import argparse import math import os from multiprocessing import Value from typing import Any, List, Optional, Union 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 transformers import CLIPTokenizer from library import deepspeed_utils, model_util, strategy_base, strategy_sd, sai_model_spec import library.train_util as train_util import library.huggingface_util as huggingface_util import library.config_util as config_util from library.config_util import ( ConfigSanitizer, BlueprintGenerator, ) import library.custom_train_functions as custom_train_functions from library.custom_train_functions import ( apply_snr_weight, 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__) imagenet_templates_small = [ "a photo of a {}", "a rendering of a {}", "a cropped photo of the {}", "the photo of a {}", "a photo of a clean {}", "a photo of a dirty {}", "a dark photo of the {}", "a photo of my {}", "a photo of the cool {}", "a close-up photo of a {}", "a bright photo of the {}", "a cropped photo of a {}", "a photo of the {}", "a good photo of the {}", "a photo of one {}", "a close-up photo of the {}", "a rendition of the {}", "a photo of the clean {}", "a rendition of a {}", "a photo of a nice {}", "a good photo of a {}", "a photo of the nice {}", "a photo of the small {}", "a photo of the weird {}", "a photo of the large {}", "a photo of a cool {}", "a photo of a small {}", ] imagenet_style_templates_small = [ "a painting in the style of {}", "a rendering in the style of {}", "a cropped painting in the style of {}", "the painting in the style of {}", "a clean painting in the style of {}", "a dirty painting in the style of {}", "a dark painting in the style of {}", "a picture in the style of {}", "a cool painting in the style of {}", "a close-up painting in the style of {}", "a bright painting in the style of {}", "a cropped painting in the style of {}", "a good painting in the style of {}", "a close-up painting in the style of {}", "a rendition in the style of {}", "a nice painting in the style of {}", "a small painting in the style of {}", "a weird painting in the style of {}", "a large painting in the style of {}", ] class TextualInversionTrainer: def __init__(self): self.vae_scale_factor = 0.18215 self.is_sdxl = False def assert_extra_args(self, args, train_dataset_group: Union[train_util.DatasetGroup, train_util.MinimalDataset], val_dataset_group: Optional[train_util.DatasetGroup]): train_dataset_group.verify_bucket_reso_steps(64) if val_dataset_group is not None: val_dataset_group.verify_bucket_reso_steps(64) 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 get_tokenize_strategy(self, args): return strategy_sd.SdTokenizeStrategy(args.v2, args.max_token_length, args.tokenizer_cache_dir) def get_tokenizers(self, tokenize_strategy: strategy_sd.SdTokenizeStrategy) -> List[Any]: return [tokenize_strategy.tokenizer] def get_latents_caching_strategy(self, args): latents_caching_strategy = strategy_sd.SdSdxlLatentsCachingStrategy( True, args.cache_latents_to_disk, args.vae_batch_size, args.skip_cache_check ) return latents_caching_strategy def assert_token_string(self, token_string, tokenizers: CLIPTokenizer): pass def get_text_encoding_strategy(self, args): return strategy_sd.SdTextEncodingStrategy(args.clip_skip) def get_models_for_text_encoding(self, args, accelerator, text_encoders) -> List[Any]: return text_encoders def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype): noise_pred = unet(noisy_latents, timesteps, text_conds[0]).sample return noise_pred def sample_images( self, accelerator, args, epoch, global_step, device, vae, tokenizers, text_encoders, unet, prompt_replacement ): train_util.sample_images( accelerator, args, epoch, global_step, device, vae, tokenizers[0], text_encoders[0], unet, prompt_replacement ) def save_weights(self, file, updated_embs, save_dtype, metadata): state_dict = {"emb_params": updated_embs[0]} if save_dtype is not None: for key in list(state_dict.keys()): v = state_dict[key] v = v.detach().clone().to("cpu").to(save_dtype) state_dict[key] = v if os.path.splitext(file)[1] == ".safetensors": from safetensors.torch import save_file save_file(state_dict, file, metadata) else: torch.save(state_dict, file) # can be loaded in Web UI def load_weights(self, file): if os.path.splitext(file)[1] == ".safetensors": from safetensors.torch import load_file data = load_file(file) else: # compatible to Web UI's file format data = torch.load(file, map_location="cpu") if type(data) != dict: raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}") if "string_to_param" in data: # textual inversion embeddings data = data["string_to_param"] if hasattr(data, "_parameters"): # support old PyTorch? data = getattr(data, "_parameters") emb = next(iter(data.values())) if type(emb) != torch.Tensor: raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}") if len(emb.size()) == 1: emb = emb.unsqueeze(0) return [emb] def train(self, args): if args.output_name is None: args.output_name = args.token_string use_template = args.use_object_template or args.use_style_template train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) setup_logging(args, reset=True) cache_latents = args.cache_latents if args.seed is not None: set_seed(args.seed) tokenize_strategy = self.get_tokenize_strategy(args) strategy_base.TokenizeStrategy.set_strategy(tokenize_strategy) tokenizers = self.get_tokenizers(tokenize_strategy) # will be removed after sample_image is refactored # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization. latents_caching_strategy = self.get_latents_caching_strategy(args) strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy) # 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 = torch.float32 if args.no_half_vae else weight_dtype # モデルを読み込む model_version, text_encoders, vae, unet = self.load_target_model(args, weight_dtype, accelerator) # Convert the init_word to token_id init_token_ids_list = [] if args.init_word is not None: for i, tokenizer in enumerate(tokenizers): init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False) if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token: accelerator.print( f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / " + f"初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: tokenizer {i+1}, length {len(init_token_ids)}" ) init_token_ids_list.append(init_token_ids) else: init_token_ids_list = [None] * len(tokenizers) # tokenizerに新しい単語を追加する。追加する単語の数はnum_vectors_per_token # token_stringが hoge の場合、"hoge", "hoge1", "hoge2", ... が追加される # add new word to tokenizer, count is num_vectors_per_token # if token_string is hoge, "hoge", "hoge1", "hoge2", ... are added self.assert_token_string(args.token_string, tokenizers) token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)] token_ids_list = [] token_embeds_list = [] for i, (tokenizer, text_encoder, init_token_ids) in enumerate(zip(tokenizers, text_encoders, init_token_ids_list)): num_added_tokens = tokenizer.add_tokens(token_strings) assert ( num_added_tokens == args.num_vectors_per_token ), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: tokenizer {i+1}, {args.token_string}" token_ids = tokenizer.convert_tokens_to_ids(token_strings) accelerator.print(f"tokens are added for tokenizer {i+1}: {token_ids}") assert ( min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1 ), f"token ids is not ordered : tokenizer {i+1}, {token_ids}" assert ( len(tokenizer) - 1 == token_ids[-1] ), f"token ids is not end of tokenize: tokenizer {i+1}, {token_ids}, {len(tokenizer)}" token_ids_list.append(token_ids) # Resize the token embeddings as we are adding new special tokens to the tokenizer text_encoder.resize_token_embeddings(len(tokenizer)) # Initialise the newly added placeholder token with the embeddings of the initializer token token_embeds = text_encoder.get_input_embeddings().weight.data if init_token_ids is not None: for i, token_id in enumerate(token_ids): token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]] # accelerator.print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min()) token_embeds_list.append(token_embeds) # load weights if args.weights is not None: embeddings_list = self.load_weights(args.weights) assert len(token_ids) == len( embeddings_list[0] ), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}" # accelerator.print(token_ids, embeddings.size()) for token_ids, embeddings, token_embeds in zip(token_ids_list, embeddings_list, token_embeds_list): for token_id, embedding in zip(token_ids, embeddings): token_embeds[token_id] = embedding # accelerator.print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min()) accelerator.print(f"weighs loaded") accelerator.print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}") # データセットを準備する if args.dataset_class is None: blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, False)) if args.dataset_config is not None: accelerator.print(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) ignored = ["train_data_dir", "reg_data_dir", "in_json"] if any(getattr(args, attr) is not None for attr in ignored): accelerator.print( "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format( ", ".join(ignored) ) ) else: use_dreambooth_method = args.in_json is None if use_dreambooth_method: accelerator.print("Use DreamBooth method.") user_config = { "datasets": [ { "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( args.train_data_dir, args.reg_data_dir ) } ] } else: logger.info("Train with captions.") user_config = { "datasets": [ { "subsets": [ { "image_dir": args.train_data_dir, "metadata_file": args.in_json, } ] } ] } blueprint = blueprint_generator.generate(user_config, args) train_dataset_group, val_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) else: train_dataset_group = train_util.load_arbitrary_dataset(args) val_dataset_group = None self.assert_extra_args(args, train_dataset_group, val_dataset_group) 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) # make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装 if use_template: accelerator.print(f"use template