train_controlnet.py

import argparse
import gc
import json
import math
import os
import random
import time
from multiprocessing import Value
from types import SimpleNamespace
import toml

from tqdm import tqdm
import torch

from library.ipex_interop import init_ipex

init_ipex()

from torch.nn.parallel import DistributedDataParallel as DDP
from accelerate.utils import set_seed
from diffusers import DDPMScheduler, ControlNetModel
from safetensors.torch import load_file

import library.model_util as model_util
import library.train_util as train_util
import library.config_util as config_util
from library.config_util import (
    ConfigSanitizer,
    BlueprintGenerator,
)
import library.huggingface_util as huggingface_util
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import (
    apply_snr_weight,
    pyramid_noise_like,
    apply_noise_offset,
)


# TODO 他のスクリプトと共通化する
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
    logs = {
        "loss/current": current_loss,
        "loss/average": avr_loss,
        "lr": lr_scheduler.get_last_lr()[0],
    }

    if args.optimizer_type.lower().startswith("DAdapt".lower()):
        logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]

    return logs


def train(args):
    # session_id = random.randint(0, 2**32)
    # training_started_at = time.time()
    train_util.verify_training_args(args)
    train_util.prepare_dataset_args(args, True)

    cache_latents = args.cache_latents
    use_user_config = args.dataset_config is not None

    if args.seed is None:
        args.seed = random.randint(0, 2**32)
    set_seed(args.seed)

    tokenizer = train_util.load_tokenizer(args)

    # データセットを準備する
    blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, True, True))
    if use_user_config:
        print(f"Load dataset config from {args.dataset_config}")
        user_config = config_util.load_user_config(args.dataset_config)
        ignored = ["train_data_dir", "conditioning_data_dir"]
        if any(getattr(args, attr) is not None for attr in ignored):
            print(
                "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
                    ", ".join(ignored)
                )
            )
    else:
        user_config = {
            "datasets": [
                {
                    "subsets": config_util.generate_controlnet_subsets_config_by_subdirs(
                        args.train_data_dir,
                        args.conditioning_data_dir,
                        args.caption_extension,
                    )
                }
            ]
        }

    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
    train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)

    current_epoch = Value("i", 0)
    current_step = Value("i", 0)
    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)

    if args.debug_dataset:
        train_util.debug_dataset(train_dataset_group)
        return
    if len(train_dataset_group) == 0:
        print(
            "No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください（train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります）"
        )
        return

    if cache_latents:
        assert (
            train_dataset_group.is_latent_cacheable()
        ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"

    # acceleratorを準備する
    print("prepare accelerator")
    accelerator = train_util.prepare_accelerator(args)
    is_main_process = accelerator.is_main_process

    # 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, accelerator, unet_use_linear_projection_in_v2=True
    )

    # DiffusersのControlNetが使用するデータを準備する
    if args.v2:
        unet.config = {
            "act_fn": "silu",
            "attention_head_dim": [5, 10, 20, 20],
            "block_out_channels": [320, 640, 1280, 1280],
            "center_input_sample": False,
            "cross_attention_dim": 1024,
            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
            "downsample_padding": 1,
            "dual_cross_attention": False,
            "flip_sin_to_cos": True,
            "freq_shift": 0,
            "in_channels": 4,
            "layers_per_block": 2,
            "mid_block_scale_factor": 1,
            "norm_eps": 1e-05,
            "norm_num_groups": 32,
            "num_class_embeds": None,
            "only_cross_attention": False,
            "out_channels": 4,
            "sample_size": 96,
            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
            "use_linear_projection": True,
            "upcast_attention": True,
            "only_cross_attention": False,
            "downsample_padding": 1,
            "use_linear_projection": True,
            "class_embed_type": None,
            "num_class_embeds": None,
            "resnet_time_scale_shift": "default",
            "projection_class_embeddings_input_dim": None,
        }
    else:
        unet.config = {
            "act_fn": "silu",
            "attention_head_dim": 8,
            "block_out_channels": [320, 640, 1280, 1280],
            "center_input_sample": False,
            "cross_attention_dim": 768,
            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
            "downsample_padding": 1,
            "flip_sin_to_cos": True,
            "freq_shift": 0,
            "in_channels": 4,
            "layers_per_block": 2,
            "mid_block_scale_factor": 1,
            "norm_eps": 1e-05,
            "norm_num_groups": 32,
            "out_channels": 4,
            "sample_size": 64,
            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
            "only_cross_attention": False,
            "downsample_padding": 1,
            "use_linear_projection": False,
            "class_embed_type": None,
            "num_class_embeds": None,
            "upcast_attention": False,
            "resnet_time_scale_shift": "default",
            "projection_class_embeddings_input_dim": None,
        }
    unet.config = SimpleNamespace(**unet.config)

