diff --git a/README-ja.md b/README-ja.md
index 27cc56c34..60249f61e 100644
--- a/README-ja.md
+++ b/README-ja.md
@@ -36,6 +36,8 @@ Python 3.10.6およびGitが必要です。
 - Python 3.10.6: https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe
 - git: https://git-scm.com/download/win
 
+Python 3.10.x、3.11.x、3.12.xでも恐らく動作しますが、3.10.6でテストしています。
+
 PowerShellを使う場合、venvを使えるようにするためには以下の手順でセキュリティ設定を変更してください。
 （venvに限らずスクリプトの実行が可能になりますので注意してください。）
 
@@ -45,7 +47,7 @@ PowerShellを使う場合、venvを使えるようにするためには以下の
 
 ## Windows環境でのインストール
 
-スクリプトはPyTorch 2.1.2でテストしています。PyTorch 2.0.1、1.12.1でも動作すると思われます。
+スクリプトはPyTorch 2.1.2でテストしています。PyTorch 2.2以降でも恐らく動作します。
 
 （なお、python -m venv～の行で「python」とだけ表示された場合、py -m venv～のようにpythonをpyに変更してください。）
 
@@ -67,10 +69,12 @@ accelerate config
 
 コマンドプロンプトでも同一です。
 
-注：`bitsandbytes==0.43.0`、`prodigyopt==1.0`、`lion-pytorch==0.0.6` は `requirements.txt` に含まれるようになりました。他のバージョンを使う場合は適宜インストールしてください。
+注：`bitsandbytes==0.44.0`、`prodigyopt==1.0`、`lion-pytorch==0.0.6` は `requirements.txt` に含まれるようになりました。他のバージョンを使う場合は適宜インストールしてください。
 
 この例では PyTorch および xfomers は2.1.2／CUDA 11.8版をインストールします。CUDA 12.1版やPyTorch 1.12.1を使う場合は適宜書き換えください。たとえば CUDA 12.1版の場合は `pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121` および `pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu121` としてください。
 
+PyTorch 2.2以降を用いる場合は、`torch==2.1.2` と `torchvision==0.16.2` 、および `xformers==0.0.23.post1` を適宜変更してください。
+
 accelerate configの質問には以下のように答えてください。（bf16で学習する場合、最後の質問にはbf16と答えてください。）
 
 ```txt
diff --git a/README.md b/README.md
index 51ac07a1f..6beee5e3a 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ This repository contains the scripts for:
 
 The file does not contain requirements for PyTorch. Because the version of PyTorch depends on the environment, it is not included in the file. Please install PyTorch first according to the environment. See installation instructions below.
 
-The scripts are tested with Pytorch 2.1.2. 2.0.1 and 1.12.1 is not tested but should work.
+The scripts are tested with Pytorch 2.1.2. PyTorch 2.2 or later will work. Please install the appropriate version of PyTorch and xformers.
 
 ## Links to usage documentation
 
@@ -52,6 +52,8 @@ Python 3.10.6 and Git:
 - Python 3.10.6: https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe
 - git: https://git-scm.com/download/win
 
+Python 3.10.x, 3.11.x, and 3.12.x will work but not tested.
+
 Give unrestricted script access to powershell so venv can work:
 
 - Open an administrator powershell window
@@ -78,10 +80,12 @@ accelerate config
 
 If `python -m venv` shows only `python`, change `python` to `py`.
 
-__Note:__ Now `bitsandbytes==0.43.0`, `prodigyopt==1.0` and `lion-pytorch==0.0.6` are included in the requirements.txt. If you'd like to use the another version, please install it manually.
+Note: Now `bitsandbytes==0.44.0`, `prodigyopt==1.0` and `lion-pytorch==0.0.6` are included in the requirements.txt. If you'd like to use the another version, please install it manually.
 
 This installation is for CUDA 11.8. If you use a different version of CUDA, please install the appropriate version of PyTorch and xformers. For example, if you use CUDA 12, please install `pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121` and `pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu121`.
 
+If you use PyTorch 2.2 or later, please change `torch==2.1.2` and `torchvision==0.16.2` and `xformers==0.0.23.post1` to the appropriate version.
+
 <!-- 
 cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
 cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
@@ -142,6 +146,197 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ## Change History
 
+### Jan 17, 2025 /  2025-01-17 Version 0.9.0
+
+- __important__ The dependent libraries are updated. Please see [Upgrade](#upgrade) and update the libraries.
+  - bitsandbytes, transformers, accelerate and huggingface_hub are updated. 
+  - If you encounter any issues, please report them.
+
+- The dev branch is merged into main. The documentation is delayed, and I apologize for that. I will gradually improve it.
+- The state just before the merge is released as Version 0.8.8, so please use it if you encounter any issues.
+- The following changes are included.
+
+#### Changes
+
+- Fixed a bug where the loss weight was incorrect when `--debiased_estimation_loss` was specified with `--v_parameterization`. PR [#1715](https://github.com/kohya-ss/sd-scripts/pull/1715) Thanks to catboxanon! See [the PR](https://github.com/kohya-ss/sd-scripts/pull/1715) for details.
+  - Removed the warning when `--v_parameterization` is specified in SDXL and SD1.5. PR [#1717](https://github.com/kohya-ss/sd-scripts/pull/1717)
+
+- There was a bug where the min_bucket_reso/max_bucket_reso in the dataset configuration did not create the correct resolution bucket if it was not divisible by bucket_reso_steps. These values are now warned and automatically rounded to a divisible value. Thanks to Maru-mee for raising the issue. Related PR [#1632](https://github.com/kohya-ss/sd-scripts/pull/1632)
+
+- `bitsandbytes` is updated to 0.44.0. Now you can use `AdEMAMix8bit` and `PagedAdEMAMix8bit` in the training script. PR [#1640](https://github.com/kohya-ss/sd-scripts/pull/1640) Thanks to sdbds!
+  - There is no abbreviation, so please specify the full path like `--optimizer_type bitsandbytes.optim.AdEMAMix8bit` (not bnb but bitsandbytes).
+
+- Fixed a bug in the cache of latents. When `flip_aug`, `alpha_mask`, and `random_crop` are different in multiple subsets in the dataset configuration file (.toml), the last subset is used instead of reflecting them correctly.
+
+- Fixed an issue where the timesteps in the batch were the same when using Huber loss. PR [#1628](https://github.com/kohya-ss/sd-scripts/pull/1628) Thanks to recris!
+
+- Improvements in OFT (Orthogonal Finetuning) Implementation
+  1. Optimization of Calculation Order:
+      - Changed the calculation order in the forward method from (Wx)R to W(xR).
+      - This has improved computational efficiency and processing speed.
+  2. Correction of Bias Application:
+      - In the previous implementation, R was incorrectly applied to the bias.
+      - The new implementation now correctly handles bias by using F.conv2d and F.linear.
+  3. Efficiency Enhancement in Matrix Operations:
+      - Introduced einsum in both the forward and merge_to methods.
+      - This has optimized matrix operations, resulting in further speed improvements.
+  4. Proper Handling of Data Types:
+      - Improved to use torch.float32 during calculations and convert results back to the original data type.
+      - This maintains precision while ensuring compatibility with the original model.
+  5. Unified Processing for Conv2d and Linear Layers:
+     - Implemented a consistent method for applying OFT to both layer types.
+  - These changes have made the OFT implementation more efficient and accurate, potentially leading to improved model performance and training stability.
+
+  - Additional Information
+    * Recommended α value for OFT constraint: We recommend using α values between 1e-4 and 1e-2. This differs slightly from the original implementation of "(α\*out_dim\*out_dim)". Our implementation uses "(α\*out_dim)", hence we recommend higher values than the 1e-5 suggested in the original implementation.
+
+    * Performance Improvement: Training speed has been improved by approximately 30%.
+
+    * Inference Environment: This implementation is compatible with and operates within Stable Diffusion web UI (SD1/2 and SDXL).
+
+- The INVERSE_SQRT, COSINE_WITH_MIN_LR, and WARMUP_STABLE_DECAY learning rate schedules are now available in the transformers library. See PR [#1393](https://github.com/kohya-ss/sd-scripts/pull/1393) for details. Thanks to sdbds!
+  - See the [transformers documentation](https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/optimizer_schedules#schedules) for details on each scheduler.
+  - `--lr_warmup_steps` and `--lr_decay_steps` can now be specified as a ratio of the number of training steps, not just the step value. Example: `--lr_warmup_steps=0.1` or `--lr_warmup_steps=10%`, etc.
+
+- When enlarging images in the script (when the size of the training image is small and bucket_no_upscale is not specified), it has been changed to use Pillow's resize and LANCZOS interpolation instead of OpenCV2's resize and Lanczos4 interpolation. The quality of the image enlargement may be slightly improved. PR [#1426](https://github.com/kohya-ss/sd-scripts/pull/1426) Thanks to sdbds!
+
+- Sample image generation during training now works on non-CUDA devices. PR [#1433](https://github.com/kohya-ss/sd-scripts/pull/1433) Thanks to millie-v!
+
+- `--v_parameterization` is available in `sdxl_train.py`. The results are unpredictable, so use with caution. PR [#1505](https://github.com/kohya-ss/sd-scripts/pull/1505) Thanks to liesened!
+
+- Fused optimizer is available for SDXL training. PR [#1259](https://github.com/kohya-ss/sd-scripts/pull/1259) Thanks to 2kpr!
+  - The memory usage during training is significantly reduced by integrating the optimizer's backward pass with step. The training results are the same as before, but if you have plenty of memory, the speed will be slower.
+  - Specify the `--fused_backward_pass` option in `sdxl_train.py`. At this time, only AdaFactor is supported. Gradient accumulation is not available.
+  - Setting mixed precision to `no` seems to use less memory than `fp16` or `bf16`.
+  - Training is possible with a memory usage of about 17GB with a batch size of 1 and fp32. If you specify the `--full_bf16` option, you can further reduce the memory usage (but the accuracy will be lower). With the same memory usage as before, you can increase the batch size.
+  - PyTorch 2.1 or later is required because it uses the new API `Tensor.register_post_accumulate_grad_hook(hook)`.
+  - Mechanism: Normally, backward -> step is performed for each parameter, so all gradients need to be temporarily stored in memory. "Fuse backward and step" reduces memory usage by performing backward/step for each parameter and reflecting the gradient immediately. The more parameters there are, the greater the effect, so it is not effective in other training scripts (LoRA, etc.) where the memory usage peak is elsewhere, and there are no plans to implement it in those training scripts.
+
+- Optimizer groups feature is added to SDXL training. PR [#1319](https://github.com/kohya-ss/sd-scripts/pull/1319)
+  - Memory usage is reduced by the same principle as Fused optimizer. The training results and speed are the same as Fused optimizer.
+  - Specify the number of groups like `--fused_optimizer_groups 10` in `sdxl_train.py`. Increasing the number of groups reduces memory usage but slows down training. Since the effect is limited to a certain number, it is recommended to specify 4-10.
+  - Any optimizer can be used, but optimizers that automatically calculate the learning rate (such as D-Adaptation and Prodigy) cannot be used. Gradient accumulation is not available.
+  - `--fused_optimizer_groups` cannot be used with `--fused_backward_pass`. When using AdaFactor, the memory usage is slightly larger than with Fused optimizer. PyTorch 2.1 or later is required.
+  - Mechanism: While Fused optimizer performs backward/step for individual parameters within the optimizer, optimizer groups reduce memory usage by grouping parameters and creating multiple optimizers to perform backward/step for each group. Fused optimizer requires implementation on the optimizer side, while optimizer groups are implemented only on the training script side.
+
+- LoRA+ is supported. PR [#1233](https://github.com/kohya-ss/sd-scripts/pull/1233) Thanks to rockerBOO!
+  - LoRA+ is a method to improve training speed by increasing the learning rate of the UP side (LoRA-B) of LoRA. Specify the multiple. The original paper recommends 16, but adjust as needed. Please see the PR for details.
+  - Specify `loraplus_lr_ratio` with `--network_args`. Example: `--network_args "loraplus_lr_ratio=16"`
+  - `loraplus_unet_lr_ratio` and `loraplus_lr_ratio` can be specified separately for U-Net and Text Encoder.
+    - Example: `--network_args "loraplus_unet_lr_ratio=16" "loraplus_text_encoder_lr_ratio=4"` or `--network_args "loraplus_lr_ratio=16" "loraplus_text_encoder_lr_ratio=4"` etc.
+  - `network_module` `networks.lora` and `networks.dylora` are available.
+
+- The feature to use the transparency (alpha channel) of the image as a mask in the loss calculation has been added. PR [#1223](https://github.com/kohya-ss/sd-scripts/pull/1223) Thanks to u-haru!
+  - The transparent part is ignored during training. Specify the `--alpha_mask` option in the training script or specify `alpha_mask = true` in the dataset configuration file.
+  - See [About masked loss](./docs/masked_loss_README.md) for details.
+
+- LoRA training in SDXL now supports block-wise learning rates and block-wise dim (rank). PR [#1331](https://github.com/kohya-ss/sd-scripts/pull/1331) 
+  - Specify the learning rate and dim (rank) for each block.
+  - See [Block-wise learning rates in LoRA](./docs/train_network_README-ja.md#階層別学習率) for details (Japanese only).
+
+- Negative learning rates can now be specified during SDXL model training. PR [#1277](https://github.com/kohya-ss/sd-scripts/pull/1277) Thanks to Cauldrath!
+  - The model is trained to move away from the training images, so the model is easily collapsed. Use with caution. A value close to 0 is recommended.
+  - When specifying from the command line, use `=` like `--learning_rate=-1e-7`.
+
+- Training scripts can now output training settings to wandb or Tensor Board logs. Specify the `--log_config` option. PR [#1285](https://github.com/kohya-ss/sd-scripts/pull/1285)  Thanks to ccharest93, plucked, rockerBOO, and VelocityRa!
+  - Some settings, such as API keys and directory specifications, are not output due to security issues.
+
+- The ControlNet training script `train_controlnet.py` for SD1.5/2.x was not working, but it has been fixed. PR [#1284](https://github.com/kohya-ss/sd-scripts/pull/1284) Thanks to sdbds!
+
+- `train_network.py` and `sdxl_train_network.py` now restore the order/position of data loading from DataSet when resuming training. PR [#1353](https://github.com/kohya-ss/sd-scripts/pull/1353) [#1359](https://github.com/kohya-ss/sd-scripts/pull/1359) Thanks to KohakuBlueleaf!
+  - This resolves the issue where the order of data loading from DataSet changes when resuming training.
+  - Specify the `--skip_until_initial_step` option to skip data loading until the specified step. If not specified, data loading starts from the beginning of the DataSet (same as before).
+  - If `--resume` is specified, the step saved in the state is used.
+  - Specify the `--initial_step` or `--initial_epoch` option to skip data loading until the specified step or epoch. Use these options in conjunction with `--skip_until_initial_step`. These options can be used without `--resume` (use them when resuming training with `--network_weights`).
+
+- An option `--disable_mmap_load_safetensors` is added to disable memory mapping when loading the model's .safetensors in SDXL. PR [#1266](https://github.com/kohya-ss/sd-scripts/pull/1266) Thanks to Zovjsra!
+  - It seems that the model file loading is faster in the WSL environment etc.
+  - Available in `sdxl_train.py`, `sdxl_train_network.py`, `sdxl_train_textual_inversion.py`, and `sdxl_train_control_net_lllite.py`.
+
+- When there is an error in the cached latents file on disk, the file name is now displayed. PR [#1278](https://github.com/kohya-ss/sd-scripts/pull/1278) Thanks to Cauldrath!
+
+- Fixed an error that occurs when specifying `--max_dataloader_n_workers` in `tag_images_by_wd14_tagger.py` when Onnx is not used. PR [#1291](
+https://github.com/kohya-ss/sd-scripts/pull/1291) issue [#1290](
+https://github.com/kohya-ss/sd-scripts/pull/1290) Thanks to frodo821!
+
+- Fixed a bug that `caption_separator` cannot be specified in the subset in the dataset settings .toml file.  [#1312](https://github.com/kohya-ss/sd-scripts/pull/1312) and [#1313](https://github.com/kohya-ss/sd-scripts/pull/1312) Thanks to rockerBOO!
+
+- Fixed a potential bug in ControlNet-LLLite training. PR [#1322](https://github.com/kohya-ss/sd-scripts/pull/1322) Thanks to aria1th!
+
+- Fixed some bugs when using DeepSpeed. Related [#1247](https://github.com/kohya-ss/sd-scripts/pull/1247)
+
+- Added a prompt option `--f` to `gen_imgs.py` to specify the file name when saving. Also, Diffusers-based keys for LoRA weights are now supported.
+
+#### 変更点
+
+- devブランチがmainにマージされました。ドキュメントの整備が遅れており申し訳ありません。少しずつ整備していきます。
+- マージ直前の状態が Version 0.8.8 としてリリースされていますので、問題があればそちらをご利用ください。
+- 以下の変更が含まれます。
+
+- SDXL の学習時に Fused optimizer が使えるようになりました。PR [#1259](https://github.com/kohya-ss/sd-scripts/pull/1259) 2kpr 氏に感謝します。
+  - optimizer の backward pass に step を統合することで学習時のメモリ使用量を大きく削減します。学習結果は未適用時と同一ですが、メモリが潤沢にある場合は速度は遅くなります。
+  - `sdxl_train.py` に `--fused_backward_pass` オプションを指定してください。現時点では optimizer は AdaFactor のみ対応しています。また gradient accumulation は使えません。
+  - mixed precision は `no` のほうが `fp16` や `bf16` よりも使用メモリ量が少ないようです。
+  - バッチサイズ 1、fp32 で 17GB 程度で学習可能なようです。`--full_bf16` オプションを指定するとさらに削減できます（精度は劣ります）。以前と同じメモリ使用量ではバッチサイズを増やせます。
+  - PyTorch 2.1 以降の新 API `Tensor.register_post_accumulate_grad_hook(hook)` を使用しているため、PyTorch 2.1 以降が必要です。
+  - 仕組み：通常は backward -> step の順で行うためすべての勾配を一時的にメモリに保持する必要があります。「backward と step の統合」はパラメータごとに backward/step を行って、勾配をすぐ反映することでメモリ使用量を削減します。パラメータ数が多いほど効果が大きいため、SDXL の学習以外（LoRA 等）ではほぼ効果がなく（メモリ使用量のピークが他の場所にあるため）、それらの学習スクリプトへの実装予定もありません。
+
+- SDXL の学習時に optimizer group 機能を追加しました。PR [#1319](https://github.com/kohya-ss/sd-scripts/pull/1319)
+  - Fused optimizer と同様の原理でメモリ使用量を削減します。学習結果や速度についても同様です。
+  - `sdxl_train.py` に `--fused_optimizer_groups 10` のようにグループ数を指定してください。グループ数を増やすとメモリ使用量が削減されますが、速度は遅くなります。ある程度の数までしか効果がないため、4~10 程度を指定すると良いでしょう。
+  - 任意の optimizer が使えますが、学習率を自動計算する optimizer （D-Adaptation や Prodigy など）は使えません。gradient accumulation は使えません。
+  - `--fused_optimizer_groups` は `--fused_backward_pass` と併用できません。AdaFactor 使用時は Fused optimizer よりも若干メモリ使用量は大きくなります。PyTorch 2.1 以降が必要です。
+  - 仕組み：Fused optimizer が optimizer 内で個別のパラメータについて backward/step を行っているのに対して、optimizer groups はパラメータをグループ化して複数の optimizer を作成し、それぞれ backward/step を行うことでメモリ使用量を削減します。Fused optimizer は optimizer 側の実装が必要ですが、optimizer groups は学習スクリプト側のみで実装されています。やはり SDXL の学習でのみ効果があります。
+
+- LoRA+ がサポートされました。PR [#1233](https://github.com/kohya-ss/sd-scripts/pull/1233) rockerBOO 氏に感謝します。
+  - LoRA の UP 側（LoRA-B）の学習率を上げることで学習速度の向上を図る手法です。倍数で指定します。元の論文では 16 が推奨されていますが、データセット等にもよりますので、適宜調整してください。PR もあわせてご覧ください。
+  - `--network_args` で `loraplus_lr_ratio` を指定します。例：`--network_args "loraplus_lr_ratio=16"`
+  - `loraplus_unet_lr_ratio` と `loraplus_lr_ratio` で、U-Net および Text Encoder に個別の値を指定することも可能です。
+    - 例：`--network_args "loraplus_unet_lr_ratio=16" "loraplus_text_encoder_lr_ratio=4"` または `--network_args "loraplus_lr_ratio=16" "loraplus_text_encoder_lr_ratio=4"` など
+  - `network_module` の `networks.lora` および `networks.dylora` で使用可能です。
+
+- 画像の透明度（アルファチャネル）をロス計算時のマスクとして使用する機能が追加されました。PR [#1223](https://github.com/kohya-ss/sd-scripts/pull/1223) u-haru 氏に感謝します。
+  - 透明部分が学習時に無視されるようになります。学習スクリプトに `--alpha_mask` オプションを指定するか、データセット設定ファイルに `alpha_mask = true` を指定してください。
+  - 詳細は [マスクロスについて](./docs/masked_loss_README-ja.md) をご覧ください。
+
+- SDXL の LoRA で階層別学習率、階層別 dim (rank) をサポートしました。PR [#1331](https://github.com/kohya-ss/sd-scripts/pull/1331) 
+  - ブロックごとに学習率および dim (rank) を指定することができます。
+  - 詳細は [LoRA の階層別学習率](./docs/train_network_README-ja.md#階層別学習率) をご覧ください。
+
+- `sdxl_train.py` での SDXL モデル学習時に負の学習率が指定できるようになりました。PR [#1277](https://github.com/kohya-ss/sd-scripts/pull/1277) Cauldrath 氏に感謝します。
+  - 学習画像から離れるように学習するため、モデルは容易に崩壊します。注意して使用してください。0 に近い値を推奨します。
+  - コマンドラインから指定する場合、`--learning_rate=-1e-7` のように`=` を使ってください。
+
+- 各学習スクリプトで学習設定を wandb や Tensor Board などのログに出力できるようになりました。`--log_config` オプションを指定してください。PR [#1285](https://github.com/kohya-ss/sd-scripts/pull/1285)  ccharest93 氏、plucked 氏、rockerBOO 氏および VelocityRa 氏に感謝します。
+  - API キーや各種ディレクトリ指定など、一部の設定はセキュリティ上の問題があるため出力されません。
+
+- SD1.5/2.x 用の ControlNet 学習スクリプト `train_controlnet.py` が動作しなくなっていたのが修正されました。PR [#1284](https://github.com/kohya-ss/sd-scripts/pull/1284) sdbds 氏に感謝します。
+
+- `train_network.py` および `sdxl_train_network.py` で、学習再開時に DataSet の読み込み順についても復元できるようになりました。PR [#1353](https://github.com/kohya-ss/sd-scripts/pull/1353) [#1359](https://github.com/kohya-ss/sd-scripts/pull/1359) KohakuBlueleaf 氏に感謝します。
+  - これにより、学習再開時に DataSet の読み込み順が変わってしまう問題が解消されます。
+  - `--skip_until_initial_step` オプションを指定すると、指定したステップまで DataSet 読み込みをスキップします。指定しない場合の動作は変わりません（DataSet の最初から読み込みます）
+  - `--resume` オプションを指定すると、state に保存されたステップ数が使用されます。
+  - `--initial_step` または `--initial_epoch` オプションを指定すると、指定したステップまたはエポックまで DataSet 読み込みをスキップします。これらのオプションは `--skip_until_initial_step` と併用してください。またこれらのオプションは `--resume` と併用しなくても使えます（`--network_weights` を用いた学習再開時などにお使いください ）。
+
+- SDXL でモデルの .safetensors を読み込む際にメモリマッピングを無効化するオプション `--disable_mmap_load_safetensors` が追加されました。PR [#1266](https://github.com/kohya-ss/sd-scripts/pull/1266) Zovjsra 氏に感謝します。
+  - WSL 環境等でモデルファイルの読み込みが高速化されるようです。
+  - `sdxl_train.py`、`sdxl_train_network.py`、`sdxl_train_textual_inversion.py`、`sdxl_train_control_net_lllite.py` で使用可能です。
+
+- ディスクにキャッシュされた latents ファイルに何らかのエラーがあったとき、そのファイル名が表示されるようになりました。 PR [#1278](https://github.com/kohya-ss/sd-scripts/pull/1278) Cauldrath 氏に感謝します。
+
+- `tag_images_by_wd14_tagger.py` で Onnx 未使用時に `--max_dataloader_n_workers` を指定するとエラーになる不具合が修正されました。 PR [#1291](
+https://github.com/kohya-ss/sd-scripts/pull/1291) issue [#1290](
+https://github.com/kohya-ss/sd-scripts/pull/1290) frodo821 氏に感謝します。
+
+- データセット設定の .toml ファイルで、`caption_separator` が subset に指定できない不具合が修正されました。 PR [#1312](https://github.com/kohya-ss/sd-scripts/pull/1312) および [#1313](https://github.com/kohya-ss/sd-scripts/pull/1313) rockerBOO 氏に感謝します。
+
+- ControlNet-LLLite 学習時の潜在バグが修正されました。 PR [#1322](https://github.com/kohya-ss/sd-scripts/pull/1322) aria1th 氏に感謝します。
+
+- DeepSpeed 使用時のいくつかのバグを修正しました。関連 [#1247](https://github.com/kohya-ss/sd-scripts/pull/1247)
+
+- `gen_imgs.py` のプロンプトオプションに、保存時のファイル名を指定する `--f` オプションを追加しました。また同スクリプトで Diffusers ベースのキーを持つ LoRA の重みに対応しました。
+
+
 ### Oct 27, 2024 / 2024-10-27:
 
 - `svd_merge_lora.py` VRAM usage has been reduced. However, main memory usage will increase (32GB is sufficient).
