352 lines
15 KiB
Python
352 lines
15 KiB
Python
# training with captions
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# XXX dropped option: hypernetwork training
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import argparse
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import gc
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import math
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import os
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from tqdm import tqdm
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import torch
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from accelerate.utils import set_seed
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import diffusers
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from diffusers import DDPMScheduler
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import library.train_util as train_util
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def collate_fn(examples):
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return examples[0]
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def train(args):
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train_util.verify_training_args(args)
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train_util.prepare_dataset_args(args, True)
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cache_latents = args.cache_latents
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if args.seed is not None:
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set_seed(args.seed) # 乱数系列を初期化する
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tokenizer = train_util.load_tokenizer(args)
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train_dataset = train_util.FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
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tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
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args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
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args.bucket_reso_steps, args.bucket_no_upscale,
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args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
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args.dataset_repeats, args.debug_dataset)
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# 学習データのdropout率を設定する
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train_dataset.set_caption_dropout(args.caption_dropout_rate, args.caption_dropout_every_n_epochs)
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train_dataset.make_buckets()
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if args.debug_dataset:
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train_util.debug_dataset(train_dataset)
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return
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if len(train_dataset) == 0:
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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。")
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return
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# acceleratorを準備する
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print("prepare accelerator")
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accelerator, unwrap_model = train_util.prepare_accelerator(args)
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# mixed precisionに対応した型を用意しておき適宜castする
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weight_dtype, save_dtype = train_util.prepare_dtype(args)
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# モデルを読み込む
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text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
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# verify load/save model formats
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if load_stable_diffusion_format:
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src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
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src_diffusers_model_path = None
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else:
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src_stable_diffusion_ckpt = None
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src_diffusers_model_path = args.pretrained_model_name_or_path
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if args.save_model_as is None:
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save_stable_diffusion_format = load_stable_diffusion_format
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use_safetensors = args.use_safetensors
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else:
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save_stable_diffusion_format = args.save_model_as.lower() == 'ckpt' or args.save_model_as.lower() == 'safetensors'
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use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
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# Diffusers版のxformers使用フラグを設定する関数
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def set_diffusers_xformers_flag(model, valid):
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# model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう
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# pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
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# 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
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# 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^)
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# Recursively walk through all the children.
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# Any children which exposes the set_use_memory_efficient_attention_xformers method
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# gets the message
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def fn_recursive_set_mem_eff(module: torch.nn.Module):
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if hasattr(module, "set_use_memory_efficient_attention_xformers"):
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module.set_use_memory_efficient_attention_xformers(valid)
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for child in module.children():
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fn_recursive_set_mem_eff(child)
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fn_recursive_set_mem_eff(model)
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# モデルに xformers とか memory efficient attention を組み込む
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if args.diffusers_xformers:
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print("Use xformers by Diffusers")
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set_diffusers_xformers_flag(unet, True)
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else:
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# 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
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print("Disable Diffusers' xformers")
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set_diffusers_xformers_flag(unet, False)
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train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
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# 学習を準備する
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if cache_latents:
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vae.to(accelerator.device, dtype=weight_dtype)
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vae.requires_grad_(False)
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vae.eval()
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with torch.no_grad():
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train_dataset.cache_latents(vae)
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vae.to("cpu")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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# 学習を準備する：モデルを適切な状態にする
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training_models = []
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if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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training_models.append(unet)
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if args.train_text_encoder:
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print("enable text encoder training")
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if args.gradient_checkpointing:
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text_encoder.gradient_checkpointing_enable()
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training_models.append(text_encoder)
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else:
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text_encoder.to(accelerator.device, dtype=weight_dtype)
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text_encoder.requires_grad_(False) # text encoderは学習しない
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if args.gradient_checkpointing:
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text_encoder.gradient_checkpointing_enable()
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text_encoder.train() # required for gradient_checkpointing
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else:
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text_encoder.eval()
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if not cache_latents:
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vae.requires_grad_(False)
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vae.eval()
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vae.to(accelerator.device, dtype=weight_dtype)
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for m in training_models:
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m.requires_grad_(True)
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params = []
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for m in training_models:
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params.extend(m.parameters())
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params_to_optimize = params
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# 学習に必要なクラスを準備する
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print("prepare optimizer, data loader etc.")
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# 8-bit Adamを使う
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if args.use_8bit_adam:
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try:
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import bitsandbytes as bnb
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except ImportError:
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raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
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print("use 8-bit Adam optimizer")
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optimizer_class = bnb.optim.AdamW8bit
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else:
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optimizer_class = torch.optim.AdamW
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# 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
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optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
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# dataloaderを準備する
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# DataLoaderのプロセス数：0はメインプロセスになる
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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
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train_dataloader = torch.utils.data.DataLoader(
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train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
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# 学習ステップ数を計算する
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if args.max_train_epochs is not None:
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args.max_train_steps = args.max_train_epochs * len(train_dataloader)
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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}")
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# lr schedulerを用意する
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lr_scheduler = diffusers.optimization.get_scheduler(
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args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
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# 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
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if args.full_fp16:
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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。"
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print("enable full fp16 training.")
