clean code and add finetune code

library/lumina_train_util.py

@@ -32,7 +32,9 @@ logger = logging.getLogger(__name__)
 # region sample images
 
 
-def batchify(prompt_dicts, batch_size=None) -> Generator[list[dict[str, str]], None, None]:
+def batchify(
+    prompt_dicts, batch_size=None
+) -> Generator[list[dict[str, str]], None, None]:
     """
     Group prompt dictionaries into batches with configurable batch size.
 
@@ -64,7 +66,15 @@ def batchify(prompt_dicts, batch_size=None) -> Generator[list[dict[str, str]], N
         seed = int(seed) if seed is not None else None
 
         # Create a key based on the parameters
-        key = (width, height, guidance_scale, seed, sample_steps, cfg_trunc_ratio, renorm_cfg)
+        key = (
+            width,
+            height,
+            guidance_scale,
+            seed,
+            sample_steps,
+            cfg_trunc_ratio,
+            renorm_cfg,
+        )
 
         # Add the prompt_dict to the corresponding batch
         if key not in batches:
@@ -131,7 +141,9 @@ def sample_images(
             if epoch is None or epoch % args.sample_every_n_epochs != 0:
                 return
         else:
-            if global_step % args.sample_every_n_steps != 0 or epoch is not None:  # steps is not divisible or end of epoch
+            if (
+                global_step % args.sample_every_n_steps != 0 or epoch is not None
+            ):  # steps is not divisible or end of epoch
                 return
 
     assert (
@@ -139,12 +151,21 @@ def sample_images(
     ), "No sample prompts found. Provide `--sample_prompts` / サンプルプロンプトが見つかりません。`--sample_prompts` を指定してください"
 
     logger.info("")
-    logger.info(f"generating sample images at step / サンプル画像生成 ステップ: {global_step}")
-    if not os.path.isfile(args.sample_prompts) and sample_prompts_gemma2_outputs is None:
-        logger.error(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}")
+    logger.info(
+        f"generating sample images at step / サンプル画像生成 ステップ: {global_step}"
+    )
+    if (
+        not os.path.isfile(args.sample_prompts)
+        and sample_prompts_gemma2_outputs is None
+    ):
+        logger.error(
+            f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}"
+        )
         return
 
-    distributed_state = PartialState()  # for multi gpu distributed inference. this is a singleton, so it's safe to use it here
+    distributed_state = (
+        PartialState()
+    )  # for multi gpu distributed inference. this is a singleton, so it's safe to use it here
 
     # unwrap nextdit and gemma2_model
     nextdit = accelerator.unwrap_model(nextdit)
@@ -163,7 +184,9 @@ def sample_images(
     rng_state = torch.get_rng_state()
     cuda_rng_state = None
     try:
-        cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
+        cuda_rng_state = (
+            torch.cuda.get_rng_state() if torch.cuda.is_available() else None
+        )
     except Exception:
         pass
 
@@ -194,7 +217,9 @@ def sample_images(
         for i in range(distributed_state.num_processes):
             per_process_prompts.append(prompts[i :: distributed_state.num_processes])
 
-        with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists:
+        with distributed_state.split_between_processes(
+            per_process_prompts
+        ) as prompt_dict_lists:
             # TODO: batch prompts together with buckets of image sizes
             for prompt_dicts in batchify(prompt_dict_lists[0], batch_size):
                 sample_image_inference(
@@ -289,7 +314,9 @@ def sample_image_inference(
         if prompt_replacement is not None:
             prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
             if negative_prompt is not None:
-                negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
+                negative_prompt = negative_prompt.replace(
+                    prompt_replacement[0], prompt_replacement[1]
+                )
 
         if negative_prompt is None:
             negative_prompt = ""
@@ -314,17 +341,26 @@ def sample_image_inference(
             gemma2_conds = sample_prompts_gemma2_outputs[prompt]
             logger.info(f"Using cached Gemma2 outputs for prompt: {prompt}")
 
-        if sample_prompts_gemma2_outputs and negative_prompt in sample_prompts_gemma2_outputs:
+        if (
+            sample_prompts_gemma2_outputs
+            and negative_prompt in sample_prompts_gemma2_outputs
+        ):
             neg_gemma2_conds = sample_prompts_gemma2_outputs[negative_prompt]
-            logger.info(f"Using cached Gemma2 outputs for negative prompt: {negative_prompt}")
+            logger.info(
+                f"Using cached Gemma2 outputs for negative prompt: {negative_prompt}"
+            )
 
         # Load sample prompts from Gemma 2
         if gemma2_model is not None:
             tokens_and_masks = tokenize_strategy.tokenize(prompt)
-            gemma2_conds = encoding_strategy.encode_tokens(tokenize_strategy, [gemma2_model], tokens_and_masks)
+            gemma2_conds = encoding_strategy.encode_tokens(
+                tokenize_strategy, [gemma2_model], tokens_and_masks
+            )
 
             tokens_and_masks = tokenize_strategy.tokenize(negative_prompt)
-            neg_gemma2_conds = encoding_strategy.encode_tokens(tokenize_strategy, [gemma2_model], tokens_and_masks)
+            neg_gemma2_conds = encoding_strategy.encode_tokens(
+                tokenize_strategy, [gemma2_model], tokens_and_masks
+            )
 
