supported in the future import library.adafactor_fused library.adafactor_fused.patch_adafactor_fused(optimizer) for param_group in optimizer.param_groups: for parameter in param_group["params"]: if parameter.requires_grad: def __grad_hook(tensor: torch.Tensor, param_group=param_group): if accelerator.sync_gradients and args.max_grad_norm != 0.0: accelerator.clip_grad_norm_(tensor, args.max_grad_norm) optimizer.step_param(tensor, param_group) tensor.grad = None parameter.register_post_accumulate_grad_hook(__grad_hook) elif args.fused_optimizer_groups: # prepare for additional optimizers and lr schedulers for i in range(1, len(optimizers)): optimizers[i] = accelerator.prepare(optimizers[i]) lr_schedulers[i] = accelerator.prepare(lr_schedulers[i]) # counters are used to determine when to step the optimizer global optimizer_hooked_count global num_parameters_per_group global parameter_optimizer_map optimizer_hooked_count = {} num_parameters_per_group = [0] * len(optimizers) parameter_optimizer_map = {} for opt_idx, optimizer in enumerate(optimizers): for param_group in optimizer.param_groups: for parameter in param_group["params"]: if parameter.requires_grad: def optimizer_hook(parameter: torch.Tensor): if accelerator.sync_gradients and args.max_grad_norm != 0.0: accelerator.clip_grad_norm_(parameter, args.max_grad_norm) i = parameter_optimizer_map[parameter] optimizer_hooked_count[i] += 1 if optimizer_hooked_count[i] == num_parameters_per_group[i]: optimizers[i].step() optimizers[i].zero_grad(set_to_none=True) parameter.register_post_accumulate_grad_hook(optimizer_hook) parameter_optimizer_map[parameter] = opt_idx num_parameters_per_group[opt_idx] += 1 # 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 accelerator.print("running training / 学習開始") accelerator.print(f" num examples / サンプル数: {train_dataset_group.num_train_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}") 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 # ) noise_scheduler = sd3_train_utils.FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=3.0) noise_scheduler_copy = copy.deepcopy(noise_scheduler) # 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( "finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs, ) # For --sample_at_first sd3_train_utils.sample_images(accelerator, args, 0, global_step, mmdit, vae, [clip_l, clip_g, t5xxl], sample_prompts_te_outputs) if len(accelerator.trackers) > 0: # log empty object to commit the sample images to wandb accelerator.log({}, step=0) # following function will be moved to sd3_train_utils def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype) schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device) timesteps = timesteps.to(accelerator.device) step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < n_dim: sigma = sigma.unsqueeze(-1) return sigma def compute_density_for_timestep_sampling( weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None ): """Compute the density for sampling the timesteps when doing SD3 training. Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. SD3 paper reference: https://arxiv.org/abs/2403.03206v1. """ if weighting_scheme == "logit_normal": # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$). u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu") u = torch.nn.functional.sigmoid(u) elif weighting_scheme == "mode": u = torch.rand(size=(batch_size,), device="cpu") u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u) else: u = torch.rand(size=(batch_size,), device="cpu") return u def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None): """Computes loss weighting scheme for SD3 training. Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528. SD3 paper reference: https://arxiv.org/abs/2403.03206v1. """ if weighting_scheme == "sigma_sqrt": weighting = (sigmas**-2.0).float() elif weighting_scheme == "cosmap": bot = 1 - 2 * sigmas + 2 * sigmas**2 weighting = 2 / (math.pi * bot) else: weighting = torch.ones_like(sigmas) return weighting loss_recorder = train_util.LossRecorder() epoch = 0 # avoid error when max_train_steps is 0 for epoch in range(num_train_epochs): accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") current_epoch.value = epoch + 1 for m in training_models: m.train() for step, batch in enumerate(train_dataloader): current_step.value = global_step if args.fused_optimizer_groups: optimizer_hooked_count = {i: 0 for i in range(len(optimizers))} # reset