Remove unnecessary files
This commit is contained in:
bmaltais
parent
7150179155
commit
0baaf6dc57
@ -1,426 +0,0 @@
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# DreamBooth training
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# XXX dropped option: fine_tune
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import gc
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import time
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import argparse
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import itertools
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import math
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import os
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import toml
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from multiprocessing import Value
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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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import library.config_util as config_util
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from library.config_util import (
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ConfigSanitizer,
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BlueprintGenerator,
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)
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import library.custom_train_functions as custom_train_functions
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from library.custom_train_functions import apply_snr_weight
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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, False)
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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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blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, True))
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if args.dataset_config is not None:
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print(f"Load dataset config from {args.dataset_config}")
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user_config = config_util.load_user_config(args.dataset_config)
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ignored = ["train_data_dir", "reg_data_dir"]
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if any(getattr(args, attr) is not None for attr in ignored):
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print(
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"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(
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", ".join(ignored)
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)
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)
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else:
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user_config = {
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"datasets": [
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{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
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]
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}
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blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
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train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
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current_epoch = Value('i',0)
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current_step = Value('i',0)
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collater = train_util.collater_class(current_epoch,current_step)
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if args.no_token_padding:
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train_dataset_group.disable_token_padding()
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if args.debug_dataset:
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train_util.debug_dataset(train_dataset_group)
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return
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if cache_latents:
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assert (
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train_dataset_group.is_latent_cacheable()
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), "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"
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# acceleratorを準備する
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print("prepare accelerator")
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if args.gradient_accumulation_steps > 1:
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print(
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f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong"
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)
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print(
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f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル（U-NetおよびText Encoder）の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です"
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)
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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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# モデルに xformers とか memory efficient attention を組み込む
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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_group.cache_latents(vae, args.vae_batch_size)
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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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train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
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unet.requires_grad_(True) # 念のため追加
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text_encoder.requires_grad_(train_text_encoder)
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if not train_text_encoder:
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print("Text Encoder is not trained.")
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if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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text_encoder.gradient_checkpointing_enable()
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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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# 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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if train_text_encoder:
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trainable_params = itertools.chain(unet.parameters(), text_encoder.parameters())
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else:
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trainable_params = unet.parameters()
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_, _, optimizer = train_util.get_optimizer(args, trainable_params)
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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_group,
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batch_size=1,
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shuffle=True,
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collate_fn=collater,
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num_workers=n_workers,
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persistent_workers=args.persistent_data_loader_workers,
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)
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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 * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
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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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# データセット側にも学習ステップを送信
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train_dataset_group.set_max_train_steps(args.max_train_steps)
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if args.stop_text_encoder_training is None:
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args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end
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# lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
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lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
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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 (
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args.mixed_precision == "fp16"
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), "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 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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)
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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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if not train_text_encoder:
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text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error
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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 train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
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print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_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(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
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)
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if accelerator.is_main_process:
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accelerator.init_trackers("dreambooth")
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loss_list = []
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loss_total = 0.0
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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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current_epoch.value = epoch+1
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# 指定したステップ数までText Encoderを学習する：epoch最初の状態
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unet.train()
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# train==True is required to enable gradient_checkpointing
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if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
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text_encoder.train()
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for step, batch in enumerate(train_dataloader):
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current_step.value = global_step
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# 指定したステップ数でText Encoderの学習を止める
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if global_step == args.stop_text_encoder_training:
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print(f"stop text encoder training at step {global_step}")
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if not args.gradient_checkpointing:
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text_encoder.train(False)
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text_encoder.requires_grad_(False)
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with accelerator.accumulate(unet):
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with torch.no_grad():
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# latentに変換
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if cache_latents:
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latents = batch["latents"].to(accelerator.device)
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else:
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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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# 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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if args.noise_offset:
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# https://www.crosslabs.org//blog/diffusion-with-offset-noise
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noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
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# Get the text embedding for conditioning
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with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
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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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)
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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="none")
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loss = loss.mean([1, 2, 3])
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loss_weights = batch["loss_weights"] # 各sampleごとのweight
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loss = loss * loss_weights
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if args.min_snr_gamma:
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loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
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loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
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accelerator.backward(loss)
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if accelerator.sync_gradients and args.max_grad_norm != 0.0:
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if train_text_encoder:
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params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
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else:
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params_to_clip = unet.parameters()
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accelerator.clip_grad_norm_(params_to_clip, 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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train_util.sample_images(
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accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
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)
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current_loss = loss.detach().item()
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if args.logging_dir is not None:
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logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
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if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
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logs["lr/d*lr"] = (
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lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
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)
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accelerator.log(logs, step=global_step)
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if epoch == 0:
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loss_list.append(current_loss)
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else:
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loss_total -= loss_list[step]
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loss_list[step] = current_loss
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loss_total += current_loss
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avr_loss = loss_total / len(loss_list)
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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 = {"loss/epoch": loss_total / len(loss_list)}
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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(
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args,
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accelerator,
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src_path,
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save_stable_diffusion_format,
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use_safetensors,
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save_dtype,
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epoch,
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num_train_epochs,
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global_step,
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unwrap_model(text_encoder),
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unwrap_model(unet),
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vae,
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)
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train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
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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(
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args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae
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)
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print("model saved.")
