diff --git a/.release b/.release index 3db656719..33206cbae 100644 --- a/.release +++ b/.release @@ -1 +1 @@ -v21.8.10 +v22.0.0 diff --git a/.vscode/settings.json b/.vscode/settings.json index c08f3e9a0..bea2e8fdf 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,4 +1,5 @@ { "python.linting.enabled": true, - "python.formatting.provider": "yapf" + "python.formatting.provider": "yapf", + "DockerRun.DisableDockerrc": true } \ No newline at end of file diff --git a/README-ja.md b/README-ja.md index 865e0d35a..29c33a659 100644 --- a/README-ja.md +++ b/README-ja.md @@ -1,3 +1,7 @@ +SDXLがサポートされました。sdxlブランチはmainブランチにマージされました。リポジトリを更新したときにはUpgradeの手順を実行してください。また accelerate のバージョンが上がっていますので、accelerate config を再度実行してください。 + +SDXL学習については[こちら](./README.md#sdxl-training)をご覧ください（英語です）。 + ## リポジトリについて Stable Diffusionの学習、画像生成、その他のスクリプトを入れたリポジトリです。 @@ -9,13 +13,12 @@ GUIやPowerShellスクリプトなど、より使いやすくする機能が[bma * DreamBooth、U-NetおよびText Encoderの学習をサポート * fine-tuning、同上 +* LoRAの学習をサポート * 画像生成 * モデル変換（Stable Diffision ckpt/safetensorsとDiffusersの相互変換） ## 使用法について -当リポジトリ内およびnote.comに記事がありますのでそちらをご覧ください（将来的にはすべてこちらへ移すかもしれません）。 - * [学習について、共通編](./docs/train_README-ja.md) : データ整備やオプションなど * [データセット設定](./docs/config_README-ja.md) * [DreamBoothの学習について](./docs/train_db_README-ja.md) @@ -41,11 +44,13 @@ 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 -以下の例ではPyTorchは1.12.1／CUDA 11.6版をインストールします。CUDA 11.3版やPyTorch 1.13を使う場合は適宜書き換えください。 +スクリプトはPyTorch 2.0.1でテストしています。PyTorch 1.12.1でも動作すると思われます。 + +以下の例ではPyTorchは2.0.1／CUDA 11.8版をインストールします。CUDA 11.6版やPyTorch 1.12.1を使う場合は適宜書き換えください。 （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。） -通常の（管理者ではない）PowerShellを開き以下を順に実行します。 +PowerShellを使う場合、通常の（管理者ではない）PowerShellを開き以下を順に実行します。 ```powershell git clone https://github.com/kohya-ss/sd-scripts.git @@ -54,43 +59,14 @@ cd sd-scripts python -m venv venv .\venv\Scripts\activate -pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 +pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118 pip install --upgrade -r requirements.txt -pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl - -cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\ -cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py -cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py +pip install xformers==0.0.20 accelerate config ``` - - -コマンドプロンプトでは以下になります。 - - -```bat -git clone https://github.com/kohya-ss/sd-scripts.git -cd sd-scripts - -python -m venv venv -.\venv\Scripts\activate - -pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 -pip install --upgrade -r requirements.txt -pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl - -copy /y .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\ -copy /y .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py -copy /y .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py - -accelerate config -``` +コマンドプロンプトでも同一です。 （注:``python -m venv venv`` のほうが ``python -m venv --system-site-packages venv`` より安全そうなため書き換えました。globalなpythonにパッケージがインストールしてあると、後者だといろいろと問題が起きます。） @@ -111,29 +87,40 @@ accelerate configの質問には以下のように答えてください。（bf1 ※場合によって ``ValueError: fp16 mixed precision requires a GPU`` というエラーが出ることがあるようです。この場合、6番目の質問（ ``What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:``）に「0」と答えてください。（id `0`のGPUが使われます。） -### PyTorchとxformersのバージョンについて +### オプション：`bitsandbytes`（8bit optimizer）を使う -他のバージョンでは学習がうまくいかない場合があるようです。特に他の理由がなければ指定のバージョンをお使いください。 +`bitsandbytes`はオプションになりました。Linuxでは通常通りpipでインストールできます（0.41.1または以降のバージョンを推奨）。 -### オプション：Lion8bitを使う +Windowsでは0.35.0または0.41.1を推奨します。 -Lion8bitを使う場合には`bitsandbytes`を0.38.0以降にアップグレードする必要があります。`bitsandbytes`をアンインストールし、Windows環境では例えば[こちら](https://github.com/jllllll/bitsandbytes-windows-webui)などからWindows版のwhlファイルをインストールしてください。たとえば以下のような手順になります。 +- `bitsandbytes` 0.35.0: 安定しているとみられるバージョンです。AdamW8bitは使用できますが、他のいくつかの8bit optimizer、学習時の`full_bf16`オプションは使用できません。 +- `bitsandbytes` 0.41.1: Lion8bit、PagedAdamW8bit、PagedLion8bitをサポートします。`full_bf16`が使用できます。 -```powershell -pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl -``` +注：`bitsandbytes` 0.35.0から0.41.0までのバージョンには問題があるようです。 https://github.com/TimDettmers/bitsandbytes/issues/659 -アップグレード時には`pip install .`でこのリポジトリを更新し、必要に応じて他のパッケージもアップグレードしてください。 +以下の手順に従い、`bitsandbytes`をインストールしてください。 -### オプション：PagedAdamW8bitとPagedLion8bitを使う +### 0.35.0を使う場合 -PagedAdamW8bitとPagedLion8bitを使う場合には`bitsandbytes`を0.39.0以降にアップグレードする必要があります。`bitsandbytes`をアンインストールし、Windows環境では例えば[こちら](https://github.com/jllllll/bitsandbytes-windows-webui)などからWindows版のwhlファイルをインストールしてください。たとえば以下のような手順になります。 +PowerShellの例です。コマンドプロンプトではcpの代わりにcopyを使ってください。 ```powershell -pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl +cd sd-scripts +.\venv\Scripts\activate +pip install bitsandbytes==0.35.0 + +cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\ +cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py +cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py ``` -アップグレード時には`pip install .`でこのリポジトリを更新し、必要に応じて他のパッケージもアップグレードしてください。 +### 0.41.1を使う場合 + +jllllll氏の配布されている[こちら](https://github.com/jllllll/bitsandbytes-windows-webui) または他の場所から、Windows用のwhlファイルをインストールしてください。 + +```powershell +python -m pip install bitsandbytes==0.41.1 --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui +``` ## アップグレード diff --git a/README.md b/README.md index a0e93bc28..93f2ced07 100644 --- a/README.md +++ b/README.md @@ -42,6 +42,12 @@ The GUI allows you to set the training parameters and generate and run the requi - [Page File Limit](#page-file-limit) - [No module called tkinter](#no-module-called-tkinter) - [FileNotFoundError](#filenotfounderror) + - [SDXL training](#sdxl-training) + - [Training scripts for SDXL](#training-scripts-for-sdxl) + - [Utility scripts for SDXL](#utility-scripts-for-sdxl) + - [Tips for SDXL training](#tips-for-sdxl-training-1) + - [Format of Textual Inversion embeddings for SDXL](#format-of-textual-inversion-embeddings-for-sdxl) + - [ControlNet-LLLite](#controlnet-lllite) - [Change History](#change-history) @@ -531,8 +537,118 @@ If you come across a `FileNotFoundError`, it is likely due to an installation is After uninstalling the local packages, redo the installation steps within the `kohya_ss` virtual environment. + +## SDXL training + +The documentation in this section will be moved to a separate document later. + +### Training scripts for SDXL + +- `sdxl_train.py` is a script for SDXL fine-tuning. The usage is almost the same as `fine_tune.py`, but it also supports DreamBooth dataset. + - `--full_bf16` option is added. Thanks to KohakuBlueleaf! + - This option enables the full bfloat16 training (includes gradients). This option is useful to reduce the GPU memory usage. + - The full bfloat16 training might be unstable. Please use it at your own risk. + - The different learning rates for each U-Net block are now supported in sdxl_train.py. Specify with `--block_lr` option. Specify 23 values separated by commas like `--block_lr 1e-3,1e-3 ... 1e-3`. + - 23 values correspond to `0: time/label embed, 1-9: input blocks 0-8, 10-12: mid blocks 0-2, 13-21: output blocks 0-8, 22: out`. +- `prepare_buckets_latents.py` now supports SDXL fine-tuning. + +- `sdxl_train_network.py` is a script for LoRA training for SDXL. The usage is almost the same as `train_network.py`. + +- Both scripts has following additional options: + - `--cache_text_encoder_outputs` and `--cache_text_encoder_outputs_to_disk`: Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions. + - `--no_half_vae`: Disable the half-precision (mixed-precision) VAE. VAE for SDXL seems to produce NaNs in some cases. This option is useful to avoid the NaNs. + +- `--weighted_captions` option is not supported yet for both scripts. + +- `sdxl_train_textual_inversion.py` is a script for Textual Inversion training for SDXL. The usage is almost the same as `train_textual_inversion.py`. + - `--cache_text_encoder_outputs` is not supported. + - There are two options for captions: + 1. Training with captions. All captions must include the token string. The token string is replaced with multiple tokens. + 2. Use `--use_object_template` or `--use_style_template` option. The captions are generated from the template. The existing captions are ignored. + - See below for the format of the embeddings. + +- `--min_timestep` and `--max_timestep` options are added to each training script. These options can be used to train U-Net with different timesteps. The default values are 0 and 1000. + +### Utility scripts for SDXL + +- `tools/cache_latents.py` is added. This script can be used to cache the latents to disk in advance. + - The options are almost the same as `sdxl_train.py'. See the help message for the usage. + - Please launch the script as follows: + `accelerate launch --num_cpu_threads_per_process 1 tools/cache_latents.py ...` + - This script should work with multi-GPU, but it is not tested in my environment. + +- `tools/cache_text_encoder_outputs.py` is added. This script can be used to cache the text encoder outputs to disk in advance. + - The options are almost the same as `cache_latents.py` and `sdxl_train.py`. See the help message for the usage. + +- `sdxl_gen_img.py` is added. This script can be used to generate images with SDXL, including LoRA, Textual Inversion and ControlNet-LLLite. See the help message for the usage. + +### Tips for SDXL training + +- The default resolution of SDXL is 1024x1024. +- The fine-tuning can be done with 24GB GPU memory with the batch size of 1. For 24GB GPU, the following options are recommended __for the fine-tuning with 24GB GPU memory__: + - Train U-Net only. + - Use gradient checkpointing. + - Use `--cache_text_encoder_outputs` option and caching latents. + - Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn't seem to work. +- The LoRA training can be done with 8GB GPU memory (10GB recommended). For reducing the GPU memory usage, the following options are recommended: + - Train U-Net only. + - Use gradient checkpointing. + - Use `--cache_text_encoder_outputs` option and caching latents. + - Use one of 8bit optimizers or Adafactor optimizer. + - Use lower dim (4 to 8 for 8GB GPU). +- `--network_train_unet_only` option is highly recommended for SDXL LoRA. Because SDXL has two text encoders, the result of the training will be unexpected. +- PyTorch 2 seems to use slightly less GPU memory than PyTorch 1. +- `--bucket_reso_steps` can be set to 32 instead of the default value 64. Smaller values than 32 will not work for SDXL training. + +Example of the optimizer settings for Adafactor with the fixed learning rate: +```toml +optimizer_type = "adafactor" +optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False" ] +lr_scheduler = "constant_with_warmup" +lr_warmup_steps = 100 +learning_rate = 4e-7 # SDXL original learning rate +``` + +### Format of Textual Inversion embeddings for SDXL + +```python +from safetensors.torch import save_file + +state_dict = {"clip_g": embs_for_text_encoder_1280, "clip_l": embs_for_text_encoder_768} +save_file(state_dict, file) +``` + +### ControlNet-LLLite + +ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [documentation](./docs/train_lllite_README.md) for details. + + ## Change History +* 2023/10/01 (v22.0.0) + - Merging main branch of sd-scripts: + - [SAI Model Spec](https://github.com/Stability-AI/ModelSpec) metadata is now supported partially. `hash_sha256` is not supported yet. + - The main items are set automatically. + - You can set title, author, description, license and tags with `--metadata_xxx` options in each training script. + - Merging scripts also support minimum SAI Model Spec metadata. See the help message for the usage. + - Metadata editor will be available soon. + + - `--v_pred_like_loss ratio` option is added. This option adds the loss like v-prediction loss in SDXL training. `0.1` means that the loss is added 10% of the v-prediction loss. The default value is None (disabled). + - In v-prediction, the loss is higher in the early timesteps (near the noise). This option can be used to increase the loss in the early timesteps. + + - Arbitrary options can be used for Diffusers' schedulers. For example `--lr_scheduler_args "lr_end=1e-8"`. + + - LoRA-FA is added experimentally. Specify `--network_module networks.lora_fa` option instead of `--network_module networks.lora`. The trained model can be used as a normal LoRA model. + - JPEG XL is supported. [#786](https://github.com/kohya-ss/sd-scripts/pull/786) + - Input perturbation noise is added. See [#798](https://github.com/kohya-ss/sd-scripts/pull/798) for details. + - Dataset subset now has `caption_prefix` and `caption_suffix` options. The strings are added to the beginning and the end of the captions before shuffling. You can specify the options in `.toml`. + - Intel ARC support with IPEX is added. [#825](https://github.com/kohya-ss/sd-scripts/pull/825) + - Other bug fixes and improvements. + - New SDXL presets + - Update wandb module version + - Add support for Chinese zh-CN localisation. You can use it with `.\gui.bat --language=zh-CN` + - Add presets support to `Finetuning`. You can add your own finetuning user presets under the `/presets/finetune/user_presets` folder. + * 2023/09/23 (v21.8.10) - Minor point upgrade. Mostly adding a new preset. @@ -544,4 +660,4 @@ If you come across a `FileNotFoundError`, it is likely due to an installation is * Input perturbation noise is added. See #798 for details. * Dataset subset now has caption_prefix and caption_suffix options. The strings are added to the beginning and the end of the captions before shuffling. You can specify the options in .toml. * Other minor changes. - - Added support for Chinese locallisation \ No newline at end of file + - Added support for Chinese locallisation diff --git a/XTI_hijack.py b/XTI_hijack.py index 36b5d3f2b..ec0849455 100644 --- a/XTI_hijack.py +++ b/XTI_hijack.py @@ -1,4 +1,11 @@ import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from typing import Union, List, Optional, Dict, Any, Tuple from diffusers.models.unet_2d_condition import UNet2DConditionOutput diff --git a/docs/train_network_README-ja.md b/docs/train_network_README-ja.md index e620a8642..2205a7736 100644 --- a/docs/train_network_README-ja.md +++ b/docs/train_network_README-ja.md @@ -181,6 +181,8 @@ 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。 `--network_args` で以下の引数を指定してください。 @@ -246,6 +248,8 @@ network_args = [ "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8, merge_lora.pyでStable DiffusionのモデルにLoRAの学習結果をマージしたり、複数のLoRAモデルをマージしたりできます。 +SDXL向けにはsdxl_merge_lora.pyを用意しています。オプション等は同一ですので、以下のmerge_lora.pyを読み替えてください。 + ### Stable DiffusionのモデルにLoRAのモデルをマージする マージ後のモデルは通常のStable Diffusionのckptと同様に扱えます。たとえば以下のようなコマンドラインになります。 @@ -276,29 +280,29 @@ python networks\merge_lora.py --sd_model ..\model\model.ckpt ### 複数のLoRAのモデルをマージする -__複数のLoRAをマージする場合は原則として `svd_merge_lora.py` を使用してください。__ 単純なup同士やdown同士のマージでは、計算結果が正しくなくなるためです。 - -`merge_lora.py` によるマージは差分抽出法でLoRAを生成する場合等、ごく限られた場合でのみ有効です。 +--concatオプションを指定すると、複数のLoRAを単純に結合して新しいLoRAモデルを作成できます。ファイルサイズ（およびdim/rank）は指定したLoRAの合計サイズになります（マージ時にdim (rank)を変更する場合は `svd_merge_lora.py` を使用してください）。 たとえば以下のようなコマンドラインになります。 ``` -python networks\merge_lora.py +python networks\merge_lora.py --save_precision bf16 --save_to ..\lora_train1\model-char1-style1-merged.safetensors - --models ..\lora_train1\last.safetensors ..\lora_train2\last.safetensors --ratios 0.6 0.4 + --models ..\lora_train1\last.safetensors ..\lora_train2\last.safetensors + --ratios 1.0 -1.0 --concat --shuffle ``` ---sd_modelオプションは指定不要です。 +--concatオプションを指定します。 + +また--shuffleオプションを追加し、重みをシャッフルします。シャッフルしないとマージ後のLoRAから元のLoRAを取り出せるため、コピー機学習などの場合には学習元データが明らかになります。ご注意ください。 --save_toオプションにマージ後のLoRAモデルの保存先を指定します（.ckptまたは.safetensors、拡張子で自動判定）。 --modelsに学習したLoRAのモデルファイルを指定します。三つ以上も指定可能です。 ---ratiosにそれぞれのモデルの比率（どのくらい重みを元モデルに反映するか）を0~1.0の数値で指定します。二つのモデルを一対一でマージす場合は、「0.5 0.5」になります。「1.0 1.0」では合計の重みが大きくなりすぎて、恐らく結果はあまり望ましくないものになると思われます。 +--ratiosにそれぞれのモデルの比率（どのくらい重みを元モデルに反映するか）を0~1.0の数値で指定します。二つのモデルを一対一でマージする場合は、「0.5 0.5」になります。「1.0 1.0」では合計の重みが大きくなりすぎて、恐らく結果はあまり望ましくないものになると思われます。 v1で学習したLoRAとv2で学習したLoRA、rank（次元数）の異なるLoRAはマージできません。U-NetだけのLoRAとU-Net+Text EncoderのLoRAはマージできるはずですが、結果は未知数です。 - ### その他のオプション * precision @@ -306,6 +310,7 @@ v1で学習したLoRAとv2で学習したLoRA、rank（次元数）の異なるL * save_precision * モデル保存時の精度をfloat、fp16、bf16から指定できます。省略時はprecisionと同じ精度になります。 +他にもいくつかのオプションがありますので、--helpで確認してください。 ## 複数のrankが異なるLoRAのモデルをマージする diff --git a/fine_tune.py b/fine_tune.py index f89e897a8..f300d4688 100644 --- a/fine_tune.py +++ b/fine_tune.py @@ -10,6 +10,13 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from accelerate.utils import set_seed from diffusers import DDPMScheduler diff --git a/finetune_gui.py b/finetune_gui.py index 872c51d47..892c8be1f 100644 --- a/finetune_gui.py +++ b/finetune_gui.py @@ -180,6 +180,7 @@ def save_configuration( def open_configuration( ask_for_file, + apply_preset, file_path, pretrained_model_name_or_path, v2, @@ -267,11 +268,27 @@ def open_configuration( sdxl_no_half_vae, min_timestep, max_timestep, + training_preset, ): # Get list of function parameters and values parameters = list(locals().items()) ask_for_file = True if ask_for_file.get('label') == 'True' else False + apply_preset = True if apply_preset.get('label') == 'True' else False + + # Check if we are "applying" a preset or a config + if apply_preset: + log.info(f'Applying preset {training_preset}...') + file_path = f'./presets/finetune/{training_preset}.json' + else: + # If not applying a preset, set the `training_preset` field to an empty string + # Find the index of the `training_preset` parameter using the `index()` method + training_preset_index = parameters.index( + ('training_preset', training_preset) + ) + + # Update the value of `training_preset` by directly assigning an empty string value + parameters[training_preset_index] = ('training_preset', '') original_file_path = file_path @@ -291,9 +308,10 @@ def open_configuration( values = [file_path] for key, value in parameters: + json_value = my_data.get(key) # Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found - if not key in ['ask_for_file', 'file_path']: - values.append(my_data.get(key, value)) + if not key in ['ask_for_file', 'apply_preset', 'file_path']: + values.append(json_value if json_value is not None else value) return tuple(values) @@ -808,6 +826,31 @@ def finetune_tab(headless=False): label='Weighted captions', value=False ) with gr.Tab('Parameters'): + + def list_presets(path): + json_files = [] + + for file in os.listdir(path): + if file.endswith('.json'): + json_files.append(os.path.splitext(file)[0]) + + user_presets_path = os.path.join(path, 'user_presets') + if os.path.isdir(user_presets_path): + for file in os.listdir(user_presets_path): + if file.endswith('.json'): + preset_name = os.path.splitext(file)[0] + json_files.append( + os.path.join('user_presets', preset_name) + ) + + return json_files + + training_preset = gr.Dropdown( + label='Presets', + choices=list_presets('./presets/finetune'), + elem_id='myDropdown', + ) + with gr.Tab('Basic', elem_id='basic_tab'): basic_training = BasicTraining( learning_rate_value='1e-5', finetuning=True @@ -960,31 +1003,64 @@ def finetune_tab(headless=False): advanced_training.max_timestep, ] - button_run.click( - train_model, - inputs=[dummy_headless] + [dummy_db_false] + settings_list, + config.button_open_config.click( + open_configuration, + inputs=[dummy_db_true, dummy_db_false, config.config_file_name] + + settings_list + + [training_preset], + outputs=[config.config_file_name] + + settings_list + + [training_preset], show_progress=False, ) - button_stop_training.click(executor.kill_command) + # config.button_open_config.click( + # open_configuration, + # inputs=[dummy_db_true, dummy_db_false, config.config_file_name] + settings_list, + # outputs=[config.config_file_name] + settings_list, + # show_progress=False, + # ) - button_print.click( - train_model, - inputs=[dummy_headless] + [dummy_db_true] + settings_list, + config.button_load_config.click( + open_configuration, + inputs=[dummy_db_false, dummy_db_false, config.config_file_name] + + settings_list + + [training_preset], + outputs=[config.config_file_name] + + settings_list + + [training_preset], show_progress=False, ) - config.button_open_config.click( + # config.button_load_config.click( + # open_configuration, + # inputs=[dummy_db_false, config.config_file_name] + settings_list, + # outputs=[config.config_file_name] + settings_list, + # show_progress=False, + # ) + + training_preset.input( open_configuration, - inputs=[dummy_db_true, config.config_file_name] + settings_list, - outputs=[config.config_file_name] + settings_list, + inputs=[dummy_db_false, dummy_db_true, config.config_file_name] + + settings_list + + [training_preset], + outputs=[gr.Textbox()] + + settings_list + + [training_preset], show_progress=False, ) - config.button_load_config.click( - open_configuration, - inputs=[dummy_db_false, config.config_file_name] + settings_list, - outputs=[config.config_file_name] + settings_list, + button_run.click( + train_model, + inputs=[dummy_headless] + [dummy_db_false] + settings_list, + show_progress=False, + ) + + button_stop_training.click(executor.kill_command) + + button_print.click( + train_model, + inputs=[dummy_headless] + [dummy_db_true] + settings_list, show_progress=False, ) diff --git a/gen_img_diffusers.py b/gen_img_diffusers.py index 273c0dd86..70ca67942 100644 --- a/gen_img_diffusers.py +++ b/gen_img_diffusers.py @@ -65,6 +65,13 @@ import diffusers import numpy as np import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass import torchvision from diffusers import ( AutoencoderKL, @@ -3145,7 +3152,7 @@ def process_batch(batch: List[BatchData], highres_fix, highres_1st=False): print("predefined seeds are exhausted") seed = None elif args.iter_same_seed: - seeds = iter_seed + seed = iter_seed else: seed = None # 前のを消す diff --git a/library/config_util.py b/library/config_util.py index 813483e7e..e8e0fda7c 100644 --- a/library/config_util.py +++ b/library/config_util.py @@ -547,17 +547,13 @@ def generate(base_dir: Optional[str]): return [] subsets_config = [] - for subdir in base_dir.iterdir(): - if not subdir.is_dir(): - continue - - subset_config = {"image_dir": str(subdir), "conditioning_data_dir": conditioning_data_dir, "caption_extension": caption_extension, "num_repeats": 1} - subsets_config.append(subset_config) + subset_config = {"image_dir": train_data_dir, "conditioning_data_dir": conditioning_data_dir, "caption_extension": caption_extension, "num_repeats": 1} + subsets_config.append(subset_config) return subsets_config subsets_config = [] - subsets_config += generate(train_data_dir, False) + subsets_config += generate(train_data_dir) return subsets_config diff --git a/library/ipex/__init__.py b/library/ipex/__init__.py new file mode 100644 index 000000000..43accd9f3 --- /dev/null +++ b/library/ipex/__init__.py @@ -0,0 +1,175 @@ +import os +import sys +import contextlib +import torch +import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import +from .hijacks import ipex_hijacks +from .attention import attention_init + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +def ipex_init(): # pylint: disable=too-many-statements + try: + #Replace cuda with xpu: + torch.cuda.current_device = torch.xpu.current_device + torch.cuda.current_stream = torch.xpu.current_stream + torch.cuda.device = torch.xpu.device + torch.cuda.device_count = torch.xpu.device_count + torch.cuda.device_of = torch.xpu.device_of + torch.cuda.get_device_name = torch.xpu.get_device_name + torch.cuda.get_device_properties = torch.xpu.get_device_properties + torch.cuda.init = torch.xpu.init + torch.cuda.is_available = torch.xpu.is_available + torch.cuda.is_initialized = torch.xpu.is_initialized + torch.cuda.is_current_stream_capturing = lambda: False + torch.cuda.set_device = torch.xpu.set_device + torch.cuda.stream = torch.xpu.stream + torch.cuda.synchronize = torch.xpu.synchronize + torch.cuda.Event = torch.xpu.Event + torch.cuda.Stream = torch.xpu.Stream + torch.cuda.FloatTensor = torch.xpu.FloatTensor + torch.Tensor.cuda = torch.Tensor.xpu + torch.Tensor.is_cuda = torch.Tensor.is_xpu + torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock + torch.cuda._initialized = torch.xpu.lazy_init._initialized + torch.cuda._lazy_seed_tracker = torch.xpu.lazy_init._lazy_seed_tracker + torch.cuda._queued_calls = torch.xpu.lazy_init._queued_calls + torch.cuda._tls = torch.xpu.lazy_init._tls + torch.cuda.threading = torch.xpu.lazy_init.threading + torch.cuda.traceback = torch.xpu.lazy_init.traceback + torch.cuda.Optional = torch.xpu.Optional + torch.cuda.__cached__ = torch.xpu.__cached__ + torch.cuda.__loader__ = torch.xpu.__loader__ + torch.cuda.ComplexFloatStorage = torch.xpu.ComplexFloatStorage + torch.cuda.Tuple = torch.xpu.Tuple + torch.cuda.streams = torch.xpu.streams + torch.cuda._lazy_new = torch.xpu._lazy_new + torch.cuda.FloatStorage = torch.xpu.FloatStorage + torch.cuda.Any = torch.xpu.Any + torch.cuda.__doc__ = torch.xpu.__doc__ + torch.cuda.default_generators = torch.xpu.default_generators + torch.cuda.HalfTensor = torch.xpu.HalfTensor + torch.cuda._get_device_index = torch.xpu._get_device_index + torch.cuda.__path__ = torch.xpu.__path__ + torch.cuda.Device = torch.xpu.Device + torch.cuda.IntTensor = torch.xpu.IntTensor + torch.cuda.ByteStorage = torch.xpu.ByteStorage + torch.cuda.set_stream = torch.xpu.set_stream + torch.cuda.BoolStorage = torch.xpu.BoolStorage + torch.cuda.os = torch.xpu.os + torch.cuda.torch = torch.xpu.torch + torch.cuda.BFloat16Storage = torch.xpu.BFloat16Storage + torch.cuda.Union = torch.xpu.Union + torch.cuda.DoubleTensor = torch.xpu.DoubleTensor + torch.cuda.ShortTensor = torch.xpu.ShortTensor + torch.cuda.LongTensor = torch.xpu.LongTensor + torch.cuda.IntStorage = torch.xpu.IntStorage + torch.cuda.LongStorage = torch.xpu.LongStorage + torch.cuda.__annotations__ = torch.xpu.__annotations__ + torch.cuda.__package__ = torch.xpu.__package__ + torch.cuda.__builtins__ = torch.xpu.__builtins__ + torch.cuda.CharTensor = torch.xpu.CharTensor + torch.cuda.List = torch.xpu.List + torch.cuda._lazy_init = torch.xpu._lazy_init + torch.cuda.BFloat16Tensor = torch.xpu.BFloat16Tensor + torch.cuda.DoubleStorage = torch.xpu.DoubleStorage + torch.cuda.ByteTensor = torch.xpu.ByteTensor + torch.cuda.StreamContext = torch.xpu.StreamContext + torch.cuda.ComplexDoubleStorage = torch.xpu.ComplexDoubleStorage + torch.cuda.ShortStorage = torch.xpu.ShortStorage + torch.cuda._lazy_call = torch.xpu._lazy_call + torch.cuda.HalfStorage = torch.xpu.HalfStorage + torch.cuda.random = torch.xpu.random + torch.cuda._device = torch.xpu._device + torch.cuda.classproperty = torch.xpu.classproperty + torch.cuda.__name__ = torch.xpu.__name__ + torch.cuda._device_t = torch.xpu._device_t + torch.cuda.warnings = torch.xpu.warnings + torch.cuda.__spec__ = torch.xpu.__spec__ + torch.cuda.BoolTensor = torch.xpu.BoolTensor + torch.cuda.CharStorage = torch.xpu.CharStorage + torch.cuda.__file__ = torch.xpu.__file__ + torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork + #torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing + + #Memory: + torch.cuda.memory = torch.xpu.memory + if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read(): + torch.xpu.empty_cache = lambda: None + torch.cuda.empty_cache = torch.xpu.empty_cache + torch.cuda.memory_stats = torch.xpu.memory_stats + torch.cuda.memory_summary = torch.xpu.memory_summary + torch.cuda.memory_snapshot = torch.xpu.memory_snapshot + torch.cuda.memory_allocated = torch.xpu.memory_allocated + torch.cuda.max_memory_allocated = torch.xpu.max_memory_allocated + torch.cuda.memory_reserved = torch.xpu.memory_reserved + torch.cuda.memory_cached = torch.xpu.memory_reserved + torch.cuda.max_memory_reserved = torch.xpu.max_memory_reserved + torch.cuda.max_memory_cached = torch.xpu.max_memory_reserved + torch.cuda.reset_peak_memory_stats = torch.xpu.reset_peak_memory_stats + torch.cuda.reset_max_memory_cached = torch.xpu.reset_peak_memory_stats + torch.cuda.reset_max_memory_allocated = torch.xpu.reset_peak_memory_stats + torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict + torch.cuda.reset_accumulated_memory_stats = torch.xpu.reset_accumulated_memory_stats + + #RNG: + torch.cuda.get_rng_state = torch.xpu.get_rng_state + torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all + torch.cuda.set_rng_state = torch.xpu.set_rng_state + torch.cuda.set_rng_state_all = torch.xpu.set_rng_state_all + torch.cuda.manual_seed = torch.xpu.manual_seed + torch.cuda.manual_seed_all = torch.xpu.manual_seed_all + torch.cuda.seed = torch.xpu.seed + torch.cuda.seed_all = torch.xpu.seed_all + torch.cuda.initial_seed = torch.xpu.initial_seed + + #AMP: + torch.cuda.amp = torch.xpu.amp + if not hasattr(torch.cuda.amp, "common"): + torch.cuda.amp.common = contextlib.nullcontext() + torch.cuda.amp.common.amp_definitely_not_available = lambda: False + try: + torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler + except Exception: # pylint: disable=broad-exception-caught + try: + from .gradscaler import gradscaler_init # pylint: disable=import-outside-toplevel, import-error + gradscaler_init() + torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler + except Exception: # pylint: disable=broad-exception-caught + torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler + + #C + torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream + ipex._C._DeviceProperties.major = 2023 + ipex._C._DeviceProperties.minor = 2 + + #Fix functions with ipex: + torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_reserved(device)), torch.xpu.get_device_properties(device).total_memory] + torch._utils._get_available_device_type = lambda: "xpu" + torch.has_cuda = True + torch.cuda.has_half = True + torch.cuda.is_bf16_supported = lambda *args, **kwargs: True + torch.cuda.is_fp16_supported = lambda *args, **kwargs: True + torch.version.cuda = "11.7" + torch.cuda.get_device_capability = lambda *args, **kwargs: [11,7] + torch.cuda.get_device_properties.major = 11 + torch.cuda.get_device_properties.minor = 7 + torch.cuda.ipc_collect = lambda *args, **kwargs: None + torch.cuda.utilization = lambda *args, **kwargs: 0 + if hasattr(torch.xpu, 'getDeviceIdListForCard'): + torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard + torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard + else: + torch.cuda.getDeviceIdListForCard = torch.xpu.get_device_id_list_per_card + torch.cuda.get_device_id_list_per_card = torch.xpu.get_device_id_list_per_card + + ipex_hijacks() + attention_init() + try: + from .diffusers import ipex_diffusers + ipex_diffusers() + except Exception: # pylint: disable=broad-exception-caught + pass + except Exception as e: + return False, e + return True, None diff --git a/library/ipex/attention.py b/library/ipex/attention.py new file mode 100644 index 000000000..84848b6a6 --- /dev/null +++ b/library/ipex/attention.py @@ -0,0 +1,157 @@ +import torch +import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +original_torch_bmm = torch.bmm +def torch_bmm(input, mat2, *, out=None): + if input.dtype != mat2.dtype: + mat2 = mat2.to(input.dtype) + + #ARC GPUs can't allocate more than 4GB to a single block, Slice it: + batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2] + block_multiply = input.element_size() + slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply + block_size = batch_size_attention * slice_block_size + + split_slice_size = batch_size_attention + if block_size > 4: + do_split = True + #Find something divisible with the input_tokens + while (split_slice_size * slice_block_size) > 4: + split_slice_size = split_slice_size // 2 + if split_slice_size <= 1: + split_slice_size = 1 + break + else: + do_split = False + + split_2_slice_size = input_tokens + if split_slice_size * slice_block_size > 4: + slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply + do_split_2 = True + #Find something divisible with the input_tokens + while (split_2_slice_size * slice_block_size2) > 4: + split_2_slice_size = split_2_slice_size // 2 + if split_2_slice_size <= 1: + split_2_slice_size = 1 + break + else: + do_split_2 = False + + if do_split: + hidden_states = torch.zeros(input.shape[0], input.shape[1], mat2.shape[2], device=input.device, dtype=input.dtype) + for i in range(batch_size_attention // split_slice_size): + start_idx = i * split_slice_size + end_idx = (i + 1) * split_slice_size + if do_split_2: + for i2 in range(input_tokens // split_2_slice_size): # pylint: disable=invalid-name + start_idx_2 = i2 * split_2_slice_size + end_idx_2 = (i2 + 1) * split_2_slice_size + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm( + input[start_idx:end_idx, start_idx_2:end_idx_2], + mat2[start_idx:end_idx, start_idx_2:end_idx_2], + out=out + ) + else: + hidden_states[start_idx:end_idx] = original_torch_bmm( + input[start_idx:end_idx], + mat2[start_idx:end_idx], + out=out + ) + else: + return original_torch_bmm(input, mat2, out=out) + return hidden_states + +original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention +def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False): + #ARC GPUs can't allocate more than 4GB to a single block, Slice it: + if len(query.shape) == 3: + batch_size_attention, query_tokens, shape_four = query.shape + shape_one = 1 + no_shape_one = True + else: + shape_one, batch_size_attention, query_tokens, shape_four = query.shape + no_shape_one = False + + block_multiply = query.element_size() + slice_block_size = shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply + block_size = batch_size_attention * slice_block_size + + split_slice_size = batch_size_attention + if block_size > 4: + do_split = True + #Find something divisible with the shape_one + while (split_slice_size * slice_block_size) > 4: + split_slice_size = split_slice_size // 2 + if split_slice_size <= 1: + split_slice_size = 1 + break + else: + do_split = False + + split_2_slice_size = query_tokens + if split_slice_size * slice_block_size > 4: + slice_block_size2 = shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply + do_split_2 = True + #Find something divisible with the batch_size_attention + while (split_2_slice_size * slice_block_size2) > 4: + split_2_slice_size = split_2_slice_size // 2 + if split_2_slice_size <= 1: + split_2_slice_size = 1 + break + else: + do_split_2 = False + + if do_split: + hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype) + for i in range(batch_size_attention // split_slice_size): + start_idx = i * split_slice_size + end_idx = (i + 1) * split_slice_size + if do_split_2: + for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name + start_idx_2 = i2 * split_2_slice_size + end_idx_2 = (i2 + 1) * split_2_slice_size + if no_shape_one: + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention( + query[start_idx:end_idx, start_idx_2:end_idx_2], + key[start_idx:end_idx, start_idx_2:end_idx_2], + value[start_idx:end_idx, start_idx_2:end_idx_2], + attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal + ) + else: + hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention( + query[:, start_idx:end_idx, start_idx_2:end_idx_2], + key[:, start_idx:end_idx, start_idx_2:end_idx_2], + value[:, start_idx:end_idx, start_idx_2:end_idx_2], + attn_mask=attn_mask[:, start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal + ) + else: + if no_shape_one: + hidden_states[start_idx:end_idx] = original_scaled_dot_product_attention( + query[start_idx:end_idx], + key[start_idx:end_idx], + value[start_idx:end_idx], + attn_mask=attn_mask[start_idx:end_idx] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal + ) + else: + hidden_states[:, start_idx:end_idx] = original_scaled_dot_product_attention( + query[:, start_idx:end_idx], + key[:, start_idx:end_idx], + value[:, start_idx:end_idx], + attn_mask=attn_mask[:, start_idx:end_idx] if attn_mask is not None else attn_mask, + dropout_p=dropout_p, is_causal=is_causal + ) + else: + return original_scaled_dot_product_attention( + query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal + ) + return hidden_states + +def attention_init(): + #ARC GPUs can't allocate more than 4GB to a single block: + torch.bmm = torch_bmm + torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention diff --git a/library/ipex/diffusers.py b/library/ipex/diffusers.py new file mode 100644 index 000000000..005ee49f0 --- /dev/null +++ b/library/ipex/diffusers.py @@ -0,0 +1,120 @@ +import torch +import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import +import diffusers #0.21.1 # pylint: disable=import-error +from diffusers.models.attention_processor import Attention + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +class SlicedAttnProcessor: # pylint: disable=too-few-public-methods + r""" + Processor for implementing sliced attention. + + Args: + slice_size (`int`, *optional*): + The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and + `attention_head_dim` must be a multiple of the `slice_size`. + """ + + def __init__(self, slice_size): + self.slice_size = slice_size + + def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): # pylint: disable=too-many-statements, too-many-locals, too-many-branches + residual = hidden_states + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + dim = query.shape[-1] + query = attn.head_to_batch_dim(query) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + batch_size_attention, query_tokens, shape_three = query.shape + hidden_states = torch.zeros( + (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype + ) + + #ARC GPUs can't allocate more than 4GB to a single block, Slice it: + block_multiply = query.element_size() + slice_block_size = self.slice_size * shape_three / 1024 / 1024 * block_multiply + block_size = query_tokens * slice_block_size + split_2_slice_size = query_tokens + if block_size > 4: + do_split_2 = True + #Find something divisible with the query_tokens + while (split_2_slice_size * slice_block_size) > 4: + split_2_slice_size = split_2_slice_size // 2 + if split_2_slice_size <= 1: + split_2_slice_size = 1 + break + else: + do_split_2 = False + + for i in range(batch_size_attention // self.slice_size): + start_idx = i * self.slice_size + end_idx = (i + 1) * self.slice_size + + if do_split_2: + for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name + start_idx_2 = i2 * split_2_slice_size + end_idx_2 = (i2 + 1) * split_2_slice_size + + query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2] + key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2] + attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None + + attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) + attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2]) + + hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice + else: + query_slice = query[start_idx:end_idx] + key_slice = key[start_idx:end_idx] + attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None + + attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) + + attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) + + hidden_states[start_idx:end_idx] = attn_slice + + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + +def ipex_diffusers(): + #ARC GPUs can't allocate more than 4GB to a single block: + diffusers.models.attention_processor.SlicedAttnProcessor = SlicedAttnProcessor diff --git a/library/ipex/gradscaler.py b/library/ipex/gradscaler.py new file mode 100644 index 000000000..530212101 --- /dev/null +++ b/library/ipex/gradscaler.py @@ -0,0 +1,179 @@ +from collections import defaultdict +import torch +import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import +import intel_extension_for_pytorch._C as core # pylint: disable=import-error, unused-import + +# pylint: disable=protected-access, missing-function-docstring, line-too-long + +OptState = ipex.cpu.autocast._grad_scaler.OptState +_MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator +_refresh_per_optimizer_state = ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state + +def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): # pylint: disable=unused-argument + per_device_inv_scale = _MultiDeviceReplicator(inv_scale) + per_device_found_inf = _MultiDeviceReplicator(found_inf) + + # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype. + # There could be hundreds of grads, so we'd like to iterate through them just once. + # However, we don't know their devices or dtypes in advance. + + # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict + # Google says mypy struggles with defaultdicts type annotations. + per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list)) # type: ignore[var-annotated] + # sync grad to master weight + if hasattr(optimizer, "sync_grad"): + optimizer.sync_grad() + with torch.no_grad(): + for group in optimizer.param_groups: + for param in group["params"]: + if param.grad is None: + continue + if (not allow_fp16) and param.grad.dtype == torch.float16: + raise ValueError("Attempting to unscale FP16 gradients.") + if param.grad.is_sparse: + # is_coalesced() == False means the sparse grad has values with duplicate indices. + # coalesce() deduplicates indices and adds all values that have the same index. + # For scaled fp16 values, there's a good chance coalescing will cause overflow, + # so we should check the coalesced _values(). + if param.grad.dtype is torch.float16: + param.grad = param.grad.coalesce() + to_unscale = param.grad._values() + else: + to_unscale = param.grad + + # -: is there a way to split by device and dtype without appending in the inner loop? + to_unscale = to_unscale.to("cpu") + per_device_and_dtype_grads[to_unscale.device][ + to_unscale.dtype + ].append(to_unscale) + + for _, per_dtype_grads in per_device_and_dtype_grads.items(): + for grads in per_dtype_grads.values(): + core._amp_foreach_non_finite_check_and_unscale_( + grads, + per_device_found_inf.get("cpu"), + per_device_inv_scale.get("cpu"), + ) + + return per_device_found_inf._per_device_tensors + +def unscale_(self, optimizer): + """ + Divides ("unscales") the optimizer's gradient tensors by the scale factor. + :meth:`unscale_` is optional, serving cases where you need to + :ref:`modify or inspect gradients` + between the backward pass(es) and :meth:`step`. + If :meth:`unscale_` is not called explicitly, gradients will be unscaled automatically during :meth:`step`. + Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients:: + ... + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) + scaler.step(optimizer) + scaler.update() + Args: + optimizer (torch.optim.Optimizer): Optimizer that owns the gradients to be unscaled. + .. warning:: + :meth:`unscale_` should only be called once per optimizer per :meth:`step` call, + and only after all gradients for that optimizer's assigned parameters have been accumulated. + Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError. + .. warning:: + :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute. + """ + if not self._enabled: + return + + self._check_scale_growth_tracker("unscale_") + + optimizer_state = self._per_optimizer_states[id(optimizer)] + + if optimizer_state["stage"] is OptState.UNSCALED: # pylint: disable=no-else-raise + raise RuntimeError( + "unscale_() has already been called on this optimizer since the last update()." + ) + elif optimizer_state["stage"] is OptState.STEPPED: + raise RuntimeError("unscale_() is being called after step().") + + # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64. + assert self._scale is not None + inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device) + found_inf = torch.full( + (1,), 0.0, dtype=torch.float32, device=self._scale.device + ) + + optimizer_state["found_inf_per_device"] = self._unscale_grads_( + optimizer, inv_scale, found_inf, False + ) + optimizer_state["stage"] = OptState.UNSCALED + +def update(self, new_scale=None): + """ + Updates the scale factor. + If any optimizer steps were skipped the scale is multiplied by ``backoff_factor`` + to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively, + the scale is multiplied by ``growth_factor`` to increase it. + Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not + used directly, it's used to fill GradScaler's internal scale tensor. So if + ``new_scale`` was a tensor, later in-place changes to that tensor will not further + affect the scale GradScaler uses internally.) + Args: + new_scale (float or :class:`torch.FloatTensor`, optional, default=None): New scale factor. + .. warning:: + :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has + been invoked for all optimizers used this iteration. + """ + if not self._enabled: + return + + _scale, _growth_tracker = self._check_scale_growth_tracker("update") + + if new_scale is not None: + # Accept a new user-defined scale. + if isinstance(new_scale, float): + self._scale.fill_(new_scale) # type: ignore[union-attr] + else: + reason = "new_scale should be a float or a 1-element torch.FloatTensor with requires_grad=False." + assert isinstance(new_scale, torch.FloatTensor), reason # type: ignore[attr-defined] + assert new_scale.numel() == 1, reason + assert new_scale.requires_grad is False, reason + self._scale.copy_(new_scale) # type: ignore[union-attr] + else: + # Consume shared inf/nan data collected from optimizers to update the scale. + # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous. + found_infs = [ + found_inf.to(device="cpu", non_blocking=True) + for state in self._per_optimizer_states.values() + for found_inf in state["found_inf_per_device"].values() + ] + + assert len(found_infs) > 0, "No inf checks were recorded prior to update." + + found_inf_combined = found_infs[0] + if len(found_infs) > 1: + for i in range(1, len(found_infs)): + found_inf_combined += found_infs[i] + + to_device = _scale.device + _scale = _scale.to("cpu") + _growth_tracker = _growth_tracker.to("cpu") + + core._amp_update_scale_( + _scale, + _growth_tracker, + found_inf_combined, + self._growth_factor, + self._backoff_factor, + self._growth_interval, + ) + + _scale = _scale.to(to_device) + _growth_tracker = _growth_tracker.to(to_device) + # To prepare for next iteration, clear the data collected from optimizers this iteration. + self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state) + +def gradscaler_init(): + torch.xpu.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler + torch.xpu.amp.GradScaler._unscale_grads_ = _unscale_grads_ + torch.xpu.amp.GradScaler.unscale_ = unscale_ + torch.xpu.amp.GradScaler.update = update + return torch.xpu.amp.GradScaler diff --git a/library/ipex/hijacks.py b/library/ipex/hijacks.py new file mode 100644 index 000000000..77ed5419a --- /dev/null +++ b/library/ipex/hijacks.py @@ -0,0 +1,196 @@ +import contextlib +import importlib +import torch +import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import + +# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return + +class CondFunc: # pylint: disable=missing-class-docstring + def __new__(cls, orig_func, sub_func, cond_func): + self = super(CondFunc, cls).__new__(cls) + if isinstance(orig_func, str): + func_path = orig_func.split('.') + for i in range(len(func_path)-1, -1, -1): + try: + resolved_obj = importlib.import_module('.'.join(func_path[:i])) + break + except ImportError: + pass + for attr_name in func_path[i:-1]: + resolved_obj = getattr(resolved_obj, attr_name) + orig_func = getattr(resolved_obj, func_path[-1]) + setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs)) + self.__init__(orig_func, sub_func, cond_func) + return lambda *args, **kwargs: self(*args, **kwargs) + def __init__(self, orig_func, sub_func, cond_func): + self.__orig_func = orig_func + self.__sub_func = sub_func + self.__cond_func = cond_func + def __call__(self, *args, **kwargs): + if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs): + return self.__sub_func(self.__orig_func, *args, **kwargs) + else: + return self.