diff --git a/_typos.toml b/_typos.toml
index ae9e06b18..bbf7728f4 100644
--- a/_typos.toml
+++ b/_typos.toml
@@ -2,6 +2,7 @@
 # Instruction:  https://github.com/marketplace/actions/typos-action#getting-started
 
 [default.extend-identifiers]
+ddPn08="ddPn08"
 
 [default.extend-words]
 NIN="NIN"
@@ -27,6 +28,7 @@ rik="rik"
 koo="koo"
 yos="yos"
 wn="wn"
+hime="hime"
 
 
 [files]
diff --git a/docs/config_README-en.md b/docs/config_README-en.md
index 83bea329b..66a50dc09 100644
--- a/docs/config_README-en.md
+++ b/docs/config_README-en.md
@@ -128,6 +128,8 @@ These are options related to the configuration of the data set. They cannot be d
 
 * `batch_size`
     * This corresponds to the command-line argument `--train_batch_size`.
+* `max_bucket_reso`, `min_bucket_reso`
+    * Specify the maximum and minimum resolutions of the bucket. It must be divisible by `bucket_reso_steps`.
 
 These settings are fixed per dataset. That means that subsets belonging to the same dataset will share these settings. For example, if you want to prepare datasets with different resolutions, you can define them as separate datasets as shown in the example above, and set different resolutions for each.
 
diff --git a/docs/config_README-ja.md b/docs/config_README-ja.md
index cc74c341b..0ed95e0eb 100644
--- a/docs/config_README-ja.md
+++ b/docs/config_README-ja.md
@@ -118,6 +118,8 @@ DreamBooth の手法と fine tuning の手法の両方とも利用可能な学
 
 * `batch_size`
     * コマンドライン引数の `--train_batch_size` と同等です。
+* `max_bucket_reso`, `min_bucket_reso`
+    * bucketの最大、最小解像度を指定します。`bucket_reso_steps` で割り切れる必要があります。
 
 これらの設定はデータセットごとに固定です。
 つまり、データセットに所属するサブセットはこれらの設定を共有することになります。
diff --git a/docs/masked_loss_README-ja.md b/docs/masked_loss_README-ja.md
new file mode 100644
index 000000000..58f042c3b
--- /dev/null
+++ b/docs/masked_loss_README-ja.md
@@ -0,0 +1,57 @@
+## マスクロスについて
+
+マスクロスは、入力画像のマスクで指定された部分だけ損失計算することで、画像の一部分だけを学習することができる機能です。
+たとえばキャラクタを学習したい場合、キャラクタ部分だけをマスクして学習することで、背景を無視して学習することができます。
+
+マスクロスのマスクには、二種類の指定方法があります。
+
+- マスク画像を用いる方法
+- 透明度（アルファチャネル）を使用する方法
+
+なお、サンプルは [ずんずんPJイラスト/3Dデータ](https://zunko.jp/con_illust.html) の「AI画像モデル用学習データ」を使用しています。
+
+### マスク画像を用いる方法
+
+学習画像それぞれに対応するマスク画像を用意する方法です。学習画像と同じファイル名のマスク画像を用意し、それを学習画像と別のディレクトリに保存します。
+
+- 学習画像
+  ![image](https://github.com/kohya-ss/sd-scripts/assets/52813779/607c5116-5f62-47de-8b66-9c4a597f0441)
+- マスク画像
+  ![image](https://github.com/kohya-ss/sd-scripts/assets/52813779/53e9b0f8-a4bf-49ed-882d-4026f84e8450)
+
+```.toml
+[[datasets.subsets]]
+image_dir = "/path/to/a_zundamon"
+caption_extension = ".txt"
+conditioning_data_dir = "/path/to/a_zundamon_mask"
+num_repeats = 8
+```
+
+マスク画像は、学習画像と同じサイズで、学習する部分を白、無視する部分を黒で描画します。グレースケールにも対応しています（127 ならロス重みが 0.5 になります）。なお、正確にはマスク画像の R チャネルが用いられます。
+
+DreamBooth 方式の dataset で、`conditioning_data_dir` で指定したディレクトリにマスク画像を保存してください。ControlNet のデータセットと同じですので、詳細は [ControlNet-LLLite](train_lllite_README-ja.md#データセットの準備) を参照してください。
+
+### 透明度（アルファチャネル）を使用する方法
+
+学習画像の透明度（アルファチャネル）がマスクとして使用されます。透明度が 0 の部分は無視され、255 の部分は学習されます。半透明の場合は、その透明度に応じてロス重みが変化します（127 ならおおむね 0.5）。
+
+![image](https://github.com/kohya-ss/sd-scripts/assets/52813779/0baa129b-446a-4aac-b98c-7208efb0e75e)
+
+※それぞれの画像は透過PNG
+
+学習時のスクリプトのオプションに `--alpha_mask` を指定するか、dataset の設定ファイルの subset で、`alpha_mask` を指定してください。たとえば、以下のようになります。
+
+```toml
+[[datasets.subsets]]
+image_dir = "/path/to/image/dir"
+caption_extension = ".txt"
+num_repeats = 8
+alpha_mask = true
+```
+
+## 学習時の注意事項
+
+- 現時点では DreamBooth 方式の dataset のみ対応しています。
+- マスクは latents のサイズ、つまり 1/8 に縮小されてから適用されます。そのため、細かい部分（たとえばアホ毛やイヤリングなど）はうまく学習できない可能性があります。マスクをわずかに拡張するなどの工夫が必要かもしれません。
+- マスクロスを用いる場合、学習対象外の部分をキャプションに含める必要はないかもしれません。（要検証）
+- `alpha_mask` の場合、マスクの有無を切り替えると latents キャッシュが自動的に再生成されます。
diff --git a/docs/masked_loss_README.md b/docs/masked_loss_README.md
new file mode 100644
index 000000000..3ac5ad211
--- /dev/null
+++ b/docs/masked_loss_README.md
@@ -0,0 +1,56 @@
+## Masked Loss
+
+Masked loss is a feature that allows you to train only part of an image by calculating the loss only for the part specified by the mask of the input image. For example, if you want to train a character, you can train only the character part by masking it, ignoring the background.
+
+There are two ways to specify the mask for masked loss.
+
+- Using a mask image
+- Using transparency (alpha channel) of the image
+
+The sample uses the "AI image model training data" from [ZunZunPJ Illustration/3D Data](https://zunko.jp/con_illust.html).
+
+### Using a mask image
+
+This is a method of preparing a mask image corresponding to each training image. Prepare a mask image with the same file name as the training image and save it in a different directory from the training image.
+
+- Training image
+  ![image](https://github.com/kohya-ss/sd-scripts/assets/52813779/607c5116-5f62-47de-8b66-9c4a597f0441)
+- Mask image
+  ![image](https://github.com/kohya-ss/sd-scripts/assets/52813779/53e9b0f8-a4bf-49ed-882d-4026f84e8450)
+
+```.toml
+[[datasets.subsets]]
+image_dir = "/path/to/a_zundamon"
+caption_extension = ".txt"
+conditioning_data_dir = "/path/to/a_zundamon_mask"
+num_repeats = 8
+```
+
+The mask image is the same size as the training image, with the part to be trained drawn in white and the part to be ignored in black. It also supports grayscale (127 gives a loss weight of 0.5). The R channel of the mask image is used currently.
+
+Use the dataset in the DreamBooth method, and save the mask image in the directory specified by `conditioning_data_dir`. It is the same as the ControlNet dataset, so please refer to [ControlNet-LLLite](train_lllite_README.md#Preparing-the-dataset) for details.
+
+### Using transparency (alpha channel) of the image
+
+The transparency (alpha channel) of the training image is used as a mask. The part with transparency 0 is ignored, the part with transparency 255 is trained. For semi-transparent parts, the loss weight changes according to the transparency (127 gives a weight of about 0.5).
+
+![image](https://github.com/kohya-ss/sd-scripts/assets/52813779/0baa129b-446a-4aac-b98c-7208efb0e75e)
+
+※Each image is a transparent PNG
+
+Specify `--alpha_mask` in the training script options or specify `alpha_mask` in the subset of the dataset configuration file. For example, it will look like this.
+
+```toml
+[[datasets.subsets]]
+image_dir = "/path/to/image/dir"
+caption_extension = ".txt"
+num_repeats = 8
+alpha_mask = true
+```
+
+## Notes on training
+
+- At the moment, only the dataset in the DreamBooth method is supported.
+- The mask is applied after the size is reduced to 1/8, which is the size of the latents. Therefore, fine details (such as ahoge or earrings) may not be learned well. Some dilations of the mask may be necessary.
+- If using masked loss, it may not be necessary to include parts that are not to be trained in the caption. (To be verified)
+- In the case of `alpha_mask`, the latents cache is automatically regenerated when the enable/disable state of the mask is switched.
diff --git a/docs/train_README-ja.md b/docs/train_README-ja.md
index d186bf243..cfa5a7d1c 100644
--- a/docs/train_README-ja.md
+++ b/docs/train_README-ja.md
@@ -648,7 +648,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
     詳細については各自お調べください。
 
-    任意のスケジューラを使う場合、任意のオプティマイザと同様に、`--scheduler_args`でオプション引数を指定してください。
+    任意のスケジューラを使う場合、任意のオプティマイザと同様に、`--lr_scheduler_args`でオプション引数を指定してください。
 
 ### オプティマイザの指定について
 
diff --git a/docs/train_README-zh.md b/docs/train_README-zh.md
index 7e00278c5..1bc47e0f5 100644
--- a/docs/train_README-zh.md
+++ b/docs/train_README-zh.md
@@ -582,7 +582,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
     有关详细信息，请自行研究。
 
-    要使用任何调度程序，请像使用任何优化器一样使用“--scheduler_args”指定可选参数。
+    要使用任何调度程序，请像使用任何优化器一样使用“--lr_scheduler_args”指定可选参数。
 ### 关于指定优化器
 
 使用 --optimizer_args 选项指定优化器选项参数。可以以key=value的格式指定多个值。此外，您可以指定多个值，以逗号分隔。例如，要指定 AdamW 优化器的参数，``--optimizer_args weight_decay=0.01 betas=.9,.999``。
diff --git a/docs/train_network_README-ja.md b/docs/train_network_README-ja.md
index 2205a7736..55c80c4b0 100644
--- a/docs/train_network_README-ja.md
+++ b/docs/train_network_README-ja.md
@@ -102,6 +102,8 @@ accelerate launch --num_cpu_threads_per_process 1 train_network.py
   * Text Encoderに関連するLoRAモジュールに、通常の学習率（--learning_rateオプションで指定）とは異なる学習率を使う時に指定します。Text Encoderのほうを若干低めの学習率（5e-5など）にしたほうが良い、という話もあるようです。
 * `--network_args`
   * 複数の引数を指定できます。後述します。
+* `--alpha_mask`
+  * 画像のアルファ値をマスクとして使用します。透過画像を学習する際に使用します。[PR #1223](https://github.com/kohya-ss/sd-scripts/pull/1223)
 
 `--network_train_unet_only` と `--network_train_text_encoder_only` の両方とも未指定時（デフォルト）はText EncoderとU-Netの両方のLoRAモジュールを有効にします。
 
@@ -181,16 +183,16 @@ python networks\extract_lora_from_dylora.py --model "foldername/dylora-model.saf
 
 詳細は[PR #355](https://github.com/kohya-ss/sd-scripts/pull/355) をご覧ください。
 
-SDXLは現在サポートしていません。
-
 フルモデルの25個のブロックの重みを指定できます。最初のブロックに該当するLoRAは存在しませんが、階層別LoRA適用等との互換性のために25個としています。またconv2d3x3に拡張しない場合も一部のブロックにはLoRAが存在しませんが、記述を統一するため常に25個の値を指定してください。
 
+SDXL では down/up 9 個、middle 3 個の値を指定してください。
+
 `--network_args` で以下の引数を指定してください。
 
 - `down_lr_weight` : U-Netのdown blocksの学習率の重みを指定します。以下が指定可能です。
-  - ブロックごとの重み : `"down_lr_weight=0,0,0,0,0,0,1,1,1,1,1,1"` のように12個の数値を指定します。
+  - ブロックごとの重み : `"down_lr_weight=0,0,0,0,0,0,1,1,1,1,1,1"` のように12個（SDXL では 9 個）の数値を指定します。
   - プリセットからの指定 : `"down_lr_weight=sine"` のように指定します（サインカーブで重みを指定します）。sine, cosine, linear, reverse_linear, zeros が指定可能です。また `"down_lr_weight=cosine+.25"` のように `+数値` を追加すると、指定した数値を加算します（0.25~1.25になります）。
-- `mid_lr_weight` : U-Netのmid blockの学習率の重みを指定します。`"down_lr_weight=0.5"` のように数値を一つだけ指定します。
+- `mid_lr_weight` : U-Netのmid blockの学習率の重みを指定します。`"down_lr_weight=0.5"` のように数値を一つだけ指定します（SDXL の場合は 3 個）。
 - `up_lr_weight` : U-Netのup blocksの学習率の重みを指定します。down_lr_weightと同様です。
 - 指定を省略した部分は1.0として扱われます。また重みを0にするとそのブロックのLoRAモジュールは作成されません。
 - `block_lr_zero_threshold` : 重みがこの値以下の場合、LoRAモジュールを作成しません。デフォルトは0です。
@@ -215,6 +217,9 @@ network_args = [ "block_lr_zero_threshold=0.1", "down_lr_weight=sine+.5", "mid_l
 
 フルモデルの25個のブロックのdim (rank)を指定できます。階層別学習率と同様に一部のブロックにはLoRAが存在しない場合がありますが、常に25個の値を指定してください。
 
+SDXL では 23 個の値を指定してください。一部のブロックにはLoRA が存在しませんが、`sdxl_train.py` の[階層別学習率](./train_SDXL-en.md) との互換性のためです。
+対応は、`0: time/label embed, 1-9: input blocks 0-8, 10-12: mid blocks 0-2, 13-21: output blocks 0-8, 22: out` です。
+
 `--network_args` で以下の引数を指定してください。
 
 - `block_dims` : 各ブロックのdim (rank)を指定します。`"block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"` のように25個の数値を指定します。
diff --git a/docs/train_network_README-zh.md b/docs/train_network_README-zh.md
index ed7a0c4ef..830014f72 100644
--- a/docs/train_network_README-zh.md
+++ b/docs/train_network_README-zh.md
@@ -101,6 +101,8 @@ LoRA的模型将会被保存在通过`--output_dir`选项指定的文件夹中
   * 当在Text Encoder相关的LoRA模块中使用与常规学习率（由`--learning_rate`选项指定）不同的学习率时，应指定此选项。可能最好将Text Encoder的学习率稍微降低（例如5e-5）。
 * `--network_args`
   * 可以指定多个参数。将在下面详细说明。
+* `--alpha_mask`
+  * 使用图像的 Alpha 值作为遮罩。这在学习透明图像时使用。[PR #1223](https://github.com/kohya-ss/sd-scripts/pull/1223)
 
 当未指定`--network_train_unet_only`和`--network_train_text_encoder_only`时（默认情况），将启用Text Encoder和U-Net的两个LoRA模块。
 
diff --git a/fine_tune.py b/fine_tune.py
index c7e6bbd2e..c79f97d25 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -91,6 +91,8 @@ def train(args):
     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
@@ -310,7 +312,11 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             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, init_kwargs=init_kwargs)
+        accelerator.init_trackers(
+            "finetuning" if args.log_tracker_name is None else args.log_tracker_name,
+            config=train_util.get_sanitized_config_or_none(args),
+            init_kwargs=init_kwargs,
+        )
 
     # For --sample_at_first
     train_util.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
@@ -354,7 +360,9 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
                 # 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, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -368,7 +376,9 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
                 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(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                    )
                     loss = loss.mean([1, 2, 3])
 
                     if args.min_snr_gamma:
@@ -376,11 +386,13 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
                     if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     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(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c
+                    )
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients and args.max_grad_norm != 0.0:
@@ -471,7 +483,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     accelerator.end_training()
 
-    if is_main_process and (args.save_state or args.save_state_on_train_end):        
+    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
diff --git a/finetune/prepare_buckets_latents.py b/finetune/prepare_buckets_latents.py
index 0389da388..019c737a6 100644
--- a/finetune/prepare_buckets_latents.py
+++ b/finetune/prepare_buckets_latents.py
@@ -11,6 +11,7 @@
 
 import torch
 from library.device_utils import init_ipex, get_preferred_device
+
 init_ipex()
 
 from torchvision import transforms
@@ -18,8 +19,10 @@
 import library.model_util as model_util
 import library.train_util as train_util
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 DEVICE = get_preferred_device()
@@ -89,7 +92,9 @@ def main(args):
 
     # bucketのサイズを計算する
     max_reso = tuple([int(t) for t in args.max_resolution.split(",")])
-    assert len(max_reso) == 2, f"illegal resolution (not 'width,height') / 画像サイズに誤りがあります。'幅,高さ'で指定してください: {args.max_resolution}"
+    assert (
+        len(max_reso) == 2
+    ), f"illegal resolution (not 'width,height') / 画像サイズに誤りがあります。'幅,高さ'で指定してください: {args.max_resolution}"
 
     bucket_manager = train_util.BucketManager(
         args.bucket_no_upscale, max_reso, args.min_bucket_reso, args.max_bucket_reso, args.bucket_reso_steps
@@ -107,7 +112,7 @@ def main(args):
     def process_batch(is_last):
         for bucket in bucket_manager.buckets:
             if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
-                train_util.cache_batch_latents(vae, True, bucket, args.flip_aug, False)
+                train_util.cache_batch_latents(vae, True, bucket, args.flip_aug, args.alpha_mask, False)
                 bucket.clear()
 
     # 読み込みの高速化のためにDataLoaderを使うオプション
@@ -208,7 +213,9 @@ def setup_parser() -> argparse.ArgumentParser:
     parser.add_argument("in_json", type=str, help="metadata file to input / 読み込むメタデータファイル")
     parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
     parser.add_argument("model_name_or_path", type=str, help="model name or path to encode latents / latentを取得するためのモデル")
-    parser.add_argument("--v2", action="store_true", help="not used (for backward compatibility) / 使用されません（互換性のため残してあります）")
+    parser.add_argument(
+        "--v2", action="store_true", help="not used (for backward compatibility) / 使用されません（互換性のため残してあります）"
+    )
     parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
     parser.add_argument(
         "--max_data_loader_n_workers",
@@ -231,10 +238,16 @@ def setup_parser() -> argparse.ArgumentParser:
         help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します",
     )
     parser.add_argument(
-        "--bucket_no_upscale", action="store_true", help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します"
+        "--bucket_no_upscale",
+        action="store_true",
+        help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します",
     )
     parser.add_argument(
-        "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度"
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help="use mixed precision / 混合精度を使う場合、その精度",
     )
     parser.add_argument(
         "--full_path",
@@ -242,7 +255,15 @@ def setup_parser() -> argparse.ArgumentParser:
         help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）",
     )
     parser.add_argument(
-        "--flip_aug", action="store_true", help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する"
+        "--flip_aug",
+        action="store_true",
+        help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する",
+    )
+    parser.add_argument(
+        "--alpha_mask",
+        type=str,
+        default="",
+        help="save alpha mask for images for loss calculation / 損失計算用に画像のアルファマスクを保存する",
     )
     parser.add_argument(
         "--skip_existing",
diff --git a/finetune/tag_images_by_wd14_tagger.py b/finetune/tag_images_by_wd14_tagger.py
index a327bbd61..cbc3d2d6b 100644
--- a/finetune/tag_images_by_wd14_tagger.py
+++ b/finetune/tag_images_by_wd14_tagger.py
@@ -11,7 +11,7 @@
 from tqdm import tqdm
 
 import library.train_util as train_util
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 
 setup_logging()
 import logging
@@ -42,8 +42,10 @@ def preprocess_image(image):
     pad_t = pad_y // 2
     image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)
 
-    interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
-    image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
+    if size > IMAGE_SIZE:
+        image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA)
+    else:
+        image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE))
 
     image = image.astype(np.float32)
     return image
@@ -112,7 +114,6 @@ def main(args):
 
     # モデルを読み込む
     if args.onnx:
-        import torch
         import onnx
         import onnxruntime as ort
 
diff --git a/gen_img.py b/gen_img.py
index 4fe898716..9427a8940 100644
--- a/gen_img.py
+++ b/gen_img.py
@@ -86,7 +86,8 @@
 """
 
 
-def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers, sdpa):
+# def replace_unet_modules(unet: diffusers.models.unets.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers, sdpa):
+def replace_unet_modules(unet, mem_eff_attn, xformers, sdpa):
     if mem_eff_attn:
         logger.info("Enable memory efficient attention for U-Net")
 
@@ -1435,6 +1436,7 @@ class BatchDataBase(NamedTuple):
     clip_prompt: str
     guide_image: Any
     raw_prompt: str
+    file_name: Optional[str]
 
 
 class BatchDataExt(NamedTuple):
@@ -1493,8 +1495,6 @@ def main(args):
     highres_fix = args.highres_fix_scale is not None
     # assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません"
 
-    if args.v_parameterization and not args.v2:
-        logger.warning("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
     if args.v2 and args.clip_skip is not None:
         logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
@@ -2316,7 +2316,7 @@ def scale_and_round(x):
             # このバッチの情報を取り出す
             (
                 return_latents,
-                (step_first, _, _, _, init_image, mask_image, _, guide_image, _),
+                (step_first, _, _, _, init_image, mask_image, _, guide_image, _, _),
                 (
                     width,
                     height,
@@ -2339,6 +2339,7 @@ def scale_and_round(x):
             prompts = []
             negative_prompts = []
             raw_prompts = []
+            filenames = []
             start_code = torch.zeros((batch_size, *noise_shape), device=device, dtype=dtype)
             noises = [
                 torch.zeros((batch_size, *noise_shape), device=device, dtype=dtype)
@@ -2371,7 +2372,7 @@ def scale_and_round(x):
             all_guide_images_are_same = True
             for i, (
                 _,
-                (_, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image, raw_prompt),
+                (_, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image, raw_prompt, filename),
                 _,
             ) in enumerate(batch):
                 prompts.append(prompt)
@@ -2379,6 +2380,7 @@ def scale_and_round(x):
                 seeds.append(seed)
                 clip_prompts.append(clip_prompt)
                 raw_prompts.append(raw_prompt)
+                filenames.append(filename)
 
                 if init_image is not None:
                     init_images.append(init_image)
@@ -2478,8 +2480,8 @@ def scale_and_round(x):
             # save image
             highres_prefix = ("0" if highres_1st else "1") if highres_fix else ""
             ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
-            for i, (image, prompt, negative_prompts, seed, clip_prompt, raw_prompt) in enumerate(
-                zip(images, prompts, negative_prompts, seeds, clip_prompts, raw_prompts)
+            for i, (image, prompt, negative_prompts, seed, clip_prompt, raw_prompt, filename) in enumerate(
+                zip(images, prompts, negative_prompts, seeds, clip_prompts, raw_prompts, filenames)
             ):
                 if highres_fix:
                     seed -= 1  # record original seed
@@ -2505,17 +2507,23 @@ def scale_and_round(x):
                     metadata.add_text("crop-top", str(crop_top))
                     metadata.add_text("crop-left", str(crop_left))
 
-                if args.use_original_file_name and init_images is not None:
-                    if type(init_images) is list:
-                        fln = os.path.splitext(os.path.basename(init_images[i % len(init_images)].filename))[0] + ".png"
-                    else:
-                        fln = os.path.splitext(os.path.basename(init_images.filename))[0] + ".png"
-                elif args.sequential_file_name:
-                    fln = f"im_{highres_prefix}{step_first + i + 1:06d}.png"