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def setup_parser() -> argparse.ArgumentParser:
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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, True, False, True)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
train_util.add_sd_saving_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(
|
||||
"--no_token_padding",
|
||||
action="store_true",
|
||||
help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--stop_text_encoder_training",
|
||||
type=int,
|
||||
default=None,
|
||||
help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
args = parser.parse_args()
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
@ -1,710 +0,0 @@
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
import importlib
|
||||
import argparse
|
||||
import gc
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import time
|
||||
import json
|
||||
import toml
|
||||
from multiprocessing import Value
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from accelerate.utils import set_seed
|
||||
from diffusers import DDPMScheduler
|
||||
|
||||
import library.train_util as train_util
|
||||
from library.train_util import (
|
||||
DreamBoothDataset,
|
||||
)
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
from library.custom_train_functions import apply_snr_weight
|
||||
|
||||
|
||||
# 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}
|
||||
|
||||
if args.network_train_unet_only:
|
||||
logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0])
|
||||
elif args.network_train_text_encoder_only:
|
||||
logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
|
||||
else:
|
||||
logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
|
||||
logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1]) # may be same to textencoder
|
||||
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value of unet.
|
||||
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_dreambooth_method = args.in_json is None
|
||||
use_user_config = args.dataset_config is not 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
|
||||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, 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", "reg_data_dir", "in_json"]
|
||||
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:
|
||||
if use_dreambooth_method:
|
||||
print("Use DreamBooth method.")
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
|
||||
]
|
||||
}
|
||||
else:
|
||||
print("Train with captions.")
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{
|
||||
"subsets": [
|
||||
{
|
||||
"image_dir": args.train_data_dir,
|
||||
"metadata_file": args.in_json,
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
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)
|
||||
collater = train_util.collater_class(current_epoch,current_step)
|
||||
|
||||
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, unwrap_model = 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)
|
||||
|
||||
# work on low-ram device
|
||||
if args.lowram:
|
||||
text_encoder.to("cuda")
|
||||
unet.to("cuda")
|
||||
|
||||
# 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_group.cache_latents(vae, args.vae_batch_size)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
# prepare network
|
||||
import sys
|
||||
|
||||
sys.path.append(os.path.dirname(__file__))
|
||||
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
|
||||
|
||||
# if a new network is added in future, add if ~ then blocks for each network (;'∀')
|
||||
network = network_module.create_network(1.0, args.network_dim, args.network_alpha, 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.")