__orig_func(*args, **kwargs) + +_utils = torch.utils.data._utils +def _shutdown_workers(self): + if torch.utils.data._utils is None or torch.utils.data._utils.python_exit_status is True or torch.utils.data._utils.python_exit_status is None: + return + if hasattr(self, "_shutdown") and not self._shutdown: + self._shutdown = True + try: + if hasattr(self, '_pin_memory_thread'): + self._pin_memory_thread_done_event.set() + self._worker_result_queue.put((None, None)) + self._pin_memory_thread.join() + self._worker_result_queue.cancel_join_thread() + self._worker_result_queue.close() + self._workers_done_event.set() + for worker_id in range(len(self._workers)): + if self._persistent_workers or self._workers_status[worker_id]: + self._mark_worker_as_unavailable(worker_id, shutdown=True) + for w in self._workers: # pylint: disable=invalid-name + w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL) + for q in self._index_queues: # pylint: disable=invalid-name + q.cancel_join_thread() + q.close() + finally: + if self._worker_pids_set: + torch.utils.data._utils.signal_handling._remove_worker_pids(id(self)) + self._worker_pids_set = False + for w in self._workers: # pylint: disable=invalid-name + if w.is_alive(): + w.terminate() + +class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods + def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument + if isinstance(device_ids, list) and len(device_ids) > 1: + print("IPEX backend doesn't support DataParallel on multiple XPU devices") + return module.to("xpu") + +def return_null_context(*args, **kwargs): # pylint: disable=unused-argument + return contextlib.nullcontext() + +def check_device(device): + return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int)) + +def return_xpu(device): + return f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device("xpu") if isinstance(device, torch.device) else "xpu" + +def ipex_no_cuda(orig_func, *args, **kwargs): + torch.cuda.is_available = lambda: False + orig_func(*args, **kwargs) + torch.cuda.is_available = torch.xpu.is_available + +original_autocast = torch.autocast +def ipex_autocast(*args, **kwargs): + if len(args) > 0 and args[0] == "cuda": + return original_autocast("xpu", *args[1:], **kwargs) + else: + return original_autocast(*args, **kwargs) + +original_torch_cat = torch.cat +def torch_cat(tensor, *args, **kwargs): + if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype): + return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs) + else: + return original_torch_cat(tensor, *args, **kwargs) + +original_interpolate = torch.nn.functional.interpolate +def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments + if antialias or align_corners is not None: + return_device = tensor.device + return_dtype = tensor.dtype + return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype) + else: + return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias) + +original_linalg_solve = torch.linalg.solve +def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name + if A.device != torch.device("cpu") or B.device != torch.device("cpu"): + return_device = A.device + return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(return_device) + else: + return original_linalg_solve(A, B, *args, **kwargs) + +def ipex_hijacks(): + CondFunc('torch.Tensor.to', + lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs), + lambda orig_func, self, device=None, *args, **kwargs: check_device(device)) + CondFunc('torch.Tensor.cuda', + lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs), + lambda orig_func, self, device=None, *args, **kwargs: check_device(device)) + CondFunc('torch.empty', + lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), + lambda orig_func, *args, device=None, **kwargs: check_device(device)) + CondFunc('torch.load', + lambda orig_func, *args, map_location=None, **kwargs: orig_func(*args, return_xpu(map_location), **kwargs), + lambda orig_func, *args, map_location=None, **kwargs: map_location is None or check_device(map_location)) + CondFunc('torch.randn', + lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), + lambda orig_func, *args, device=None, **kwargs: check_device(device)) + CondFunc('torch.ones', + lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), + lambda orig_func, *args, device=None, **kwargs: check_device(device)) + CondFunc('torch.zeros', + lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), + lambda orig_func, *args, device=None, **kwargs: check_device(device)) + CondFunc('torch.tensor', + lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), + lambda orig_func, *args, device=None, **kwargs: check_device(device)) + CondFunc('torch.linspace', + lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs), + lambda orig_func, *args, device=None, **kwargs: check_device(device)) + + CondFunc('torch.Generator', + lambda orig_func, device=None: torch.xpu.Generator(device), + lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu") + + CondFunc('torch.batch_norm', + lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input, + weight if weight is not None else torch.ones(input.size()[1], device=input.device), + bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs), + lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu")) + CondFunc('torch.instance_norm', + lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input, + weight if weight is not None else torch.ones(input.size()[1], device=input.device), + bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs), + lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu")) + + #Functions with dtype errors: + CondFunc('torch.nn.modules.GroupNorm.forward', + lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)), + lambda orig_func, self, input: input.dtype != self.weight.data.dtype) + CondFunc('torch.nn.modules.linear.Linear.forward', + lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)), + lambda orig_func, self, input: input.dtype != self.weight.data.dtype) + CondFunc('torch.nn.modules.conv.Conv2d.forward', + lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)), + lambda orig_func, self, input: input.dtype != self.weight.data.dtype) + CondFunc('torch.nn.functional.layer_norm', + lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs: + orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs), + lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs: + weight is not None and input.dtype != weight.data.dtype) + + #Diffusers Float64 (ARC GPUs doesn't support double or Float64): + if not torch.xpu.has_fp64_dtype(): + CondFunc('torch.from_numpy', + lambda orig_func, ndarray: orig_func(ndarray.astype('float32')), + lambda orig_func, ndarray: ndarray.dtype == float) + + #Broken functions when torch.cuda.is_available is True: + CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__', + lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs), + lambda orig_func, *args, **kwargs: True) + + #Functions that make compile mad with CondFunc: + torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers + torch.nn.DataParallel = DummyDataParallel + torch.autocast = ipex_autocast + torch.cat = torch_cat + torch.linalg.solve = linalg_solve + torch.nn.functional.interpolate = interpolate + torch.backends.cuda.sdp_kernel = return_null_context diff --git a/library/model_util.py b/library/model_util.py index 860c170b2..00a3c0495 100644 --- a/library/model_util.py +++ b/library/model_util.py @@ -4,6 +4,13 @@ import math import os import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass import diffusers from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline # , UNet2DConditionModel diff --git a/library/sdxl_model_util.py b/library/sdxl_model_util.py index 6647b4394..2f0154cae 100644 --- a/library/sdxl_model_util.py +++ b/library/sdxl_model_util.py @@ -258,6 +258,10 @@ def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dty te1_sd[k.replace("conditioner.embedders.0.transformer.", "")] = state_dict.pop(k) elif k.startswith("conditioner.embedders.1.model."): te2_sd[k] = state_dict.pop(k) + + # 一部のposition_idsがないモデルへの対応 / add position_ids for some models + if "text_model.embeddings.position_ids" not in te1_sd: + te1_sd["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0) info1 = _load_state_dict_on_device(text_model1, te1_sd, device=map_location) # remain fp32 print("text encoder 1:", info1) diff --git a/localizations/zh-CN.json b/localizations/zh-CN.json new file mode 100644 index 000000000..4d6037538 --- /dev/null +++ b/localizations/zh-CN.json @@ -0,0 +1,361 @@ + + { + + "-Need to add resources here": "-需要在这里添加资源", + "(Experimental, Optional) Since the latent is close to a normal distribution, it may be a good idea to specify a value around 1/10 the noise offset.": "（实验性，可选）由于潜在变量接近正态分布，指定一个接近噪声偏移1/10的值可能是个好主意。", + "(Optional) Add training comment to be included in metadata": "(可选)增加训练注释到元数据", + "(Optional) Enforce number of epoch": "(可选)强制每批数量", + "(Optional) Save only the specified number of models (old models will be deleted)": "（可选）仅保存指定数量的模型（旧模型将被删除）", + "(Optional) Save only the specified number of states (old models will be deleted)": "（可选）仅保存指定数量的状态（旧模型将被删除）", + "(Optional) Stable Diffusion base model": "（可选）稳定扩散基础模型", + "(Optional) Stable Diffusion model": "（可选）稳定扩散模型", + "(Optional) The model is saved every specified steps": "（可选）模型每隔指定的步数保存一次", + "(Optional)": "（可选）", + "About SDXL training": "关于SDXL培训", + "Adaptive noise scale": "自适应噪声比例", + "Additional parameters": "额外参数", + "Advanced options": "高级选项", + "Advanced parameters": "高级参数", + "Advanced": "增强", + "ashleykleynhans runpod docker builds": "ashleykleynhans runpod docker构建", + "Automatically determine the dim(rank) from the weight file.": "从权重文件自动确定dim（排名）。", + "Autosave": "自动保存", + "Basic Captioning": "基本字幕", + "Basic": "基础", + "Batch size": "批量大小", + "BLIP Captioning": "BLIP字幕", + "Bucket resolution steps": "桶分辨率步骤", + "Built with Gradio": "使用Gradio构建", + "Cache latents to disk": "缓存潜变量到磁盘", + "Cache latents": "缓存潜变量", + "Caption file extension": "标题文件扩展名", + "Caption text": "标题文本", + "caption": "标题", + "Change History": "更改历史", + "Class prompt": "Class类提示", + "Color augmentation": "颜色增强", + "Configuration file": "配置文件", + "constant_with_warmup": "带预热的常数", + "constant": "常数", + "Conv Dimension (Rank)": "卷积维度(Rank)", + "Conv Dimension": "卷积维度", + "Convert model": "转换模型", + "Copy info to Folders Tab": "复制信息到文件夹", + "cosine_with_restarts": "带重启的余弦函数学习率的方法", + "cosine": "余弦函数", + "CrossAttention": "交叉注意力", + "DANGER!!! -- Insecure folder renaming -- DANGER!!!": "危险！！！-- 不安全的文件夹重命名 -- 危险！！！", + "Dataset folder": "数据集文件夹", + "Dataset preparation": "数据集准备", + "Dataset Preparation": "数据集准备", + "Dataset repeats": "数据集重复", + "Desired LoRA rank": "期望的LoRA秩", + "Destination training directory": "训练结果目录", + "Device": "设备", + "DIM from weights": "从权重获取DIM", + "Directory containing the images to caption": "包含要添加标题的图像的目录", + "Directory containing the training images": "直接包含训练图片", + "Directory where formatted training and regularisation folders will be placed": "训练和正则化文件会被取代", + "Disable CP decomposition": "禁用CP分解", + "Do not copy other files in the input folder to the output folder": "不要将输入文件夹中的其他文件复制到输出文件夹", + "Do not copy other files": "不复制其他文件", + "Don't upscale bucket resolution": "不要放大桶分辨率", + "Dreambooth/LoRA Dataset balancing": "Dreambooth/LoRA数据集平衡", + "Dreambooth/LoRA Folder preparation": "Dreambooth/LoRA文件准备", + "Dropout caption every n epochs": "每n个时代丢弃标题", + "DyLoRA model": "DyLoRA模型", + "Dynamic method": "动态方法", + "Dynamic parameter": "动态参数", + "e.g., \"by some artist\". Leave empty if you only want to add a prefix or postfix.": "例如，\"由某个艺术家创作\"。如果您只想添加前缀或后缀，请留空。", + "e.g., \"by some artist\". Leave empty if you want to replace with nothing.": "例如，\"由某个艺术家创作\"。如果您想用空白替换，请留空。", + "Enable buckets": "启用数据容器buckets", + "Enable for Hugging Face's stabilityai models": "启用Hugging Face的stabilityai模型", + "Enter one sample prompt per line to generate multiple samples per cycle. Optional specifiers include: --w (width), --h (height), --d (seed), --l (cfg scale), --s (sampler steps) and --n (negative prompt). To modify sample prompts during training, edit the prompt.txt file in the samples directory.": "每行输入一个样本提示以生成每个周期的多个样本。可选指定符包括：--w（宽度），--h（高度），--d（种子），--l（cfg比例），--s（采样器步骤）和--n（负提示）。要在训练期间修改样本提示，请编辑样本目录中的prompt.txt文件。", + "Epoch": "数量增加", + "Error": "错误", + "Example of the optimizer settings for Adafactor with the fixed learning rate:": "具有固定学习率的Adafactor优化器设置的示例：", + "Extract DyLoRA": "提取DyLoRA", + "Extract LoRA model": "提取LoRA模型", + "Extract LoRA": "提取LoRA", + "Extract LyCORIS LoCon": "提取LyCORIS LoCon", + "Extract LyCORIS LoCON": "提取LyCORIS LoCON", + "FileNotFoundError": "FileNotFoundError", + "Find text": "查找文本", + "Finetune": "微调", + "Finetuned model": "微调模型", + "Finetuning Resource Guide": "微调资源指南", + "fixed": "固定", + "Flip augmentation": "翻转增强", + "float16": "float16", + "Folders": "文件夹", + "Full bf16 training (experimental)": "完全bf16训练（实验性）", + "Full fp16 training (experimental)": "完全fp16训练（实验性）", + "Generate caption files for the grouped images based on their folder name": "根据其文件夹名称为分组图片生成标题文件", + "Generate caption metadata": "生成标题元数据", + "Generate Captions": "生成标题", + "Generate image buckets metadata": "生成图像存储桶元数据", + "GIT Captioning": "GIT字幕", + "Gradient accumulate steps": "渐变积累步骤", + "Gradient checkpointing": "渐变检查点", + "Group size": "Group大小", + "Guidelines for SDXL Finetuning": "SDXL微调指南", + "Guides": "指南", + "How to Create a LoRA Part 1: Dataset Preparation:": "如何创建LoRA第1部分：数据集准备：", + "If unchecked, tensorboard will be used as the default for logging.": "如果未选中，tensorboard将用作日志记录的默认选项。", + "If you have valuable resources to add, kindly create a PR on Github.": "如果您有有价值的资源要添加，请在Github上创建一个PR。", + "Ignore Imported Tags Above Word Count": "忽略高于字数计数的导入标签", + "Image folder to caption": "要添加标题的图像文件夹", + "Image folder": "图片文件夹", + "Include images in subfolders as well": "同时包括子文件夹中的图片", + "Include Subfolders": "包括子文件夹", + "Init word": "初始化词", + "Input folder": "输入文件夹", + "Install Location": "安装位置", + "Installation": "安装", + "Instance prompt": "实例提示", + "Keep n tokens": "保留n个令牌", + "Launching the GUI on Linux and macOS": "在Linux和macOS上启动GUI", + "Launching the GUI on Windows": "在Windows上启动GUI", + "Learning rate": "学习率", + "linear": "线性", + "Linux and macOS Upgrade": "Linux和macOS升级", + "Linux and macOS": "Linux和macOS", + "Linux Pre-requirements": "Linux预先要求", + "Load": "加载", + "Loading...": "载入中...", + "Local docker build": "本地Docker构建", + "Logging folder": "日志文件夹", + "LoRA model \"A\"": "LoRA模型“A”", + "LoRA model \"B\"": "LoRA模型“B”", + "LoRA model \"C\"": "LoRA模型“C”", + "LoRA model \"D\"": "LoRA模型“D”", + "LoRA model": "LoRA模型", + "LoRA network weights": "LoRA网络权重", + "LoRA": "LoRA", + "LR number of cycles": "学习率周期数", + "LR power": "学习率功率", + "LR scheduler extra arguments": "学习率调度器额外参数", + "LR Scheduler": "学习率调度器", + "LR warmup (% of steps)": "学习率预热（%的步数）", + "LyCORIS model": "LyCORIS模型", + "Macos is not great at the moment.": "目前MacOS的支持不是很好。", + "Manual Captioning": "手动字幕", + "Manual installation": "手动安装", + "Max bucket resolution": "最大存储桶分辨率", + "Max length": "最大长度", + "Max num workers for DataLoader": "DataLoader的最大工作人员数量", + "Max resolution": "最大分辨率", + "Max Timestep": "最大时间步", + "Max Token Length": "最大令牌长度", + "Max train epoch": "每批数量", + "Max train steps": "最大训练步数", + "Maximum bucket resolution": "最大数据容器存储桶分辨率", + "Maximum size in pixel a bucket can be (>= 64)": "可以达到的最大像素尺寸（>= 64）", + "Memory efficient attention": "内存高效注意力", + "Merge LoRA (SVD)": "合并LoRA（SVD）", + "Merge LoRA": "合并LoRA", + "Merge LyCORIS": "合并LyCORIS", + "Merge model": "合并模型", + "Merge precision": "合并精度", + "Merge ratio model A": "模型A合并比例", + "Merge ratio model B": "模型B合并比例", + "Merge ratio model C": "模型C合并比例", + "Merge ratio model D": "模型D合并比例", + "Min bucket resolution": "最小数据容器存储桶分辨率", + "Min length": "最小长度", + "Min SNR gamma": "最小SNR伽玛", + "Min Timestep": "最小时间步", + "Minimum bucket resolution": "最小数据容器存储桶分辨率", + "Minimum size in pixel a bucket can be": "数据容器存储桶的最小像素大小", + "Mixed precision": "混合精度", + "Mnimum difference": "最小差异", + "Mode": "模式", + "Model A merge ratio (eg: 0.5 mean 50%)": "模型A合并比率（例如：0.5意味着50%）", + "Model B merge ratio (eg: 0.5 mean 50%)": "模型B合并比率（例如：0.5意味着50%）", + "Model C merge ratio (eg: 0.5 mean 50%)": "模型C合并比率（例如：0.5意味着50%）", + "Model D merge ratio (eg: 0.5 mean 50%)": "模型D合并比率（例如：0.5意味着50%）", + "Model output folder": "模型输出文件夹", + "Model output name": "模型输出文件夹", + "Model Quick Pick": "快速选择模型", + "Module dropout": "模块丢失", + "Network Dimension (Rank)": "网络维度（秩）", + "Network Dimension": "网络维度", + "Network dropout": "网络丢失", + "No module called tkinter": "没有名为tkinter的模块", + "No token padding": "无令牌填充", + "Noise offset type": "噪声偏移类型", + "Noise offset": "噪声偏移", + "Number of beams": "beam的数量 - 由于同时考虑多个解决方案，beam搜索能够减少错误累积，从而提高最终解决方案的质量。", + "Number of CPU threads per core": "每个核心的CPU线程数", + "Number of images to group together": "要一起分组的图像数量", + "Number of updates steps to accumulate before performing a backward/update pass": "执行反向/更新传递之前需要积累的更新步骤数", + "object template": "对象模板", + "Only for SD v2 models. By scaling the loss according to the time step, the weights of global noise prediction and local noise prediction become the same, and the improvement of details may be expected.": "仅适用于SD v2模型。