         # Unpack Gemma2 outputs
         gemma2_hidden_states, _, gemma2_attn_mask = gemma2_conds
@@ -340,10 +376,18 @@ def sample_image_inference(
         )
 
     # Stack conditioning
-    cond_hidden_states = torch.stack([text_cond[0] for text_cond in text_conds]).to(accelerator.device)
-    cond_attn_masks = torch.stack([text_cond[1] for text_cond in text_conds]).to(accelerator.device)
-    uncond_hidden_states = torch.stack([text_cond[2] for text_cond in text_conds]).to(accelerator.device)
-    uncond_attn_masks = torch.stack([text_cond[3] for text_cond in text_conds]).to(accelerator.device)
+    cond_hidden_states = torch.stack([text_cond[0] for text_cond in text_conds]).to(
+        accelerator.device
+    )
+    cond_attn_masks = torch.stack([text_cond[1] for text_cond in text_conds]).to(
+        accelerator.device
+    )
+    uncond_hidden_states = torch.stack([text_cond[2] for text_cond in text_conds]).to(
+        accelerator.device
+    )
+    uncond_attn_masks = torch.stack([text_cond[3] for text_cond in text_conds]).to(
+        accelerator.device
+    )
 
     # sample image
     weight_dtype = vae.dtype  # TOFO give dtype as argument
@@ -362,7 +406,9 @@ def sample_image_inference(
     noise = noise.repeat(cond_hidden_states.shape[0], 1, 1, 1)
 
     scheduler = FlowMatchEulerDiscreteScheduler(shift=6.0)
-    timesteps, num_inference_steps = retrieve_timesteps(scheduler, num_inference_steps=sample_steps)
+    timesteps, num_inference_steps = retrieve_timesteps(
+        scheduler, num_inference_steps=sample_steps
+    )
 
     # if controlnet_image is not None:
     #     controlnet_image = Image.open(controlnet_image).convert("RGB")
@@ -422,7 +468,9 @@ def sample_image_inference(
             import wandb
 
             # not to commit images to avoid inconsistency between training and logging steps
-            wandb_tracker.log({f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False)  # positive prompt as a caption
+            wandb_tracker.log(
+                {f"sample_{i}": wandb.Image(image, caption=prompt)}, commit=False
+            )  # positive prompt as a caption
 
     vae.to(org_vae_device)
     clean_memory_on_device(accelerator.device)
@@ -437,7 +485,9 @@ def time_shift(mu: float, sigma: float, t: torch.Tensor):
     return t
 
 
-def get_lin_function(x1: float = 256, x2: float = 4096, y1: float = 0.5, y2: float = 1.15) -> Callable[[float], float]:
+def get_lin_function(
+    x1: float = 256, x2: float = 4096, y1: float = 0.5, y2: float = 1.15
+) -> Callable[[float], float]:
     """
     Get linear function
 
@@ -481,7 +531,9 @@ def get_schedule(
     # shifting the schedule to favor high timesteps for higher signal images
     if shift:
         # eastimate mu based on linear estimation between two points
-        mu = get_lin_function(y1=base_shift, y2=max_shift, x1=256, x2=4096)(image_seq_len)
+        mu = get_lin_function(y1=base_shift, y2=max_shift, x1=256, x2=4096)(
+            image_seq_len
+        )
         timesteps = time_shift(mu, 1.0, timesteps)
 
     return timesteps.tolist()
@@ -520,9 +572,13 @@ def retrieve_timesteps(
         second element is the number of inference steps.
     """
     if timesteps is not None and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
     if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
         if not accepts_timesteps:
             raise ValueError(
                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
@@ -532,7 +588,9 @@ def retrieve_timesteps(
         timesteps = scheduler.timesteps
         num_inference_steps = len(timesteps)
     elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
         if not accept_sigmas:
             raise ValueError(
                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
@@ -593,7 +651,9 @@ def denoise(
         # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image
         current_timestep = 1 - t / scheduler.config.num_train_timesteps
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        current_timestep = current_timestep * torch.ones(img.shape[0], device=img.device)
+        current_timestep = current_timestep * torch.ones(
+            img.shape[0], device=img.device
+        )
 
         noise_pred_cond = model(
             img,
@@ -610,12 +670,20 @@ def denoise(
                 cap_feats=neg_txt,  # Gemma2的hidden states作为caption features
                 cap_mask=neg_txt_mask.to(dtype=torch.int32),  # Gemma2的attention mask
             )
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+            noise_pred = noise_pred_uncond + guidance_scale * (
+                noise_pred_cond - noise_pred_uncond
+            )
             # apply normalization after classifier-free guidance
             if float(renorm_cfg) > 0.0:
-                cond_norm = torch.linalg.vector_norm(noise_pred_cond, dim=tuple(range(1, len(noise_pred_cond.shape))), keepdim=True)
+                cond_norm = torch.linalg.vector_norm(
+                    noise_pred_cond,
+                    dim=tuple(range(1, len(noise_pred_cond.shape))),
+                    keepdim=True,
+                )
                 max_new_norm = cond_norm * float(renorm_cfg)
-                noise_norm = torch.linalg.vector_norm(noise_pred, dim=tuple(range(1, len(noise_pred.shape))), keepdim=True)
+                noise_norm = torch.linalg.vector_norm(
+                    noise_pred, dim=tuple(range(1, len(noise_pred.shape))), keepdim=True
+                )
                 if noise_norm >= max_new_norm:
                     noise_pred = noise_pred * (max_new_norm / noise_norm)
         else:
@@ -640,7 +708,11 @@ def denoise(
 