|
||||
|
||||
trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
|
||||
optimizer_name, optimizer_args, 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=collater,
|
||||
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)
|
||||
if is_main_process:
|
||||
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。"
|
||||
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)
|
||||
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
|
||||
unet.train()
|
||||
text_encoder.train()
|
||||
|
||||
# set top parameter requires_grad = True for gradient checkpointing works
|
||||
if type(text_encoder) == DDP:
|
||||
text_encoder.module.text_model.embeddings.requires_grad_(True)
|
||||
else:
|
||||
text_encoder.text_model.embeddings.requires_grad_(True)
|
||||
else:
|
||||
unet.eval()
|
||||
text_encoder.eval()
|
||||
|
||||
# support DistributedDataParallel
|
||||
if type(text_encoder) == DDP:
|
||||
text_encoder = text_encoder.module
|
||||
unet = unet.module
|
||||
network = network.module
|
||||
|
||||
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)
|
||||
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
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
|
||||
if is_main_process:
|
||||
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_group.num_train_images}")
|
||||
print(f" num reg images / 正則化画像の数: {train_dataset_group.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: {', '.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}")
|
||||
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}")
|
||||
|
||||
# TODO refactor metadata creation and move to util
|
||||
metadata = {
|
||||
"ss_session_id": session_id, # random integer indicating which group of epochs the model came from
|
||||
"ss_training_started_at": training_started_at, # unix timestamp
|
||||
"ss_output_name": args.output_name,
|
||||
"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_group.num_train_images,
|
||||
"ss_num_reg_images": train_dataset_group.num_reg_images,
|
||||
"ss_num_batches_per_epoch": len(train_dataloader),
|
||||
"ss_num_epochs": num_train_epochs,
|
||||
"ss_gradient_checkpointing": args.gradient_checkpointing,
|
||||
"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_network_alpha": args.network_alpha, # some networks may not use this value
|
||||
"ss_mixed_precision": args.mixed_precision,
|
||||
"ss_full_fp16": bool(args.full_fp16),
|
||||
"ss_v2": bool(args.v2),
|
||||
"ss_clip_skip": args.clip_skip,
|
||||
"ss_max_token_length": args.max_token_length,
|
||||
"ss_cache_latents": bool(args.cache_latents),
|
||||
"ss_seed": args.seed,
|
||||
"ss_lowram": args.lowram,
|
||||
"ss_noise_offset": args.noise_offset,
|
||||
"ss_training_comment": args.training_comment, # will not be updated after training
|
||||
"ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
|
||||
"ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
|
||||
"ss_max_grad_norm": args.max_grad_norm,
|
||||
"ss_caption_dropout_rate": args.caption_dropout_rate,
|
||||
"ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs,
|
||||
"ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,
|
||||
"ss_face_crop_aug_range": args.face_crop_aug_range,
|
||||
"ss_prior_loss_weight": args.prior_loss_weight,
|
||||
}
|
||||
|
||||
if use_user_config:
|
||||
# save metadata of multiple datasets
|
||||
# NOTE: pack "ss_datasets" value as json one time
|
||||
# or should also pack nested collections as json?
|
||||
datasets_metadata = []
|
||||
tag_frequency = {} # merge tag frequency for metadata editor
|
||||
dataset_dirs_info = {} # merge subset dirs for metadata editor
|
||||
|
||||
for dataset in train_dataset_group.datasets:
|
||||
is_dreambooth_dataset = isinstance(dataset, DreamBoothDataset)
|
||||
dataset_metadata = {
|
||||
"is_dreambooth": is_dreambooth_dataset,
|
||||
"batch_size_per_device": dataset.batch_size,
|
||||
"num_train_images": dataset.num_train_images, # includes repeating
|
||||
"num_reg_images": dataset.num_reg_images,
|
||||
"resolution": (dataset.width, dataset.height),
|
||||
"enable_bucket": bool(dataset.enable_bucket),
|
||||
"min_bucket_reso": dataset.min_bucket_reso,
|
||||
"max_bucket_reso": dataset.max_bucket_reso,
|
||||
"tag_frequency": dataset.tag_frequency,
|
||||
"bucket_info": dataset.bucket_info,
|
||||
}
|
||||
|
||||
subsets_metadata = []
|
||||
for subset in dataset.subsets:
|
||||
subset_metadata = {
|
||||
"img_count": subset.img_count,
|
||||
"num_repeats": subset.num_repeats,
|
||||
"color_aug": bool(subset.color_aug),
|
||||
"flip_aug": bool(subset.flip_aug),
|
||||
"random_crop": bool(subset.random_crop),
|
||||
"shuffle_caption": bool(subset.shuffle_caption),
|
||||
"keep_tokens": subset.keep_tokens,
|
||||
}
|
||||
|
||||
image_dir_or_metadata_file = None
|
||||
if subset.image_dir:
|
||||
image_dir = os.path.basename(subset.image_dir)
|
||||
subset_metadata["image_dir"] = image_dir
|
||||
image_dir_or_metadata_file = image_dir
|
||||