通过根据时间步长缩放损失，全局噪声预测和局部噪声预测的权重变得相同，可以期望细节的改进。", + "Open": "打开", + "Optimizer extra arguments": "优化器额外参数", + "Optimizer": "优化器", + "Optional: CUDNN 8.6": "可选：CUDNN 8.6", + "Original": "原始", + "Output folder": "输出文件夹", + "Output": "输出", + "Overwrite existing captions in folder": "覆盖文件夹中现有的标题", + "Page File Limit": "页面文件限制", + "PagedAdamW8bit": "分页AdamW8位", + "PagedLion8bit": "分页Lion8位", + "Parameters": "参数", + "path for the checkpoint file to save...": "保存检查点文件的路径...", + "path for the LoRA file to save...": "保存LoRA文件的路径...", + "path for the new LoRA file to save...": "保存新LoRA文件的路径...", + "path to \"last-state\" state folder to resume from": "从中恢复的“最后状态”状态文件夹的路径", + "Path to the DyLoRA model to extract from": "要从中提取的DyLoRA模型的路径", + "Path to the finetuned model to extract": "要提取的微调模型的路径", + "Path to the LoRA A model": "LoRA A模型的路径", + "Path to the LoRA B model": "LoRA B模型的路径", + "Path to the LoRA C model": "LoRA C模型的路径", + "Path to the LoRA D model": "LoRA D模型的路径", + "Path to the LoRA model to verify": "要验证的LoRA模型的路径", + "Path to the LoRA to resize": "要调整大小的LoRA的路径", + "Path to the LyCORIS model": "LyCORIS模型的路径", + "path where to save the extracted LoRA model...": "保存提取出的LoRA模型的路径...", + "Persistent data loader": "持久数据加载器", + "polynomial": "多项式", + "Postfix to add to BLIP caption": "添加到BLIP标题的后缀", + "Postfix to add to caption": "添加到标题的后缀", + "Pre-built Runpod template": "预构建的Runpod模板", + "Prefix to add to BLIP caption": "添加到BLIP标题的前缀", + "Prefix to add to caption": "添加到标题的前缀", + "Prepare training data": "准备训练数据", + "Print training command": "打印训练命令", + "Prior loss weight": "先验损失权重", + "Prodigy": "神童", + "Provide a SD file path IF you want to merge it with LoRA files": "如果您想将其与LoRA文件合并，请提供SD文件路径", + "Provide a SD file path that you want to merge with the LyCORIS file": "提供您想与LyCORIS文件合并的SD文件路径", + "PyTorch 2 seems to use slightly less GPU memory than PyTorch 1.": "PyTorch 2似乎使用的GPU内存比PyTorch 1略少。", + "Quick Tags": "快速标签", + "Random crop instead of center crop": "随机裁剪而非中心裁剪", + "Rank dropout": "排名丢失", + "Rate of caption dropout": "标题丢失率", + "Recommended value of 0.5 when used": "使用时推荐值为0.5", + "Recommended value of 5 when used": "使用时推荐值为5", + "recommended values are 0.05 - 0.15": "推荐值为0.05 - 0.15", + "Regularisation folder": "正则化文件夹", + "Regularisation images": "正则化图像", + "Repeats": "重复", + "Replacement text": "替换文本", + "Required bitsandbytes >= 0.36.0": "所需的bitsandbytes >= 0.36.0", + "Resize LoRA": "调整LoRA尺寸", + "Resize model": "调整模型大小", + "Resolution (width,height)": "分辨率（宽度,高度）", + "Resource Contributions": "资源贡献", + "Resume from saved training state": "从保存的训练状态恢复", + "Resume TI training": "恢复TI训练", + "Runpod": "Runpod", + "Sample every n epochs": "每n个时代采样一次", + "Sample every n steps": "每n步采样一次", + "Sample image generation during training": "培训期间的样本图像生成", + "Sample prompts": "样本提示", + "Sample sampler": "样本采样器", + "Samples": "样例", + "Save dtype": "保存数据类型", + "Save every N epochs": "每N个epochs保存", + "Save every N steps": "每N步保存一次", + "Save last N steps state": "保存最后N步状态", + "Save last N steps": "保存最后N步", + "Save precision": "保存精度", + "Save to": "保存到", + "Save trained model as": "保存训练模型为", + "Save training state": "保存训练状态", + "Save": "保存", + "Scale v prediction loss": "缩放v预测损失", + "Scale weight norms": "缩放权重规范", + "SD Model": "SD模型", + "SDXL model": "SDXL模型", + "Set the Max resolution to at least 1024x1024, as this is the standard resolution for SDXL. ": "将 最大分辨率 设置为至少 1024x1024，因为这是 SDXL 的标准分辨率。", + "Set the Max resolution to at least 1024x1024, as this is the standard resolution for SDXL.": "将最大分辨率设置为至少1024x1024，因为这是SDXL的标准分辨率。", + "Setup": "设置", + "SGDNesterov": "SGD Nesterov", + "SGDNesterov8bit": "SGD Nesterov 8位", + "Shuffle caption": "随机标题", + "Source LoRA": "源LoRA", + "Source model type": "源模型类型", + "Source model": "模型来源", + "Sparsity": "稀疏性", + "Stable Diffusion base model": "稳定扩散基础模型", + "Stable Diffusion original model: ckpt or safetensors file": "稳定扩散原始模型：ckpt或safetensors文件", + "Start tensorboard": "开始 tensorboard", + "Start training": "开始训练", + "Starting GUI Service": "启动GUI服务", + "Stop tensorboard": "结束 tensorboard", + "Stop text encoder training": "停止文本编码器训练", + "Stop training": "停止训练", + "style template": "样式模板", + "sv_fro": "sv_fro", + "Target model folder": "目标模型文件夹", + "Target model name": "目标模型名称", + "Target model precision": "目标模型精度", + "Target model type": "目标模型类型", + "Template": "模板", + "Text Encoder learning rate": "文本编码器学习率", + "The fine-tuning can be done with 24GB GPU memory with the batch size of 1.": "微调可以在具有1个批量大小的24GB GPU内存上完成。", + "The GUI allows you to set the training parameters and generate and run the required CLI commands to train the model.": "该GUI允许您设置训练参数，并生成并运行训练模型所需的CLI命令。", + "This guide is a resource compilation to facilitate the development of robust LoRA models.": "该指南是一个资源汇编，以促进强大LoRA模型的开发。", + "This section provide Dreambooth tools to help setup your dataset…": "这些选择帮助设置自己的数据集", + "This section provide LoRA tools to help setup your dataset…": "本节提供LoRA工具以帮助您设置数据集...", + "This section provide Various Finetuning guides and information…": "本节提供各种微调指南和信息", + "This utility allows quick captioning and tagging of images.": "此工具允许快速地为图像添加标题和标签。", + "This utility allows you to create simple caption files for each image in a folder.": "此工具允许您为文件夹中的每个图像创建简单的标题文件。", + "This utility can be used to convert from one stable diffusion model format to another.": "该工具可用于将一个稳定扩散模型格式转换为另一种格式", + "This utility can extract a DyLoRA network from a finetuned model.": "该工具可以从微调模型中提取DyLoRA网络。", + "This utility can extract a LoRA network from a finetuned model.": "该工具可以从微调模型中提取LoRA网络。", + "This utility can extract a LyCORIS LoCon network from a finetuned model.": "该工具可以从微调模型中提取LyCORIS LoCon网络。", + "This utility can merge a LyCORIS model into a SD checkpoint.": "该工具可以将LyCORIS模型合并到SD检查点中。", + "This utility can merge two LoRA networks together into a new LoRA.": "该工具可以将两个LoRA网络合并为一个新的LoRA。", + "This utility can merge up to 4 LoRA together or alternatively merge up to 4 LoRA into a SD checkpoint.": "该工具可以合并多达4个LoRA，或者选择性地将多达4个LoRA合并到SD检查点中。", + "This utility can resize a LoRA.": "该工具可以调整LoRA的大小。", + "This utility can verify a LoRA network to make sure it is properly trained.": "该工具可以验证LoRA网络以确保其得到适当的训练。", + "This utility uses BLIP to caption files for each image in a folder.": "此工具使用BLIP为文件夹中的每张图像添加标题。", + "This utility will create the necessary folder structure for the training images and optional regularization images needed for the kohys_ss Dreambooth/LoRA method to function correctly.": "为训练文件创建文件夹", + "This utility will ensure that each concept folder in the dataset folder is used equally during the training process of the dreambooth machine learning model, regardless of the number of images in each folder. It will do this by renaming the concept folders to indicate the number of times they should be repeated during training.": "此工具将确保在训练dreambooth机器学习模型的过程中，数据集文件夹中的每个概念文件夹都将被平等地使用，无论每个文件夹中有多少图像。它将通过重命名概念文件夹来指示在训练期间应重复使用它们的次数。", + "This utility will group images in a folder based on their aspect ratio.": "此工具将根据它们的纵横比将文件夹中的图像分组。", + "This utility will use GIT to caption files for each images in a folder.": "此工具将使用GIT为文件夹中的每张图像添加标题。", + "This utility will use WD14 to caption files for each images in a folder.": "此工具将使用WD14为文件夹中的每张图像添加标题。", + "Tips for SDXL training": "SDXL培训提示", + "Token string": "令牌字符串", + "Train a custom model using kohya finetune python code": "使用kohya微调Python代码训练个性化模型", + "Train a custom model using kohya train network LoRA python code…": "使用kohya训练网络LoRA Python代码训练自定义模型", + "Train batch size": "训练批次大小", + "Train Network": "训练网络", + "Train text encoder": "训练文本编码器", + "Train U-Net only.": "仅训练 U-Net", + "Training config folder": "训练配置文件夹", + "Training Image folder": "训练图像文件夹", + "Training images": "训练图像", + "Training steps per concept per epoch": "每个周期每个概念的训练步骤", + "Training": "训练", + "Troubleshooting": "故障排除", + "Tutorials": "教程", + "Unet learning rate": "Unet学习率", + "UNet linear projection": "UNet 线性投影", + "Upgrading": "升级", + "Use --cache_text_encoder_outputs option and caching latents.": "使用 --cache_text_encoder_outputs 选项和缓存潜在变量。", + "Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn’t seem to work.": "使用 Adafactor 优化器。 RMSprop 8bit 或 Adagrad 8bit 可能有效。 AdamW 8bit 好像不行。", + "Use beam search": "使用beam搜索-启发式图搜索算法,beam搜索可以用来生成更准确和自然的文本。", + "Use gradient checkpointing.": "使用梯度检查点。", + "Use latent files": "使用潜在文件", + "Use sparse biais": "使用稀疏偏见", + "Users can obtain and/or generate an api key in the their user settings on the website: https://wandb.ai/login": "用户可以在以下网站的用户设置中获取和/或生成API密钥：https://wandb.ai/login", + "V Pred like loss": "v预测损失", + "Values greater than 0 will make the model more img2img focussed. 0 = image only": "大于0的值会使模型更加聚焦在 img2img 上。0 = 仅图像。这应该表示时间步参数,大于0会使模型更加侧重 img2img 生成,0则仅关注图像生成。", + "Values lower than 1000 will make the model more img2img focussed. 1000 = noise only": "小于1000的值会使模型更加聚焦在 img2img 上。1000 = 仅噪声。这也应该表示时间步参数,小于1000会使模型更加侧重 img2img 生成,1000则仅从噪声生成图像。", + "Vectors": "向量", + "Verbose": "详细输出", + "WANDB API Key": "WANDB API 密钥。", + "WARNING! The use of this utility on the wrong folder can lead to unexpected folder renaming!!!": "警告！在错误的文件夹上使用此工具可能导致意外的文件夹重命名！", + "WD14 Captioning": "WD14字幕", + "Windows Upgrade": "Windows升级", + "Train a custom model using kohya dreambooth python code…": "使用kohya的dreambooth Python代码训练个性化模型", + "Training comment": "训练注释", + "Train a TI using kohya textual inversion python code…": "使用kohya的文本反转Python代码训练TI模型", + "Train a custom model using kohya finetune python code…": "使用kohya的微调Python代码训练个性化模型" + +} \ No newline at end of file diff --git a/modules.txt b/modules.txt new file mode 100644 index 000000000..7c9399841 Binary files /dev/null and b/modules.txt differ diff --git a/networks/extract_lora_from_models.py b/networks/extract_lora_from_models.py index 3bc529773..7bdfceafb 100644 --- a/networks/extract_lora_from_models.py +++ b/networks/extract_lora_from_models.py @@ -198,7 +198,7 @@ def str_to_dtype(p): if not args.no_metadata: title = os.path.splitext(os.path.basename(args.save_to))[0] sai_metadata = sai_model_spec.build_metadata( - None, args.v2, args.v_parameterization, False, True, False, time.time(), title=title + None, args.v2, args.v_parameterization, args.sdxl, True, False, time.time(), title=title ) metadata.update(sai_metadata) diff --git a/networks/lora_diffusers.py b/networks/lora_diffusers.py index c41111bea..47d75ac4d 100644 --- a/networks/lora_diffusers.py +++ b/networks/lora_diffusers.py @@ -117,7 +117,7 @@ def __init__( super().__init__() self.lora_name = lora_name - if org_module.__class__.__name__ == "Conv2d": + if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv": in_dim = org_module.in_channels out_dim = org_module.out_channels else: @@ -126,7 +126,7 @@ def __init__( self.lora_dim = lora_dim - if org_module.__class__.__name__ == "Conv2d": + if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv": kernel_size = org_module.kernel_size stride = org_module.stride padding = org_module.padding @@ -166,7 +166,8 @@ def unapply_to(self): self.org_module[0].forward = self.org_forward # forward with lora - def forward(self, x): + # scale is used LoRACompatibleConv, but we ignore it because we have multiplier + def forward(self, x, scale=1.0): if not self.enabled: return self.org_forward(x) return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale @@ -318,8 +319,12 @@ def create_modules( for name, module in root_module.named_modules(): if module.__class__.__name__ in target_replace_modules: for child_name, child_module in module.named_modules(): - is_linear = child_module.__class__.__name__ == "Linear" - is_conv2d = child_module.__class__.__name__ == "Conv2d" + is_linear = ( + child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "LoRACompatibleLinear" + ) + is_conv2d = ( + child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv" + ) if is_linear or is_conv2d: lora_name = prefix + "." + name + "." + child_name @@ -359,7 +364,7 @@ def create_modules( skipped_te += skipped print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.") if len(skipped_te) > 0: - print(f"skipped {len(skipped_te)} modules because of missing weight.") + print(f"skipped {len(skipped_te)} modules because of missing weight for text encoder.") # extend U-Net target modules to include Conv2d 3x3 target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 @@ -368,7 +373,7 @@ def create_modules( self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules) print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.") if len(skipped_un) > 0: - print(f"skipped {len(skipped_un)} modules because of missing weight.") + print(f"skipped {len(skipped_un)} modules because of missing weight for U-Net.") # assertion names = set() diff --git a/networks/merge_lora.py b/networks/merge_lora.py index c8d743f56..71492621e 100644 --- a/networks/merge_lora.py +++ b/networks/merge_lora.py @@ -110,7 +110,7 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype): module.weight = torch.nn.Parameter(weight) -def merge_lora_models(models, ratios, merge_dtype): +def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False): base_alphas = {} # alpha for merged model base_dims = {} @@ -158,6 +158,12 @@ def merge_lora_models(models, ratios, merge_dtype): for key in lora_sd.keys(): if "alpha" in key: continue + if "lora_up" in key and concat: + concat_dim = 1 + elif "lora_down" in key and concat: + concat_dim = 0 + else: + concat_dim = None lora_module_name = key[: key.rfind(".lora_")] @@ -165,12 +171,16 @@ def merge_lora_models(models, ratios, merge_dtype): alpha = alphas[lora_module_name] scale = math.sqrt(alpha / base_alpha) * ratio + scale = abs(scale) if "lora_up" in key else scale # マイナスの重みに対応する。 if key in merged_sd: assert ( - merged_sd[key].size() == lora_sd[key].size() + merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません" - merged_sd[key] = merged_sd[key] + lora_sd[key] * scale + if concat_dim is not None: + merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim) + else: + merged_sd[key] = merged_sd[key] + lora_sd[key] * scale else: merged_sd[key] = lora_sd[key] * scale @@ -178,6 +188,13 @@ def merge_lora_models(models, ratios, merge_dtype): for lora_module_name, alpha in base_alphas.items(): key = lora_module_name + ".alpha" merged_sd[key] = torch.tensor(alpha) + if shuffle: + key_down = lora_module_name + ".lora_down.weight" + key_up = lora_module_name + ".lora_up.weight" + dim = merged_sd[key_down].shape[0] + perm = torch.randperm(dim) + merged_sd[key_down] = merged_sd[key_down][perm] + merged_sd[key_up] = merged_sd[key_up][:,perm] print("merged model") print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}") @@ -256,7 +273,7 @@ def str_to_dtype(p): args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, sai_metadata, save_dtype, vae ) else: - state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype) + state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle) print(f"calculating hashes and creating metadata...") @@ -317,7 +334,19 @@ def setup_parser() -> argparse.ArgumentParser: help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / " + "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）", ) - + parser.add_argument( + "--concat", + action="store_true", + help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / " + + "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）", + ) + parser.add_argument( + "--shuffle", + action="store_true", + help="shuffle lora weight./ " + + "LoRAの重みをシャッフルする", + ) + return parser diff --git a/networks/resize_lora.py b/networks/resize_lora.py index 7b7406347..03fc545e7 100644 --- a/networks/resize_lora.py +++ b/networks/resize_lora.py @@ -219,8 +219,8 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn for key, value in tqdm(lora_sd.items()): weight_name = None if 'lora_down' in key: - block_down_name = key.split(".")[0] - weight_name = key.split(".")[-1] + block_down_name = key.rsplit('.lora_down', 1)[0] + weight_name = key.rsplit(".", 1)[-1] lora_down_weight = value else: continue @@ -283,7 +283,10 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn def resize(args): + if args.save_to is None or not (args.save_to.endswith('.ckpt') or args.save_to.endswith('.pt') or args.save_to.endswith('.pth') or args.save_to.endswith('.safetensors')): + raise Exception("The --save_to argument must be specified and must be a .ckpt , .pt, .pth or .safetensors file.") + def str_to_dtype(p): if p == 'float': return torch.float diff --git a/networks/sdxl_merge_lora.py b/networks/sdxl_merge_lora.py index 0608c01f9..c513eb59f 100644 --- a/networks/sdxl_merge_lora.py +++ b/networks/sdxl_merge_lora.py @@ -113,7 +113,7 @@ def merge_to_sd_model(text_encoder1, text_encoder2, unet, models, ratios, merge_ module.weight = torch.nn.Parameter(weight) -def merge_lora_models(models, ratios, merge_dtype): +def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False): base_alphas = {} # alpha for merged model base_dims = {} @@ -161,6 +161,13 @@ def merge_lora_models(models, ratios, merge_dtype): for key in tqdm(lora_sd.keys()): if "alpha" in key: continue + + if "lora_up" in key and concat: + concat_dim = 1 + elif "lora_down" in key and concat: + concat_dim = 0 + else: + concat_dim = None lora_module_name = key[: key.rfind(".lora_")] @@ -168,12 +175,16 @@ def merge_lora_models(models, ratios, merge_dtype): alpha = alphas[lora_module_name] scale = math.sqrt(alpha / base_alpha) * ratio - + scale = abs(scale) if "lora_up" in key else scale # マイナスの重みに対応する。 + if key in merged_sd: assert ( - merged_sd[key].size() == lora_sd[key].size() + merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません" - merged_sd[key] = merged_sd[key] + lora_sd[key] * scale + if concat_dim is not None: + merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim) + else: + merged_sd[key] = merged_sd[key] + lora_sd[key] * scale else: merged_sd[key] = lora_sd[key] * scale @@ -181,6 +192,13 @@ def merge_lora_models(models, ratios, merge_dtype): for lora_module_name, alpha in base_alphas.items(): key = lora_module_name + ".alpha" merged_sd[key] = torch.tensor(alpha) + if shuffle: + key_down = lora_module_name + ".lora_down.weight" + key_up = lora_module_name + ".lora_up.weight" + dim = merged_sd[key_down].shape[0] + perm = torch.randperm(dim) + merged_sd[key_down] = merged_sd[key_down][perm] + merged_sd[key_up] = merged_sd[key_up][:,perm] print("merged model") print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}") @@ -252,7 +270,7 @@ def str_to_dtype(p): args.save_to, text_model1, text_model2, unet, 0, 0, ckpt_info, vae, logit_scale, sai_metadata, save_dtype ) else: - state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype) + state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle) print(f"calculating hashes and creating metadata...") @@ -307,6 +325,18 @@ def setup_parser() -> argparse.ArgumentParser: help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / " + "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）", ) + parser.add_argument( + "--concat", + action="store_true", + help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / " + + "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）", + ) + parser.add_argument( + "--shuffle", + action="store_true", + help="shuffle lora weight./ " + + "LoRAの重みをシャッフルする", + ) return parser diff --git a/presets/finetune/SDXL - AI_Now PagedAdamW8bit v1.0.json b/presets/finetune/SDXL - AI_Now PagedAdamW8bit v1.0.json new file mode 100644 index 000000000..0821e5829 --- /dev/null +++ b/presets/finetune/SDXL - AI_Now PagedAdamW8bit v1.0.json @@ -0,0 +1,71 @@ +{ + "adaptive_noise_scale": 0.00375, + "additional_parameters": "", + "batch_size": "4", + "block_lr": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "cache_latents": true, + "cache_latents_to_disk": false, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0, + "caption_extension": ".txt", + "clip_skip": "1", + "color_aug": false, + "create_buckets": true, + "create_caption": true, + "dataset_repeats": "1", + "epoch": 240, + "flip_aug": false, + "full_bf16": true, + "full_fp16": false, + "full_path": true, + "gradient_accumulation_steps": 6.0, + "gradient_checkpointing": true, + "keep_tokens": "0", + "learning_rate": 5e-05, + "lr_scheduler": "constant", + "lr_scheduler_args": "", + "lr_warmup": 0, + "max_bucket_reso": "1024", + "max_data_loader_n_workers": "0", + "max_resolution": "1024,1024", + "max_timestep": 900, + "max_token_length": "75", + "max_train_epochs": "240", + "mem_eff_attn": false, + "min_bucket_reso": "64", + "min_snr_gamma": 5, + "min_timestep": 100, + "mixed_precision": "bf16", + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "noise_offset": 0.0375, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "optimizer": "PagedAdamW8bit", + "optimizer_args": "", + "persistent_data_loader_workers": false, + "random_crop": false, + "save_every_n_epochs": 240, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_precision": "bf16", + "scale_v_pred_loss_like_noise_pred": false, + "sdxl_cache_text_encoder_outputs": true, + "sdxl_checkbox": true, + "sdxl_no_half_vae": true, + "seed": "1234", + "shuffle_caption": false, + "train_batch_size": 2, + "train_text_encoder": false, + "use_latent_files": "No", + "use_wandb": false, + "v2": false, + "v_parameterization": false, + "v_pred_like_loss": 0, + "vae_batch_size": 0, + "weighted_captions": false, + "xformers": "xformers" +} \ No newline at end of file diff --git a/presets/finetune/adafactor.json b/presets/finetune/adafactor.json index 0e0149dc7..6f7a10926 100644 --- a/presets/finetune/adafactor.json +++ b/presets/finetune/adafactor.json @@ -1,61 +1,49 @@ { - "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", - "v2": false, - "v_parameterization": false, - "train_dir": "D:/dataset/paige_spiranac/ft", - "image_folder": "D:\\dataset\\paige_spiranac\\lora\\img4_g8\\16_paige_spiranac", - "output_dir": "D:/models/test", - "logging_dir": "D:/dataset/paige_spiranac/ft/logs", - "max_resolution": "512,512", - "min_bucket_reso": "256", - "max_bucket_reso": "1024", - "batch_size": "1", - "flip_aug": false, - "caption_metadata_filename": "meta_cap.json", - "latent_metadata_filename": "meta_lat.json", - "full_path": true, - "learning_rate": "1e-6", - "lr_scheduler": "adafactor", - "lr_warmup": "10", - "dataset_repeats": "10", - "train_batch_size": 4, - "epoch": "2", - "save_every_n_epochs": "1", - "mixed_precision": "bf16", - "save_precision": "fp16", - "seed": "1234", - "num_cpu_threads_per_process": 2, - "train_text_encoder": true, - "create_caption": true, - "create_buckets": false, - "save_model_as": "safetensors", - "caption_extension": ".txt", - "use_8bit_adam": false, - "xformers": true, - "clip_skip": 1, - "save_state": false, - "resume": "", - "gradient_checkpointing": false, - "gradient_accumulation_steps": 1.0, - "mem_eff_attn": false, - "shuffle_caption": true, - "output_name": "paige_spiranac_v1.5e", - "max_token_length": "150", - "max_train_epochs": "", - "max_data_loader_n_workers": "0", - "full_fp16": false, - "color_aug": false, - "model_list": "runwayml/stable-diffusion-v1-5", - "cache_latents": true, - "use_latent_files": "No", - "keep_tokens": 1, - "persistent_data_loader_workers": false, - "bucket_no_upscale": true, - "random_crop": false, - "bucket_reso_steps": 1.0, - "caption_dropout_every_n_epochs": 0.0, - "caption_dropout_rate": 0.1, - "optimizer": "Adafactor", - "optimizer_args": "scale_parameter=True relative_step=True warmup_init=True weight_decay=2", - "noise_offset": "" + "batch_size": "1", + "bucket_no_upscale": true, + "bucket_reso_steps": 1.0, + "cache_latents": true, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0.1, + "caption_extension": ".txt", + "clip_skip": 1, + "color_aug": false, + "create_buckets": false, + "create_caption": true, + "dataset_repeats": "10", + "epoch": "2", + "flip_aug": false, + "full_fp16": false, + "full_path": true, + "gradient_accumulation_steps": 1.0, + "gradient_checkpointing": false, + "keep_tokens": 1, + "learning_rate": "1e-6", + "lr_scheduler": "adafactor", + "lr_warmup": "10", + "max_bucket_reso": "1024", + "max_data_loader_n_workers": "0", + "max_resolution": "512,512", + "max_token_length": "150", + "max_train_epochs": "", + "mem_eff_attn": false, + "min_bucket_reso": "256", + "mixed_precision": "bf16", + "noise_offset": "", + "num_cpu_threads_per_process": 2, + "optimizer": "Adafactor", + "optimizer_args": "scale_parameter=True relative_step=True warmup_init=True weight_decay=2", + "persistent_data_loader_workers": false, + "random_crop": false, + "save_every_n_epochs": "1", + "save_precision": "fp16", + "seed": "1234", + "shuffle_caption": true, + "train_batch_size": 4, + "train_text_encoder": true, + "use_8bit_adam": false, + "use_latent_files": "No", + "v2": false, + "v_parameterization": false, + "xformers": true } \ No newline at end of file diff --git a/presets/finetune/lion.json b/presets/finetune/lion.json index 982c8a869..0f74105e6 100644 --- a/presets/finetune/lion.json +++ b/presets/finetune/lion.json @@ -1,61 +1,49 @@ { - "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", - "v2": false, - "v_parameterization": false, - "train_dir": "D:/dataset/paige_spiranac/ft", - "image_folder": "D:\\dataset\\paige_spiranac\\lora\\img4_g8\\16_paige_spiranac", - "output_dir": "D:/models/test", - "logging_dir": "D:/dataset/paige_spiranac/ft/logs", - "max_resolution": "512,512", - "min_bucket_reso": "256", - "max_bucket_reso": "1024", - "batch_size": "1", - "flip_aug": false, - "caption_metadata_filename": "meta_cap.json", - "latent_metadata_filename": "meta_lat.json", - "full_path": true, - "learning_rate": "0.0000166666666", - "lr_scheduler": "cosine", - "lr_warmup": "10", - "dataset_repeats": "10", - "train_batch_size": 4, - "epoch": "2", - "save_every_n_epochs": "1", - "mixed_precision": "bf16", - "save_precision": "fp16", - "seed": "1234", - "num_cpu_threads_per_process": 2, - "train_text_encoder": true, - "create_caption": true, - "create_buckets": false, - "save_model_as": "safetensors", - "caption_extension": ".txt", - "use_8bit_adam": false, - "xformers": true, - "clip_skip": 1, - "save_state": false, - "resume": "", - "gradient_checkpointing": false, - "gradient_accumulation_steps": 1.0, - "mem_eff_attn": false, - "shuffle_caption": true, - "output_name": "paige_spiranac_v1.5e", - "max_token_length": "150", - "max_train_epochs": "", - "max_data_loader_n_workers": "0", - "full_fp16": false, - "color_aug": false, - "model_list": "runwayml/stable-diffusion-v1-5", - "cache_latents": true, - "use_latent_files": "No", - "keep_tokens": 1, - "persistent_data_loader_workers": false, - "bucket_no_upscale": true, - "random_crop": false, - "bucket_reso_steps": 1.0, - "caption_dropout_every_n_epochs": 0.0, - "caption_dropout_rate": 0.1, - "optimizer": "Lion", - "optimizer_args": "", - "noise_offset": "" + "batch_size": "1", + "bucket_no_upscale": true, + "bucket_reso_steps": 1.0, + "cache_latents": true, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0.1, + "caption_extension": ".txt", + "clip_skip": 1, + "color_aug": false, + "create_buckets": false, + "create_caption": true, + "dataset_repeats": "10", + "epoch": "2", + "flip_aug": false, + "full_fp16": false, + "full_path": true, + "gradient_accumulation_steps": 1.0, + "gradient_checkpointing": false, + "keep_tokens": 1, + "learning_rate": "0.0000166666666", + "lr_scheduler": "cosine", + "lr_warmup": "10", + "max_bucket_reso": "1024", + "max_data_loader_n_workers": "0", + "max_resolution": "512,512", + "max_token_length": "150", + "max_train_epochs": "", + "mem_eff_attn": false, + "min_bucket_reso": "256", + "mixed_precision": "bf16", + "noise_offset": "", + "num_cpu_threads_per_process": 2, + "optimizer": "Lion", + "optimizer_args": "", + "persistent_data_loader_workers": false, + "random_crop": false, + "save_every_n_epochs": "1", + "save_precision": "fp16", + "seed": "1234", + "shuffle_caption": true, + "train_batch_size": 4, + "train_text_encoder": true, + "use_8bit_adam": false, + "use_latent_files": "No", + "v2": false, + "v_parameterization": false, + "xformers": true } \ No newline at end of file diff --git a/presets/finetune/prepare_presets.md b/presets/finetune/prepare_presets.md new file mode 100644 index 000000000..48e2101fe --- /dev/null +++ b/presets/finetune/prepare_presets.md @@ -0,0 +1,7 @@ +# Preparing presets for users + +Run the followinf command to prepare new presets for release to users: + +``` +python.exe .\tools\prepare_presets.py .\presets\finetune\*.json +``` \ No newline at end of file diff --git a/presets/lora/SDXL - LoRA AI_Now ADamW v1.0.json b/presets/lora/SDXL - LoRA AI_Now ADamW v1.0.json new file mode 100644 index 000000000..da25fba46 --- /dev/null +++ b/presets/lora/SDXL - LoRA AI_Now ADamW v1.0.json @@ -0,0 +1,97 @@ +{ + "LoRA_type": "Standard", + "adaptive_noise_scale": 0.00375, + "additional_parameters": "", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "cache_latents": true, + "cache_latents_to_disk": true, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0, + "caption_extension": ".txt-no", + "clip_skip": "1", + "color_aug": false, + "conv_alpha": 32, + "conv_block_alphas": "", + "conv_block_dims": "", + "conv_dim": 32, + "decompose_both": false, + "dim_from_weights": false, + "down_lr_weight": "", + "enable_bucket": true, + "epoch": 160, + "factor": -1, + "flip_aug": false, + "full_bf16": false, + "full_fp16": false, + "gradient_accumulation_steps": 1, + "gradient_checkpointing": true, + "keep_tokens": "0", + "learning_rate": 0.0001, + "lora_network_weights": "", + "lr_scheduler": "constant", + "lr_scheduler_args": "", + "lr_scheduler_num_cycles": "1", + "lr_scheduler_power": "", + "lr_warmup": 0, + "max_bucket_reso": 2048, + "max_data_loader_n_workers": "0", + "max_resolution": "1024,1024", + "max_timestep": 900, + "max_token_length": "75", + "max_train_epochs": "", + "max_train_steps": "320", + "mem_eff_attn": false, + "mid_lr_weight": "", + "min_bucket_reso": 256, + "min_snr_gamma": 5, + "min_timestep": 100, + "mixed_precision": "bf16", + "module_dropout": 0, + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "network_alpha": 32, + "network_dim": 32, + "network_dropout": 0, + "no_token_padding": false, + "noise_offset": 0.0375, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "optimizer": "AdamW", + "optimizer_args": "", + "persistent_data_loader_workers": false, + "prior_loss_weight": 1.0, + "random_crop": false, + "rank_dropout": 0, + "save_every_n_epochs": 5, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_precision": "bf16", + "scale_v_pred_loss_like_noise_pred": false, + "scale_weight_norms": 0, + "sdxl": true, + "sdxl_cache_text_encoder_outputs": false, + "sdxl_no_half_vae": true, + "seed": "12345", + "shuffle_caption": false, + "stop_text_encoder_training": 0, + "text_encoder_lr": 0.0001, + "train_batch_size": 4, + "train_on_input": true, + "training_comment": "trigger: lxndrn woman", + "unet_lr": 0.0001, + "unit": 1, + "up_lr_weight": "", + "use_cp": false, + "use_wandb": false, + "v2": false, + "v_parameterization": false, + "v_pred_like_loss": 0, + "vae_batch_size": 0, + "weighted_captions": false, + "xformers": "xformers" +} \ No newline at end of file diff --git a/presets/lora/SDXL - LoRA kudou-reira dadaptadam v1.0.json b/presets/lora/SDXL - LoRA kudou-reira dadaptadam v1.0.json new file mode 100644 index 000000000..3e5af9f62 --- /dev/null +++ b/presets/lora/SDXL - LoRA kudou-reira dadaptadam v1.0.json @@ -0,0 +1,94 @@ +{ + "LoRA_type": "Standard", + "adaptive_noise_scale": 0, + "additional_parameters": "--network_train_unet_only", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "cache_latents": true, + "cache_latents_to_disk": true, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0, + "caption_extension": ".txt", + "clip_skip": "1", + "color_aug": false, + "conv_alpha": 1, + "conv_alphas": "", + "conv_dim": 1, + "conv_dims": "", + "decompose_both": false, + "dim_from_weights": false, + "down_lr_weight": "", + "enable_bucket": true, + "epoch": 25, + "factor": -1, + "flip_aug": false, + "full_bf16": false, + "full_fp16": false, + "gradient_accumulation_steps": 1.0, + "gradient_checkpointing": true, + "keep_tokens": 2, + "learning_rate": 1.0, + "lora_network_weights": "", + "lr_scheduler": "cosine", + "lr_scheduler_num_cycles": "", + "lr_scheduler_power": "", + "lr_warmup": 5, + "max_bucket_reso": 2048, + "max_data_loader_n_workers": "0", + "max_resolution": "1024,1024", + "max_timestep": 1000, + "max_token_length": "225", + "max_train_epochs": "", + "mem_eff_attn": false, + "mid_lr_weight": "", + "min_bucket_reso": 256, + "min_snr_gamma": 5, + "min_timestep": 0, + "mixed_precision": "bf16", + "module_dropout": 0, + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "network_alpha": 256, + "network_dim": 256, + "network_dropout": 0, + "no_token_padding": false, + "noise_offset": 0.0357, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "optimizer": "DAdaptAdam", + "optimizer_args": "\"decouple=True\" \"weight_decay=0.2\" \"betas=0.9,0.99\" \"growth_rate=1.02\"", + "persistent_data_loader_workers": false, + "prior_loss_weight": 1.0, + "random_crop": false, + "rank_dropout": 0, + "save_every_n_epochs": 1, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_precision": "bf16", + "scale_v_pred_loss_like_noise_pred": false, + "scale_weight_norms": 0, + "sdxl": true, + "sdxl_cache_text_encoder_outputs": false, + "sdxl_no_half_vae": true, + "seed": "1337", + "shuffle_caption": true, + "stop_text_encoder_training": 0, + "text_encoder_lr": 1.0, + "train_batch_size": 6, + "train_on_input": true, + "training_comment": "", + "unet_lr": 1.0, + "unit": 1, + "up_lr_weight": "", + "use_cp": false, + "use_wandb": true, + "v2": false, + "v_parameterization": false, + "vae_batch_size": 0, + "weighted_captions": false, + "xformers": true +} \ No newline at end of file diff --git a/presets/lora/SDXL - LoRA kudou-reira dadaptadam v1.1.json b/presets/lora/SDXL - LoRA kudou-reira dadaptadam v1.1.json new file mode 100644 index 000000000..5cb0296bc --- /dev/null +++ b/presets/lora/SDXL - LoRA kudou-reira dadaptadam v1.1.json @@ -0,0 +1,94 @@ +{ + "LoRA_type": "Standard", + "adaptive_noise_scale": 0, + "additional_parameters": "--network_train_unet_only", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "cache_latents": true, + "cache_latents_to_disk": true, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0, + "caption_extension": ".txt", + "clip_skip": "1", + "color_aug": false, + "conv_alpha": 1, + "conv_alphas": "", + "conv_dim": 