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add fine tuning FLUX.1 (WIP)
This commit is contained in:
Kohya S
729
flux_train.py
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729
flux_train.py
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# training with captions
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import argparse
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import copy
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import math
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import os
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from multiprocessing import Value
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from typing import List
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import toml
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from tqdm import tqdm
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import torch
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from library.device_utils import init_ipex, clean_memory_on_device
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init_ipex()
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from accelerate.utils import set_seed
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from library import deepspeed_utils, flux_train_utils, flux_utils, strategy_base, strategy_flux
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from library.sd3_train_utils import load_prompts, FlowMatchEulerDiscreteScheduler
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import library.train_util as train_util
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from library.utils import setup_logging, add_logging_arguments
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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import library.config_util as config_util
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# import library.sdxl_train_util as sdxl_train_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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from library.custom_train_functions import apply_masked_loss, add_custom_train_arguments
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def train(args):
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train_util.verify_training_args(args)
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train_util.prepare_dataset_args(args, True)
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# sdxl_train_util.verify_sdxl_training_args(args)
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deepspeed_utils.prepare_deepspeed_args(args)
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setup_logging(args, reset=True)
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# assert (
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# not args.weighted_captions
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# ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません"
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if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs:
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logger.warning(
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"cache_text_encoder_outputs_to_disk is enabled, so cache_text_encoder_outputs is also enabled / cache_text_encoder_outputs_to_diskが有効になっているため、cache_text_encoder_outputsも有効になります"
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)
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args.cache_text_encoder_outputs = 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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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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# prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization.
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if args.cache_latents:
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latents_caching_strategy = strategy_flux.FluxLatentsCachingStrategy(
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args.cache_latents_to_disk, args.vae_batch_size, args.skip_latents_validity_check
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)
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strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy)
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# データセットを準備する
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if args.dataset_class is None:
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blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True))
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if args.dataset_config is not None:
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logger.info(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", "in_json"]
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if any(getattr(args, attr) is not None for attr in ignored):
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logger.warning(
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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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if use_dreambooth_method:
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logger.info("Using DreamBooth method.")
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user_config = {
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"datasets": [
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{
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"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(
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args.train_data_dir, args.reg_data_dir
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)
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}
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]
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}
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else:
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logger.info("Training with captions.")
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user_config = {
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"datasets": [
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{
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"subsets": [
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{
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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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]
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}
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blueprint = blueprint_generator.generate(user_config, args)
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train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
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else:
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train_dataset_group = train_util.load_arbitrary_dataset(args)
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current_epoch = Value("i", 0)
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current_step = Value("i", 0)
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ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
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collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
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train_dataset_group.verify_bucket_reso_steps(16) # TODO これでいいか確認
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if args.debug_dataset:
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if args.cache_text_encoder_outputs:
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strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(
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strategy_flux.FluxTextEncoderOutputsCachingStrategy(
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args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False
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)
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)
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train_dataset_group.set_current_strategies()
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train_util.debug_dataset(train_dataset_group, True)
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return
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if len(train_dataset_group) == 0:
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logger.error(
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"No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。"
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)
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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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if args.cache_text_encoder_outputs:
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assert (
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train_dataset_group.is_text_encoder_output_cacheable()
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), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"
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# acceleratorを準備する
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logger.info("prepare accelerator")
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accelerator = 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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name = "schnell" if "schnell" in args.pretrained_model_name_or_path else "dev"
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# load VAE for caching latents
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ae = None
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if cache_latents:
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ae = flux_utils.load_ae(name, args.ae, weight_dtype, "cpu")
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ae.to(accelerator.device, dtype=weight_dtype)
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ae.requires_grad_(False)
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ae.eval()
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train_dataset_group.new_cache_latents(ae, accelerator.is_main_process)
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ae.to("cpu") # if no sampling, vae can be deleted
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clean_memory_on_device(accelerator.device)
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accelerator.wait_for_everyone()
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# prepare tokenize strategy
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if args.t5xxl_max_token_length is None:
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if name == "schnell":
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t5xxl_max_token_length = 256
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else:
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t5xxl_max_token_length = 512
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else:
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t5xxl_max_token_length = args.t5xxl_max_token_length
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flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length)
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strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy)
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# load clip_l, t5xxl for caching text encoder outputs
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clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu")
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t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu")
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clip_l.eval()
