From c1b14fcdd673bf0cba791af8bdb6a3b8f8859375 Mon Sep 17 00:00:00 2001 From: Kohya S Date: Thu, 12 Jan 2023 20:47:08 +0900 Subject: [PATCH] initial version of TI --- library/train_util.py | 24 +- train_textual_inversion.py | 492 +++++++++++++++++++++++++++++++++++++ 2 files changed, 513 insertions(+), 3 deletions(-) create mode 100644 train_textual_inversion.py diff --git a/library/train_util.py b/library/train_util.py index 7a0f794b..378c0c29 100644 --- a/library/train_util.py +++ b/library/train_util.py @@ -104,9 +104,14 @@ class BaseDataset(torch.utils.data.Dataset): self.image_data: dict[str, ImageInfo] = {} + self.replacements = {} + def disable_token_padding(self): self.token_padding_disabled = True + def add_replacement(self, str_from, str_to): + self.replacements[str_from] = str_to + def process_caption(self, caption): if self.shuffle_caption: tokens = caption.strip().split(",") @@ -119,6 +124,17 @@ class BaseDataset(torch.utils.data.Dataset): random.shuffle(tokens) tokens = keep_tokens + tokens caption = ",".join(tokens).strip() + + for str_from, str_to in self.replacements.items(): + if str_from == "": + # replace all + if type(str_to) == list: + caption = random.choice(str_to) + else: + caption = str_to + else: + caption = caption.replace(str_from, str_to) + return caption def get_input_ids(self, caption): @@ -589,7 +605,7 @@ class FineTuningDataset(BaseDataset): else: # わりといい加減だがいい方法が思いつかん abs_path = glob_images(train_data_dir, image_key) - assert len(abs_path) >= 1, f"no image / 画像がありません: {abs_path}" + assert len(abs_path) >= 1, f"no image / 画像がありません: {image_key}" abs_path = abs_path[0] caption = img_md.get('caption') @@ -689,15 +705,17 @@ class FineTuningDataset(BaseDataset): return npz_file_norm, npz_file_flip -def debug_dataset(train_dataset): +def debug_dataset(train_dataset, show_input_ids=False): print(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}") print("Escape for exit. / Escキーで中断、終了します") k = 0 for example in train_dataset: if example['latents'] is not None: print("sample has latents from npz file") - for j, (ik, cap, lw) in enumerate(zip(example['image_keys'], example['captions'], example['loss_weights'])): + for j, (ik, cap, lw, iid) in enumerate(zip(example['image_keys'], example['captions'], example['loss_weights'], example['input_ids'])): print(f'{ik}, size: {train_dataset.image_data[ik].image_size}, caption: "{cap}", loss weight: {lw}') + if show_input_ids: + print(f"input ids: {iid}") if example['images'] is not None: im = example['images'][j] im = ((im.numpy() + 1.0) * 127.5).astype(np.uint8) diff --git a/train_textual_inversion.py b/train_textual_inversion.py new file mode 100644 index 00000000..5fd5e05e --- /dev/null +++ b/train_textual_inversion.py @@ -0,0 +1,492 @@ +import importlib +import argparse +import gc +import math +import os + +from tqdm import tqdm +import torch +from accelerate.utils import set_seed +import diffusers +from diffusers import DDPMScheduler + +import library.train_util as train_util +from library.train_util import DreamBoothDataset, FineTuningDataset + +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 {}", +] + + +def collate_fn(examples): + return examples[0] + + +def train(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) + + cache_latents = args.cache_latents + use_dreambooth_method = args.in_json is None + + if args.seed is not None: + set_seed(args.seed) + + tokenizer = train_util.load_tokenizer(args) + + # acceleratorを準備する + print("prepare accelerator") + accelerator, unwrap_model = 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) + + # モデルを読み込む + text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype) + + # Convert the init_word to token_id + if args.init_word is not None: + init_token_id = tokenizer.encode(args.init_word, add_special_tokens=False) + assert len( + init_token_id) == 1, f"init word {args.init_word} is not converted to single token / 初期化単語が二つ以上のトークンに変換されます。別の単語を使ってください" + init_token_id = init_token_id[0] + else: + init_token_id = None + + # add new word to tokenizer, count is num_vectors_per_token + token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)] + 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: {args.token_string}" + + token_ids = tokenizer.convert_tokens_to_ids(token_strings) + print(f"tokens are added: {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" + assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}" + + # 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_id is not None: + for token_id in token_ids: + token_embeds[token_id] = token_embeds[init_token_id] + print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min()) + + # load weights + if args.weights