|
||||
if is_dreambooth_dataset:
|
||||
subset_metadata["class_tokens"] = subset.class_tokens
|
||||
subset_metadata["is_reg"] = subset.is_reg
|
||||
if subset.is_reg:
|
||||
image_dir_or_metadata_file = None # not merging reg dataset
|
||||
else:
|
||||
metadata_file = os.path.basename(subset.metadata_file)
|
||||
subset_metadata["metadata_file"] = metadata_file
|
||||
image_dir_or_metadata_file = metadata_file # may overwrite
|
||||
|
||||
subsets_metadata.append(subset_metadata)
|
||||
|
||||
# merge dataset dir: not reg subset only
|
||||
# TODO update additional-network extension to show detailed dataset config from metadata
|
||||
if image_dir_or_metadata_file is not None:
|
||||
# datasets may have a certain dir multiple times
|
||||
v = image_dir_or_metadata_file
|
||||
i = 2
|
||||
while v in dataset_dirs_info:
|
||||
v = image_dir_or_metadata_file + f" ({i})"
|
||||
i += 1
|
||||
image_dir_or_metadata_file = v
|
||||
|
||||
dataset_dirs_info[image_dir_or_metadata_file] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}
|
||||
|
||||
dataset_metadata["subsets"] = subsets_metadata
|
||||
datasets_metadata.append(dataset_metadata)
|
||||
|
||||
# merge tag frequency:
|
||||
for ds_dir_name, ds_freq_for_dir in dataset.tag_frequency.items():
|
||||
# あるディレクトリが複数のdatasetで使用されている場合、一度だけ数える
|
||||
# もともと繰り返し回数を指定しているので、キャプション内でのタグの出現回数と、それが学習で何度使われるかは一致しない
|
||||
# なので、ここで複数datasetの回数を合算してもあまり意味はない
|
||||
if ds_dir_name in tag_frequency:
|
||||
continue
|
||||
tag_frequency[ds_dir_name] = ds_freq_for_dir
|
||||
|
||||
metadata["ss_datasets"] = json.dumps(datasets_metadata)
|
||||
metadata["ss_tag_frequency"] = json.dumps(tag_frequency)
|
||||
metadata["ss_dataset_dirs"] = json.dumps(dataset_dirs_info)
|
||||
else:
|
||||
# conserving backward compatibility when using train_dataset_dir and reg_dataset_dir
|
||||
assert (
|
||||
len(train_dataset_group.datasets) == 1
|
||||
), f"There should be a single dataset but {len(train_dataset_group.datasets)} found. This seems to be a bug. / データセットは1個だけ存在するはずですが、実際には{len(train_dataset_group.datasets)}個でした。プログラムのバグかもしれません。"
|
||||
|
||||
dataset = train_dataset_group.datasets[0]
|
||||
|
||||
dataset_dirs_info = {}
|
||||
reg_dataset_dirs_info = {}
|
||||
if use_dreambooth_method:
|
||||
for subset in dataset.subsets:
|
||||
info = reg_dataset_dirs_info if subset.is_reg else dataset_dirs_info
|
||||
info[os.path.basename(subset.image_dir)] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}
|
||||
else:
|
||||
for subset in dataset.subsets:
|
||||
dataset_dirs_info[os.path.basename(subset.metadata_file)] = {
|
||||
"n_repeats": subset.num_repeats,
|
||||
"img_count": subset.img_count,
|
||||
}
|
||||
|
||||
metadata.update(
|
||||
{
|
||||
"ss_batch_size_per_device": args.train_batch_size,
|
||||
"ss_total_batch_size": total_batch_size,
|
||||
"ss_resolution": args.resolution,
|
||||
"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_enable_bucket": bool(dataset.enable_bucket),
|
||||
"ss_bucket_no_upscale": bool(dataset.bucket_no_upscale),
|
||||
"ss_min_bucket_reso": dataset.min_bucket_reso,
|
||||
"ss_max_bucket_reso": dataset.max_bucket_reso,
|
||||
"ss_keep_tokens": args.keep_tokens,
|
||||
"ss_dataset_dirs": json.dumps(dataset_dirs_info),
|
||||
"ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info),
|
||||
"ss_tag_frequency": json.dumps(dataset.tag_frequency),
|
||||
"ss_bucket_info": json.dumps(dataset.bucket_info),
|
||||
}
|
||||
)
|
||||
|
||||
# add extra args
|
||||
if args.network_args:
|
||||
metadata["ss_network_args"] = json.dumps(net_kwargs)
|
||||
# for key, value in net_kwargs.items():
|
||||
# metadata["ss_arg_" + key] = value
|
||||
|
||||
# model name and hash
|
||||
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)
|
||||
metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(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)
|
||||
metadata["ss_new_vae_hash"] = train_util.calculate_sha256(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()}
|
||||
|
||||
# make minimum metadata for filtering
|
||||
minimum_keys = ["ss_network_module", "ss_network_dim", "ss_network_alpha", "ss_network_args"]
|
||||
minimum_metadata = {}
|
||||
for key in minimum_keys:
|
||||
if key in metadata:
|
||||
minimum_metadata[key] = metadata[key]
|
||||
|
||||
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")
|
||||
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
del train_dataset_group
|
||||
for epoch in range(num_train_epochs):
|
||||
if is_main_process:
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
current_epoch.value = epoch+1
|
||||
|
||||
metadata["ss_epoch"] = str(epoch + 1)
|
||||
|
||||
network.on_epoch_start(text_encoder, unet)
|
||||
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
current_step.value = global_step
|
||||
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)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), 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