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t5xxl.eval()
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clip_l.requires_grad_(False)
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t5xxl.requires_grad_(False)
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text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask)
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strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy)
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# cache text encoder outputs
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sample_prompts_te_outputs = None
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if args.cache_text_encoder_outputs:
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# Text Encodes are eval and no grad here
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clip_l.to(accelerator.device)
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t5xxl.to(accelerator.device)
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text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy(
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args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask
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)
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strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy)
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with accelerator.autocast():
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train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], accelerator.is_main_process)
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# cache sample prompt's embeddings to free text encoder's memory
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if args.sample_prompts is not None:
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logger.info(f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}")
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tokenize_strategy: strategy_flux.FluxTokenizeStrategy = strategy_base.TokenizeStrategy.get_strategy()
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text_encoding_strategy: strategy_flux.FluxTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy()
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prompts = load_prompts(args.sample_prompts)
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sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs
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with accelerator.autocast(), torch.no_grad():
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for prompt_dict in prompts:
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for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]:
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if p not in sample_prompts_te_outputs:
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logger.info(f"cache Text Encoder outputs for prompt: {p}")
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tokens_and_masks = tokenize_strategy.tokenize(p)
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sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens(
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tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask
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)
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accelerator.wait_for_everyone()
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# now we can delete Text Encoders to free memory
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clip_l = None
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t5xxl = None
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# load FLUX
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# if we load to cpu, flux.to(fp8) takes a long time
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flux = flux_utils.load_flow_model(name, args.pretrained_model_name_or_path, weight_dtype, "cpu")
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if args.gradient_checkpointing:
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flux.enable_gradient_checkpointing()
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flux.requires_grad_(True)
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if not cache_latents:
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# load VAE here if not cached
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ae = flux_utils.load_ae(name, args.ae, weight_dtype, "cpu")
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ae.requires_grad_(False)
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ae.eval()
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ae.to(accelerator.device, dtype=weight_dtype)
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training_models = []
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params_to_optimize = []
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training_models.append(flux)
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params_to_optimize.append({"params": list(flux.parameters()), "lr": args.learning_rate})
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# calculate number of trainable parameters
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n_params = 0
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for group in params_to_optimize:
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for p in group["params"]:
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n_params += p.numel()
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accelerator.print(f"number of trainable parameters: {n_params}")
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# 学習に必要なクラスを準備する
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accelerator.print("prepare optimizer, data loader etc.")
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if args.fused_optimizer_groups:
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# fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
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# Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each group of parameters.
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# This balances memory usage and management complexity.
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# calculate total number of parameters
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n_total_params = sum(len(params["params"]) for params in params_to_optimize)
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params_per_group = math.ceil(n_total_params / args.fused_optimizer_groups)
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# split params into groups, keeping the learning rate the same for all params in a group
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# this will increase the number of groups if the learning rate is different for different params (e.g. U-Net and text encoders)
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grouped_params = []
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param_group = []
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param_group_lr = -1
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for group in params_to_optimize:
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lr = group["lr"]
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for p in group["params"]:
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# if the learning rate is different for different params, start a new group
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if lr != param_group_lr:
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if param_group:
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grouped_params.append({"params": param_group, "lr": param_group_lr})
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param_group = []
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param_group_lr = lr
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param_group.append(p)
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# if the group has enough parameters, start a new group
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if len(param_group) == params_per_group:
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grouped_params.append({"params": param_group, "lr": param_group_lr})
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param_group = []
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param_group_lr = -1
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if param_group:
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grouped_params.append({"params": param_group, "lr": param_group_lr})
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# prepare optimizers for each group
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optimizers = []
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for group in grouped_params:
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_, _, optimizer = train_util.get_optimizer(args, trainable_params=[group])
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optimizers.append(optimizer)
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optimizer = optimizers[0] # avoid error in the following code
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logger.info(f"using {len(optimizers)} optimizers for fused optimizer groups")
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else:
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_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
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# prepare dataloader
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# strategies are set here because they cannot be referenced in another process. Copy them with the dataset
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# some strategies can be None
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train_dataset_group.set_current_strategies()
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# DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
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n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers
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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=collator,
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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(
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len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
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)
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accelerator.print(
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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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)
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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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# lr schedulerを用意する
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if args.fused_optimizer_groups:
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# prepare lr schedulers for each optimizer
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lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers]
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lr_scheduler = lr_schedulers[0] # avoid error in the following code
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else:
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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/bf16学習を行う モデル全体をfp16/bf16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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accelerator.print("enable full fp16 training.")