+                if filename is not None:
+                    fln = filename
                 else:
-                    fln = f"im_{ts_str}_{highres_prefix}{i:03d}_{seed}.png"
+                    if args.use_original_file_name and init_images is not None:
+                        if type(init_images) is list:
+                            fln = os.path.splitext(os.path.basename(init_images[i % len(init_images)].filename))[0] + ".png"
+                        else:
+                            fln = os.path.splitext(os.path.basename(init_images.filename))[0] + ".png"
+                    elif args.sequential_file_name:
+                        fln = f"im_{highres_prefix}{step_first + i + 1:06d}.png"
+                    else:
+                        fln = f"im_{ts_str}_{highres_prefix}{i:03d}_{seed}.png"
 
-                image.save(os.path.join(args.outdir, fln), pnginfo=metadata)
+                if fln.endswith(".webp"):
+                    image.save(os.path.join(args.outdir, fln), pnginfo=metadata, quality=100)  # lossy
+                else:
+                    image.save(os.path.join(args.outdir, fln), pnginfo=metadata)
 
             if not args.no_preview and not highres_1st and args.interactive:
                 try:
@@ -2562,6 +2570,7 @@ def scale_and_round(x):
             # repeat prompt
             for pi in range(args.images_per_prompt if len(raw_prompts) == 1 else len(raw_prompts)):
                 raw_prompt = raw_prompts[pi] if len(raw_prompts) > 1 else raw_prompts[0]
+                filename = None
 
                 if pi == 0 or len(raw_prompts) > 1:
                     # parse prompt: if prompt is not changed, skip parsing
@@ -2783,6 +2792,12 @@ def scale_and_round(x):
                                 logger.info(f"gradual latent unsharp params: {gl_unsharp_params}")
                                 continue
 
+                            m = re.match(r"f (.+)", parg, re.IGNORECASE)
+                            if m:  # filename
+                                filename = m.group(1)
+                                logger.info(f"filename: {filename}")
+                                continue
+
                         except ValueError as ex:
                             logger.error(f"Exception in parsing / 解析エラー: {parg}")
                             logger.error(f"{ex}")
@@ -2873,7 +2888,16 @@ def scale_and_round(x):
                 b1 = BatchData(
                     False,
                     BatchDataBase(
-                        global_step, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image, raw_prompt
+                        global_step,
+                        prompt,
+                        negative_prompt,
+                        seed,
+                        init_image,
+                        mask_image,
+                        clip_prompt,
+                        guide_image,
+                        raw_prompt,
+                        filename,
                     ),
                     BatchDataExt(
                         width,
@@ -2916,7 +2940,7 @@ def setup_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
 
     add_logging_arguments(parser)
-    
+
     parser.add_argument(
         "--sdxl", action="store_true", help="load Stable Diffusion XL model / Stable Diffusion XLのモデルを読み込む"
     )
diff --git a/gen_img_diffusers.py b/gen_img_diffusers.py
index 2c40f1a06..04db4e9b4 100644
--- a/gen_img_diffusers.py
+++ b/gen_img_diffusers.py
@@ -2216,8 +2216,6 @@ def main(args):
     highres_fix = args.highres_fix_scale is not None
     # assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません"
 
-    if args.v_parameterization and not args.v2:
-        logger.warning("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
     if args.v2 and args.clip_skip is not None:
         logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
diff --git a/library/adafactor_fused.py b/library/adafactor_fused.py
new file mode 100644
index 000000000..bdfc32ced
--- /dev/null
+++ b/library/adafactor_fused.py
@@ -0,0 +1,106 @@
+import math
+import torch
+from transformers import Adafactor
+
+@torch.no_grad()
+def adafactor_step_param(self, p, group):
+    if p.grad is None:
+        return
+    grad = p.grad
+    if grad.dtype in {torch.float16, torch.bfloat16}:
+        grad = grad.float()
+    if grad.is_sparse:
+        raise RuntimeError("Adafactor does not support sparse gradients.")
+
+    state = self.state[p]
+    grad_shape = grad.shape
+
+    factored, use_first_moment = Adafactor._get_options(group, grad_shape)
+    # State Initialization
+    if len(state) == 0:
+        state["step"] = 0
+
+        if use_first_moment:
+            # Exponential moving average of gradient values
+            state["exp_avg"] = torch.zeros_like(grad)
+        if factored:
+            state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
+            state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
+        else:
+            state["exp_avg_sq"] = torch.zeros_like(grad)
+
+        state["RMS"] = 0
+    else:
+        if use_first_moment:
+            state["exp_avg"] = state["exp_avg"].to(grad)
+        if factored:
+            state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
+            state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
+        else:
+            state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
+
+    p_data_fp32 = p
+    if p.dtype in {torch.float16, torch.bfloat16}:
+        p_data_fp32 = p_data_fp32.float()
+
+    state["step"] += 1
+    state["RMS"] = Adafactor._rms(p_data_fp32)
+    lr = Adafactor._get_lr(group, state)
+
+    beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
+    update = (grad ** 2) + group["eps"][0]
+    if factored:
+        exp_avg_sq_row = state["exp_avg_sq_row"]
+        exp_avg_sq_col = state["exp_avg_sq_col"]
+
+        exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t))
+        exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t))
+
+        # Approximation of exponential moving average of square of gradient
+        update = Adafactor._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
+        update.mul_(grad)
+    else:
+        exp_avg_sq = state["exp_avg_sq"]
+
+        exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t))
+        update = exp_avg_sq.rsqrt().mul_(grad)
+
+    update.div_((Adafactor._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
+    update.mul_(lr)
+
+    if use_first_moment:
+        exp_avg = state["exp_avg"]
+        exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"]))
+        update = exp_avg
+
+    if group["weight_decay"] != 0:
+        p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr))
+
+    p_data_fp32.add_(-update)
+
+    if p.dtype in {torch.float16, torch.bfloat16}:
+        p.copy_(p_data_fp32)
+
+
+@torch.no_grad()
+def adafactor_step(self, closure=None):
+    """
+    Performs a single optimization step
+
+    Arguments:
+        closure (callable, optional): A closure that reevaluates the model
+            and returns the loss.
+    """
+    loss = None
+    if closure is not None:
+        loss = closure()
+
+    for group in self.param_groups:
+        for p in group["params"]:
+            adafactor_step_param(self, p, group)
+
+    return loss
+
+def patch_adafactor_fused(optimizer: Adafactor):
+    optimizer.step_param = adafactor_step_param.__get__(optimizer)
+    optimizer.step = adafactor_step.__get__(optimizer)
diff --git a/library/config_util.py b/library/config_util.py
index d75d03b03..10b2457f3 100644
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -86,11 +86,13 @@ class DreamBoothSubsetParams(BaseSubsetParams):
     class_tokens: Optional[str] = None
     caption_extension: str = ".caption"
     cache_info: bool = False
+    alpha_mask: bool = False
 
 
 @dataclass
 class FineTuningSubsetParams(BaseSubsetParams):
     metadata_file: Optional[str] = None
+    alpha_mask: bool = False
 
 
 @dataclass
@@ -191,6 +193,7 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]
         "keep_tokens": int,
         "keep_tokens_separator": str,
         "secondary_separator": str,
+        "caption_separator": str,
         "enable_wildcard": bool,
         "token_warmup_min": int,
         "token_warmup_step": Any(float, int),
@@ -212,11 +215,13 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]
     DB_SUBSET_DISTINCT_SCHEMA = {
         Required("image_dir"): str,
         "is_reg": bool,
+        "alpha_mask": bool,
     }
     # FT means FineTuning
     FT_SUBSET_DISTINCT_SCHEMA = {
         Required("metadata_file"): str,
         "image_dir": str,
+        "alpha_mask": bool,
     }
     CN_SUBSET_ASCENDABLE_SCHEMA = {
         "caption_extension": str,
@@ -523,6 +528,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
           shuffle_caption: {subset.shuffle_caption}
           keep_tokens: {subset.keep_tokens}
           keep_tokens_separator: {subset.keep_tokens_separator}
+          caption_separator: {subset.caption_separator}
           secondary_separator: {subset.secondary_separator}
           enable_wildcard: {subset.enable_wildcard}
           caption_dropout_rate: {subset.caption_dropout_rate}
@@ -536,6 +542,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
           random_crop: {subset.random_crop}
           token_warmup_min: {subset.token_warmup_min},
           token_warmup_step: {subset.token_warmup_step},
+          alpha_mask: {subset.alpha_mask},
       """
                 ),
                 "  ",
diff --git a/library/custom_train_functions.py b/library/custom_train_functions.py
index 406e0e36e..faf443048 100644
--- a/library/custom_train_functions.py
+++ b/library/custom_train_functions.py
@@ -96,10 +96,13 @@ def add_v_prediction_like_loss(loss, timesteps, noise_scheduler, v_pred_like_los
     return loss
 
 
-def apply_debiased_estimation(loss, timesteps, noise_scheduler):
+def apply_debiased_estimation(loss, timesteps, noise_scheduler, v_prediction=False):
     snr_t = torch.stack([noise_scheduler.all_snr[t] for t in timesteps])  # batch_size
     snr_t = torch.minimum(snr_t, torch.ones_like(snr_t) * 1000)  # if timestep is 0, snr_t is inf, so limit it to 1000
-    weight = 1 / torch.sqrt(snr_t)
+    if v_prediction:
+        weight = 1 / (snr_t + 1)
+    else:
+        weight = 1 / torch.sqrt(snr_t)
     loss = weight * loss
     return loss
 
@@ -480,12 +483,20 @@ def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale):
 
 
 def apply_masked_loss(loss, batch):
-    # mask image is -1 to 1. we need to convert it to 0 to 1
-    mask_image = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1)  # use R channel
+    if "conditioning_images" in batch:
+        # conditioning image is -1 to 1. we need to convert it to 0 to 1
+        mask_image = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1)  # use R channel
+        mask_image = mask_image / 2 + 0.5
+        # print(f"conditioning_image: {mask_image.shape}")
+    elif "alpha_masks" in batch and batch["alpha_masks"] is not None:
+        # alpha mask is 0 to 1
+        mask_image = batch["alpha_masks"].to(dtype=loss.dtype).unsqueeze(1) # add channel dimension
+        # print(f"mask_image: {mask_image.shape}, {mask_image.mean()}")
+    else:
+        return loss
 
     # resize to the same size as the loss
     mask_image = torch.nn.functional.interpolate(mask_image, size=loss.shape[2:], mode="area")
-    mask_image = mask_image / 2 + 0.5
     loss = loss * mask_image
     return loss
 
diff --git a/library/ipex/attention.py b/library/ipex/attention.py
index d989ad53d..2bc62f65c 100644
--- a/library/ipex/attention.py
+++ b/library/ipex/attention.py
@@ -5,7 +5,7 @@
 
 # pylint: disable=protected-access, missing-function-docstring, line-too-long
 
-# ARC GPUs can't allocate more than 4GB to a single block so we slice the attetion layers
+# ARC GPUs can't allocate more than 4GB to a single block so we slice the attention layers
 
 sdpa_slice_trigger_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 4))
 attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 4))
diff --git a/library/sdxl_model_util.py b/library/sdxl_model_util.py
index f03f1bae5..4fad78a1c 100644
--- a/library/sdxl_model_util.py
+++ b/library/sdxl_model_util.py
@@ -1,4 +1,5 @@
 import torch
+import safetensors
 from accelerate import init_empty_weights
 from accelerate.utils.modeling import set_module_tensor_to_device
 from safetensors.torch import load_file, save_file
@@ -8,8 +9,10 @@
 from library import model_util
 from library import sdxl_original_unet
 from .utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 VAE_SCALE_FACTOR = 0.13025
@@ -163,17 +166,20 @@ def _load_state_dict_on_device(model, state_dict, device, dtype=None):
     raise RuntimeError("Error(s) in loading state_dict for {}:\n\t{}".format(model.__class__.__name__, "\n\t".join(error_msgs)))
 
 
-def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None):
+def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None, disable_mmap=False):
     # model_version is reserved for future use
     # dtype is used for full_fp16/bf16 integration. Text Encoder will remain fp32, because it runs on CPU when caching
 
     # Load the state dict
     if model_util.is_safetensors(ckpt_path):
         checkpoint = None
-        try:
-            state_dict = load_file(ckpt_path, device=map_location)
-        except:
-            state_dict = load_file(ckpt_path)  # prevent device invalid Error
+        if disable_mmap:
+            state_dict = safetensors.torch.load(open(ckpt_path, "rb").read())
+        else:
+            try:
+                state_dict = load_file(ckpt_path, device=map_location)
+            except:
+                state_dict = load_file(ckpt_path)  # prevent device invalid Error
         epoch = None
         global_step = None
     else:
diff --git a/library/sdxl_train_util.py b/library/sdxl_train_util.py
index a29013e34..b74bea91a 100644
--- a/library/sdxl_train_util.py
+++ b/library/sdxl_train_util.py
@@ -5,6 +5,7 @@
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from accelerate import init_empty_weights
@@ -13,8 +14,10 @@
 from library import model_util, sdxl_model_util, train_util, sdxl_original_unet
 from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline
 from .utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 TOKENIZER1_PATH = "openai/clip-vit-large-patch14"
@@ -44,6 +47,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
                 weight_dtype,
                 accelerator.device if args.lowram else "cpu",
                 model_dtype,
+                args.disable_mmap_load_safetensors,
             )
 
             # work on low-ram device
@@ -60,7 +64,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
 
 
 def _load_target_model(
-    name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None
+    name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None, disable_mmap=False
 ):
     # model_dtype only work with full fp16/bf16
     name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
@@ -75,7 +79,7 @@ def _load_target_model(
             unet,
             logit_scale,
             ckpt_info,
-        ) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device, model_dtype)
+        ) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device, model_dtype, disable_mmap)
     else:
         # Diffusers model is loaded to CPU
         from diffusers import StableDiffusionXLPipeline
@@ -332,6 +336,11 @@ def add_sdxl_training_arguments(parser: argparse.ArgumentParser):
         action="store_true",
         help="cache text encoder outputs to disk / text encoderの出力をディスクにキャッシュする",
     )
+    parser.add_argument(
+        "--disable_mmap_load_safetensors",
+        action="store_true",
+        help="disable mmap load for safetensors. Speed up model loading in WSL environment / safetensorsのmmapロードを無効にする。WSL環境等でモデル読み込みを高速化できる",
+    )
 
 
 def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCaching: bool = True):
diff --git a/library/train_util.py b/library/train_util.py
index 0fec565db..100ef475d 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -42,7 +42,11 @@
 from torchvision import transforms
 from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection
 import transformers
-from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
+from diffusers.optimization import (
+    SchedulerType as DiffusersSchedulerType,
+    TYPE_TO_SCHEDULER_FUNCTION as DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION,
+)
+from transformers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
 from diffusers import (
     StableDiffusionPipeline,
     DDPMScheduler,
@@ -71,7 +75,7 @@
 import library.huggingface_util as huggingface_util
 import library.sai_model_spec as sai_model_spec
 import library.deepspeed_utils as deepspeed_utils
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 
 setup_logging()
 import logging
@@ -159,6 +163,7 @@ def __init__(self, image_key: str, num_repeats: int, caption: str, is_reg: bool,
         self.text_encoder_outputs1: Optional[torch.Tensor] = None
         self.text_encoder_outputs2: Optional[torch.Tensor] = None
         self.text_encoder_pool2: Optional[torch.Tensor] = None
+        self.alpha_mask: Optional[torch.Tensor] = None  # alpha mask can be flipped in runtime
 
 
 class BucketManager:
@@ -361,6 +366,7 @@ class BaseSubset:
     def __init__(
         self,
         image_dir: Optional[str],
+        alpha_mask: Optional[bool],
         num_repeats: int,
         shuffle_caption: bool,
         caption_separator: str,
@@ -381,6 +387,7 @@ def __init__(
         token_warmup_step: Union[float, int],
     ) -> None:
         self.image_dir = image_dir
+        self.alpha_mask = alpha_mask if alpha_mask is not None else False
         self.num_repeats = num_repeats
         self.shuffle_caption = shuffle_caption
         self.caption_separator = caption_separator
@@ -412,6 +419,7 @@ def __init__(
         class_tokens: Optional[str],
         caption_extension: str,
         cache_info: bool,
+        alpha_mask: bool,
         num_repeats,
         shuffle_caption,
         caption_separator: str,
@@ -435,6 +443,7 @@ def __init__(
 
         super().__init__(
             image_dir,
+            alpha_mask,
             num_repeats,
             shuffle_caption,
             caption_separator,
@@ -473,6 +482,7 @@ def __init__(
         self,
         image_dir,
         metadata_file: str,
+        alpha_mask: bool,
         num_repeats,
         shuffle_caption,
         caption_separator,
@@ -496,6 +506,7 @@ def __init__(
 
         super().__init__(
             image_dir,
+            alpha_mask,
             num_repeats,
             shuffle_caption,
             caption_separator,
@@ -554,6 +565,7 @@ def __init__(
 
         super().__init__(
             image_dir,
+            False,  # alpha_mask
             num_repeats,
             shuffle_caption,
             caption_separator,
@@ -641,6 +653,34 @@ def __init__(
         # caching
         self.caching_mode = None  # None, 'latents', 'text'
 
+    def adjust_min_max_bucket_reso_by_steps(
+        self, resolution: Tuple[int, int], min_bucket_reso: int, max_bucket_reso: int, bucket_reso_steps: int
+    ) -> Tuple[int, int]:
+        # make min/max bucket reso to be multiple of bucket_reso_steps
+        if min_bucket_reso % bucket_reso_steps != 0:
+            adjusted_min_bucket_reso = min_bucket_reso - min_bucket_reso % bucket_reso_steps
+            logger.warning(
+                f"min_bucket_reso is adjusted to be multiple of bucket_reso_steps"
+                f" / min_bucket_resoがbucket_reso_stepsの倍数になるように調整されました: {min_bucket_reso} -> {adjusted_min_bucket_reso}"
+            )
+            min_bucket_reso = adjusted_min_bucket_reso
+        if max_bucket_reso % bucket_reso_steps != 0:
+            adjusted_max_bucket_reso = max_bucket_reso + bucket_reso_steps - max_bucket_reso % bucket_reso_steps
+            logger.warning(
+                f"max_bucket_reso is adjusted to be multiple of bucket_reso_steps"
+                f" / max_bucket_resoがbucket_reso_stepsの倍数になるように調整されました: {max_bucket_reso} -> {adjusted_max_bucket_reso}"
+            )
+            max_bucket_reso = adjusted_max_bucket_reso
+
+        assert (
+            min(resolution) >= min_bucket_reso
+        ), f"min_bucket_reso must be equal or less than resolution / min_bucket_resoは最小解像度より大きくできません。解像度を大きくするかmin_bucket_resoを小さくしてください"
+        assert (
+            max(resolution) <= max_bucket_reso
+        ), f"max_bucket_reso must be equal or greater than resolution / max_bucket_resoは最大解像度より小さくできません。解像度を小さくするかmin_bucket_resoを大きくしてください"
+
+        return min_bucket_reso, max_bucket_reso
+
     def set_seed(self, seed):
         self.seed = seed
 
@@ -649,8 +689,16 @@ def set_caching_mode(self, mode):
 
     def set_current_epoch(self, epoch):
         if not self.current_epoch == epoch:  # epochが切り替わったらバケツをシャッフルする
-            self.shuffle_buckets()
-        self.current_epoch = epoch
+            if epoch > self.current_epoch:
+                logger.info("epoch is incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch))
+                num_epochs = epoch - self.current_epoch
+                for _ in range(num_epochs):
+                    self.current_epoch += 1
+                    self.shuffle_buckets()
+                # self.current_epoch seem to be set to 0 again in the next epoch. it may be caused by skipped_dataloader?
