642 lines
28 KiB
Python
642 lines
28 KiB
Python
from torch.nn.parallel import DistributedDataParallel as DDP
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import importlib
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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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import random
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import time
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import json
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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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from diffusers import DDPMScheduler
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import library.train_util as train_util
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from library.train_util import (
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DreamBoothDataset,
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)
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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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def collate_fn(examples):
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return examples[0]
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# TODO 他のスクリプトと共通化する
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def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
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logs = {"loss/current": current_loss, "loss/average": avr_loss}
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if args.network_train_unet_only:
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logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0])
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elif args.network_train_text_encoder_only:
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logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
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else:
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logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
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logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1]) # may be same to textencoder
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if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value of unet.
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logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]['d']*lr_scheduler.optimizers[-1].param_groups[0]['lr']
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return logs
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def train(args):
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session_id = random.randint(0, 2**32)
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training_started_at = time.time()
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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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use_dreambooth_method = args.in_json is None
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use_user_config = args.dataset_config is not None
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if args.seed is not None:
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set_seed(args.seed)
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tokenizer = train_util.load_tokenizer(args)
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# データセットを準備する
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blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, True))
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if use_user_config:
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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", "in_json"]
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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(', '.join(ignored)))
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else:
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if use_dreambooth_method:
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print("Use DreamBooth method.")
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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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else:
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print("Train with captions.")
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user_config = {
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"datasets": [{
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"subsets": [{
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"image_dir": args.train_data_dir,
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"metadata_file": args.in_json,
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}]
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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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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 len(train_dataset_group) == 0:
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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）")
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return
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if cache_latents:
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assert 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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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, _ = train_util.load_target_model(args, weight_dtype)
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# work on low-ram device
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if args.lowram:
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text_encoder.to("cuda")
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unet.to("cuda")
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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)
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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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# prepare network
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print("import network module:", args.network_module)
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network_module = importlib.import_module(args.network_module)
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net_kwargs = {}
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if args.network_args is not None:
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for net_arg in args.network_args:
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key, value = net_arg.split('=')
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net_kwargs[key] = value
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# if a new network is added in future, add if ~ then blocks for each network (;'∀')
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network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
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if network is None:
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return
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if args.network_weights is not None:
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print("load network weights from:", args.network_weights)
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network.load_weights(args.network_weights)
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train_unet = not args.network_train_text_encoder_only
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train_text_encoder = not args.network_train_unet_only
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network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
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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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network.enable_gradient_checkpointing() # may have no effect
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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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trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
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optimizer_name, optimizer_args, 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, batch_size=1, shuffle=True, 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 = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
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num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
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num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
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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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network.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_unet and train_text_encoder:
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unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler)
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elif train_unet:
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unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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unet, network, optimizer, train_dataloader, lr_scheduler)
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elif train_text_encoder:
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text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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text_encoder, network, optimizer, train_dataloader, lr_scheduler)
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else:
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network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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network, optimizer, train_dataloader, lr_scheduler)
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unet.requires_grad_(False)
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unet.to(accelerator.device, dtype=weight_dtype)
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text_encoder.requires_grad_(False)
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text_encoder.to(accelerator.device)
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if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
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unet.train()
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text_encoder.train()
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# set top parameter requires_grad = True for gradient checkpointing works
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if type(text_encoder) == DDP:
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text_encoder.module.text_model.embeddings.requires_grad_(True)
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else:
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text_encoder.text_model.embeddings.requires_grad_(True)
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else:
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unet.eval()
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text_encoder.eval()
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# support DistributedDataParallel
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if type(text_encoder) == DDP:
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text_encoder = text_encoder.module
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unet = unet.module
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network = network.module
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network.prepare_grad_etc(text_encoder, unet)
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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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# 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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# TODO: find a way to handle total batch size when there are multiple datasets
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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: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
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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 accumulation 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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# TODO refactor metadata creation and move to util
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metadata = {
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"ss_session_id": session_id, # random integer indicating which group of epochs the model came from
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"ss_training_started_at": training_started_at, # unix timestamp
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"ss_output_name": args.output_name,
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"ss_learning_rate": args.learning_rate,
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"ss_text_encoder_lr": args.text_encoder_lr,
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"ss_unet_lr": args.unet_lr,
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"ss_num_train_images": train_dataset_group.num_train_images,
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"ss_num_reg_images": train_dataset_group.num_reg_images,
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"ss_num_batches_per_epoch": len(train_dataloader),
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"ss_num_epochs": num_train_epochs,
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"ss_gradient_checkpointing": args.gradient_checkpointing,
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"ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
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"ss_max_train_steps": args.max_train_steps,
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"ss_lr_warmup_steps": args.lr_warmup_steps,
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"ss_lr_scheduler": args.lr_scheduler,
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"ss_network_module": args.network_module,
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"ss_network_dim": args.network_dim, # None means default because another network than LoRA may have another default dim
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"ss_network_alpha": args.network_alpha, # some networks may not use this value
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"ss_mixed_precision": args.mixed_precision,
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"ss_full_fp16": bool(args.full_fp16),
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"ss_v2": bool(args.v2),
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"ss_clip_skip": args.clip_skip,
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"ss_max_token_length": args.max_token_length,
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"ss_cache_latents": bool(args.cache_latents),
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"ss_seed": args.seed,
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"ss_lowram": args.lowram,
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"ss_noise_offset": args.noise_offset,
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"ss_training_comment": args.training_comment, # will not be updated after training
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"ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
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"ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
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"ss_max_grad_norm": args.max_grad_norm,
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"ss_caption_dropout_rate": args.caption_dropout_rate,
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"ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs,
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"ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,
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"ss_face_crop_aug_range": args.face_crop_aug_range,
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"ss_prior_loss_weight": args.prior_loss_weight,
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}
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if use_user_config:
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# save metadata of multiple datasets
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# NOTE: pack "ss_datasets" value as json one time
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# or should also pack nested collections as json?