1, + "conv_dims": "", + "decompose_both": false, + "dim_from_weights": false, + "down_lr_weight": "", + "enable_bucket": true, + "epoch": 25, + "factor": -1, + "flip_aug": false, + "full_bf16": false, + "full_fp16": false, + "gradient_accumulation_steps": 1.0, + "gradient_checkpointing": true, + "keep_tokens": 2, + "learning_rate": 1.0, + "lora_network_weights": "", + "lr_scheduler": "cosine", + "lr_scheduler_num_cycles": "", + "lr_scheduler_power": "", + "lr_warmup": 5, + "max_bucket_reso": 2048, + "max_data_loader_n_workers": "0", + "max_resolution": "1024,1024", + "max_timestep": 1000, + "max_token_length": "225", + "max_train_epochs": "", + "mem_eff_attn": false, + "mid_lr_weight": "", + "min_bucket_reso": 256, + "min_snr_gamma": 5, + "min_timestep": 0, + "mixed_precision": "bf16", + "module_dropout": 0, + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "network_alpha": 64, + "network_dim": 64, + "network_dropout": 0, + "no_token_padding": false, + "noise_offset": 0.0357, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "optimizer": "DAdaptAdam", + "optimizer_args": "\"decouple=True\" \"weight_decay=0.1\" \"betas=0.9,0.91\"", + "persistent_data_loader_workers": false, + "prior_loss_weight": 1.0, + "random_crop": false, + "rank_dropout": 0, + "save_every_n_epochs": 1, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_precision": "bf16", + "scale_v_pred_loss_like_noise_pred": false, + "scale_weight_norms": 0, + "sdxl": true, + "sdxl_cache_text_encoder_outputs": false, + "sdxl_no_half_vae": true, + "seed": "1337", + "shuffle_caption": true, + "stop_text_encoder_training": 0, + "text_encoder_lr": 1.0, + "train_batch_size": 6, + "train_on_input": true, + "training_comment": "", + "unet_lr": 1.0, + "unit": 1, + "up_lr_weight": "", + "use_cp": false, + "use_wandb": true, + "v2": false, + "v_parameterization": false, + "vae_batch_size": 0, + "weighted_captions": false, + "xformers": true +} \ No newline at end of file diff --git a/presets/lora/SDXL - LoRA kudou-reira prodigy v4.0.json b/presets/lora/SDXL - LoRA kudou-reira prodigy v4.0.json new file mode 100644 index 000000000..755743728 --- /dev/null +++ b/presets/lora/SDXL - LoRA kudou-reira prodigy v4.0.json @@ -0,0 +1,94 @@ +{ + "LoRA_type": "Standard", + "adaptive_noise_scale": 0, + "additional_parameters": "--lr_scheduler_type \"CosineAnnealingLR\" --lr_scheduler_args \"T_max=30\" --network_train_unet_only", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "cache_latents": true, + "cache_latents_to_disk": true, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0, + "caption_extension": ".txt", + "clip_skip": "1", + "color_aug": false, + "conv_alpha": 1, + "conv_block_alphas": "", + "conv_block_dims": "", + "conv_dim": 1, + "decompose_both": false, + "dim_from_weights": false, + "down_lr_weight": "", + "enable_bucket": true, + "epoch": 30, + "factor": -1, + "flip_aug": false, + "full_bf16": false, + "full_fp16": false, + "gradient_accumulation_steps": 1.0, + "gradient_checkpointing": true, + "keep_tokens": 2, + "learning_rate": 1.0, + "lora_network_weights": "", + "lr_scheduler": "cosine_with_restarts", + "lr_scheduler_num_cycles": "", + "lr_scheduler_power": "", + "lr_warmup": 0, + "max_bucket_reso": 2048, + "max_data_loader_n_workers": "0", + "max_resolution": "1024,1024", + "max_timestep": 1000, + "max_token_length": "225", + "max_train_epochs": "", + "mem_eff_attn": false, + "mid_lr_weight": "", + "min_bucket_reso": 256, + "min_snr_gamma": 5, + "min_timestep": 0, + "mixed_precision": "bf16", + "module_dropout": 0, + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "network_alpha": 256, + "network_dim": 256, + "network_dropout": 0, + "no_token_padding": false, + "noise_offset": 0.0357, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "optimizer": "Prodigy", + "optimizer_args": "decouple=True weight_decay=0.45 d_coef=2 use_bias_correction=True safeguard_warmup=True", + "persistent_data_loader_workers": false, + "prior_loss_weight": 1.0, + "random_crop": false, + "rank_dropout": 0, + "save_every_n_epochs": 1, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_precision": "bf16", + "scale_v_pred_loss_like_noise_pred": false, + "scale_weight_norms": 0, + "sdxl": true, + "sdxl_cache_text_encoder_outputs": false, + "sdxl_no_half_vae": true, + "seed": "1337", + "shuffle_caption": true, + "stop_text_encoder_training_pct": 0, + "text_encoder_lr": 1.0, + "train_batch_size": 6, + "train_on_input": true, + "training_comment": "", + "unet_lr": 1.0, + "unit": 1, + "up_lr_weight": "", + "use_cp": false, + "use_wandb": true, + "v2": false, + "v_parameterization": false, + "vae_batch_size": 0, + "weighted_captions": false, + "xformers": true +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 7b9b71870..9ffc17fb3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ -accelerate==0.19.0 +accelerate==0.23.0 +diffusers[torch]==0.21.2 # albumentations==1.3.0 aiofiles==23.2.1 altair==4.2.2 @@ -14,6 +15,12 @@ huggingface-hub==0.15.1 invisible-watermark==0.2.0 lion-pytorch==0.0.6 lycoris_lora==1.8.3 +# for BLIP captioning +# requests==2.28.2 +# timm==0.6.12 +# fairscale==0.4.13 +# for WD14 captioning +# tensorflow==2.10.1 # open clip for SDXL open-clip-torch==2.20.0 opencv-python==4.7.0.68 @@ -26,6 +33,6 @@ tk==0.1.0 toml==0.10.2 transformers==4.30.2 voluptuous==0.13.1 -wandb==0.15.0 +wandb==0.15.11 # for kohya_ss library -e . # no_verify leave this to specify not checking this a verification stage diff --git a/sdxl_gen_img.py b/sdxl_gen_img.py index c506ad3fc..ac01b76e0 100755 --- a/sdxl_gen_img.py +++ b/sdxl_gen_img.py @@ -17,6 +17,13 @@ import diffusers import numpy as np import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass import torchvision from diffusers import ( AutoencoderKL, @@ -37,7 +44,7 @@ from einops import rearrange from tqdm import tqdm from torchvision import transforms -from transformers import CLIPTextModel, CLIPTokenizer, CLIPModel, CLIPTextConfig +from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPImageProcessor import PIL from PIL import Image from PIL.PngImagePlugin import PngInfo @@ -61,6 +68,8 @@ LATENT_CHANNELS = 4 DOWNSAMPLING_FACTOR = 8 +CLIP_VISION_MODEL = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" + # region モジュール入れ替え部 """ 高速化のためのモジュール入れ替え @@ -320,6 +329,10 @@ def __init__( self.scheduler = scheduler self.safety_checker = None + self.clip_vision_model: CLIPVisionModelWithProjection = None + self.clip_vision_processor: CLIPImageProcessor = None + self.clip_vision_strength = 0.0 + # Textual Inversion self.token_replacements_list = [] for _ in range(len(self.text_encoders)): @@ -535,6 +548,21 @@ def __call__( num_sub_prompts = len(text_pool) // batch_size text_pool = text_pool[num_sub_prompts - 1 :: num_sub_prompts] # last subprompt + if init_image is not None and self.clip_vision_model is not None: + print(f"encode by clip_vision_model and apply clip_vision_strength={self.clip_vision_strength}") + vision_input = self.clip_vision_processor(init_image, return_tensors="pt", device=self.device) + pixel_values = vision_input["pixel_values"].to(self.device, dtype=text_embeddings.dtype) + + clip_vision_embeddings = self.clip_vision_model(pixel_values=pixel_values, output_hidden_states=True, return_dict=True) + clip_vision_embeddings = clip_vision_embeddings.image_embeds + + if len(clip_vision_embeddings) == 1 and batch_size > 1: + clip_vision_embeddings = clip_vision_embeddings.repeat((batch_size, 1)) + + clip_vision_embeddings = clip_vision_embeddings * self.clip_vision_strength + assert clip_vision_embeddings.shape == text_pool.shape, f"{clip_vision_embeddings.shape} != {text_pool.shape}" + text_pool = clip_vision_embeddings # replace: same as ComfyUI (?) + c_vector = torch.cat([text_pool, c_vector], dim=1) uc_vector = torch.cat([uncond_pool, uc_vector], dim=1) @@ -745,7 +773,7 @@ def __call__( return None if return_latents: - return (latents, False) + return latents latents = 1 / sdxl_model_util.VAE_SCALE_FACTOR * latents if vae_batch_size >= batch_size: @@ -1767,6 +1795,19 @@ def resize_images(imgs, size): init_images = load_images(args.image_path) assert len(init_images) > 0, f"No image / 画像がありません: {args.image_path}" print(f"loaded {len(init_images)} images for img2img") + + # CLIP Vision + if args.clip_vision_strength is not None: + print(f"load CLIP Vision model: {CLIP_VISION_MODEL}") + vision_model = CLIPVisionModelWithProjection.from_pretrained(CLIP_VISION_MODEL, projection_dim=1280) + vision_model.to(device, dtype) + processor = CLIPImageProcessor.from_pretrained(CLIP_VISION_MODEL) + + pipe.clip_vision_model = vision_model + pipe.clip_vision_processor = processor + pipe.clip_vision_strength = args.clip_vision_strength + print(f"CLIP Vision model loaded.") + else: init_images = None @@ -1780,7 +1821,7 @@ def resize_images(imgs, size): # promptがないとき、画像のPngInfoから取得する if init_images is not None and len(prompt_list) == 0 and not args.interactive: - print("get prompts from images' meta data") + print("get prompts from images' metadata") for img in init_images: if "prompt" in img.text: prompt = img.text["prompt"] @@ -2656,6 +2697,12 @@ def setup_parser() -> argparse.ArgumentParser: nargs="*", help="ControlNet guidance ratio for steps / ControlNetでガイドするステップ比率", ) + parser.add_argument( + "--clip_vision_strength", + type=float, + default=None, + help="enable CLIP Vision Conditioning for img2img with this strength / img2imgでCLIP Vision Conditioningを有効にしてこのstrengthで処理する", + ) # # parser.add_argument( # "--control_net_image_path", type=str, default=None, nargs="*", help="image for ControlNet guidance / ControlNetでガイドに使う画像" # ) diff --git a/sdxl_minimal_inference.py b/sdxl_minimal_inference.py index 5c8a0bd89..ff865629e 100644 --- a/sdxl_minimal_inference.py +++ b/sdxl_minimal_inference.py @@ -9,6 +9,13 @@ from einops import repeat import numpy as np import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from tqdm import tqdm from transformers import CLIPTokenizer from diffusers import EulerDiscreteScheduler diff --git a/sdxl_train.py b/sdxl_train.py index 195467b00..6b255d679 100644 --- a/sdxl_train.py +++ b/sdxl_train.py @@ -10,6 +10,13 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from accelerate.utils import set_seed from diffusers import DDPMScheduler from library import sdxl_model_util diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py index 09cf16438..61ebfb581 100644 --- a/sdxl_train_control_net_lllite.py +++ b/sdxl_train_control_net_lllite.py @@ -1,3 +1,6 @@ +# cond_imageをU-Netのforwardで渡すバージョンのControlNet-LLLite検証用学習コード +# training code for ControlNet-LLLite with passing cond_image to U-Net's forward + import argparse import gc import json @@ -11,8 +14,16 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from torch.nn.parallel import DistributedDataParallel as DDP from accelerate.utils import set_seed +import accelerate from diffusers import DDPMScheduler, ControlNetModel from safetensors.torch import load_file from library import sai_model_spec, sdxl_model_util, sdxl_original_unet, sdxl_train_util @@ -34,7 +45,7 @@ apply_noise_offset, scale_v_prediction_loss_like_noise_prediction, ) -import networks.control_net_lllite as control_net_lllite +import networks.control_net_lllite_for_train as control_net_lllite_for_train # TODO 他のスクリプトと共通化する @@ -141,9 +152,6 @@ def train(args): ckpt_info, ) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype) - # モデルに xformers とか memory efficient attention を組み込む - train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa) - # 学習を準備する if cache_latents: vae.to(accelerator.device, dtype=vae_dtype) @@ -177,22 +185,53 @@ def train(args): ) accelerator.wait_for_everyone() - # prepare ControlNet - network = control_net_lllite.ControlNetLLLite(unet, args.cond_emb_dim, args.network_dim, args.network_dropout) - network.apply_to() + # prepare ControlNet-LLLite + control_net_lllite_for_train.replace_unet_linear_and_conv2d() if args.network_weights is not None: - info = network.load_weights(args.network_weights) - accelerator.print(f"load ControlNet weights from {args.network_weights}: {info}") + accelerator.print(f"initialize U-Net with ControlNet-LLLite") + with accelerate.init_empty_weights(): + unet_lllite = control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite() + unet_lllite.to(accelerator.device, dtype=weight_dtype) + + unet_sd = unet.state_dict() + info = unet_lllite.load_lllite_weights(args.network_weights, unet_sd) + accelerator.print(f"load ControlNet-LLLite weights from {args.network_weights}: {info}") + else: + # cosumes large memory, so send to GPU before creating the LLLite model + accelerator.print("sending U-Net to GPU") + unet.to(accelerator.device, dtype=weight_dtype) + unet_sd = unet.state_dict() + + # init LLLite weights + accelerator.print(f"initialize U-Net with ControlNet-LLLite") + + if args.lowram: + with accelerate.init_on_device(accelerator.device): + unet_lllite = control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite() + else: + unet_lllite = control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite() + unet_lllite.to(weight_dtype) + + info = unet_lllite.load_lllite_weights(None, unet_sd) + accelerator.print(f"init U-Net with ControlNet-LLLite weights: {info}") + del unet_sd, unet + + unet: control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite = unet_lllite + del unet_lllite + + unet.apply_lllite(args.cond_emb_dim, args.network_dim, args.network_dropout) + + # モデルに xformers とか memory efficient attention を組み込む + train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() - network.enable_gradient_checkpointing() # may have no effect # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") - trainable_params = list(network.prepare_optimizer_params()) + trainable_params = list(unet.prepare_params()) print(f"trainable params count: {len(trainable_params)}") print(f"number of trainable parameters: {sum(p.numel() for p in trainable_params if p.requires_grad)}") @@ -225,37 +264,32 @@ def train(args): lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする - if args.full_fp16: - assert ( - args.mixed_precision == "fp16" - ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" - accelerator.print("enable full fp16 training.") - unet.to(weight_dtype) - network.to(weight_dtype) - elif args.full_bf16: - assert ( - args.mixed_precision == "bf16" - ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" - accelerator.print("enable full bf16 training.") - unet.to(weight_dtype) - network.to(weight_dtype) + # if args.full_fp16: + # assert ( + # args.mixed_precision == "fp16" + # ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + # accelerator.print("enable full fp16 training.") + # unet.to(weight_dtype) + # elif args.full_bf16: + # assert ( + # args.mixed_precision == "bf16" + # ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" + # accelerator.print("enable full bf16 training.") + # unet.to(weight_dtype) + + unet.to(weight_dtype) # acceleratorがなんかよろしくやってくれるらしい - unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, network, optimizer, train_dataloader, lr_scheduler - ) - network: control_net_lllite.ControlNetLLLite + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) # transform DDP after prepare (train_network here only) - unet, network = train_util.transform_models_if_DDP([unet, network]) + unet = train_util.transform_models_if_DDP([unet])[0] 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: unet.eval() - network.prepare_grad_etc() - # TextEncoderの出力をキャッシュするときにはCPUへ移動する if args.cache_text_encoder_outputs: # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16 @@ -321,7 +355,13 @@ def train(args): del train_dataset_group # function for saving/removing - def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False): + def save_model( + ckpt_name, + unwrapped_nw: control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite, + steps, + epoch_no, + force_sync_upload=False, + ): os.makedirs(args.output_dir, exist_ok=True) ckpt_file = os.path.join(args.output_dir, ckpt_name) @@ -329,7 +369,7 @@ def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False sai_metadata = train_util.get_sai_model_spec(None, args, True, True, False) sai_metadata["modelspec.architecture"] = sai_model_spec.ARCH_SD_XL_V1_BASE + "/control-net-lllite" - unwrapped_nw.save_weights(ckpt_file, save_dtype, sai_metadata) + unwrapped_nw.save_lllite_weights(ckpt_file, save_dtype, sai_metadata) if args.huggingface_repo_id is not None: huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload) @@ -344,11 +384,9 @@ def remove_model(old_ckpt_name): accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") current_epoch.value = epoch + 1 - network.on_epoch_start() # train() - for