update scripts

scripts/dev/README.md

@@ -14,6 +14,41 @@ The command to install PyTorch is as follows:
 
 ### Recent Updates
 
+Dec 15, 2024:
+
+- RAdamScheduleFree optimizer is supported. PR [#1830](https://github.com/kohya-ss/sd-scripts/pull/1830) Thanks to nhamanasu!
+  - Update to `schedulefree==1.4` is required. Please update individually or with `pip install --use-pep517 --upgrade -r requirements.txt`.
+  - Available with `--optimizer_type=RAdamScheduleFree`. No need to specify warm up steps as well as learning rate scheduler.
+
+Dec 7, 2024:
+
+- The option to specify the model name during ControlNet training was different in each script. It has been unified. Please specify `--controlnet_model_name_or_path`. PR [#1821](https://github.com/kohya-ss/sd-scripts/pull/1821) Thanks to sdbds!
+<!-- 
+Also, the ControlNet training script for SD has been changed from `train_controlnet.py` to `train_control_net.py`.
+  - `train_controlnet.py` is still available, but it will be removed in the future.
+-->
+
+- Fixed an issue where the saved model would be corrupted (pos_embed would not be saved) when `--enable_scaled_pos_embed` was specified in `sd3_train.py`.
+
+Dec 3, 2024:
+
+-`--blocks_to_swap` now works in FLUX.1 ControlNet training. Sample commands for 24GB VRAM and 16GB VRAM are added [here](#flux1-controlnet-training).
+
+Dec 2, 2024:
+
+- FLUX.1 ControlNet training is supported. PR [#1813](https://github.com/kohya-ss/sd-scripts/pull/1813). Thanks to minux302!  See PR and [here](#flux1-controlnet-training) for details.
+  - Not fully tested. Feedback is welcome.
+  - 80GB VRAM is required for 1024x1024 resolution, and 48GB VRAM is required for 512x512 resolution.
+  - Currently, it only works in Linux environment (or Windows WSL2) because DeepSpeed is required.
+  - Multi-GPU training is not tested.
+
+Dec 1, 2024:
+
+- Pseudo Huber loss is now available for FLUX.1 and SD3.5 training. See PR [#1808](https://github.com/kohya-ss/sd-scripts/pull/1808)  for details. Thanks to recris!
+  - Specify `--loss_type huber` or `--loss_type smooth_l1` to use it. `--huber_c` and `--huber_scale` are also available.
+
+- [Prodigy + ScheduleFree](https://github.com/LoganBooker/prodigy-plus-schedule-free) is supported. See PR [#1811](https://github.com/kohya-ss/sd-scripts/pull/1811) for details. Thanks to rockerBOO!
+
 Nov 14, 2024:
 
 - Improved the implementation of block swap and made it available for both FLUX.1 and SD3 LoRA training. See [FLUX.1 LoRA training](#flux1-lora-training) etc. for how to use the new options. Training is possible with about 8-10GB of VRAM.
@@ -28,6 +63,7 @@ Nov 14, 2024:
   - [Key Features for FLUX.1 LoRA training](#key-features-for-flux1-lora-training)
   - [Specify rank for each layer in FLUX.1](#specify-rank-for-each-layer-in-flux1)
   - [Specify blocks to train in FLUX.1 LoRA training](#specify-blocks-to-train-in-flux1-lora-training)
+- [FLUX.1 ControlNet training](#flux1-controlnet-training)
 - [FLUX.1 OFT training](#flux1-oft-training)
 - [Inference for FLUX.1 with LoRA model](#inference-for-flux1-with-lora-model)
 - [FLUX.1 fine-tuning](#flux1-fine-tuning)
@@ -245,6 +281,30 @@ example:
 
 If you specify one of `train_double_block_indices` or `train_single_block_indices`, the other will be trained as usual. 
 
+### FLUX.1 ControlNet training
+We have added a new training script for ControlNet training. The script is flux_train_control_net.py. See --help for options.
+
+Sample command is below. It will work with 80GB VRAM GPUs.
+```
+accelerate launch --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_train_control_net.py
+--pretrained_model_name_or_path flux1-dev.safetensors --clip_l clip_l.safetensors --t5xxl t5xxl_fp16.safetensors
+--ae ae.safetensors --save_model_as safetensors --sdpa --persistent_data_loader_workers
+--max_data_loader_n_workers 1 --seed 42 --gradient_checkpointing --mixed_precision bf16
+--optimizer_type adamw8bit --learning_rate 2e-5 
+--highvram --max_train_epochs 1 --save_every_n_steps 1000 --dataset_config dataset.toml
+--output_dir /path/to/output/dir --output_name flux-cn
+--timestep_sampling shift --discrete_flow_shift 3.1582 --model_prediction_type raw --guidance_scale 1.0 --deepspeed
+```
+
+For 24GB VRAM GPUs, you can train with 16 blocks swapped and caching latents and text encoder outputs with the batch size of 1. Remove `--deepspeed` . Sample command is below. Not fully tested.
+```
+ --blocks_to_swap 16 --cache_latents_to_disk --cache_text_encoder_outputs_to_disk 
+```
+
+The training can be done with 16GB VRAM GPUs with around 30 blocks swapped. 
+
+`--gradient_accumulation_steps` is also available. The default value is 1 (no accumulation), but according to the original PR, 8 is used.
+
 ### FLUX.1 OFT training
 
 You can train OFT with almost the same options as LoRA, such as `--timestamp_sampling`. The following points are different.

scripts/dev/fine_tune.py

@@ -380,9 +380,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
-                    args, noise_scheduler, latents
-                )
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -394,11 +392,10 @@ def train(args):
                 else:
                     target = noise
 
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
                 if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred or args.debiased_estimation_loss:
                     # do not mean over batch dimension for snr weight or scale v-pred loss
-                    loss = train_util.conditional_loss(
-                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                    )
+                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                     loss = loss.mean([1, 2, 3])
 
                     if args.min_snr_gamma:
@@ -410,9 +407,7 @@ def train(args):
 
                     loss = loss.mean()  # mean over batch dimension
                 else:
-                    loss = train_util.conditional_loss(
-                        noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c
-                    )
+                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "mean", huber_c)
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients and args.max_grad_norm != 0.0:

scripts/dev/flux_train.py

@@ -666,9 +666,8 @@ def train(args):
                 target = noise - latents
 
                 # calculate loss
-                loss = train_util.conditional_loss(
-                    model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None
-                )
+                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):