 # region train
 def get_sigmas(
-    noise_scheduler: FlowMatchEulerDiscreteScheduler, timesteps: Tensor, device: torch.device, n_dim=4, dtype=torch.float32
+    noise_scheduler: FlowMatchEulerDiscreteScheduler,
+    timesteps: Tensor,
+    device: torch.device,
+    n_dim=4,
+    dtype=torch.float32,
 ) -> Tensor:
     """
     Get sigmas for timesteps
@@ -667,7 +739,11 @@ def get_sigmas(
 
 
 def compute_density_for_timestep_sampling(
-    weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
+    weighting_scheme: str,
+    batch_size: int,
+    logit_mean: float = None,
+    logit_std: float = None,
+    mode_scale: float = None,
 ):
     """
     Compute the density for sampling the timesteps when doing SD3 training.
@@ -688,7 +764,9 @@ def compute_density_for_timestep_sampling(
     """
     if weighting_scheme == "logit_normal":
         # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
-        u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
+        u = torch.normal(
+            mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu"
+        )
         u = torch.nn.functional.sigmoid(u)
     elif weighting_scheme == "mode":
         u = torch.rand(size=(batch_size,), device="cpu")
@@ -722,7 +800,9 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None) -> Tensor
     return weighting
 
 
-def get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype) -> Tuple[Tensor, Tensor, Tensor]:
+def get_noisy_model_input_and_timesteps(
+    args, noise_scheduler, latents, noise, device, dtype
+) -> Tuple[Tensor, Tensor, Tensor]:
     """
     Get noisy model input and timesteps.
 
@@ -753,27 +833,27 @@ def get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, d
 
         timesteps = t * 1000.0
         t = t.view(-1, 1, 1, 1)
-        noisy_model_input = (1 - t) * latents + t * noise
+        noisy_model_input = (1 - t) * noise + t * latents
     elif args.timestep_sampling == "shift":
         shift = args.discrete_flow_shift
         logits_norm = torch.randn(bsz, device=device)
-        logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
+        logits_norm = (
+            logits_norm * args.sigmoid_scale
+        )  # larger scale for more uniform sampling
         timesteps = logits_norm.sigmoid()
         timesteps = (timesteps * shift) / (1 + (shift - 1) * timesteps)
 
         t = timesteps.view(-1, 1, 1, 1)
         timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        noisy_model_input = (1 - t) * noise + t * latents
     elif args.timestep_sampling == "nextdit_shift":
-        logits_norm = torch.randn(bsz, device=device)
-        logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
-        timesteps = logits_norm.sigmoid()
-        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))
-        timesteps = time_shift(mu, 1.0, timesteps)
+        t = torch.rand((bsz,), device=device)
+        mu = get_lin_function(y1=0.5, y2=1.15)((h // 16) * (w // 16)) # lumina use //16
+        t = time_shift(mu, 1.0, t)
 
-        t = timesteps.view(-1, 1, 1, 1)
-        timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        timesteps = t * 1000.0
+        t = t.view(-1, 1, 1, 1)
+        noisy_model_input = (1 - t) * noise + t * latents
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly
@@ -788,8 +868,10 @@ def get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, d
         timesteps = noise_scheduler.timesteps[indices].to(device=device)
 
         # Add noise according to flow matching.
-        sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
-        noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
+        sigmas = get_sigmas(
+            noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype
+        )
+        noisy_model_input = sigmas * latents + (1.0 - sigmas) * noise
 
     return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas
 
@@ -821,7 +903,9 @@ def apply_model_prediction_type(
 
         # these weighting schemes use a uniform timestep sampling
         # and instead post-weight the loss
-        weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
+        weighting = compute_loss_weighting_for_sd3(
+            weighting_scheme=args.weighting_scheme, sigmas=sigmas
+        )
 
     return model_pred, weighting
 
@@ -863,15 +947,27 @@ def save_models(
 
 
 def save_lumina_model_on_train_end(
-    args: argparse.Namespace, save_dtype: torch.dtype, epoch: int, global_step: int, lumina: lumina_models.NextDiT
+    args: argparse.Namespace,
+    save_dtype: torch.dtype,
+    epoch: int,
+    global_step: int,
+    lumina: lumina_models.NextDiT,
 ):
     def sd_saver(ckpt_file, epoch_no, global_step):
         sai_metadata = train_util.get_sai_model_spec(
-            None, args, False, False, False, is_stable_diffusion_ckpt=True, lumina="lumina2"
+            None,
+            args,
+            False,
+            False,
+            False,
+            is_stable_diffusion_ckpt=True,
+            lumina="lumina2",
         )
         save_models(ckpt_file, lumina, sai_metadata, save_dtype, args.mem_eff_save)
 
-    train_util.save_sd_model_on_train_end_common(args, True, True, epoch, global_step, sd_saver, None)
+    train_util.save_sd_model_on_train_end_common(
+        args, True, True, epoch, global_step, sd_saver, None
+    )
 