counter for each step with accelerator.accumulate(*training_models): 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(): # encode images to latents. images are [-1, 1] latents = vae.encode(batch["images"].to(vae_dtype)).to(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 * sdxl_model_util.VAE_SCALE_FACTOR latents = sd3_models.SDVAE.process_in(latents) text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None) if text_encoder_outputs_list is not None: lg_out, t5_out, lg_pooled = text_encoder_outputs_list if args.use_t5xxl_cache_only: lg_out = None lg_pooled = None else: lg_out = None t5_out = None lg_pooled = None if lg_out is None or (train_clip_l or train_clip_g): # not cached or training, so get from text encoders input_ids_clip_l, input_ids_clip_g, _, l_attn_mask, g_attn_mask, _ = batch["input_ids_list"] with torch.set_grad_enabled(args.train_text_encoder): # TODO support weighted captions # text models in sd3_models require "cpu" for input_ids input_ids_clip_l = input_ids_clip_l.to("cpu") input_ids_clip_g = input_ids_clip_g.to("cpu") lg_out, _, lg_pooled = text_encoding_strategy.encode_tokens( sd3_tokenize_strategy, [clip_l, clip_g, None], [input_ids_clip_l, input_ids_clip_g, None, l_attn_mask, g_attn_mask, None], ) if t5_out is None: _, _, input_ids_t5xxl, _, _, t5_attn_mask = batch["input_ids_list"] with torch.no_grad(): input_ids_t5xxl = input_ids_t5xxl.to("cpu") if t5_out is None else None _, t5_out, _ = text_encoding_strategy.encode_tokens( sd3_tokenize_strategy, [None, None, t5xxl], [None, None, input_ids_t5xxl, None, None, t5_attn_mask] ) context, lg_pooled = text_encoding_strategy.concat_encodings(lg_out, t5_out, lg_pooled) # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # Sample a random timestep for each image # for weighting schemes where we sample timesteps non-uniformly u = compute_density_for_timestep_sampling( weighting_scheme=args.weighting_scheme, batch_size=bsz, logit_mean=args.logit_mean, logit_std=args.logit_std, mode_scale=args.mode_scale, ) indices = (u * noise_scheduler_copy.config.num_train_timesteps).long() timesteps = noise_scheduler_copy.timesteps[indices].to(device=accelerator.device) # Add noise according to flow matching. sigmas = get_sigmas(timesteps, n_dim=latents.ndim, dtype=weight_dtype) noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents # debug: NaN check for all inputs if torch.any(torch.isnan(noisy_model_input)): accelerator.print("NaN found in noisy_model_input, replacing with zeros") noisy_model_input = torch.nan_to_num(noisy_model_input, 0, out=noisy_model_input) if torch.any(torch.isnan(context)): accelerator.print("NaN found in context, replacing with zeros") context = torch.nan_to_num(context, 0, out=context) if torch.any(torch.isnan(lg_pooled)): accelerator.print("NaN found in pool, replacing with zeros") lg_pooled = torch.nan_to_num(lg_pooled, 0, out=lg_pooled) # call model with accelerator.autocast(): model_pred = mmdit(noisy_model_input, timesteps, context=context, y=lg_pooled) # Follow: Section 5 of https://arxiv.org/abs/2206.00364. # Preconditioning of the model outputs. model_pred = model_pred * (-sigmas) + noisy_model_input # these weighting schemes use a uniform timestep sampling # and instead post-weight the loss weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas) # flow matching loss target = latents # Compute regular loss. TODO simplify this loss = torch.mean( (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1), 1, ) loss = loss.mean() accelerator.backward(loss) if not (args.fused_backward_pass or args.fused_optimizer_groups): if accelerator.sync_gradients and args.max_grad_norm != 0.0: params_to_clip = [] for m in training_models: params_to_clip.extend(m.parameters()) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True) else: # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook lr_scheduler.step() if args.fused_optimizer_groups: for i in range(1, len(optimizers)): lr_schedulers[i].step() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 sd3_train_utils.sample_images( accelerator, args, None, global_step, mmdit, vae, [clip_l, clip_g, t5xxl], sample_prompts_te_outputs ) # 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: sd3_train_utils.save_sd3_model_on_epoch_end_or_stepwise( args, False, accelerator, save_dtype, epoch, num_train_epochs, global_step, accelerator.unwrap_model(clip_l) if args.save_clip else