|
||||
with accelerator.autocast():
|
||||
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
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
|
||||
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 = network.get_trainable_params()
|
||||
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
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"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_total / len(loss_list)}
|
||||
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)
|
||||
metadata["ss_training_finished_at"] = str(time.time())
|
||||
print(f"saving checkpoint: {ckpt_file}")
|
||||
unwrap_model(network).save_weights(ckpt_file, save_dtype, minimum_metadata 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)
|
||||
|
||||
if is_main_process:
|
||||
saving = 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)
|
||||
|
||||
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
# end of epoch
|
||||
|
||||
metadata["ss_epoch"] = str(num_train_epochs)
|
||||
metadata["ss_training_finished_at"] = str(time.time())
|
||||
|
||||
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, minimum_metadata if args.no_metadata else metadata)
|
||||
print("model saved.")
|
||||
|
||||
|
||||
def setup_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, True, True, True)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
custom_train_functions.add_custom_train_arguments(parser)
|
||||
|
||||
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="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("--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_alpha",
|
||||
type=float,
|
||||
default=1,
|
||||
help="alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）",
|
||||
)
|
||||
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"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--training_comment", type=str, default=None, help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列"
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
args = parser.parse_args()
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
@ -1,589 +0,0 @@
|
||||
import importlib
|
||||
import argparse
|
||||
import gc
|
||||
import math
|
||||
import os
|
||||
import toml
|
||||
from multiprocessing import Value
|
||||
|
||||
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
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
from library.custom_train_functions import apply_snr_weight
|
||||
|
||||
imagenet_templates_small = [
|
||||
"a photo of a {}",
|
||||
"a rendering of a {}",
|
||||
"a cropped photo of the {}",
|
||||
"the photo of a {}",
|
||||
"a photo of a clean {}",
|
||||
"a photo of a dirty {}",
|
||||
"a dark photo of the {}",
|
||||
"a photo of my {}",
|
||||
"a photo of the cool {}",
|
||||
"a close-up photo of a {}",
|
||||
"a bright photo of the {}",
|
||||
"a cropped photo of a {}",
|
||||
"a photo of the {}",
|
||||
"a good photo of the {}",
|
||||
"a photo of one {}",
|
||||
"a close-up photo of the {}",
|
||||
"a rendition of the {}",
|
||||
"a photo of the clean {}",
|
||||
"a rendition of a {}",
|
||||
"a photo of a nice {}",
|
||||
"a good photo of a {}",
|
||||
"a photo of the nice {}",
|
||||
"a photo of the small {}",
|
||||
"a photo of the weird {}",
|
||||
"a photo of the large {}",
|
||||
"a photo of a cool {}",
|
||||
"a photo of a small {}",
|
||||
]
|
||||
|
||||
imagenet_style_templates_small = [
|
||||
"a painting in the style of {}",
|
||||
"a rendering in the style of {}",
|
||||
"a cropped painting in the style of {}",
|
||||
"the painting in the style of {}",
|
||||
"a clean painting in the style of {}",
|
||||
"a dirty painting in the style of {}",
|
||||
"a dark painting in the style of {}",
|
||||
"a picture in the style of {}",
|
||||
"a cool painting in the style of {}",
|
||||
"a close-up painting in the style of {}",
|
||||
"a bright painting in the style of {}",
|
||||
"a cropped painting in the style of {}",
|
||||
"a good painting in the style of {}",
|
||||
"a close-up painting in the style of {}",
|
||||
"a rendition in the style of {}",
|
||||
"a nice painting in the style of {}",
|
||||
"a small painting in the style of {}",
|
||||
"a weird painting in the style of {}",
|
||||
"a large painting in the style of {}",
|
||||
]
|
||||
|
||||
|
||||
def train(args):
|
||||
if args.output_name is None:
|
||||
args.output_name = args.token_string
|
||||
use_template = args.use_object_template or args.use_style_template
|
||||
|
||||
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)
|
||||
|
||||
tokenizer = train_util.load_tokenizer(args)
|
||||
|
||||
# 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)
|
||||
|
||||
# Convert the init_word to token_id
|
||||
if args.init_word is not None:
|
||||
init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
|
||||
if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
|
||||
print(
|
||||
f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