 
 # epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合してている
@@ -901,7 +997,15 @@ def save_lumina_model_on_epoch_end_or_stepwise(
     """
 
     def sd_saver(ckpt_file: str, epoch_no: int, global_step: int):
-        sai_metadata = train_util.get_sai_model_spec({}, args, False, False, False, is_stable_diffusion_ckpt=True, lumina="lumina2")
+        sai_metadata = train_util.get_sai_model_spec(
+            {},
+            args,
+            False,
+            False,
+            False,
+            is_stable_diffusion_ckpt=True,
+            lumina="lumina2",
+        )
         save_models(ckpt_file, lumina, sai_metadata, save_dtype, args.mem_eff_save)
 
     train_util.save_sd_model_on_epoch_end_or_stepwise_common(
@@ -927,7 +1031,11 @@ def add_lumina_train_arguments(parser: argparse.ArgumentParser):
         type=str,
         help="path to gemma2 model (*.sft or *.safetensors), should be float16 / gemma2のパス（*.sftまたは*.safetensors）、float16が前提",
     )
-    parser.add_argument("--ae", type=str, help="path to ae (*.sft or *.safetensors) / aeのパス（*.sftまたは*.safetensors）")
+    parser.add_argument(
+        "--ae",
+        type=str,
+        help="path to ae (*.sft or *.safetensors) / aeのパス（*.sftまたは*.safetensors）",
+    )
     parser.add_argument(
         "--gemma2_max_token_length",
         type=int,