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datasets_metadata = []
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tag_frequency = {} # merge tag frequency for metadata editor
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dataset_dirs_info = {} # merge subset dirs for metadata editor
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for dataset in train_dataset_group.datasets:
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is_dreambooth_dataset = isinstance(dataset, DreamBoothDataset)
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dataset_metadata = {
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"is_dreambooth": is_dreambooth_dataset,
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"batch_size_per_device": dataset.batch_size,
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"num_train_images": dataset.num_train_images, # includes repeating
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"num_reg_images": dataset.num_reg_images,
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"resolution": (dataset.width, dataset.height),
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"enable_bucket": bool(dataset.enable_bucket),
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"min_bucket_reso": dataset.min_bucket_reso,
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"max_bucket_reso": dataset.max_bucket_reso,
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"tag_frequency": dataset.tag_frequency,
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"bucket_info": dataset.bucket_info,
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}
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subsets_metadata = []
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for subset in dataset.subsets:
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subset_metadata = {
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"img_count": subset.img_count,
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"num_repeats": subset.num_repeats,
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"color_aug": bool(subset.color_aug),
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"flip_aug": bool(subset.flip_aug),
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"random_crop": bool(subset.random_crop),
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"shuffle_caption": bool(subset.shuffle_caption),
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"keep_tokens": subset.keep_tokens,
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}
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image_dir_or_metadata_file = None
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if subset.image_dir:
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image_dir = os.path.basename(subset.image_dir)
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subset_metadata["image_dir"] = image_dir
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image_dir_or_metadata_file = image_dir
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if is_dreambooth_dataset:
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subset_metadata["class_tokens"] = subset.class_tokens
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subset_metadata["is_reg"] = subset.is_reg
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if subset.is_reg:
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image_dir_or_metadata_file = None # not merging reg dataset
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else:
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metadata_file = os.path.basename(subset.metadata_file)
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subset_metadata["metadata_file"] = metadata_file
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image_dir_or_metadata_file = metadata_file # may overwrite
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subsets_metadata.append(subset_metadata)
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# merge dataset dir: not reg subset only
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# TODO update additional-network extension to show detailed dataset config from metadata
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if image_dir_or_metadata_file is not None:
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# datasets may have a certain dir multiple times
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v = image_dir_or_metadata_file
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i = 2
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while v in dataset_dirs_info:
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v = image_dir_or_metadata_file + f" ({i})"
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i += 1
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image_dir_or_metadata_file = v
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dataset_dirs_info[image_dir_or_metadata_file] = {
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"n_repeats": subset.num_repeats,
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"img_count": subset.img_count
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}
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dataset_metadata["subsets"] = subsets_metadata
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datasets_metadata.append(dataset_metadata)
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# merge tag frequency:
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for ds_dir_name, ds_freq_for_dir in dataset.tag_frequency.items():
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# 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
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# 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
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# 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
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if ds_dir_name in tag_frequency:
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continue
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tag_frequency[ds_dir_name] = ds_freq_for_dir
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metadata["ss_datasets"] = json.dumps(datasets_metadata)
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metadata["ss_tag_frequency"] = json.dumps(tag_frequency)
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metadata["ss_dataset_dirs"] = json.dumps(dataset_dirs_info)
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else:
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# conserving backward compatibility when using train_dataset_dir and reg_dataset_dir
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assert len(
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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。"
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dataset = train_dataset_group.datasets[0]
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dataset_dirs_info = {}
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reg_dataset_dirs_info = {}
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if use_dreambooth_method:
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for subset in dataset.subsets:
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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),
|
||
})
|
||
|
||
if args.network_args:
|
||
for key, value in net_kwargs.items():
|
||
metadata["ss_arg_" + key] = value
|
||
|
||
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()}
|
||
|
||
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
|
||
for epoch in range(num_train_epochs):
|
||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||
train_dataset_group.set_current_epoch(epoch + 1)
|
||
|
||
metadata["ss_epoch"] = str(epoch+1)
|
||
|
||
network.on_epoch_start(text_encoder, unet)
|
||
|
||
for step, batch in enumerate(train_dataloader):
|
||
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
|
||
|
||
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, None 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)
|
||
|
||
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())
|
||
|
||
is_main_process = accelerator.is_main_process
|
||
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, None if args.no_metadata else metadata)
|
||
print("model saved.")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
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)
|
||
|
||
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")
|
||
|
||
args = parser.parse_args()
|
||
train(args) |