From 71e03559e27b52a79c89e2a077a9d801831dbcb3 Mon Sep 17 00:00:00 2001 From: Kohya S Date: Tue, 20 Feb 2024 08:27:11 +0900 Subject: [PATCH] support LoRA training for Stable Cascade Stage C --- README.md | 55 ++ library/stable_cascade.py | 3 + library/stable_cascade_utils.py | 4 +- networks/lora.py | 14 +- stable_cascade_gen_img.py | 56 +- stable_cascade_train_c_network.py | 1090 +++++++++++++++++++++++++++++ 6 files changed, 1217 insertions(+), 5 deletions(-) create mode 100644 stable_cascade_train_c_network.py diff --git a/README.md b/README.md index c9a13afc..8c6e9123 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,32 @@ If the latents cache files for SD/SDXL exist (extension `*.npz`), it will be rea After that, run `finetune/prepare_buckets_latents.py` with the `--stable_cascade` option to create latents cache files for Stable Cascade (suffix `_sc_latents.npz` is added). +## LoRA training + +`stable_cascade_train_c_network.py` is used for LoRA training. The main options are the same as `train_network.py`, and the same options as `stable_cascade_train_stage_c.py` have been added. + +__This is an experimental feature, so the format of the saved weights may change in the future and become incompatible.__ + +There is no compatibility with the official LoRA, and the implementation of Text Encoder embedding training (Pivotal Tuning) in the official implementation is not implemented here. + +Text Encoder LoRA training is implemented, but untested. + +## Image generation + +Basic image generation functionality is available in `stable_cascade_gen_img.py`. See `--help` for usage. + +When using LoRA, specify `--network_module networks.lora --network_mul 1 --network_weights lora_weights.safetensors`. + +The following prompt options are available. + + * `--n` Negative prompt up to the next option. + * `--w` Specifies the width of the generated image. + * `--h` Specifies the height of the generated image. + * `--d` Specifies the seed of the generated image. + * `--l` Specifies the CFG scale of the generated image. + * `--s` Specifies the number of steps in the generation. + * `--t` Specifies the t_start of the generation. + * `--f` Specifies the shift of the generation. # Stable Cascade Stage C の学習 @@ -92,6 +118,35 @@ SD/SDXL 向けの latents キャッシュファイル（拡張子 `*.npz`）が その後、`finetune/prepare_buckets_latents.py` をオプション `--stable_cascade` を指定して実行すると、Stable Cascade 向けの latents キャッシュファイル（接尾辞 `_sc_latents.npz` が付きます）が作成されます。 + +## LoRA 等の学習 + +LoRA の学習は `stable_cascade_train_c_network.py` で行います。主なオプションは `train_network.py` と同様で、`stable_cascade_train_stage_c.py` と同様のオプションが追加されています。 + +__実験的機能のため、保存される重みのフォーマットは将来的に変更され、互換性がなくなる可能性があります。__ + +公式の LoRA と重みの互換性はありません。また公式で実装されている Text Encoder の embedding 学習（Pivotal Tuning）も実装されていません。 + +Text Encoder の LoRA 学習は実装してありますが、未テストです。 + +## 画像生成 + +最低限の画像生成機能が `stable_cascade_gen_img.py` にあります。使用法は `--help` を参照してください。 + +LoRA 使用時は `--network_module networks.lora --network_mul 1 --network_weights lora_weights.safetensors` のように指定します。 + +プロンプトオプションとして以下が使用できます。 + + * `--n` Negative prompt up to the next option. + * `--w` Specifies the width of the generated image. + * `--h` Specifies the height of the generated image. + * `--d` Specifies the seed of the generated image. + * `--l` Specifies the CFG scale of the generated image. + * `--s` Specifies the number of steps in the generation. + * `--t` Specifies the t_start of the generation. + * `--f` Specifies the shift of the generation. + + --- __SDXL is now supported. The sdxl branch has been merged into the main branch. If you update the repository, please follow the upgrade instructions. Also, the version of accelerate has been updated, so please run accelerate config again.__ The documentation for SDXL training is [here](./README.md#sdxl-training). diff --git a/library/stable_cascade.py b/library/stable_cascade.py index cbccaa9b..a3cd3614 100644 --- a/library/stable_cascade.py +++ b/library/stable_cascade.py @@ -12,6 +12,9 @@ import torch.utils.checkpoint import torchvision +MODEL_VERSION_STABLE_CASCADE = "stable_cascade" + + # region VectorQuantize # from torchtools https://github.com/pabloppp/pytorch-tools diff --git a/library/stable_cascade_utils.py b/library/stable_cascade_utils.py index fe280419..6c99289a 100644 --- a/library/stable_cascade_utils.py +++ b/library/stable_cascade_utils.py @@ -193,7 +193,7 @@ def load_previewer_model(previewer_checkpoint_path, dtype=None, device="cpu") -> return previewer -def get_sai_model_spec(args): +def get_sai_model_spec(args, lora=False): timestamp = time.time() reso = args.resolution @@ -212,7 +212,7 @@ def get_sai_model_spec(args): False, False, False, - False, + lora, False, timestamp, title=title, diff --git a/networks/lora.py b/networks/lora.py index 948b30b0..f7e93595 100644 --- a/networks/lora.py +++ b/networks/lora.py @@ -841,9 +841,14 @@ class LoRANetwork(torch.nn.Module): is_linear = child_module.