KohyaSS/fine_tune.py

# training with captions
# XXX dropped option: hypernetwork training

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


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

    if args.seed is not None:
        set_seed(args.seed)                           # 乱数系列を初期化する

    tokenizer = train_util.load_tokenizer(args)

    train_dataset = train_util.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 the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプション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,
        load_stable_diffusion_format,
    ) = train_util.load_target_model(args, weight_dtype)

    # verify load/save model formats
    if load_stable_diffusion_format:
        src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
        src_diffusers_model_path = None
    else:
        src_stable_diffusion_ckpt = None
        src_diffusers_model_path = args.pretrained_model_name_or_path

    if args.save_model_as is None:
        save_stable_diffusion_format = load_stable_diffusion_format
        use_safetensors = args.use_safetensors
    else:
        save_stable_diffusion_format = (
            args.save_model_as.lower() == 'ckpt'
            or args.save_model_as.lower() == 'safetensors'
        )
        use_safetensors = args.use_safetensors or (
            'safetensors' in args.save_model_as.lower()
        )

    # Diffusers版のxformers使用フラグを設定する関数
    def set_diffusers_xformers_flag(model, valid):
        #   model.set_use_memory_efficient_attention_xformers(valid)            # 次のリリースでなくなりそう
        # pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
        # U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
        # 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)

        # Recursively walk through all the children.
        # Any children which exposes the set_use_memory_efficient_attention_xformers method
        # gets the message
        def fn_recursive_set_mem_eff(module: torch.nn.Module):
            if hasattr(module, 'set_use_memory_efficient_attention_xformers'):
                module.set_use_memory_efficient_attention_xformers(valid)

            for child in module.children():
                fn_recursive_set_mem_eff(child)

        fn_recursive_set_mem_eff(model)

    # モデルに xformers とか memory efficient attention を組み込む
    if args.diffusers_xformers:
        print('Use xformers by Diffusers')
        set_diffusers_xformers_flag(unet, True)
    else:
        # Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
        print("Disable Diffusers' xformers")
        set_diffusers_xformers_flag(unet, False)
        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()

    # 学習を準備する：モデルを適切な状態にする
    training_models = []
    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()
    training_models.append(unet)

    if args.train_text_encoder:
        print('enable text encoder training')
        if args.gradient_checkpointing:
            text_encoder.gradient_checkpointing_enable()
        training_models.append(text_encoder)
    else:
        text_encoder.to(accelerator.device, dtype=weight_dtype)
        text_encoder.requires_grad_(False)             # text encoderは学習しない
        if args.gradient_checkpointing:
            text_encoder.gradient_checkpointing_enable()
            text_encoder.train()                # required for gradient_checkpointing
        else:
            text_encoder.eval()

    if not cache_latents:
        vae.requires_grad_(False)
        vae.eval()
        vae.to(accelerator.device, dtype=weight_dtype)

    for m in training_models:
        m.requires_grad_(True)
    params = []
    for m in training_models:
        params.extend(m.parameters())
    params_to_optimize = params

    # 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

    # 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(params_to_optimize, 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,
    )

    # 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.')
        unet.to(weight_dtype)
        text_encoder.to(weight_dtype)

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

    # 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 examples / サンプル数: {train_dataset.num_train_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('finetuning')

    for epoch in range(num_train_epochs):
        print(f'epoch {epoch+1}/{num_train_epochs}')
        for m in training_models:
            m.train()

        loss_total = 0
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(
                training_models[0]
            ):  # 複数モデルに対応していない模様だがとりあえずこうしておく
                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(args.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,
                        None if not args.full_fp16 else 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='mean'
                )

                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    params_to_clip = []
                    for m in training_models:
                        params_to_clip.extend(m.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)

            # 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()        # 平均なのでbatch sizeは関係ないはず
            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:
            src_path = (
                src_stable_diffusion_ckpt
                if save_stable_diffusion_format
                else src_diffusers_model_path
            )
            train_util.save_sd_model_on_epoch_end(
                args,
                accelerator,
                src_path,
                save_stable_diffusion_format,
                use_safetensors,
                save_dtype,
                epoch,
                num_train_epochs,
                global_step,
                unwrap_model(text_encoder),
                unwrap_model(unet),
                vae,
            )

    is_main_process = accelerator.is_main_process
    if is_main_process:
        unet = unwrap_model(unet)
        text_encoder = unwrap_model(text_encoder)

    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:
        src_path = (
            src_stable_diffusion_ckpt
            if save_stable_diffusion_format
            else src_diffusers_model_path
        )
        train_util.save_sd_model_on_train_end(
            args,
            src_path,
            save_stable_diffusion_format,
            use_safetensors,
            save_dtype,
            epoch,
            global_step,
            text_encoder,
            unet,
            vae,
        )
        print('model saved.')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    train_util.add_sd_models_arguments(parser)
    train_util.add_dataset_arguments(parser, False, True)
    train_util.add_training_arguments(parser, False)
    train_util.add_sd_saving_arguments(parser)

    parser.add_argument(
        '--diffusers_xformers',
        action='store_true',
        help='use xformers by diffusers / Diffusersでxformersを使用する',
    )
    parser.add_argument(
        '--train_text_encoder',
        action='store_true',
        help='train text encoder / text encoderも学習する',
    )

    args = parser.parse_args()
    train(args)