scripts/dev/flux_train_control_net.py

@@ -0,0 +1,877 @@
+# 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
+import time
+from concurrent.futures import ThreadPoolExecutor
+from multiprocessing import Value
+from typing import List, Optional, Tuple, Union
+
+import toml
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+
+from library import utils
+from library.device_utils import clean_memory_on_device, init_ipex
+
+init_ipex()
+
+from accelerate.utils import set_seed
+
+import library.train_util as train_util
+from library import (
+    deepspeed_utils,
+    flux_train_utils,
+    flux_utils,
+    strategy_base,
+    strategy_flux,
+)
+from library.sd3_train_utils import FlowMatchEulerDiscreteScheduler
+from library.utils import add_logging_arguments, setup_logging
+
+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 (
+    BlueprintGenerator,
+    ConfigSanitizer,
+)
+from library.custom_train_functions import add_custom_train_arguments, apply_masked_loss
+
+
+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
+
+    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_flux.FluxLatentsCachingStrategy(
+            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(False, False, True, 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", "conditioning_data_dir"]
+            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:
+            user_config = {
+                "datasets": [
+                    {
+                        "subsets": config_util.generate_controlnet_subsets_config_by_subdirs(
+                            args.train_data_dir, args.conditioning_data_dir, args.caption_extension
+                        )
+                    }
+                ]
+            }
+
+        blueprint = blueprint_generator.generate(user_config, args)
+        train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
+    else:
+        train_dataset_group = train_util.load_arbitrary_dataset(args)
+
+    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
+
+    _, is_schnell, _, _ = flux_utils.analyze_checkpoint_state(args.pretrained_model_name_or_path)
+    if args.debug_dataset:
+        if args.cache_text_encoder_outputs:
+            strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(
+                strategy_flux.FluxTextEncoderOutputsCachingStrategy(
+                    args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, args.skip_cache_check, False
+                )
+            )
+        t5xxl_max_token_length = (
+            args.t5xxl_max_token_length if args.t5xxl_max_token_length is not None else (256 if is_schnell else 512)
+        )
+        strategy_base.TokenizeStrategy.set_strategy(strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length))
+
+        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 = flux_utils.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.t5xxl_max_token_length is None:
+        if is_schnell:
+            t5xxl_max_token_length = 256
+        else:
+            t5xxl_max_token_length = 512
+    else:
+        t5xxl_max_token_length = args.t5xxl_max_token_length
+
+    flux_tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length)
+    strategy_base.TokenizeStrategy.set_strategy(flux_tokenize_strategy)
+
+    # load clip_l, t5xxl for caching text encoder outputs
+    clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu", args.disable_mmap_load_safetensors)
+    t5xxl = flux_utils.load_t5xxl(args.t5xxl, weight_dtype, "cpu", args.disable_mmap_load_safetensors)
+    clip_l.eval()
+    t5xxl.eval()
+    clip_l.requires_grad_(False)
+    t5xxl.requires_grad_(False)
+
+    text_encoding_strategy = strategy_flux.FluxTextEncodingStrategy(args.apply_t5_attn_mask)
+    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
+        clip_l.to(accelerator.device)
+        t5xxl.to(accelerator.device)
+
+        text_encoder_caching_strategy = strategy_flux.FluxTextEncoderOutputsCachingStrategy(
+            args.cache_text_encoder_outputs_to_disk, args.text_encoder_batch_size, False, False, args.apply_t5_attn_mask
+        )
+        strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_caching_strategy)
+
+        with accelerator.autocast():
+            train_dataset_group.new_cache_text_encoder_outputs([clip_l, t5xxl], 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_flux.FluxTextEncodingStrategy = 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 = flux_tokenize_strategy.tokenize(p)
+                            sample_prompts_te_outputs[p] = text_encoding_strategy.encode_tokens(
+                                flux_tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask
+                            )
+
+        accelerator.wait_for_everyone()
+
+        # now we can delete Text Encoders to free memory
+        clip_l = None
+        t5xxl = None
+        clean_memory_on_device(accelerator.device)
+
+    # load FLUX
+    is_schnell, flux = flux_utils.load_flow_model(
+        args.pretrained_model_name_or_path, weight_dtype, "cpu", args.disable_mmap_load_safetensors
+    )
+    flux.requires_grad_(False)
+
+    # load controlnet
+    controlnet_dtype = torch.float32 if args.deepspeed else weight_dtype
+    controlnet = flux_utils.load_controlnet(
+        args.controlnet_model_name_or_path, is_schnell, controlnet_dtype, accelerator.device, args.disable_mmap_load_safetensors
+    )
+    controlnet.train()
+
+    if args.gradient_checkpointing:
+        if not args.deepspeed:
+            flux.enable_gradient_checkpointing(cpu_offload=args.cpu_offload_checkpointing)
+        controlnet.enable_gradient_checkpointing(cpu_offload=args.cpu_offload_checkpointing)
+
+    # 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)
+        flux.move_to_device_except_swap_blocks(accelerator.device)  # reduce peak memory usage
+        # ControlNet only has two blocks, so we can keep it on GPU
+        # controlnet.enable_block_swap(args.blocks_to_swap, accelerator.device)
+    else:
+        flux.to(accelerator.device)
+
+    if not cache_latents:
+        # load VAE here if not cached
+        ae = flux_utils.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(controlnet)
+    name_and_params = list(controlnet.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(controlnet.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.")
+        flux.to(weight_dtype)
+        controlnet.to(weight_dtype)
+        if clip_l is not None:
+            clip_l.to(weight_dtype)
+            t5xxl.to(weight_dtype)  # TODO check works with fp16 or not
+    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.")
+        flux.to(weight_dtype)
+        controlnet.to(weight_dtype)
+        if clip_l is not None:
+            clip_l.to(weight_dtype)
+            t5xxl.to(weight_dtype)
+
+    # if we don't cache text encoder outputs, move them to device
+    if not args.cache_text_encoder_outputs:
+        clip_l.to(accelerator.device)
+        t5xxl.to(accelerator.device)
+
+    clean_memory_on_device(accelerator.device)
+
+    if args.deepspeed:
+        ds_model = deepspeed_utils.prepare_deepspeed_model(args, mmdit=controlnet)
+        # 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
+        controlnet = accelerator.prepare(controlnet)  # , device_placement=[not is_swapping_blocks])
+        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:
+        flux.prepare_block_swap_before_forward()
+
+    # For --sample_at_first
+    optimizer_eval_fn()
+    flux_train_utils.sample_images(
+        accelerator, args, 0, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs, controlnet=controlnet
+    )
+    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(
+                            flux_tokenize_strategy, [clip_l, t5xxl], input_ids, args.apply_t5_attn_mask
+                        )
+                text_encoder_conds = [c.to(weight_dtype) for c in text_encoder_conds]
+
+                # TODO support some features for noise implemented in get_noise_noisy_latents_and_timesteps
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+
+                # get noisy model input and timesteps
+                noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps(
+                    args, noise_scheduler_copy, latents, noise, accelerator.device, weight_dtype
+                )
+
+                # pack latents and get img_ids
+                packed_noisy_model_input = flux_utils.pack_latents(noisy_model_input)  # b, c, h*2, w*2 -> b, h*w, c*4
+                packed_latent_height, packed_latent_width = noisy_model_input.shape[2] // 2, noisy_model_input.shape[3] // 2
+                img_ids = (
+                    flux_utils.prepare_img_ids(bsz, packed_latent_height, packed_latent_width)
+                    .to(device=accelerator.device)
+                    .to(weight_dtype)
+                )
+
+                # get guidance: ensure args.guidance_scale is float
+                guidance_vec = torch.full((bsz,), float(args.guidance_scale), device=accelerator.device, dtype=weight_dtype)
+
+                # call model
+                l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds
+                if not args.apply_t5_attn_mask:
+                    t5_attn_mask = None
+
+                with accelerator.autocast():
+                    block_samples, block_single_samples = controlnet(
+                        img=packed_noisy_model_input,
+                        img_ids=img_ids,
+                        controlnet_cond=batch["conditioning_images"].to(accelerator.device).to(weight_dtype),
+                        txt=t5_out,
+                        txt_ids=txt_ids,
+                        y=l_pooled,
+                        timesteps=timesteps / 1000,
+                        guidance=guidance_vec,
+                        txt_attention_mask=t5_attn_mask,
+                    )
+                    # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
+                    model_pred = flux(
+                        img=packed_noisy_model_input,
+                        img_ids=img_ids,
+                        txt=t5_out,
+                        txt_ids=txt_ids,
+                        y=l_pooled,
+                        block_controlnet_hidden_states=block_samples,
+                        block_controlnet_single_hidden_states=block_single_samples,
+                        timesteps=timesteps / 1000,
+                        guidance=guidance_vec,
+                        txt_attention_mask=t5_attn_mask,
+                    )
+
+                # unpack latents
+                model_pred = flux_utils.unpack_latents(model_pred, packed_latent_height, packed_latent_width)
+
+                # apply model prediction type
+                model_pred, weighting = flux_train_utils.apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas)
+
+                # flow matching loss: this is different from SD3
+                target = noise - latents
+
+                # calculate loss
+                loss = train_util.conditional_loss(
+                    model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None
+                )
+                if weighting is not None:
+                    loss = loss * weighting
+                if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
+                    loss = apply_masked_loss(loss, batch)
+                loss = loss.mean([1, 2, 3])
+
+                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
+                loss = loss * loss_weights
+                loss = loss.mean()
+
+                # backward
+                accelerator.backward(loss)
+
+                if not (args.fused_backward_pass or args.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()
+                flux_train_utils.sample_images(
+                    accelerator,
+                    args,
+                    None,
+                    global_step,
+                    flux,
+                    ae,
+                    [clip_l, t5xxl],
+                    sample_prompts_te_outputs,
+                    controlnet=controlnet,
+                )
+
+                # 指定ステップごとにモデルを保存
+                if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
+                    accelerator.wait_for_everyone()
+                    if accelerator.is_main_process:
+                        flux_train_utils.save_flux_model_on_epoch_end_or_stepwise(
+                            args,
+                            False,
+                            accelerator,
+                            save_dtype,
+                            epoch,
+                            num_train_epochs,
+                            global_step,
+                            accelerator.unwrap_model(controlnet),
+                        )
+                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:
+                flux_train_utils.save_flux_model_on_epoch_end_or_stepwise(
+                    args,
+                    True,
+                    accelerator,
+                    save_dtype,
+                    epoch,
+                    num_train_epochs,
+                    global_step,
+                    accelerator.unwrap_model(controlnet),
+                )
+
+        flux_train_utils.sample_images(
+            accelerator, args, epoch + 1, global_step, flux, ae, [clip_l, t5xxl], sample_prompts_te_outputs, controlnet=controlnet
+        )
+        optimizer_train_fn()
+
+    is_main_process = accelerator.is_main_process
+    # if is_main_process:
+    controlnet = accelerator.unwrap_model(controlnet)
+
+    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:
+        flux_train_utils.save_flux_model_on_train_end(args, save_dtype, epoch, global_step, controlnet)
+        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, False, 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)
+    flux_train_utils.add_flux_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(
+        "--double_blocks_to_swap",
+        type=int,
+        default=None,
+        help="[Deprecated] use 'blocks_to_swap' instead / 代わりに 'blocks_to_swap' を使用してください",
+    )
+    parser.add_argument(
+        "--single_blocks_to_swap",
+        type=int,
+        default=None,
+        help="[Deprecated] use 'blocks_to_swap' instead / 代わりに 'blocks_to_swap' 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)

scripts/dev/flux_train_network.py

@@ -6,12 +6,21 @@ from typing import Any, Optional
 
 import torch
 from accelerate import Accelerator
-from library.device_utils import init_ipex, clean_memory_on_device
+
+from library.device_utils import clean_memory_on_device, init_ipex
 
 init_ipex()
 
-from library import flux_models, flux_train_utils, flux_utils, sd3_train_utils, strategy_base, strategy_flux, train_util
 import train_network
+from library import (
+    flux_models,
+    flux_train_utils,
+    flux_utils,
+    sd3_train_utils,
+    strategy_base,
+    strategy_flux,
+    train_util,
+)
 from library.utils import setup_logging
 
 setup_logging()
@@ -468,7 +477,7 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
                 )
                 target[diff_output_pr_indices] = model_pred_prior.to(target.dtype)
 
-        return model_pred, target, timesteps, None, weighting
+        return model_pred, target, timesteps, weighting
 
     def post_process_loss(self, loss, args, timesteps, noise_scheduler):
         return loss

scripts/dev/library/flux_models.py

@@ -2,15 +2,15 @@
 # license: Apache-2.0 License
 
 
-from concurrent.futures import Future, ThreadPoolExecutor
-from dataclasses import dataclass
 import math
 import os
 import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
 from typing import Dict, List, Optional, Union
 
 from library import utils
-from library.device_utils import init_ipex, clean_memory_on_device
+from library.device_utils import clean_memory_on_device, init_ipex
 
 init_ipex()
 
@@ -18,6 +18,7 @@ import torch
 from einops import rearrange
 from torch import Tensor, nn
 from torch.utils.checkpoint import checkpoint
+
 from library import custom_offloading_utils
 