    controlnet = ControlNetModel.from_unet(unet)

    if args.controlnet_model_name_or_path:
        filename = args.controlnet_model_name_or_path
        if os.path.isfile(filename):
            if os.path.splitext(filename)[1] == ".safetensors":
                state_dict = load_file(filename)
            else:
                state_dict = torch.load(filename)
            state_dict = model_util.convert_controlnet_state_dict_to_diffusers(state_dict)
            controlnet.load_state_dict(state_dict)
        elif os.path.isdir(filename):
            controlnet = ControlNetModel.from_pretrained(filename)

    # 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, args.sdpa)

    # 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_group.cache_latents(
                vae,
                args.vae_batch_size,
                args.cache_latents_to_disk,
                accelerator.is_main_process,
            )
        vae.to("cpu")
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()

        accelerator.wait_for_everyone()

    if args.gradient_checkpointing:
        controlnet.enable_gradient_checkpointing()

    # 学習に必要なクラスを準備する
    accelerator.print("prepare optimizer, data loader etc.")

    trainable_params = controlnet.parameters()

    _, _, optimizer = train_util.get_optimizer(args, trainable_params)

    # 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_group,
        batch_size=1,
        shuffle=True,
        collate_fn=collator,
        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 * math.ceil(
            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
        )
        accelerator.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}")

    # データセット側にも学習ステップを送信
    train_dataset_group.set_max_train_steps(args.max_train_steps)

    # lr schedulerを用意する
    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)

    # 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。"
        accelerator.print("enable full fp16 training.")
        controlnet.to(weight_dtype)

    # acceleratorがなんかよろしくやってくれるらしい
    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        controlnet, optimizer, train_dataloader, lr_scheduler
    )

    unet.requires_grad_(False)
    text_encoder.requires_grad_(False)
    unet.to(accelerator.device)
    text_encoder.to(accelerator.device)

    # transform DDP after prepare
    controlnet = controlnet.module if isinstance(controlnet, DDP) else controlnet

    controlnet.train()

    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
    train_util.resume_from_local_or_hf_if_specified(accelerator, args)

    # 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
    # TODO: find a way to handle total batch size when there are multiple datasets
    accelerator.print("running training / 学習開始")
    accelerator.print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
    accelerator.print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
    accelerator.print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
    accelerator.print(f"  num epochs / epoch数: {num_train_epochs}")
    accelerator.print(f"  batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
    # 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}")
    accelerator.print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
    accelerator.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:
        init_kwargs = {}
        if args.wandb_run_name:
            init_kwargs['wandb'] = {'name': args.wandb_run_name}
        if args.log_tracker_config is not None:
            init_kwargs = toml.load(args.log_tracker_config)
        accelerator.init_trackers(
            "controlnet_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
        )

    loss_recorder = train_util.LossRecorder()
    del train_dataset_group

    # function for saving/removing
    def save_model(ckpt_name, model, force_sync_upload=False):
        os.makedirs(args.output_dir, exist_ok=True)
        ckpt_file = os.path.join(args.output_dir, ckpt_name)

        accelerator.print(f"\nsaving checkpoint: {ckpt_file}")

        state_dict = model_util.convert_controlnet_state_dict_to_sd(model.state_dict())

        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(ckpt_file)[1] == ".safetensors":
            from safetensors.torch import save_file

            save_file(state_dict, ckpt_file)
        else:
            torch.save(state_dict, ckpt_file)

        if args.huggingface_repo_id is not None:
            huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)

    def remove_model(old_ckpt_name):
        old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
        if os.path.exists(old_ckpt_file):
            accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
            os.remove(old_ckpt_file)

    # For --sample_at_first
    train_util.sample_images(
        accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, controlnet=controlnet
    )