+            else:
+                logger.warning("epoch is not incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch))
+                self.current_epoch = epoch
 
     def set_current_step(self, step):
         self.current_step = step
@@ -915,7 +963,7 @@ def make_buckets(self):
             logger.info(f"mean ar error (without repeats): {mean_img_ar_error}")
 
         # データ参照用indexを作る。このindexはdatasetのshuffleに用いられる
-        self.buckets_indices: List(BucketBatchIndex) = []
+        self.buckets_indices: List[BucketBatchIndex] = []
         for bucket_index, bucket in enumerate(self.bucket_manager.buckets):
             batch_count = int(math.ceil(len(bucket) / self.batch_size))
             for batch_index in range(batch_count):
@@ -978,9 +1026,26 @@ def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_proc
         # sort by resolution
         image_infos.sort(key=lambda info: info.bucket_reso[0] * info.bucket_reso[1])
 
-        # split by resolution
-        batches = []
-        batch = []
+        # split by resolution and some conditions
+        class Condition:
+            def __init__(self, reso, flip_aug, alpha_mask, random_crop):
+                self.reso = reso
+                self.flip_aug = flip_aug
+                self.alpha_mask = alpha_mask
+                self.random_crop = random_crop
+
+            def __eq__(self, other):
+                return (
+                    self.reso == other.reso
+                    and self.flip_aug == other.flip_aug
+                    and self.alpha_mask == other.alpha_mask
+                    and self.random_crop == other.random_crop
+                )
+
+        batches: List[Tuple[Condition, List[ImageInfo]]] = []
+        batch: List[ImageInfo] = []
+        current_condition = None
+
         logger.info("checking cache validity...")
         for info in tqdm(image_infos):
             subset = self.image_to_subset[info.image_key]
@@ -994,33 +1059,38 @@ def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_proc
                 if not is_main_process:  # store to info only
                     continue
 
-                cache_available = is_disk_cached_latents_is_expected(info.bucket_reso, info.latents_npz, subset.flip_aug)
+                cache_available = is_disk_cached_latents_is_expected(
+                    info.bucket_reso, info.latents_npz, subset.flip_aug, subset.alpha_mask
+                )
 
                 if cache_available:  # do not add to batch
                     continue
 
-            # if last member of batch has different resolution, flush the batch
-            if len(batch) > 0 and batch[-1].bucket_reso != info.bucket_reso:
-                batches.append(batch)
+            # if batch is not empty and condition is changed, flush the batch. Note that current_condition is not None if batch is not empty
+            condition = Condition(info.bucket_reso, subset.flip_aug, subset.alpha_mask, subset.random_crop)
+            if len(batch) > 0 and current_condition != condition:
+                batches.append((current_condition, batch))
                 batch = []
 
             batch.append(info)
+            current_condition = condition
 
             # if number of data in batch is enough, flush the batch
             if len(batch) >= vae_batch_size:
-                batches.append(batch)
+                batches.append((current_condition, batch))
                 batch = []
+                current_condition = None
 
         if len(batch) > 0:
-            batches.append(batch)
+            batches.append((current_condition, batch))
 
         if cache_to_disk and not is_main_process:  # if cache to disk, don't cache latents in non-main process, set to info only
             return
 
         # iterate batches: batch doesn't have image, image will be loaded in cache_batch_latents and discarded
         logger.info("caching latents...")
-        for batch in tqdm(batches, smoothing=1, total=len(batches)):
-            cache_batch_latents(vae, cache_to_disk, batch, subset.flip_aug, subset.random_crop)
+        for condition, batch in tqdm(batches, smoothing=1, total=len(batches)):
+            cache_batch_latents(vae, cache_to_disk, batch, condition.flip_aug, condition.alpha_mask, condition.random_crop)
 
     # weight_dtypeを指定するとText Encoderそのもの、およひ出力がweight_dtypeになる
     # SDXLでのみ有効だが、datasetのメソッドとする必要があるので、sdxl_train_util.pyではなくこちらに実装する
@@ -1088,8 +1158,8 @@ def cache_text_encoder_outputs(
     def get_image_size(self, image_path):
         return imagesize.get(image_path)
 
-    def load_image_with_face_info(self, subset: BaseSubset, image_path: str):
-        img = load_image(image_path)
+    def load_image_with_face_info(self, subset: BaseSubset, image_path: str, alpha_mask=False):
+        img = load_image(image_path, alpha_mask)
 
         face_cx = face_cy = face_w = face_h = 0
         if subset.face_crop_aug_range is not None:
@@ -1166,6 +1236,7 @@ def __getitem__(self, index):
         input_ids_list = []
         input_ids2_list = []
         latents_list = []
+        alpha_mask_list = []
         images = []
         original_sizes_hw = []
         crop_top_lefts = []
@@ -1190,21 +1261,28 @@ def __getitem__(self, index):
                 crop_ltrb = image_info.latents_crop_ltrb  # calc values later if flipped
                 if not flipped:
                     latents = image_info.latents
+                    alpha_mask = image_info.alpha_mask
                 else:
                     latents = image_info.latents_flipped
+                    alpha_mask = None if image_info.alpha_mask is None else torch.flip(image_info.alpha_mask, [1])
 
                 image = None
             elif image_info.latents_npz is not None:  # FineTuningDatasetまたはcache_latents_to_disk=Trueの場合
-                latents, original_size, crop_ltrb, flipped_latents = load_latents_from_disk(image_info.latents_npz)
+                latents, original_size, crop_ltrb, flipped_latents, alpha_mask = load_latents_from_disk(image_info.latents_npz)
                 if flipped:
                     latents = flipped_latents
+                    alpha_mask = None if alpha_mask is None else alpha_mask[:, ::-1].copy()  # copy to avoid negative stride problem
                     del flipped_latents
                 latents = torch.FloatTensor(latents)
+                if alpha_mask is not None:
+                    alpha_mask = torch.FloatTensor(alpha_mask)
 
                 image = None
             else:
                 # 画像を読み込み、必要ならcropする
-                img, face_cx, face_cy, face_w, face_h = self.load_image_with_face_info(subset, image_info.absolute_path)
+                img, face_cx, face_cy, face_w, face_h = self.load_image_with_face_info(
+                    subset, image_info.absolute_path, subset.alpha_mask
+                )
                 im_h, im_w = img.shape[0:2]
 
                 if self.enable_bucket:
@@ -1236,16 +1314,33 @@ def __getitem__(self, index):
                 # augmentation
                 aug = self.aug_helper.get_augmentor(subset.color_aug)
                 if aug is not None:
-                    img = aug(image=img)["image"]
+                    # augment RGB channels only
+                    img_rgb = img[:, :, :3]
+                    img_rgb = aug(image=img_rgb)["image"]
+                    img[:, :, :3] = img_rgb
 
                 if flipped:
                     img = img[:, ::-1, :].copy()  # copy to avoid negative stride problem
 
+                if subset.alpha_mask:
+                    if img.shape[2] == 4:
+                        alpha_mask = img[:, :, 3]  # [H,W]
+                        alpha_mask = alpha_mask.astype(np.float32) / 255.0  # 0.0~1.0
+                        alpha_mask = torch.FloatTensor(alpha_mask)
+                    else:
+                        alpha_mask = torch.ones((img.shape[0], img.shape[1]), dtype=torch.float32)
+                else:
+                    alpha_mask = None
+
+                img = img[:, :, :3]  # remove alpha channel
+
                 latents = None
                 image = self.image_transforms(img)  # -1.0~1.0のtorch.Tensorになる
+                del img
 
             images.append(image)
             latents_list.append(latents)
+            alpha_mask_list.append(alpha_mask)
 
             target_size = (image.shape[2], image.shape[1]) if image is not None else (latents.shape[2] * 8, latents.shape[1] * 8)
 
@@ -1331,6 +1426,23 @@ def __getitem__(self, index):
             example["text_encoder_outputs2_list"] = torch.stack(text_encoder_outputs2_list)
             example["text_encoder_pool2_list"] = torch.stack(text_encoder_pool2_list)
 
+        # if one of alpha_masks is not None, we need to replace None with ones
+        none_or_not = [x is None for x in alpha_mask_list]
+        if all(none_or_not):
+            example["alpha_masks"] = None
+        elif any(none_or_not):
+            for i in range(len(alpha_mask_list)):
+                if alpha_mask_list[i] is None:
+                    if images[i] is not None:
+                        alpha_mask_list[i] = torch.ones((images[i].shape[1], images[i].shape[2]), dtype=torch.float32)
+                    else:
+                        alpha_mask_list[i] = torch.ones(
+                            (latents_list[i].shape[1] * 8, latents_list[i].shape[2] * 8), dtype=torch.float32
+                        )
+            example["alpha_masks"] = torch.stack(alpha_mask_list)
+        else:
+            example["alpha_masks"] = torch.stack(alpha_mask_list)
+
         if images[0] is not None:
             images = torch.stack(images)
             images = images.to(memory_format=torch.contiguous_format).float()
@@ -1361,6 +1473,7 @@ def get_item_for_caching(self, bucket, bucket_batch_size, image_index):
         resized_sizes = []
         bucket_reso = None
         flip_aug = None
+        alpha_mask = None
         random_crop = None
 
         for image_key in bucket[image_index : image_index + bucket_batch_size]:
@@ -1369,10 +1482,13 @@ def get_item_for_caching(self, bucket, bucket_batch_size, image_index):
 
             if flip_aug is None:
                 flip_aug = subset.flip_aug
+                alpha_mask = subset.alpha_mask
                 random_crop = subset.random_crop
                 bucket_reso = image_info.bucket_reso
             else:
+                # TODO そもそも混在してても動くようにしたほうがいい
                 assert flip_aug == subset.flip_aug, "flip_aug must be same in a batch"
+                assert alpha_mask == subset.alpha_mask, "alpha_mask must be same in a batch"
                 assert random_crop == subset.random_crop, "random_crop must be same in a batch"
                 assert bucket_reso == image_info.bucket_reso, "bucket_reso must be same in a batch"
 
@@ -1409,6 +1525,7 @@ def get_item_for_caching(self, bucket, bucket_batch_size, image_index):
         example["absolute_paths"] = absolute_paths
         example["resized_sizes"] = resized_sizes
         example["flip_aug"] = flip_aug
+        example["alpha_mask"] = alpha_mask
         example["random_crop"] = random_crop
         example["bucket_reso"] = bucket_reso
         return example
@@ -1444,12 +1561,9 @@ def __init__(
 
         self.enable_bucket = enable_bucket
         if self.enable_bucket:
-            assert (
-                min(resolution) >= min_bucket_reso
-            ), f"min_bucket_reso must be equal or less than resolution / min_bucket_resoは最小解像度より大きくできません。解像度を大きくするかmin_bucket_resoを小さくしてください"
-            assert (
-                max(resolution) <= max_bucket_reso
-            ), f"max_bucket_reso must be equal or greater than resolution / max_bucket_resoは最大解像度より小さくできません。解像度を小さくするかmin_bucket_resoを大きくしてください"
+            min_bucket_reso, max_bucket_reso = self.adjust_min_max_bucket_reso_by_steps(
+                resolution, min_bucket_reso, max_bucket_reso, bucket_reso_steps
+            )
             self.min_bucket_reso = min_bucket_reso
             self.max_bucket_reso = max_bucket_reso
             self.bucket_reso_steps = bucket_reso_steps
@@ -1812,6 +1926,9 @@ def __init__(
 
             self.enable_bucket = enable_bucket
             if self.enable_bucket:
+                min_bucket_reso, max_bucket_reso = self.adjust_min_max_bucket_reso_by_steps(
+                    resolution, min_bucket_reso, max_bucket_reso, bucket_reso_steps
+                )
                 self.min_bucket_reso = min_bucket_reso
                 self.max_bucket_reso = max_bucket_reso
                 self.bucket_reso_steps = bucket_reso_steps
@@ -1892,6 +2009,7 @@ def __init__(
                 None,
                 subset.caption_extension,
                 subset.cache_info,
+                False,
                 subset.num_repeats,
                 subset.shuffle_caption,
                 subset.caption_separator,
@@ -2028,9 +2146,7 @@ def __getitem__(self, index):
                 # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}"
                 # resize to target
                 if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]:
-                    cond_img = cv2.resize(
-                        cond_img, (int(target_size_hw[1]), int(target_size_hw[0])), interpolation=cv2.INTER_LANCZOS4
-                    )
+                    cond_img = pil_resize(cond_img, (int(target_size_hw[1]), int(target_size_hw[0])))
 
             if flipped:
                 cond_img = cond_img[:, ::-1, :].copy()  # copy to avoid negative stride
@@ -2117,31 +2233,44 @@ def disable_token_padding(self):
             dataset.disable_token_padding()
 
 
-def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool):
+def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool, alpha_mask: bool):
     expected_latents_size = (reso[1] // 8, reso[0] // 8)  # bucket_resoはWxHなので注意
 
     if not os.path.exists(npz_path):
         return False
 
-    npz = np.load(npz_path)
-    if "latents" not in npz or "original_size" not in npz or "crop_ltrb" not in npz:  # old ver?
-        return False
-    if npz["latents"].shape[1:3] != expected_latents_size:
-        return False
-
-    if flip_aug:
-        if "latents_flipped" not in npz:
+    try:
+        npz = np.load(npz_path)
+        if "latents" not in npz or "original_size" not in npz or "crop_ltrb" not in npz:  # old ver?
             return False
-        if npz["latents_flipped"].shape[1:3] != expected_latents_size:
+        if npz["latents"].shape[1:3] != expected_latents_size:
             return False
 
+        if flip_aug:
+            if "latents_flipped" not in npz:
+                return False
+            if npz["latents_flipped"].shape[1:3] != expected_latents_size:
+                return False
+
+        if alpha_mask:
+            if "alpha_mask" not in npz:
+                return False
+            if (npz["alpha_mask"].shape[1], npz["alpha_mask"].shape[0]) != reso:  # HxW => WxH != reso
+                return False
+        else:
+            if "alpha_mask" in npz:
+                return False
+    except Exception as e:
+        logger.error(f"Error loading file: {npz_path}")
+        raise e
+
     return True
 
 
 # 戻り値は、latents_tensor, (original_size width, original_size height), (crop left, crop top)
 def load_latents_from_disk(
     npz_path,
-) -> Tuple[Optional[torch.Tensor], Optional[List[int]], Optional[List[int]], Optional[torch.Tensor]]:
+) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
     npz = np.load(npz_path)
     if "latents" not in npz:
         raise ValueError(f"error: npz is old format. please re-generate {npz_path}")
@@ -2150,13 +2279,16 @@ def load_latents_from_disk(
     original_size = npz["original_size"].tolist()
     crop_ltrb = npz["crop_ltrb"].tolist()
     flipped_latents = npz["latents_flipped"] if "latents_flipped" in npz else None
-    return latents, original_size, crop_ltrb, flipped_latents
+    alpha_mask = npz["alpha_mask"] if "alpha_mask" in npz else None
+    return latents, original_size, crop_ltrb, flipped_latents, alpha_mask
 
 
-def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_ltrb, flipped_latents_tensor=None):
+def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_ltrb, flipped_latents_tensor=None, alpha_mask=None):
     kwargs = {}
     if flipped_latents_tensor is not None:
         kwargs["latents_flipped"] = flipped_latents_tensor.float().cpu().numpy()
+    if alpha_mask is not None:
+        kwargs["alpha_mask"] = alpha_mask.float().cpu().numpy()
     np.savez(
         npz_path,
         latents=latents_tensor.float().cpu().numpy(),
@@ -2228,6 +2360,13 @@ def debug_dataset(train_dataset, show_input_ids=False):
                         if os.name == "nt":
                             cv2.imshow("cond_img", cond_img)
 
+                    if "alpha_masks" in example and example["alpha_masks"] is not None:
+                        alpha_mask = example["alpha_masks"][j]
+                        logger.info(f"alpha mask size: {alpha_mask.size()}")
+                        alpha_mask = (alpha_mask.numpy() * 255.0).astype(np.uint8)
+                        if os.name == "nt":
+                            cv2.imshow("alpha_mask", alpha_mask)
+
                     if os.name == "nt":  # only windows
                         cv2.imshow("img", im)
                         k = cv2.waitKey()
@@ -2345,24 +2484,35 @@ def load_arbitrary_dataset(args, tokenizer) -> MinimalDataset:
     return train_dataset_group
 
 
-def load_image(image_path):
-    image = Image.open(image_path)
-    if not image.mode == "RGB":
-        image = image.convert("RGB")
-    img = np.array(image, np.uint8)
-    return img
+def load_image(image_path, alpha=False):
+    try:
+        with Image.open(image_path) as image:
+            if alpha:
+                if not image.mode == "RGBA":
+                    image = image.convert("RGBA")
+            else:
+                if not image.mode == "RGB":
+                    image = image.convert("RGB")
+            img = np.array(image, np.uint8)
+            return img
+    except (IOError, OSError) as e:
+        logger.error(f"Error loading file: {image_path}")
+        raise e
 
 
 # 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top, crop right, crop bottom)
 def trim_and_resize_if_required(
-    random_crop: bool, image: Image.Image, reso, resized_size: Tuple[int, int]
+    random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int]
 ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int, int, int]]:
     image_height, image_width = image.shape[0:2]
     original_size = (image_width, image_height)  # size before resize
 
     if image_width != resized_size[0] or image_height != resized_size[1]:
         # リサイズする
-        image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+        if image_width > resized_size[0] and image_height > resized_size[1]:
+            image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+        else:
+            image = pil_resize(image, resized_size)
 
     image_height, image_width = image.shape[0:2]
 
@@ -2387,7 +2537,7 @@ def trim_and_resize_if_required(
 
 
 def cache_batch_latents(
-    vae: AutoencoderKL, cache_to_disk: bool, image_infos: List[ImageInfo], flip_aug: bool, random_crop: bool
+    vae: AutoencoderKL, cache_to_disk: bool, image_infos: List[ImageInfo], flip_aug: bool, use_alpha_mask: bool, random_crop: bool
 ) -> None:
     r"""
     requires image_infos to have: absolute_path, bucket_reso, resized_size, latents_npz
@@ -2399,16 +2549,30 @@ def cache_batch_latents(
     latents_original_size and latents_crop_ltrb are also set
     """
     images = []
+    alpha_masks: List[np.ndarray] = []
     for info in image_infos:
-        image = load_image(info.absolute_path) if info.image is None else np.array(info.image, np.uint8)
+        image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8)
         # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要
         image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size)
-        image = IMAGE_TRANSFORMS(image)
-        images.append(image)
 
         info.latents_original_size = original_size
         info.latents_crop_ltrb = crop_ltrb
 
+        if use_alpha_mask:
+            if image.shape[2] == 4:
+                alpha_mask = image[:, :, 3]  # [H,W]
+                alpha_mask = alpha_mask.astype(np.float32) / 255.0
+                alpha_mask = torch.FloatTensor(alpha_mask)  # [H,W]
+            else:
+                alpha_mask = torch.ones_like(image[:, :, 0], dtype=torch.float32)  # [H,W]
+        else:
+            alpha_mask = None
+        alpha_masks.append(alpha_mask)
+
+        image = image[:, :, :3]  # remove alpha channel if exists
+        image = IMAGE_TRANSFORMS(image)
+        images.append(image)
+
     img_tensors = torch.stack(images, dim=0)
     img_tensors = img_tensors.to(device=vae.device, dtype=vae.dtype)
 
@@ -2422,17 +2586,25 @@ def cache_batch_latents(
     else:
         flipped_latents = [None] * len(latents)
 
-    for info, latent, flipped_latent in zip(image_infos, latents, flipped_latents):
+    for info, latent, flipped_latent, alpha_mask in zip(image_infos, latents, flipped_latents, alpha_masks):
         # check NaN
         if torch.isnan(latents).any() or (flipped_latent is not None and torch.isnan(flipped_latent).any()):
             raise RuntimeError(f"NaN detected in latents: {info.absolute_path}")
 
         if cache_to_disk:
-            save_latents_to_disk(info.latents_npz, latent, info.latents_original_size, info.latents_crop_ltrb, flipped_latent)
+            save_latents_to_disk(
+                info.latents_npz,
+                latent,
+                info.latents_original_size,
+                info.latents_crop_ltrb,
+                flipped_latent,
+                alpha_mask,
+            )
         else:
             info.latents = latent
             if flip_aug:
                 info.latents_flipped = flipped_latent
+            info.alpha_mask = alpha_mask
 
     if not HIGH_VRAM:
         clean_memory_on_device(vae.device)
@@ -2852,11 +3024,29 @@ def add_sd_models_arguments(parser: argparse.ArgumentParser):
 
 
 def add_optimizer_arguments(parser: argparse.ArgumentParser):
+    def int_or_float(value):
+        if value.endswith("%"):
+            try:
+                return float(value[:-1]) / 100.0
+            except ValueError:
+                raise argparse.ArgumentTypeError(f"Value '{value}' is not a valid percentage")
+        try:
+            float_value = float(value)
+            if float_value >= 1:
+                return int(value)
+            return float(value)
+        except ValueError:
+            raise argparse.ArgumentTypeError(f"'{value}' is not an int or float")
+
     parser.add_argument(
         "--optimizer_type",
         type=str,
         default="",
-        help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, AdaFactor",
+        help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, "
+        "Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, "
+        "DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, "
+        "AdaFactor. "
+        "Also, you can use any optimizer by specifying the full path to the class, like 'bitsandbytes.optim.AdEMAMix8bit' or 'bitsandbytes.optim.PagedAdEMAMix8bit'.",
     )
 
     # backward compatibility
@@ -2904,9 +3094,17 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
     )
     parser.add_argument(
         "--lr_warmup_steps",
-        type=int,
+        type=int_or_float,
         default=0,
-        help="Number of steps for the warmup in the lr scheduler (default is 0) / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）",
+        help="Int number of steps for the warmup in the lr scheduler (default is 0) or float with ratio of train steps"
+        " / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）、または学習ステップの比率（1未満のfloat値の場合）",
+    )
+    parser.add_argument(
+        "--lr_decay_steps",
+        type=int_or_float,
+        default=0,
+        help="Int number of steps for the decay in the lr scheduler (default is 0) or float (<1) with ratio of train steps"
+        " / 学習率のスケジューラを減衰させるステップ数（デフォルト0）、または学習ステップの比率（1未満のfloat値の場合）",
     )
     parser.add_argument(
         "--lr_scheduler_num_cycles",
@@ -2920,6 +3118,26 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
         default=1,
         help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power",
     )
+    parser.add_argument(
+        "--fused_backward_pass",
+        action="store_true",
+        help="Combines backward pass and optimizer step to reduce VRAM usage. Only available in SDXL"
+        + " / バックワードパスとオプティマイザステップを組み合わせてVRAMの使用量を削減します。SDXLでのみ有効",
+    )
+    parser.add_argument(
+        "--lr_scheduler_timescale",
+        type=int,
+        default=None,
+        help="Inverse sqrt timescale for inverse sqrt scheduler,defaults to `num_warmup_steps`"
+        + " / 逆平方根スケジューラのタイムスケール、デフォルトは`num_warmup_steps`",
+    )
+    parser.add_argument(
+        "--lr_scheduler_min_lr_ratio",
+        type=float,
+        default=None,
+        help="The minimum learning rate as a ratio of the initial learning rate for cosine with min lr scheduler and warmup decay scheduler"
+        + " / 初期学習率の比率としての最小学習率を指定する、cosine with min lr と warmup decay スケジューラ で有効",
+    )
 
 
 def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool):
@@ -3087,7 +3305,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
     )
     parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed")
     parser.add_argument(
-        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / grandient checkpointingを有効にする"
+        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / gradient checkpointingを有効にする"
     )
     parser.add_argument(
         "--gradient_accumulation_steps",
@@ -3170,6 +3388,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=None,
         help="specify WandB API key to log in before starting training (optional). / WandB APIキーを指定して学習開始前にログインする（オプション）",
     )
+    parser.add_argument("--log_config", action="store_true", help="log training configuration / 学習設定をログに出力する")
 
     parser.add_argument(
         "--noise_offset",
@@ -3379,6 +3598,42 @@ def add_masked_loss_arguments(parser: argparse.ArgumentParser):
     )
 
 
+def get_sanitized_config_or_none(args: argparse.Namespace):
+    # if `--log_config` is enabled, return args for logging. if not, return None.