 # USE_REENTRANT = True
@@ -1013,6 +1014,8 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
+        block_controlnet_hidden_states=None,
+        block_controlnet_single_hidden_states=None,
         guidance: Tensor | None = None,
         txt_attention_mask: Tensor | None = None,
     ) -> Tensor:
@@ -1031,18 +1034,29 @@ class Flux(nn.Module):
 
         ids = torch.cat((txt_ids, img_ids), dim=1)
         pe = self.pe_embedder(ids)
+        if block_controlnet_hidden_states is not None:
+            controlnet_depth = len(block_controlnet_hidden_states)
+        if block_controlnet_single_hidden_states is not None:
+            controlnet_single_depth = len(block_controlnet_single_hidden_states)
 
         if not self.blocks_to_swap:
-            for block in self.double_blocks:
+            for block_idx, block in enumerate(self.double_blocks):
                 img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                if block_controlnet_hidden_states is not None and controlnet_depth > 0:
+                    img = img + block_controlnet_hidden_states[block_idx % controlnet_depth]
+
             img = torch.cat((txt, img), 1)
-            for block in self.single_blocks:
+            for block_idx, block in enumerate(self.single_blocks):
                 img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                if block_controlnet_single_hidden_states is not None and controlnet_single_depth > 0:
+                    img = img + block_controlnet_single_hidden_states[block_idx % controlnet_single_depth]
         else:
             for block_idx, block in enumerate(self.double_blocks):
                 self.offloader_double.wait_for_block(block_idx)
 
                 img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                if block_controlnet_hidden_states is not None and controlnet_depth > 0:
+                    img = img + block_controlnet_hidden_states[block_idx % controlnet_depth]
 
                 self.offloader_double.submit_move_blocks(self.double_blocks, block_idx)
 
@@ -1052,6 +1066,8 @@ class Flux(nn.Module):
                 self.offloader_single.wait_for_block(block_idx)
 
                 img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                if block_controlnet_single_hidden_states is not None and controlnet_single_depth > 0:
+                    img = img + block_controlnet_single_hidden_states[block_idx % controlnet_single_depth]
 
                 self.offloader_single.submit_move_blocks(self.single_blocks, block_idx)
 
@@ -1066,6 +1082,246 @@ class Flux(nn.Module):
         return img
 
 
+def zero_module(module):
+    for p in module.parameters():
+        nn.init.zeros_(p)
+    return module
+
+
+class ControlNetFlux(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, params: FluxParams, controlnet_depth=2, controlnet_single_depth=0):
+        super().__init__()
+
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
+        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(controlnet_depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
+                for _ in range(controlnet_single_depth)
+            ]
+        )
+
+        self.gradient_checkpointing = False
+        self.cpu_offload_checkpointing = False
+        self.blocks_to_swap = None
+
+        self.offloader_double = None
+        self.offloader_single = None
+        self.num_double_blocks = len(self.double_blocks)
+        self.num_single_blocks = len(self.single_blocks)
+
+        # add ControlNet blocks
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(controlnet_depth):
+            controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = zero_module(controlnet_block)
+            self.controlnet_blocks.append(controlnet_block)
+        self.controlnet_blocks_for_single = nn.ModuleList([])
+        for _ in range(controlnet_single_depth):
+            controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = zero_module(controlnet_block)
+            self.controlnet_blocks_for_single.append(controlnet_block)
+        self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.gradient_checkpointing = False
+        self.input_hint_block = nn.Sequential(
+            nn.Conv2d(3, 16, 3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, 3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, 3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, 3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            nn.SiLU(),
+            zero_module(nn.Conv2d(16, 16, 3, padding=1))
+        )
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def enable_gradient_checkpointing(self, cpu_offload: bool = False):
+        self.gradient_checkpointing = True
+        self.cpu_offload_checkpointing = cpu_offload
+
+        self.time_in.enable_gradient_checkpointing()
+        self.vector_in.enable_gradient_checkpointing()
+        if self.guidance_in.__class__ != nn.Identity:
+            self.guidance_in.enable_gradient_checkpointing()
+
+        for block in self.double_blocks + self.single_blocks:
+            block.enable_gradient_checkpointing(cpu_offload=cpu_offload)
+
+        print(f"FLUX: Gradient checkpointing enabled. CPU offload: {cpu_offload}")
+
+    def disable_gradient_checkpointing(self):
+        self.gradient_checkpointing = False
+        self.cpu_offload_checkpointing = False
+
+        self.time_in.disable_gradient_checkpointing()
+        self.vector_in.disable_gradient_checkpointing()
+        if self.guidance_in.__class__ != nn.Identity:
+            self.guidance_in.disable_gradient_checkpointing()
+
+        for block in self.double_blocks + self.single_blocks:
+            block.disable_gradient_checkpointing()
+
+        print("FLUX: Gradient checkpointing disabled.")
+
+    def enable_block_swap(self, num_blocks: int, device: torch.device):
+        self.blocks_to_swap = num_blocks
+        double_blocks_to_swap = num_blocks // 2
+        single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2
+
+        assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, (
+            f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. "
+            f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks."
+        )
+
+        self.offloader_double = custom_offloading_utils.ModelOffloader(
+            self.double_blocks, self.num_double_blocks, double_blocks_to_swap, device  # , debug=True
+        )
+        self.offloader_single = custom_offloading_utils.ModelOffloader(
+            self.single_blocks, self.num_single_blocks, single_blocks_to_swap, device  # , debug=True
+        )
+        print(
+            f"FLUX: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}."
+        )
+
+    def move_to_device_except_swap_blocks(self, device: torch.device):
+        # assume model is on cpu. do not move blocks to device to reduce temporary memory usage
+        if self.blocks_to_swap:
+            save_double_blocks = self.double_blocks
+            save_single_blocks = self.single_blocks
+            self.double_blocks = None
+            self.single_blocks = None
+
+        self.to(device)
+
+        if self.blocks_to_swap:
+            self.double_blocks = save_double_blocks
+            self.single_blocks = save_single_blocks
+
+    def prepare_block_swap_before_forward(self):
+        if self.blocks_to_swap is None or self.blocks_to_swap == 0:
+            return
+        self.offloader_double.prepare_block_devices_before_forward(self.double_blocks)
+        self.offloader_single.prepare_block_devices_before_forward(self.single_blocks)
+
+    def forward(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        controlnet_cond: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor | None = None,
+        txt_attention_mask: Tensor | None = None,
+    ) -> tuple[tuple[Tensor]]:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        controlnet_cond = self.input_hint_block(controlnet_cond)
+        controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        controlnet_cond = self.pos_embed_input(controlnet_cond)
+        img = img + controlnet_cond
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        block_samples = ()
+        block_single_samples = ()
+        if not self.blocks_to_swap:
+            for block in self.double_blocks:
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                block_samples = block_samples + (img,)
+
+            img = torch.cat((txt, img), 1)
+            for block in self.single_blocks:
+                img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                block_single_samples = block_single_samples + (img,)
+        else:
+            for block_idx, block in enumerate(self.double_blocks):
+                self.offloader_double.wait_for_block(block_idx)
+
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                block_samples = block_samples + (img,)
+
+                self.offloader_double.submit_move_blocks(self.double_blocks, block_idx)
+
+            img = torch.cat((txt, img), 1)
+
+            for block_idx, block in enumerate(self.single_blocks):
+                self.offloader_single.wait_for_block(block_idx)
+
+                img = block(img, vec=vec, pe=pe, txt_attention_mask=txt_attention_mask)
+                block_single_samples = block_single_samples + (img,)
+
+                self.offloader_single.submit_move_blocks(self.single_blocks, block_idx)
+
+        controlnet_block_samples = ()
+        controlnet_single_block_samples = ()
+        for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks):
+            block_sample = controlnet_block(block_sample)
+            controlnet_block_samples = controlnet_block_samples + (block_sample,)
+        for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks_for_single):
+            block_sample = controlnet_block(block_sample)
+            controlnet_single_block_samples = controlnet_single_block_samples + (block_sample,)
+
+        return controlnet_block_samples, controlnet_single_block_samples
+
+
 """
 class FluxUpper(nn.Module):
     ""

scripts/dev/library/flux_train_utils.py

@@ -40,6 +40,7 @@ def sample_images(
     text_encoders,
     sample_prompts_te_outputs,
     prompt_replacement=None,
+    controlnet=None
 ):
     if steps == 0:
         if not args.sample_at_first:
@@ -67,6 +68,8 @@ def sample_images(
     flux = accelerator.unwrap_model(flux)
     if text_encoders is not None:
         text_encoders = [accelerator.unwrap_model(te) for te in text_encoders]
+    if controlnet is not None:
+        controlnet = accelerator.unwrap_model(controlnet)
     # print([(te.parameters().__next__().device if te is not None else None) for te in text_encoders])
 
     prompts = train_util.load_prompts(args.sample_prompts)
@@ -98,6 +101,7 @@ def sample_images(
                     steps,
                     sample_prompts_te_outputs,
                     prompt_replacement,
+                    controlnet
                 )
     else:
         # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available)
@@ -121,6 +125,7 @@ def sample_images(
                         steps,
                         sample_prompts_te_outputs,
                         prompt_replacement,
+                        controlnet
                     )
 
     torch.set_rng_state(rng_state)
@@ -142,6 +147,7 @@ def sample_image_inference(
     steps,
     sample_prompts_te_outputs,
     prompt_replacement,
+    controlnet
 ):
     assert isinstance(prompt_dict, dict)
     # negative_prompt = prompt_dict.get("negative_prompt")
@@ -150,7 +156,7 @@ def sample_image_inference(
     height = prompt_dict.get("height", 512)
     scale = prompt_dict.get("scale", 3.5)
     seed = prompt_dict.get("seed")
-    # controlnet_image = prompt_dict.get("controlnet_image")
+    controlnet_image = prompt_dict.get("controlnet_image")
     prompt: str = prompt_dict.get("prompt", "")
     # sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler)
 
@@ -169,7 +175,6 @@ def sample_image_inference(
 
     # if negative_prompt is None:
     #     negative_prompt = ""
-
     height = max(64, height - height % 16)  # round to divisible by 16
     width = max(64, width - width % 16)  # round to divisible by 16
     logger.info(f"prompt: {prompt}")
@@ -223,10 +228,15 @@ def sample_image_inference(
     img_ids = flux_utils.prepare_img_ids(1, packed_latent_height, packed_latent_width).to(accelerator.device, weight_dtype)
     t5_attn_mask = t5_attn_mask.to(accelerator.device) if args.apply_t5_attn_mask else None
 