|
||||
)
|
||||
else:
|
||||
init_token_ids = None
|
||||
|
||||
# add new word to tokenizer, count is num_vectors_per_token
|
||||
token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
|
||||
num_added_tokens = tokenizer.add_tokens(token_strings)
|
||||
assert (
|
||||
num_added_tokens == args.num_vectors_per_token
|
||||
), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
|
||||
|
||||
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
|
||||
print(f"tokens are added: {token_ids}")
|
||||
assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
|
||||
assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
|
||||
|
||||
# Resize the token embeddings as we are adding new special tokens to the tokenizer
|
||||
text_encoder.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Initialise the newly added placeholder token with the embeddings of the initializer token
|
||||
token_embeds = text_encoder.get_input_embeddings().weight.data
|
||||
if init_token_ids is not None:
|
||||
for i, token_id in enumerate(token_ids):
|
||||
token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]]
|
||||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||||
|
||||
# load weights
|
||||
if args.weights is not None:
|
||||
embeddings = load_weights(args.weights)
|
||||
assert len(token_ids) == len(
|
||||
embeddings
|
||||
), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
|
||||
# print(token_ids, embeddings.size())
|
||||
for token_id, embedding in zip(token_ids, embeddings):
|
||||
token_embeds[token_id] = embedding
|
||||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||||
print(f"weighs loaded")
|
||||
|
||||
print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
|
||||
|
||||
# データセットを準備する
|
||||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
|
||||
if args.dataset_config is not None:
|
||||
print(f"Load dataset config from {args.dataset_config}")
|
||||
user_config = config_util.load_user_config(args.dataset_config)
|
||||
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
|
||||
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:
|
||||
use_dreambooth_method = args.in_json is None
|
||||
if use_dreambooth_method:
|
||||
print("Use DreamBooth method.")
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
|
||||
]
|
||||
}
|
||||
else:
|
||||
print("Train with captions.")
|
||||
user_config = {
|
||||
"datasets": [
|
||||
{
|
||||
"subsets": [
|
||||
{
|
||||
"image_dir": args.train_data_dir,
|
||||
"metadata_file": args.in_json,
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
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)
|
||||
collater = train_util.collater_class(current_epoch,current_step)
|
||||
|
||||
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
|
||||
if use_template:
|
||||
print("use template for training captions. is object: {args.use_object_template}")
|
||||
templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
|
||||
replace_to = " ".join(token_strings)
|
||||
captions = []
|
||||
for tmpl in templates:
|
||||
captions.append(tmpl.format(replace_to))
|
||||
train_dataset_group.add_replacement("", captions)
|
||||
|
||||
if args.num_vectors_per_token > 1:
|
||||
prompt_replacement = (args.token_string, replace_to)
|
||||
else:
|
||||
prompt_replacement = None
|
||||
else:
|
||||
if args.num_vectors_per_token > 1:
|
||||
replace_to = " ".join(token_strings)
|
||||
train_dataset_group.add_replacement(args.token_string, replace_to)
|
||||
prompt_replacement = (args.token_string, replace_to)
|
||||
else:
|
||||
prompt_replacement = None
|
||||
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group, show_input_ids=True)
|
||||
return
|
||||
if len(train_dataset_group) == 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
|
||||
|
||||
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"
|
||||
|
||||
# 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_group.cache_latents(vae, args.vae_batch_size)
|
||||
vae.to("cpu")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
trainable_params = text_encoder.get_input_embeddings().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=collater,
|
||||
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)
|
||||
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)
|
||||
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
|
||||
index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
|
||||
# print(len(index_no_updates), torch.sum(index_no_updates))
|
||||
orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
|
||||
|
||||
# Freeze all parameters except for the token embeddings in text encoder
|
||||
text_encoder.requires_grad_(True)
|
||||
text_encoder.text_model.encoder.requires_grad_(False)
|
||||
text_encoder.text_model.final_layer_norm.requires_grad_(False)
|
||||
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
|
||||
# text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
|
||||
|
||||
unet.requires_grad_(False)
|
||||