+    # when `--log_config is enabled, filter out sensitive values from args
+    # if wandb is not enabled, the log is not exposed to the public, but it is fine to filter out sensitive values to be safe
+
+    if not args.log_config:
+        return None
+
+    sensitive_args = ["wandb_api_key", "huggingface_token"]
+    sensitive_path_args = [
+        "pretrained_model_name_or_path",
+        "vae",
+        "tokenizer_cache_dir",
+        "train_data_dir",
+        "conditioning_data_dir",
+        "reg_data_dir",
+        "output_dir",
+        "logging_dir",
+    ]
+    filtered_args = {}
+    for k, v in vars(args).items():
+        # filter out sensitive values and convert to string if necessary
+        if k not in sensitive_args + sensitive_path_args:
+            # Accelerate values need to have type `bool`,`str`, `float`, `int`, or `None`.
+            if v is None or isinstance(v, bool) or isinstance(v, str) or isinstance(v, float) or isinstance(v, int):
+                filtered_args[k] = v
+            # accelerate does not support lists
+            elif isinstance(v, list):
+                filtered_args[k] = f"{v}"
+            # accelerate does not support objects
+            elif isinstance(v, object):
+                filtered_args[k] = f"{v}"
+
+    return filtered_args
+
+
 # verify command line args for training
 def verify_command_line_training_args(args: argparse.Namespace):
     # if wandb is enabled, the command line is exposed to the public
@@ -3443,10 +3698,6 @@ def verify_training_args(args: argparse.Namespace):
         global HIGH_VRAM
         HIGH_VRAM = True
 
-    if args.v_parameterization and not args.v2:
-        logger.warning(
-            "v_parameterization should be with v2 not v1 or sdxl / v1やsdxlでv_parameterizationを使用することは想定されていません"
-        )
     if args.v2 and args.clip_skip is not None:
         logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
@@ -3610,8 +3861,20 @@ def add_dataset_arguments(
         action="store_true",
         help="enable buckets for multi aspect ratio training / 複数解像度学習のためのbucketを有効にする",
     )
-    parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度")
-    parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最大解像度")
+    parser.add_argument(
+        "--min_bucket_reso",
+        type=int,
+        default=256,
+        help="minimum resolution for buckets, must be divisible by bucket_reso_steps "
+        " / bucketの最小解像度、bucket_reso_stepsで割り切れる必要があります",
+    )
+    parser.add_argument(
+        "--max_bucket_reso",
+        type=int,
+        default=1024,
+        help="maximum resolution for buckets, must be divisible by bucket_reso_steps "
+        " / bucketの最大解像度、bucket_reso_stepsで割り切れる必要があります",
+    )
     parser.add_argument(
         "--bucket_reso_steps",
         type=int,
@@ -3636,6 +3899,11 @@ def add_dataset_arguments(
         default=0,
         help="tag length reaches maximum on N steps (or N*max_train_steps if N<1) / N（N<1ならN*max_train_steps）ステップでタグ長が最大になる。デフォルトは0（最初から最大）",
     )
+    parser.add_argument(
+        "--alpha_mask",
+        action="store_true",
+        help="use alpha channel as mask for training / 画像のアルファチャンネルをlossのマスクに使用する",
+    )
 
     parser.add_argument(
         "--dataset_class",
@@ -3824,7 +4092,7 @@ def task():
 
 
 def get_optimizer(args, trainable_params):
-    # "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, Adafactor"
+    # "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
     if args.use_8bit_adam:
@@ -3846,6 +4114,14 @@ def get_optimizer(args, trainable_params):
         optimizer_type = "AdamW"
     optimizer_type = optimizer_type.lower()
 
+    if args.fused_backward_pass:
+        assert (
+            optimizer_type == "Adafactor".lower()
+        ), "fused_backward_pass currently only works with optimizer_type Adafactor / fused_backward_passは現在optimizer_type Adafactorでのみ機能します"
+        assert (
+            args.gradient_accumulation_steps == 1
+        ), "fused_backward_pass does not work with gradient_accumulation_steps > 1 / fused_backward_passはgradient_accumulation_steps>1では機能しません"
+
     # 引数を分解する
     optimizer_kwargs = {}
     if args.optimizer_args is not None and len(args.optimizer_args) > 0:
@@ -3869,6 +4145,7 @@ def get_optimizer(args, trainable_params):
 
     lr = args.learning_rate
     optimizer = None
+    optimizer_class = None
 
     if optimizer_type == "Lion".lower():
         try:
@@ -3926,7 +4203,8 @@ def get_optimizer(args, trainable_params):
                     "No PagedLion8bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedLion8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください"
                 )
 
-        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+        if optimizer_class is not None:
+            optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
     elif optimizer_type == "PagedAdamW".lower():
         logger.info(f"use PagedAdamW optimizer | {optimizer_kwargs}")
@@ -4102,6 +4380,7 @@ def get_optimizer(args, trainable_params):
         optimizer_class = getattr(optimizer_module, optimizer_type)
         optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
+    # for logging
     optimizer_name = optimizer_class.__module__ + "." + optimizer_class.__name__
     optimizer_args = ",".join([f"{k}={v}" for k, v in optimizer_kwargs.items()])
 
@@ -4117,10 +4396,18 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
     Unified API to get any scheduler from its name.
     """
     name = args.lr_scheduler
-    num_warmup_steps: Optional[int] = args.lr_warmup_steps
     num_training_steps = args.max_train_steps * num_processes  # * args.gradient_accumulation_steps
+    num_warmup_steps: Optional[int] = (
+        int(args.lr_warmup_steps * num_training_steps) if isinstance(args.lr_warmup_steps, float) else args.lr_warmup_steps
+    )
+    num_decay_steps: Optional[int] = (
+        int(args.lr_decay_steps * num_training_steps) if isinstance(args.lr_decay_steps, float) else args.lr_decay_steps
+    )
+    num_stable_steps = num_training_steps - num_warmup_steps - num_decay_steps
     num_cycles = args.lr_scheduler_num_cycles
     power = args.lr_scheduler_power
+    timescale = args.lr_scheduler_timescale
+    min_lr_ratio = args.lr_scheduler_min_lr_ratio
 
     lr_scheduler_kwargs = {}  # get custom lr_scheduler kwargs
     if args.lr_scheduler_args is not None and len(args.lr_scheduler_args) > 0:
@@ -4156,15 +4443,17 @@ def wrap_check_needless_num_warmup_steps(return_vals):
         # logger.info(f"adafactor scheduler init lr {initial_lr}")
         return wrap_check_needless_num_warmup_steps(transformers.optimization.AdafactorSchedule(optimizer, initial_lr))
 
+    if name == DiffusersSchedulerType.PIECEWISE_CONSTANT.value:
+        name = DiffusersSchedulerType(name)
+        schedule_func = DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION[name]
+        return schedule_func(optimizer, **lr_scheduler_kwargs)  # step_rules and last_epoch are given as kwargs
+
     name = SchedulerType(name)
     schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
 
     if name == SchedulerType.CONSTANT:
         return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs))
 
-    if name == SchedulerType.PIECEWISE_CONSTANT:
-        return schedule_func(optimizer, **lr_scheduler_kwargs)  # step_rules and last_epoch are given as kwargs
-
     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
         raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
@@ -4172,6 +4461,9 @@ def wrap_check_needless_num_warmup_steps(return_vals):
     if name == SchedulerType.CONSTANT_WITH_WARMUP:
         return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **lr_scheduler_kwargs)
 
+    if name == SchedulerType.INVERSE_SQRT:
+        return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, timescale=timescale, **lr_scheduler_kwargs)
+
     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
         raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
@@ -4190,7 +4482,46 @@ def wrap_check_needless_num_warmup_steps(return_vals):
             optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power, **lr_scheduler_kwargs
         )
 
-    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, **lr_scheduler_kwargs)
+    if name == SchedulerType.COSINE_WITH_MIN_LR:
+        return schedule_func(
+            optimizer,
+            num_warmup_steps=num_warmup_steps,
+            num_training_steps=num_training_steps,
+            num_cycles=num_cycles / 2,
+            min_lr_rate=min_lr_ratio,
+            **lr_scheduler_kwargs,
+        )
+
+    # these schedulers do not require `num_decay_steps`
+    if name == SchedulerType.LINEAR or name == SchedulerType.COSINE:
+        return schedule_func(
+            optimizer,
+            num_warmup_steps=num_warmup_steps,
+            num_training_steps=num_training_steps,
+            **lr_scheduler_kwargs,
+        )
+
+    # All other schedulers require `num_decay_steps`
+    if num_decay_steps is None:
+        raise ValueError(f"{name} requires `num_decay_steps`, please provide that argument.")
+    if name == SchedulerType.WARMUP_STABLE_DECAY:
+        return schedule_func(
+            optimizer,
+            num_warmup_steps=num_warmup_steps,
+            num_stable_steps=num_stable_steps,
+            num_decay_steps=num_decay_steps,
+            num_cycles=num_cycles / 2,
+            min_lr_ratio=min_lr_ratio if min_lr_ratio is not None else 0.0,
+            **lr_scheduler_kwargs,
+        )
+
+    return schedule_func(
+        optimizer,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+        num_decay_steps=num_decay_steps,
+        **lr_scheduler_kwargs,
+    )
 
 
 def prepare_dataset_args(args: argparse.Namespace, support_metadata: bool):
@@ -4865,34 +5196,27 @@ def save_sd_model_on_train_end_common(
 
 
 def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device):
-
-    # TODO: if a huber loss is selected, it will use constant timesteps for each batch
-    # as. In the future there may be a smarter way
+    timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
 
     if args.loss_type == "huber" or args.loss_type == "smooth_l1":
-        timesteps = torch.randint(min_timestep, max_timestep, (1,), device="cpu")
-        timestep = timesteps.item()
-
         if args.huber_schedule == "exponential":
             alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
-            huber_c = math.exp(-alpha * timestep)
+            huber_c = torch.exp(-alpha * timesteps)
         elif args.huber_schedule == "snr":
-            alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]
+            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 = args.huber_c
+            huber_c = torch.full((b_size,), args.huber_c)
         else:
             raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!")
-
-        timesteps = timesteps.repeat(b_size).to(device)
+        huber_c = huber_c.to(device)
     elif args.loss_type == "l2":
-        timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=device)
-        huber_c = 1  # may be anything, as it's not used
+        huber_c = None  # may be anything, as it's not used
     else:
         raise NotImplementedError(f"Unknown loss type {args.loss_type}")
-    timesteps = timesteps.long()
 
+    timesteps = timesteps.long().to(device)
     return timesteps, huber_c
 
 
@@ -4931,20 +5255,21 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     return noise, noisy_latents, timesteps, huber_c
 
 
-# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already
 def conditional_loss(
-    model_pred: torch.Tensor, target: torch.Tensor, reduction: str = "mean", loss_type: str = "l2", huber_c: float = 0.1
+    model_pred: torch.Tensor, target: torch.Tensor, reduction: str, loss_type: str, huber_c: Optional[torch.Tensor]
 ):
 
     if loss_type == "l2":
         loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction)
     elif loss_type == "huber":
+        huber_c = huber_c.view(-1, 1, 1, 1)
         loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)
         if reduction == "mean":
             loss = torch.mean(loss)
         elif reduction == "sum":
             loss = torch.sum(loss)
     elif loss_type == "smooth_l1":
+        huber_c = huber_c.view(-1, 1, 1, 1)
         loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)
         if reduction == "mean":
             loss = torch.mean(loss)
@@ -5229,7 +5554,7 @@ def sample_images_common(
     clean_memory_on_device(accelerator.device)
 
     torch.set_rng_state(rng_state)
-    if cuda_rng_state is not None:
+    if torch.cuda.is_available() and cuda_rng_state is not None:
         torch.cuda.set_rng_state(cuda_rng_state)
     vae.to(org_vae_device)
 
@@ -5263,11 +5588,13 @@ def sample_image_inference(
 
     if seed is not None:
         torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed(seed)
     else:
         # True random sample image generation
         torch.seed()
-        torch.cuda.seed()
+        if torch.cuda.is_available():
+            torch.cuda.seed()
 
     scheduler = get_my_scheduler(
         sample_sampler=sampler_name,
@@ -5302,8 +5629,9 @@ def sample_image_inference(
             controlnet_image=controlnet_image,
         )
 
-    with torch.cuda.device(torch.cuda.current_device()):
-        torch.cuda.empty_cache()
+    if torch.cuda.is_available():
+        with torch.cuda.device(torch.cuda.current_device()):
+            torch.cuda.empty_cache()
 
     image = pipeline.latents_to_image(latents)[0]
 
@@ -5390,6 +5718,8 @@ def add(self, *, epoch: int, step: int, loss: float) -> None:
         if epoch == 0:
             self.loss_list.append(loss)
         else:
+            while len(self.loss_list) <= step:
+                self.loss_list.append(0.0)
             self.loss_total -= self.loss_list[step]
             self.loss_list[step] = loss
         self.loss_total += loss
diff --git a/library/utils.py b/library/utils.py
index 3037c055d..49d46a546 100644
--- a/library/utils.py
+++ b/library/utils.py
@@ -7,6 +7,9 @@
 from diffusers import EulerAncestralDiscreteScheduler
 import diffusers.schedulers.scheduling_euler_ancestral_discrete
 from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteSchedulerOutput
+import cv2
+from PIL import Image
+import numpy as np
 
 
 def fire_in_thread(f, *args, **kwargs):
@@ -79,6 +82,24 @@ def setup_logging(args=None, log_level=None, reset=False):
         logger.info(msg_init)
 
 
+def pil_resize(image, size, interpolation=Image.LANCZOS):
+    has_alpha = image.shape[2] == 4 if len(image.shape) == 3 else False
+
+    if has_alpha:
+        pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA))
+    else:
+        pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    resized_pil = pil_image.resize(size, interpolation)
+
+    # Convert back to cv2 format
+    if has_alpha:
+        resized_cv2 = cv2.cvtColor(np.array(resized_pil), cv2.COLOR_RGBA2BGRA)
+    else:
+        resized_cv2 = cv2.cvtColor(np.array(resized_pil), cv2.COLOR_RGB2BGR)
+
+    return resized_cv2
+
 
 # TODO make inf_utils.py
 
diff --git a/networks/check_lora_weights.py b/networks/check_lora_weights.py
index 794659c94..f8eab53ba 100644
--- a/networks/check_lora_weights.py
+++ b/networks/check_lora_weights.py
@@ -18,7 +18,7 @@ def main(file):
 
     keys = list(sd.keys())
     for key in keys:
-        if "lora_up" in key or "lora_down" in key:
+        if "lora_up" in key or "lora_down" in key or "lora_A" in key or "lora_B" in key or "oft_" in key:
             values.append((key, sd[key]))
     print(f"number of LoRA modules: {len(values)}")
 
diff --git a/networks/control_net_lllite_for_train.py b/networks/control_net_lllite_for_train.py
index 65b3520cf..366451b7f 100644
--- a/networks/control_net_lllite_for_train.py
+++ b/networks/control_net_lllite_for_train.py
@@ -7,8 +7,10 @@
 import torch
 from library import sdxl_original_unet
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 # input_blocksに適用するかどうか / if True, input_blocks are not applied
@@ -103,19 +105,15 @@ def set_lllite(self, depth, cond_emb_dim, name, mlp_dim, dropout=None, multiplie
         add_lllite_modules(self, in_dim, depth, cond_emb_dim, mlp_dim)
 
         self.cond_image = None
-        self.cond_emb = None
 
     def set_cond_image(self, cond_image):
         self.cond_image = cond_image
-        self.cond_emb = None
 
     def forward(self, x):
         if not self.enabled:
             return super().forward(x)
 
-        if self.cond_emb is None:
-            self.cond_emb = self.lllite_conditioning1(self.cond_image)
-        cx = self.cond_emb
+        cx = self.lllite_conditioning1(self.cond_image)  # make forward and backward compatible
 
         # reshape / b,c,h,w -> b,h*w,c
         n, c, h, w = cx.shape
@@ -159,9 +157,7 @@ def forward(self, x):  # , cond_image=None):
         if not self.enabled:
             return super().forward(x)
 
-        if self.cond_emb is None:
-            self.cond_emb = self.lllite_conditioning1(self.cond_image)
-        cx = self.cond_emb
+        cx = self.lllite_conditioning1(self.cond_image)
 
         cx = torch.cat([cx, self.down(x)], dim=1)
         cx = self.mid(cx)
diff --git a/networks/dylora.py b/networks/dylora.py
index 637f33450..b0925453c 100644
--- a/networks/dylora.py
+++ b/networks/dylora.py
@@ -18,10 +18,13 @@
 import torch
 from torch import nn
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
+
 class DyLoRAModule(torch.nn.Module):
     """
     replaces forward method of the original Linear, instead of replacing the original Linear module.