-    with accelerator.autocast(), torch.no_grad():
-        x = denoise(flux, noise, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=scale, t5_attn_mask=t5_attn_mask)
+    if controlnet_image is not None:
+        controlnet_image = Image.open(controlnet_image).convert("RGB")
+        controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS)
+        controlnet_image = torch.from_numpy((np.array(controlnet_image) / 127.5) - 1)
+        controlnet_image = controlnet_image.permute(2, 0, 1).unsqueeze(0).to(weight_dtype).to(accelerator.device)
+
+    with accelerator.autocast(), torch.no_grad():
+        x = denoise(flux, noise, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=scale, t5_attn_mask=t5_attn_mask, controlnet=controlnet, controlnet_img=controlnet_image)
 
-    x = x.float()
     x = flux_utils.unpack_latents(x, packed_latent_height, packed_latent_width)
 
     # latent to image
@@ -301,18 +311,39 @@ def denoise(
     timesteps: list[float],
     guidance: float = 4.0,
     t5_attn_mask: Optional[torch.Tensor] = None,
+    controlnet: Optional[flux_models.ControlNetFlux] = None,
+    controlnet_img: Optional[torch.Tensor] = None,
 ):
     # this is ignored for schnell
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+
+
     for t_curr, t_prev in zip(tqdm(timesteps[:-1]), timesteps[1:]):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
         model.prepare_block_swap_before_forward()
+        if controlnet is not None:
+            block_samples, block_single_samples = controlnet(
+                img=img,
+                img_ids=img_ids,
+                controlnet_cond=controlnet_img,
+                txt=txt,
+                txt_ids=txt_ids,
+                y=vec,
+                timesteps=t_vec,
+                guidance=guidance_vec,
+                txt_attention_mask=t5_attn_mask,
+            )
+        else:
+            block_samples = None
+            block_single_samples = None
         pred = model(
             img=img,
             img_ids=img_ids,
             txt=txt,
             txt_ids=txt_ids,
             y=vec,
+            block_controlnet_hidden_states=block_samples,
+            block_controlnet_single_hidden_states=block_single_samples,
             timesteps=t_vec,
             guidance=guidance_vec,
             txt_attention_mask=t5_attn_mask,
@@ -432,7 +463,7 @@ def get_noisy_model_input_and_timesteps(
         sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
         noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
 
-    return noisy_model_input, timesteps, sigmas
+    return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas
 
 
 def apply_model_prediction_type(args, model_pred, noisy_model_input, sigmas):
@@ -532,6 +563,12 @@ def add_flux_train_arguments(parser: argparse.ArgumentParser):
         help="path to t5xxl (*.sft or *.safetensors), should be float16 / t5xxl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(
+        "--controlnet_model_name_or_path",
+        type=str,
+        default=None,
+        help="path to controlnet (*.sft or *.safetensors) / controlnetのパス（*.sftまたは*.safetensors）"
+    )
     parser.add_argument(
         "--t5xxl_max_token_length",
         type=int,

scripts/dev/library/flux_utils.py

@@ -1,14 +1,14 @@
-from dataclasses import replace
 import json
 import os
+from dataclasses import replace
 from typing import List, Optional, Tuple, Union
+
 import einops
 import torch
-
-from safetensors.torch import load_file
-from safetensors import safe_open
 from accelerate import init_empty_weights
-from transformers import CLIPTextModel, CLIPConfig, T5EncoderModel, T5Config
+from safetensors import safe_open
+from safetensors.torch import load_file
+from transformers import CLIPConfig, CLIPTextModel, T5Config, T5EncoderModel
 
 from library.utils import setup_logging
 
@@ -153,6 +153,22 @@ def load_ae(
     return ae
 
 
+def load_controlnet(
+    ckpt_path: Optional[str], is_schnell: bool, dtype: torch.dtype, device: Union[str, torch.device], disable_mmap: bool = False
+):
+    logger.info("Building ControlNet")
+    name = MODEL_NAME_DEV if not is_schnell else MODEL_NAME_SCHNELL
+    with torch.device(device):
+        controlnet = flux_models.ControlNetFlux(flux_models.configs[name].params).to(dtype)
+
+    if ckpt_path is not None:
+        logger.info(f"Loading state dict from {ckpt_path}")
+        sd = load_safetensors(ckpt_path, device=str(device), disable_mmap=disable_mmap, dtype=dtype)
+        info = controlnet.load_state_dict(sd, strict=False, assign=True)
+        logger.info(f"Loaded ControlNet: {info}")
+    return controlnet    
+
+
 def load_clip_l(
     ckpt_path: Optional[str],
     dtype: torch.dtype,

scripts/dev/library/sd3_models.py

@@ -870,8 +870,10 @@ class MMDiT(nn.Module):
         self.use_scaled_pos_embed = use_scaled_pos_embed
 
         if self.use_scaled_pos_embed:
-            # remove pos_embed to free up memory up to 0.4 GB
-            self.pos_embed = None
+            # # remove pos_embed to free up memory up to 0.4 GB -> this causes error because pos_embed is not saved
+            # self.pos_embed = None
+            # move pos_embed to CPU to free up memory up to 0.4 GB
+            self.pos_embed = self.pos_embed.cpu()
 
             # remove duplicates and sort latent sizes in ascending order
             latent_sizes = list(set(latent_sizes))
@@ -1017,22 +1019,35 @@ class MMDiT(nn.Module):
                 patched_size = patched_size_
                 break
         if patched_size is None:
-            raise ValueError(f"Area {area} is too large for the given latent sizes {self.resolution_area_to_latent_size}.")
+            # raise ValueError(f"Area {area} is too large for the given latent sizes {self.resolution_area_to_latent_size}.")
+            # use largest latent size
+            patched_size = self.resolution_area_to_latent_size[-1][1]
 
         pos_embed = self.resolution_pos_embeds[patched_size]
-        pos_embed_size = round(math.sqrt(pos_embed.shape[1]))
+        pos_embed_size = round(math.sqrt(pos_embed.shape[1]))  # max size, patched_size * POS_EMBED_MAX_RATIO
         if h > pos_embed_size or w > pos_embed_size:
             # # fallback to normal pos_embed
             # return self.cropped_pos_embed(h * p, w * p, device=device, random_crop=random_crop)
             # extend pos_embed size
             logger.warning(
-                f"Using normal pos_embed for size {h}x{w} as it exceeds the scaled pos_embed size {pos_embed_size}. Image is too tall or wide."
+                f"Add new pos_embed for size {h}x{w} as it exceeds the scaled pos_embed size {pos_embed_size}. Image is too tall or wide."
             )
-            pos_embed_size = max(h, w)
-            pos_embed = get_scaled_2d_sincos_pos_embed(self.hidden_size, pos_embed_size, sample_size=patched_size)
+            patched_size = max(h, w)
+            grid_size = int(patched_size * MMDiT.POS_EMBED_MAX_RATIO)
+            pos_embed_size = grid_size
+            pos_embed = get_scaled_2d_sincos_pos_embed(self.hidden_size, grid_size, sample_size=patched_size)
             pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0)
             self.resolution_pos_embeds[patched_size] = pos_embed
-            logger.info(f"Updated pos_embed for size {pos_embed_size}x{pos_embed_size}")
+            logger.info(f"Added pos_embed for size {patched_size}x{patched_size}")
+
+            # print(torch.allclose(pos_embed.to(torch.float32).cpu(), self.pos_embed.to(torch.float32).cpu(), atol=5e-2))
+            # diff = pos_embed.to(torch.float32).cpu() - self.pos_embed.to(torch.float32).cpu()
+            # print(diff.abs().max(), diff.abs().mean())
+
+            # insert to resolution_area_to_latent_size, by adding and sorting
+            area = pos_embed_size**2
+            self.resolution_area_to_latent_size.append((area, patched_size))
+            self.resolution_area_to_latent_size = sorted(self.resolution_area_to_latent_size)
 
         if not random_crop:
             top = (pos_embed_size - h) // 2

scripts/dev/library/train_util.py

@@ -21,7 +21,7 @@ from typing import (
     Optional,
     Sequence,
     Tuple,
-    Union,
+    Union
 )
 from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs, PartialState
 import glob
@@ -3905,7 +3905,16 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         "--huber_c",
         type=float,
         default=0.1,
-        help="The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 0.1 / Huber損失のパラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは0.1",
+        help="The Huber loss decay parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 0.1"
+        " / Huber損失の減衰パラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは0.1",
+    )
+
+    parser.add_argument(
+        "--huber_scale",
+        type=float,
+        default=1.0,
+        help="The Huber loss scale parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 1.0"
+        " / Huber損失のスケールパラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは1.0",
     )
 
     parser.add_argument(
@@ -4598,7 +4607,7 @@ def resume_from_local_or_hf_if_specified(accelerator, args):
     accelerator.load_state(dirname)
 
 
-def get_optimizer(args, trainable_params):
+def get_optimizer(args, trainable_params) -> tuple[str, str, object]:
     # "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, AdEMAMix8bit, PagedAdEMAMix8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, Adafactor"
 
     optimizer_type = args.optimizer_type
@@ -4878,17 +4887,21 @@ def get_optimizer(args, trainable_params):
             import schedulefree as sf
         except ImportError:
             raise ImportError("No schedulefree / schedulefreeがインストールされていないようです")
-        if optimizer_type == "AdamWScheduleFree".lower():
+        
+        if optimizer_type == "RAdamScheduleFree".lower():
+            optimizer_class = sf.RAdamScheduleFree
+            logger.info(f"use RAdamScheduleFree optimizer | {optimizer_kwargs}")
+        elif optimizer_type == "AdamWScheduleFree".lower():
             optimizer_class = sf.AdamWScheduleFree
             logger.info(f"use AdamWScheduleFree optimizer | {optimizer_kwargs}")
         elif optimizer_type == "SGDScheduleFree".lower():
             optimizer_class = sf.SGDScheduleFree
             logger.info(f"use SGDScheduleFree optimizer | {optimizer_kwargs}")
         else:
-            raise ValueError(f"Unknown optimizer type: {optimizer_type}")
-        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
-        # make optimizer as train mode: we don't need to call train again, because eval will not be called in training loop
-        optimizer.train()
+            optimizer_class = None
+
+        if optimizer_class is not None:
+            optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
     if optimizer is None:
         # 任意のoptimizerを使う
@@ -4990,6 +5003,10 @@ def get_optimizer(args, trainable_params):
     optimizer_name = optimizer_class.__module__ + "." + optimizer_class.__name__
     optimizer_args = ",".join([f"{k}={v}" for k, v in optimizer_kwargs.items()])
 