is not None: + embeddings = load_weights(args.weights) + assert len(token_ids) == len( + embeddings), 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)}" + print(token_ids, embeddings.size()) + for token_id, embedding in zip(token_ids, embeddings): + token_embeds[token_id] = embedding + print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min()) + print(f"weighs loaded") + + 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 use_dreambooth_method: + print("Use DreamBooth method.") + train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir, + tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens, + args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso, args.prior_loss_weight, + args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.debug_dataset) + else: + print("Train with captions.") + train_dataset = FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir, + tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens, + args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso, + args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, + args.dataset_repeats, args.debug_dataset) + + # 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: + print("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.add_replacement("", captions) + elif args.num_vectors_per_token > 1: + replace_to = " ".join(token_strings) + train_dataset.add_replacement(args.token_string, replace_to) + + train_dataset.make_buckets() + + if args.debug_dataset: + train_util.debug_dataset(train_dataset, show_input_ids=True) + return + if len(train_dataset) == 0: + 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 + + # 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) + + # 学習を準備する + if cache_latents: + vae.to(accelerator.device, dtype=weight_dtype) + vae.requires_grad_(False) + vae.eval() + with torch.no_grad(): + train_dataset.cache_latents(vae) + vae.to("cpu") + if torch.cuda.is_available(): + torch.cuda.empty_cache() + gc.collect() + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + text_encoder.gradient_checkpointing_enable() + + # 学習に必要なクラスを準備する + print("prepare optimizer, data loader etc.") + + # 8-bit Adamを使う + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです") + print("use 8-bit Adam optimizer") + optimizer_class = bnb.optim.AdamW8bit + else: + optimizer_class = torch.optim.AdamW + + trainable_params = text_encoder.get_input_embeddings().parameters() + + # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略 + optimizer = optimizer_class(trainable_params, lr=args.learning_rate) + + # dataloaderを準備する + # DataLoaderのプロセス数：0はメインプロセスになる + n_workers = min(8, os.cpu_count() - 1) # cpu_count-1 ただし最大8 + train_dataloader = torch.utils.data.DataLoader( + train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers) + + # lr schedulerを用意する + lr_scheduler = diffusers.optimization.get_scheduler( + args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps) + + # acceleratorがなんかよろしくやってくれるらしい + text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + text_encoder, optimizer, train_dataloader, lr_scheduler) + + index_no_updates = torch.arange(len(tokenizer)) < token_ids[0] + print(len(index_no_updates), torch.sum(index_no_updates)) + orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone() + + # Freeze all parameters except for the token embeddings in text encoder + text_encoder.requires_grad_(True) + text_encoder.text_model.encoder.requires_grad_(False) + text_encoder.text_model.final_layer_norm.requires_grad_(False) + 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 + unet.train() + else: + unet.eval() + + if not cache_latents: + vae.requires_grad_(False) + vae.eval() + vae.to(accelerator.device, dtype=weight_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) + text_encoder.to(weight_dtype) + + # resumeする + if args.resume is not None: + print(f"resume training from state: {args.resume}") + accelerator.load_state(args.resume) + + # 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) + + # 学習する + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + print("running training / 学習開始") + print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset.num_train_images}") + print(f" num reg images / 正則化画像の数: {train_dataset.num_reg_images}") + print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}") + print(f" num epochs / epoch数: {num_train_epochs}") + print(f" batch size per device / バッチサイズ: {args.train_batch_size}") + 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}") + print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") + 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) + + if accelerator.is_main_process: + accelerator.init_trackers("textual_inversion") + + for