@@ -195,7 +198,7 @@ def create_network(
             conv_alpha = 1.0
         else:
             conv_alpha = float(conv_alpha)
-            
+
     if unit is not None:
         unit = int(unit)
     else:
@@ -211,6 +214,16 @@ def create_network(
         unit=unit,
         varbose=True,
     )
+
+    loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
+    loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
+    loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
+    loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
+    loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
+    loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
+    if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
+        network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
+
     return network
 
 
@@ -280,6 +293,10 @@ def __init__(
         self.alpha = alpha
         self.apply_to_conv = apply_to_conv
 
+        self.loraplus_lr_ratio = None
+        self.loraplus_unet_lr_ratio = None
+        self.loraplus_text_encoder_lr_ratio = None
+
         if modules_dim is not None:
             logger.info("create LoRA network from weights")
         else:
@@ -320,9 +337,9 @@ def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules
                             lora = module_class(lora_name, child_module, self.multiplier, dim, alpha, unit)
                             loras.append(lora)
             return loras
-        
+
         text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
-        
+
         self.text_encoder_loras = []
         for i, text_encoder in enumerate(text_encoders):
             if len(text_encoders) > 1:
@@ -331,7 +348,7 @@ def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules
             else:
                 index = None
                 logger.info("create LoRA for Text Encoder")
-            
+
             text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
             self.text_encoder_loras.extend(text_encoder_loras)
 
@@ -346,6 +363,14 @@ def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules
         self.unet_loras = create_modules(True, unet, target_modules)
         logger.info(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
 
+    def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
+        self.loraplus_lr_ratio = loraplus_lr_ratio
+        self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
+        self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
+
+        logger.info(f"LoRA+ UNet LR Ratio: {self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio}")
+        logger.info(f"LoRA+ Text Encoder LR Ratio: {self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio}")
+
     def set_multiplier(self, multiplier):
         self.multiplier = multiplier
         for lora in self.text_encoder_loras + self.unet_loras:
@@ -406,27 +431,53 @@ def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
         logger.info(f"weights are merged")
     """
 
+    # 二つのText Encoderに別々の学習率を設定できるようにするといいかも
     def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
         self.requires_grad_(True)
         all_params = []
 
-        def enumerate_params(loras):
-            params = []
+        def assemble_params(loras, lr, ratio):
+            param_groups = {"lora": {}, "plus": {}}
             for lora in loras:
-                params.extend(lora.parameters())
+                for name, param in lora.named_parameters():
+                    if ratio is not None and "lora_B" in name:
+                        param_groups["plus"][f"{lora.lora_name}.{name}"] = param
+                    else:
+                        param_groups["lora"][f"{lora.lora_name}.{name}"] = param
+
+            params = []
+            for key in param_groups.keys():
+                param_data = {"params": param_groups[key].values()}
+
+                if len(param_data["params"]) == 0:
+                    continue
+
+                if lr is not None:
+                    if key == "plus":
+                        param_data["lr"] = lr * ratio
+                    else:
+                        param_data["lr"] = lr
+
+                if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
+                    continue
+
+                params.append(param_data)
+
             return params
 
         if self.text_encoder_loras:
-            param_data = {"params": enumerate_params(self.text_encoder_loras)}
-            if text_encoder_lr is not None:
-                param_data["lr"] = text_encoder_lr
-            all_params.append(param_data)
+            params = assemble_params(
+                self.text_encoder_loras,
+                text_encoder_lr if text_encoder_lr is not None else default_lr,
+                self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio,
+            )
+            all_params.extend(params)
 
         if self.unet_loras:
-            param_data = {"params": enumerate_params(self.unet_loras)}
-            if unet_lr is not None:
-                param_data["lr"] = unet_lr
-            all_params.append(param_data)
+            params = assemble_params(
+                self.unet_loras, default_lr if unet_lr is None else unet_lr, self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio
+            )
+            all_params.extend(params)
 
         return all_params
 
diff --git a/networks/lora.py b/networks/lora.py
index d1208040f..6f33f1a1e 100644
--- a/networks/lora.py
+++ b/networks/lora.py
@@ -12,6 +12,7 @@
 import torch
 import re
 from library.utils import setup_logging
+from library.sdxl_original_unet import SdxlUNet2DConditionModel
 
 setup_logging()
 import logging
@@ -385,14 +386,14 @@ def to_out_forward(self, x):
         return out
 
 
-def parse_block_lr_kwargs(nw_kwargs):
+def parse_block_lr_kwargs(is_sdxl: bool, nw_kwargs: Dict) -> Optional[List[float]]:
     down_lr_weight = nw_kwargs.get("down_lr_weight", None)
     mid_lr_weight = nw_kwargs.get("mid_lr_weight", None)
     up_lr_weight = nw_kwargs.get("up_lr_weight", None)
 
     # 以上のいずれにも設定がない場合は無効としてNoneを返す
     if down_lr_weight is None and mid_lr_weight is None and up_lr_weight is None:
-        return None, None, None
+        return None
 
     # extract learning rate weight for each block
     if down_lr_weight is not None:
@@ -401,18 +402,16 @@ def parse_block_lr_kwargs(nw_kwargs):
             down_lr_weight = [(float(s) if s else 0.0) for s in down_lr_weight.split(",")]
 
     if mid_lr_weight is not None:
-        mid_lr_weight = float(mid_lr_weight)
+        mid_lr_weight = [(float(s) if s else 0.0) for s in mid_lr_weight.split(",")]
 
     if up_lr_weight is not None:
         if "," in up_lr_weight:
             up_lr_weight = [(float(s) if s else 0.0) for s in up_lr_weight.split(",")]
 
-    down_lr_weight, mid_lr_weight, up_lr_weight = get_block_lr_weight(
-        down_lr_weight, mid_lr_weight, up_lr_weight, float(nw_kwargs.get("block_lr_zero_threshold", 0.0))
+    return get_block_lr_weight(
+        is_sdxl, down_lr_weight, mid_lr_weight, up_lr_weight, float(nw_kwargs.get("block_lr_zero_threshold", 0.0))
     )
 
-    return down_lr_weight, mid_lr_weight, up_lr_weight
-
 
 def create_network(
     multiplier: float,
@@ -424,6 +423,9 @@ def create_network(
     neuron_dropout: Optional[float] = None,
     **kwargs,
 ):
+    # if unet is an instance of SdxlUNet2DConditionModel or subclass, set is_sdxl to True
+    is_sdxl = unet is not None and issubclass(unet.__class__, SdxlUNet2DConditionModel)
+
     if network_dim is None:
         network_dim = 4  # default
     if network_alpha is None:
@@ -441,21 +443,21 @@ def create_network(
 
     # block dim/alpha/lr
     block_dims = kwargs.get("block_dims", None)
-    down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs)
+    block_lr_weight = parse_block_lr_kwargs(is_sdxl, kwargs)
 
     # 以上のいずれかに指定があればblockごとのdim(rank)を有効にする
-    if block_dims is not None or down_lr_weight is not None or mid_lr_weight is not None or up_lr_weight is not None:
+    if block_dims is not None or block_lr_weight is not None:
         block_alphas = kwargs.get("block_alphas", None)
         conv_block_dims = kwargs.get("conv_block_dims", None)
         conv_block_alphas = kwargs.get("conv_block_alphas", None)
 
         block_dims, block_alphas, conv_block_dims, conv_block_alphas = get_block_dims_and_alphas(
-            block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
+            is_sdxl, block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
         )
 
         # remove block dim/alpha without learning rate
         block_dims, block_alphas, conv_block_dims, conv_block_alphas = remove_block_dims_and_alphas(
-            block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight
+            is_sdxl, block_dims, block_alphas, conv_block_dims, conv_block_alphas, block_lr_weight
         )
 
     else:
@@ -488,10 +490,20 @@ def create_network(
         conv_block_dims=conv_block_dims,
         conv_block_alphas=conv_block_alphas,
         varbose=True,
+        is_sdxl=is_sdxl,
     )
 
-    if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
-        network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)
+    loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
+    loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
+    loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
+    loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
+    loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
+    loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
+    if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
+        network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
+
+    if block_lr_weight is not None:
+        network.set_block_lr_weight(block_lr_weight)
 
     return network
 
@@ -501,9 +513,13 @@ def create_network(
 # block_dims, block_alphas は両方ともNoneまたは両方とも値が入っている
 # conv_dim, conv_alpha は両方ともNoneまたは両方とも値が入っている
 def get_block_dims_and_alphas(
-    block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
+    is_sdxl, block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
 ):
-    num_total_blocks = LoRANetwork.NUM_OF_BLOCKS * 2 + 1
+    if not is_sdxl:
+        num_total_blocks = LoRANetwork.NUM_OF_BLOCKS * 2 + LoRANetwork.NUM_OF_MID_BLOCKS
+    else:
+        # 1+9+3+9+1=23, no LoRA for emb_layers (0)
+        num_total_blocks = 1 + LoRANetwork.SDXL_NUM_OF_BLOCKS * 2 + LoRANetwork.SDXL_NUM_OF_MID_BLOCKS + 1
 
     def parse_ints(s):
         return [int(i) for i in s.split(",")]
@@ -514,9 +530,10 @@ def parse_floats(s):
     # block_dimsとblock_alphasをパースする。必ず値が入る
     if block_dims is not None:
         block_dims = parse_ints(block_dims)
-        assert (
-            len(block_dims) == num_total_blocks
-        ), f"block_dims must have {num_total_blocks} elements / block_dimsは{num_total_blocks}個指定してください"
+        assert len(block_dims) == num_total_blocks, (
+            f"block_dims must have {num_total_blocks} elements but {len(block_dims)} elements are given"
+            + f" / block_dimsは{num_total_blocks}個指定してください（指定された個数: {len(block_dims)}）"
+        )
     else:
         logger.warning(
             f"block_dims is not specified. all dims are set to {network_dim} / block_dimsが指定されていません。すべてのdimは{network_dim}になります"
@@ -567,15 +584,25 @@ def parse_floats(s):
     return block_dims, block_alphas, conv_block_dims, conv_block_alphas
 
 
-# 層別学習率用に層ごとの学習率に対する倍率を定義する、外部から呼び出される可能性を考慮しておく
+# 層別学習率用に層ごとの学習率に対する倍率を定義する、外部から呼び出せるようにclass外に出しておく
+# 戻り値は block ごとの倍率のリスト
 def get_block_lr_weight(
-    down_lr_weight, mid_lr_weight, up_lr_weight, zero_threshold
-) -> Tuple[List[float], List[float], List[float]]:
+    is_sdxl,
+    down_lr_weight: Union[str, List[float]],
+    mid_lr_weight: List[float],
+    up_lr_weight: Union[str, List[float]],
+    zero_threshold: float,
+) -> Optional[List[float]]:
     # パラメータ未指定時は何もせず、今までと同じ動作とする
     if up_lr_weight is None and mid_lr_weight is None and down_lr_weight is None:
-        return None, None, None
+        return None
 
-    max_len = LoRANetwork.NUM_OF_BLOCKS  # フルモデル相当でのup,downの層の数
+    if not is_sdxl:
+        max_len_for_down_or_up = LoRANetwork.NUM_OF_BLOCKS
+        max_len_for_mid = LoRANetwork.NUM_OF_MID_BLOCKS
+    else:
+        max_len_for_down_or_up = LoRANetwork.SDXL_NUM_OF_BLOCKS
+        max_len_for_mid = LoRANetwork.SDXL_NUM_OF_MID_BLOCKS
 
     def get_list(name_with_suffix) -> List[float]:
         import math
@@ -585,15 +612,18 @@ def get_list(name_with_suffix) -> List[float]:
         base_lr = float(tokens[1]) if len(tokens) > 1 else 0.0
 
         if name == "cosine":
-            return [math.sin(math.pi * (i / (max_len - 1)) / 2) + base_lr for i in reversed(range(max_len))]
+            return [
+                math.sin(math.pi * (i / (max_len_for_down_or_up - 1)) / 2) + base_lr
+                for i in reversed(range(max_len_for_down_or_up))
+            ]
         elif name == "sine":
-            return [math.sin(math.pi * (i / (max_len - 1)) / 2) + base_lr for i in range(max_len)]
+            return [math.sin(math.pi * (i / (max_len_for_down_or_up - 1)) / 2) + base_lr for i in range(max_len_for_down_or_up)]
         elif name == "linear":
-            return [i / (max_len - 1) + base_lr for i in range(max_len)]
+            return [i / (max_len_for_down_or_up - 1) + base_lr for i in range(max_len_for_down_or_up)]
         elif name == "reverse_linear":
-            return [i / (max_len - 1) + base_lr for i in reversed(range(max_len))]
+            return [i / (max_len_for_down_or_up - 1) + base_lr for i in reversed(range(max_len_for_down_or_up))]
         elif name == "zeros":
-            return [0.0 + base_lr] * max_len
+            return [0.0 + base_lr] * max_len_for_down_or_up
         else:
             logger.error(
                 "Unknown lr_weight argument %s is used. Valid arguments:  / 不明なlr_weightの引数 %s が使われました。有効な引数:\n\tcosine, sine, linear, reverse_linear, zeros"
@@ -606,20 +636,36 @@ def get_list(name_with_suffix) -> List[float]:
     if type(up_lr_weight) == str:
         up_lr_weight = get_list(up_lr_weight)
 
-    if (up_lr_weight != None and len(up_lr_weight) > max_len) or (down_lr_weight != None and len(down_lr_weight) > max_len):
-        logger.warning("down_weight or up_weight is too long. Parameters after %d-th are ignored." % max_len)
-        logger.warning("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。" % max_len)
-        up_lr_weight = up_lr_weight[:max_len]
-        down_lr_weight = down_lr_weight[:max_len]
+    if (up_lr_weight != None and len(up_lr_weight) > max_len_for_down_or_up) or (
+        down_lr_weight != None and len(down_lr_weight) > max_len_for_down_or_up
+    ):
+        logger.warning("down_weight or up_weight is too long. Parameters after %d-th are ignored." % max_len_for_down_or_up)
+        logger.warning("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。" % max_len_for_down_or_up)
+        up_lr_weight = up_lr_weight[:max_len_for_down_or_up]
+        down_lr_weight = down_lr_weight[:max_len_for_down_or_up]
+
+    if mid_lr_weight != None and len(mid_lr_weight) > max_len_for_mid:
+        logger.warning("mid_weight is too long. Parameters after %d-th are ignored." % max_len_for_mid)
+        logger.warning("mid_weightが長すぎます。%d個目以降のパラメータは無視されます。" % max_len_for_mid)
+        mid_lr_weight = mid_lr_weight[:max_len_for_mid]
+
+    if (up_lr_weight != None and len(up_lr_weight) < max_len_for_down_or_up) or (
+        down_lr_weight != None and len(down_lr_weight) < max_len_for_down_or_up
+    ):
+        logger.warning("down_weight or up_weight is too short. Parameters after %d-th are filled with 1." % max_len_for_down_or_up)
+        logger.warning(
+            "down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。" % max_len_for_down_or_up
+        )
 
-    if (up_lr_weight != None and len(up_lr_weight) < max_len) or (down_lr_weight != None and len(down_lr_weight) < max_len):
-        logger.warning("down_weight or up_weight is too short. Parameters after %d-th are filled with 1." % max_len)
-        logger.warning("down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。" % max_len)
+        if down_lr_weight != None and len(down_lr_weight) < max_len_for_down_or_up:
+            down_lr_weight = down_lr_weight + [1.0] * (max_len_for_down_or_up - len(down_lr_weight))
+        if up_lr_weight != None and len(up_lr_weight) < max_len_for_down_or_up:
+            up_lr_weight = up_lr_weight + [1.0] * (max_len_for_down_or_up - len(up_lr_weight))
 
-        if down_lr_weight != None and len(down_lr_weight) < max_len:
-            down_lr_weight = down_lr_weight + [1.0] * (max_len - len(down_lr_weight))
-        if up_lr_weight != None and len(up_lr_weight) < max_len:
-            up_lr_weight = up_lr_weight + [1.0] * (max_len - len(up_lr_weight))
+    if mid_lr_weight != None and len(mid_lr_weight) < max_len_for_mid:
+        logger.warning("mid_weight is too short. Parameters after %d-th are filled with 1." % max_len_for_mid)
+        logger.warning("mid_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。" % max_len_for_mid)
+        mid_lr_weight = mid_lr_weight + [1.0] * (max_len_for_mid - len(mid_lr_weight))
 
     if (up_lr_weight != None) or (mid_lr_weight != None) or (down_lr_weight != None):
         logger.info("apply block learning rate / 階層別学習率を適用します。")
@@ -627,78 +673,139 @@ def get_list(name_with_suffix) -> List[float]:
             down_lr_weight = [w if w > zero_threshold else 0 for w in down_lr_weight]
             logger.info(f"down_lr_weight (shallower -> deeper, 浅い層->深い層): {down_lr_weight}")
         else:
+            down_lr_weight = [1.0] * max_len_for_down_or_up
             logger.info("down_lr_weight: all 1.0, すべて1.0")
 
         if mid_lr_weight != None:
-            mid_lr_weight = mid_lr_weight if mid_lr_weight > zero_threshold else 0
+            mid_lr_weight = [w if w > zero_threshold else 0 for w in mid_lr_weight]
             logger.info(f"mid_lr_weight: {mid_lr_weight}")
         else:
-            logger.info("mid_lr_weight: 1.0")
+            mid_lr_weight = [1.0] * max_len_for_mid
+            logger.info("mid_lr_weight: all 1.0, すべて1.0")
 
         if up_lr_weight != None:
             up_lr_weight = [w if w > zero_threshold else 0 for w in up_lr_weight]
             logger.info(f"up_lr_weight (deeper -> shallower, 深い層->浅い層): {up_lr_weight}")
         else:
+            up_lr_weight = [1.0] * max_len_for_down_or_up
             logger.info("up_lr_weight: all 1.0, すべて1.0")
 
-    return down_lr_weight, mid_lr_weight, up_lr_weight
+    lr_weight = down_lr_weight + mid_lr_weight + up_lr_weight
+
+    if is_sdxl:
+        lr_weight = [1.0] + lr_weight + [1.0]  # add 1.0 for emb_layers and out
+
+    assert (not is_sdxl and len(lr_weight) == LoRANetwork.NUM_OF_BLOCKS * 2 + LoRANetwork.NUM_OF_MID_BLOCKS) or (
+        is_sdxl and len(lr_weight) == 1 + LoRANetwork.SDXL_NUM_OF_BLOCKS * 2 + LoRANetwork.SDXL_NUM_OF_MID_BLOCKS + 1
+    ), f"lr_weight length is invalid: {len(lr_weight)}"
+
+    return lr_weight
 
 
 # lr_weightが0のblockをblock_dimsから除外する、外部から呼び出す可能性を考慮しておく
 def remove_block_dims_and_alphas(
-    block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight
+    is_sdxl, block_dims, block_alphas, conv_block_dims, conv_block_alphas, block_lr_weight: Optional[List[float]]
 ):
-    # set 0 to block dim without learning rate to remove the block
-    if down_lr_weight != None:
-        for i, lr in enumerate(down_lr_weight):
+    if block_lr_weight is not None:
+        for i, lr in enumerate(block_lr_weight):
             if lr == 0:
                 block_dims[i] = 0
                 if conv_block_dims is not None:
                     conv_block_dims[i] = 0
-    if mid_lr_weight != None:
-        if mid_lr_weight == 0:
-            block_dims[LoRANetwork.NUM_OF_BLOCKS] = 0
-            if conv_block_dims is not None:
-                conv_block_dims[LoRANetwork.NUM_OF_BLOCKS] = 0
-    if up_lr_weight != None:
-        for i, lr in enumerate(up_lr_weight):
-            if lr == 0:
-                block_dims[LoRANetwork.NUM_OF_BLOCKS + 1 + i] = 0
-                if conv_block_dims is not None:
-                    conv_block_dims[LoRANetwork.NUM_OF_BLOCKS + 1 + i] = 0
-
     return block_dims, block_alphas, conv_block_dims, conv_block_alphas
 
 
 # 外部から呼び出す可能性を考慮しておく
-def get_block_index(lora_name: str) -> int:
+def get_block_index(lora_name: str, is_sdxl: bool = False) -> int:
     block_idx = -1  # invalid lora name
-
-    m = RE_UPDOWN.search(lora_name)
-    if m:
-        g = m.groups()
-        i = int(g[1])
-        j = int(g[3])
-        if g[2] == "resnets":
-            idx = 3 * i + j
-        elif g[2] == "attentions":
-            idx = 3 * i + j
-        elif g[2] == "upsamplers" or g[2] == "downsamplers":
-            idx = 3 * i + 2
-
-        if g[0] == "down":
-            block_idx = 1 + idx  # 0に該当するLoRAは存在しない
-        elif g[0] == "up":
-            block_idx = LoRANetwork.NUM_OF_BLOCKS + 1 + idx
-
-    elif "mid_block_" in lora_name:
-        block_idx = LoRANetwork.NUM_OF_BLOCKS  # idx=12
+    if not is_sdxl:
+        m = RE_UPDOWN.search(lora_name)
+        if m:
+            g = m.groups()
+            i = int(g[1])
+            j = int(g[3])
+            if g[2] == "resnets":
+                idx = 3 * i + j
+            elif g[2] == "attentions":
+                idx = 3 * i + j
+            elif g[2] == "upsamplers" or g[2] == "downsamplers":
+                idx = 3 * i + 2
+
+            if g[0] == "down":
+                block_idx = 1 + idx  # 0に該当するLoRAは存在しない
+            elif g[0] == "up":
+                block_idx = LoRANetwork.NUM_OF_BLOCKS + 1 + idx
+        elif "mid_block_" in lora_name:
+            block_idx = LoRANetwork.NUM_OF_BLOCKS  # idx=12
+    else:
+        # copy from sdxl_train
+        if lora_name.startswith("lora_unet_"):
+            name = lora_name[len("lora_unet_") :]
+            if name.startswith("time_embed_") or name.startswith("label_emb_"):  # No LoRA
+                block_idx = 0  # 0
+            elif name.startswith("input_blocks_"):  # 1-9
+                block_idx = 1 + int(name.split("_")[2])
+            elif name.startswith("middle_block_"):  # 10-12
+                block_idx = 10 + int(name.split("_")[2])
+            elif name.startswith("output_blocks_"):  # 13-21
+                block_idx = 13 + int(name.split("_")[2])
+            elif name.startswith("out_"):  # 22, out, no LoRA
+                block_idx = 22
 
     return block_idx
 
 
+def convert_diffusers_to_sai_if_needed(weights_sd):
+    # only supports U-Net LoRA modules
+
+    found_up_down_blocks = False
+    for k in list(weights_sd.keys()):
+        if "down_blocks" in k:
+            found_up_down_blocks = True
+            break
+        if "up_blocks" in k:
+            found_up_down_blocks = True
+            break
+    if not found_up_down_blocks:
+        return
+
+    from library.sdxl_model_util import make_unet_conversion_map
+
+    unet_conversion_map = make_unet_conversion_map()
+    unet_conversion_map = {hf.replace(".", "_")[:-1]: sd.replace(".", "_")[:-1] for sd, hf in unet_conversion_map}
+
+    # # add extra conversion
+    # unet_conversion_map["up_blocks_1_upsamplers_0"] = "lora_unet_output_blocks_2_2_conv"
+
+    logger.info(f"Converting LoRA keys from Diffusers to SAI")
+    lora_unet_prefix = "lora_unet_"
+    for k in list(weights_sd.keys()):
+        if not k.startswith(lora_unet_prefix):
+            continue
+
+        unet_module_name = k[len(lora_unet_prefix) :].split(".")[0]
+
+        # search for conversion: this is slow because the algorithm is O(n^2), but the number of keys is small
+        for hf_module_name, sd_module_name in unet_conversion_map.items():
+            if hf_module_name in unet_module_name:
+                new_key = (
+                    lora_unet_prefix
+                    + unet_module_name.replace(hf_module_name, sd_module_name)
+                    + k[len(lora_unet_prefix) + len(unet_module_name) :]
+                )
+                weights_sd[new_key] = weights_sd.pop(k)
+                found = True
+                break
+
+        if not found:
+            logger.warning(f"Key {k} is not found in unet_conversion_map")
+
+
 # Create network from weights for inference, weights are not loaded here (because can be merged)
 def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs):
+    # if unet is an instance of SdxlUNet2DConditionModel or subclass, set is_sdxl to True
+    is_sdxl = unet is not None and issubclass(unet.__class__, SdxlUNet2DConditionModel)
+
     if weights_sd is None:
         if os.path.splitext(file)[1] == ".safetensors":
             from safetensors.torch import load_file, safe_open
@@ -707,6 +814,10 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
         else:
             weights_sd = torch.load(file, map_location="cpu")
 
+    # if keys are Diffusers based, convert to SAI based
+    if is_sdxl:
+        convert_diffusers_to_sai_if_needed(weights_sd)
+
     # get dim/alpha mapping
     modules_dim = {}
     modules_alpha = {}
@@ -730,19 +841,28 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
     module_class = LoRAInfModule if for_inference else LoRAModule
 
     network = LoRANetwork(
-        text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
+        text_encoder,
+        unet,
+        multiplier=multiplier,
+        modules_dim=modules_dim,
+        modules_alpha=modules_alpha,
+        module_class=module_class,
+        is_sdxl=is_sdxl,
     )
 
     # block lr
-    down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs)
-    if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
-        network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)
+    block_lr_weight = parse_block_lr_kwargs(is_sdxl, kwargs)
+    if block_lr_weight is not None:
+        network.set_block_lr_weight(block_lr_weight)
 
     return network, weights_sd
 
 
 class LoRANetwork(torch.nn.Module):
     NUM_OF_BLOCKS = 12  # フルモデル相当でのup,downの層の数
+    NUM_OF_MID_BLOCKS = 1
+    SDXL_NUM_OF_BLOCKS = 9  # SDXLのモデルでのinput/outputの層の数 total=1(base) 9(input) + 3(mid) + 9(output) + 1(out) = 23
+    SDXL_NUM_OF_MID_BLOCKS = 3
 
     UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
     UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
@@ -774,6 +894,7 @@ def __init__(
         modules_alpha: Optional[Dict[str, int]] = None,
         module_class: Type[object] = LoRAModule,
         varbose: Optional[bool] = False,
+        is_sdxl: Optional[bool] = False,
     ) -> None:
         """
         LoRA network: すごく引数が多いが、パターンは以下の通り
@@ -794,6 +915,10 @@ def __init__(
         self.rank_dropout = rank_dropout
         self.module_dropout = module_dropout
 
+        self.loraplus_lr_ratio = None
+        self.loraplus_unet_lr_ratio = None
+        self.loraplus_text_encoder_lr_ratio = None
+
         if modules_dim is not None:
             logger.info(f"create LoRA network from weights")
         elif block_dims is not None:
@@ -855,7 +980,7 @@ def create_modules(
                                     alpha = modules_alpha[lora_name]
                             elif is_unet and block_dims is not None:
                                 # U-Netでblock_dims指定あり
-                                block_idx = get_block_index(lora_name)
+                                block_idx = get_block_index(lora_name, is_sdxl)
                                 if is_linear or is_conv2d_1x1:
                                     dim = block_dims[block_idx]
                                     alpha = block_alphas[block_idx]
@@ -925,9 +1050,7 @@ def create_modules(
             for name in skipped:
                 logger.info(f"\t{name}")
 
-        self.up_lr_weight: List[float] = None
-        self.down_lr_weight: List[float] = None
-        self.mid_lr_weight: float = None
+        self.block_lr_weight = None
         self.block_lr = False
 
         # assertion
@@ -958,12 +1081,12 @@ def load_weights(self, file):
 
     def apply_to(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True):
         if apply_text_encoder:
-            logger.info("enable LoRA for text encoder")
+            logger.info(f"enable LoRA for text encoder: {len(self.text_encoder_loras)} modules")
         else:
             self.text_encoder_loras = []
 
         if apply_unet:
-            logger.info("enable LoRA for U-Net")
+            logger.info(f"enable LoRA for U-Net: {len(self.unet_loras)} modules")
         else:
             self.unet_loras = []
 
@@ -1004,81 +1127,117 @@ def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
         logger.info(f"weights are merged")
 
     # 層別学習率用に層ごとの学習率に対する倍率を定義する　引数の順番が逆だがとりあえず気にしない
-    def set_block_lr_weight(
-        self,
-        up_lr_weight: List[float] = None,
-        mid_lr_weight: float = None,
-        down_lr_weight: List[float] = None,
-    ):
+    def set_block_lr_weight(self, block_lr_weight: Optional[List[float]]):
         self.block_lr = True
-        self.down_lr_weight = down_lr_weight
-        self.mid_lr_weight = mid_lr_weight
-        self.up_lr_weight = up_lr_weight
-
-    def get_lr_weight(self, lora: LoRAModule) -> float:
-        lr_weight = 1.0
-        block_idx = get_block_index(lora.lora_name)
-        if block_idx < 0:
-            return lr_weight
-
-        if block_idx < LoRANetwork.NUM_OF_BLOCKS:
-            if self.down_lr_weight != None:
-                lr_weight = self.down_lr_weight[block_idx]
-        elif block_idx == LoRANetwork.NUM_OF_BLOCKS:
-            if self.mid_lr_weight != None:
-                lr_weight = self.mid_lr_weight
-        elif block_idx > LoRANetwork.NUM_OF_BLOCKS:
-            if self.up_lr_weight != None:
-                lr_weight = self.up_lr_weight[block_idx - LoRANetwork.NUM_OF_BLOCKS - 1]
-
-        return lr_weight
+        self.block_lr_weight = block_lr_weight
+
+    def get_lr_weight(self, block_idx: int) -> float:
+        if not self.block_lr or self.block_lr_weight is None:
+            return 1.0
+        return self.block_lr_weight[block_idx]
+
+    def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
+        self.loraplus_lr_ratio = loraplus_lr_ratio
+        self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
+        self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
+
+        logger.info(f"LoRA+ UNet LR Ratio: {self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio}")
+        logger.info(f"LoRA+ Text Encoder LR Ratio: {self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio}")
 
     # 二つのText Encoderに別々の学習率を設定できるようにするといいかも
     def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
+        # TODO warn if optimizer is not compatible with LoRA+ (but it will cause error so we don't need to check it here?)