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flux.to(weight_dtype)
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if clip_l is not None:
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clip_l.to(weight_dtype)
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t5xxl.to(weight_dtype) # TODO check works with fp16 or not
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elif args.full_bf16:
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assert (
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args.mixed_precision == "bf16"
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), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。"
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accelerator.print("enable full bf16 training.")
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flux.to(weight_dtype)
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if clip_l is not None:
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clip_l.to(weight_dtype)
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t5xxl.to(weight_dtype)
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# if we don't cache text encoder outputs, move them to device
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if not args.cache_text_encoder_outputs:
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clip_l.to(accelerator.device)
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t5xxl.to(accelerator.device)
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clean_memory_on_device(accelerator.device)
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if args.deepspeed:
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ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=flux)
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# most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007
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ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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ds_model, optimizer, train_dataloader, lr_scheduler
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)
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training_models = [ds_model]
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else:
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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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flux = accelerator.prepare(flux)
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optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
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# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
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if args.full_fp16:
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# During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
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# -> But we think it's ok to patch accelerator even if deepspeed is enabled.
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train_util.patch_accelerator_for_fp16_training(accelerator)
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# resumeする
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train_util.resume_from_local_or_hf_if_specified(accelerator, args)
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if args.fused_backward_pass:
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# use fused optimizer for backward pass: other optimizers will be supported in the future
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import library.adafactor_fused
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library.adafactor_fused.patch_adafactor_fused(optimizer)
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for param_group in optimizer.param_groups:
|
||||
for parameter in param_group["params"]:
|
||||
if parameter.requires_grad:
|
||||
|
||||
def __grad_hook(tensor: torch.Tensor, param_group=param_group):
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
|
||||
optimizer.step_param(tensor, param_group)
|
||||
tensor.grad = None
|
||||
|
||||
parameter.register_post_accumulate_grad_hook(__grad_hook)
|
||||
|
||||
elif args.fused_optimizer_groups:
|
||||
# prepare for additional optimizers and lr schedulers
|
||||
for i in range(1, len(optimizers)):
|
||||
optimizers[i] = accelerator.prepare(optimizers[i])
|
||||
lr_schedulers[i] = accelerator.prepare(lr_schedulers[i])
|
||||
|
||||
# counters are used to determine when to step the optimizer
|
||||
global optimizer_hooked_count
|
||||
global num_parameters_per_group
|
||||
global parameter_optimizer_map
|
||||
|
||||
optimizer_hooked_count = {}
|
||||
num_parameters_per_group = [0] * len(optimizers)