epoch in range(num_train_epochs): + print(f"epoch {epoch+1}/{num_train_epochs}") + + text_encoder.train() + + loss_total = 0 + bef_epo_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone() + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(text_encoder): + with torch.no_grad(): + if "latents" in batch and batch["latents"] is not None: + latents = batch["latents"].to(accelerator.device) + else: + # latentに変換 + latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample() + latents = latents * 0.18215 + b_size = latents.shape[0] + + # Get the text embedding for conditioning + input_ids = batch["input_ids"].to(accelerator.device) + encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float) # weight_dtype) use float instead of fp16/bf16 because text encoder is float + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents, device=latents.device) + + # Sample a random timestep for each image + timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Predict the noise residual + noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + if args.v_parameterization: + # v-parameterization training + target = noise_scheduler.get_velocity(latents, noise, timesteps) + else: + target = noise + + loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = loss.mean([1, 2, 3]) + + loss_weights = batch["loss_weights"] # 各sampleごとのweight + loss = loss * loss_weights + + loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし + + accelerator.backward(loss) + if accelerator.sync_gradients: + params_to_clip = text_encoder.get_input_embeddings().parameters() + accelerator.clip_grad_norm_(params_to_clip, 1.0) # 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(): + unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[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 + + current_loss = loss.detach().item() + if args.logging_dir is not None: + logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]} + 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 = {"epoch_loss": loss_total / len(train_dataloader)} + accelerator.log(logs, step=epoch+1) + + accelerator.wait_for_everyone() + + updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone() + d = updated_embs - bef_epo_embs + print(bef_epo_embs.size(), updated_embs.size(), d.mean(), d.min()) + + if args.save_every_n_epochs is not None: + model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name + + def save_func(): + ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + '.' + args.save_model_as + ckpt_file = os.path.join(args.output_dir, ckpt_name) + print(f"saving checkpoint: {ckpt_file}") + save_weights(ckpt_file, updated_embs, save_dtype) + + def remove_old_func(old_epoch_no): + old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + '.' + args.save_model_as + old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name) + if os.path.exists(old_ckpt_file): + print(f"removing old checkpoint: {old_ckpt_file}") + os.remove(old_ckpt_file) + + saving, remove_epoch_no = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs) + if saving and args.save_state: + train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1, remove_epoch_no) + + # end of epoch + + is_main_process = accelerator.is_main_process + if is_main_process: + text_encoder = unwrap_model(text_encoder) + + accelerator.end_training() + + if args.save_state: + train_util.save_state_on_train_end(args, accelerator) + + updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone() + + del accelerator # この後メモリを使うのでこれは消す + + if is_main_process: + os.makedirs(args.output_dir, exist_ok=True) + + model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name + ckpt_name = model_name + '.' + args.save_model_as + ckpt_file = os.path.join(args.output_dir, ckpt_name) + + print(f"save trained model to {ckpt_file}") + save_weights(ckpt_file, updated_embs, save_dtype) + print("model saved.") + + +def save_weights(file, updated_embs, save_dtype): + state_dict = {"emb_params": updated_embs} + + 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) + else: + torch.save(state_dict, file) # can be loaded in Web UI + + +def load_weights(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}") + print(data.keys()) + + 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 + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + train_util.add_sd_models_arguments(parser) + train_util.add_dataset_arguments(parser, True, True) + train_util.add_training_arguments(parser, True) + + 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="word to initialize vector / ベクトルを初期化に使用する単語、tokenizerで一語になること") + 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") + + args = parser.parse_args() + train(args)