for training captions. is object: {args.use_object_template}") templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small replace_to = " ".join(token_strings) captions = [] for tmpl in templates: captions.append(tmpl.format(replace_to)) train_dataset_group.add_replacement("", captions) # サンプル生成用 if args.num_vectors_per_token > 1: prompt_replacement = (args.token_string, replace_to) else: prompt_replacement = None else: # サンプル生成用 if args.num_vectors_per_token > 1: replace_to = " ".join(token_strings) train_dataset_group.add_replacement(args.token_string, replace_to) prompt_replacement = (args.token_string, replace_to) else: prompt_replacement = None if args.debug_dataset: train_util.debug_dataset(train_dataset_group, show_input_ids=True) return if len(train_dataset_group) == 0: accelerator.print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください") return if cache_latents: assert ( train_dataset_group.is_latent_cacheable() ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" # モデルに xformers とか memory efficient attention を組み込む train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, 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) # 学習を準備する if cache_latents: vae.to(accelerator.device, dtype=vae_dtype) vae.requires_grad_(False) vae.eval() train_dataset_group.new_cache_latents(vae, accelerator) clean_memory_on_device(accelerator.device) accelerator.wait_for_everyone() if args.gradient_checkpointing: unet.enable_gradient_checkpointing() for text_encoder in text_encoders: text_encoder.gradient_checkpointing_enable() # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") trainable_params = [] for text_encoder in text_encoders: trainable_params += text_encoder.get_input_embeddings().parameters() _, _, optimizer = train_util.get_optimizer(args, trainable_params) # 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 lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) # acceleratorがなんかよろしくやってくれるらしい optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) text_encoders = [accelerator.prepare(text_encoder) for text_encoder in text_encoders] index_no_updates_list = [] orig_embeds_params_list = [] for tokenizer, token_ids, text_encoder in zip(tokenizers, token_ids_list, text_encoders): index_no_updates = torch.arange(len(tokenizer)) < token_ids[0] index_no_updates_list.append(index_no_updates) # accelerator.print(len(index_no_updates), torch.sum(index_no_updates)) orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone() orig_embeds_params_list.append(orig_embeds_params) # Freeze all parameters except for the token embeddings in text encoder text_encoder.requires_grad_(True) unwrapped_text_encoder = accelerator.unwrap_model(text_encoder) unwrapped_text_encoder.text_model.encoder.requires_grad_(False) unwrapped_text_encoder.text_model.final_layer_norm.requires_grad_(False) unwrapped_text_encoder.text_model.embeddings.position_embedding.requires_grad_(False) # text_encoder.text_model.embeddings.token_embedding.requires_grad_(True) unet.requires_grad_(False) unet.to(accelerator.device, dtype=weight_dtype) if args.gradient_checkpointing: # according to TI example in Diffusers, train is required # TODO U-Netをオリジナルに置き換えたのでいらないはずなので、後で確認して消す unet.train() else: unet.eval() text_encoding_strategy = self.get_text_encoding_strategy(args) strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy) 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) for text_encoder in text_encoders: text_encoder.to(weight_dtype) if args.full_bf16: for text_encoder in text_encoders: text_encoder.to(weight_dtype) # 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 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: {args.train_batch_size}") 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 ccumulation 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 ) 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( "textual_inversion" 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, ) # function for saving/removing def save_model(ckpt_name, embs_list, 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}") sai_metadata = train_util.get_sai_model_spec(None, args, self.is_sdxl, False, True) self.save_weights(ckpt_file, embs_list, save_dtype, sai_metadata) 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, tokenizers, text_encoders, unet, prompt_replacement, ) if len(accelerator.trackers) > 0: # log empty object to commit the sample images to wandb accelerator.log({}, step=0) # training loop for epoch in range(num_train_epochs): accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") current_epoch.value = epoch + 1 for text_encoder in text_encoders: text_encoder.train() loss_total = 0 for step, batch in enumerate(train_dataloader): current_step.value = global_step with accelerator.accumulate(text_encoders[0]): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype) latents = latents * self.vae_scale_factor if batch["masks"] is not None: masked_latents = vae.encode( batch["masked_images"].to(dtype=vae_dtype) ).latent_dist.sample().to(dtype=weight_dtype) masked_latents = masked_latents * self.vae_scale_factor # Resize the mask to latents shape as we concatenate the mask to the latents mask = torch.nn.functional.interpolate( batch["masks"].to(weight_dtype), size=latents.shape[2:] ) # Get the text embedding for conditioning input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]] text_encoder_conds = text_encoding_strategy.encode_tokens( tokenize_strategy, self.get_models_for_text_encoding(args, accelerator, text_encoders), input_ids ) if args.full_fp16: text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds] # 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 ) if batch["masks"] is not None: # Concatenate the noised latents with the mask and the masked latents noisy_latents = torch.cat([noisy_latents, mask, masked_latents], dim=1) # 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 huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler) loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c) if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None): 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, args.v_parameterization) 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 = accelerator.unwrap_model(text_encoder).get_input_embeddings().parameters() accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=True) # Let's make sure we don't update any embedding weights besides the newly added token with torch.no_grad(): for text_encoder, orig_embeds_params, index_no_updates in zip( text_encoders, orig_embeds_params_list, index_no_updates_list ): # if full_fp16/bf16, input_embeddings_weight is fp16/bf16, orig_embeds_params is fp32 input_embeddings_weight = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight input_embeddings_weight[index_no_updates] = orig_embeds_params.to(input_embeddings_weight.dtype)[ index_no_updates ] # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 self.sample_images( accelerator, args, None, global_step, accelerator.device, vae, tokenizers, text_encoders, unet, prompt_replacement, ) # 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: updated_embs_list = [] for text_encoder, token_ids in zip(text_encoders, token_ids_list): updated_embs = ( accelerator.unwrap_model(text_encoder) .get_input_embeddings() .weight[token_ids] .data.detach() .clone() ) updated_embs_list.append(updated_embs) ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step) save_model(ckpt_name, updated_embs_list, 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() if len(accelerator.trackers) > 0: logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])} if ( args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower() ): # tracking d*lr value logs["lr/d*lr"] = ( lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"] ) accelerator.log(logs, step=global_step) loss_total += current_loss avr_loss = loss_total / (step + 1) logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break if len(accelerator.trackers) > 0: logs = {"loss/epoch": loss_total / len(train_dataloader)} accelerator.log(logs, step=epoch + 1) accelerator.wait_for_everyone() updated_embs_list = [] for text_encoder, token_ids in zip(text_encoders, token_ids_list): updated_embs = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone() updated_embs_list.append(updated_embs) 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 accelerator.is_main_process and saving: ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1) save_model(ckpt_name, updated_embs_list, epoch + 1, global_step) 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, tokenizers, text_encoders, unet, prompt_replacement, ) accelerator.log({}) # end of epoch is_main_process = accelerator.is_main_process if is_main_process: text_encoder = accelerator.unwrap_model(text_encoder) updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone() 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, updated_embs_list, 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) sai_model_spec.add_model_spec_arguments(parser) train_util.add_dataset_arguments(parser, True, True, False) 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, False) parser.add_argument( "--save_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"], help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）", ) parser.add_argument( "--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み" ) parser.add_argument( "--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数" ) parser.add_argument( "--token_string", type=str, default=None, help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること", ) parser.add_argument( "--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可" ) parser.add_argument( "--use_object_template", action="store_true", help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する", ) parser.add_argument( "--use_style_template", action="store_true", help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する", ) 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 = TextualInversionTrainer() trainer.train(args)