__class__.__name__ == "Linear" is_conv2d = child_module.__class__.__name__ == "Conv2d" is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1) + is_group_conv2d = is_conv2d and child_module.groups > 1 - if is_linear or is_conv2d: - lora_name = prefix + "." + name + "." + child_name + # if is_group_conv2d: + # logger.info(f"skip group conv2d: {name}.{child_name}") + # continue + + if is_linear or (is_conv2d and not is_group_conv2d): + lora_name = prefix + "." + name + ("." + child_name if child_name else "") lora_name = lora_name.replace(".", "_") dim = None @@ -915,6 +920,11 @@ class LoRANetwork(torch.nn.Module): if modules_dim is not None or self.conv_lora_dim is not None or conv_block_dims is not None: target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 + # XXX temporary solution for Stable Cascade Stage C: replace all modules + if "StageC" in unet.__class__.__name__: + logger.info("replace all modules for Stable Cascade Stage C") + target_modules = ["Linear", "Conv2d"] + self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules) logger.info(f"create LoRA for U-Net: {len(self.unet_loras)} modules.") diff --git a/stable_cascade_gen_img.py b/stable_cascade_gen_img.py index b7e5fe4e..a98c1a68 100644 --- a/stable_cascade_gen_img.py +++ b/stable_cascade_gen_img.py @@ -1,4 +1,5 @@ import argparse +import importlib import math import os import random @@ -66,6 +67,39 @@ def main(args): else: previewer = None + # LoRA + for i, network_module in enumerate(args.network_module): + print("import network module:", network_module) + imported_module = importlib.import_module(network_module) + + network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i] + + net_kwargs = {} + if args.network_args and i < len(args.network_args): + network_args = args.network_args[i] + # TODO escape special chars + network_args = network_args.split(";") + for net_arg in network_args: + key, value = net_arg.split("=") + net_kwargs[key] = value + + if args.network_weights is None or len(args.network_weights) <= i: + raise ValueError("No weight. Weight is required.") + + network_weight = args.network_weights[i] + print("load network weights from:", network_weight) + + network, weights_sd = imported_module.create_network_from_weights( + network_mul, network_weight, effnet, text_model, generator_c, for_inference=True, **net_kwargs + ) + if network is None: + return + + mergeable = network.is_mergeable() + assert mergeable, "not-mergeable network is not supported yet." + + network.merge_to(text_model, generator_c, weights_sd, dtype, device) + # 謎のクラス gdf gdf_c = sc.GDF( schedule=sc.CosineSchedule(clamp_range=[0.0001, 0.9999]), @@ -122,7 +156,7 @@ def main(args): cfg = 4 timesteps = 20 shift = 2 - t_start = 1.0 # t_start is not an option, but it is a parameter + t_start = 1.0 negative_prompt = "" seed = None @@ -299,6 +333,26 @@ if __name__ == "__main__": parser.add_argument("--fp16", action="store_true") parser.add_argument("--outdir", type=str, default="../outputs", help="dir to write results to / 生成画像の出力先") parser.add_argument("--lowvram", action="store_true", help="if specified, use low VRAM mode") + parser.add_argument( + "--network_module", + type=str, + default=None, + nargs="*", + help="additional network module to use / 追加ネットワークを使う時そのモジュール名", + ) + parser.add_argument( + "--network_weights", type=str, default=None, nargs="*", help="additional network weights to load / 追加ネットワークの重み" + ) + parser.add_argument( + "--network_mul", type=float, default=None, nargs="*", help="additional network multiplier / 追加ネットワークの効果の倍率" + ) + parser.add_argument( + "--network_args", + type=str, + default=None, + nargs="*", + help="additional arguments for network (key=value) / ネットワークへの追加の引数", + ) args = parser.parse_args() main(args) diff --git a/stable_cascade_train_c_network.py b/stable_cascade_train_c_network.py new file mode 100644 index 00000000..173c40d4 --- /dev/null +++ b/stable_cascade_train_c_network.py @@ -0,0 +1,1090 @@ +import importlib +import argparse +import math +import os +import