+    if hasattr(optimizer, "train") and callable(optimizer.train):
+        # make optimizer as train mode before training for schedulefree optimizer. the optimizer will be in eval mode in sampling and saving.
+        optimizer.train()
+
     return optimizer_name, optimizer_args, optimizer
 
 
@@ -5821,29 +5838,10 @@ def save_sd_model_on_train_end_common(
             huggingface_util.upload(args, out_dir, "/" + model_name, force_sync_upload=True)
 
 
-def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device):
+def get_timesteps(min_timestep, max_timestep, b_size, device):
     timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
-
-    if args.loss_type == "huber" or args.loss_type == "smooth_l1":
-        if args.huber_schedule == "exponential":
-            alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
-            huber_c = torch.exp(-alpha * timesteps)
-        elif args.huber_schedule == "snr":
-            alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps)
-            sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5
-            huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c
-        elif args.huber_schedule == "constant":
-            huber_c = torch.full((b_size,), args.huber_c)
-        else:
-            raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!")
-        huber_c = huber_c.to(device)
-    elif args.loss_type == "l2":
-        huber_c = None  # may be anything, as it's not used
-    else:
-        raise NotImplementedError(f"Unknown loss type {args.loss_type}")
-
     timesteps = timesteps.long().to(device)
-    return timesteps, huber_c
+    return timesteps
 
 
 def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
@@ -5865,7 +5863,7 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     min_timestep = 0 if args.min_timestep is None else args.min_timestep
     max_timestep = noise_scheduler.config.num_train_timesteps if args.max_timestep is None else args.max_timestep
 
-    timesteps, huber_c = get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, latents.device)
+    timesteps = get_timesteps(min_timestep, max_timestep, b_size, latents.device)
 
     # Add noise to the latents according to the noise magnitude at each timestep
     # (this is the forward diffusion process)
@@ -5878,11 +5876,34 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     else:
         noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
-    return noise, noisy_latents, timesteps, huber_c
+    return noise, noisy_latents, timesteps
+
+
+def get_huber_threshold_if_needed(args, timesteps: torch.Tensor, noise_scheduler) -> Optional[torch.Tensor]:
+    if not (args.loss_type == "huber" or args.loss_type == "smooth_l1"):
+        return None
+
+    b_size = timesteps.shape[0]
+    if args.huber_schedule == "exponential":
+        alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
+        result = torch.exp(-alpha * timesteps) * args.huber_scale
+    elif args.huber_schedule == "snr":
+        if not hasattr(noise_scheduler, "alphas_cumprod"):
+            raise NotImplementedError("Huber schedule 'snr' is not supported with the current model.")
+        alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps.cpu())
+        sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        result = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c
+        result = result.to(timesteps.device)
+    elif args.huber_schedule == "constant":
+        result = torch.full((b_size,), args.huber_c * args.huber_scale, device=timesteps.device)
+    else:
+        raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!")
+
+    return result
 
 
 def conditional_loss(
-    model_pred: torch.Tensor, target: torch.Tensor, reduction: str, loss_type: str, huber_c: Optional[torch.Tensor]
+    model_pred: torch.Tensor, target: torch.Tensor, loss_type: str, reduction: str, huber_c: Optional[torch.Tensor] = None
 ):
     if loss_type == "l2":
         loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction)
@@ -5903,7 +5924,7 @@ def conditional_loss(
         elif reduction == "sum":
             loss = torch.sum(loss)
     else:
-        raise NotImplementedError(f"Unsupported Loss Type {loss_type}")
+        raise NotImplementedError(f"Unsupported Loss Type: {loss_type}")
     return loss
 
 
@@ -5913,7 +5934,7 @@ def append_lr_to_logs(logs, lr_scheduler, optimizer_type, including_unet=True):
         names.append("unet")
     names.append("text_encoder1")
     names.append("text_encoder2")
-    names.append("text_encoder3") # SD3
+    names.append("text_encoder3")  # SD3
 
     append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names)
 

scripts/dev/pytest.ini

@@ -0,0 +1,8 @@
+[pytest]
+minversion = 6.0
+testpaths =
+    tests
+filterwarnings =
+    ignore::DeprecationWarning
+    ignore::UserWarning
+		ignore::FutureWarning

scripts/dev/requirements.txt

@@ -9,7 +9,7 @@ pytorch-lightning==1.9.0
 bitsandbytes==0.44.0
 prodigyopt==1.0
 lion-pytorch==0.0.6
-schedulefree==1.2.7
+schedulefree==1.4
 tensorboard
 safetensors==0.4.4
 # gradio==3.16.2

scripts/dev/sd3_train.py

@@ -675,8 +675,8 @@ def train(args):
     progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
     global_step = 0
 
-    # noise_scheduler = sd3_train_utils.FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=3.0)
-    # noise_scheduler_copy = copy.deepcopy(noise_scheduler)
+    # only used to get timesteps, etc. TODO manage timesteps etc. separately
+    dummy_scheduler = sd3_train_utils.FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=3.0)
 
     if accelerator.is_main_process:
         init_kwargs = {}
@@ -844,9 +844,8 @@ def train(args):
                 #     1,
                 # )
                 # calculate loss
-                loss = train_util.conditional_loss(
-                    model_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=None
-                )
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, dummy_scheduler)
+                loss = train_util.conditional_loss(model_pred.float(), target.float(), args.loss_type, "none", huber_c)
                 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])

scripts/dev/sd3_train_network.py

@@ -378,7 +378,7 @@ class Sd3NetworkTrainer(train_network.NetworkTrainer):
 
                 target[diff_output_pr_indices] = model_pred_prior.to(target.dtype)
 
-        return model_pred, target, timesteps, None, weighting
+        return model_pred, target, timesteps, weighting
 
     def post_process_loss(self, loss, args, timesteps, noise_scheduler):
         return loss

scripts/dev/sdxl_train.py

@@ -695,9 +695,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
-                    args, noise_scheduler, latents
-                )
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -711,6 +709,7 @@ def train(args):
                 else:
                     target = noise
 
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
                 if (
                     args.min_snr_gamma
                     or args.scale_v_pred_loss_like_noise_pred
@@ -719,9 +718,7 @@ def train(args):
                     or args.masked_loss
                 ):
                     # do not mean over batch dimension for snr weight or scale v-pred loss
-                    loss = train_util.conditional_loss(
-                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                    )
+                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                     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])
@@ -737,9 +734,7 @@ def train(args):
 
                     loss = loss.mean()  # mean over batch dimension
                 else:
-                    loss = train_util.conditional_loss(
-                        noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c
-                    )
+                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "mean", huber_c)
 
                 accelerator.backward(loss)
 

scripts/dev/sdxl_train_control_net.py

@@ -184,12 +184,12 @@ def train(args):
 
     # make control net
     logger.info("make ControlNet")
-    if args.controlnet_model_path:
+    if args.controlnet_model_name_or_path:
         with init_empty_weights():
             control_net = SdxlControlNet()
 
-        logger.info(f"load ControlNet from {args.controlnet_model_path}")
-        filename = args.controlnet_model_path
+        logger.info(f"load ControlNet from {args.controlnet_model_name_or_path}")
+        filename = args.controlnet_model_name_or_path
         if os.path.splitext(filename)[1] == ".safetensors":
             state_dict = load_file(filename)
         else:
@@ -512,9 +512,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
-                    args, noise_scheduler, latents
-                )
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)
 
@@ -533,9 +531,8 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(
-                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                )
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
@@ -678,7 +675,7 @@ def setup_parser() -> argparse.ArgumentParser:
     sdxl_train_util.add_sdxl_training_arguments(parser)
 
     parser.add_argument(
-        "--controlnet_model_path",
+        "--controlnet_model_name_or_path",
         type=str,
         default=None,
         help="controlnet model name or path / controlnetのモデル名またはパス",

scripts/dev/sdxl_train_control_net_lllite.py

@@ -463,9 +463,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
-                    args, noise_scheduler, latents
-                )
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -484,9 +482,8 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(
-                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                )
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight

scripts/dev/sdxl_train_control_net_lllite_old.py

@@ -12,6 +12,7 @@ from tqdm import tqdm
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -324,7 +325,9 @@ def train(args):
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
         accelerator.init_trackers(
-            "lllite_control_net_train" 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
+            "lllite_control_net_train" 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,
         )
 
     loss_recorder = train_util.LossRecorder()
@@ -406,7 +409,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -426,7 +429,8 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight

scripts/dev/train_control_net.py

@@ -0,0 +1,669 @@
+import argparse
+import json
+import math
+import os
+import random
+import time
+from multiprocessing import Value
+
+# from omegaconf import OmegaConf
+import toml
+
+from tqdm import tqdm
+
+import torch
+from library import deepspeed_utils
+from library.device_utils import init_ipex, clean_memory_on_device
+
+init_ipex()
+
+from torch.nn.parallel import DistributedDataParallel as DDP
+from accelerate.utils import set_seed
+from diffusers import DDPMScheduler, ControlNetModel
+from safetensors.torch import load_file
+
+import library.model_util as model_util
+import library.train_util as train_util
+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
+from library.custom_train_functions import (
+    apply_snr_weight,
+    pyramid_noise_like,
+    apply_noise_offset,
+)
+from library.utils import setup_logging, add_logging_arguments
+
+setup_logging()
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+# TODO 他のスクリプトと共通化する
+def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
+    logs = {
+        "loss/current": current_loss,
+        "loss/average": avr_loss,
+        "lr": lr_scheduler.get_last_lr()[0],
+    }
+
+    if args.optimizer_type.lower().startswith("DAdapt".lower()):
+        logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
+
+    return logs
+
+
+def train(args):
+    # session_id = random.randint(0, 2**32)
+    # training_started_at = time.time()
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, True)
+    setup_logging(args, reset=True)
+
+    cache_latents = args.cache_latents
+    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 = train_util.load_tokenizer(args)
+
+    # データセットを準備する
+    blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, True, True))
+    if use_user_config:
+        logger.info(f"Load dataset config from {args.dataset_config}")
+        user_config = config_util.load_user_config(args.dataset_config)
+        ignored = ["train_data_dir", "conditioning_data_dir"]
+        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:
+        user_config = {
+            "datasets": [
+                {
+                    "subsets": config_util.generate_controlnet_subsets_config_by_subdirs(
+                        args.train_data_dir,
+                        args.conditioning_data_dir,
+                        args.caption_extension,
+                    )
+                }
+            ]
+        }
+
+    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
+
+    current_epoch = Value("i", 0)
+    current_step = Value("i", 0)
+    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(64)
+
+    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"
+
+    # acceleratorを準備する
+    logger.info("prepare 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)
+
+    # モデルを読み込む
+    text_encoder, vae, unet, _ = train_util.load_target_model(
+        args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=True
+    )
+
+    # DiffusersのControlNetが使用するデータを準備する
+    if args.v2:
+        unet.config = {
+            "act_fn": "silu",
+            "attention_head_dim": [5, 10, 20, 20],
+            "block_out_channels": [320, 640, 1280, 1280],
+            "center_input_sample": False,
+            "cross_attention_dim": 1024,
+            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
+            "downsample_padding": 1,
+            "dual_cross_attention": False,
+            "flip_sin_to_cos": True,
+            "freq_shift": 0,
+            "in_channels": 4,
+            "layers_per_block": 2,
+            "mid_block_scale_factor": 1,
+            "mid_block_type": "UNetMidBlock2DCrossAttn",
+            "norm_eps": 1e-05,
+            "norm_num_groups": 32,
+            "num_attention_heads": [5, 10, 20, 20],
+            "num_class_embeds": None,
+            "only_cross_attention": False,
+            "out_channels": 4,
+            "sample_size": 96,
+            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
+            "use_linear_projection": True,
+            "upcast_attention": True,
+            "only_cross_attention": False,
+            "downsample_padding": 1,
+            "use_linear_projection": True,
+            "class_embed_type": None,
+            "num_class_embeds": None,
+            "resnet_time_scale_shift": "default",
+            "projection_class_embeddings_input_dim": None,
+        }
+    else:
+        unet.config = {
+            "act_fn": "silu",
+            "attention_head_dim": 8,
+            "block_out_channels": [320, 640, 1280, 1280],
+            "center_input_sample": False,
+            "cross_attention_dim": 768,
+            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
+            "downsample_padding": 1,
+            "flip_sin_to_cos": True,
+            "freq_shift": 0,
+            "in_channels": 4,
+            "layers_per_block": 2,
+            "mid_block_scale_factor": 1,
+            "mid_block_type": "UNetMidBlock2DCrossAttn",
+            "norm_eps": 1e-05,
+            "norm_num_groups": 32,
+            "num_attention_heads": 8,
+            "out_channels": 4,
+            "sample_size": 64,
+            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
+            "only_cross_attention": False,
+            "downsample_padding": 1,
+            "use_linear_projection": False,
+            "class_embed_type": None,
+            "num_class_embeds": None,
+            "upcast_attention": False,
+            "resnet_time_scale_shift": "default",
+            "projection_class_embeddings_input_dim": None,
+        }
+    # unet.config = OmegaConf.create(unet.config)
+
+    # make unet.config iterable and accessible by attribute
+    class CustomConfig:
+        def __init__(self, **kwargs):
+            self.__dict__.update(kwargs)
+
+        def __getattr__(self, name):
+            if name in self.__dict__:
+                return self.__dict__[name]
+            else:
+                raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
+
+        def __contains__(self, name):
+            return name in self.__dict__
+
+    unet.config = CustomConfig(**unet.config)
+
+    controlnet = ControlNetModel.from_unet(unet)
+
+    if args.controlnet_model_name_or_path:
+        filename = args.controlnet_model_name_or_path
+        if os.path.isfile(filename):
+            if os.path.splitext(filename)[1] == ".safetensors":
+                state_dict = load_file(filename)
+            else:
+                state_dict = torch.load(filename)
+            state_dict = model_util.convert_controlnet_state_dict_to_diffusers(state_dict)
+            controlnet.load_state_dict(state_dict)
+        elif os.path.isdir(filename):
+            controlnet = ControlNetModel.from_pretrained(filename)
+
+    # 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)
+
+    # 学習を準備する
+    if cache_latents:
+        vae.to(accelerator.device, dtype=weight_dtype)
+        vae.requires_grad_(False)
+        vae.eval()
+        with torch.no_grad():
+            train_dataset_group.cache_latents(
+                vae,
+                args.vae_batch_size,
+                args.cache_latents_to_disk,
+                accelerator.is_main_process,
+            )
+        vae.to("cpu")
+        clean_memory_on_device(accelerator.device)
+
+        accelerator.wait_for_everyone()
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+        controlnet.enable_gradient_checkpointing()
+
+    # 学習に必要なクラスを準備する
+    accelerator.print("prepare optimizer, data loader etc.")
+
+    trainable_params = list(controlnet.parameters())
+
+    _, _, optimizer = train_util.get_optimizer(args, trainable_params)
+
+    # dataloaderを準備する
+    # 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
+    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
+    if args.full_fp16:
+        assert (
+            args.mixed_precision == "fp16"
+        ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
+        accelerator.print("enable full fp16 training.")
+        controlnet.to(weight_dtype)
+
+    # acceleratorがなんかよろしくやってくれるらしい
+    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        controlnet, optimizer, train_dataloader, lr_scheduler
+    )
+
+    if args.fused_backward_pass:
+        import library.adafactor_fused
+
+        library.adafactor_fused.patch_adafactor_fused(optimizer)
+        for param_group in optimizer.param_groups:
+            for parameter in param_group["params"]:
+                if parameter.requires_grad:
+
+                    def __grad_hook(tensor: torch.Tensor, param_group=param_group):
+                        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                            accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
+                        optimizer.step_param(tensor, param_group)
+                        tensor.grad = None
+
+                    parameter.register_post_accumulate_grad_hook(__grad_hook)
+
+    unet.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    unet.to(accelerator.device)
+    text_encoder.to(accelerator.device)
+
+    # transform DDP after prepare
+    controlnet = controlnet.module if isinstance(controlnet, DDP) else controlnet
+
+    controlnet.train()
+
+    if not cache_latents:
+        vae.requires_grad_(False)
+        vae.eval()
+        vae.to(accelerator.device, dtype=weight_dtype)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
+    if args.full_fp16:
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+
+    # resumeする
+    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
+    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])}"
+    )
+    # logger.info(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 = DDPMScheduler(
+        beta_start=0.00085,
+        beta_end=0.012,
+        beta_schedule="scaled_linear",
+        num_train_timesteps=1000,
+        clip_sample=False,
+    )
+    if accelerator.is_main_process:
+        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(
+            "controlnet_train" 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,
+        )
+
+    loss_recorder = train_util.LossRecorder()
+    del train_dataset_group
+
+    # function for saving/removing
+    def save_model(ckpt_name, model, 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}")
+
+        state_dict = model_util.convert_controlnet_state_dict_to_sd(model.state_dict())
+
+        if save_dtype is not None:
+            for key in list(state_dict.keys()):
+                v = state_dict[key]
+                v = v.detach().clone().to("cpu").to(save_dtype)
+                state_dict[key] = v
+
+        if os.path.splitext(ckpt_file)[1] == ".safetensors":
+            from safetensors.torch import save_file
+
+            save_file(state_dict, ckpt_file)
+        else:
+            torch.save(state_dict, ckpt_file)
+
+        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
+    train_util.sample_images(
+        accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, controlnet=controlnet
+    )
+    if len(accelerator.trackers) > 0:
+        # log empty object to commit the sample images to wandb
+        accelerator.log({}, step=0)
+
+    # training loop
+    for epoch in range(num_train_epochs):
+        if is_main_process:
+            accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
+        current_epoch.value = epoch + 1
+
+        for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
+            with accelerator.accumulate(controlnet):
+                with torch.no_grad():
+                    if "latents" in batch and batch["latents"] is not None:
+                        latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
+                    else:
+                        # latentに変換
+                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+                    latents = latents * 0.18215
+                b_size = latents.shape[0]
+
+                input_ids = batch["input_ids"].to(accelerator.device)
+                encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents, device=latents.device)
+                if args.noise_offset:
+                    noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
+                elif args.multires_noise_iterations:
+                    noise = pyramid_noise_like(
+                        noise,
+                        latents.device,
+                        args.multires_noise_iterations,
+                        args.multires_noise_discount,
+                    )
+
+                # Sample a random timestep for each image
+                timesteps = train_util.get_timesteps(0, noise_scheduler.config.num_train_timesteps, b_size, latents.device)
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)
+
+                with accelerator.autocast():
+                    down_block_res_samples, mid_block_res_sample = controlnet(
+                        noisy_latents,
+                        timesteps,
+                        encoder_hidden_states=encoder_hidden_states,
+                        controlnet_cond=controlnet_image,
+                        return_dict=False,
+                    )
+
+                    # Predict the noise residual
+                    noise_pred = unet(
+                        noisy_latents,
+                        timesteps,
+                        encoder_hidden_states,
+                        down_block_additional_residuals=[sample.to(dtype=weight_dtype) for sample in down_block_res_samples],
+                        mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
+                    ).sample
+
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
+
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
+                loss = loss.mean([1, 2, 3])
+
+                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
+                loss = loss * loss_weights
+
+                if args.min_snr_gamma:
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
+
+                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
+
+                accelerator.backward(loss)
+                if not args.fused_backward_pass:
+                    if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                        params_to_clip = controlnet.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()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                train_util.sample_images(
+                    accelerator,
+                    args,
+                    None,
+                    global_step,
+                    accelerator.device,
+                    vae,
+                    tokenizer,
+                    text_encoder,
+                    unet,
+                    controlnet=controlnet,
+                )
+
+                # 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(controlnet),
+                        )
+
+                        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.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 len(accelerator.trackers) > 0:
+                logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
+                accelerator.log(logs, step=global_step)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if len(accelerator.trackers) > 0:
+            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(controlnet))
+
+                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)
+
+        train_util.sample_images(
+            accelerator,
+            args,
+            epoch + 1,
+            global_step,
+            accelerator.device,
+            vae,
+            tokenizer,
+            text_encoder,
+            unet,
+            controlnet=controlnet,
+        )
+
+        # end of epoch
+    if is_main_process:
+        controlnet = accelerator.unwrap_model(controlnet)
+
+    accelerator.end_training()
+
+    if is_main_process and (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→printで使うので消さずにおく
+
+    if is_main_process:
+        ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
+        save_model(ckpt_name, controlnet, force_sync_upload=True)
+
+        logger.info("model saved.")
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+
+    add_logging_arguments(parser)
+    train_util.add_sd_models_arguments(parser)
+    train_util.add_dataset_arguments(parser, False, True, True)
+    train_util.add_training_arguments(parser, False)
+    deepspeed_utils.add_deepspeed_arguments(parser)
+    train_util.add_optimizer_arguments(parser)
+    config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
+
+    parser.add_argument(
+        "--save_model_as",
+        type=str,
+        default="safetensors",
+        choices=[None, "ckpt", "pt", "safetensors"],
+        help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）",
+    )
+    parser.add_argument(
+        "--controlnet_model_name_or_path",
+        type=str,
+        default=None,
+        help="controlnet model name or path / controlnetのモデル名またはパス",
+    )
+    parser.add_argument(
+        "--conditioning_data_dir",
+        type=str,
+        default=None,
+        help="conditioning data directory / 条件付けデータのディレクトリ",
+    )
+
+    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)

