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.bucket_reso_steps, args.bucket_no_upscale, 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.bucket_reso_steps, args.bucket_no_upscale, 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(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, 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 * len(train_dataloader) 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}") # 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) 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 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}") train_dataset.set_current_epoch(epoch + 1) 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) # weight_dtype) use float instead of fp16/bf16 because text encoder is float encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.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 = {"loss/epoch": 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 = 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) # 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}") 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, False) 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)