sys +import random +import time +import json +from multiprocessing import Value +import toml + +from tqdm import tqdm + +import torch +from library.device_utils import init_ipex, clean_memory_on_device + +init_ipex() + +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.huggingface_util as huggingface_util +import library.custom_train_functions as custom_train_functions +import library.stable_cascade_utils as sc_utils +import library.stable_cascade as sc +from library.sdxl_train_util import add_sdxl_training_arguments +from library.custom_train_functions import ( + apply_snr_weight, + get_weighted_text_embeddings, + prepare_scheduler_for_custom_training, + scale_v_prediction_loss_like_noise_prediction, + add_v_prediction_like_loss, + apply_debiased_estimation, +) +from library.utils import setup_logging, add_logging_arguments + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +class NetworkTrainer: + def __init__(self): + pass + + # TODO 他のスクリプトと共通化する + def generate_step_logs( + self, args: argparse.Namespace, current_loss, avr_loss, lr_scheduler, keys_scaled=None, mean_norm=None, maximum_norm=None + ): + logs = {"loss/current": current_loss, "loss/average": avr_loss} + + if keys_scaled is not None: + logs["max_norm/keys_scaled"] = keys_scaled + logs["max_norm/average_key_norm"] = mean_norm + logs["max_norm/max_key_norm"] = maximum_norm + + lrs = lr_scheduler.get_last_lr() + + if args.network_train_text_encoder_only or len(lrs) <= 2: # not block lr (or single block) + if args.network_train_unet_only: + logs["lr/unet"] = float(lrs[0]) + elif args.network_train_text_encoder_only: + logs["lr/textencoder"] = float(lrs[0]) + else: + logs["lr/textencoder"] = float(lrs[0]) + logs["lr/unet"] = float(lrs[-1]) # may be same to textencoder + + if ( + args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".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"] + ) + else: + idx = 0 + if not args.network_train_unet_only: + logs["lr/textencoder"] = float(lrs[0]) + idx = 1 + + for i in range(idx, len(lrs)): + logs[f"lr/group{i}"] = float(lrs[i]) + if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower(): + logs[f"lr/d*lr/group{i}"] = ( + lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"] + ) + + return logs + + def assert_extra_args(self, args, train_dataset_group): + pass + + # def load_target_model(self, args, weight_dtype, accelerator): + # text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator) + # return model_util.get_model_version_str_for_sd1_sd2(args.v2, args.v_parameterization), text_encoder, vae, unet + + def load_tokenizer(self, args): + tokenizer = sc_utils.load_tokenizer(args) + return tokenizer + + def is_text_encoder_outputs_cached(self, args): + return False + + def is_train_text_encoder(self, args): + return not args.network_train_unet_only and not self.is_text_encoder_outputs_cached(args) + + def cache_text_encoder_outputs_if_needed( + self, args, accelerator, unet, vae, tokenizers, text_encoders, dataset: train_util.DatasetGroup, weight_dtype + ): + if args.cache_text_encoder_outputs: + if not args.lowram: + # メモリ消費を減らす + logger.info("move vae and unet to cpu to save memory") + org_vae_device = vae.device + org_unet_device = unet.device + vae.to("cpu") + unet.to("cpu") + clean_memory_on_device(accelerator.device) + + # When TE is not be trained, it will not be prepared so we need to use explicit autocast + # Text Encodes are eval and no grad + with torch.no_grad(), accelerator.autocast(): + dataset.cache_text_encoder_outputs( + tokenizers, + text_encoders, + accelerator.device, + weight_dtype, + args.cache_text_encoder_outputs_to_disk, + accelerator.is_main_process, + sc_utils.TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX, + ) + accelerator.wait_for_everyone() + + text_encoders[0].to("cpu", dtype=torch.float32) # Text Encoder doesn't work with fp16 on CPU + clean_memory_on_device(accelerator.device) + + if not args.lowram: + logger.info("move vae and unet back to original device") + vae.to(org_vae_device) + unet.to(org_unet_device) + else: + # Text Encoderから毎回出力を取得するので、GPUに乗せておく + text_encoders[0].to(accelerator.device, dtype=weight_dtype) + + def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype): + if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None: + input_ids1 = batch["input_ids"].to(accelerator.device) + # unwrap_model is fine for models not wrapped by accelerator + encoder_hidden_states, pool = train_util.get_hidden_states_stable_cascade( + args.max_token_length, + input_ids1, + tokenizers[0], + text_encoders[0], + None if not args.full_fp16 else weight_dtype, + accelerator, + ) + else: + encoder_hidden_states = batch["text_encoder_outputs1_list"].to(accelerator.device).to(weight_dtype) + pool = batch["text_encoder_pool2_list"].to(accelerator.device).to(weight_dtype) + + return encoder_hidden_states, pool + + def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype): + noise_pred = unet(noisy_latents, timesteps, text_conds).sample + return noise_pred + + def all_reduce_network(self, accelerator, network): + for param in network.parameters(): + if param.grad is not None: + param.grad = accelerator.reduce(param.grad, reduction="mean") + + def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet): + train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet) + + def train(self, args): + session_id = random.randint(0, 2**32) + training_started_at = time.time() + + train_util.verify_training_args(args) + train_util.prepare_dataset_args(args, True) + setup_logging(args, reset=True) + + cache_latents = args.cache_latents + use_dreambooth_method = args.in_json is None + use_user_config = args.dataset_config is not None + + if args.seed is None: + args.seed = random.randint(0, 2**32) + set_seed(args.seed) + + # tokenizerは単体またはリスト、tokenizersは必ずリスト：既存のコードとの互換性のため + tokenizer = self.load_tokenizer(args) + tokenizers = tokenizer if isinstance(tokenizer, list) else [tokenizer] + + # データセットを準備する + if args.dataset_class is None: + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, True)) + if use_user_config: + logger.info(f"Loading 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): + logger.warning( + "ignoring the 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: + logger.info("Using DreamBooth method.") + user_config = { + "datasets": [ + { + "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( + args.train_data_dir, args.reg_data_dir + ) + } + ] + } + else: + logger.info("Training 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) + else: + # use arbitrary dataset class + train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer) + + current_epoch = Value("i", 0) + current_step = Value("i", 0) + ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None + collator = train_util.collator_class(current_epoch, current_step, ds_for_collator) + + if args.debug_dataset: + train_util.debug_dataset(train_dataset_group) + return + if len(train_dataset_group) == 0: + logger.error( + "No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください（train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります）" + ) + return + + if cache_latents: + assert ( + train_dataset_group.is_latent_cacheable() + ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" + + self.assert_extra_args(args, train_dataset_group) + + # acceleratorを準備する + logger.info("preparing accelerator") + accelerator = train_util.prepare_accelerator(args) + is_main_process = accelerator.is_main_process + + # mixed precisionに対応した型を用意しておき適宜castする + weight_dtype, save_dtype = train_util.prepare_dtype(args) + effnet_dtype = torch.float32 if args.no_half_vae else weight_dtype + + # モデルを読み込む + # model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator) + loading_device = accelerator.device if args.lowram else "cpu" + effnet = sc_utils.load_effnet(args.effnet_checkpoint_path, loading_device) + stage_c = sc_utils.load_stage_c_model(args.stage_c_checkpoint_path, dtype=weight_dtype, device=loading_device) + text_encoder = sc_utils.load_clip_text_model(args.text_model_checkpoint_path, dtype=weight_dtype, device=loading_device) + model_version = sc.MODEL_VERSION_STABLE_CASCADE + + if args.sample_at_first or args.sample_every_n_steps is not None or args.sample_every_n_epochs is not None: + # Previewer is small enough to be loaded on CPU + previewer = sc_utils.load_previewer_model(args.previewer_checkpoint_path, dtype=torch.float32, device="cpu") + previewer.eval() + else: + previewer = None + + # text_encoder is List[CLIPTextModel] or CLIPTextModel + text_encoders = text_encoder if isinstance(text_encoder, list) else [text_encoder] + + # # モデルに xformers とか memory efficient attention を組み込む + # train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa) + # if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える + # vae.set_use_memory_efficient_attention_xformers(args.xformers) + + # 差分追加学習のためにモデルを読み込む + sys.path.append(os.path.dirname(__file__)) + accelerator.print("import