scripts/dev/train_controlnet.py

@@ -1,42 +1,4 @@
-import argparse
-import json
-import math
-import os
-import random
-import time
-from multiprocessing import Value
-
-# from omegaconf import OmegaConf
-import toml
-
-from tqdm import tqdm
-
-import torch
-from library import deepspeed_utils
-from library.device_utils import init_ipex, clean_memory_on_device
-
-init_ipex()
-
-from torch.nn.parallel import DistributedDataParallel as DDP
-from accelerate.utils import set_seed
-from diffusers import DDPMScheduler, ControlNetModel
-from safetensors.torch import load_file
-
-import library.model_util as model_util
-import library.train_util as train_util
-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
-from library.custom_train_functions import (
-    apply_snr_weight,
-    pyramid_noise_like,
-    apply_noise_offset,
-)
-from library.utils import setup_logging, add_logging_arguments
+from library.utils import setup_logging
 
 setup_logging()
 import logging
@@ -44,623 +6,14 @@ import logging
 logger = logging.getLogger(__name__)
 
 
-# TODO 他のスクリプトと共通化する
-def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
-    logs = {
-        "loss/current": current_loss,
-        "loss/average": avr_loss,
-        "lr": lr_scheduler.get_last_lr()[0],
-    }
-
-    if args.optimizer_type.lower().startswith("DAdapt".lower()):
-        logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
-
-    return logs
-
-
-def train(args):
-    # session_id = random.randint(0, 2**32)
-    # training_started_at = time.time()
-    train_util.verify_training_args(args)
-    train_util.prepare_dataset_args(args, True)
-    setup_logging(args, reset=True)
-
-    cache_latents = args.cache_latents
-    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 = train_util.load_tokenizer(args)
-
-    # データセットを準備する
-    blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, True, True))
-    if use_user_config:
-        logger.info(f"Load dataset config from {args.dataset_config}")
-        user_config = config_util.load_user_config(args.dataset_config)
-        ignored = ["train_data_dir", "conditioning_data_dir"]
-        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:
-        user_config = {
-            "datasets": [
-                {
-                    "subsets": config_util.generate_controlnet_subsets_config_by_subdirs(
-                        args.train_data_dir,
-                        args.conditioning_data_dir,
-                        args.caption_extension,
-                    )
-                }
-            ]
-        }
-
-    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
-    train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
-
-    current_epoch = Value("i", 0)
-    current_step = Value("i", 0)
-    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(64)
-
-    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"
-
-    # acceleratorを準備する
-    logger.info("prepare 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)
-
-    # モデルを読み込む
-    text_encoder, vae, unet, _ = train_util.load_target_model(
-        args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=True
-    )
-
-    # DiffusersのControlNetが使用するデータを準備する
-    if args.v2:
-        unet.config = {
-            "act_fn": "silu",
-            "attention_head_dim": [5, 10, 20, 20],
-            "block_out_channels": [320, 640, 1280, 1280],
-            "center_input_sample": False,
-            "cross_attention_dim": 1024,
-            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
-            "downsample_padding": 1,
-            "dual_cross_attention": False,
-            "flip_sin_to_cos": True,
-            "freq_shift": 0,
-            "in_channels": 4,
-            "layers_per_block": 2,
-            "mid_block_scale_factor": 1,
-            "mid_block_type": "UNetMidBlock2DCrossAttn",
-            "norm_eps": 1e-05,
-            "norm_num_groups": 32,
-            "num_attention_heads": [5, 10, 20, 20],
-            "num_class_embeds": None,
-            "only_cross_attention": False,
-            "out_channels": 4,
-            "sample_size": 96,
-            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
-            "use_linear_projection": True,
-            "upcast_attention": True,
-            "only_cross_attention": False,
-            "downsample_padding": 1,
-            "use_linear_projection": True,
-            "class_embed_type": None,
-            "num_class_embeds": None,
-            "resnet_time_scale_shift": "default",
-            "projection_class_embeddings_input_dim": None,
-        }
-    else:
-        unet.config = {
-            "act_fn": "silu",
-            "attention_head_dim": 8,
-            "block_out_channels": [320, 640, 1280, 1280],
-            "center_input_sample": False,
-            "cross_attention_dim": 768,
-            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
-            "downsample_padding": 1,
-            "flip_sin_to_cos": True,
-            "freq_shift": 0,
-            "in_channels": 4,
-            "layers_per_block": 2,
-            "mid_block_scale_factor": 1,
-            "mid_block_type": "UNetMidBlock2DCrossAttn",
-            "norm_eps": 1e-05,
-            "norm_num_groups": 32,
-            "num_attention_heads": 8,
-            "out_channels": 4,
-            "sample_size": 64,
-            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
-            "only_cross_attention": False,
-            "downsample_padding": 1,
-            "use_linear_projection": False,
-            "class_embed_type": None,
-            "num_class_embeds": None,
-            "upcast_attention": False,
-            "resnet_time_scale_shift": "default",
-            "projection_class_embeddings_input_dim": None,
-        }
-    # unet.config = OmegaConf.create(unet.config)
-
-    # make unet.config iterable and accessible by attribute
-    class CustomConfig:
-        def __init__(self, **kwargs):
-            self.__dict__.update(kwargs)
-
-        def __getattr__(self, name):
-            if name in self.__dict__:
-                return self.__dict__[name]
-            else:
-                raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
-
-        def __contains__(self, name):
-            return name in self.__dict__
-
-    unet.config = CustomConfig(**unet.config)
-
-    controlnet = ControlNetModel.from_unet(unet)
-
-    if args.controlnet_model_name_or_path:
-        filename = args.controlnet_model_name_or_path
-        if os.path.isfile(filename):
-            if os.path.splitext(filename)[1] == ".safetensors":
-                state_dict = load_file(filename)
-            else:
-                state_dict = torch.load(filename)
-            state_dict = model_util.convert_controlnet_state_dict_to_diffusers(state_dict)
-            controlnet.load_state_dict(state_dict)
-        elif os.path.isdir(filename):
-            controlnet = ControlNetModel.from_pretrained(filename)
-
-    # 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)
-
-    # 学習を準備する
-    if cache_latents:
-        vae.to(accelerator.device, dtype=weight_dtype)
-        vae.requires_grad_(False)
-        vae.eval()
-        with torch.no_grad():
-            train_dataset_group.cache_latents(
-                vae,
-                args.vae_batch_size,
-                args.cache_latents_to_disk,
-                accelerator.is_main_process,
-            )
-        vae.to("cpu")
-        clean_memory_on_device(accelerator.device)
-
-        accelerator.wait_for_everyone()
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        controlnet.enable_gradient_checkpointing()
-
-    # 学習に必要なクラスを準備する
-    accelerator.print("prepare optimizer, data loader etc.")
-
-    trainable_params = list(controlnet.parameters())
-
-    _, _, optimizer = train_util.get_optimizer(args, trainable_params)
-
-    # dataloaderを準備する
-    # 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
-    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
-
-    # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
-    if args.full_fp16:
-        assert (
-            args.mixed_precision == "fp16"
-        ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
-        accelerator.print("enable full fp16 training.")
-        controlnet.to(weight_dtype)
-
-    # acceleratorがなんかよろしくやってくれるらしい
-    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        controlnet, optimizer, train_dataloader, lr_scheduler
-    )
-
-    if args.fused_backward_pass:
-        import library.adafactor_fused
-        library.adafactor_fused.patch_adafactor_fused(optimizer)
-        for param_group in optimizer.param_groups:
-            for parameter in param_group["params"]:
-                if parameter.requires_grad:
-                    def __grad_hook(tensor: torch.Tensor, param_group=param_group):
-                        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
-                            accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
-                        optimizer.step_param(tensor, param_group)
-                        tensor.grad = None
-
-                    parameter.register_post_accumulate_grad_hook(__grad_hook)
-
-    unet.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-    unet.to(accelerator.device)
-    text_encoder.to(accelerator.device)
-
-    # transform DDP after prepare
-    controlnet = controlnet.module if isinstance(controlnet, DDP) else controlnet
-
-    controlnet.train()
-
-    if not cache_latents:
-        vae.requires_grad_(False)
-        vae.eval()
-        vae.to(accelerator.device, dtype=weight_dtype)
-
-    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
-    if args.full_fp16:
-        train_util.patch_accelerator_for_fp16_training(accelerator)
-
-    # resumeする
-    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
-    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])}"
-    )
-    # logger.info(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 = DDPMScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=1000,
-        clip_sample=False,
-    )
-    if accelerator.is_main_process:
-        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(
-            "controlnet_train" 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,
-        )
-
-    loss_recorder = train_util.LossRecorder()
-    del train_dataset_group
-
-    # function for saving/removing
-    def save_model(ckpt_name, model, 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}")
-
-        state_dict = model_util.convert_controlnet_state_dict_to_sd(model.state_dict())
-
-        if save_dtype is not None:
-            for key in list(state_dict.keys()):
-                v = state_dict[key]
-                v = v.detach().clone().to("cpu").to(save_dtype)
-                state_dict[key] = v
-
-        if os.path.splitext(ckpt_file)[1] == ".safetensors":
-            from safetensors.torch import save_file
-
-            save_file(state_dict, ckpt_file)
-        else:
-            torch.save(state_dict, ckpt_file)
-
-        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
-    train_util.sample_images(
-        accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, controlnet=controlnet
-    )
-    if len(accelerator.trackers) > 0:
-        # log empty object to commit the sample images to wandb
-        accelerator.log({}, step=0)
-
-    # training loop
-    for epoch in range(num_train_epochs):
-        if is_main_process:
-            accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
-        current_epoch.value = epoch + 1
-
-        for step, batch in enumerate(train_dataloader):
-            current_step.value = global_step
-            with accelerator.accumulate(controlnet):
-                with torch.no_grad():
-                    if "latents" in batch and batch["latents"] is not None:
-                        latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
-                    else:
-                        # latentに変換
-                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
-                    latents = latents * 0.18215
-                b_size = latents.shape[0]
-
-                input_ids = batch["input_ids"].to(accelerator.device)
-                encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents, device=latents.device)
-                if args.noise_offset:
-                    noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
-                elif args.multires_noise_iterations:
-                    noise = pyramid_noise_like(
-                        noise,
-                        latents.device,
-                        args.multires_noise_iterations,
-                        args.multires_noise_discount,
-                    )
-
-                # Sample a random timestep for each image
-                timesteps, huber_c = train_util.get_timesteps_and_huber_c(
-                    args, 0, noise_scheduler.config.num_train_timesteps, noise_scheduler, b_size, latents.device
-                )
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)
-
-                with accelerator.autocast():
-                    down_block_res_samples, mid_block_res_sample = controlnet(
-                        noisy_latents,
-                        timesteps,
-                        encoder_hidden_states=encoder_hidden_states,
-                        controlnet_cond=controlnet_image,
-                        return_dict=False,
-                    )
-
-                    # Predict the noise residual
-                    noise_pred = unet(
-                        noisy_latents,
-                        timesteps,
-                        encoder_hidden_states,
-                        down_block_additional_residuals=[sample.to(dtype=weight_dtype) for sample in down_block_res_samples],
-                        mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
-                    ).sample
-
-                if args.v_parameterization:
-                    # v-parameterization training
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    target = noise
-
-                loss = train_util.conditional_loss(
-                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                )
-                loss = loss.mean([1, 2, 3])
-
-                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
-                loss = loss * loss_weights
-
-                if args.min_snr_gamma:
-                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-
-                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
-
-                accelerator.backward(loss)
-                if not args.fused_backward_pass:
-                    if accelerator.sync_gradients and args.max_grad_norm != 0.0:
-                        params_to_clip = controlnet.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()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-                train_util.sample_images(
-                    accelerator,
-                    args,
-                    None,
-                    global_step,
-                    accelerator.device,
-                    vae,
-                    tokenizer,
-                    text_encoder,
-                    unet,
-                    controlnet=controlnet,
-                )
-
-                # 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(controlnet),
-                        )
-
-                        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.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 len(accelerator.trackers) > 0:
-                logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
-                accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-        if len(accelerator.trackers) > 0:
-            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(controlnet))
-
-                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)
-
-        train_util.sample_images(
-            accelerator,
-            args,
-            epoch + 1,
-            global_step,
-            accelerator.device,
-            vae,
-            tokenizer,
-            text_encoder,
-            unet,
-            controlnet=controlnet,
-        )
-
-        # end of epoch
-    if is_main_process:
-        controlnet = accelerator.unwrap_model(controlnet)
-
-    accelerator.end_training()
-
-    if is_main_process and (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→printで使うので消さずにおく
-
-    if is_main_process:
-        ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
-        save_model(ckpt_name, controlnet, force_sync_upload=True)
-
-        logger.info("model saved.")
-
-
-def setup_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser()
-
-    add_logging_arguments(parser)
-    train_util.add_sd_models_arguments(parser)
-    train_util.add_dataset_arguments(parser, False, True, True)
-    train_util.add_training_arguments(parser, False)
-    deepspeed_utils.add_deepspeed_arguments(parser)
-    train_util.add_optimizer_arguments(parser)
-    config_util.add_config_arguments(parser)
-    custom_train_functions.add_custom_train_arguments(parser)
-
-    parser.add_argument(
-        "--save_model_as",
-        type=str,
-        default="safetensors",
-        choices=[None, "ckpt", "pt", "safetensors"],
-        help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）",
-    )
-    parser.add_argument(
-        "--controlnet_model_name_or_path",
-        type=str,
-        default=None,
-        help="controlnet model name or path / controlnetのモデル名またはパス",
-    )
-    parser.add_argument(
-        "--conditioning_data_dir",
-        type=str,
-        default=None,
-        help="conditioning data directory / 条件付けデータのディレクトリ",
-    )
-
-    return parser
-
+from library import train_util
+from train_control_net import setup_parser, train
 