+        # if (
+        #     self.loraplus_lr_ratio is not None
+        #     or self.loraplus_text_encoder_lr_ratio is not None
+        #     or self.loraplus_unet_lr_ratio is not None
+        # ):
+        #     assert (
+        #         optimizer_type.lower() != "prodigy" and "dadapt" not in optimizer_type.lower()
+        #     ), "LoRA+ and Prodigy/DAdaptation is not supported / LoRA+とProdigy/DAdaptationの組み合わせはサポートされていません"
+
         self.requires_grad_(True)
+
         all_params = []
+        lr_descriptions = []
 
-        def enumerate_params(loras):
-            params = []
+        def assemble_params(loras, lr, ratio):
+            param_groups = {"lora": {}, "plus": {}}
             for lora in loras:
-                params.extend(lora.parameters())
-            return params
+                for name, param in lora.named_parameters():
+                    if ratio is not None and "lora_up" in name:
+                        param_groups["plus"][f"{lora.lora_name}.{name}"] = param
+                    else:
+                        param_groups["lora"][f"{lora.lora_name}.{name}"] = param
+
+            params = []
+            descriptions = []
+            for key in param_groups.keys():
+                param_data = {"params": param_groups[key].values()}
+
+                if len(param_data["params"]) == 0:
+                    continue
+
+                if lr is not None:
+                    if key == "plus":
+                        param_data["lr"] = lr * ratio
+                    else:
+                        param_data["lr"] = lr
+
+                if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
+                    logger.info("NO LR skipping!")
+                    continue
+
+                params.append(param_data)
+                descriptions.append("plus" if key == "plus" else "")
+
+            return params, descriptions
 
         if self.text_encoder_loras:
-            param_data = {"params": enumerate_params(self.text_encoder_loras)}
-            if text_encoder_lr is not None:
-                param_data["lr"] = text_encoder_lr
-            all_params.append(param_data)
+            params, descriptions = assemble_params(
+                self.text_encoder_loras,
+                text_encoder_lr if text_encoder_lr is not None else default_lr,
+                self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio,
+            )
+            all_params.extend(params)
+            lr_descriptions.extend(["textencoder" + (" " + d if d else "") for d in descriptions])
 
         if self.unet_loras:
             if self.block_lr:
+                is_sdxl = False
+                for lora in self.unet_loras:
+                    if "input_blocks" in lora.lora_name or "output_blocks" in lora.lora_name:
+                        is_sdxl = True
+                        break
+
                 # 学習率のグラフをblockごとにしたいので、blockごとにloraを分類
                 block_idx_to_lora = {}
                 for lora in self.unet_loras:
-                    idx = get_block_index(lora.lora_name)
+                    idx = get_block_index(lora.lora_name, is_sdxl)
                     if idx not in block_idx_to_lora:
                         block_idx_to_lora[idx] = []
                     block_idx_to_lora[idx].append(lora)
 
                 # blockごとにパラメータを設定する
                 for idx, block_loras in block_idx_to_lora.items():
-                    param_data = {"params": enumerate_params(block_loras)}
-
-                    if unet_lr is not None:
-                        param_data["lr"] = unet_lr * self.get_lr_weight(block_loras[0])
-                    elif default_lr is not None:
-                        param_data["lr"] = default_lr * self.get_lr_weight(block_loras[0])
-                    if ("lr" in param_data) and (param_data["lr"] == 0):
-                        continue
-                    all_params.append(param_data)
+                    params, descriptions = assemble_params(
+                        block_loras,
+                        (unet_lr if unet_lr is not None else default_lr) * self.get_lr_weight(idx),
+                        self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
+                    )
+                    all_params.extend(params)
+                    lr_descriptions.extend([f"unet_block{idx}" + (" " + d if d else "") for d in descriptions])
 
             else:
-                param_data = {"params": enumerate_params(self.unet_loras)}
-                if unet_lr is not None:
-                    param_data["lr"] = unet_lr
-                all_params.append(param_data)
+                params, descriptions = assemble_params(
+                    self.unet_loras,
+                    unet_lr if unet_lr is not None else default_lr,
+                    self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
+                )
+                all_params.extend(params)
+                lr_descriptions.extend(["unet" + (" " + d if d else "") for d in descriptions])
 
-        return all_params
+        return all_params, lr_descriptions
 
     def enable_gradient_checkpointing(self):
         # not supported
diff --git a/networks/oft.py b/networks/oft.py
index 461a98698..6321def3b 100644
--- a/networks/oft.py
+++ b/networks/oft.py
@@ -4,13 +4,17 @@
 import os
 from typing import Dict, List, Optional, Tuple, Type, Union
 from diffusers import AutoencoderKL
+import einops
 from transformers import CLIPTextModel
 import numpy as np
 import torch
+import torch.nn.functional as F
 import re
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
@@ -45,11 +49,16 @@ def __init__(
 
         if type(alpha) == torch.Tensor:
             alpha = alpha.detach().numpy()
-        self.constraint = alpha * out_dim
+        
+        # constraint in original paper is alpha * out_dim * out_dim, but we use alpha * out_dim for backward compatibility
+        # original alpha is 1e-6, so we use 1e-3 or 1e-4 for alpha
+        self.constraint = alpha * out_dim 
+        
         self.register_buffer("alpha", torch.tensor(alpha))
 
         self.block_size = out_dim // self.num_blocks
         self.oft_blocks = torch.nn.Parameter(torch.zeros(self.num_blocks, self.block_size, self.block_size))
+        self.I = torch.eye(self.block_size).unsqueeze(0).repeat(self.num_blocks, 1, 1)  # cpu
 
         self.out_dim = out_dim
         self.shape = org_module.weight.shape
@@ -69,27 +78,36 @@ def get_weight(self, multiplier=None):
         norm_Q = torch.norm(block_Q.flatten())
         new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
         block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
-        I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
-        block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
 
-        block_R_weighted = self.multiplier * block_R + (1 - self.multiplier) * I
-        R = torch.block_diag(*block_R_weighted)
-
-        return R
+        if self.I.device != block_Q.device:
+            self.I = self.I.to(block_Q.device)
+        I = self.I
+        block_R = torch.matmul(I + block_Q, (I - block_Q).float().inverse())
+        block_R_weighted = self.multiplier * (block_R - I) + I
+        return block_R_weighted
 
     def forward(self, x, scale=None):
-        x = self.org_forward(x)
         if self.multiplier == 0.0:
-            return x
-
-        R = self.get_weight().to(x.device, dtype=x.dtype)
-        if x.dim() == 4:
-            x = x.permute(0, 2, 3, 1)
-            x = torch.matmul(x, R)
-            x = x.permute(0, 3, 1, 2)
-        else:
-            x = torch.matmul(x, R)
-        return x
+            return self.org_forward(x)
+        org_module = self.org_module[0]
+        org_dtype = x.dtype
+
+        R = self.get_weight().to(torch.float32)
+        W = org_module.weight.to(torch.float32)
+
+        if len(W.shape) == 4:  # Conv2d
+            W_reshaped = einops.rearrange(W, "(k n) ... -> k n ...", k=self.num_blocks, n=self.block_size)
+            RW = torch.einsum("k n m, k n ... -> k m ...", R, W_reshaped)
+            RW = einops.rearrange(RW, "k m ... -> (k m) ...")
+            result = F.conv2d(
+                x, RW.to(org_dtype), org_module.bias, org_module.stride, org_module.padding, org_module.dilation, org_module.groups
+            )
+        else:  # Linear
+            W_reshaped = einops.rearrange(W, "(k n) m -> k n m", k=self.num_blocks, n=self.block_size)
+            RW = torch.einsum("k n m, k n p -> k m p", R, W_reshaped)
+            RW = einops.rearrange(RW, "k m p -> (k m) p")
+            result = F.linear(x, RW.to(org_dtype), org_module.bias)
+        return result
 
 
 class OFTInfModule(OFTModule):
@@ -115,18 +133,19 @@ def forward(self, x, scale=None):
             return self.org_forward(x)
         return super().forward(x, scale)
 
-    def merge_to(self, multiplier=None, sign=1):
-        R = self.get_weight(multiplier) * sign
-
+    def merge_to(self, multiplier=None):
         # get org weight
         org_sd = self.org_module[0].state_dict()
-        org_weight = org_sd["weight"]
-        R = R.to(org_weight.device, dtype=org_weight.dtype)
+        org_weight = org_sd["weight"].to(torch.float32)
 
-        if org_weight.dim() == 4:
-            weight = torch.einsum("oihw, op -> pihw", org_weight, R)
-        else:
-            weight = torch.einsum("oi, op -> pi", org_weight, R)
+        R = self.get_weight(multiplier).to(torch.float32)
+
+        weight = org_weight.reshape(self.num_blocks, self.block_size, -1)
+        weight = torch.einsum("k n m, k n ... -> k m ...", R, weight)
+        weight = weight.reshape(org_weight.shape)
+
+        # convert back to original dtype
+        weight = weight.to(org_sd["weight"].dtype)
 
         # set weight to org_module
         org_sd["weight"] = weight
@@ -145,8 +164,16 @@ def create_network(
 ):
     if network_dim is None:
         network_dim = 4  # default
-    if network_alpha is None:
-        network_alpha = 1.0
+    if network_alpha is None:  # should be set
+        logger.info(
+            "network_alpha is not set, use default value 1e-3 / network_alphaが設定されていないのでデフォルト値 1e-3 を使用します"
+        )
+        network_alpha = 1e-3
+    elif network_alpha >= 1:
+        logger.warning(
+            "network_alpha is too large (>=1, maybe default value is too large), please consider to set smaller value like 1e-3"
+            " / network_alphaが大きすぎるようです(>=1, デフォルト値が大きすぎる可能性があります)。1e-3のような小さな値を推奨"
+        )
 
     enable_all_linear = kwargs.get("enable_all_linear", None)
     enable_conv = kwargs.get("enable_conv", None)
@@ -190,12 +217,11 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
         else:
             if dim is None:
                 dim = param.size()[0]
-            if has_conv2d is None and param.dim() == 4:
+            if has_conv2d is None and "in_layers_2" in name:
                 has_conv2d = True
-            if all_linear is None:
-                if param.dim() == 3 and "attn" not in name:
-                    all_linear = True
-        if dim is not None and alpha is not None and has_conv2d is not None:
+            if all_linear is None and "_ff_" in name:
+                all_linear = True
+        if dim is not None and alpha is not None and has_conv2d is not None and all_linear is not None:
             break
     if has_conv2d is None:
         has_conv2d = False
@@ -241,7 +267,7 @@ def __init__(
         self.alpha = alpha
 
         logger.info(
-            f"create OFT network. num blocks: {self.dim}, constraint: {self.alpha}, multiplier: {self.multiplier}, enable_conv: {enable_conv}"
+            f"create OFT network. num blocks: {self.dim}, constraint: {self.alpha}, multiplier: {self.multiplier}, enable_conv: {enable_conv}, enable_all_linear: {enable_all_linear}"
         )
 
         # create module instances
diff --git a/requirements.txt b/requirements.txt
index 977c5cd91..e6e1bf6fc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,12 +1,12 @@
-accelerate==0.25.0
-transformers==4.36.2
+accelerate==0.30.0
+transformers==4.44.0
 diffusers[torch]==0.25.0
 ftfy==6.1.1
 # albumentations==1.3.0
 opencv-python==4.8.1.78
 einops==0.7.0
 pytorch-lightning==1.9.0
-bitsandbytes==0.43.0
+bitsandbytes==0.44.0
 prodigyopt==1.0
 lion-pytorch==0.0.6
 tensorboard
@@ -16,7 +16,7 @@ altair==4.2.2
 easygui==0.98.3
 toml==0.10.2
 voluptuous==0.13.1
-huggingface-hub==0.20.1
+huggingface-hub==0.24.5
 # for Image utils
 imagesize==1.4.1
 # for BLIP captioning
diff --git a/sdxl_train.py b/sdxl_train.py
index 46d7860be..b533b2749 100644
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -272,7 +272,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
     # 学習を準備する：モデルを適切な状態にする
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
-    train_unet = args.learning_rate > 0
+    train_unet = args.learning_rate != 0
     train_text_encoder1 = False
     train_text_encoder2 = False
 
@@ -284,8 +284,8 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             text_encoder2.gradient_checkpointing_enable()
         lr_te1 = args.learning_rate_te1 if args.learning_rate_te1 is not None else args.learning_rate  # 0 means not train
         lr_te2 = args.learning_rate_te2 if args.learning_rate_te2 is not None else args.learning_rate  # 0 means not train
-        train_text_encoder1 = lr_te1 > 0
-        train_text_encoder2 = lr_te2 > 0
+        train_text_encoder1 = lr_te1 != 0
+        train_text_encoder2 = lr_te2 != 0
 
         # caching one text encoder output is not supported
         if not train_text_encoder1:
@@ -345,8 +345,8 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     # calculate number of trainable parameters
     n_params = 0
-    for params in params_to_optimize:
-        for p in params["params"]:
+    for group in params_to_optimize:
+        for p in group["params"]:
             n_params += p.numel()
 
     accelerator.print(f"train unet: {train_unet}, text_encoder1: {train_text_encoder1}, text_encoder2: {train_text_encoder2}")
@@ -355,7 +355,53 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     # 学習に必要なクラスを準備する
     accelerator.print("prepare optimizer, data loader etc.")
-    _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
+
+    if args.fused_optimizer_groups:
+        # 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 group of parameters.
+        # This balances memory usage and management complexity.