None, accelerator.unwrap_model(clip_g) if args.save_clip else None, accelerator.unwrap_model(t5xxl) if args.save_t5xxl else None, accelerator.unwrap_model(mmdit), vae, ) current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず if len(accelerator.trackers) > 0: logs = {"loss": current_loss} train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=train_mmdit) accelerator.log(logs, step=global_step) 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 global_step >= args.max_train_steps: break if len(accelerator.trackers) > 0: logs = {"loss/epoch": loss_recorder.moving_average} accelerator.log(logs, step=epoch + 1) accelerator.wait_for_everyone() if args.save_every_n_epochs is not None: if accelerator.is_main_process: sd3_train_utils.save_sd3_model_on_epoch_end_or_stepwise( args, True, accelerator, save_dtype, epoch, num_train_epochs, global_step, accelerator.unwrap_model(clip_l) if args.save_clip else None, accelerator.unwrap_model(clip_g) if args.save_clip else None, accelerator.unwrap_model(t5xxl) if args.save_t5xxl else None, accelerator.unwrap_model(mmdit), vae, ) sd3_train_utils.sample_images( accelerator, args, epoch + 1, global_step, mmdit, vae, [clip_l, clip_g, t5xxl], sample_prompts_te_outputs ) is_main_process = accelerator.is_main_process # if is_main_process: mmdit = accelerator.unwrap_model(mmdit) clip_l = accelerator.unwrap_model(clip_l) clip_g = accelerator.unwrap_model(clip_g) if t5xxl is not None: t5xxl = accelerator.unwrap_model(t5xxl) accelerator.end_training() if args.save_state or args.save_state_on_train_end: 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: sd3_train_utils.save_sd3_model_on_train_end( args, save_dtype, epoch, global_step, clip_l if args.save_clip else None, clip_g if args.save_clip else None, t5xxl if args.save_t5xxl else None, mmdit, vae, ) 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, False) train_util.add_masked_loss_arguments(parser) deepspeed_utils.add_deepspeed_arguments(parser) 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) sd3_train_utils.add_sd3_training_arguments(parser) parser.add_argument( "--train_text_encoder", action="store_true", help="train text encoder (CLIP-L and G) / text encoderも学習する" ) # parser.add_argument("--train_t5xxl", action="store_true", help="train T5-XXL / T5-XXLも学習する") parser.add_argument( "--use_t5xxl_cache_only", action="store_true", help="cache T5-XXL outputs only / T5-XXLの出力のみキャッシュする" ) parser.add_argument( "--t5xxl_max_token_length", type=int, default=None, help="maximum token length for T5-XXL. 256 if omitted / T5-XXLの最大トークン数。省略時は256", ) parser.add_argument( "--apply_lg_attn_mask", action="store_true", help="apply attention mask (zero embs) to CLIP-L and G / CLIP-LとGにアテンションマスク（ゼロ埋め）を適用する", ) parser.add_argument( "--apply_t5_attn_mask", action="store_true", help="apply attention mask (zero embs) to T5-XXL / T5-XXLにアテンションマスク（ゼロ埋め）を適用する", ) # TE training is disabled temporarily # parser.add_argument( # "--learning_rate_te1", # type=float, # default=None, # help="learning rate for text encoder 1 (ViT-L) / text encoder 1 (ViT-L)の学習率", # ) # parser.add_argument( # "--learning_rate_te2", # type=float, # default=None, # help="learning rate for text encoder 2 (BiG-G) / text encoder 2 (BiG-G)の学習率", # ) # parser.add_argument( # "--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する" # ) # 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", # ) # parser.add_argument( # "--block_lr", # type=str, # default=None, # help=f"learning rates for each block of U-Net, comma-separated, {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / " # + f"U-Netの各ブロックの学習率、カンマ区切り、{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値", # ) parser.add_argument( "--fused_optimizer_groups", type=int, default=None, help="number of optimizers for fused backward pass and optimizer step / fused backward passとoptimizer stepのためのoptimizer数", ) parser.add_argument( "--skip_latents_validity_check", action="store_true", help="[Deprecated] use 'skip_cache_check' instead / 代わりに 'skip_cache_check' を使用してください", ) parser.add_argument( "--skip_cache_check", action="store_true", help="skip cache (latents and text encoder outputs) check / キャッシュ（latentsとtext encoder outputs）のチェックをスキップする", ) parser.add_argument( "--num_last_block_to_freeze", type=int, default=None, help="freeze last n blocks of MM-DIT / MM-DITの最後のnブロックを凍結する", ) 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) train(args)