lumina_train.py

@@ -0,0 +1,953 @@
+# training with captions
+
+# Swap blocks between CPU and GPU:
+# This implementation is inspired by and based on the work of 2kpr.
+# Many thanks to 2kpr for the original concept and implementation of memory-efficient offloading.
+# The original idea has been adapted and extended to fit the current project's needs.
+
+# Key features:
+# - CPU offloading during forward and backward passes
+# - Use of fused optimizer and grad_hook for efficient gradient processing
+# - Per-block fused optimizer instances
+
+import argparse
+import copy
+import math
+import os
+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 library import (
+    deepspeed_utils,
+    lumina_train_util,
+    lumina_util,
+    strategy_base,
+    strategy_lumina,
+)
+from library.sd3_train_utils import FlowMatchEulerDiscreteScheduler
+
+import library.train_util as train_util
+
+from library.utils import setup_logging, add_logging_arguments
+
+setup_logging()
+import logging
+
+logger = logging.getLogger(__name__)
+
+import library.config_util as config_util
+
+# import library.sdxl_train_util as sdxl_train_util
+from library.config_util import (
+    ConfigSanitizer,
+    BlueprintGenerator,
+)
+from library.custom_train_functions import apply_masked_loss, add_custom_train_arguments
+
+
+def train(args):
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, True)
+    # sdxl_train_util.verify_sdxl_training_args(args)
+    deepspeed_utils.prepare_deepspeed_args(args)
+    setup_logging(args, reset=True)
+
+    # temporary: backward compatibility for deprecated options. remove in the future
+    if not args.skip_cache_check:
+        args.skip_cache_check = args.skip_latents_validity_check
+
+    # assert (
+    #     not args.weighted_captions
+    # ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません"
+    if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs:
+        logger.warning(
+            "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"
+        )
+        args.cache_text_encoder_outputs = True
+
+    if args.cpu_offload_checkpointing and not args.gradient_checkpointing:
+        logger.warning(
+            "cpu_offload_checkpointing is enabled, so gradient_checkpointing is also enabled / cpu_offload_checkpointingが有効になっているため、gradient_checkpointingも有効になります"
+        )
+        args.gradient_checkpointing = True
+
+    # assert (
+    #     args.blocks_to_swap is None or args.blocks_to_swap == 0
+    # ) or not args.cpu_offload_checkpointing, "blocks_to_swap is not supported with cpu_offload_checkpointing / blocks_to_swapはcpu_offload_checkpointingと併用できません"
+
+    cache_latents = args.cache_latents
+    use_dreambooth_method = args.in_json is None
+
+    if args.seed is not None:
+        set_seed(args.seed)  # 乱数系列を初期化する
+
+    # prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization.
+    if args.cache_latents:
+        latents_caching_strategy = strategy_lumina.LuminaLatentsCachingStrategy(
+            args.cache_latents_to_disk, args.vae_batch_size, args.skip_cache_check
+        )
+        strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy)
+
+    # データセットを準備する
+    if args.dataset_class is None:
+        blueprint_generator = BlueprintGenerator(
+            ConfigSanitizer(True, True, args.masked_loss, True)
+        )
+        if args.dataset_config is not None:
+            logger.info(f"Load dataset config from {args.dataset_config}")
+            user_config = config_util.load_user_config(args.dataset_config)
+            ignored = ["train_data_dir", "in_json"]
+            if any(getattr(args, attr) is not None for attr in ignored):
+                logger.warning(
+                    "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
+                        ", ".join(ignored)
+                    )
+                )
+        else:
+            if use_dreambooth_method:
+                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)
+        train_dataset_group, val_dataset_group = (
+            config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
+        )
+    else:
+        train_dataset_group = train_util.load_arbitrary_dataset(args)
+        val_dataset_group = None
+
+    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)
+
+    train_dataset_group.verify_bucket_reso_steps(16)  # TODO これでいいか確認
+
+    if args.debug_dataset:
+        if args.cache_text_encoder_outputs:
+            strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(
+                strategy_lumina.LuminaTextEncoderOutputsCachingStrategy(
+                    args.cache_text_encoder_outputs_to_disk,
+                    args.text_encoder_batch_size,
+                    args.skip_cache_check,
+                    False,
+                )
+            )
+        strategy_base.TokenizeStrategy.set_strategy(
+            strategy_lumina.LuminaTokenizeStrategy()
+        )
+
+        train_dataset_group.set_current_strategies()
+        train_util.debug_dataset(train_dataset_group, True)
+        return
+    if len(train_dataset_group) == 0:
+        logger.error(
+            "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。"
+        )
+        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"
+
+    if args.cache_text_encoder_outputs:
+        assert (
+            train_dataset_group.is_text_encoder_output_cacheable()
+        ), "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"
+
+    # acceleratorを準備する
+    logger.info("prepare accelerator")
+    accelerator = train_util.prepare_accelerator(args)
+
+    # mixed precisionに対応した型を用意しておき適宜castする
+    weight_dtype, save_dtype = train_util.prepare_dtype(args)
+
+    # モデルを読み込む
+
+    # load VAE for caching latents
+    ae = None
+    if cache_latents:
+        ae = lumina_util.load_ae(
+            args.ae, weight_dtype, "cpu", args.disable_mmap_load_safetensors
+        )
+        ae.to(accelerator.device, dtype=weight_dtype)
+        ae.requires_grad_(False)
+        ae.eval()
+
+        train_dataset_group.new_cache_latents(ae, accelerator)
+
+        ae.to("cpu")  # if no sampling, vae can be deleted
+        clean_memory_on_device(accelerator.device)
+
+        accelerator.wait_for_everyone()
+
+    # prepare tokenize strategy
+    if args.gemma2_max_token_length is None:
+        gemma2_max_token_length = 256
+    else:
+        gemma2_max_token_length = args.gemma2_max_token_length
+
+    lumina_tokenize_strategy = strategy_lumina.LuminaTokenizeStrategy(
+        gemma2_max_token_length
+    )
+    strategy_base.TokenizeStrategy.set_strategy(lumina_tokenize_strategy)
+
+    # load gemma2 for caching text encoder outputs
+    gemma2 = lumina_util.load_gemma2(
+        args.gemma2, weight_dtype, "cpu", args.disable_mmap_load_safetensors
+    )
+    gemma2.eval()
+    gemma2.requires_grad_(False)
+
+    text_encoding_strategy = strategy_lumina.LuminaTextEncodingStrategy()
+    strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy)
+
+    # cache text encoder outputs
+    sample_prompts_te_outputs = None
+    if args.cache_text_encoder_outputs:
+        # Text Encodes are eval and no grad here
+        gemma2.to(accelerator.device)
+
+        text_encoder_caching_strategy = (
+            strategy_lumina.LuminaTextEncoderOutputsCachingStrategy(
+                args.cache_text_encoder_outputs_to_disk,
+                args.text_encoder_batch_size,
+                False,
+                False,
+            )
+        )
+        strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(
+            text_encoder_caching_strategy
+        )
+
+        with accelerator.autocast():
+            train_dataset_group.new_cache_text_encoder_outputs([gemma2], accelerator)
+
+        # cache sample prompt's embeddings to free text encoder's memory
+        if args.sample_prompts is not None:
+            logger.info(
+                f"cache Text Encoder outputs for sample prompt: {args.sample_prompts}"
+            )
+
+            text_encoding_strategy: strategy_lumina.LuminaTextEncodingStrategy = (
+                strategy_base.TextEncodingStrategy.get_strategy()
+            )
+
+            prompts = train_util.load_prompts(args.sample_prompts)
+            sample_prompts_te_outputs = {}  # key: prompt, value: text encoder outputs
+            with accelerator.autocast(), torch.no_grad():
+                for prompt_dict in prompts:
+                    for p in [
+                        prompt_dict.get("prompt", ""),
+                        prompt_dict.get("negative_prompt", ""),
