Remove defunct code for train_controlnet.py

train_controlnet.py

@@ -6,577 +6,8 @@ import logging
 logger = logging.getLogger(__name__)
 
 
-<<<<<<< HEAD
-# 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)
-    setup_logging(args, reset=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:
-        logger.info(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):
-            logger.warning(
-                "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:
-        logger.error(
-            "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
-    logger.info("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")
-        clean_memory_on_device(accelerator.device)
-        
-        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 persistent_workers が使えないので注意
-    n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
-
-    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])}"
-    )
-    # logger.info(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).to(dtype=weight_dtype)
-                    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 = train_util.get_timesteps(args, 0, noise_scheduler.config.num_train_timesteps, b_size)
-                huber_c = train_util.get_huber_c(args, noise_scheduler, timesteps.item(), latents.device)
-
-                # 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 = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
-                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 or args.save_state_on_train_end):
-        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)
-
-        logger.info("model saved.")
-
-
-def setup_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser()
-
-    add_logging_arguments(parser)
-    train_util.add_sd_models_arguments(parser)
-    train_util.add_dataset_arguments(parser, False, True, True)
-    train_util.add_training_arguments(parser, False)
-    deepspeed_utils.add_deepspeed_arguments(parser)
-    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
-
-=======
 from library import train_util
 from train_control_net import setup_parser, train
->>>>>>> hina/feature/val-loss
 
 if __name__ == "__main__":
     logger.warning(