train_textual_inversion.py

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_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:
      print(
          f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 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: length {len(init_token_ids)}")
  else:
    init_token_ids = 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_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)]]
      # 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
  elif args.use_lion_optimizer:
    try:
      import lion_pytorch
    except ImportError:
      raise ImportError("No lion_pytorch / lion_pytorch がインストールされていないようです")
    print("use Lion optimizer")
    optimizer_class = lion_pytorch.Lion
  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)
        if args.noise_offset:
          # https://www.crosslabs.org//blog/diffusion-with-offset-noise
          noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), 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="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")

  args = parser.parse_args()
  train(args)