+                    ]:
+                        if p not in sample_prompts_te_outputs:
+                            logger.info(f"cache Text Encoder outputs for prompt: {p}")
+                            tokens_and_masks = lumina_tokenize_strategy.tokenize(p)
+                            sample_prompts_te_outputs[p] = (
+                                text_encoding_strategy.encode_tokens(
+                                    lumina_tokenize_strategy,
+                                    [gemma2],
+                                    tokens_and_masks,
+                                )
+                            )
+
+        accelerator.wait_for_everyone()
+
+        # now we can delete Text Encoders to free memory
+        gemma2 = None
+        clean_memory_on_device(accelerator.device)
+
+    # load lumina
+    nextdit = lumina_util.load_lumina_model(
+        args.pretrained_model_name_or_path,
+        loading_dtype,
+        torch.device("cpu"),
+        disable_mmap=args.disable_mmap_load_safetensors,
+        use_flash_attn=args.use_flash_attn,
+    )
+
+    if args.gradient_checkpointing:
+        nextdit.enable_gradient_checkpointing(
+            cpu_offload=args.cpu_offload_checkpointing
+        )
+
+    nextdit.requires_grad_(True)
+
+    # block swap
+
+    # backward compatibility
+    # if args.blocks_to_swap is None:
+    #     blocks_to_swap = args.double_blocks_to_swap or 0
+    #     if args.single_blocks_to_swap is not None:
+    #         blocks_to_swap += args.single_blocks_to_swap // 2
+    #     if blocks_to_swap > 0:
+    #         logger.warning(
+    #             "double_blocks_to_swap and single_blocks_to_swap are deprecated. Use blocks_to_swap instead."
+    #             " / double_blocks_to_swapとsingle_blocks_to_swapは非推奨です。blocks_to_swapを使ってください。"
+    #         )
+    #         logger.info(
+    #             f"double_blocks_to_swap={args.double_blocks_to_swap} and single_blocks_to_swap={args.single_blocks_to_swap} are converted to blocks_to_swap={blocks_to_swap}."
+    #         )
+    #         args.blocks_to_swap = blocks_to_swap
+    #     del blocks_to_swap
+
+    # is_swapping_blocks = args.blocks_to_swap is not None and args.blocks_to_swap > 0
+    # if is_swapping_blocks:
+    #     # Swap blocks between CPU and GPU to reduce memory usage, in forward and backward passes.
+    #     # This idea is based on 2kpr's great work. Thank you!
+    #     logger.info(f"enable block swap: blocks_to_swap={args.blocks_to_swap}")
+    #     flux.enable_block_swap(args.blocks_to_swap, accelerator.device)
+
+    if not cache_latents:
+        # load VAE here if not cached
+        ae = lumina_util.load_ae(args.ae, weight_dtype, "cpu")
+        ae.requires_grad_(False)
+        ae.eval()
+        ae.to(accelerator.device, dtype=weight_dtype)
+
+    training_models = []
+    params_to_optimize = []
+    training_models.append(nextdit)
+    name_and_params = list(nextdit.named_parameters())
+    # single param group for now
+    params_to_optimize.append(
+        {"params": [p for _, p in name_and_params], "lr": args.learning_rate}
+    )
+    param_names = [[n for n, _ in name_and_params]]
+
+    # calculate number of trainable parameters
+    n_params = 0
+    for group in params_to_optimize:
+        for p in group["params"]:
+            n_params += p.numel()
+
+    accelerator.print(f"number of trainable parameters: {n_params}")
+
+    # 学習に必要なクラスを準備する
+    accelerator.print("prepare optimizer, data loader etc.")
+
+    if args.blockwise_fused_optimizers:
+        # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
+        # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters.
+        # This balances memory usage and management complexity.
+
+        # split params into groups. currently different learning rates are not supported
+        grouped_params = []
+        param_group = {}
+        for group in params_to_optimize:
+            named_parameters = list(nextdit.named_parameters())
+            assert len(named_parameters) == len(
+                group["params"]
+            ), "number of parameters does not match"
+            for p, np in zip(group["params"], named_parameters):
+                # determine target layer and block index for each parameter
+                block_type = "other"  # double, single or other
+                if np[0].startswith("double_blocks"):
+                    block_index = int(np[0].split(".")[1])
+                    block_type = "double"
+                elif np[0].startswith("single_blocks"):
+                    block_index = int(np[0].split(".")[1])
+                    block_type = "single"
+                else:
+                    block_index = -1
+
+                param_group_key = (block_type, block_index)
+                if param_group_key not in param_group:
+                    param_group[param_group_key] = []
+                param_group[param_group_key].append(p)
+
+        block_types_and_indices = []
+        for param_group_key, param_group in param_group.items():
+            block_types_and_indices.append(param_group_key)
+            grouped_params.append({"params": param_group, "lr": args.learning_rate})
+
+            num_params = 0
+            for p in param_group:
+                num_params += p.numel()
+            accelerator.print(f"block {param_group_key}: {num_params} parameters")
+
+        # prepare optimizers for each group
+        optimizers = []
+        for group in grouped_params:
+            _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group])
+            optimizers.append(optimizer)
+        optimizer = optimizers[0]  # avoid error in the following code
+
+        logger.info(
+            f"using {len(optimizers)} optimizers for blockwise fused optimizers"
+        )
+
+        if train_util.is_schedulefree_optimizer(optimizers[0], args):
+            raise ValueError(
+                "Schedule-free optimizer is not supported with blockwise fused optimizers"
+            )
+        optimizer_train_fn = lambda: None  # dummy function
+        optimizer_eval_fn = lambda: None  # dummy function
+    else:
+        _, _, optimizer = train_util.get_optimizer(
+            args, trainable_params=params_to_optimize
+        )
+        optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(
+            optimizer, args
+        )
+
+    # prepare dataloader
+    # strategies are set here because they cannot be referenced in another process. Copy them with the dataset
+    # some strategies can be None
+    train_dataset_group.set_current_strategies()
+
+    # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
+    n_workers = min(
+        args.max_data_loader_n_workers, os.cpu_count()
+    )  # cpu_count or max_data_loader_n_workers
+    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
+    if args.blockwise_fused_optimizers:
+        # prepare lr schedulers for each optimizer
+        lr_schedulers = [
+            train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
+            for optimizer in optimizers
+        ]
+        lr_scheduler = lr_schedulers[0]  # avoid error in the following code
+    else:
+        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.")
+        nextdit.to(weight_dtype)
+        if gemma2 is not None:
+            gemma2.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.")
+        nextdit.to(weight_dtype)
+        if gemma2 is not None:
+            gemma2.to(weight_dtype)
+
+    # if we don't cache text encoder outputs, move them to device
+    if not args.cache_text_encoder_outputs:
+        gemma2.to(accelerator.device)
+
+    clean_memory_on_device(accelerator.device)
+
+    if args.deepspeed:
+        ds_model = deepspeed_utils.prepare_deepspeed_model(args, nextdit=nextdit)
+        # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007
+        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            ds_model, optimizer, train_dataloader, lr_scheduler
+        )
+        training_models = [ds_model]
+
+    else:
+        # accelerator does some magic
+        # if we doesn't swap blocks, we can move the model to device
+        nextdit = accelerator.prepare(
+            nextdit, device_placement=[not is_swapping_blocks]
+        )
+        if is_swapping_blocks:
+            accelerator.unwrap_model(nextdit).move_to_device_except_swap_blocks(
+                accelerator.device
+            )  # reduce peak memory usage