network module:", args.network_module) + network_module = importlib.import_module(args.network_module) + + if args.base_weights is not None: + # base_weights が指定されている場合は、指定された重みを読み込みマージする + for i, weight_path in enumerate(args.base_weights): + if args.base_weights_multiplier is None or len(args.base_weights_multiplier) <= i: + multiplier = 1.0 + else: + multiplier = args.base_weights_multiplier[i] + + accelerator.print(f"merging module: {weight_path} with multiplier {multiplier}") + + module, weights_sd = network_module.create_network_from_weights( + multiplier, weight_path, effnet, text_encoder, stage_c, for_inference=True + ) + module.merge_to(text_encoder, stage_c, weights_sd, weight_dtype, accelerator.device if args.lowram else "cpu") + + accelerator.print(f"all weights merged: {', '.join(args.base_weights)}") + + # 学習を準備する + if cache_latents: + effnet.to(accelerator.device, dtype=effnet_dtype) + effnet.requires_grad_(False) + effnet.eval() + with torch.no_grad(): + train_dataset_group.cache_latents( + effnet, + args.vae_batch_size, + args.cache_latents_to_disk, + accelerator.is_main_process, + train_util.STABLE_CASCADE_LATENTS_CACHE_SUFFIX, + 32, + ) + effnet.to("cpu") + clean_memory_on_device(accelerator.device) + + accelerator.wait_for_everyone() + + # 必要ならテキストエンコーダーの出力をキャッシュする: Text Encoderはcpuまたはgpuへ移される + # cache text encoder outputs if needed: Text Encoder is moved to cpu or gpu + self.cache_text_encoder_outputs_if_needed( + args, accelerator, stage_c, effnet, tokenizers, text_encoders, train_dataset_group, weight_dtype + ) + + # prepare network + 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 (;'∀') + if args.dim_from_weights: + network, _ = network_module.create_network_from_weights( + 1, args.network_weights, effnet, text_encoder, stage_c, **net_kwargs + ) + else: + if "dropout" not in net_kwargs: + # workaround for LyCORIS (;^ω^) + net_kwargs["dropout"] = args.network_dropout + + network = network_module.create_network( + 1.0, + args.network_dim, + args.network_alpha, + effnet, + text_encoder, + stage_c, + neuron_dropout=args.network_dropout, + **net_kwargs, + ) + if network is None: + return + network_has_multiplier = hasattr(network, "set_multiplier") + + if hasattr(network, "prepare_network"): + network.prepare_network(args) + if args.scale_weight_norms and not hasattr(network, "apply_max_norm_regularization"): + logger.warning( + "warning: scale_weight_norms is specified but the network does not support it / scale_weight_normsが指定されていますが、ネットワークが対応していません" + ) + args.scale_weight_norms = False + + train_unet = not args.network_train_text_encoder_only + train_text_encoder = self.is_train_text_encoder(args) + network.apply_to(text_encoder, stage_c, train_text_encoder, train_unet) + + if args.network_weights is not None: + info = network.load_weights(args.network_weights) + accelerator.print(f"load network weights from {args.network_weights}: {info}") + + if args.gradient_checkpointing: + stage_c.set_gradient_checkpointing(True) + for t_enc in text_encoders: + t_enc.gradient_checkpointing_enable() + del t_enc + network.enable_gradient_checkpointing() # may have no effect + + # 学習に必要なクラスを準備する + accelerator.print("prepare optimizer, data loader etc.") + + # 後方互換性を確保するよ + try: + trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate) + except TypeError: + accelerator.print( + "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)" + ) + 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=collator, + num_workers=n_workers, + persistent_workers=args.persistent_data_loader_workers, + ) + + # 学習ステップ数を計算する + if args.max_train_epochs is not None: + args.max_train_steps = args.max_train_epochs * math.ceil( + len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps + ) + accelerator.print( + f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}" + ) + + # データセット側にも学習ステップを送信 + train_dataset_group.set_max_train_steps(args.max_train_steps) + + # lr schedulerを用意する + lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) + + # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする + if args.full_fp16: + assert ( + args.mixed_precision == "fp16" + ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + accelerator.print("enable full fp16 training.") + network.to(weight_dtype) + elif args.full_bf16: + assert ( + args.mixed_precision == "bf16" + ), "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。" + accelerator.print("enable full bf16 training.") + network.to(weight_dtype) + + unet_weight_dtype = te_weight_dtype = weight_dtype + # Experimental Feature: Put base model into fp8 to save vram + if args.fp8_base: + assert torch.