    # training loop
    for epoch in range(num_train_epochs):
        if is_main_process:
            accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
        current_epoch.value = epoch + 1

        for step, batch in enumerate(train_dataloader):
            current_step.value = global_step
            with accelerator.accumulate(controlnet):
                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]

                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)
                if args.noise_offset:
                    noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
                elif args.multires_noise_iterations:
                    noise = pyramid_noise_like(
                        noise,
                        latents.device,
                        args.multires_noise_iterations,
                        args.multires_noise_discount,
                    )

                # 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)

                controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)

                with accelerator.autocast():
                    down_block_res_samples, mid_block_res_sample = controlnet(
                        noisy_latents,
                        timesteps,
                        encoder_hidden_states=encoder_hidden_states,
                        controlnet_cond=controlnet_image,
                        return_dict=False,
                    )

                    # Predict the noise residual
                    noise_pred = unet(
                        noisy_latents,
                        timesteps,
                        encoder_hidden_states,
                        down_block_additional_residuals=[sample.to(dtype=weight_dtype) for sample in down_block_res_samples],
                        mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
                    ).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

                if args.min_snr_gamma:
                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)

                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし

                accelerator.backward(loss)
                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
                    params_to_clip = controlnet.parameters()
                    accelerator.clip_grad_norm_(params_to_clip, 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

                train_util.sample_images(
                    accelerator,
                    args,
                    None,
                    global_step,
                    accelerator.device,
                    vae,
                    tokenizer,
                    text_encoder,
                    unet,
                    controlnet=controlnet,
                )

                # 指定ステップごとにモデルを保存
                if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
                    accelerator.wait_for_everyone()
                    if accelerator.is_main_process:
                        ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
                        save_model(
                            ckpt_name,
                            accelerator.unwrap_model(controlnet),
                        )

                        if args.save_state:
                            train_util.save_and_remove_state_stepwise(args, accelerator, global_step)

                        remove_step_no = train_util.get_remove_step_no(args, global_step)
                        if remove_step_no is not None:
                            remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)
                            remove_model(remove_ckpt_name)

            current_loss = loss.detach().item()
            loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
            avr_loss: float = loss_recorder.moving_average
            logs = {"avr_loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
            progress_bar.set_postfix(**logs)

            if args.logging_dir is not None:
                logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
                accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

        if args.logging_dir is not None:
            logs = {"loss/epoch": loss_recorder.moving_average}
            accelerator.log(logs, step=epoch + 1)

        accelerator.wait_for_everyone()

        # 指定エポックごとにモデルを保存
        if args.save_every_n_epochs is not None:
            saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs
            if is_main_process and saving:
                ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
                save_model(ckpt_name, accelerator.unwrap_model(controlnet))

                remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)
                if remove_epoch_no is not None:
                    remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
                    remove_model(remove_ckpt_name)

                if args.save_state:
                    train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)

        train_util.sample_images(
            accelerator,
            args,
            epoch + 1,
            global_step,
            accelerator.device,
            vae,
            tokenizer,
            text_encoder,
            unet,
            controlnet=controlnet,
        )

        # end of epoch
    if is_main_process:
        controlnet = accelerator.unwrap_model(controlnet)

    accelerator.end_training()

    if is_main_process and 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→printで使うので消さずにおく

    if is_main_process:
        ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
        save_model(ckpt_name, controlnet, force_sync_upload=True)

        print("model saved.")


def setup_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()

    train_util.add_sd_models_arguments(parser)
    train_util.add_dataset_arguments(parser, False, True, True)
    train_util.add_training_arguments(parser, False)
    train_util.add_optimizer_arguments(parser)
    config_util.add_config_arguments(parser)
    custom_train_functions.add_custom_train_arguments(parser)

    parser.add_argument(
        "--save_model_as",
        type=str,
        default="safetensors",
        choices=[None, "ckpt", "pt", "safetensors"],
        help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）",
    )
    parser.add_argument(
        "--controlnet_model_name_or_path",
        type=str,
        default=None,
        help="controlnet model name or path / controlnetのモデル名またはパス",
    )
    parser.add_argument(
        "--conditioning_data_dir",
        type=str,
        default=None,
        help="conditioning data directory / 条件付けデータのディレクトリ",
    )

    return parser


if __name__ == "__main__":
    parser = setup_parser()

    args = parser.parse_args()
    args = train_util.read_config_from_file(args, parser)

    train(args)