+        optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            optimizer, train_dataloader, lr_scheduler
+        )
+
+    # 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:
+        # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
+        # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+
+    # resumeする
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
+
+    if args.fused_backward_pass:
+        # use fused optimizer for backward pass: other optimizers will be supported in the future
+        import library.adafactor_fused
+
+        library.adafactor_fused.patch_adafactor_fused(optimizer)
+
+        for param_group, param_name_group in zip(optimizer.param_groups, param_names):
+            for parameter, param_name in zip(param_group["params"], param_name_group):
+                if parameter.requires_grad:
+
+                    def create_grad_hook(p_name, p_group):
+                        def grad_hook(tensor: torch.Tensor):
+                            if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                                accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
+                            optimizer.step_param(tensor, p_group)
+                            tensor.grad = None
+
+                        return grad_hook
+
+                    parameter.register_post_accumulate_grad_hook(
+                        create_grad_hook(param_name, param_group)
+                    )
+
+    elif args.blockwise_fused_optimizers:
+        # 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 grad_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(grad_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,
+        )
+
+    if is_swapping_blocks:
+        accelerator.unwrap_model(nextdit).prepare_block_swap_before_forward()
+
+    # For --sample_at_first
+    optimizer_eval_fn()
+    lumina_train_util.sample_images(
+        accelerator,
+        args,
+        0,
+        global_step,
+        nextdit,
+        ae,
+        gemma2,
+        sample_prompts_te_outputs,
+    )
+    optimizer_train_fn()
+    if len(accelerator.trackers) > 0:
+        # log empty object to commit the sample images to wandb
+        accelerator.log({}, step=0)
+
+    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.blockwise_fused_optimizers:
+                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"].to(ae.dtype)).to(
+                            accelerator.device, dtype=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)
+
+                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(
+                            lumina_tokenize_strategy,
+                            [gemma2],
+                            input_ids,
+                        )
+                        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)
+
+                # get noisy model input and timesteps
+                noisy_model_input, timesteps, sigmas = (
+                    lumina_train_util.get_noisy_model_input_and_timesteps(
+                        args,
+                        noise_scheduler_copy,
+                        latents,
+                        noise,
+                        accelerator.device,
+                        weight_dtype,
+                    )
+                )
+                # call model
+                gemma2_hidden_states, input_ids, gemma2_attn_mask = 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 = nextdit(
+                        x=img,  # image latents (B, C, H, W)
+                        t=timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
+                        cap_feats=gemma2_hidden_states,  # Gemma2的hidden states作为caption features
+                        cap_mask=gemma2_attn_mask.to(
+                            dtype=torch.int32
+                        ),  # Gemma2的attention mask
+                    )
+                # apply model prediction type
+                model_pred, weighting = lumina_train_util.apply_model_prediction_type(
+                    args, model_pred, noisy_model_input, sigmas
+                )
+
+                # flow matching loss: this is different from SD3
+                target = noise - latents
+
+                # calculate loss
+                huber_c = train_util.get_huber_threshold_if_needed(
+                    args, timesteps, noise_scheduler
+                )
+                loss = train_util.conditional_loss(
+                    model_pred.float(), target.float(), args.loss_type, "none", huber_c
+                )
+                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.blockwise_fused_optimizers):
+                    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.blockwise_fused_optimizers:
+                        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
+
+                optimizer_eval_fn()
+                lumina_train_util.sample_images(
+                    accelerator,
+                    args,
+                    None,
+                    global_step,
+                    nextdit,
+                    ae,
+                    gemma2,
+                    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:
+                        lumina_train_util.save_lumina_model_on_epoch_end_or_stepwise(
+                            args,
+                            False,
+                            accelerator,
+                            save_dtype,
+                            epoch,
+                            num_train_epochs,
+                            global_step,
+                            accelerator.unwrap_model(nextdit),
+                        )
+                optimizer_train_fn()
+
+            current_loss = loss.detach().item()  # 平均なのでbatch sizeは関係ないはず
+            if len(accelerator.trackers) > 0:
+                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 len(accelerator.trackers) > 0:
+            logs = {"loss/epoch": loss_recorder.moving_average}
+            accelerator.log(logs, step=epoch + 1)
+
+        accelerator.wait_for_everyone()
+
+        optimizer_eval_fn()
+        if args.save_every_n_epochs is not None:
+            if accelerator.is_main_process:
+                lumina_train_util.save_lumina_model_on_epoch_end_or_stepwise(
+                    args,
+                    True,
+                    accelerator,
+                    save_dtype,
+                    epoch,
+                    num_train_epochs,
+                    global_step,
+                    accelerator.unwrap_model(nextdit),
+                )
+
+        lumina_train_util.sample_images(
+            accelerator,
+            args,
+            epoch + 1,
+            global_step,
+            nextdit,
+            ae,
+            gemma2,
+            sample_prompts_te_outputs,
+        )
+        optimizer_train_fn()
+
+    is_main_process = accelerator.is_main_process
+    # if is_main_process:
+    nextdit = accelerator.unwrap_model(nextdit)
+
+    accelerator.end_training()
+    optimizer_eval_fn()
+
+    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:
+        lumina_train_util.save_lumina_model_on_train_end(
+            args, save_dtype, epoch, global_step, nextdit
+        )
+        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
+    train_util.add_dit_training_arguments(parser)
+    lumina_train_util.add_lumina_train_arguments(parser)
+
+    parser.add_argument(
+        "--mem_eff_save",
+        action="store_true",
+        help="[EXPERIMENTAL] use memory efficient custom model saving method / メモリ効率の良い独自のモデル保存方法を使う",
+    )
+
+    parser.add_argument(
+        "--fused_optimizer_groups",
+        type=int,
+        default=None,
+        help="**this option is not working** will be removed in the future / このオプションは動作しません。将来削除されます",
+    )
+    parser.add_argument(
+        "--blockwise_fused_optimizers",
+        action="store_true",
+        help="enable blockwise optimizers for fused backward pass and optimizer step / fused backward passとoptimizer step のためブロック単位のoptimizerを有効にする",
+    )
+    parser.add_argument(
+        "--skip_latents_validity_check",
+        action="store_true",
+        help="[Deprecated] use 'skip_cache_check' instead / 代わりに 'skip_cache_check' を使用してください",
+    )
+    parser.add_argument(
+        "--cpu_offload_checkpointing",
+        action="store_true",
+        help="[EXPERIMENTAL] enable offloading of tensors to CPU during checkpointing / チェックポイント時にテンソルをCPUにオフロードする",
+    )
+    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)

