Upload lora-scripts/sd-scripts/train_textual_inversion.py with huggingface_hub

lora-scripts/sd-scripts/train_textual_inversion.py

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+import argparse
+import math
+import os
+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 transformers import CLIPTokenizer
+from library import deepspeed_utils, model_util
+
+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):
+        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 assert_token_string(self, token_string, tokenizers: CLIPTokenizer):
+        pass
+
+    def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype):
+        with torch.enable_grad():
+            input_ids = batch["input_ids"].to(accelerator.device)
+            encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizers[0], text_encoders[0], None)
+            return encoder_hidden_states
+
+    def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype):
+        noise_pred = unet(noisy_latents, timesteps, text_conds).sample
+        return noise_pred
+
+    def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet, prompt_replacement):
+        train_util.sample_images(
+            accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, 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)
+
+        tokenizer_or_list = self.load_tokenizer(args)  # list of tokenizer or tokenizer
+        tokenizers = tokenizer_or_list if isinstance(tokenizer_or_list, list) else [tokenizer_or_list]
+
+        # 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_encoder_or_list, vae, unet = self.load_target_model(args, weight_dtype, accelerator)
+        text_encoders = [text_encoder_or_list] if not isinstance(text_encoder_or_list, list) else text_encoder_or_list
+
+        if len(text_encoders) > 1 and args.gradient_accumulation_steps > 1:
+            accelerator.print(
+                "accelerate doesn't seem to support gradient_accumulation_steps for multiple models (text encoders) / "
+                + "accelerateでは複数のモデル（テキストエンコーダー）のgradient_accumulation_stepsはサポートされていないようです"
+            )
+
+        # 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, tokenizer=tokenizer_or_list)
+            train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
+        else:
+            train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer_or_list)
+
+        self.assert_extra_args(args, train_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()
+            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()
+
+        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)
+
+        # 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)
+
+        # acceleratorがなんかよろしくやってくれるらしい
+        if len(text_encoders) == 1:
+            text_encoder_or_list, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                text_encoder_or_list, optimizer, train_dataloader, lr_scheduler
+            )
+
+        elif len(text_encoders) == 2:
+            text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                text_encoders[0], text_encoders[1], optimizer, train_dataloader, lr_scheduler
+            )
+
+            text_encoder_or_list = text_encoders = [text_encoder1, text_encoder2]
+
+        else:
+            raise NotImplementedError()
+
+        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()
+
+        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, 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,
+            tokenizer_or_list,
+            text_encoder_or_list,
+            unet,
+            prompt_replacement,
+        )
+
+        # 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
+
+                    # Get the text embedding for conditioning
+                    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
+                    )
+
+                    # Predict the noise residual
+                    with accelerator.autocast():
+                        noise_pred = self.call_unet(
+                            args, accelerator, unet, noisy_latents, timesteps, text_encoder_conds, batch, weight_dtype
+                        )
+
+                    if args.v_parameterization:
+                        # v-parameterization training
+                        target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                    else:
+                        target = noise
+
+                    loss = 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 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,
+                        tokenizer_or_list,
+                        text_encoder_or_list,
+                        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 args.logging_dir is not None:
+                    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 args.logging_dir is not None:
+                logs = {"loss/epoch": loss_total / len(train_dataloader)}
+                accelerator.log(logs, step=epoch + 1)
+
+            accelerator.wait_for_everyone()
+
+            updated_embs_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,
+                tokenizer_or_list,
+                text_encoder_or_list,
+                unet,
+                prompt_replacement,
+            )
+
+            # 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)
+    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)