__version__ >= "2.1.0", "fp8_base requires torch>=2.1.0 / fp8を使う場合はtorch>=2.1.0が必要です。" + assert ( + args.mixed_precision != "no" + ), "fp8_base requires mixed precision='fp16' or 'bf16' / fp8を使う場合はmixed_precision='fp16'または'bf16'が必要です。" + accelerator.print("enable fp8 training.") + unet_weight_dtype = torch.float8_e4m3fn + te_weight_dtype = torch.float8_e4m3fn + + stage_c.requires_grad_(False) + stage_c.to(dtype=unet_weight_dtype) + for t_enc in text_encoders: + t_enc.requires_grad_(False) + + # in case of cpu, dtype is already set to fp32 because cpu does not support fp8/fp16/bf16 + if t_enc.device.type != "cpu": + t_enc.to(dtype=te_weight_dtype) + # nn.Embedding not support FP8 + t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) + + # acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good + if train_unet: + stage_c = accelerator.prepare(stage_c) + else: + stage_c.to(accelerator.device, dtype=unet_weight_dtype) # move to device because unet is not prepared by accelerator + if train_text_encoder: + if len(text_encoders) > 1: + text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders] + else: + text_encoder = accelerator.prepare(text_encoder) + text_encoders = [text_encoder] + else: + pass # if text_encoder is not trained, no need to prepare. and device and dtype are already set + + network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler) + + if args.gradient_checkpointing: + # according to TI example in Diffusers, train is required + # Stage C also requires grad for gradient checkpointing to work for compatibility + stage_c.train() + for t_enc in text_encoders: + t_enc.train() + + # set top parameter requires_grad = True for gradient checkpointing works + if train_text_encoder: + t_enc.text_model.embeddings.requires_grad_(True) + + else: + stage_c.eval() + for t_enc in text_encoders: + t_enc.eval() + + del t_enc + + accelerator.unwrap_model(network).prepare_grad_etc(text_encoder, stage_c) + + if not cache_latents: # キャッシュしない場合はVAEを使うのでVAEを準備する + effnet.requires_grad_(False) + effnet.eval() + effnet.to(accelerator.device, dtype=effnet_dtype) + + # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする + if args.full_fp16: + train_util.patch_accelerator_for_fp16_training(accelerator) + + # resumeする + train_util.resume_from_local_or_hf_if_specified(accelerator, args) + + # epoch数を計算する + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0): + args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1 + + # 学習する + # TODO: find a way to handle total batch size when there are multiple datasets + 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 train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}") + accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}") + accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}") + accelerator.print(f" num epochs / epoch数: {num_train_epochs}") + accelerator.print( + f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}" + ) + # 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}") + + # 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 have alpha + "ss_network_dropout": args.network_dropout, # some networks may not have dropout + "ss_mixed_precision": args.mixed_precision, + "ss_full_fp16": bool(args.full_fp16), + "ss_v2": False, # bool(args.v2), + "ss_base_model_version": model_version, + "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_multires_noise_iterations": args.multires_noise_iterations, + "ss_multires_noise_discount": args.multires_noise_discount, + "ss_adaptive_noise_scale": args.adaptive_noise_scale, + "ss_zero_terminal_snr": args.zero_terminal_snr, + "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, + "ss_min_snr_gamma": args.min_snr_gamma, + "ss_scale_weight_norms": args.scale_weight_norms, + "ss_ip_noise_gamma": args.ip_noise_gamma, + "ss_debiased_estimation": bool(args.debiased_estimation_loss), + } + + 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) + + # 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_metadata = {} + for key in train_util.SS_METADATA_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 + + # GDF は Guided Diffusion かしらん + gdf = sc.GDF( + schedule=sc.CosineSchedule(clamp_range=[0.0001, 0.9999]), + input_scaler=sc.VPScaler(), + target=sc.EpsilonTarget(), + noise_cond=sc.CosineTNoiseCond(), + loss_weight=sc.AdaptiveLossWeight() if