step, batch in enumerate(train_dataloader): current_step.value = global_step - with accelerator.accumulate(network): + with accelerator.accumulate(unet): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device) @@ -405,10 +443,9 @@ def remove_model(old_ckpt_name): with accelerator.autocast(): # conditioning imageをControlNetに渡す / pass conditioning image to ControlNet # 内部でcond_embに変換される / it will be converted to cond_emb inside - network.set_cond_image(controlnet_image) # それらの値を使いつつ、U-Netでノイズを予測する / predict noise with U-Net using those values - noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) + noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding, controlnet_image) if args.v_parameterization: # v-parameterization training @@ -433,7 +470,7 @@ def remove_model(old_ckpt_name): accelerator.backward(loss) if accelerator.sync_gradients and args.max_grad_norm != 0.0: - params_to_clip = network.get_trainable_params() + params_to_clip = unet.get_trainable_params() accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() @@ -452,7 +489,7 @@ def remove_model(old_ckpt_name): accelerator.wait_for_everyone() if accelerator.is_main_process: ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step) - save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch) + save_model(ckpt_name, accelerator.unwrap_model(unet), global_step, epoch) if args.save_state: train_util.save_and_remove_state_stepwise(args, accelerator, global_step) @@ -491,7 +528,7 @@ def remove_model(old_ckpt_name): saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs if is_main_process and saving: ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1) - save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1) + save_model(ckpt_name, accelerator.unwrap_model(unet), global_step, epoch + 1) remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1) if remove_epoch_no is not None: @@ -506,7 +543,7 @@ def remove_model(old_ckpt_name): # end of epoch if is_main_process: - network = accelerator.unwrap_model(network) + unet = accelerator.unwrap_model(unet) accelerator.end_training() @@ -515,7 +552,7 @@ def remove_model(old_ckpt_name): if is_main_process: ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as) - save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True) + save_model(ckpt_name, unet, global_step, num_train_epochs, force_sync_upload=True) print("model saved.") diff --git a/sdxl_train_control_net_lllite_alt.py b/sdxl_train_control_net_lllite_old.py similarity index 87% rename from sdxl_train_control_net_lllite_alt.py rename to sdxl_train_control_net_lllite_old.py index 757194a10..f8169bdbf 100644 --- a/sdxl_train_control_net_lllite_alt.py +++ b/sdxl_train_control_net_lllite_old.py @@ -1,6 +1,3 @@ -# cond_imageをU-Netのforwardで渡すバージョンのControlNet-LLLite検証用学習コード -# training code for ControlNet-LLLite with passing cond_image to U-Net's forward - import argparse import gc import json @@ -14,9 +11,15 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from torch.nn.parallel import DistributedDataParallel as DDP from accelerate.utils import set_seed -import accelerate from diffusers import DDPMScheduler, ControlNetModel from safetensors.torch import load_file from library import sai_model_spec, sdxl_model_util, sdxl_original_unet, sdxl_train_util @@ -38,7 +41,7 @@ apply_noise_offset, scale_v_prediction_loss_like_noise_prediction, ) -import networks.control_net_lllite_for_train as control_net_lllite_for_train +import networks.control_net_lllite as control_net_lllite # TODO 他のスクリプトと共通化する @@ -145,6 +148,9 @@ def train(args): ckpt_info, ) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype) + # モデルに xformers とか memory efficient attention を組み込む + train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa) + # 学習を準備する if cache_latents: vae.to(accelerator.device, dtype=vae_dtype) @@ -178,53 +184,22 @@ def train(args): ) accelerator.wait_for_everyone() - # prepare ControlNet-LLLite - control_net_lllite_for_train.replace_unet_linear_and_conv2d() + # prepare ControlNet + network = control_net_lllite.ControlNetLLLite(unet, args.cond_emb_dim, args.network_dim, args.network_dropout) + network.apply_to() if args.network_weights is not None: - accelerator.print(f"initialize U-Net with ControlNet-LLLite") - with accelerate.init_empty_weights(): - unet_lllite = control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite() - unet_lllite.to(accelerator.device, dtype=weight_dtype) - - unet_sd = unet.state_dict() - info = unet_lllite.load_lllite_weights(args.network_weights, unet_sd) - accelerator.print(f"load ControlNet-LLLite weights from {args.network_weights}: {info}") - else: - # cosumes large memory, so send to GPU before creating the LLLite model - accelerator.print("sending U-Net to GPU") - unet.to(accelerator.device, dtype=weight_dtype) - unet_sd = unet.state_dict() - - # init LLLite weights - accelerator.print(f"initialize U-Net with ControlNet-LLLite") - - if args.lowram: - with accelerate.init_on_device(accelerator.device): - unet_lllite = control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite() - else: - unet_lllite = control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite() - unet_lllite.to(weight_dtype) - - info = unet_lllite.load_lllite_weights(None, unet_sd) - accelerator.print(f"init U-Net with ControlNet-LLLite weights: {info}") - del unet_sd, unet - - unet: control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite = unet_lllite - del unet_lllite - - unet.apply_lllite(args.cond_emb_dim, args.network_dim, args.network_dropout) - - # モデルに xformers とか memory efficient attention を組み込む - train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa) + info = network.load_weights(args.network_weights) + accelerator.print(f"load ControlNet weights from {args.network_weights}: {info}") if args.gradient_checkpointing: unet.enable_gradient_checkpointing() + network.enable_gradient_checkpointing() # may have no effect # 学習に必要なクラスを準備する accelerator.print("prepare optimizer, data loader etc.") - trainable_params = list(unet.prepare_params()) + trainable_params = list(network.prepare_optimizer_params()) print(f"trainable params count: {len(trainable_params)}") print(f"number of trainable parameters: {sum(p.numel() for p in trainable_params if p.requires_grad)}") @@ -257,32 +232,37 @@ def train(args): lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) # 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする - # if args.full_fp16: - # assert ( - # args.mixed_precision == "fp16" - # ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" - # accelerator.print("enable full fp16 training.") - # unet.to(weight_dtype) - # elif args.full_bf16: - # assert ( - # args.mixed_precision == "bf16" - # ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" - # accelerator.print("enable full bf16 training.") - # unet.to(weight_dtype) - - unet.to(weight_dtype) + if args.full_fp16: + assert ( + args.mixed_precision == "fp16" + ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + accelerator.print("enable full fp16 training.") + unet.to(weight_dtype) + network.to(weight_dtype) + elif args.full_bf16: + assert ( + args.mixed_precision == "bf16" + ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" + accelerator.print("enable full bf16 training.") + unet.to(weight_dtype) + network.to(weight_dtype) # 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) + unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, network, optimizer, train_dataloader, lr_scheduler + ) + network: control_net_lllite.ControlNetLLLite # transform DDP after prepare (train_network here only) - unet = train_util.transform_models_if_DDP([unet])[0] + unet, network = train_util.transform_models_if_DDP([unet, network]) 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: unet.eval() + network.prepare_grad_etc() + # TextEncoderの出力をキャッシュするときにはCPUへ移動する if args.cache_text_encoder_outputs: # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16 @@ -348,13 +328,7 @@ def train(args): del train_dataset_group # function for saving/removing - def save_model( - ckpt_name, - unwrapped_nw: control_net_lllite_for_train.SdxlUNet2DConditionModelControlNetLLLite, - steps, - epoch_no, - force_sync_upload=False, - ): + def save_model(ckpt_name, unwrapped_nw, steps, epoch_no, force_sync_upload=False): os.makedirs(args.output_dir, exist_ok=True) ckpt_file = os.path.join(args.output_dir, ckpt_name) @@ -362,7 +336,7 @@ def save_model( sai_metadata = train_util.get_sai_model_spec(None, args, True, True, False) sai_metadata["modelspec.architecture"] = sai_model_spec.ARCH_SD_XL_V1_BASE + "/control-net-lllite" - unwrapped_nw.save_lllite_weights(ckpt_file, save_dtype, sai_metadata) + unwrapped_nw.save_weights(ckpt_file, save_dtype, sai_metadata) if args.huggingface_repo_id is not None: huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload) @@ -377,9 +351,11 @@ def remove_model(old_ckpt_name): accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") current_epoch.value = epoch + 1 + network.on_epoch_start() # train() + for step, batch in enumerate(train_dataloader): current_step.value = global_step - with accelerator.accumulate(unet): + with accelerator.accumulate(network): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: latents = batch["latents"].to(accelerator.device) @@ -436,9 +412,10 @@ def remove_model(old_ckpt_name): with accelerator.autocast(): # conditioning imageをControlNetに渡す / pass conditioning image to ControlNet # 内部でcond_embに変換される / it will be converted to cond_emb inside + network.set_cond_image(controlnet_image) # それらの値を使いつつ、U-Netでノイズを予測する / predict noise with U-Net using those values - noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding, controlnet_image) + noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) if args.v_parameterization: # v-parameterization training @@ -463,7 +440,7 @@ def remove_model(old_ckpt_name): accelerator.backward(loss) if accelerator.sync_gradients and args.max_grad_norm != 0.0: - params_to_clip = unet.get_trainable_params() + params_to_clip = network.get_trainable_params() accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() @@ -482,7 +459,7 @@ def remove_model(old_ckpt_name): accelerator.wait_for_everyone() if accelerator.is_main_process: ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step) - save_model(ckpt_name, accelerator.unwrap_model(unet), global_step, epoch) + save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch) if args.save_state: train_util.save_and_remove_state_stepwise(args, accelerator, global_step) @@ -521,7 +498,7 @@ def remove_model(old_ckpt_name): saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs if is_main_process and saving: ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1) - save_model(ckpt_name, accelerator.unwrap_model(unet), global_step, epoch + 1) + save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1) remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1) if remove_epoch_no is not None: @@ -536,7 +513,7 @@ def remove_model(old_ckpt_name): # end of epoch if is_main_process: - unet = accelerator.unwrap_model(unet) + network = accelerator.unwrap_model(network) accelerator.end_training() @@ -545,7 +522,7 @@ def remove_model(old_ckpt_name): if is_main_process: ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as) - save_model(ckpt_name, unet, global_step, num_train_epochs, force_sync_upload=True) + save_model(ckpt_name, network, global_step, num_train_epochs, force_sync_upload=True) print("model saved.") diff --git a/sdxl_train_network.py b/sdxl_train_network.py index 8d3a81c3a..2de57c0ac 100644 --- a/sdxl_train_network.py +++ b/sdxl_train_network.py @@ -1,5 +1,12 @@ import argparse import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from library import sdxl_model_util, sdxl_train_util, train_util import train_network diff --git a/sdxl_train_textual_inversion.py b/sdxl_train_textual_inversion.py index 123ca35a1..f5cca17b2 100644 --- a/sdxl_train_textual_inversion.py +++ b/sdxl_train_textual_inversion.py @@ -3,6 +3,13 @@ import regex import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass import open_clip from library import sdxl_model_util, sdxl_train_util, train_util diff --git a/tools/merge_models.py b/tools/merge_models.py index dd04ea463..391bfe677 100644 --- a/tools/merge_models.py +++ b/tools/merge_models.py @@ -51,7 +51,7 @@ def merge(args): print(f"Model {model} does not exist") exit() - assert len(args.models) == len(args.ratios) or args.ratios is None, "ratios must be the same length as models" + assert args.ratios is None or len(args.models) == len(args.ratios), "ratios must be the same length as models" # load and merge ratio = 1.0 / len(args.models) # default @@ -113,13 +113,13 @@ def merge(args): # add supplementary keys' value (including VAE and TextEncoder) if len(supplementary_key_ratios) > 0: print("add first model's value") - with safe_open(model, framework="pt", device=args.device) as f: + with safe_open(args.models[0], framework="pt", device=args.device) as f: for key in tqdm(f.keys()): _, new_key = replace_text_encoder_key(key) if new_key not in supplementary_key_ratios: continue - if is_unet_key(new_key): # not VAE or TextEncoder + if is_unet_key(new_key): # not VAE or TextEncoder print(f"Key {new_key} not in all models, ratio = {supplementary_key_ratios[new_key]}") value = f.get_tensor(key) # original key diff --git a/tools/prepare_presets.py b/tools/prepare_presets.py index 8c00af457..a94b87644 100644 --- a/tools/prepare_presets.py +++ b/tools/prepare_presets.py @@ -3,7 +3,7 @@ import glob def remove_items_with_keywords(json_file_path): - keywords = ["pretrained_model_name_or_path", "dir", "save_model_as", "save_state", "resume", "output_name", "model_list", "sample_", "wandb_api_key"] + keywords = ["pretrained_model_name_or_path", "train_dir", "output_dir", "logging_dir", "image_folder", "dir", "caption_metadata_filename", "latent_metadata_filename", "save_model_as", "save_state", "resume", "output_name", "model_list", "sample_", "wandb_api_key"] with open(json_file_path) as file: data = json.load(file) diff --git a/train_controlnet.py b/train_controlnet.py index 988304f62..42da44125 100644 --- a/train_controlnet.py +++ b/train_controlnet.py @@ -11,6 +11,13 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from torch.nn.parallel import DistributedDataParallel as DDP from accelerate.utils import set_seed from diffusers import DDPMScheduler, ControlNetModel diff --git a/train_db.py b/train_db.py index 6dde7e9bf..feb147787 100644 --- a/train_db.py +++ b/train_db.py @@ -11,6 +11,13 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from accelerate.utils import set_seed from diffusers import DDPMScheduler diff --git a/train_network.py b/train_network.py index f752607e9..1a1713259 100644 --- a/train_network.py +++ b/train_network.py @@ -12,6 +12,16 @@ from tqdm import tqdm import torch + +try: + import intel_extension_for_pytorch as ipex + + if torch.xpu.is_available(): + from library.ipex import ipex_init + + ipex_init() +except Exception: + pass from accelerate.utils import set_seed from diffusers import DDPMScheduler from library import model_util @@ -419,7 +429,12 @@ def train(self, args): t_enc.train() # set top parameter requires_grad = True for gradient checkpointing works - t_enc.text_model.embeddings.requires_grad_(True) + if train_text_encoder: + t_enc.text_model.embeddings.requires_grad_(True) + + # set top parameter requires_grad = True for gradient checkpointing works + if not train_text_encoder: # train U-Net only + unet.parameters().__next__().requires_grad_(True) else: unet.eval() for t_enc in text_encoders: @@ -509,6 +524,7 @@ def train(self, args): "ss_prior_loss_weight": args.prior_loss_weight, "ss_min_snr_gamma": args.min_snr_gamma, "ss_scale_weight_norms": args.scale_weight_norms, + "ss_ip_noise_gamma": args.ip_noise_gamma, } if use_user_config: diff --git a/train_textual_inversion.py b/train_textual_inversion.py index b65d524cf..1c7b7fcb2 100644 --- a/train_textual_inversion.py +++ b/train_textual_inversion.py @@ -7,6 +7,13 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from accelerate.utils import set_seed from diffusers import DDPMScheduler from transformers import CLIPTokenizer diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py index 79c64cbeb..2c5673be1 100644 --- a/train_textual_inversion_XTI.py +++ b/train_textual_inversion_XTI.py @@ -8,6 +8,13 @@ from tqdm import tqdm import torch +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + from library.ipex import ipex_init + ipex_init() +except Exception: + pass from accelerate.utils import set_seed import diffusers from diffusers import DDPMScheduler