KohyaSS/train_network.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


def collate_fn(examples):
  return examples[0]


def train(args):
  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)

  # 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)
  train_dataset.make_buckets()

  if args.debug_dataset:
    train_util.debug_dataset(train_dataset)
    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

  # 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)

  # 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()

  # prepare network
  print("import network module:", args.network_module)
  network_module = importlib.import_module(args.network_module)

  net_kwargs = {}
  if args.network_args is not None:
    for net_arg in args.network_args:
      key, value = net_arg.split('=')
      net_kwargs[key] = value

  network = network_module.create_network(1.0, args.network_dim, vae, text_encoder, unet, **net_kwargs)
  if network is None:
    return

  if args.network_weights is not None:
    print("load network weights from:", args.network_weights)
    network.load_weights(args.network_weights)

  train_unet = not args.network_train_text_encoder_only
  train_text_encoder = not args.network_train_unet_only
  network.apply_to(text_encoder, unet, train_text_encoder, train_unet)

  if args.gradient_checkpointing:
    unet.enable_gradient_checkpointing()
    text_encoder.gradient_checkpointing_enable()
    network.enable_gradient_checkpointing()                   # may have no effect

  # 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 = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)

  # 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)

  # 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)

  # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
  if args.full_fp16:
    assert args.mixed_precision == "fp16", "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
    print("enable full fp16 training.")
    network.to(weight_dtype)

  # acceleratorがなんかよろしくやってくれるらしい
  if train_unet and train_text_encoder:
    unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler)
  elif train_unet:
    unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        unet,  network, optimizer, train_dataloader, lr_scheduler)
  elif train_text_encoder:
    text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        text_encoder, network, optimizer, train_dataloader, lr_scheduler)
  else:
    network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        network, optimizer, train_dataloader, lr_scheduler)

  unet.requires_grad_(False)
  unet.to(accelerator.device, dtype=weight_dtype)
  text_encoder.requires_grad_(False)
  text_encoder.to(accelerator.device, dtype=weight_dtype)
  if args.gradient_checkpointing:                       # according to TI example in Diffusers, train is required
    unet.train()
    text_encoder.train()
  else:
    unet.eval()
    text_encoder.eval()

  network.prepare_grad_etc(text_encoder, unet)

  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)

  # 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 accumulation 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}")

  metadata = {
      "ss_learning_rate": args.learning_rate,
      "ss_text_encoder_lr": args.text_encoder_lr,
      "ss_unet_lr": args.unet_lr,
      "ss_num_train_images": train_dataset.num_train_images,          # includes repeating TODO more detailed data
      "ss_num_reg_images": train_dataset.num_reg_images,
      "ss_num_batches_per_epoch": len(train_dataloader),
      "ss_num_epochs": num_train_epochs,
      "ss_batch_size_per_device": args.train_batch_size,
      "ss_total_batch_size": total_batch_size,
      "ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
      "ss_max_train_steps": args.max_train_steps,
      "ss_lr_warmup_steps": args.lr_warmup_steps,
      "ss_lr_scheduler": args.lr_scheduler,
      "ss_network_module": args.network_module,
      "ss_network_dim": args.network_dim,      # None means default because another network than LoRA may have another default dim
      "ss_mixed_precision": args.mixed_precision,
      "ss_full_fp16": bool(args.full_fp16),
      "ss_v2": bool(args.v2),
      "ss_resolution": args.resolution,
      "ss_clip_skip": args.clip_skip,
      "ss_max_token_length": args.max_token_length,
      "ss_color_aug": bool(args.color_aug),
      "ss_flip_aug": bool(args.flip_aug),
      "ss_random_crop": bool(args.random_crop),
      "ss_shuffle_caption": bool(args.shuffle_caption),
      "ss_cache_latents": bool(args.cache_latents),
      "ss_enable_bucket": bool(train_dataset.enable_bucket),        # TODO move to BaseDataset from DB/FT
      "ss_min_bucket_reso": args.min_bucket_reso,                   # TODO get from dataset
      "ss_max_bucket_reso": args.max_bucket_reso,
      "ss_seed": args.seed
  }

  # uncomment if another network is added
  # for key, value in net_kwargs.items():
  #   metadata["ss_arg_" + key] = value

  if args.pretrained_model_name_or_path is not None:
    sd_model_name = args.pretrained_model_name_or_path
    if os.path.exists(sd_model_name):
      metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)
      sd_model_name = os.path.basename(sd_model_name)
    metadata["ss_sd_model_name"] = sd_model_name

  if args.vae is not None:
    vae_name = args.vae
    if os.path.exists(vae_name):
      metadata["ss_vae_hash"] = train_util.model_hash(vae_name)
      vae_name = os.path.basename(vae_name)
    metadata["ss_vae_name"] = vae_name

  metadata = {k: str(v) for k, v in metadata.items()}

  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("network_train")

  for epoch in range(num_train_epochs):
    print(f"epoch {epoch+1}/{num_train_epochs}")
    metadata["ss_epoch"] = str(epoch+1)

    network.on_epoch_start(text_encoder, unet)

    loss_total = 0
    for step, batch in enumerate(train_dataloader):
      with accelerator.accumulate(network):
        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]

        with torch.set_grad_enabled(train_text_encoder):
          # 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, weight_dtype)

        # 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 = network.get_trainable_params()
          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)

      # 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()

    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}")
        unwrap_model(network).save_weights(ckpt_file, save_dtype, None if args.no_metadata else metadata)

      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

  metadata["ss_epoch"] = str(num_train_epochs)

  is_main_process = accelerator.is_main_process
  if is_main_process:
    network = unwrap_model(network)

  accelerator.end_training()

  if args.save_state:
    train_util.save_state_on_train_end(args, accelerator)

  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}")
    network.save_weights(ckpt_file, save_dtype, None if args.no_metadata else metadata)
    print("model saved.")


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("--no_metadata", action='store_true', help="do not save metadata in output model / メタデータを出力先モデルに保存しない")
  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("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率")
  parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率")

  parser.add_argument("--network_weights", type=str, default=None,
                      help="pretrained weights for network / 学習するネットワークの初期重み")
  parser.add_argument("--network_module", type=str, default=None, help='network module to train / 学習対象のネットワークのモジュール')
  parser.add_argument("--network_dim", type=int, default=None,
                      help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）')
  parser.add_argument("--network_args", type=str, default=None, nargs='*',
                      help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
  parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")
  parser.add_argument("--network_train_text_encoder_only", action="store_true",
                      help="only training Text Encoder part / Text Encoder関連部分のみ学習する")

  args = parser.parse_args()
  train(args)