args.adaptive_loss_weight else sc.P2LossWeight(), + ) + + 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( + "network_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs + ) + + loss_recorder = train_util.LossRecorder() + del train_dataset_group + + # callback for step start + if hasattr(accelerator.unwrap_model(network), "on_step_start"): + on_step_start = accelerator.unwrap_model(network).on_step_start + else: + on_step_start = lambda *args, **kwargs: None + + # function for saving/removing + def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False): + os.makedirs(args.output_dir, exist_ok=True) + ckpt_file = os.path.join(args.output_dir, ckpt_name) + + accelerator.print(f"\nsaving checkpoint: {ckpt_file}") + metadata["ss_training_finished_at"] = str(time.time()) + metadata["ss_steps"] = str(steps) + metadata["ss_epoch"] = str(epoch_no) + + metadata_to_save = minimum_metadata if args.no_metadata else metadata + sai_metadata = sc_utils.get_sai_model_spec(args) + metadata_to_save.update(sai_metadata) + + unwrapped_nw.save_weights(ckpt_file, save_dtype, metadata_to_save) + if args.huggingface_repo_id is not None: + huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload) + + def remove_model(old_ckpt_name): + old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name) + if os.path.exists(old_ckpt_file): + accelerator.print(f"removing old checkpoint: {old_ckpt_file}") + os.remove(old_ckpt_file) + + # For --sample_at_first + sc_utils.sample_images(accelerator, args, 0, global_step, previewer, tokenizer, text_encoder, stage_c, gdf) + + # training loop + for epoch in range(num_train_epochs): + accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") + current_epoch.value = epoch + 1 + + metadata["ss_epoch"] = str(epoch + 1) + + accelerator.unwrap_model(network).on_epoch_start(text_encoder, stage_c) + + for step, batch in enumerate(train_dataloader): + current_step.value = global_step + with accelerator.accumulate(network): + on_step_start(text_encoder, stage_c) + + 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 = effnet(batch["images"].to(effnet_dtype)).to(weight_dtype) + + # 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) + + # get multiplier for each sample + if network_has_multiplier: + multipliers = batch["network_multipliers"] + # if all multipliers are same, use single multiplier + if torch.all(multipliers == multipliers[0]): + multipliers = multipliers[0].item() + else: + raise NotImplementedError("multipliers for each sample is not supported yet") + # print(f"set multiplier: {multipliers}") + accelerator.unwrap_model(network).set_multiplier(multipliers) + + with torch.set_grad_enabled(train_text_encoder), accelerator.autocast(): + # Get the text embedding for conditioning + # TODO support weighted captions + encoder_hidden_states, pool = self.get_text_cond( + args, accelerator, batch, tokenizers, text_encoders, weight_dtype + ) + + pool = pool.unsqueeze(1) # add extra dimension b,1280 -> b,1,1280 + + # ensure the hidden state will require grad + if args.gradient_checkpointing: + latents.requires_grad_(True) # I'm not sure if this is correct + encoder_hidden_states.requires_grad_(True) + pool.requires_grad_(True) + + # FORWARD PASS + with torch.no_grad(): + noised, noise, target, logSNR, noise_cond, loss_weight = gdf.diffuse(latents, shift=1, loss_shift=1) + + zero_img_emb = torch.zeros(noised.shape[0], 768, device=accelerator.device) + with accelerator.autocast(): + pred = stage_c( + noised, noise_cond, clip_text=encoder_hidden_states, clip_text_pooled=pool, clip_img=zero_img_emb + ) + loss = torch.nn.functional.mse_loss(pred, target, reduction="none").mean(dim=[1, 2, 3]) + loss_adjusted = (loss * loss_weight).mean() + + if args.adaptive_loss_weight: + gdf.loss_weight.update_buckets(logSNR, loss) # use loss instead of loss_adjusted + + accelerator.backward(loss_adjusted) + if accelerator.sync_gradients: + self.all_reduce_network(accelerator, network) # sync DDP grad manually + if args.max_grad_norm != 0.0: + params_to_clip = accelerator.unwrap_model(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) + + if args.scale_weight_norms: + keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization( + args.scale_weight_norms, accelerator.device + ) + max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm} + else: + keys_scaled, mean_norm, maximum_norm = None, None, None + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + + sc_utils.sample_images(accelerator, args, None, global_step, previewer, tokenizer, text_encoder, stage_c, gdf) + + # 指定ステップごとにモデルを保存 + if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step) + save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch) + + if args.save_state: + train_util.save_and_remove_state_stepwise(args, accelerator, global_step) + + remove_step_no = train_util.get_remove_step_no(args, global_step) + if remove_step_no is not None: + remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no) + remove_model(remove_ckpt_name) + + current_loss = loss_adjusted.detach().item() + loss_recorder.add(epoch=epoch, step=step, loss=current_loss) + avr_loss: float = loss_recorder.moving_average + logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if args.scale_weight_norms: + progress_bar.set_postfix(**{**max_mean_logs, **logs}) + + if args.logging_dir is not None: + logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm) + accelerator.log(logs, step=global_step) + + if global_step >= args.max_train_steps: + break + + if args.logging_dir is not None: + logs = {"loss/epoch": loss_recorder.moving_average} + accelerator.log(logs, step=epoch + 1) + + accelerator.wait_for_everyone() + + # 指定エポックごとにモデルを保存 + if args.save_every_n_epochs is not None: + saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs + if is_main_process and saving: + ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1) + save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1) + + remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1) + if remove_epoch_no is not None: + remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no) + remove_model(remove_ckpt_name) + + if args.save_state: + train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1) + + sc_utils.sample_images(accelerator, args, epoch + 1, global_step, previewer, tokenizer, text_encoder, stage_c, gdf) + + # end of epoch + + # metadata["ss_epoch"] = str(num_train_epochs) + metadata["ss_training_finished_at"] = str(time.time()) + + if is_main_process: + network = accelerator.unwrap_model(network) + + accelerator.end_training() + + if is_main_process and args.save_state: + train_util.save_state_on_train_end(args, accelerator) + + if is_main_process: + ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as) + save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True) + + logger.info("model saved.") + + +def setup_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser() + + add_logging_arguments(parser) + sc_utils.add_effnet_arguments(parser) + sc_utils.add_stage_c_arguments(parser) + sc_utils.add_text_model_arguments(parser) + sc_utils.add_previewer_arguments(parser) + sc_utils.add_training_arguments(parser) + train_util.add_tokenizer_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) + add_sdxl_training_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("--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_dropout", + type=float, + default=None, + help="Drops neurons out of training every step (0 or None is default behavior (no dropout), 1 would drop all neurons) / 訓練時に毎ステップでニューロンをdropする（0またはNoneはdropoutなし、1は全ニューロンをdropout）", + ) + parser.add_argument( + "--network_args", + type=str, + default=None, + nargs="*", + help="additional arguments 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", + ) + parser.add_argument( + "--dim_from_weights", + action="store_true", + help="automatically determine dim (rank) from network_weights / dim (rank)をnetwork_weightsで指定した重みから自動で決定する", + ) + parser.add_argument( + "--scale_weight_norms", + type=float, + default=None, + help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point) / 重みの値をスケーリングして勾配爆発を防ぐ（1が初期値としては適当）", + ) + parser.add_argument( + "--base_weights", + type=str, + default=None, + nargs="*", + help="network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みファイル", + ) + parser.add_argument( + "--base_weights_multiplier", + type=float, + default=None, + nargs="*", + help="multiplier for network weights to merge into the model before training / 学習前にあらかじめモデルにマージするnetworkの重みの倍率", + ) + parser.add_argument( + "--no_half_vae", + action="store_true", + help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う", + ) + return parser + + +if __name__ == "__main__": + parser = setup_parser() + + args = parser.parse_args() + args = train_util.read_config_from_file(args, parser) + + trainer = NetworkTrainer() + trainer.train(args)