|
||||
parameter_optimizer_map = {}
|
||||
|
||||
for opt_idx, optimizer in enumerate(optimizers):
|
||||
for param_group in optimizer.param_groups:
|
||||
for parameter in param_group["params"]:
|
||||
if parameter.requires_grad:
|
||||
|
||||
def optimizer_hook(parameter: torch.Tensor):
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
accelerator.clip_grad_norm_(parameter, args.max_grad_norm)
|
||||
|
||||
i = parameter_optimizer_map[parameter]
|
||||
optimizer_hooked_count[i] += 1
|
||||
if optimizer_hooked_count[i] == num_parameters_per_group[i]:
|
||||
optimizers[i].step()
|
||||
optimizers[i].zero_grad(set_to_none=True)
|
||||
|
||||
parameter.register_post_accumulate_grad_hook(optimizer_hook)
|
||||
parameter_optimizer_map[parameter] = opt_idx
|
||||
num_parameters_per_group[opt_idx] += 1
|
||||
|
||||
# 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
|
||||
accelerator.print("running training / 学習開始")
|
||||
accelerator.print(f" num examples / サンプル数: {train_dataset_group.num_train_images}")
|
||||
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
accelerator.print(
|
||||
f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
|
||||
)
|
||||
# accelerator.print(
|
||||
# f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
|
||||
# )
|
||||
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
noise_scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.discrete_flow_shift)
|
||||
noise_scheduler_copy = copy.deepcopy(noise_scheduler)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
init_kwargs = {}
|
||||
if args.wandb_run_name:
|
||||
init_kwargs["wandb"] = {"name": args.wandb_run_name}
|
||||
if args.log_tracker_config is not None:
|
||||
init_kwargs = toml.load(args.log_tracker_config)
|
||||
accelerator.init_trackers(
|
||||
"finetuning" if args.log_tracker_name is None else args.log_tracker_name,
|
||||
config=train_util.get_sanitized_config_or_none(args),
|
||||
init_kwargs=init_kwargs,
|
||||
)
|
||||
|
||||
# For --sample_at_first
|
||||
flux_train_utils.sample_images(accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
epoch = 0 # avoid error when max_train_steps is 0
|
||||
for epoch in range(num_train_epochs):
|
||||
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
||||
current_epoch.value = epoch + 1
|
||||
|
||||
for m in training_models:
|
||||
m.train()
|
||||
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
current_step.value = global_step
|
||||
|
||||
if args.fused_optimizer_groups:
|
||||
optimizer_hooked_count = {i: 0 for i in range(len(optimizers))} # reset counter for each step
|
||||
|
||||
with accelerator.accumulate(*training_models):
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device, dtype=weight_dtype)
|
||||
else:
|
||||
with torch.no_grad():
|
||||
# encode images to latents. images are [-1, 1]
|
||||
latents = ae.encode(batch["images"])
|
||||
|
||||
# NaNが含まれていれば警告を表示し0に置き換える
|
||||
if torch.any(torch.isnan(latents)):
|
||||
accelerator.print("NaN found in latents, replacing with zeros")
|
||||
latents = torch.nan_to_num(latents, 0, out=latents)
|
||||
|
||||
text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None)
|
||||
if text_encoder_outputs_list is not None:
|
||||
text_encoder_conds = text_encoder_outputs_list
|
||||
else:
|
||||
# not cached or training, so get from text encoders
|
||||
tokens_and_masks = batch["input_ids_list"]
|
||||
with torch.no_grad():
|
||||
input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]]
|
||||
text_encoder_conds = text_encoding_strategy.encode_tokens(
|
||||
tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask
|
||||
)
|
||||
if args.full_fp16:
|
||||
text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds]
|
||||
|
||||
# TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents)
|
||||
bsz = latents.shape[0]
|
||||
|
||||
# get noisy model input and timesteps
|
||||
noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps(
|
||||
args, noise_scheduler, latents, noise, accelerator.device, weight_dtype
|
||||
)
|
||||
|
||||
# pack latents and get img_ids
|
||||
packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input) # b, c, h*2, w*2 -> b, h*w, c*4
|
||||
packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2
|
||||
img_ids = flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width).to(device=accelerator.device)
|
||||
|
||||
# get guidance
|
||||
guidance_vec = torch.full((bsz,), args.guidance_scale, device=accelerator.device)
|
||||
|
||||
# call model
|
||||
l_pooled, t5_out, txt_ids = text_encoder_conds
|
||||
with accelerator.autocast():
|
||||
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
|
||||
model_pred = flux(
|
||||