unet.to(accelerator.device, dtype=weight_dtype)
|
||||
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
|
||||
unet.train()
|
||||
else:
|
||||
unet.eval()
|
||||
|
||||
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)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
# 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)
|
||||
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
|
||||
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_group.num_train_images}")
|
||||
print(f" num reg images / 正則化画像の数: {train_dataset_group.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 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("textual_inversion")
|
||||
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
current_epoch.value = epoch+1
|
||||
|
||||
text_encoder.train()
|
||||
|
||||
loss_total = 0
|
||||
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
current_step.value = global_step
|
||||
with accelerator.accumulate(text_encoder):
|
||||
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]
|
||||
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
# weight_dtype) use float instead of fp16/bf16 because text encoder is float
|
||||
encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
if args.noise_offset:
|
||||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), 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])
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
|
||||
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 and args.max_grad_norm != 0.0:
|
||||
params_to_clip = text_encoder.get_input_embeddings().parameters()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
# Let's make sure we don't update any embedding weights besides the newly added token
|
||||
with torch.no_grad():
|
||||
unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[
|
||||
index_no_updates
|
||||
]
|
||||
|
||||
# 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, prompt_replacement
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
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 = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
|
||||
|
||||
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}")
|
||||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||||
|
||||
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 = 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)
|
||||
|
||||
train_util.sample_images(
|
||||
accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
|
||||
)
|
||||
|
||||
# end of epoch
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone()
|
||||
|
||||
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}")
|
||||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||||
print("model saved.")
|
||||
|
||||
|
||||
def save_weights(file, updated_embs, save_dtype):
|
||||
state_dict = {"emb_params": updated_embs}
|
||||
|
||||
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(file)[1] == ".safetensors":
|
||||
from safetensors.torch import save_file
|
||||
|
||||
save_file(state_dict, file)
|
||||
else:
|
||||
torch.save(state_dict, file) # can be loaded in Web UI
|
||||
|
||||
|
||||
def load_weights(file):
|
||||
if os.path.splitext(file)[1] == ".safetensors":
|
||||
from safetensors.torch import load_file
|
||||
|
||||
data = load_file(file)
|
||||
else:
|
||||
# compatible to Web UI's file format
|
||||
data = torch.load(file, map_location="cpu")
|
||||
if type(data) != dict:
|
||||
raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}")
|
||||
|
||||
if "string_to_param" in data: # textual inversion embeddings
|
||||
data = data["string_to_param"]
|
||||
if hasattr(data, "_parameters"): # support old PyTorch?
|
||||
data = getattr(data, "_parameters")
|
||||
|
||||
emb = next(iter(data.values()))
|
||||
if type(emb) != torch.Tensor:
|
||||
raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}")
|
||||
|
||||
if len(emb.size()) == 1:
|
||||
emb = emb.unsqueeze(0)
|
||||
|
||||
return emb
|
||||
|
||||
|
||||
def setup_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, True, True, False)
|
||||
train_util.add_training_arguments(parser, True)
|
||||
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="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("--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み")
|
||||
parser.add_argument(
|
||||
"--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--token_string",
|
||||
type=str,
|
||||
default=None,
|
||||
help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
|
||||
)
|
||||
parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
|
||||
parser.add_argument(
|
||||
"--use_object_template",
|
||||
action="store_true",
|
||||
help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_style_template",
|
||||
action="store_true",
|
||||
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
args = parser.parse_args()
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
Loading…
Reference in New Issue
Block a user