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unet.to(weight_dtype)
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text_encoder.to(weight_dtype)
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# acceleratorがなんかよろしくやってくれるらしい
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if args.train_text_encoder:
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unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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unet, text_encoder, optimizer, train_dataloader, lr_scheduler)
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else:
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unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
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# 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
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if args.full_fp16:
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train_util.patch_accelerator_for_fp16_training(accelerator)
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# resumeする
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if args.resume is not None:
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print(f"resume training from state: {args.resume}")
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accelerator.load_state(args.resume)
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# epoch数を計算する
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
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num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
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if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
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args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
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# 学習する
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total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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print("running training / 学習開始")
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print(f" num examples / サンプル数: {train_dataset.num_train_images}")
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print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
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print(f" num epochs / epoch数: {num_train_epochs}")
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print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
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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}")
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print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
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print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
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progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
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global_step = 0
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noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
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num_train_timesteps=1000, clip_sample=False)
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if accelerator.is_main_process:
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accelerator.init_trackers("finetuning")
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for epoch in range(num_train_epochs):
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print(f"epoch {epoch+1}/{num_train_epochs}")
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train_dataset.epoch_current = epoch + 1
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for m in training_models:
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m.train()
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loss_total = 0
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for step, batch in enumerate(train_dataloader):
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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
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with torch.no_grad():
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if "latents" in batch and batch["latents"] is not None:
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latents = batch["latents"].to(accelerator.device)
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else:
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# latentに変換
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latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
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latents = latents * 0.18215
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b_size = latents.shape[0]
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with torch.set_grad_enabled(args.train_text_encoder):
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# Get the text embedding for conditioning
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input_ids = batch["input_ids"].to(accelerator.device)
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encoder_hidden_states = train_util.get_hidden_states(
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args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype)
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# Sample noise that we'll add to the latents
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noise = torch.randn_like(latents, device=latents.device)
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# Sample a random timestep for each image
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timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
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timesteps = timesteps.long()
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# Add noise to the latents according to the noise magnitude at each timestep
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# (this is the forward diffusion process)
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noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
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# Predict the noise residual
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noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
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if args.v_parameterization:
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# v-parameterization training
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target = noise_scheduler.get_velocity(latents, noise, timesteps)
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else:
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target = noise
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loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
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accelerator.backward(loss)
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if accelerator.sync_gradients:
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params_to_clip = []
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for m in training_models:
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params_to_clip.extend(m.parameters())
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accelerator.clip_grad_norm_(params_to_clip, 1.0) # args.max_grad_norm)
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad(set_to_none=True)
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# Checks if the accelerator has performed an optimization step behind the scenes
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if accelerator.sync_gradients:
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progress_bar.update(1)
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global_step += 1
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current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
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if args.logging_dir is not None:
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logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
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accelerator.log(logs, step=global_step)
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loss_total += current_loss
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avr_loss = loss_total / (step+1)
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logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
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progress_bar.set_postfix(**logs)
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if global_step >= args.max_train_steps:
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break
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if args.logging_dir is not None:
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logs = {"epoch_loss": loss_total / len(train_dataloader)}
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accelerator.log(logs, step=epoch+1)
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accelerator.wait_for_everyone()
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if args.save_every_n_epochs is not None:
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src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
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train_util.save_sd_model_on_epoch_end(args, accelerator, src_path, save_stable_diffusion_format, use_safetensors,
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save_dtype, epoch, num_train_epochs, global_step, unwrap_model(text_encoder), unwrap_model(unet), vae)
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is_main_process = accelerator.is_main_process
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if is_main_process:
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unet = unwrap_model(unet)
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text_encoder = unwrap_model(text_encoder)
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accelerator.end_training()
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if args.save_state:
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train_util.save_state_on_train_end(args, accelerator)
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del accelerator # この後メモリを使うのでこれは消す
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if is_main_process:
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src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
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train_util.save_sd_model_on_train_end(args, src_path, save_stable_diffusion_format, use_safetensors,
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save_dtype, epoch, global_step, text_encoder, unet, vae)
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print("model saved.")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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train_util.add_sd_models_arguments(parser)
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train_util.add_dataset_arguments(parser, False, True, True)
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train_util.add_training_arguments(parser, False)
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train_util.add_sd_saving_arguments(parser)
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parser.add_argument("--diffusers_xformers", action='store_true',
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help='use xformers by diffusers / Diffusersでxformersを使用する')
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parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
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args = parser.parse_args()
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train(args)
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