 if __name__ == "__main__":
+    logger.warning(
+        "The module 'train_controlnet.py' is deprecated. Please use 'train_control_net.py' instead"
+        " / 'train_controlnet.py'は非推奨です。代わりに'train_control_net.py'を使用してください。"
+    )
     parser = setup_parser()
 
     args = parser.parse_args()

scripts/dev/train_db.py

@@ -370,9 +370,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
-                    args, noise_scheduler, latents
-                )
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -384,9 +382,8 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(
-                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                )
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                 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])

scripts/dev/train_network.py

@@ -61,6 +61,7 @@ class NetworkTrainer:
         avr_loss,
         lr_scheduler,
         lr_descriptions,
+        optimizer=None,
         keys_scaled=None,
         mean_norm=None,
         maximum_norm=None,
@@ -93,6 +94,30 @@ class NetworkTrainer:
                 logs[f"lr/d*lr/{lr_desc}"] = (
                     lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                 )
+            if (
+                args.optimizer_type.lower().endswith("ProdigyPlusScheduleFree".lower()) and optimizer is not None
+            ):  # tracking d*lr value of unet.
+                logs["lr/d*lr"] = (
+                    optimizer.param_groups[0]["d"] * optimizer.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"]
+                    )
+                if (
+                    args.optimizer_type.lower().endswith("ProdigyPlusScheduleFree".lower()) and optimizer is not None
+                ):  
+                    logs[f"lr/d*lr/group{i}"] = (
+                        optimizer.param_groups[i]["d"] * optimizer.param_groups[i]["lr"]
+                    )
 
         return logs
 
@@ -192,7 +217,7 @@ class NetworkTrainer:
     ):
         # Sample noise, sample a random timestep for each image, and add noise to the latents,
         # with noise offset and/or multires noise if specified
-        noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+        noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
         # ensure the hidden state will require grad
         if args.gradient_checkpointing:
@@ -244,7 +269,7 @@ class NetworkTrainer:
                 network.set_multiplier(1.0)  # may be overwritten by "network_multipliers" in the next step
                 target[diff_output_pr_indices] = noise_pred_prior.to(target.dtype)
 
-        return noise_pred, target, timesteps, huber_c, None
+        return noise_pred, target, timesteps, None
 
     def post_process_loss(self, loss, args, timesteps, noise_scheduler):
         if args.min_snr_gamma:
@@ -806,6 +831,7 @@ class NetworkTrainer:
             "ss_ip_noise_gamma_random_strength": args.ip_noise_gamma_random_strength,
             "ss_loss_type": args.loss_type,
             "ss_huber_schedule": args.huber_schedule,
+            "ss_huber_scale": args.huber_scale,
             "ss_huber_c": args.huber_c,
             "ss_fp8_base": bool(args.fp8_base),
             "ss_fp8_base_unet": bool(args.fp8_base_unet),
@@ -1193,7 +1219,7 @@ class NetworkTrainer:
                                     text_encoder_conds[i] = encoded_text_encoder_conds[i]
 
                     # sample noise, call unet, get target
-                    noise_pred, target, timesteps, huber_c, weighting = self.get_noise_pred_and_target(
+                    noise_pred, target, timesteps, weighting = self.get_noise_pred_and_target(
                         args,
                         accelerator,
                         noise_scheduler,
@@ -1206,9 +1232,8 @@ class NetworkTrainer:
                         train_unet,
                     )
 
-                    loss = train_util.conditional_loss(
-                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                    )
+                    huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                    loss = train_util.conditional_loss(noise_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):
@@ -1279,7 +1304,7 @@ class NetworkTrainer:
 
                 if len(accelerator.trackers) > 0:
                     logs = self.generate_step_logs(
-                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, keys_scaled, mean_norm, maximum_norm
+                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, optimizer, keys_scaled, mean_norm, maximum_norm
                     )
                     accelerator.log(logs, step=global_step)
 

scripts/dev/train_textual_inversion.py

@@ -585,7 +585,7 @@ class TextualInversionTrainer:
 
                     # Sample noise, sample a random timestep for each image, and add noise to the latents,
                     # with noise offset and/or multires noise if specified
-                    noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(
                         args, noise_scheduler, latents
                     )
 
@@ -601,9 +601,8 @@ class TextualInversionTrainer:
                     else:
                         target = noise
 
-                    loss = train_util.conditional_loss(
-                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
-                    )
+                    huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                     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])

scripts/dev/train_textual_inversion_XTI.py

@@ -407,7 +407,9 @@ def train(args):
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
         accelerator.init_trackers(
-            "textual_inversion" 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
+            "textual_inversion" 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,
         )
 
     # function for saving/removing
@@ -461,7 +463,7 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -473,7 +475,8 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
+                loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
                 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])