lumina_train_network.py

@@ -15,7 +15,6 @@ from accelerate import Accelerator
 import train_network
 from library import (
     lumina_models,
-    flux_train_utils,
     lumina_util,
     lumina_train_util,
     sd3_train_utils,
@@ -250,36 +249,10 @@ class LuminaNetworkTrainer(train_network.NetworkTrainer):
     ):
         assert isinstance(noise_scheduler, sd3_train_utils.FlowMatchEulerDiscreteScheduler)
         noise = torch.randn_like(latents)
-        bsz = latents.shape[0]
-
-        # Sample a random timestep for each image
-        # for weighting schemes where we sample timesteps non-uniformly
-        u = lumina_train_util.compute_density_for_timestep_sampling(
-            weighting_scheme=args.weighting_scheme,
-            batch_size=bsz,
-            logit_mean=args.logit_mean,
-            logit_std=args.logit_std,
-            mode_scale=args.mode_scale,
+        # get noisy model input and timesteps
+        noisy_model_input, timesteps, sigmas = lumina_train_util.get_noisy_model_input_and_timesteps(
+            args, noise_scheduler, latents, noise, accelerator.device, weight_dtype
         )
-        indices = (u * noise_scheduler.config.num_train_timesteps).long()
-        timesteps = noise_scheduler.timesteps[indices].to(device=latents.device)
-
-        def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
-            sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
-            schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
-            timesteps = timesteps.to(accelerator.device)
-            step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
-
-            sigma = sigmas[step_indices].flatten()
-            while len(sigma.shape) < n_dim:
-                sigma = sigma.unsqueeze(-1)
-            return sigma
-
-        # Add noise according to flow matching.
-        # zt = (1 - texp) * x + texp * z1
-        # Lumina2 reverses the lerp i.e., sigma of 1.0 should mean `latents`
-        sigmas = get_sigmas(timesteps, n_dim=latents.ndim, dtype=latents.dtype)
-        noisy_model_input = (1.0 - sigmas) * noise + sigmas * latents
 
         # ensure the hidden state will require grad
         if args.gradient_checkpointing:
@@ -310,7 +283,7 @@ class LuminaNetworkTrainer(train_network.NetworkTrainer):
         )
 
         # apply model prediction type
-        model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas)
+        model_pred, weighting = lumina_train_util.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas)
 
         # flow matching loss
         target = latents - noise
@@ -336,7 +309,7 @@ class LuminaNetworkTrainer(train_network.NetworkTrainer):
                 # model_pred_prior = lumina_util.unpack_latents(
                 #     model_pred_prior, packed_latent_height, packed_latent_width
                 # )
-                model_pred_prior, _ = flux_train_utils.apply_model_prediction_type(
+                model_pred_prior, _ = lumina_train_util.apply_model_prediction_type(
                     args,
                     model_pred_prior,
                     noisy_model_input[diff_output_pr_indices],