img=packed_noisy_model_input,
|
||||
img_ids=img_ids,
|
||||
txt=t5_out,
|
||||
txt_ids=txt_ids,
|
||||
y=l_pooled,
|
||||
timesteps=timesteps / 1000,
|
||||
guidance=guidance_vec,
|
||||
)
|
||||
|
||||
# unpack latents
|
||||
model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width)
|
||||
|
||||
# apply model prediction type
|
||||
model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas)
|
||||
|
||||
# flow matching loss: this is different from SD3
|
||||
target = noise - latents
|
||||
|
||||
# calculate loss
|
||||
loss = train_util.conditional_loss(
|
||||
model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None
|
||||
)
|
||||
if weighting is not None:
|
||||
loss = loss * weighting
|
||||
if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
|
||||
loss = apply_masked_loss(loss, batch)
|
||||
loss = loss.mean([1, 2, 3])
|
||||
|
||||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||||
loss = loss * loss_weights
|
||||
loss = loss.mean()
|
||||
|
||||
# backward
|
||||
accelerator.backward(loss)
|
||||
|
||||
if not (args.fused_backward_pass or args.fused_optimizer_groups):
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = []
|
||||
for m in training_models:
|
||||
params_to_clip.extend(m.parameters())
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
else:
|
||||
# optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
|
||||
lr_scheduler.step()
|
||||
if args.fused_optimizer_groups:
|
||||
for i in range(1, len(optimizers)):
|
||||
lr_schedulers[i].step()
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
|
||||
flux_train_utils.sample_images(
|
||||
accelerator, args, epoch, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs
|
||||
)
|
||||
|
||||
# 指定ステップごとにモデルを保存
|
||||
if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
|
||||
accelerator.wait_for_everyone()
|
||||
if accelerator.is_main_process:
|
||||
flux_train_utils.save_flux_model_on_epoch_end_or_stepwise(
|
||||
args,
|
||||
False,
|
||||
accelerator,
|
||||
save_dtype,
|
||||
epoch,
|
||||
num_train_epochs,
|
||||
global_step,
|
||||
accelerator.unwrap_model(flux),
|
||||
)
|
||||
|
||||
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss}
|
||||
train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True)
|
||||
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
if accelerator.is_main_process:
|
||||
flux_train_utils.save_flux_model_on_epoch_end_or_stepwise(
|
||||
args,
|
||||
True,
|
||||
accelerator,
|
||||
save_dtype,
|
||||
epoch,
|
||||
num_train_epochs,
|
||||
global_step,
|
||||
accelerator.unwrap_model(flux),
|
||||
)
|
||||
|
||||
flux_train_utils.sample_images(
|
||||
accelerator, args, epoch + 1, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs
|
||||
)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
# if is_main_process:
|
||||
flux = accelerator.unwrap_model(flux)
|
||||
clip_l = accelerator.unwrap_model(clip_l)
|
||||
clip_g = accelerator.unwrap_model(clip_g)
|
||||
if t5xxl is not None:
|
||||
t5xxl = accelerator.unwrap_model(t5xxl)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state or args.save_state_on_train_end:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
|
||||
if is_main_process:
|
||||
flux_train_utils.save_flux_model_on_train_end(args, save_dtype, epoch, global_step, flux, ae)
|
||||
logger.info("model saved.")
|
||||
|
||||
|
||||
def setup_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
add_logging_arguments(parser)
|
||||
train_util.add_sd_models_arguments(parser) # TODO split this
|
||||
train_util.add_dataset_arguments(parser, True, True, True)
|
||||
train_util.add_training_arguments(parser, False)
|
||||
train_util.add_masked_loss_arguments(parser)
|
||||
deepspeed_utils.add_deepspeed_arguments(parser)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
train_util.add_optimizer_arguments(parser)
|
||||
config_util.add_config_arguments(parser)
|
||||
add_custom_train_arguments(parser) # TODO remove this from here
|
||||
flux_train_utils.add_flux_train_arguments(parser)
|
||||
|
||||
parser.add_argument(
|
||||
"--fused_optimizer_groups",
|
||||
type=int,
|
||||
default=None,
|
||||
help="number of optimizers for fused backward pass and optimizer step / fused backward passとoptimizer stepのためのoptimizer数",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--skip_latents_validity_check",
|
||||
action="store_true",
|
||||
help="skip latents validity check / latentsの正当性チェックをスキップする",
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
args = parser.parse_args()
|
||||
train_util.verify_command_line_training_args(args)
|
||||
args = train_util.read_config_from_file(args, parser)
|
||||
|
||||
train(args)
|
||||
Reference in New Issue
Block a user