+
+        # calculate total number of parameters
+        n_total_params = sum(len(params["params"]) for params in params_to_optimize)
+        params_per_group = math.ceil(n_total_params / args.fused_optimizer_groups)
+
+        # split params into groups, keeping the learning rate the same for all params in a group
+        # this will increase the number of groups if the learning rate is different for different params (e.g. U-Net and text encoders)
+        grouped_params = []
+        param_group = []
+        param_group_lr = -1
+        for group in params_to_optimize:
+            lr = group["lr"]
+            for p in group["params"]:
+                # if the learning rate is different for different params, start a new group
+                if lr != param_group_lr:
+                    if param_group:
+                        grouped_params.append({"params": param_group, "lr": param_group_lr})
+                        param_group = []
+                    param_group_lr = lr
+
+                param_group.append(p)
+
+                # if the group has enough parameters, start a new group
+                if len(param_group) == params_per_group:
+                    grouped_params.append({"params": param_group, "lr": param_group_lr})
+                    param_group = []
+                    param_group_lr = -1
+
+        if param_group:
+            grouped_params.append({"params": param_group, "lr": param_group_lr})
+
+        # 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 fused optimizer groups")
+
+    else:
+        _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
 
     # 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
@@ -382,7 +428,12 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
     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)
+    if args.fused_optimizer_groups:
+        # 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:
@@ -450,6 +501,57 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
     # 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 in optimizer.param_groups:
+            for parameter in param_group["params"]:
+                if parameter.requires_grad:
+
+                    def __grad_hook(tensor: torch.Tensor, param_group=param_group):
+                        if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                            accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
+                        optimizer.step_param(tensor, param_group)
+                        tensor.grad = None
+
+                    parameter.register_post_accumulate_grad_hook(__grad_hook)
+
+    elif args.fused_optimizer_groups:
+        # prepare for additional optimizers and lr schedulers
+        for i in range(1, len(optimizers)):
+            optimizers[i] = accelerator.prepare(optimizers[i])
+            lr_schedulers[i] = accelerator.prepare(lr_schedulers[i])
+
+        # counters are used to determine when to step the optimizer
+        global optimizer_hooked_count
+        global num_parameters_per_group
+        global parameter_optimizer_map
+
+        optimizer_hooked_count = {}
+        num_parameters_per_group = [0] * len(optimizers)
+        parameter_optimizer_map = {}
+
+        for opt_idx, optimizer in enumerate(optimizers):
+            for param_group in optimizer.param_groups:
+                for parameter in param_group["params"]:
+                    if parameter.requires_grad:
+
+                        def optimizer_hook(parameter: torch.Tensor):
+                            if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                                accelerator.clip_grad_norm_(parameter, args.max_grad_norm)
+
+                            i = parameter_optimizer_map[parameter]
+                            optimizer_hooked_count[i] += 1
+                            if optimizer_hooked_count[i] == num_parameters_per_group[i]:
+                                optimizers[i].step()
+                                optimizers[i].zero_grad(set_to_none=True)
+
+                        parameter.register_post_accumulate_grad_hook(optimizer_hook)
+                        parameter_optimizer_map[parameter] = opt_idx
+                        num_parameters_per_group[opt_idx] += 1
+
     # epoch数を計算する
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
@@ -487,7 +589,11 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             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, init_kwargs=init_kwargs)
+        accelerator.init_trackers(
+            "finetuning" if args.log_tracker_name is None else args.log_tracker_name,
+            config=train_util.get_sanitized_config_or_none(args),
+            init_kwargs=init_kwargs,
+        )
 
     # For --sample_at_first
     sdxl_train_util.sample_images(
@@ -504,6 +610,10 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
         for step, batch in enumerate(train_dataloader):
             current_step.value = global_step
+
+            if args.fused_optimizer_groups:
+                optimizer_hooked_count = {i: 0 for i in range(len(optimizers))}  # reset counter for each step
+
             with accelerator.accumulate(*training_models):
                 if "latents" in batch and batch["latents"] is not None:
                     latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
@@ -582,7 +692,9 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
                 # 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, huber_c = 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
 
@@ -590,7 +702,11 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
                 with accelerator.autocast():
                     noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)
 
-                target = noise
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
 
                 if (
                     args.min_snr_gamma
@@ -600,34 +716,46 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
                     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)
-                    if args.masked_loss:
+                    loss = train_util.conditional_loss(
+                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=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])
 
                     if args.min_snr_gamma:
-                        loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+                        loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
                     if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     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(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c
+                    )
 
                 accelerator.backward(loss)
-                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)
+                if not (args.fused_backward_pass or args.fused_optimizer_groups):
+                    if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                        params_to_clip = []
+                        for m in training_models:
+                            params_to_clip.extend(m.parameters())
+                        accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad(set_to_none=True)
+                else:
+                    # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
+                    lr_scheduler.step()
+                    if args.fused_optimizer_groups:
+                        for i in range(1, len(optimizers)):
+                            lr_schedulers[i].step()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
@@ -736,7 +864,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     accelerator.end_training()
 
-    if args.save_state or args.save_state_on_train_end:        
+    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
@@ -805,6 +933,12 @@ def setup_parser() -> argparse.ArgumentParser:
         help=f"learning rates for each block of U-Net, comma-separated, {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / "
         + f"U-Netの各ブロックの学習率、カンマ区切り、{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値",
     )
+    parser.add_argument(
+        "--fused_optimizer_groups",
+        type=int,
+        default=None,
+        help="number of optimizers for fused backward pass and optimizer step / fused backward passとoptimizer stepのためのoptimizer数",
+    )
     return parser
 
 
diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py
index f89c3628f..0e67cde5c 100644
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -15,6 +15,7 @@
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -288,6 +289,9 @@ def train(args):
     # acceleratorがなんかよろしくやってくれるらしい
     unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
 
+    if isinstance(unet, DDP):
+        unet._set_static_graph() # avoid error for multiple use of the parameter
+
     if args.gradient_checkpointing:
         unet.train()  # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる
     else:
@@ -353,7 +357,7 @@ 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, 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()
@@ -439,7 +443,9 @@ def remove_model(old_ckpt_name):
 
                 # 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, huber_c = 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
 
@@ -458,7 +464,9 @@ def remove_model(old_ckpt_name):
                 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 = 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
@@ -471,13 +479,13 @@ def remove_model(old_ckpt_name):
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients and args.max_grad_norm != 0.0:
-                    params_to_clip = unet.get_trainable_params()
+                    params_to_clip = accelerator.unwrap_model(unet).get_trainable_params()
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
diff --git a/sdxl_train_control_net_lllite_old.py b/sdxl_train_control_net_lllite_old.py
index e85e978c1..4a01f9e2c 100644
--- a/sdxl_train_control_net_lllite_old.py
+++ b/sdxl_train_control_net_lllite_old.py
@@ -324,7 +324,7 @@ 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, 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()
@@ -439,7 +439,7 @@ def remove_model(old_ckpt_name):
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/tools/cache_latents.py b/tools/cache_latents.py
index 32101de3f..2f0098b42 100644
--- a/tools/cache_latents.py
+++ b/tools/cache_latents.py
@@ -19,8 +19,10 @@
 from library.utils import setup_logging, add_logging_arguments
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
+
 def cache_to_disk(args: argparse.Namespace) -> None:
     setup_logging(args, reset=True)
     train_util.prepare_dataset_args(args, True)
@@ -109,7 +111,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
     else:
         _, vae, _, _ = train_util.load_target_model(args, weight_dtype, accelerator)
 
-    if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える
+    if torch.__version__ >= "2.0.0":  # PyTorch 2.0.0 以上対応のxformersなら以下が使える
         vae.set_use_memory_efficient_attention_xformers(args.xformers)
     vae.to(accelerator.device, dtype=vae_dtype)
     vae.requires_grad_(False)
@@ -138,6 +140,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
         b_size = len(batch["images"])
         vae_batch_size = b_size if args.vae_batch_size is None else args.vae_batch_size
         flip_aug = batch["flip_aug"]
+        alpha_mask = batch["alpha_mask"]
         random_crop = batch["random_crop"]
         bucket_reso = batch["bucket_reso"]
 
@@ -156,14 +159,16 @@ def cache_to_disk(args: argparse.Namespace) -> None:
                 image_info.latents_npz = os.path.splitext(absolute_path)[0] + ".npz"
 
                 if args.skip_existing:
-                    if train_util.is_disk_cached_latents_is_expected(image_info.bucket_reso, image_info.latents_npz, flip_aug):
+                    if train_util.is_disk_cached_latents_is_expected(
+                        image_info.bucket_reso, image_info.latents_npz, flip_aug, alpha_mask
+                    ):
                         logger.warning(f"Skipping {image_info.latents_npz} because it already exists.")
                         continue
 
                 image_infos.append(image_info)
 
             if len(image_infos) > 0:
-                train_util.cache_batch_latents(vae, True, image_infos, flip_aug, random_crop)
+                train_util.cache_batch_latents(vae, True, image_infos, flip_aug, alpha_mask, random_crop)
 
     accelerator.wait_for_everyone()
     accelerator.print(f"Finished caching latents for {len(train_dataset_group)} batches.")
diff --git a/tools/detect_face_rotate.py b/tools/detect_face_rotate.py
index bbc643edc..d2a4d9cfb 100644
--- a/tools/detect_face_rotate.py
+++ b/tools/detect_face_rotate.py
@@ -15,7 +15,7 @@
 from anime_face_detector import create_detector
 from tqdm import tqdm
 import numpy as np
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -172,7 +172,10 @@ def process(args):
         if scale != 1.0:
           w = int(w * scale + .5)
           h = int(h * scale + .5)
-          face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA if scale < 1.0 else cv2.INTER_LANCZOS4)
+          if scale < 1.0:
+            face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA)
+          else:
+            face_img = pil_resize(face_img, (w, h))
           cx = int(cx * scale + .5)
           cy = int(cy * scale + .5)
           fw = int(fw * scale + .5)
diff --git a/tools/resize_images_to_resolution.py b/tools/resize_images_to_resolution.py
index b8069fc1d..0f9e00b1e 100644
--- a/tools/resize_images_to_resolution.py
+++ b/tools/resize_images_to_resolution.py
@@ -6,7 +6,7 @@
 import math
 from PIL import Image
 import numpy as np
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -24,9 +24,9 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
 
   # Select interpolation method
   if interpolation == 'lanczos4':
-    cv2_interpolation = cv2.INTER_LANCZOS4
+    pil_interpolation = Image.LANCZOS
   elif interpolation == 'cubic':
-    cv2_interpolation = cv2.INTER_CUBIC
+    pil_interpolation = Image.BICUBIC
   else:
     cv2_interpolation = cv2.INTER_AREA
 
@@ -64,7 +64,10 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
         new_width = int(img.shape[1] * math.sqrt(scale_factor))
 
         # Resize image
-        img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
+        if cv2_interpolation:
+          img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
+        else:
+          img = pil_resize(img, (new_width, new_height), interpolation=pil_interpolation)
       else:
         new_height, new_width = img.shape[0:2]
 
diff --git a/train_controlnet.py b/train_controlnet.py
index f4c94e8d9..6938c4bcc 100644
--- a/train_controlnet.py
+++ b/train_controlnet.py
@@ -5,7 +5,8 @@
 import random
 import time
 from multiprocessing import Value
-from types import SimpleNamespace
+
+# from omegaconf import OmegaConf
 import toml
 
 from tqdm import tqdm
@@ -13,6 +14,7 @@
 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
@@ -105,6 +107,8 @@ def train(args):
     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
@@ -148,8 +152,10 @@ def train(args):
             "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,
@@ -179,8 +185,10 @@ def train(args):
             "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"],
@@ -193,7 +201,23 @@ def train(args):
             "resnet_time_scale_shift": "default",
             "projection_class_embeddings_input_dim": None,
         }
-    unet.config = SimpleNamespace(**unet.config)
+    # 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)
 
@@ -226,7 +250,7 @@ def train(args):
             )
         vae.to("cpu")
         clean_memory_on_device(accelerator.device)
-        
+
         accelerator.wait_for_everyone()
 
     if args.gradient_checkpointing:
@@ -235,7 +259,7 @@ def train(args):
     # 学習に必要なクラスを準備する
     accelerator.print("prepare optimizer, data loader etc.")
 
-    trainable_params = controlnet.parameters()
+    trainable_params = list(controlnet.parameters())
 
     _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
@@ -344,7 +368,9 @@ def train(args):
         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, init_kwargs=init_kwargs
+            "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()
@@ -420,7 +446,9 @@ def remove_model(old_ckpt_name):
                     )
 
                 # 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)
+                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)
@@ -452,7 +480,9 @@ def remove_model(old_ckpt_name):
                 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 = 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
diff --git a/train_db.py b/train_db.py
index 1de504ed8..e7cf3cde3 100644
--- a/train_db.py
+++ b/train_db.py
@@ -93,6 +93,8 @@ def train(args):
     if args.no_token_padding:
         train_dataset_group.disable_token_padding()
 
+    train_dataset_group.verify_bucket_reso_steps(64)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
         return
@@ -290,7 +292,7 @@ def train(args):
             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("dreambooth" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
+        accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs)
 
     # For --sample_at_first
     train_util.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
@@ -359,7 +361,7 @@ def train(args):
                     target = noise
 
                 loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
-                if args.masked_loss:
+                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])
 
@@ -371,7 +373,7 @@ def train(args):
                 if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/train_network.py b/train_network.py
index c99d37247..7bf125dca 100644
--- a/train_network.py
+++ b/train_network.py
@@ -53,7 +53,15 @@ def __init__(self):
 
     # TODO 他のスクリプトと共通化する
     def generate_step_logs(
-        self, args: argparse.Namespace, current_loss, avr_loss, lr_scheduler, keys_scaled=None, mean_norm=None, maximum_norm=None
+        self,
+        args: argparse.Namespace,
+        current_loss,
+        avr_loss,
+        lr_scheduler,
+        lr_descriptions,
+        keys_scaled=None,
+        mean_norm=None,
+        maximum_norm=None,
     ):
         logs = {"loss/current": current_loss, "loss/average": avr_loss}
 
@@ -63,39 +71,31 @@ def generate_step_logs(
             logs["max_norm/max_key_norm"] = maximum_norm
 
         lrs = lr_scheduler.get_last_lr()
-
-        if args.network_train_text_encoder_only or len(lrs) <= 2:  # not block lr (or single block)
-            if args.network_train_unet_only:
-                logs["lr/unet"] = float(lrs[0])
-            elif args.network_train_text_encoder_only:
-                logs["lr/textencoder"] = float(lrs[0])
+        for i, lr in enumerate(lrs):
+            if lr_descriptions is not None:
+                lr_desc = lr_descriptions[i]
             else:
-                logs["lr/textencoder"] = float(lrs[0])
-                logs["lr/unet"] = float(lrs[-1])  # may be same to textencoder
-
-            if (
-                args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
-            ):  # tracking d*lr value of unet.
-                logs["lr/d*lr"] = (
-                    lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
+                idx = i - (0 if args.network_train_unet_only else -1)
+                if idx == -1:
+                    lr_desc = "textencoder"
+                else:
+                    if len(lrs) > 2:
+                        lr_desc = f"group{idx}"
+                    else:
+                        lr_desc = "unet"
+
+            logs[f"lr/{lr_desc}"] = lr
+
+            if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
+                # tracking d*lr value
+                logs[f"lr/d*lr/{lr_desc}"] = (
+                    lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                 )
-        else:
-            idx = 0
-            if not args.network_train_unet_only:
-                logs["lr/textencoder"] = float(lrs[0])
-                idx = 1
-
-            for i in range(idx, len(lrs)):
-                logs[f"lr/group{i}"] = float(lrs[i])
-                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
-                    logs[f"lr/d*lr/group{i}"] = (
-                        lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
-                    )
 
         return logs
 
     def assert_extra_args(self, args, train_dataset_group):
-        pass
+        train_dataset_group.verify_bucket_reso_steps(64)
 
     def load_target_model(self, args, weight_dtype, accelerator):
         text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
@@ -323,6 +323,7 @@ def train(self, args):
         network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
 
         if args.network_weights is not None:
+            # FIXME consider alpha of weights
             info = network.load_weights(args.network_weights)
             accelerator.print(f"load network weights from {args.network_weights}: {info}")
 
@@ -338,12 +339,30 @@ def train(self, args):
 
         # 後方互換性を確保するよ
         try:
-            trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
-        except TypeError:
-            accelerator.print(
-                "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
-            )
+            results = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
+            if type(results) is tuple:
+                trainable_params = results[0]
+                lr_descriptions = results[1]
+            else:
+                trainable_params = results
+                lr_descriptions = None
+        except TypeError as e:
+            # logger.warning(f"{e}")
+            # accelerator.print(
+            #     "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
+            # )
             trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
+            lr_descriptions = None
+
+        # if len(trainable_params) == 0:
+        #     accelerator.print("no trainable parameters found / 学習可能なパラメータが見つかりませんでした")
+        # for params in trainable_params:
+        #     for k, v in params.items():
+        #         if type(v) == float:
+        #             pass
+        #         else:
+        #             v = len(v)
+        #         accelerator.print(f"trainable_params: {k} = {v}")
 
         optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
 
@@ -474,15 +493,26 @@ def train(self, args):
         # before resuming make hook for saving/loading to save/load the network weights only
         def save_model_hook(models, weights, output_dir):
             # pop weights of other models than network to save only network weights
-            if accelerator.is_main_process:
+            # only main process or deepspeed https://github.com/huggingface/diffusers/issues/2606
+            if accelerator.is_main_process or args.deepspeed:
                 remove_indices = []
                 for i, model in enumerate(models):
                     if not isinstance(model, type(accelerator.unwrap_model(network))):
                         remove_indices.append(i)
                 for i in reversed(remove_indices):
-                    weights.pop(i)
+                    if len(weights) > i:
+                        weights.pop(i)
                 # print(f"save model hook: {len(weights)} weights will be saved")
 
+            # save current ecpoch and step
+            train_state_file = os.path.join(output_dir, "train_state.json")
+            # +1 is needed because the state is saved before current_step is set from global_step
+            logger.info(f"save train state to {train_state_file} at epoch {current_epoch.value} step {current_step.value+1}")
+            with open(train_state_file, "w", encoding="utf-8") as f:
+                json.dump({"current_epoch": current_epoch.value, "current_step": current_step.value + 1}, f)
+
+        steps_from_state = None
+
         def load_model_hook(models, input_dir):
             # remove models except network
             remove_indices = []
@@ -493,6 +523,15 @@ def load_model_hook(models, input_dir):
                 models.pop(i)
             # print(f"load model hook: {len(models)} models will be loaded")
 
+            # load current epoch and step to
+            nonlocal steps_from_state
+            train_state_file = os.path.join(input_dir, "train_state.json")
+            if os.path.exists(train_state_file):
+                with open(train_state_file, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                steps_from_state = data["current_step"]
+                logger.info(f"load train state from {train_state_file}: {data}")
+
         accelerator.register_save_state_pre_hook(save_model_hook)
         accelerator.register_load_state_pre_hook(load_model_hook)
 
@@ -736,7 +775,54 @@ def load_model_hook(models, input_dir):
             if key in metadata:
                 minimum_metadata[key] = metadata[key]
 
-        progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
+        # calculate steps to skip when resuming or starting from a specific step
+        initial_step = 0
+        if args.initial_epoch is not None or args.initial_step is not None:
+            # if initial_epoch or initial_step is specified, steps_from_state is ignored even when resuming
+            if steps_from_state is not None:
+                logger.warning(
+                    "steps from the state is ignored because initial_step is specified / initial_stepが指定されているため、stateからのステップ数は無視されます"
+                )
+            if args.initial_step is not None:
+                initial_step = args.initial_step
+            else:
+                # num steps per epoch is calculated by num_processes and gradient_accumulation_steps
+                initial_step = (args.initial_epoch - 1) * math.ceil(
+                    len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
+                )
+        else:
+            # if initial_epoch and initial_step are not specified, steps_from_state is used when resuming
+            if steps_from_state is not None:
+                initial_step = steps_from_state
+                steps_from_state = None
+
+        if initial_step > 0:
+            assert (
+                args.max_train_steps > initial_step
+            ), f"max_train_steps should be greater than initial step / max_train_stepsは初期ステップより大きい必要があります: {args.max_train_steps} vs {initial_step}"
+
+        progress_bar = tqdm(
+            range(args.max_train_steps - initial_step), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps"
+        )
+
+        epoch_to_start = 0
+        if initial_step > 0:
+            if args.skip_until_initial_step:
+                # if skip_until_initial_step is specified, load data and discard it to ensure the same data is used
+                if not args.resume:
+                    logger.info(
+                        f"initial_step is specified but not resuming. lr scheduler will be started from the beginning / initial_stepが指定されていますがresumeしていないため、lr schedulerは最初から始まります"
+                    )
+                logger.info(f"skipping {initial_step} steps / {initial_step}ステップをスキップします")
+                initial_step *= args.gradient_accumulation_steps
+
+                # set epoch to start to make initial_step less than len(train_dataloader)
+                epoch_to_start = initial_step // math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+            else:
+                # if not, only epoch no is skipped for informative purpose
+                epoch_to_start = initial_step // math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+                initial_step = 0  # do not skip
+
         global_step = 0
 
         noise_scheduler = DDPMScheduler(
@@ -753,7 +839,9 @@ def load_model_hook(models, input_dir):
             if args.log_tracker_config is not None:
                 init_kwargs = toml.load(args.log_tracker_config)
             accelerator.init_trackers(
-                "network_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
+                "network_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()
@@ -793,7 +881,13 @@ def remove_model(old_ckpt_name):
         self.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
 
         # training loop
-        for epoch in range(num_train_epochs):
+        if initial_step > 0:  # only if skip_until_initial_step is specified
+            for skip_epoch in range(epoch_to_start):  # skip epochs
+                logger.info(f"skipping epoch {skip_epoch+1} because initial_step (multiplied) is {initial_step}")
+                initial_step -= len(train_dataloader)
+            global_step = initial_step
+
+        for epoch in range(epoch_to_start, num_train_epochs):
             accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
             current_epoch.value = epoch + 1
 
@@ -801,8 +895,17 @@ def remove_model(old_ckpt_name):
 
             accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet)
 
-            for step, batch in enumerate(train_dataloader):
+            skipped_dataloader = None
+            if initial_step > 0:
+                skipped_dataloader = accelerator.skip_first_batches(train_dataloader, initial_step - 1)
+                initial_step = 1
+
+            for step, batch in enumerate(skipped_dataloader or train_dataloader):
                 current_step.value = global_step
+                if initial_step > 0:
+                    initial_step -= 1
+                    continue
+
                 with accelerator.accumulate(training_model):
                     on_step_start(text_encoder, unet)
 
@@ -881,7 +984,7 @@ def remove_model(old_ckpt_name):
                     loss = train_util.conditional_loss(
                         noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
                     )
-                    if args.masked_loss:
+                    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])
 
@@ -895,7 +998,7 @@ def remove_model(old_ckpt_name):
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
@@ -950,7 +1053,9 @@ def remove_model(old_ckpt_name):
                     progress_bar.set_postfix(**{**max_mean_logs, **logs})
 
                 if args.logging_dir is not None:
-                    logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
+                    logs = self.generate_step_logs(
+                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, keys_scaled, mean_norm, maximum_norm
+                    )
                     accelerator.log(logs, step=global_step)
 
                 if global_step >= args.max_train_steps:
@@ -1101,6 +1206,28 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
     )
+    parser.add_argument(
+        "--skip_until_initial_step",
+        action="store_true",
+        help="skip training until initial_step is reached / initial_stepに到達するまで学習をスキップする",
+    )
+    parser.add_argument(
+        "--initial_epoch",
+        type=int,
+        default=None,
+        help="initial epoch number, 1 means first epoch (same as not specifying). NOTE: initial_epoch/step doesn't affect to lr scheduler. Which means lr scheduler will start from 0 without `--resume`."
+        + " / 初期エポック数、1で最初のエポック（未指定時と同じ）。注意：initial_epoch/stepはlr schedulerに影響しないため、`--resume`しない場合はlr schedulerは0から始まる",
+    )
+    parser.add_argument(
+        "--initial_step",
+        type=int,
+        default=None,
+        help="initial step number including all epochs, 0 means first step (same as not specifying). overwrites initial_epoch."
+        + " / 初期ステップ数、全エポックを含むステップ数、0で最初のステップ（未指定時と同じ）。initial_epochを上書きする",
+    )
+    # parser.add_argument("--loraplus_lr_ratio", default=None, type=float, help="LoRA+ learning rate ratio")
+    # parser.add_argument("--loraplus_unet_lr_ratio", default=None, type=float, help="LoRA+ UNet learning rate ratio")
+    # parser.add_argument("--loraplus_text_encoder_lr_ratio", default=None, type=float, help="LoRA+ text encoder learning rate ratio")
     return parser
 
 
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 10fce2677..37349da7d 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -99,7 +99,7 @@ def __init__(self):
         self.is_sdxl = False
 
     def assert_extra_args(self, args, train_dataset_group):
-        pass
+        train_dataset_group.verify_bucket_reso_steps(64)
 
     def load_target_model(self, args, weight_dtype, accelerator):
         text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
@@ -510,7 +510,7 @@ def train(self, 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, 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
@@ -589,7 +589,7 @@ def remove_model(old_ckpt_name):
                         target = noise
 
                     loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
-                    if args.masked_loss:
+                    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])
 
@@ -603,7 +603,7 @@ def remove_model(old_ckpt_name):
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
                     if args.debiased_estimation_loss:
-                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                        loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                     loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py
index ddd03d532..fac0787b9 100644
--- a/train_textual_inversion_XTI.py
+++ b/train_textual_inversion_XTI.py
@@ -407,7 +407,7 @@ 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, 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
@@ -474,7 +474,7 @@ def remove_model(old_ckpt_name):
                     target = noise
 
                 loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
-                if args.masked_loss:
+                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])
 
@@ -486,7 +486,7 @@ def remove_model(old_ckpt_name):
                 if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
-                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
+                    loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし