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.github/dependabot.yml
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.github/dependabot.yml
vendored
@@ -1,7 +0,0 @@
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---
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version: 2
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updates:
|
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- package-ecosystem: "github-actions"
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directory: "/"
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schedule:
|
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interval: "monthly"
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4
.github/workflows/typos.yml
vendored
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.github/workflows/typos.yml
vendored
@@ -15,7 +15,7 @@ jobs:
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runs-on: ubuntu-latest
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|
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v3
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|
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- name: typos-action
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uses: crate-ci/typos@v1.16.26
|
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uses: crate-ci/typos@v1.13.10
|
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|
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83
README-ja.md
83
README-ja.md
@@ -1,7 +1,3 @@
|
||||
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。
|
||||
|
||||
@@ -13,12 +9,13 @@ 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)
|
||||
@@ -44,13 +41,11 @@ PowerShellを使う場合、venvを使えるようにするためには以下の
|
||||
|
||||
## Windows環境でのインストール
|
||||
|
||||
スクリプトはPyTorch 2.0.1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。
|
||||
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。
|
||||
|
||||
（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。）
|
||||
|
||||
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。
|
||||
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
|
||||
@@ -59,14 +54,43 @@ cd sd-scripts
|
||||
python -m venv venv
|
||||
.\venv\Scripts\activate
|
||||
|
||||
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
|
||||
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 xformers==0.0.20
|
||||
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
|
||||
|
||||
accelerate config
|
||||
```
|
||||
|
||||
コマンドプロンプトでも同一です。
|
||||
<!--
|
||||
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
|
||||
pip install --use-pep517 --upgrade -r requirements.txt
|
||||
pip install -U -I --no-deps xformers==0.0.16
|
||||
-->
|
||||
|
||||
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
|
||||
```
|
||||
|
||||
（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。）
|
||||
|
||||
@@ -87,41 +111,30 @@ 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。）
|
||||
|
||||
### オプション：`bitsandbytes`（8bit optimizer）を使う
|
||||
### PyTorchとxformersのバージョンについて
|
||||
|
||||
`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）。
|
||||
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。
|
||||
|
||||
Windowsでは0.35.0または0.41.1を推奨します。
|
||||
### オプション：Lion8bitを使う
|
||||
|
||||
- `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。
|
||||
|
||||
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
|
||||
|
||||
以下の手順に従い、`bitsandbytes`をインストールしてください。
|
||||
|
||||
### 0.35.0を使う場合
|
||||
|
||||
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。
|
||||
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。
|
||||
|
||||
```powershell
|
||||
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
|
||||
pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl
|
||||
```
|
||||
|
||||
### 0.41.1を使う場合
|
||||
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。
|
||||
|
||||
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。
|
||||
### オプション：PagedAdamW8bitとPagedLion8bitを使う
|
||||
|
||||
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
|
||||
python -m pip install bitsandbytes==0.41.1 --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
|
||||
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
|
||||
```
|
||||
|
||||
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。
|
||||
|
||||
## アップグレード
|
||||
|
||||
新しいリリースがあった場合、以下のコマンドで更新できます。
|
||||
|
||||
514
README.md
514
README.md
@@ -1,11 +1,9 @@
|
||||
__SDXL is now supported. The sdxl branch has been merged into the main branch. If you update the repository, please follow the upgrade instructions. Also, the version of accelerate has been updated, so please run accelerate config again.__ The documentation for SDXL training is [here](./README.md#sdxl-training).
|
||||
|
||||
This repository contains training, generation and utility scripts for Stable Diffusion.
|
||||
|
||||
[__Change History__](#change-history) is moved to the bottom of the page.
|
||||
[__Change History__](#change-history) is moved to the bottom of the page.
|
||||
更新履歴は[ページ末尾](#change-history)に移しました。
|
||||
|
||||
[日本語版READMEはこちら](./README-ja.md)
|
||||
[日本語版README](./README-ja.md)
|
||||
|
||||
For easier use (GUI and PowerShell scripts etc...), please visit [the repository maintained by bmaltais](https://github.com/bmaltais/kohya_ss). Thanks to @bmaltais!
|
||||
|
||||
@@ -18,13 +16,135 @@ This repository contains the scripts for:
|
||||
* Image generation
|
||||
* Model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)
|
||||
|
||||
__Stable Diffusion web UI now seems to support LoRA trained by ``sd-scripts``.__ Thank you for great work!!!
|
||||
|
||||
## About SDXL training
|
||||
|
||||
The feature of SDXL training is now available in sdxl branch as an experimental feature.
|
||||
|
||||
Sep 3, 2023: The feature will be merged into the main branch soon. Following are the changes from the previous version.
|
||||
|
||||
- ControlNet-LLLite is added. See [documentation](./docs/train_lllite_README.md) for details.
|
||||
- JPEG XL is supported. [#786](https://github.com/kohya-ss/sd-scripts/pull/786)
|
||||
- Peak memory usage is reduced. [#791](https://github.com/kohya-ss/sd-scripts/pull/791)
|
||||
- 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`.
|
||||
- Other minor changes.
|
||||
- Thanks for contributions from Isotr0py, vvern999, lansing and others!
|
||||
|
||||
Aug 13, 2023:
|
||||
|
||||
- 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.
|
||||
|
||||
Aug 12, 2023:
|
||||
|
||||
- The default value of noise offset when omitted has been changed to 0 from 0.0357.
|
||||
- The different learning rates for each U-Net block are now supported. 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`.
|
||||
|
||||
Aug 6, 2023:
|
||||
|
||||
- [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.
|
||||
- SDXL LoRA has `sdxl_base_v1-0` now for `ss_base_model_version` metadata item, instead of `v0-9`.
|
||||
|
||||
Aug 4, 2023:
|
||||
|
||||
- `bitsandbytes` is now optional. Please install it if you want to use it. The insructions are in the later section.
|
||||
- `albumentations` is not required anymore.
|
||||
- An issue for pooled output for Textual Inversion training is fixed.
|
||||
- `--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"`.
|
||||
- `sdxl_gen_imgs.py` supports batch size > 1.
|
||||
- Fix ControlNet to work with attention couple and reginal LoRA in `gen_img_diffusers.py`.
|
||||
|
||||
Summary of the feature:
|
||||
|
||||
- `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_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.
|
||||
- However, bitsandbytes==0.35 doesn't seem to support this. Please use a newer version of bitsandbytes or another optimizer.
|
||||
- I cannot find bitsandbytes>0.35.0 that works correctly on Windows.
|
||||
- In addition, the full bfloat16 training might be unstable. Please use it at your own risk.
|
||||
- `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.
|
||||
- The image generation during training is now available. `--no_half_vae` option also works to avoid black images.
|
||||
|
||||
- `--weighted_captions` option is not supported yet for both scripts.
|
||||
- `--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.
|
||||
|
||||
- `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.
|
||||
- `token_string` must be alphabet only currently, due to the limitation of the open-clip tokenizer.
|
||||
- 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.
|
||||
|
||||
- `sdxl_gen_img.py` is added. This script can be used to generate images with SDXL, including LoRA. See the help message for the usage.
|
||||
- Textual Inversion is supported, but the name for the embeds in the caption becomes alphabet only. For example, `neg_hand_v1.safetensors` can be activated with `neghandv`.
|
||||
|
||||
`requirements.txt` is updated to support SDXL training.
|
||||
|
||||
### 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 (-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
|
||||
|
||||
```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)
|
||||
```
|
||||
|
||||
## About requirements.txt
|
||||
|
||||
These files do not contain requirements for PyTorch. Because the versions of them depend on your environment. Please install PyTorch at first (see installation guide below.)
|
||||
|
||||
The scripts are tested with Pytorch 2.0.1. 1.12.1 is not tested but should work.
|
||||
The scripts are tested with PyTorch 1.12.1 and 2.0.1, Diffusers 0.18.2.
|
||||
|
||||
## Links to usage documentation
|
||||
## Links to how-to-use documents
|
||||
|
||||
Most of the documents are written in Japanese.
|
||||
|
||||
@@ -64,9 +184,9 @@ cd sd-scripts
|
||||
python -m venv venv
|
||||
.\venv\Scripts\activate
|
||||
|
||||
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
|
||||
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 xformers==0.0.20
|
||||
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
|
||||
|
||||
accelerate config
|
||||
```
|
||||
@@ -95,6 +215,31 @@ note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is o
|
||||
|
||||
(Single GPU with id `0` will be used.)
|
||||
|
||||
### Experimental: Use PyTorch 2.0
|
||||
|
||||
In this case, you need to install PyTorch 2.0 and xformers 0.0.20. Instead of the above, please type the following:
|
||||
|
||||
```powershell
|
||||
git clone https://github.com/kohya-ss/sd-scripts.git
|
||||
cd sd-scripts
|
||||
|
||||
python -m venv venv
|
||||
.\venv\Scripts\activate
|
||||
|
||||
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 xformers==0.0.20
|
||||
|
||||
accelerate config
|
||||
```
|
||||
|
||||
Answers to accelerate config should be the same as above.
|
||||
|
||||
### about PyTorch and xformers
|
||||
|
||||
Other versions of PyTorch and xformers seem to have problems with training.
|
||||
If there is no other reason, please install the specified version.
|
||||
|
||||
### Optional: Use `bitsandbytes` (8bit optimizer)
|
||||
|
||||
For 8bit optimizer, you need to install `bitsandbytes`. For Linux, please install `bitsandbytes` as usual (0.41.1 or later is recommended.)
|
||||
@@ -161,187 +306,214 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
|
||||
|
||||
[BLIP](https://github.com/salesforce/BLIP): BSD-3-Clause
|
||||
|
||||
|
||||
## 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
|
||||
|
||||
### Dec 24, 2023 / 2023/12/24
|
||||
### 15 Jun. 2023, 2023/06/15
|
||||
|
||||
- Fixed to work `tools/convert_diffusers20_original_sd.py`. Thanks to Disty0! PR [#1016](https://github.com/kohya-ss/sd-scripts/pull/1016)
|
||||
- Prodigy optimizer is supported in each training script. It is a member of D-Adaptation and is effective for DyLoRA training. [PR #585](https://github.com/kohya-ss/sd-scripts/pull/585) Please see the PR for details. Thanks to sdbds!
|
||||
- Install the package with `pip install prodigyopt`. Then specify the option like `--optimizer_type="prodigy"`.
|
||||
- Arbitrary Dataset is supported in each training script (except XTI). You can use it by defining a Dataset class that returns images and captions.
|
||||
- Prepare a Python script and define a class that inherits `train_util.MinimalDataset`. Then specify the option like `--dataset_class package.module.DatasetClass` in each training script.
|
||||
- Please refer to `MinimalDataset` for implementation. I will prepare a sample later.
|
||||
- The following features have been added to the generation script.
|
||||
- Added an option `--highres_fix_disable_control_net` to disable ControlNet in the 2nd stage of Highres. Fix. Please try it if the image is disturbed by some ControlNet such as Canny.
|
||||
- Added Variants similar to sd-dynamic-propmpts in the prompt.
|
||||
- If you specify `{spring|summer|autumn|winter}`, one of them will be randomly selected.
|
||||
- If you specify `{2$$chocolate|vanilla|strawberry}`, two of them will be randomly selected.
|
||||
- If you specify `{1-2$$ and $$chocolate|vanilla|strawberry}`, one or two of them will be randomly selected and connected by ` and `.
|
||||
- You can specify the number of candidates in the range `0-2`. You cannot omit one side like `-2` or `1-`.
|
||||
- It can also be specified for the prompt option.
|
||||
- If you specify `e` or `E`, all candidates will be selected and the prompt will be repeated multiple times (`--images_per_prompt` is ignored). It may be useful for creating X/Y plots.
|
||||
- You can also specify `--am {e$$0.2|0.4|0.6|0.8|1.0},{e$$0.4|0.7|1.0} --d 1234`. In this case, 15 prompts will be generated with 5*3.
|
||||
- There is no weighting function.
|
||||
|
||||
- `tools/convert_diffusers20_original_sd.py` が動かなくなっていたのが修正されました。Disty0 氏に感謝します。 PR [#1016](https://github.com/kohya-ss/sd-scripts/pull/1016)
|
||||
- 各学習スクリプトでProdigyオプティマイザがサポートされました。D-Adaptationの仲間でDyLoRAの学習に有効とのことです。 [PR #585](https://github.com/kohya-ss/sd-scripts/pull/585) 詳細はPRをご覧ください。sdbds氏に感謝します。
|
||||
- `pip install prodigyopt` としてパッケージをインストールしてください。また `--optimizer_type="prodigy"` のようにオプションを指定します。
|
||||
- 各学習スクリプトで任意のDatasetをサポートしました（XTIを除く）。画像とキャプションを返すDatasetクラスを定義することで、学習スクリプトから利用できます。
|
||||
- Pythonスクリプトを用意し、`train_util.MinimalDataset`を継承するクラスを定義してください。そして各学習スクリプトのオプションで `--dataset_class package.module.DatasetClass` のように指定してください。
|
||||
- 実装方法は `MinimalDataset` を参考にしてください。のちほどサンプルを用意します。
|
||||
- 生成スクリプトに以下の機能追加を行いました。
|
||||
- Highres. Fixの2nd stageでControlNetを無効化するオプション `--highres_fix_disable_control_net` を追加しました。Canny等一部のControlNetで画像が乱れる場合にお試しください。
|
||||
- プロンプトでsd-dynamic-propmptsに似たVariantをサポートしました。
|
||||
- `{spring|summer|autumn|winter}` のように指定すると、いずれかがランダムに選択されます。
|
||||
- `{2$$chocolate|vanilla|strawberry}` のように指定すると、いずれか2個がランダムに選択されます。
|
||||
- `{1-2$$ and $$chocolate|vanilla|strawberry}` のように指定すると、1個か2個がランダムに選択され ` and ` で接続されます。
|
||||
- 個数のレンジ指定では`0-2`のように0個も指定可能です。`-2`や`1-`のような片側の省略はできません。
|
||||
- プロンプトオプションに対しても指定可能です。
|
||||
- `{e$$chocolate|vanilla|strawberry}` のように`e`または`E`を指定すると、すべての候補が選択されプロンプトが複数回繰り返されます（`--images_per_prompt`は無視されます）。X/Y plotの作成に便利かもしれません。
|
||||
- `--am {e$$0.2|0.4|0.6|0.8|1.0},{e$$0.4|0.7|1.0} --d 1234`のような指定も可能です。この場合、5*3で15回のプロンプトが生成されます。
|
||||
- Weightingの機能はありません。
|
||||
|
||||
### 8 Jun. 2023, 2023/06/08
|
||||
|
||||
### Dec 21, 2023 / 2023/12/21
|
||||
- Fixed a bug where clip skip did not work when training with weighted captions (`--weighted_captions` specified) and when generating sample images during training.
|
||||
- 重みづけキャプションでの学習時（`--weighted_captions`指定時）および学習中のサンプル画像生成時にclip skipが機能しない不具合を修正しました。
|
||||
|
||||
- The issues in multi-GPU training are fixed. Thanks to Isotr0py! PR [#989](https://github.com/kohya-ss/sd-scripts/pull/989) and [#1000](https://github.com/kohya-ss/sd-scripts/pull/1000)
|
||||
- `--ddp_gradient_as_bucket_view` and `--ddp_bucket_view`options are added to `sdxl_train.py`. Please specify these options for multi-GPU training.
|
||||
- IPEX support is updated. Thanks to Disty0!
|
||||
- Fixed the bug that the size of the bucket becomes less than `min_bucket_reso`. Thanks to Cauldrath! PR [#1008](https://github.com/kohya-ss/sd-scripts/pull/1008)
|
||||
- `--sample_at_first` option is added to each training script. This option is useful to generate images at the first step, before training. Thanks to shirayu! PR [#907](https://github.com/kohya-ss/sd-scripts/pull/907)
|
||||
- `--ss` option is added to the sampling prompt in training. You can specify the scheduler for the sampling like `--ss euler_a`. Thanks to shirayu! PR [#906](https://github.com/kohya-ss/sd-scripts/pull/906)
|
||||
- `keep_tokens_separator` is added to the dataset config. This option is useful to keep (prevent from shuffling) the tokens in the captions. See [#975](https://github.com/kohya-ss/sd-scripts/pull/975) for details. Thanks to Linaqruf!
|
||||
- You can specify the separator with an option like `--keep_tokens_separator "|||"` or with `keep_tokens_separator: "|||"` in `.toml`. The tokens before `|||` are not shuffled.
|
||||
- Attention processor hook is added. See [#961](https://github.com/kohya-ss/sd-scripts/pull/961) for details. Thanks to rockerBOO!
|
||||
- The optimizer `PagedAdamW` is added. Thanks to xzuyn! PR [#955](https://github.com/kohya-ss/sd-scripts/pull/955)
|
||||
- NaN replacement in SDXL VAE is sped up. Thanks to liubo0902! PR [#1009](https://github.com/kohya-ss/sd-scripts/pull/1009)
|
||||
- Fixed the path error in `finetune/make_captions.py`. Thanks to CjangCjengh! PR [#986](https://github.com/kohya-ss/sd-scripts/pull/986)
|
||||
### 6 Jun. 2023, 2023/06/06
|
||||
|
||||
- マルチGPUでの学習の不具合を修正しました。Isotr0py 氏に感謝します。 PR [#989](https://github.com/kohya-ss/sd-scripts/pull/989) および [#1000](https://github.com/kohya-ss/sd-scripts/pull/1000)
|
||||
- `sdxl_train.py` に `--ddp_gradient_as_bucket_view` と `--ddp_bucket_view` オプションが追加されました。マルチGPUでの学習時にはこれらのオプションを指定してください。
|
||||
- IPEX サポートが更新されました。Disty0 氏に感謝します。
|
||||
- Aspect Ratio Bucketing で bucket のサイズが `min_bucket_reso` 未満になる不具合を修正しました。Cauldrath 氏に感謝します。 PR [#1008](https://github.com/kohya-ss/sd-scripts/pull/1008)
|
||||
- 各学習スクリプトに `--sample_at_first` オプションが追加されました。学習前に画像を生成することで、学習結果が比較しやすくなります。shirayu 氏に感謝します。 PR [#907](https://github.com/kohya-ss/sd-scripts/pull/907)
|
||||
- 学習時のプロンプトに `--ss` オプションが追加されました。`--ss euler_a` のようにスケジューラを指定できます。shirayu 氏に感謝します。 PR [#906](https://github.com/kohya-ss/sd-scripts/pull/906)
|
||||
- データセット設定に `keep_tokens_separator` が追加されました。キャプション内のトークンをどの位置までシャッフルしないかを指定できます。詳細は [#975](https://github.com/kohya-ss/sd-scripts/pull/975) を参照してください。Linaqruf 氏に感謝します。
|
||||
- オプションで `--keep_tokens_separator "|||"` のように指定するか、`.toml` で `keep_tokens_separator: "|||"` のように指定します。`|||` の前のトークンはシャッフルされません。
|
||||
- Attention processor hook が追加されました。詳細は [#961](https://github.com/kohya-ss/sd-scripts/pull/961) を参照してください。rockerBOO 氏に感謝します。
|
||||
- オプティマイザ `PagedAdamW` が追加されました。xzuyn 氏に感謝します。 PR [#955](https://github.com/kohya-ss/sd-scripts/pull/955)
|
||||
- 学習時、SDXL VAE で NaN が発生した時の置き換えが高速化されました。liubo0902 氏に感謝します。 PR [#1009](https://github.com/kohya-ss/sd-scripts/pull/1009)
|
||||
- `finetune/make_captions.py` で相対パス指定時のエラーが修正されました。CjangCjengh 氏に感謝します。 PR [#986](https://github.com/kohya-ss/sd-scripts/pull/986)
|
||||
- Fix `train_network.py` to probably work with older versions of LyCORIS.
|
||||
- `gen_img_diffusers.py` now supports `BREAK` syntax.
|
||||
- `train_network.py`がLyCORISの以前のバージョンでも恐らく動作するよう修正しました。
|
||||
- `gen_img_diffusers.py` で `BREAK` 構文をサポートしました。
|
||||
|
||||
### Dec 3, 2023 / 2023/12/3
|
||||
### 3 Jun. 2023, 2023/06/03
|
||||
|
||||
- `finetune\tag_images_by_wd14_tagger.py` now supports the separator other than `,` with `--caption_separator` option. Thanks to KohakuBlueleaf! PR [#913](https://github.com/kohya-ss/sd-scripts/pull/913)
|
||||
- Min SNR Gamma with V-predicition (SD 2.1) is fixed. Thanks to feffy380! PR[#934](https://github.com/kohya-ss/sd-scripts/pull/934)
|
||||
- See [#673](https://github.com/kohya-ss/sd-scripts/issues/673) for details.
|
||||
- `--min_diff` and `--clamp_quantile` options are added to `networks/extract_lora_from_models.py`. Thanks to wkpark! PR [#936](https://github.com/kohya-ss/sd-scripts/pull/936)
|
||||
- The default values are same as the previous version.
|
||||
- Deep Shrink hires fix is supported in `sdxl_gen_img.py` and `gen_img_diffusers.py`.
|
||||
- `--ds_timesteps_1` and `--ds_timesteps_2` options denote the timesteps of the Deep Shrink for the first and second stages.
|
||||
- `--ds_depth_1` and `--ds_depth_2` options denote the depth (block index) of the Deep Shrink for the first and second stages.
|
||||
- `--ds_ratio` option denotes the ratio of the Deep Shrink. `0.5` means the half of the original latent size for the Deep Shrink.
|
||||
- `--dst1`, `--dst2`, `--dsd1`, `--dsd2` and `--dsr` prompt options are also available.
|
||||
- Max Norm Regularization is now available in `train_network.py`. [PR #545](https://github.com/kohya-ss/sd-scripts/pull/545) Thanks to AI-Casanova!
|
||||
- Max Norm Regularization is a technique to stabilize network training by limiting the norm of network weights. It may be effective in suppressing overfitting of LoRA and improving stability when used with other LoRAs. See PR for details.
|
||||
- Specify as `--scale_weight_norms=1.0`. It seems good to try from `1.0`.
|
||||
- The networks other than LoRA in this repository (such as LyCORIS) do not support this option.
|
||||
|
||||
- `finetune\tag_images_by_wd14_tagger.py` で `--caption_separator` オプションでカンマ以外の区切り文字を指定できるようになりました。KohakuBlueleaf 氏に感謝します。 PR [#913](https://github.com/kohya-ss/sd-scripts/pull/913)
|
||||
- V-predicition (SD 2.1) での Min SNR Gamma が修正されました。feffy380 氏に感謝します。 PR[#934](https://github.com/kohya-ss/sd-scripts/pull/934)
|
||||
- 詳細は [#673](https://github.com/kohya-ss/sd-scripts/issues/673) を参照してください。
|
||||
- `networks/extract_lora_from_models.py` に `--min_diff` と `--clamp_quantile` オプションが追加されました。wkpark 氏に感謝します。 PR [#936](https://github.com/kohya-ss/sd-scripts/pull/936)
|
||||
- デフォルト値は前のバージョンと同じです。
|
||||
- `sdxl_gen_img.py` と `gen_img_diffusers.py` で Deep Shrink hires fix をサポートしました。
|
||||
- `--ds_timesteps_1` と `--ds_timesteps_2` オプションは Deep Shrink の第一段階と第二段階の timesteps を指定します。
|
||||
- `--ds_depth_1` と `--ds_depth_2` オプションは Deep Shrink の第一段階と第二段階の深さ（ブロックの index）を指定します。
|
||||
- `--ds_ratio` オプションは Deep Shrink の比率を指定します。`0.5` を指定すると Deep Shrink 適用時の latent は元のサイズの半分になります。
|
||||
- `--dst1`、`--dst2`、`--dsd1`、`--dsd2`、`--dsr` プロンプトオプションも使用できます。
|
||||
- Three types of dropout have been added to `train_network.py` and LoRA network.
|
||||
- Dropout is a technique to suppress overfitting and improve network performance by randomly setting some of the network outputs to 0.
|
||||
- `--network_dropout` is a normal dropout at the neuron level. In the case of LoRA, it is applied to the output of down. Proposed in [PR #545](https://github.com/kohya-ss/sd-scripts/pull/545) Thanks to AI-Casanova!
|
||||
- `--network_dropout=0.1` specifies the dropout probability to `0.1`.
|
||||
- Note that the specification method is different from LyCORIS.
|
||||
- For LoRA network, `--network_args` can specify `rank_dropout` to dropout each rank with specified probability. Also `module_dropout` can be specified to dropout each module with specified probability.
|
||||
- Specify as `--network_args "rank_dropout=0.2" "module_dropout=0.1"`.
|
||||
- `--network_dropout`, `rank_dropout`, and `module_dropout` can be specified at the same time.
|
||||
- Values of 0.1 to 0.3 may be good to try. Values greater than 0.5 should not be specified.
|
||||
- `rank_dropout` and `module_dropout` are original techniques of this repository. Their effectiveness has not been verified yet.
|
||||
- The networks other than LoRA in this repository (such as LyCORIS) do not support these options.
|
||||
|
||||
### Nov 5, 2023 / 2023/11/5
|
||||
- Added an option `--scale_v_pred_loss_like_noise_pred` to scale v-prediction loss like noise prediction in each training script.
|
||||
- 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.
|
||||
- See [this article](https://xrg.hatenablog.com/entry/2023/06/02/202418) by xrg for details (written in Japanese). Thanks to xrg for the great suggestion!
|
||||
|
||||
- `sdxl_train.py` now supports different learning rates for each Text Encoder.
|
||||
- Example:
|
||||
- `--learning_rate 1e-6`: train U-Net only
|
||||
- `--train_text_encoder --learning_rate 1e-6`: train U-Net and two Text Encoders with the same learning rate (same as the previous version)
|
||||
- `--train_text_encoder --learning_rate 1e-6 --learning_rate_te1 1e-6 --learning_rate_te2 1e-6`: train U-Net and two Text Encoders with the different learning rates
|
||||
- `--train_text_encoder --learning_rate 0 --learning_rate_te1 1e-6 --learning_rate_te2 1e-6`: train two Text Encoders only
|
||||
- `--train_text_encoder --learning_rate 1e-6 --learning_rate_te1 1e-6 --learning_rate_te2 0`: train U-Net and one Text Encoder only
|
||||
- `--train_text_encoder --learning_rate 0 --learning_rate_te1 0 --learning_rate_te2 1e-6`: train one Text Encoder only
|
||||
- Max Norm Regularizationが`train_network.py`で使えるようになりました。[PR #545](https://github.com/kohya-ss/sd-scripts/pull/545) AI-Casanova氏に感謝します。
|
||||
- Max Norm Regularizationは、ネットワークの重みのノルムを制限することで、ネットワークの学習を安定させる手法です。LoRAの過学習の抑制、他のLoRAと併用した時の安定性の向上が期待できるかもしれません。詳細はPRを参照してください。
|
||||
- `--scale_weight_norms=1.0`のように `--scale_weight_norms` で指定してください。`1.0`から試すと良いようです。
|
||||
- LyCORIS等、当リポジトリ以外のネットワークは現時点では未対応です。
|
||||
|
||||
- `train_db.py` and `fine_tune.py` now support different learning rates for Text Encoder. Specify with `--learning_rate_te` option.
|
||||
- To train Text Encoder with `fine_tune.py`, specify `--train_text_encoder` option too. `train_db.py` trains Text Encoder by default.
|
||||
- `train_network.py` およびLoRAに計三種類のdropoutを追加しました。
|
||||
- dropoutはネットワークの一部の出力をランダムに0にすることで、過学習の抑制、ネットワークの性能向上等を図る手法です。
|
||||
- `--network_dropout` はニューロン単位の通常のdropoutです。LoRAの場合、downの出力に対して適用されます。[PR #545](https://github.com/kohya-ss/sd-scripts/pull/545) で提案されました。AI-Casanova氏に感謝します。
|
||||
- `--network_dropout=0.1` などとすることで、dropoutの確率を指定できます。
|
||||
- LyCORISとは指定方法が異なりますのでご注意ください。
|
||||
- LoRAの場合、`--network_args`に`rank_dropout`を指定することで各rankを指定確率でdropoutします。また同じくLoRAの場合、`--network_args`に`module_dropout`を指定することで各モジュールを指定確率でdropoutします。
|
||||
- `--network_args "rank_dropout=0.2" "module_dropout=0.1"` のように指定します。
|
||||
- `--network_dropout`、`rank_dropout` 、 `module_dropout` は同時に指定できます。
|
||||
- それぞれの値は0.1~0.3程度から試してみると良いかもしれません。0.5を超える値は指定しない方が良いでしょう。
|
||||
- `rank_dropout`および`module_dropout`は当リポジトリ独自の手法です。有効性の検証はまだ行っていません。
|
||||
- これらのdropoutはLyCORIS等、当リポジトリ以外のネットワークは現時点では未対応です。
|
||||
|
||||
- Fixed the bug that Text Encoder is not trained when block lr is specified in `sdxl_train.py`.
|
||||
- 各学習スクリプトにv-prediction lossをnoise predictionと同様の値にスケールするオプション`--scale_v_pred_loss_like_noise_pred`を追加しました。
|
||||
- タイムステップに応じてlossをスケールすることで、 大域的なノイズの予測と局所的なノイズの予測の重みが同じになり、ディテールの改善が期待できるかもしれません。
|
||||
- 詳細はxrg氏のこちらの記事をご参照ください：[noise_predictionモデルとv_predictionモデルの損失 - 勾配降下党青年局](https://xrg.hatenablog.com/entry/2023/06/02/202418) xrg氏の素晴らしい記事に感謝します。
|
||||
|
||||
- Debiased Estimation loss is added to each training script. Thanks to sdbds!
|
||||
- Specify `--debiased_estimation_loss` option to enable it. See PR [#889](https://github.com/kohya-ss/sd-scripts/pull/889) for details.
|
||||
- Training of Text Encoder is improved in `train_network.py` and `sdxl_train_network.py`. Thanks to KohakuBlueleaf! PR [#895](https://github.com/kohya-ss/sd-scripts/pull/895)
|
||||
- The moving average of the loss is now displayed in the progress bar in each training script. Thanks to shirayu! PR [#899](https://github.com/kohya-ss/sd-scripts/pull/899)
|
||||
- PagedAdamW32bit optimizer is supported. Specify `--optimizer_type=PagedAdamW32bit`. Thanks to xzuyn! PR [#900](https://github.com/kohya-ss/sd-scripts/pull/900)
|
||||
- Other bug fixes and improvements.
|
||||
### 31 May 2023, 2023/05/31
|
||||
|
||||
- `sdxl_train.py` で、二つのText Encoderそれぞれに独立した学習率が指定できるようになりました。サンプルは上の英語版を参照してください。
|
||||
- `train_db.py` および `fine_tune.py` で Text Encoder に別の学習率を指定できるようになりました。`--learning_rate_te` オプションで指定してください。
|
||||
- `fine_tune.py` で Text Encoder を学習するには `--train_text_encoder` オプションをあわせて指定してください。`train_db.py` はデフォルトで学習します。
|
||||
- `sdxl_train.py` で block lr を指定すると Text Encoder が学習されない不具合を修正しました。
|
||||
- Debiased Estimation loss が各学習スクリプトに追加されました。sdbsd 氏に感謝します。
|
||||
- `--debiased_estimation_loss` を指定すると有効になります。詳細は PR [#889](https://github.com/kohya-ss/sd-scripts/pull/889) を参照してください。
|
||||
- `train_network.py` と `sdxl_train_network.py` でText Encoderの学習が改善されました。KohakuBlueleaf 氏に感謝します。 PR [#895](https://github.com/kohya-ss/sd-scripts/pull/895)
|
||||
- 各学習スクリプトで移動平均のlossがプログレスバーに表示されるようになりました。shirayu 氏に感謝します。 PR [#899](https://github.com/kohya-ss/sd-scripts/pull/899)
|
||||
- PagedAdamW32bit オプティマイザがサポートされました。`--optimizer_type=PagedAdamW32bit` と指定してください。xzuyn 氏に感謝します。 PR [#900](https://github.com/kohya-ss/sd-scripts/pull/900)
|
||||
- その他のバグ修正と改善。
|
||||
- Show warning when image caption file does not exist during training. [PR #533](https://github.com/kohya-ss/sd-scripts/pull/533) Thanks to TingTingin!
|
||||
- Warning is also displayed when using class+identifier dataset. Please ignore if it is intended.
|
||||
- `train_network.py` now supports merging network weights before training. [PR #542](https://github.com/kohya-ss/sd-scripts/pull/542) Thanks to u-haru!
|
||||
- `--base_weights` option specifies LoRA or other model files (multiple files are allowed) to merge.
|
||||
- `--base_weights_multiplier` option specifies multiplier of the weights to merge (multiple values are allowed). If omitted or less than `base_weights`, 1.0 is used.
|
||||
- This is useful for incremental learning. See PR for details.
|
||||
- Show warning and continue training when uploading to HuggingFace fails.
|
||||
|
||||
- 学習時に画像のキャプションファイルが存在しない場合、警告が表示されるようになりました。 [PR #533](https://github.com/kohya-ss/sd-scripts/pull/533) TingTingin氏に感謝します。
|
||||
- class+identifier方式のデータセットを利用している場合も警告が表示されます。意図している通りの場合は無視してください。
|
||||
- `train_network.py` に学習前にモデルにnetworkの重みをマージする機能が追加されました。 [PR #542](https://github.com/kohya-ss/sd-scripts/pull/542) u-haru氏に感謝します。
|
||||
- `--base_weights` オプションでLoRA等のモデルファイル（複数可）を指定すると、それらの重みをマージします。
|
||||
- `--base_weights_multiplier` オプションでマージする重みの倍率（複数可）を指定できます。省略時または`base_weights`よりも数が少ない場合は1.0になります。
|
||||
- 差分追加学習などにご利用ください。詳細はPRをご覧ください。
|
||||
- HuggingFaceへのアップロードに失敗した場合、警告を表示しそのまま学習を続行するよう変更しました。
|
||||
|
||||
### 25 May 2023, 2023/05/25
|
||||
|
||||
- [D-Adaptation v3.0](https://github.com/facebookresearch/dadaptation) is now supported. [PR #530](https://github.com/kohya-ss/sd-scripts/pull/530) Thanks to sdbds!
|
||||
- `--optimizer_type` now accepts `DAdaptAdamPreprint`, `DAdaptAdanIP`, and `DAdaptLion`.
|
||||
- `DAdaptAdam` is now new. The old `DAdaptAdam` is available with `DAdaptAdamPreprint`.
|
||||
- Simply specifying `DAdaptation` will use `DAdaptAdamPreprint` (same behavior as before).
|
||||
- You need to install D-Adaptation v3.0. After activating venv, please do `pip install -U dadaptation`.
|
||||
- See PR and D-Adaptation documentation for details.
|
||||
- [D-Adaptation v3.0](https://github.com/facebookresearch/dadaptation)がサポートされました。 [PR #530](https://github.com/kohya-ss/sd-scripts/pull/530) sdbds氏に感謝します。
|
||||
- `--optimizer_type`に`DAdaptAdamPreprint`、`DAdaptAdanIP`、`DAdaptLion` が追加されました。
|
||||
- `DAdaptAdam`が新しくなりました。今までの`DAdaptAdam`は`DAdaptAdamPreprint`で使用できます。
|
||||
- 単に `DAdaptation` を指定すると`DAdaptAdamPreprint`が使用されます（今までと同じ動き）。
|
||||
- D-Adaptation v3.0のインストールが必要です。venvを有効にした後 `pip install -U dadaptation` としてください。
|
||||
- 詳細はPRおよびD-Adaptationのドキュメントを参照してください。
|
||||
|
||||
### 22 May 2023, 2023/05/22
|
||||
|
||||
- Fixed several bugs.
|
||||
- The state is saved even when the `--save_state` option is not specified in `fine_tune.py` and `train_db.py`. [PR #521](https://github.com/kohya-ss/sd-scripts/pull/521) Thanks to akshaal!
|
||||
- Cannot load LoRA without `alpha`. [PR #527](https://github.com/kohya-ss/sd-scripts/pull/527) Thanks to Manjiz!
|
||||
- Minor changes to console output during sample generation. [PR #515](https://github.com/kohya-ss/sd-scripts/pull/515) Thanks to yanhuifair!
|
||||
- The generation script now uses xformers for VAE as well.
|
||||
- いくつかのバグ修正を行いました。
|
||||
- `fine_tune.py`と`train_db.py`で`--save_state`オプション未指定時にもstateが保存される。 [PR #521](https://github.com/kohya-ss/sd-scripts/pull/521) akshaal氏に感謝します。
|
||||
- `alpha`を持たないLoRAを読み込めない。[PR #527](https://github.com/kohya-ss/sd-scripts/pull/527) Manjiz氏に感謝します。
|
||||
- サンプル生成時のコンソール出力の軽微な変更。[PR #515](https://github.com/kohya-ss/sd-scripts/pull/515) yanhuifair氏に感謝します。
|
||||
- 生成スクリプトでVAEについてもxformersを使うようにしました。
|
||||
|
||||
### 16 May 2023, 2023/05/16
|
||||
|
||||
- Fixed an issue where an error would occur if the encoding of the prompt file was different from the default. [PR #510](https://github.com/kohya-ss/sd-scripts/pull/510) Thanks to sdbds!
|
||||
- Please save the prompt file in UTF-8.
|
||||
- プロンプトファイルのエンコーディングがデフォルトと異なる場合にエラーが発生する問題を修正しました。 [PR #510](https://github.com/kohya-ss/sd-scripts/pull/510) sdbds氏に感謝します。
|
||||
- プロンプトファイルはUTF-8で保存してください。
|
||||
|
||||
### 15 May 2023, 2023/05/15
|
||||
|
||||
- Added [English translation of documents](https://github.com/darkstorm2150/sd-scripts#links-to-usage-documentation) by darkstorm2150. Thank you very much!
|
||||
- The prompt for sample generation during training can now be specified in `.toml` or `.json`. [PR #504](https://github.com/kohya-ss/sd-scripts/pull/504) Thanks to Linaqruf!
|
||||
- For details on prompt description, please see the PR.
|
||||
|
||||
- darkstorm2150氏に[ドキュメント類を英訳](https://github.com/darkstorm2150/sd-scripts#links-to-usage-documentation)していただきました。ありがとうございます！
|
||||
- 学習中のサンプル生成のプロンプトを`.toml`または`.json`で指定可能になりました。 [PR #504](https://github.com/kohya-ss/sd-scripts/pull/504) Linaqruf氏に感謝します。
|
||||
- プロンプト記述の詳細は当該PRをご覧ください。
|
||||
|
||||
### 11 May 2023, 2023/05/11
|
||||
|
||||
- Added an option `--dim_from_weights` to `train_network.py` to automatically determine the dim(rank) from the weight file. [PR #491](https://github.com/kohya-ss/sd-scripts/pull/491) Thanks to AI-Casanova!
|
||||
- It is useful in combination with `resize_lora.py`. Please see the PR for details.
|
||||
- Fixed a bug where the noise resolution was incorrect with Multires noise. [PR #489](https://github.com/kohya-ss/sd-scripts/pull/489) Thanks to sdbds!
|
||||
- Please see the PR for details.
|
||||
- The image generation scripts can now use img2img and highres fix at the same time.
|
||||
- Fixed a bug where the hint image of ControlNet was incorrectly BGR instead of RGB in the image generation scripts.
|
||||
- Added a feature to the image generation scripts to use the memory-efficient VAE.
|
||||
- If you specify a number with the `--vae_slices` option, the memory-efficient VAE will be used. The maximum output size will be larger, but it will be slower. Please specify a value of about `16` or `32`.
|
||||
- The implementation of the VAE is in `library/slicing_vae.py`.
|
||||
|
||||
- `train_network.py`にdim(rank)を重みファイルから自動決定するオプション`--dim_from_weights`が追加されました。 [PR #491](https://github.com/kohya-ss/sd-scripts/pull/491) AI-Casanova氏に感謝します。
|
||||
- `resize_lora.py`と組み合わせると有用です。詳細はPRもご参照ください。
|
||||
- Multires noiseでノイズ解像度が正しくない不具合が修正されました。 [PR #489](https://github.com/kohya-ss/sd-scripts/pull/489) sdbds氏に感謝します。
|
||||
- 詳細は当該PRをご参照ください。
|
||||
- 生成スクリプトでimg2imgとhighres fixを同時に使用できるようにしました。
|
||||
- 生成スクリプトでControlNetのhint画像が誤ってBGRだったのをRGBに修正しました。
|
||||
- 生成スクリプトで省メモリ化VAEを使えるよう機能追加しました。
|
||||
- `--vae_slices`オプションに数値を指定すると、省メモリ化VAEを用います。出力可能な最大サイズが大きくなりますが、遅くなります。`16`または`32`程度の値を指定してください。
|
||||
- VAEの実装は`library/slicing_vae.py`にあります。
|
||||
|
||||
### 7 May 2023, 2023/05/07
|
||||
|
||||
- The documentation has been moved to the `docs` folder. If you have links, please change them.
|
||||
- Removed `gradio` from `requirements.txt`.
|
||||
- DAdaptAdaGrad, DAdaptAdan, and DAdaptSGD are now supported by DAdaptation. [PR#455](https://github.com/kohya-ss/sd-scripts/pull/455) Thanks to sdbds!
|
||||
- DAdaptation needs to be installed. Also, depending on the optimizer, DAdaptation may need to be updated. Please update with `pip install --upgrade dadaptation`.
|
||||
- Added support for pre-calculation of LoRA weights in image generation scripts. Specify `--network_pre_calc`.
|
||||
- The prompt option `--am` is available. Also, it is disabled when Regional LoRA is used.
|
||||
- Added Adaptive noise scale to each training script. Specify a number with `--adaptive_noise_scale` to enable it.
|
||||
- __Experimental option. It may be removed or changed in the future.__
|
||||
- This is an original implementation that automatically adjusts the value of the noise offset according to the absolute value of the mean of each channel of the latents. It is expected that appropriate noise offsets will be set for bright and dark images, respectively.
|
||||
- Specify it together with `--noise_offset`.
|
||||
- The actual value of the noise offset is calculated as `noise_offset + abs(mean(latents, dim=(2,3))) * adaptive_noise_scale`. Since the latent is close to a normal distribution, it may be a good idea to specify a value of about 1/10 to the same as the noise offset.
|
||||
- Negative values can also be specified, in which case the noise offset will be clipped to 0 or more.
|
||||
- Other minor fixes.
|
||||
|
||||
- ドキュメントを`docs`フォルダに移動しました。リンク等を張られている場合は変更をお願いいたします。
|
||||
- `requirements.txt`から`gradio`を削除しました。
|
||||
- DAdaptationで新しくDAdaptAdaGrad、DAdaptAdan、DAdaptSGDがサポートされました。[PR#455](https://github.com/kohya-ss/sd-scripts/pull/455) sdbds氏に感謝します。
|
||||
- dadaptationのインストールが必要です。またオプティマイザによってはdadaptationの更新が必要です。`pip install --upgrade dadaptation`で更新してください。
|
||||
- 画像生成スクリプトでLoRAの重みの事前計算をサポートしました。`--network_pre_calc`を指定してください。
|
||||
- プロンプトオプションの`--am`が利用できます。またRegional LoRA使用時には無効になります。
|
||||
- 各学習スクリプトにAdaptive noise scaleを追加しました。`--adaptive_noise_scale`で数値を指定すると有効になります。
|
||||
- __実験的オプションです。将来的に削除、仕様変更される可能性があります。__
|
||||
- Noise offsetの値を、latentsの各チャネルの平均値の絶対値に応じて自動調整するオプションです。独自の実装で、明るい画像、暗い画像に対してそれぞれ適切なnoise offsetが設定されることが期待されます。
|
||||
- `--noise_offset` と同時に指定してください。
|
||||
- 実際のNoise offsetの値は `noise_offset + abs(mean(latents, dim=(2,3))) * adaptive_noise_scale` で計算されます。 latentは正規分布に近いためnoise_offsetの1/10～同程度の値を指定するとよいかもしれません。
|
||||
- 負の値も指定でき、その場合はnoise offsetは0以上にclipされます。
|
||||
- その他の細かい修正を行いました。
|
||||
|
||||
Please read [Releases](https://github.com/kohya-ss/sd-scripts/releases) for recent updates.
|
||||
最近の更新情報は [Release](https://github.com/kohya-ss/sd-scripts/releases) をご覧ください。
|
||||
|
||||
@@ -1,11 +1,4 @@
|
||||
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
|
||||
|
||||
|
||||
20
_typos.toml
20
_typos.toml
@@ -9,25 +9,7 @@ parms="parms"
|
||||
nin="nin"
|
||||
extention="extention" # Intentionally left
|
||||
nd="nd"
|
||||
shs="shs"
|
||||
sts="sts"
|
||||
scs="scs"
|
||||
cpc="cpc"
|
||||
coc="coc"
|
||||
cic="cic"
|
||||
msm="msm"
|
||||
usu="usu"
|
||||
ici="ici"
|
||||
lvl="lvl"
|
||||
dii="dii"
|
||||
muk="muk"
|
||||
ori="ori"
|
||||
hru="hru"
|
||||
rik="rik"
|
||||
koo="koo"
|
||||
yos="yos"
|
||||
wn="wn"
|
||||
|
||||
|
||||
[files]
|
||||
extend-exclude = ["_typos.toml", "venv"]
|
||||
extend-exclude = ["_typos.toml"]
|
||||
|
||||
@@ -374,10 +374,6 @@ classがひとつで対象が複数の場合、正則化画像フォルダはひ
|
||||
|
||||
サンプル出力するステップ数またはエポック数を指定します。この数ごとにサンプル出力します。両方指定するとエポック数が優先されます。
|
||||
|
||||
- `--sample_at_first`
|
||||
|
||||
学習開始前にサンプル出力します。学習前との比較ができます。
|
||||
|
||||
- `--sample_prompts`
|
||||
|
||||
サンプル出力用プロンプトのファイルを指定します。
|
||||
|
||||
@@ -181,8 +181,6 @@ 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。
|
||||
@@ -248,8 +246,6 @@ 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。
|
||||
@@ -280,29 +276,29 @@ python networks\merge_lora.py --sd_model ..\model\model.ckpt
|
||||
|
||||
### 複数のLoRAのモデルをマージする
|
||||
|
||||
--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）。
|
||||
__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。
|
||||
|
||||
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 --save_precision bf16
|
||||
python networks\merge_lora.py
|
||||
--save_to ..\lora_train1\model-char1-style1-merged.safetensors
|
||||
--models ..\lora_train1\last.safetensors ..\lora_train2\last.safetensors
|
||||
--ratios 1.0 -1.0 --concat --shuffle
|
||||
--models ..\lora_train1\last.safetensors ..\lora_train2\last.safetensors --ratios 0.6 0.4
|
||||
```
|
||||
|
||||
--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。
|
||||
--sd_modelオプションは指定不要です。
|
||||
|
||||
--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。
|
||||
--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。
|
||||
|
||||
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
|
||||
@@ -310,7 +306,6 @@ 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
|
||||
|
||||
|
||||
71
fine_tune.py
71
fine_tune.py
@@ -10,16 +10,6 @@ import toml
|
||||
|
||||
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
|
||||
|
||||
@@ -35,7 +25,6 @@ from library.custom_train_functions import (
|
||||
get_weighted_text_embeddings,
|
||||
prepare_scheduler_for_custom_training,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
|
||||
|
||||
@@ -84,8 +73,8 @@ def train(args):
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group)
|
||||
@@ -196,20 +185,14 @@ def train(args):
|
||||
|
||||
for m in training_models:
|
||||
m.requires_grad_(True)
|
||||
|
||||
trainable_params = []
|
||||
if args.learning_rate_te is None or not args.train_text_encoder:
|
||||
for m in training_models:
|
||||
trainable_params.extend(m.parameters())
|
||||
else:
|
||||
trainable_params = [
|
||||
{"params": list(unet.parameters()), "lr": args.learning_rate},
|
||||
{"params": list(text_encoder.parameters()), "lr": args.learning_rate_te},
|
||||
]
|
||||
params = []
|
||||
for m in training_models:
|
||||
params.extend(m.parameters())
|
||||
params_to_optimize = params
|
||||
|
||||
# 学習に必要なクラスを準備する
|
||||
accelerator.print("prepare optimizer, data loader etc.")
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params=trainable_params)
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
|
||||
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
@@ -218,7 +201,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -253,6 +236,9 @@ def train(args):
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
# transform DDP after prepare
|
||||
text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
@@ -295,10 +281,6 @@ def train(args):
|
||||
init_kwargs = toml.load(args.log_tracker_config)
|
||||
accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
|
||||
|
||||
# For --sample_at_first
|
||||
train_util.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
for epoch in range(num_train_epochs):
|
||||
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
||||
current_epoch.value = epoch + 1
|
||||
@@ -306,6 +288,7 @@ def train(args):
|
||||
for m in training_models:
|
||||
m.train()
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
current_step.value = global_step
|
||||
with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく
|
||||
@@ -349,17 +332,15 @@ def train(args):
|
||||
else:
|
||||
target = noise
|
||||
|
||||
if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred or args.debiased_estimation_loss:
|
||||
if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred:
|
||||
# do not mean over batch dimension for snr weight or scale v-pred loss
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||||
loss = loss.mean([1, 2, 3])
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # mean over batch dimension
|
||||
else:
|
||||
@@ -408,20 +389,26 @@ def train(args):
|
||||
|
||||
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss}
|
||||
train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True)
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if (
|
||||
args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
|
||||
): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
# TODO moving averageにする
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step + 1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
@@ -480,12 +467,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
|
||||
parser.add_argument("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する")
|
||||
parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
|
||||
parser.add_argument(
|
||||
"--learning_rate_te",
|
||||
type=float,
|
||||
default=None,
|
||||
help="learning rate for text encoder, default is same as unet / Text Encoderの学習率、デフォルトはunetと同じ",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
@@ -13,7 +13,7 @@ import torch
|
||||
from torchvision import transforms
|
||||
from torchvision.transforms.functional import InterpolationMode
|
||||
sys.path.append(os.path.dirname(__file__))
|
||||
from blip.blip import blip_decoder, is_url
|
||||
from blip.blip import blip_decoder
|
||||
import library.train_util as train_util
|
||||
|
||||
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
@@ -76,8 +76,6 @@ def main(args):
|
||||
cwd = os.getcwd()
|
||||
print("Current Working Directory is: ", cwd)
|
||||
os.chdir("finetune")
|
||||
if not is_url(args.caption_weights) and not os.path.isfile(args.caption_weights):
|
||||
args.caption_weights = os.path.join("..", args.caption_weights)
|
||||
|
||||
print(f"load images from {args.train_data_dir}")
|
||||
train_data_dir_path = Path(args.train_data_dir)
|
||||
|
||||
@@ -52,9 +52,6 @@ def collate_fn_remove_corrupted(batch):
|
||||
|
||||
|
||||
def main(args):
|
||||
r"""
|
||||
transformers 4.30.2で、バッチサイズ>1でも動くようになったので、以下コメントアウト
|
||||
|
||||
# GITにバッチサイズが1より大きくても動くようにパッチを当てる: transformers 4.26.0用
|
||||
org_prepare_input_ids_for_generation = GenerationMixin._prepare_input_ids_for_generation
|
||||
curr_batch_size = [args.batch_size] # ループの最後で件数がbatch_size未満になるので入れ替えられるように
|
||||
@@ -68,7 +65,6 @@ def main(args):
|
||||
return input_ids
|
||||
|
||||
GenerationMixin._prepare_input_ids_for_generation = _prepare_input_ids_for_generation_patch
|
||||
"""
|
||||
|
||||
print(f"load images from {args.train_data_dir}")
|
||||
train_data_dir_path = Path(args.train_data_dir)
|
||||
@@ -85,7 +81,7 @@ def main(args):
|
||||
def run_batch(path_imgs):
|
||||
imgs = [im for _, im in path_imgs]
|
||||
|
||||
# curr_batch_size[0] = len(path_imgs)
|
||||
curr_batch_size[0] = len(path_imgs)
|
||||
inputs = git_processor(images=imgs, return_tensors="pt").to(DEVICE) # 画像はpil形式
|
||||
generated_ids = git_model.generate(pixel_values=inputs.pixel_values, max_length=args.max_length)
|
||||
captions = git_processor.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
@@ -215,7 +215,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
help="max resolution in fine tuning (width,height) / fine tuning時の最大画像サイズ 「幅,高さ」（使用メモリ量に関係します）",
|
||||
)
|
||||
parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度")
|
||||
parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最大解像度")
|
||||
parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最小解像度")
|
||||
parser.add_argument(
|
||||
"--bucket_reso_steps",
|
||||
type=int,
|
||||
|
||||
@@ -1,14 +1,16 @@
|
||||
import argparse
|
||||
import csv
|
||||
import glob
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
from huggingface_hub import hf_hub_download
|
||||
from PIL import Image
|
||||
import cv2
|
||||
from tqdm import tqdm
|
||||
import numpy as np
|
||||
from tensorflow.keras.models import load_model
|
||||
from huggingface_hub import hf_hub_download
|
||||
import torch
|
||||
from pathlib import Path
|
||||
|
||||
import library.train_util as train_util
|
||||
|
||||
@@ -18,7 +20,6 @@ IMAGE_SIZE = 448
|
||||
# wd-v1-4-swinv2-tagger-v2 / wd-v1-4-vit-tagger / wd-v1-4-vit-tagger-v2/ wd-v1-4-convnext-tagger / wd-v1-4-convnext-tagger-v2
|
||||
DEFAULT_WD14_TAGGER_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
|
||||
FILES = ["keras_metadata.pb", "saved_model.pb", "selected_tags.csv"]
|
||||
FILES_ONNX = ["model.onnx"]
|
||||
SUB_DIR = "variables"
|
||||
SUB_DIR_FILES = ["variables.data-00000-of-00001", "variables.index"]
|
||||
CSV_FILE = FILES[-1]
|
||||
@@ -80,10 +81,7 @@ def main(args):
|
||||
# https://github.com/toriato/stable-diffusion-webui-wd14-tagger/issues/22
|
||||
if not os.path.exists(args.model_dir) or args.force_download:
|
||||
print(f"downloading wd14 tagger model from hf_hub. id: {args.repo_id}")
|
||||
files = FILES
|
||||
if args.onnx:
|
||||
files += FILES_ONNX
|
||||
for file in files:
|
||||
for file in FILES:
|
||||
hf_hub_download(args.repo_id, file, cache_dir=args.model_dir, force_download=True, force_filename=file)
|
||||
for file in SUB_DIR_FILES:
|
||||
hf_hub_download(
|
||||
@@ -98,46 +96,7 @@ def main(args):
|
||||
print("using existing wd14 tagger model")
|
||||
|
||||
# 画像を読み込む
|
||||
if args.onnx:
|
||||
import onnx
|
||||
import onnxruntime as ort
|
||||
|
||||
onnx_path = f"{args.model_dir}/model.onnx"
|
||||
print("Running wd14 tagger with onnx")
|
||||
print(f"loading onnx model: {onnx_path}")
|
||||
|
||||
if not os.path.exists(onnx_path):
|
||||
raise Exception(
|
||||
f"onnx model not found: {onnx_path}, please redownload the model with --force_download"
|
||||
+ " / onnxモデルが見つかりませんでした。--force_downloadで再ダウンロードしてください"
|
||||
)
|
||||
|
||||
model = onnx.load(onnx_path)
|
||||
input_name = model.graph.input[0].name
|
||||
try:
|
||||
batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_value
|
||||
except:
|
||||
batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_param
|
||||
|
||||
if args.batch_size != batch_size and type(batch_size) != str:
|
||||
# some rebatch model may use 'N' as dynamic axes
|
||||
print(
|
||||
f"Batch size {args.batch_size} doesn't match onnx model batch size {batch_size}, use model batch size {batch_size}"
|
||||
)
|
||||
args.batch_size = batch_size
|
||||
|
||||
del model
|
||||
|
||||
ort_sess = ort.InferenceSession(
|
||||
onnx_path,
|
||||
providers=["CUDAExecutionProvider"]
|
||||
if "CUDAExecutionProvider" in ort.get_available_providers()
|
||||
else ["CPUExecutionProvider"],
|
||||
)
|
||||
else:
|
||||
from tensorflow.keras.models import load_model
|
||||
|
||||
model = load_model(f"{args.model_dir}")
|
||||
model = load_model(args.model_dir)
|
||||
|
||||
# label_names = pd.read_csv("2022_0000_0899_6549/selected_tags.csv")
|
||||
# 依存ライブラリを増やしたくないので自力で読むよ
|
||||
@@ -160,21 +119,13 @@ def main(args):
|
||||
|
||||
tag_freq = {}
|
||||
|
||||
caption_separator = args.caption_separator
|
||||
stripped_caption_separator = caption_separator.strip()
|
||||
undesired_tags = set(args.undesired_tags.split(stripped_caption_separator))
|
||||
undesired_tags = set(args.undesired_tags.split(","))
|
||||
|
||||
def run_batch(path_imgs):
|
||||
imgs = np.array([im for _, im in path_imgs])
|
||||
|
||||
if args.onnx:
|
||||
if len(imgs) < args.batch_size:
|
||||
imgs = np.concatenate([imgs, np.zeros((args.batch_size - len(imgs), IMAGE_SIZE, IMAGE_SIZE, 3))], axis=0)
|
||||
probs = ort_sess.run(None, {input_name: imgs})[0] # onnx output numpy
|
||||
probs = probs[: len(path_imgs)]
|
||||
else:
|
||||
probs = model(imgs, training=False)
|
||||
probs = probs.numpy()
|
||||
probs = model(imgs, training=False)
|
||||
probs = probs.numpy()
|
||||
|
||||
for (image_path, _), prob in zip(path_imgs, probs):
|
||||
# 最初の4つはratingなので無視する
|
||||
@@ -196,7 +147,7 @@ def main(args):
|
||||
|
||||
if tag_name not in undesired_tags:
|
||||
tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
|
||||
general_tag_text += caption_separator + tag_name
|
||||
general_tag_text += ", " + tag_name
|
||||
combined_tags.append(tag_name)
|
||||
elif i >= len(general_tags) and p >= args.character_threshold:
|
||||
tag_name = character_tags[i - len(general_tags)]
|
||||
@@ -205,36 +156,18 @@ def main(args):
|
||||
|
||||
if tag_name not in undesired_tags:
|
||||
tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
|
||||
character_tag_text += caption_separator + tag_name
|
||||
character_tag_text += ", " + tag_name
|
||||
combined_tags.append(tag_name)
|
||||
|
||||
# 先頭のカンマを取る
|
||||
if len(general_tag_text) > 0:
|
||||
general_tag_text = general_tag_text[len(caption_separator) :]
|
||||
general_tag_text = general_tag_text[2:]
|
||||
if len(character_tag_text) > 0:
|
||||
character_tag_text = character_tag_text[len(caption_separator) :]
|
||||
character_tag_text = character_tag_text[2:]
|
||||
|
||||
caption_file = os.path.splitext(image_path)[0] + args.caption_extension
|
||||
tag_text = ", ".join(combined_tags)
|
||||
|
||||
tag_text = caption_separator.join(combined_tags)
|
||||
|
||||
if args.append_tags:
|
||||
# Check if file exists
|
||||
if os.path.exists(caption_file):
|
||||
with open(caption_file, "rt", encoding="utf-8") as f:
|
||||
# Read file and remove new lines
|
||||
existing_content = f.read().strip("\n") # Remove newlines
|
||||
|
||||
# Split the content into tags and store them in a list
|
||||
existing_tags = [tag.strip() for tag in existing_content.split(stripped_caption_separator) if tag.strip()]
|
||||
|
||||
# Check and remove repeating tags in tag_text
|
||||
new_tags = [tag for tag in combined_tags if tag not in existing_tags]
|
||||
|
||||
# Create new tag_text
|
||||
tag_text = caption_separator.join(existing_tags + new_tags)
|
||||
|
||||
with open(caption_file, "wt", encoding="utf-8") as f:
|
||||
with open(os.path.splitext(image_path)[0] + args.caption_extension, "wt", encoding="utf-8") as f:
|
||||
f.write(tag_text + "\n")
|
||||
if args.debug:
|
||||
print(f"\n{image_path}:\n Character tags: {character_tag_text}\n General tags: {general_tag_text}")
|
||||
@@ -350,21 +283,12 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
help="comma-separated list of undesired tags to remove from the output / 出力から除外したいタグのカンマ区切りのリスト",
|
||||
)
|
||||
parser.add_argument("--frequency_tags", action="store_true", help="Show frequency of tags for images / 画像ごとのタグの出現頻度を表示する")
|
||||
parser.add_argument("--onnx", action="store_true", help="use onnx model for inference / onnxモデルを推論に使用する")
|
||||
parser.add_argument("--append_tags", action="store_true", help="Append captions instead of overwriting / 上書きではなくキャプションを追記する")
|
||||
parser.add_argument(
|
||||
"--caption_separator",
|
||||
type=str,
|
||||
default=", ",
|
||||
help="Separator for captions, include space if needed / キャプションの区切り文字、必要ならスペースを含めてください",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# スペルミスしていたオプションを復元する
|
||||
|
||||
@@ -65,16 +65,6 @@ import re
|
||||
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,
|
||||
@@ -105,7 +95,7 @@ import library.train_util as train_util
|
||||
from networks.lora import LoRANetwork
|
||||
import tools.original_control_net as original_control_net
|
||||
from tools.original_control_net import ControlNetInfo
|
||||
from library.original_unet import UNet2DConditionModel, InferUNet2DConditionModel
|
||||
from library.original_unet import UNet2DConditionModel
|
||||
from library.original_unet import FlashAttentionFunction
|
||||
|
||||
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
|
||||
@@ -378,7 +368,7 @@ class PipelineLike:
|
||||
vae: AutoencoderKL,
|
||||
text_encoder: CLIPTextModel,
|
||||
tokenizer: CLIPTokenizer,
|
||||
unet: InferUNet2DConditionModel,
|
||||
unet: UNet2DConditionModel,
|
||||
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
||||
clip_skip: int,
|
||||
clip_model: CLIPModel,
|
||||
@@ -957,7 +947,7 @@ class PipelineLike:
|
||||
text_emb_last = torch.stack(text_emb_last)
|
||||
else:
|
||||
text_emb_last = text_embeddings
|
||||
|
||||
|
||||
for i, t in enumerate(tqdm(timesteps)):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = latents.repeat((num_latent_input, 1, 1, 1))
|
||||
@@ -2196,7 +2186,6 @@ def main(args):
|
||||
)
|
||||
original_unet.load_state_dict(unet.state_dict())
|
||||
unet = original_unet
|
||||
unet: InferUNet2DConditionModel = InferUNet2DConditionModel(unet)
|
||||
|
||||
# VAEを読み込む
|
||||
if args.vae is not None:
|
||||
@@ -2353,20 +2342,13 @@ def main(args):
|
||||
vae = sli_vae
|
||||
del sli_vae
|
||||
vae.to(dtype).to(device)
|
||||
vae.eval()
|
||||
|
||||
text_encoder.to(dtype).to(device)
|
||||
unet.to(dtype).to(device)
|
||||
|
||||
text_encoder.eval()
|
||||
unet.eval()
|
||||
|
||||
if clip_model is not None:
|
||||
clip_model.to(dtype).to(device)
|
||||
clip_model.eval()
|
||||
if vgg16_model is not None:
|
||||
vgg16_model.to(dtype).to(device)
|
||||
vgg16_model.eval()
|
||||
|
||||
# networkを組み込む
|
||||
if args.network_module:
|
||||
@@ -2374,19 +2356,12 @@ def main(args):
|
||||
network_default_muls = []
|
||||
network_pre_calc = args.network_pre_calc
|
||||
|
||||
# merge関連の引数を統合する
|
||||
if args.network_merge:
|
||||
network_merge = len(args.network_module) # all networks are merged
|
||||
elif args.network_merge_n_models:
|
||||
network_merge = args.network_merge_n_models
|
||||
else:
|
||||
network_merge = 0
|
||||
|
||||
for i, network_module in enumerate(args.network_module):
|
||||
print("import network module:", network_module)
|
||||
imported_module = importlib.import_module(network_module)
|
||||
|
||||
network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
|
||||
network_default_muls.append(network_mul)
|
||||
|
||||
net_kwargs = {}
|
||||
if args.network_args and i < len(args.network_args):
|
||||
@@ -2397,32 +2372,31 @@ def main(args):
|
||||
key, value = net_arg.split("=")
|
||||
net_kwargs[key] = value
|
||||
|
||||
if args.network_weights is None or len(args.network_weights) <= i:
|
||||
if args.network_weights and i < len(args.network_weights):
|
||||
network_weight = args.network_weights[i]
|
||||
print("load network weights from:", network_weight)
|
||||
|
||||
if model_util.is_safetensors(network_weight) and args.network_show_meta:
|
||||
from safetensors.torch import safe_open
|
||||
|
||||
with safe_open(network_weight, framework="pt") as f:
|
||||
metadata = f.metadata()
|
||||
if metadata is not None:
|
||||
print(f"metadata for: {network_weight}: {metadata}")
|
||||
|
||||
network, weights_sd = imported_module.create_network_from_weights(
|
||||
network_mul, network_weight, vae, text_encoder, unet, for_inference=True, **net_kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError("No weight. Weight is required.")
|
||||
|
||||
network_weight = args.network_weights[i]
|
||||
print("load network weights from:", network_weight)
|
||||
|
||||
if model_util.is_safetensors(network_weight) and args.network_show_meta:
|
||||
from safetensors.torch import safe_open
|
||||
|
||||
with safe_open(network_weight, framework="pt") as f:
|
||||
metadata = f.metadata()
|
||||
if metadata is not None:
|
||||
print(f"metadata for: {network_weight}: {metadata}")
|
||||
|
||||
network, weights_sd = imported_module.create_network_from_weights(
|
||||
network_mul, network_weight, vae, text_encoder, unet, for_inference=True, **net_kwargs
|
||||
)
|
||||
if network is None:
|
||||
return
|
||||
|
||||
mergeable = network.is_mergeable()
|
||||
if network_merge and not mergeable:
|
||||
if args.network_merge and not mergeable:
|
||||
print("network is not mergiable. ignore merge option.")
|
||||
|
||||
if not mergeable or i >= network_merge:
|
||||
# not merging
|
||||
if not args.network_merge or not mergeable:
|
||||
network.apply_to(text_encoder, unet)
|
||||
info = network.load_state_dict(weights_sd, False) # network.load_weightsを使うようにするとよい
|
||||
print(f"weights are loaded: {info}")
|
||||
@@ -2436,7 +2410,6 @@ def main(args):
|
||||
network.backup_weights()
|
||||
|
||||
networks.append(network)
|
||||
network_default_muls.append(network_mul)
|
||||
else:
|
||||
network.merge_to(text_encoder, unet, weights_sd, dtype, device)
|
||||
|
||||
@@ -2509,10 +2482,6 @@ def main(args):
|
||||
if args.diffusers_xformers:
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
|
||||
# Deep Shrink
|
||||
if args.ds_depth_1 is not None:
|
||||
unet.set_deep_shrink(args.ds_depth_1, args.ds_timesteps_1, args.ds_depth_2, args.ds_timesteps_2, args.ds_ratio)
|
||||
|
||||
# Extended Textual Inversion および Textual Inversionを処理する
|
||||
if args.XTI_embeddings:
|
||||
diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI
|
||||
@@ -2736,18 +2705,9 @@ def main(args):
|
||||
|
||||
size = None
|
||||
for i, network in enumerate(networks):
|
||||
if (i < 3 and args.network_regional_mask_max_color_codes is None) or i < args.network_regional_mask_max_color_codes:
|
||||
if i < 3:
|
||||
np_mask = np.array(mask_images[0])
|
||||
|
||||
if args.network_regional_mask_max_color_codes:
|
||||
# カラーコードでマスクを指定する
|
||||
ch0 = (i + 1) & 1
|
||||
ch1 = ((i + 1) >> 1) & 1
|
||||
ch2 = ((i + 1) >> 2) & 1
|
||||
np_mask = np.all(np_mask == np.array([ch0, ch1, ch2]) * 255, axis=2)
|
||||
np_mask = np_mask.astype(np.uint8) * 255
|
||||
else:
|
||||
np_mask = np_mask[:, :, i]
|
||||
np_mask = np_mask[:, :, i]
|
||||
size = np_mask.shape
|
||||
else:
|
||||
np_mask = np.full(size, 255, dtype=np.uint8)
|
||||
@@ -3097,13 +3057,6 @@ def main(args):
|
||||
clip_prompt = None
|
||||
network_muls = None
|
||||
|
||||
# Deep Shrink
|
||||
ds_depth_1 = None # means no override
|
||||
ds_timesteps_1 = args.ds_timesteps_1
|
||||
ds_depth_2 = args.ds_depth_2
|
||||
ds_timesteps_2 = args.ds_timesteps_2
|
||||
ds_ratio = args.ds_ratio
|
||||
|
||||
prompt_args = raw_prompt.strip().split(" --")
|
||||
prompt = prompt_args[0]
|
||||
print(f"prompt {prompt_index+1}/{len(prompt_list)}: {prompt}")
|
||||
@@ -3175,51 +3128,10 @@ def main(args):
|
||||
print(f"network mul: {network_muls}")
|
||||
continue
|
||||
|
||||
# Deep Shrink
|
||||
m = re.match(r"dsd1 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink depth 1
|
||||
ds_depth_1 = int(m.group(1))
|
||||
print(f"deep shrink depth 1: {ds_depth_1}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dst1 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink timesteps 1
|
||||
ds_timesteps_1 = int(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink timesteps 1: {ds_timesteps_1}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dsd2 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink depth 2
|
||||
ds_depth_2 = int(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink depth 2: {ds_depth_2}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dst2 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink timesteps 2
|
||||
ds_timesteps_2 = int(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink timesteps 2: {ds_timesteps_2}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dsr ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink ratio
|
||||
ds_ratio = float(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink ratio: {ds_ratio}")
|
||||
continue
|
||||
|
||||
except ValueError as ex:
|
||||
print(f"Exception in parsing / 解析エラー: {parg}")
|
||||
print(ex)
|
||||
|
||||
# override Deep Shrink
|
||||
if ds_depth_1 is not None:
|
||||
if ds_depth_1 < 0:
|
||||
ds_depth_1 = args.ds_depth_1 or 3
|
||||
unet.set_deep_shrink(ds_depth_1, ds_timesteps_1, ds_depth_2, ds_timesteps_2, ds_ratio)
|
||||
|
||||
# prepare seed
|
||||
if seeds is not None: # given in prompt
|
||||
# 数が足りないなら前のをそのまま使う
|
||||
@@ -3233,7 +3145,7 @@ def main(args):
|
||||
print("predefined seeds are exhausted")
|
||||
seed = None
|
||||
elif args.iter_same_seed:
|
||||
seed = iter_seed
|
||||
seeds = iter_seed
|
||||
else:
|
||||
seed = None # 前のを消す
|
||||
|
||||
@@ -3445,22 +3357,13 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
)
|
||||
parser.add_argument("--network_mul", type=float, default=None, nargs="*", help="additional network multiplier / 追加ネットワークの効果の倍率")
|
||||
parser.add_argument(
|
||||
"--network_args", type=str, default=None, nargs="*", help="additional arguments for network (key=value) / ネットワークへの追加の引数"
|
||||
"--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"
|
||||
)
|
||||
parser.add_argument("--network_show_meta", action="store_true", help="show metadata of network model / ネットワークモデルのメタデータを表示する")
|
||||
parser.add_argument(
|
||||
"--network_merge_n_models", type=int, default=None, help="merge this number of networks / この数だけネットワークをマージする"
|
||||
)
|
||||
parser.add_argument("--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする")
|
||||
parser.add_argument(
|
||||
"--network_pre_calc", action="store_true", help="pre-calculate network for generation / ネットワークのあらかじめ計算して生成する"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--network_regional_mask_max_color_codes",
|
||||
type=int,
|
||||
default=None,
|
||||
help="max color codes for regional mask (default is None, mask by channel) / regional maskの最大色数（デフォルトはNoneでチャンネルごとのマスク）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--textual_inversion_embeddings",
|
||||
type=str,
|
||||
@@ -3480,7 +3383,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
"--max_embeddings_multiples",
|
||||
type=int,
|
||||
default=None,
|
||||
help="max embedding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
|
||||
help="max embeding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--clip_guidance_scale",
|
||||
@@ -3539,7 +3442,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
"--highres_fix_upscaler_args",
|
||||
type=str,
|
||||
default=None,
|
||||
help="additional arguments for upscaler (key=value) / upscalerへの追加の引数",
|
||||
help="additional argmuments for upscaler (key=value) / upscalerへの追加の引数",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--highres_fix_disable_control_net",
|
||||
@@ -3569,30 +3472,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
# "--control_net_image_path", type=str, default=None, nargs="*", help="image for ControlNet guidance / ControlNetでガイドに使う画像"
|
||||
# )
|
||||
|
||||
# Deep Shrink
|
||||
parser.add_argument(
|
||||
"--ds_depth_1",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Enable Deep Shrink with this depth 1, valid values are 0 to 3 / Deep Shrinkをこのdepthで有効にする",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ds_timesteps_1",
|
||||
type=int,
|
||||
default=650,
|
||||
help="Apply Deep Shrink depth 1 until this timesteps / Deep Shrink depth 1を適用するtimesteps",
|
||||
)
|
||||
parser.add_argument("--ds_depth_2", type=int, default=None, help="Deep Shrink depth 2 / Deep Shrinkのdepth 2")
|
||||
parser.add_argument(
|
||||
"--ds_timesteps_2",
|
||||
type=int,
|
||||
default=650,
|
||||
help="Apply Deep Shrink depth 2 until this timesteps / Deep Shrink depth 2を適用するtimesteps",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ds_ratio", type=float, default=0.5, help="Deep Shrink ratio for downsampling / Deep Shrinkのdownsampling比率"
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
|
||||
@@ -51,9 +51,7 @@ class BaseSubsetParams:
|
||||
image_dir: Optional[str] = None
|
||||
num_repeats: int = 1
|
||||
shuffle_caption: bool = False
|
||||
caption_separator: str = ',',
|
||||
keep_tokens: int = 0
|
||||
keep_tokens_separator: str = None,
|
||||
color_aug: bool = False
|
||||
flip_aug: bool = False
|
||||
face_crop_aug_range: Optional[Tuple[float, float]] = None
|
||||
@@ -161,7 +159,6 @@ class ConfigSanitizer:
|
||||
"random_crop": bool,
|
||||
"shuffle_caption": bool,
|
||||
"keep_tokens": int,
|
||||
"keep_tokens_separator": str,
|
||||
"token_warmup_min": int,
|
||||
"token_warmup_step": Any(float,int),
|
||||
"caption_prefix": str,
|
||||
@@ -463,7 +460,6 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
|
||||
num_repeats: {subset.num_repeats}
|
||||
shuffle_caption: {subset.shuffle_caption}
|
||||
keep_tokens: {subset.keep_tokens}
|
||||
keep_tokens_separator: {subset.keep_tokens_separator}
|
||||
caption_dropout_rate: {subset.caption_dropout_rate}
|
||||
caption_dropout_every_n_epoches: {subset.caption_dropout_every_n_epochs}
|
||||
caption_tag_dropout_rate: {subset.caption_tag_dropout_rate}
|
||||
@@ -551,13 +547,17 @@ def generate_controlnet_subsets_config_by_subdirs(train_data_dir: Optional[str]
|
||||
return []
|
||||
|
||||
subsets_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)
|
||||
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)
|
||||
|
||||
return subsets_config
|
||||
|
||||
subsets_config = []
|
||||
subsets_config += generate(train_data_dir)
|
||||
subsets_config += generate(train_data_dir, False)
|
||||
|
||||
return subsets_config
|
||||
|
||||
|
||||
@@ -57,13 +57,10 @@ def fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler):
|
||||
noise_scheduler.alphas_cumprod = alphas_cumprod
|
||||
|
||||
|
||||
def apply_snr_weight(loss, timesteps, noise_scheduler, gamma, v_prediction=False):
|
||||
def apply_snr_weight(loss, timesteps, noise_scheduler, gamma):
|
||||
snr = torch.stack([noise_scheduler.all_snr[t] for t in timesteps])
|
||||
min_snr_gamma = torch.minimum(snr, torch.full_like(snr, gamma))
|
||||
if v_prediction:
|
||||
snr_weight = torch.div(min_snr_gamma, snr+1).float().to(loss.device)
|
||||
else:
|
||||
snr_weight = torch.div(min_snr_gamma, snr).float().to(loss.device)
|
||||
gamma_over_snr = torch.div(torch.ones_like(snr) * gamma, snr)
|
||||
snr_weight = torch.minimum(gamma_over_snr, torch.ones_like(gamma_over_snr)).float().to(loss.device) # from paper
|
||||
loss = loss * snr_weight
|
||||
return loss
|
||||
|
||||
@@ -89,12 +86,6 @@ def add_v_prediction_like_loss(loss, timesteps, noise_scheduler, v_pred_like_los
|
||||
loss = loss + loss / scale * v_pred_like_loss
|
||||
return loss
|
||||
|
||||
def apply_debiased_estimation(loss, timesteps, noise_scheduler):
|
||||
snr_t = torch.stack([noise_scheduler.all_snr[t] for t in timesteps]) # batch_size
|
||||
snr_t = torch.minimum(snr_t, torch.ones_like(snr_t) * 1000) # if timestep is 0, snr_t is inf, so limit it to 1000
|
||||
weight = 1/torch.sqrt(snr_t)
|
||||
loss = weight * loss
|
||||
return loss
|
||||
|
||||
# TODO train_utilと分散しているのでどちらかに寄せる
|
||||
|
||||
@@ -117,11 +108,6 @@ def add_custom_train_arguments(parser: argparse.ArgumentParser, support_weighted
|
||||
default=None,
|
||||
help="add v-prediction like loss multiplied by this value / v-prediction lossをこの値をかけたものをlossに加算する",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--debiased_estimation_loss",
|
||||
action="store_true",
|
||||
help="debiased estimation loss / debiased estimation loss",
|
||||
)
|
||||
if support_weighted_captions:
|
||||
parser.add_argument(
|
||||
"--weighted_captions",
|
||||
|
||||
@@ -1,169 +0,0 @@
|
||||
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
|
||||
|
||||
# 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.UntypedStorage.cuda = torch.UntypedStorage.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
|
||||
|
||||
ipex_hijacks()
|
||||
if not torch.xpu.has_fp64_dtype():
|
||||
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
|
||||
@@ -1,151 +0,0 @@
|
||||
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_32_bit(input, mat2, *, out=None):
|
||||
# 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
|
||||
split_2_slice_size = input_tokens
|
||||
if split_slice_size * slice_block_size > 4:
|
||||
slice_block_size_2 = 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_size_2) > 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
|
||||
else:
|
||||
do_split = 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_32_bit(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_three = query.shape
|
||||
shape_four = 1
|
||||
else:
|
||||
batch_size_attention, query_tokens, shape_three, shape_four = query.shape
|
||||
|
||||
block_multiply = query.element_size()
|
||||
slice_block_size = query_tokens * shape_three * 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 batch_size_attention
|
||||
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
|
||||
split_2_slice_size = query_tokens
|
||||
if split_slice_size * slice_block_size > 4:
|
||||
slice_block_size_2 = split_slice_size * shape_three * shape_four / 1024 / 1024 * block_multiply
|
||||
do_split_2 = True
|
||||
# Find something divisible with the query_tokens
|
||||
while (split_2_slice_size * slice_block_size_2) > 4:
|
||||
split_2_slice_size = split_2_slice_size // 2
|
||||
if split_2_slice_size <= 1:
|
||||
split_2_slice_size = 1
|
||||
break
|
||||
split_3_slice_size = shape_three
|
||||
if split_2_slice_size * slice_block_size_2 > 4:
|
||||
slice_block_size_3 = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * block_multiply
|
||||
do_split_3 = True
|
||||
# Find something divisible with the shape_three
|
||||
while (split_3_slice_size * slice_block_size_3) > 4:
|
||||
split_3_slice_size = split_3_slice_size // 2
|
||||
if split_3_slice_size <= 1:
|
||||
split_3_slice_size = 1
|
||||
break
|
||||
else:
|
||||
do_split_3 = False
|
||||
else:
|
||||
do_split_2 = False
|
||||
else:
|
||||
do_split = 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 do_split_3:
|
||||
for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name
|
||||
start_idx_3 = i3 * split_3_slice_size
|
||||
end_idx_3 = (i3 + 1) * split_3_slice_size
|
||||
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_scaled_dot_product_attention(
|
||||
query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
|
||||
key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
|
||||
value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
|
||||
attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] 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:
|
||||
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
|
||||
@@ -1,120 +0,0 @@
|
||||
import torch
|
||||
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
|
||||
import diffusers #0.24.0 # 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
|
||||
@@ -1,183 +0,0 @@
|
||||
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
|
||||
|
||||
device_supports_fp64 = torch.xpu.has_fp64_dtype()
|
||||
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<working-with-unscaled-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
|
||||
if device_supports_fp64:
|
||||
inv_scale = self._scale.double().reciprocal().float()
|
||||
else:
|
||||
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
|
||||
@@ -1,252 +0,0 @@
|
||||
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)
|
||||
|
||||
# Embedding BF16
|
||||
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)
|
||||
|
||||
# Latent antialias:
|
||||
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)
|
||||
|
||||
if torch.xpu.has_fp64_dtype():
|
||||
original_torch_bmm = torch.bmm
|
||||
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
|
||||
else:
|
||||
# 64 bit attention workarounds for Alchemist:
|
||||
try:
|
||||
from .attention import torch_bmm_32_bit as original_torch_bmm
|
||||
from .attention import scaled_dot_product_attention_32_bit as original_scaled_dot_product_attention
|
||||
except Exception: # pylint: disable=broad-exception-caught
|
||||
original_torch_bmm = torch.bmm
|
||||
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
|
||||
|
||||
# dtype errors:
|
||||
def torch_bmm(input, mat2, *, out=None):
|
||||
if input.dtype != mat2.dtype:
|
||||
mat2 = mat2.to(input.dtype)
|
||||
return original_torch_bmm(input, mat2, out=out)
|
||||
|
||||
def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
|
||||
if query.dtype != key.dtype:
|
||||
key = key.to(dtype=query.dtype)
|
||||
if query.dtype != value.dtype:
|
||||
value = value.to(dtype=query.dtype)
|
||||
return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
|
||||
|
||||
@property
|
||||
def is_cuda(self):
|
||||
return self.device.type == 'xpu'
|
||||
|
||||
def ipex_hijacks():
|
||||
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.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.UntypedStorage.__init__',
|
||||
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.UntypedStorage.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.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.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.load',
|
||||
lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs:
|
||||
orig_func(orig_func, f, map_location=return_xpu(map_location), pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs),
|
||||
lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs: check_device(map_location))
|
||||
if hasattr(torch.xpu, "Generator"):
|
||||
CondFunc('torch.Generator',
|
||||
lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
|
||||
lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
|
||||
else:
|
||||
CondFunc('torch.Generator',
|
||||
lambda orig_func, device=None: orig_func(return_xpu(device)),
|
||||
lambda orig_func, device=None: check_device(device))
|
||||
|
||||
# TiledVAE and ControlNet:
|
||||
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)
|
||||
# Training:
|
||||
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)
|
||||
# BF16:
|
||||
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)
|
||||
# SwinIR BF16:
|
||||
CondFunc('torch.nn.functional.pad',
|
||||
lambda orig_func, input, pad, mode='constant', value=None: orig_func(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16),
|
||||
lambda orig_func, input, pad, mode='constant', value=None: mode == 'reflect' and input.dtype == torch.bfloat16)
|
||||
|
||||
# Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
|
||||
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:
|
||||
# Pin Memory:
|
||||
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.nn.DataParallel = DummyDataParallel
|
||||
torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
|
||||
|
||||
torch.autocast = ipex_autocast
|
||||
torch.backends.cuda.sdp_kernel = return_null_context
|
||||
torch.UntypedStorage.is_cuda = is_cuda
|
||||
|
||||
torch.nn.functional.interpolate = interpolate
|
||||
torch.linalg.solve = linalg_solve
|
||||
|
||||
torch.bmm = torch_bmm
|
||||
torch.cat = torch_cat
|
||||
torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
|
||||
@@ -4,13 +4,6 @@
|
||||
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
|
||||
@@ -1307,19 +1300,19 @@ def load_vae(vae_id, dtype):
|
||||
|
||||
def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64):
|
||||
max_width, max_height = max_reso
|
||||
max_area = max_width * max_height
|
||||
max_area = (max_width // divisible) * (max_height // divisible)
|
||||
|
||||
resos = set()
|
||||
|
||||
width = int(math.sqrt(max_area) // divisible) * divisible
|
||||
resos.add((width, width))
|
||||
size = int(math.sqrt(max_area)) * divisible
|
||||
resos.add((size, size))
|
||||
|
||||
width = min_size
|
||||
while width <= max_size:
|
||||
height = min(max_size, int((max_area // width) // divisible) * divisible)
|
||||
if height >= min_size:
|
||||
resos.add((width, height))
|
||||
resos.add((height, width))
|
||||
size = min_size
|
||||
while size <= max_size:
|
||||
width = size
|
||||
height = min(max_size, (max_area // (width // divisible)) * divisible)
|
||||
resos.add((width, height))
|
||||
resos.add((height, width))
|
||||
|
||||
# # make additional resos
|
||||
# if width >= height and width - divisible >= min_size:
|
||||
@@ -1329,7 +1322,7 @@ def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64)
|
||||
# resos.add((width, height - divisible))
|
||||
# resos.add((height - divisible, width))
|
||||
|
||||
width += divisible
|
||||
size += divisible
|
||||
|
||||
resos = list(resos)
|
||||
resos.sort()
|
||||
|
||||
@@ -131,7 +131,7 @@ DOWN_BLOCK_TYPES = ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDo
|
||||
UP_BLOCK_TYPES = ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"]
|
||||
|
||||
|
||||
# region memory efficient attention
|
||||
# region memory effcient attention
|
||||
|
||||
# FlashAttentionを使うCrossAttention
|
||||
# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py
|
||||
@@ -361,23 +361,6 @@ def get_timestep_embedding(
|
||||
return emb
|
||||
|
||||
|
||||
# Deep Shrink: We do not common this function, because minimize dependencies.
|
||||
def resize_like(x, target, mode="bicubic", align_corners=False):
|
||||
org_dtype = x.dtype
|
||||
if org_dtype == torch.bfloat16:
|
||||
x = x.to(torch.float32)
|
||||
|
||||
if x.shape[-2:] != target.shape[-2:]:
|
||||
if mode == "nearest":
|
||||
x = F.interpolate(x, size=target.shape[-2:], mode=mode)
|
||||
else:
|
||||
x = F.interpolate(x, size=target.shape[-2:], mode=mode, align_corners=align_corners)
|
||||
|
||||
if org_dtype == torch.bfloat16:
|
||||
x = x.to(org_dtype)
|
||||
return x
|
||||
|
||||
|
||||
class SampleOutput:
|
||||
def __init__(self, sample):
|
||||
self.sample = sample
|
||||
@@ -586,9 +569,6 @@ class CrossAttention(nn.Module):
|
||||
self.use_memory_efficient_attention_mem_eff = False
|
||||
self.use_sdpa = False
|
||||
|
||||
# Attention processor
|
||||
self.processor = None
|
||||
|
||||
def set_use_memory_efficient_attention(self, xformers, mem_eff):
|
||||
self.use_memory_efficient_attention_xformers = xformers
|
||||
self.use_memory_efficient_attention_mem_eff = mem_eff
|
||||
@@ -610,28 +590,7 @@ class CrossAttention(nn.Module):
|
||||
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
|
||||
return tensor
|
||||
|
||||
def set_processor(self):
|
||||
return self.processor
|
||||
|
||||
def get_processor(self):
|
||||
return self.processor
|
||||
|
||||
def forward(self, hidden_states, context=None, mask=None, **kwargs):
|
||||
if self.processor is not None:
|
||||
(
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
attention_mask,
|
||||
) = translate_attention_names_from_diffusers(
|
||||
hidden_states=hidden_states, context=context, mask=mask, **kwargs
|
||||
)
|
||||
return self.processor(
|
||||
attn=self,
|
||||
hidden_states=hidden_states,
|
||||
encoder_hidden_states=context,
|
||||
attention_mask=mask,
|
||||
**kwargs
|
||||
)
|
||||
def forward(self, hidden_states, context=None, mask=None):
|
||||
if self.use_memory_efficient_attention_xformers:
|
||||
return self.forward_memory_efficient_xformers(hidden_states, context, mask)
|
||||
if self.use_memory_efficient_attention_mem_eff:
|
||||
@@ -744,21 +703,6 @@ class CrossAttention(nn.Module):
|
||||
out = self.to_out[0](out)
|
||||
return out
|
||||
|
||||
def translate_attention_names_from_diffusers(
|
||||
hidden_states: torch.FloatTensor,
|
||||
context: Optional[torch.FloatTensor] = None,
|
||||
mask: Optional[torch.FloatTensor] = None,
|
||||
# HF naming
|
||||
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None
|
||||
):
|
||||
# translate from hugging face diffusers
|
||||
context = context if context is not None else encoder_hidden_states
|
||||
|
||||
# translate from hugging face diffusers
|
||||
mask = mask if mask is not None else attention_mask
|
||||
|
||||
return hidden_states, context, mask
|
||||
|
||||
# feedforward
|
||||
class GEGLU(nn.Module):
|
||||
@@ -1186,7 +1130,6 @@ class UpBlock2D(nn.Module):
|
||||
# pop res hidden states
|
||||
res_hidden_states = res_hidden_states_tuple[-1]
|
||||
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
||||
|
||||
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
||||
|
||||
if self.training and self.gradient_checkpointing:
|
||||
@@ -1278,7 +1221,6 @@ class CrossAttnUpBlock2D(nn.Module):
|
||||
# pop res hidden states
|
||||
res_hidden_states = res_hidden_states_tuple[-1]
|
||||
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
||||
|
||||
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
||||
|
||||
if self.training and self.gradient_checkpointing:
|
||||
@@ -1389,7 +1331,7 @@ class UNet2DConditionModel(nn.Module):
|
||||
self.out_channels = OUT_CHANNELS
|
||||
|
||||
self.sample_size = sample_size
|
||||
self.prepare_config(sample_size=sample_size)
|
||||
self.prepare_config()
|
||||
|
||||
# state_dictの書式が変わるのでmoduleの持ち方は変えられない
|
||||
|
||||
@@ -1476,8 +1418,8 @@ class UNet2DConditionModel(nn.Module):
|
||||
self.conv_out = nn.Conv2d(BLOCK_OUT_CHANNELS[0], OUT_CHANNELS, kernel_size=3, padding=1)
|
||||
|
||||
# region diffusers compatibility
|
||||
def prepare_config(self, *args, **kwargs):
|
||||
self.config = SimpleNamespace(**kwargs)
|
||||
def prepare_config(self):
|
||||
self.config = SimpleNamespace()
|
||||
|
||||
@property
|
||||
def dtype(self) -> torch.dtype:
|
||||
@@ -1577,6 +1519,7 @@ class UNet2DConditionModel(nn.Module):
|
||||
# 2. pre-process
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
# downblockはforwardで必ずencoder_hidden_statesを受け取るようにしても良さそうだけど、
|
||||
@@ -1661,255 +1604,3 @@ class UNet2DConditionModel(nn.Module):
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
return timesteps
|
||||
|
||||
|
||||
class InferUNet2DConditionModel:
|
||||
def __init__(self, original_unet: UNet2DConditionModel):
|
||||
self.delegate = original_unet
|
||||
|
||||
# override original model's forward method: because forward is not called by `__call__`
|
||||
# overriding `__call__` is not enough, because nn.Module.forward has a special handling
|
||||
self.delegate.forward = self.forward
|
||||
|
||||
# override original model's up blocks' forward method
|
||||
for up_block in self.delegate.up_blocks:
|
||||
if up_block.__class__.__name__ == "UpBlock2D":
|
||||
|
||||
def resnet_wrapper(func, block):
|
||||
def forward(*args, **kwargs):
|
||||
return func(block, *args, **kwargs)
|
||||
|
||||
return forward
|
||||
|
||||
up_block.forward = resnet_wrapper(self.up_block_forward, up_block)
|
||||
|
||||
elif up_block.__class__.__name__ == "CrossAttnUpBlock2D":
|
||||
|
||||
def cross_attn_up_wrapper(func, block):
|
||||
def forward(*args, **kwargs):
|
||||
return func(block, *args, **kwargs)
|
||||
|
||||
return forward
|
||||
|
||||
up_block.forward = cross_attn_up_wrapper(self.cross_attn_up_block_forward, up_block)
|
||||
|
||||
# Deep Shrink
|
||||
self.ds_depth_1 = None
|
||||
self.ds_depth_2 = None
|
||||
self.ds_timesteps_1 = None
|
||||
self.ds_timesteps_2 = None
|
||||
self.ds_ratio = None
|
||||
|
||||
# call original model's methods
|
||||
def __getattr__(self, name):
|
||||
return getattr(self.delegate, name)
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.delegate(*args, **kwargs)
|
||||
|
||||
def set_deep_shrink(self, ds_depth_1, ds_timesteps_1=650, ds_depth_2=None, ds_timesteps_2=None, ds_ratio=0.5):
|
||||
if ds_depth_1 is None:
|
||||
print("Deep Shrink is disabled.")
|
||||
self.ds_depth_1 = None
|
||||
self.ds_timesteps_1 = None
|
||||
self.ds_depth_2 = None
|
||||
self.ds_timesteps_2 = None
|
||||
self.ds_ratio = None
|
||||
else:
|
||||
print(
|
||||
f"Deep Shrink is enabled: [depth={ds_depth_1}/{ds_depth_2}, timesteps={ds_timesteps_1}/{ds_timesteps_2}, ratio={ds_ratio}]"
|
||||
)
|
||||
self.ds_depth_1 = ds_depth_1
|
||||
self.ds_timesteps_1 = ds_timesteps_1
|
||||
self.ds_depth_2 = ds_depth_2 if ds_depth_2 is not None else -1
|
||||
self.ds_timesteps_2 = ds_timesteps_2 if ds_timesteps_2 is not None else 1000
|
||||
self.ds_ratio = ds_ratio
|
||||
|
||||
def up_block_forward(self, _self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
||||
for resnet in _self.resnets:
|
||||
# pop res hidden states
|
||||
res_hidden_states = res_hidden_states_tuple[-1]
|
||||
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
||||
|
||||
# Deep Shrink
|
||||
if res_hidden_states.shape[-2:] != hidden_states.shape[-2:]:
|
||||
hidden_states = resize_like(hidden_states, res_hidden_states)
|
||||
|
||||
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
||||
hidden_states = resnet(hidden_states, temb)
|
||||
|
||||
if _self.upsamplers is not None:
|
||||
for upsampler in _self.upsamplers:
|
||||
hidden_states = upsampler(hidden_states, upsample_size)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def cross_attn_up_block_forward(
|
||||
self,
|
||||
_self,
|
||||
hidden_states,
|
||||
res_hidden_states_tuple,
|
||||
temb=None,
|
||||
encoder_hidden_states=None,
|
||||
upsample_size=None,
|
||||
):
|
||||
for resnet, attn in zip(_self.resnets, _self.attentions):
|
||||
# pop res hidden states
|
||||
res_hidden_states = res_hidden_states_tuple[-1]
|
||||
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
||||
|
||||
# Deep Shrink
|
||||
if res_hidden_states.shape[-2:] != hidden_states.shape[-2:]:
|
||||
hidden_states = resize_like(hidden_states, res_hidden_states)
|
||||
|
||||
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
||||
hidden_states = resnet(hidden_states, temb)
|
||||
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
||||
|
||||
if _self.upsamplers is not None:
|
||||
for upsampler in _self.upsamplers:
|
||||
hidden_states = upsampler(hidden_states, upsample_size)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.FloatTensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
return_dict: bool = True,
|
||||
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
||||
mid_block_additional_residual: Optional[torch.Tensor] = None,
|
||||
) -> Union[Dict, Tuple]:
|
||||
r"""
|
||||
current implementation is a copy of `UNet2DConditionModel.forward()` with Deep Shrink.
|
||||
"""
|
||||
|
||||
r"""
|
||||
Args:
|
||||
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
|
||||
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
|
||||
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
|
||||
return_dict (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to return a dict instead of a plain tuple.
|
||||
|
||||
Returns:
|
||||
`SampleOutput` or `tuple`:
|
||||
`SampleOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
|
||||
"""
|
||||
|
||||
_self = self.delegate
|
||||
|
||||
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
||||
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
|
||||
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
||||
# on the fly if necessary.
|
||||
# デフォルトではサンプルは「2^アップサンプルの数」、つまり64の倍数である必要がある
|
||||
# ただそれ以外のサイズにも対応できるように、必要ならアップサンプルのサイズを変更する
|
||||
# 多分画質が悪くなるので、64で割り切れるようにしておくのが良い
|
||||
default_overall_up_factor = 2**_self.num_upsamplers
|
||||
|
||||
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
||||
# 64で割り切れないときはupsamplerにサイズを伝える
|
||||
forward_upsample_size = False
|
||||
upsample_size = None
|
||||
|
||||
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
||||
# logger.info("Forward upsample size to force interpolation output size.")
|
||||
forward_upsample_size = True
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
timesteps = _self.handle_unusual_timesteps(sample, timesteps) # 変な時だけ処理
|
||||
|
||||
t_emb = _self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
# timestepsは重みを含まないので常にfloat32のテンソルを返す
|
||||
# しかしtime_embeddingはfp16で動いているかもしれないので、ここでキャストする必要がある
|
||||
# time_projでキャストしておけばいいんじゃね？
|
||||
t_emb = t_emb.to(dtype=_self.dtype)
|
||||
emb = _self.time_embedding(t_emb)
|
||||
|
||||
# 2. pre-process
|
||||
sample = _self.conv_in(sample)
|
||||
|
||||
down_block_res_samples = (sample,)
|
||||
for depth, downsample_block in enumerate(_self.down_blocks):
|
||||
# Deep Shrink
|
||||
if self.ds_depth_1 is not None:
|
||||
if (depth == self.ds_depth_1 and timesteps[0] >= self.ds_timesteps_1) or (
|
||||
self.ds_depth_2 is not None
|
||||
and depth == self.ds_depth_2
|
||||
and timesteps[0] < self.ds_timesteps_1
|
||||
and timesteps[0] >= self.ds_timesteps_2
|
||||
):
|
||||
org_dtype = sample.dtype
|
||||
if org_dtype == torch.bfloat16:
|
||||
sample = sample.to(torch.float32)
|
||||
sample = F.interpolate(sample, scale_factor=self.ds_ratio, mode="bicubic", align_corners=False).to(org_dtype)
|
||||
|
||||
# downblockはforwardで必ずencoder_hidden_statesを受け取るようにしても良さそうだけど、
|
||||
# まあこちらのほうがわかりやすいかもしれない
|
||||
if downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# skip connectionにControlNetの出力を追加する
|
||||
if down_block_additional_residuals is not None:
|
||||
down_block_res_samples = list(down_block_res_samples)
|
||||
for i in range(len(down_block_res_samples)):
|
||||
down_block_res_samples[i] += down_block_additional_residuals[i]
|
||||
down_block_res_samples = tuple(down_block_res_samples)
|
||||
|
||||
# 4. mid
|
||||
sample = _self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
|
||||
|
||||
# ControlNetの出力を追加する
|
||||
if mid_block_additional_residual is not None:
|
||||
sample += mid_block_additional_residual
|
||||
|
||||
# 5. up
|
||||
for i, upsample_block in enumerate(_self.up_blocks):
|
||||
is_final_block = i == len(_self.up_blocks) - 1
|
||||
|
||||
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
||||
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # skip connection
|
||||
|
||||
# if we have not reached the final block and need to forward the upsample size, we do it here
|
||||
# 前述のように最後のブロック以外ではupsample_sizeを伝える
|
||||
if not is_final_block and forward_upsample_size:
|
||||
upsample_size = down_block_res_samples[-1].shape[2:]
|
||||
|
||||
if upsample_block.has_cross_attention:
|
||||
sample = upsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
res_hidden_states_tuple=res_samples,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
upsample_size=upsample_size,
|
||||
)
|
||||
else:
|
||||
sample = upsample_block(
|
||||
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
|
||||
)
|
||||
|
||||
# 6. post-process
|
||||
sample = _self.conv_norm_out(sample)
|
||||
sample = _self.conv_act(sample)
|
||||
sample = _self.conv_out(sample)
|
||||
|
||||
if not return_dict:
|
||||
return (sample,)
|
||||
|
||||
return SampleOutput(sample=sample)
|
||||
|
||||
@@ -133,12 +133,6 @@ def convert_sdxl_text_encoder_2_checkpoint(checkpoint, max_length):
|
||||
# logit_scale はDiffusersには含まれないが、保存時に戻したいので別途返す
|
||||
logit_scale = checkpoint.get(SDXL_KEY_PREFIX + "logit_scale", None)
|
||||
|
||||
# temporary workaround for text_projection.weight.weight for Playground-v2
|
||||
if "text_projection.weight.weight" in new_sd:
|
||||
print(f"convert_sdxl_text_encoder_2_checkpoint: convert text_projection.weight.weight to text_projection.weight")
|
||||
new_sd["text_projection.weight"] = new_sd["text_projection.weight.weight"]
|
||||
del new_sd["text_projection.weight.weight"]
|
||||
|
||||
return new_sd, logit_scale
|
||||
|
||||
|
||||
@@ -264,7 +258,7 @@ 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)
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
|
||||
import math
|
||||
from types import SimpleNamespace
|
||||
from typing import Any, Optional
|
||||
from typing import Optional
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
@@ -41,7 +41,7 @@ TIME_EMBED_DIM = 320 * 4
|
||||
|
||||
USE_REENTRANT = True
|
||||
|
||||
# region memory efficient attention
|
||||
# region memory effcient attention
|
||||
|
||||
# FlashAttentionを使うCrossAttention
|
||||
# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py
|
||||
@@ -266,23 +266,6 @@ def get_timestep_embedding(
|
||||
return emb
|
||||
|
||||
|
||||
# Deep Shrink: We do not common this function, because minimize dependencies.
|
||||
def resize_like(x, target, mode="bicubic", align_corners=False):
|
||||
org_dtype = x.dtype
|
||||
if org_dtype == torch.bfloat16:
|
||||
x = x.to(torch.float32)
|
||||
|
||||
if x.shape[-2:] != target.shape[-2:]:
|
||||
if mode == "nearest":
|
||||
x = F.interpolate(x, size=target.shape[-2:], mode=mode)
|
||||
else:
|
||||
x = F.interpolate(x, size=target.shape[-2:], mode=mode, align_corners=align_corners)
|
||||
|
||||
if org_dtype == torch.bfloat16:
|
||||
x = x.to(org_dtype)
|
||||
return x
|
||||
|
||||
|
||||
class GroupNorm32(nn.GroupNorm):
|
||||
def forward(self, x):
|
||||
if self.weight.dtype != torch.float32:
|
||||
@@ -1013,6 +996,109 @@ class SdxlUNet2DConditionModel(nn.Module):
|
||||
[GroupNorm32(32, self.model_channels), nn.SiLU(), nn.Conv2d(self.model_channels, self.out_channels, 3, padding=1)]
|
||||
)
|
||||
|
||||
# FreeU
|
||||
self.freeU = False
|
||||
self.freeUB1 = 1.0
|
||||
self.freeUB2 = 1.0
|
||||
self.freeUS1 = 1.0
|
||||
self.freeUS2 = 1.0
|
||||
self.freeURThres = 1
|
||||
|
||||
# implementation of FreeU
|
||||
# FreeU: Free Lunch in Diffusion U-Net https://arxiv.org/abs/2309.11497
|
||||
|
||||
def set_free_u_enabled(self, enabled: bool, b1=1.0, b2=1.0, s1=1.0, s2=1.0, rthresh=1):
|
||||
print(f"FreeU: {enabled}, b1={b1}, b2={b2}, s1={s1}, s2={s2}, rthresh={rthresh}")
|
||||
self.freeU = enabled
|
||||
self.freeUB1 = b1
|
||||
self.freeUB2 = b2
|
||||
self.freeUS1 = s1
|
||||
self.freeUS2 = s2
|
||||
self.freeURThres = rthresh
|
||||
|
||||
def spectral_modulation(self, skip_feature, sl=1.0, rthresh=1):
|
||||
"""
|
||||
スキップ特徴を周波数領域で修正する関数
|
||||
|
||||
:param skip_feature: スキップ特徴のテンソル [b, c, H, W]
|
||||
:param sl: スケーリング係数
|
||||
:param rthresh: 周波数の閾値
|
||||
:return: 修正されたスキップ特徴
|
||||
"""
|
||||
|
||||
import torch.fft
|
||||
|
||||
r"""
|
||||
# 論文に従った実装
|
||||
|
||||
org_dtype = skip_feature.dtype
|
||||
if org_dtype == torch.bfloat16:
|
||||
skip_feature = skip_feature.to(torch.float32)
|
||||
|
||||
# FFTを計算
|
||||
F = torch.fft.fftn(skip_feature, dim=(2, 3))
|
||||
|
||||
# 周波数領域での座標を計算
|
||||
freq_x = torch.fft.fftfreq(skip_feature.size(2), d=1 / skip_feature.size(2)).to(skip_feature.device)
|
||||
freq_y = torch.fft.fftfreq(skip_feature.size(3), d=1 / skip_feature.size(3)).to(skip_feature.device)
|
||||
|
||||
# 2Dグリッドを作成
|
||||
freq_x = freq_x[:, None] # [H, 1]
|
||||
freq_y = freq_y[None, :] # [1, W]
|
||||
|
||||
# ラジアス（距離）を計算
|
||||
r = torch.sqrt(freq_x**2 + freq_y**2)
|
||||
# 32,32: tensor(0., device='cuda:0') tensor(22.6274, device='cuda:0') tensor(12.2521, device='cuda:0')
|
||||
# 64,64: tensor(0., device='cuda:0') tensor(45.2548, device='cuda:0') tensor(24.4908, device='cuda:0')
|
||||
# 128,128: tensor(0., device='cuda:0') tensor(90.5097, device='cuda:0') tensor(48.9748, device='cuda:0')
|
||||
|
||||
# マスクを作成
|
||||
mask = torch.ones_like(r)
|
||||
mask[r < rthresh] = sl
|
||||
|
||||
# b,c,H,Wの形状にブロードキャスト
|
||||
# TODO shapeごとに同じなのでキャッシュすると良さそう
|
||||
mask = mask[None, None, :, :]
|
||||
|
||||
# 周波数領域での要素ごとの乗算
|
||||
F_prime = F * mask
|
||||
|
||||
# 逆FFTを計算
|
||||
modified_skip_feature = torch.fft.ifftn(F_prime, dim=(2, 3))
|
||||
|
||||
modified_skip_feature = modified_skip_feature.real # 実部のみを取得
|
||||
"""
|
||||
|
||||
# 公式リポジトリの実装
|
||||
|
||||
org_dtype = skip_feature.dtype
|
||||
|
||||
x = skip_feature
|
||||
threshold = rthresh
|
||||
scale = sl
|
||||
|
||||
# FFT
|
||||
x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
|
||||
x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
|
||||
|
||||
B, C, H, W = x_freq.shape
|
||||
mask = torch.ones((B, C, H, W), device=x.device)
|
||||
|
||||
crow, ccol = H // 2, W // 2
|
||||
mask[..., crow - threshold : crow + threshold, ccol - threshold : ccol + threshold] = scale
|
||||
x_freq = x_freq * mask
|
||||
|
||||
# IFFT
|
||||
x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
|
||||
x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
|
||||
|
||||
modified_skip_feature = x_filtered
|
||||
|
||||
# if org_dtype == torch.bfloat16:
|
||||
modified_skip_feature = modified_skip_feature.to(org_dtype)
|
||||
|
||||
return modified_skip_feature
|
||||
|
||||
# region diffusers compatibility
|
||||
def prepare_config(self):
|
||||
self.config = SimpleNamespace()
|
||||
@@ -1094,14 +1180,32 @@ class SdxlUNet2DConditionModel(nn.Module):
|
||||
|
||||
# h = x.type(self.dtype)
|
||||
h = x
|
||||
|
||||
for module in self.input_blocks:
|
||||
h = call_module(module, h, emb, context)
|
||||
hs.append(h)
|
||||
|
||||
if self.freeU:
|
||||
ch = h.shape[1]
|
||||
s = self.freeUS1 if ch == 1280 else (self.freeUS2 if ch == 640 else 1.0)
|
||||
if s == 1.0:
|
||||
h_mod = h
|
||||
else:
|
||||
h_mod = self.spectral_modulation(h, s, self.freeURThres)
|
||||
hs.append(h_mod)
|
||||
else:
|
||||
hs.append(h)
|
||||
|
||||
h = call_module(self.middle_block, h, emb, context)
|
||||
|
||||
for module in self.output_blocks:
|
||||
if self.freeU:
|
||||
ch = h.shape[1]
|
||||
if ch == 1280:
|
||||
h[:, : ch // 2] = h[:, : ch // 2] * self.freeUB1
|
||||
elif ch == 640:
|
||||
h[:, : ch // 2] = h[:, : ch // 2] * self.freeUB2
|
||||
# else:
|
||||
# print(f"disable freeU: {ch}")
|
||||
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = call_module(module, h, emb, context)
|
||||
|
||||
@@ -1111,121 +1215,6 @@ class SdxlUNet2DConditionModel(nn.Module):
|
||||
return h
|
||||
|
||||
|
||||
class InferSdxlUNet2DConditionModel:
|
||||
def __init__(self, original_unet: SdxlUNet2DConditionModel, **kwargs):
|
||||
self.delegate = original_unet
|
||||
|
||||
# override original model's forward method: because forward is not called by `__call__`
|
||||
# overriding `__call__` is not enough, because nn.Module.forward has a special handling
|
||||
self.delegate.forward = self.forward
|
||||
|
||||
# Deep Shrink
|
||||
self.ds_depth_1 = None
|
||||
self.ds_depth_2 = None
|
||||
self.ds_timesteps_1 = None
|
||||
self.ds_timesteps_2 = None
|
||||
self.ds_ratio = None
|
||||
|
||||
# call original model's methods
|
||||
def __getattr__(self, name):
|
||||
return getattr(self.delegate, name)
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.delegate(*args, **kwargs)
|
||||
|
||||
def set_deep_shrink(self, ds_depth_1, ds_timesteps_1=650, ds_depth_2=None, ds_timesteps_2=None, ds_ratio=0.5):
|
||||
if ds_depth_1 is None:
|
||||
print("Deep Shrink is disabled.")
|
||||
self.ds_depth_1 = None
|
||||
self.ds_timesteps_1 = None
|
||||
self.ds_depth_2 = None
|
||||
self.ds_timesteps_2 = None
|
||||
self.ds_ratio = None
|
||||
else:
|
||||
print(
|
||||
f"Deep Shrink is enabled: [depth={ds_depth_1}/{ds_depth_2}, timesteps={ds_timesteps_1}/{ds_timesteps_2}, ratio={ds_ratio}]"
|
||||
)
|
||||
self.ds_depth_1 = ds_depth_1
|
||||
self.ds_timesteps_1 = ds_timesteps_1
|
||||
self.ds_depth_2 = ds_depth_2 if ds_depth_2 is not None else -1
|
||||
self.ds_timesteps_2 = ds_timesteps_2 if ds_timesteps_2 is not None else 1000
|
||||
self.ds_ratio = ds_ratio
|
||||
|
||||
def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
|
||||
r"""
|
||||
current implementation is a copy of `SdxlUNet2DConditionModel.forward()` with Deep Shrink.
|
||||
"""
|
||||
_self = self.delegate
|
||||
|
||||
# broadcast timesteps to batch dimension
|
||||
timesteps = timesteps.expand(x.shape[0])
|
||||
|
||||
hs = []
|
||||
t_emb = get_timestep_embedding(timesteps, _self.model_channels) # , repeat_only=False)
|
||||
t_emb = t_emb.to(x.dtype)
|
||||
emb = _self.time_embed(t_emb)
|
||||
|
||||
assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"
|
||||
assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}"
|
||||
# assert x.dtype == _self.dtype
|
||||
emb = emb + _self.label_emb(y)
|
||||
|
||||
def call_module(module, h, emb, context):
|
||||
x = h
|
||||
for layer in module:
|
||||
# print(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if context is not None else None)
|
||||
if isinstance(layer, ResnetBlock2D):
|
||||
x = layer(x, emb)
|
||||
elif isinstance(layer, Transformer2DModel):
|
||||
x = layer(x, context)
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
# h = x.type(self.dtype)
|
||||
h = x
|
||||
|
||||
for depth, module in enumerate(_self.input_blocks):
|
||||
# Deep Shrink
|
||||
if self.ds_depth_1 is not None:
|
||||
if (depth == self.ds_depth_1 and timesteps[0] >= self.ds_timesteps_1) or (
|
||||
self.ds_depth_2 is not None
|
||||
and depth == self.ds_depth_2
|
||||
and timesteps[0] < self.ds_timesteps_1
|
||||
and timesteps[0] >= self.ds_timesteps_2
|
||||
):
|
||||
# print("downsample", h.shape, self.ds_ratio)
|
||||
org_dtype = h.dtype
|
||||
if org_dtype == torch.bfloat16:
|
||||
h = h.to(torch.float32)
|
||||
h = F.interpolate(h, scale_factor=self.ds_ratio, mode="bicubic", align_corners=False).to(org_dtype)
|
||||
|
||||
h = call_module(module, h, emb, context)
|
||||
hs.append(h)
|
||||
|
||||
h = call_module(_self.middle_block, h, emb, context)
|
||||
|
||||
for module in _self.output_blocks:
|
||||
# Deep Shrink
|
||||
if self.ds_depth_1 is not None:
|
||||
if hs[-1].shape[-2:] != h.shape[-2:]:
|
||||
# print("upsample", h.shape, hs[-1].shape)
|
||||
h = resize_like(h, hs[-1])
|
||||
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = call_module(module, h, emb, context)
|
||||
|
||||
# Deep Shrink: in case of depth 0
|
||||
if self.ds_depth_1 == 0 and h.shape[-2:] != x.shape[-2:]:
|
||||
# print("upsample", h.shape, x.shape)
|
||||
h = resize_like(h, x)
|
||||
|
||||
h = h.type(x.dtype)
|
||||
h = call_module(_self.out, h, emb, context)
|
||||
|
||||
return h
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import time
|
||||
|
||||
|
||||
@@ -51,6 +51,8 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
|
||||
torch.cuda.empty_cache()
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
text_encoder1, text_encoder2, unet = train_util.transform_models_if_DDP([text_encoder1, text_encoder2, unet])
|
||||
|
||||
return load_stable_diffusion_format, text_encoder1, text_encoder2, vae, unet, logit_scale, ckpt_info
|
||||
|
||||
|
||||
|
||||
@@ -62,7 +62,7 @@ def cat_h(sliced):
|
||||
return x
|
||||
|
||||
|
||||
def resblock_forward(_self, num_slices, input_tensor, temb, **kwargs):
|
||||
def resblock_forward(_self, num_slices, input_tensor, temb):
|
||||
assert _self.upsample is None and _self.downsample is None
|
||||
assert _self.norm1.num_groups == _self.norm2.num_groups
|
||||
assert temb is None
|
||||
|
||||
@@ -3,7 +3,6 @@
|
||||
import argparse
|
||||
import ast
|
||||
import asyncio
|
||||
import datetime
|
||||
import importlib
|
||||
import json
|
||||
import pathlib
|
||||
@@ -19,7 +18,7 @@ from typing import (
|
||||
Tuple,
|
||||
Union,
|
||||
)
|
||||
from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs
|
||||
from accelerate import Accelerator
|
||||
import gc
|
||||
import glob
|
||||
import math
|
||||
@@ -97,7 +96,6 @@ try:
|
||||
except:
|
||||
pass
|
||||
|
||||
# JPEG-XL on Linux
|
||||
try:
|
||||
from jxlpy import JXLImagePlugin
|
||||
|
||||
@@ -105,14 +103,6 @@ try:
|
||||
except:
|
||||
pass
|
||||
|
||||
# JPEG-XL on Windows
|
||||
try:
|
||||
import pillow_jxl
|
||||
|
||||
IMAGE_EXTENSIONS.extend([".jxl", ".JXL"])
|
||||
except:
|
||||
pass
|
||||
|
||||
IMAGE_TRANSFORMS = transforms.Compose(
|
||||
[
|
||||
transforms.ToTensor(),
|
||||
@@ -149,13 +139,6 @@ class ImageInfo:
|
||||
|
||||
class BucketManager:
|
||||
def __init__(self, no_upscale, max_reso, min_size, max_size, reso_steps) -> None:
|
||||
if max_size is not None:
|
||||
if max_reso is not None:
|
||||
assert max_size >= max_reso[0], "the max_size should be larger than the width of max_reso"
|
||||
assert max_size >= max_reso[1], "the max_size should be larger than the height of max_reso"
|
||||
if min_size is not None:
|
||||
assert max_size >= min_size, "the max_size should be larger than the min_size"
|
||||
|
||||
self.no_upscale = no_upscale
|
||||
if max_reso is None:
|
||||
self.max_reso = None
|
||||
@@ -349,9 +332,7 @@ class BaseSubset:
|
||||
image_dir: Optional[str],
|
||||
num_repeats: int,
|
||||
shuffle_caption: bool,
|
||||
caption_separator: str,
|
||||
keep_tokens: int,
|
||||
keep_tokens_separator: str,
|
||||
color_aug: bool,
|
||||
flip_aug: bool,
|
||||
face_crop_aug_range: Optional[Tuple[float, float]],
|
||||
@@ -367,9 +348,7 @@ class BaseSubset:
|
||||
self.image_dir = image_dir
|
||||
self.num_repeats = num_repeats
|
||||
self.shuffle_caption = shuffle_caption
|
||||
self.caption_separator = caption_separator
|
||||
self.keep_tokens = keep_tokens
|
||||
self.keep_tokens_separator = keep_tokens_separator
|
||||
self.color_aug = color_aug
|
||||
self.flip_aug = flip_aug
|
||||
self.face_crop_aug_range = face_crop_aug_range
|
||||
@@ -395,9 +374,7 @@ class DreamBoothSubset(BaseSubset):
|
||||
caption_extension: str,
|
||||
num_repeats,
|
||||
shuffle_caption,
|
||||
caption_separator: str,
|
||||
keep_tokens,
|
||||
keep_tokens_separator,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
face_crop_aug_range,
|
||||
@@ -416,9 +393,7 @@ class DreamBoothSubset(BaseSubset):
|
||||
image_dir,
|
||||
num_repeats,
|
||||
shuffle_caption,
|
||||
caption_separator,
|
||||
keep_tokens,
|
||||
keep_tokens_separator,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
face_crop_aug_range,
|
||||
@@ -451,9 +426,7 @@ class FineTuningSubset(BaseSubset):
|
||||
metadata_file: str,
|
||||
num_repeats,
|
||||
shuffle_caption,
|
||||
caption_separator,
|
||||
keep_tokens,
|
||||
keep_tokens_separator,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
face_crop_aug_range,
|
||||
@@ -472,9 +445,7 @@ class FineTuningSubset(BaseSubset):
|
||||
image_dir,
|
||||
num_repeats,
|
||||
shuffle_caption,
|
||||
caption_separator,
|
||||
keep_tokens,
|
||||
keep_tokens_separator,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
face_crop_aug_range,
|
||||
@@ -504,9 +475,7 @@ class ControlNetSubset(BaseSubset):
|
||||
caption_extension: str,
|
||||
num_repeats,
|
||||
shuffle_caption,
|
||||
caption_separator,
|
||||
keep_tokens,
|
||||
keep_tokens_separator,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
face_crop_aug_range,
|
||||
@@ -525,9 +494,7 @@ class ControlNetSubset(BaseSubset):
|
||||
image_dir,
|
||||
num_repeats,
|
||||
shuffle_caption,
|
||||
caption_separator,
|
||||
keep_tokens,
|
||||
keep_tokens_separator,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
face_crop_aug_range,
|
||||
@@ -662,33 +629,15 @@ class BaseDataset(torch.utils.data.Dataset):
|
||||
caption = ""
|
||||
else:
|
||||
if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0:
|
||||
fixed_tokens = []
|
||||
flex_tokens = []
|
||||
if (
|
||||
hasattr(subset, "keep_tokens_separator")
|
||||
and subset.keep_tokens_separator
|
||||
and subset.keep_tokens_separator in caption
|
||||
):
|
||||
fixed_part, flex_part = caption.split(subset.keep_tokens_separator, 1)
|
||||
fixed_tokens = [t.strip() for t in fixed_part.split(subset.caption_separator) if t.strip()]
|
||||
flex_tokens = [t.strip() for t in flex_part.split(subset.caption_separator) if t.strip()]
|
||||
else:
|
||||
tokens = [t.strip() for t in caption.strip().split(subset.caption_separator)]
|
||||
flex_tokens = tokens[:]
|
||||
if subset.keep_tokens > 0:
|
||||
fixed_tokens = flex_tokens[: subset.keep_tokens]
|
||||
flex_tokens = tokens[subset.keep_tokens :]
|
||||
|
||||
tokens = [t.strip() for t in caption.strip().split(",")]
|
||||
if subset.token_warmup_step < 1: # 初回に上書きする
|
||||
subset.token_warmup_step = math.floor(subset.token_warmup_step * self.max_train_steps)
|
||||
if subset.token_warmup_step and self.current_step < subset.token_warmup_step:
|
||||
tokens_len = (
|
||||
math.floor(
|
||||
(self.current_step) * ((len(flex_tokens) - subset.token_warmup_min) / (subset.token_warmup_step))
|
||||
)
|
||||
math.floor((self.current_step) * ((len(tokens) - subset.token_warmup_min) / (subset.token_warmup_step)))
|
||||
+ subset.token_warmup_min
|
||||
)
|
||||
flex_tokens = flex_tokens[:tokens_len]
|
||||
tokens = tokens[:tokens_len]
|
||||
|
||||
def dropout_tags(tokens):
|
||||
if subset.caption_tag_dropout_rate <= 0:
|
||||
@@ -699,6 +648,12 @@ class BaseDataset(torch.utils.data.Dataset):
|
||||
l.append(token)
|
||||
return l
|
||||
|
||||
fixed_tokens = []
|
||||
flex_tokens = tokens[:]
|
||||
if subset.keep_tokens > 0:
|
||||
fixed_tokens = flex_tokens[: subset.keep_tokens]
|
||||
flex_tokens = tokens[subset.keep_tokens :]
|
||||
|
||||
if subset.shuffle_caption:
|
||||
random.shuffle(flex_tokens)
|
||||
|
||||
@@ -1742,9 +1697,7 @@ class ControlNetDataset(BaseDataset):
|
||||
subset.caption_extension,
|
||||
subset.num_repeats,
|
||||
subset.shuffle_caption,
|
||||
subset.caption_separator,
|
||||
subset.keep_tokens,
|
||||
subset.keep_tokens_separator,
|
||||
subset.color_aug,
|
||||
subset.flip_aug,
|
||||
subset.face_crop_aug_range,
|
||||
@@ -2687,7 +2640,7 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
|
||||
"--optimizer_type",
|
||||
type=str,
|
||||
default="",
|
||||
help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, AdaFactor",
|
||||
help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW8bit, Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, AdaFactor",
|
||||
)
|
||||
|
||||
# backward compatibility
|
||||
@@ -2893,22 +2846,6 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
|
||||
parser.add_argument(
|
||||
"--full_bf16", action="store_true", help="bf16 training including gradients / 勾配も含めてbf16で学習する"
|
||||
) # TODO move to SDXL training, because it is not supported by SD1/2
|
||||
parser.add_argument(
|
||||
"--ddp_timeout",
|
||||
type=int,
|
||||
default=None,
|
||||
help="DDP timeout (min, None for default of accelerate) / DDPのタイムアウト（分、Noneでaccelerateのデフォルト）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ddp_gradient_as_bucket_view",
|
||||
action="store_true",
|
||||
help="enable gradient_as_bucket_view for DDP / DDPでgradient_as_bucket_viewを有効にする",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ddp_static_graph",
|
||||
action="store_true",
|
||||
help="enable static_graph for DDP / DDPでstatic_graphを有効にする",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--clip_skip",
|
||||
type=int,
|
||||
@@ -2935,12 +2872,6 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
|
||||
default=None,
|
||||
help="name of tracker to use for logging, default is script-specific default name / ログ出力に使用するtrackerの名前、省略時はスクリプトごとのデフォルト名",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--wandb_run_name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="The name of the specific wandb session / wandb ログに表示される特定の実行の名前",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--log_tracker_config",
|
||||
type=str,
|
||||
@@ -3017,7 +2948,6 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
|
||||
parser.add_argument(
|
||||
"--sample_every_n_steps", type=int, default=None, help="generate sample images every N steps / 学習中のモデルで指定ステップごとにサンプル出力する"
|
||||
)
|
||||
parser.add_argument("--sample_at_first", action="store_true", help="generate sample images before training / 学習前にサンプル出力する")
|
||||
parser.add_argument(
|
||||
"--sample_every_n_epochs",
|
||||
type=int,
|
||||
@@ -3151,8 +3081,9 @@ def add_dataset_arguments(
|
||||
):
|
||||
# dataset common
|
||||
parser.add_argument("--train_data_dir", type=str, default=None, help="directory for train images / 学習画像データのディレクトリ")
|
||||
parser.add_argument("--shuffle_caption", action="store_true", help="shuffle separated caption / 区切られたcaptionの各要素をshuffleする")
|
||||
parser.add_argument("--caption_separator", type=str, default=",", help="separator for caption / captionの区切り文字")
|
||||
parser.add_argument(
|
||||
"--shuffle_caption", action="store_true", help="shuffle comma-separated caption / コンマで区切られたcaptionの各要素をshuffleする"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--caption_extension", type=str, default=".caption", help="extension of caption files / 読み込むcaptionファイルの拡張子"
|
||||
)
|
||||
@@ -3168,13 +3099,6 @@ def add_dataset_arguments(
|
||||
default=0,
|
||||
help="keep heading N tokens when shuffling caption tokens (token means comma separated strings) / captionのシャッフル時に、先頭からこの個数のトークンをシャッフルしないで残す（トークンはカンマ区切りの各部分を意味する）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--keep_tokens_separator",
|
||||
type=str,
|
||||
default="",
|
||||
help="A custom separator to divide the caption into fixed and flexible parts. Tokens before this separator will not be shuffled. If not specified, '--keep_tokens' will be used to determine the fixed number of tokens."
|
||||
+ " / captionを固定部分と可変部分に分けるためのカスタム区切り文字。この区切り文字より前のトークンはシャッフルされない。指定しない場合、'--keep_tokens'が固定部分のトークン数として使用される。",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--caption_prefix",
|
||||
type=str,
|
||||
@@ -3426,7 +3350,7 @@ def resume_from_local_or_hf_if_specified(accelerator, args):
|
||||
|
||||
|
||||
def get_optimizer(args, trainable_params):
|
||||
# "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, Adafactor"
|
||||
# "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, PagedAdamW8bit, Lion8bit, PagedLion8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, Adafactor"
|
||||
|
||||
optimizer_type = args.optimizer_type
|
||||
if args.use_8bit_adam:
|
||||
@@ -3530,34 +3454,6 @@ def get_optimizer(args, trainable_params):
|
||||
|
||||
optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
|
||||
|
||||
elif optimizer_type == "PagedAdamW".lower():
|
||||
print(f"use PagedAdamW optimizer | {optimizer_kwargs}")
|
||||
try:
|
||||
import bitsandbytes as bnb
|
||||
except ImportError:
|
||||
raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです")
|
||||
try:
|
||||
optimizer_class = bnb.optim.PagedAdamW
|
||||
except AttributeError:
|
||||
raise AttributeError(
|
||||
"No PagedAdamW. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamWが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください"
|
||||
)
|
||||
optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
|
||||
|
||||
elif optimizer_type == "PagedAdamW32bit".lower():
|
||||
print(f"use 32-bit PagedAdamW optimizer | {optimizer_kwargs}")
|
||||
try:
|
||||
import bitsandbytes as bnb
|
||||
except ImportError:
|
||||
raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです")
|
||||
try:
|
||||
optimizer_class = bnb.optim.PagedAdamW32bit
|
||||
except AttributeError:
|
||||
raise AttributeError(
|
||||
"No PagedAdamW32bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamW32bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください"
|
||||
)
|
||||
optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
|
||||
|
||||
elif optimizer_type == "SGDNesterov".lower():
|
||||
print(f"use SGD with Nesterov optimizer | {optimizer_kwargs}")
|
||||
if "momentum" not in optimizer_kwargs:
|
||||
@@ -3876,19 +3772,11 @@ def prepare_accelerator(args: argparse.Namespace):
|
||||
if args.wandb_api_key is not None:
|
||||
wandb.login(key=args.wandb_api_key)
|
||||
|
||||
kwargs_handlers = (
|
||||
InitProcessGroupKwargs(timeout=datetime.timedelta(minutes=args.ddp_timeout)) if args.ddp_timeout else None,
|
||||
DistributedDataParallelKwargs(gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, static_graph=args.ddp_static_graph)
|
||||
if args.ddp_gradient_as_bucket_view or args.ddp_static_graph
|
||||
else None,
|
||||
)
|
||||
kwargs_handlers = list(filter(lambda x: x is not None, kwargs_handlers))
|
||||
accelerator = Accelerator(
|
||||
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
||||
mixed_precision=args.mixed_precision,
|
||||
log_with=log_with,
|
||||
project_dir=logging_dir,
|
||||
kwargs_handlers=kwargs_handlers,
|
||||
)
|
||||
return accelerator
|
||||
|
||||
@@ -3957,6 +3845,17 @@ def _load_target_model(args: argparse.Namespace, weight_dtype, device="cpu", une
|
||||
return text_encoder, vae, unet, load_stable_diffusion_format
|
||||
|
||||
|
||||
# TODO remove this function in the future
|
||||
def transform_if_model_is_DDP(text_encoder, unet, network=None):
|
||||
# Transform text_encoder, unet and network from DistributedDataParallel
|
||||
return (model.module if type(model) == DDP else model for model in [text_encoder, unet, network] if model is not None)
|
||||
|
||||
|
||||
def transform_models_if_DDP(models):
|
||||
# Transform text_encoder, unet and network from DistributedDataParallel
|
||||
return [model.module if type(model) == DDP else model for model in models if model is not None]
|
||||
|
||||
|
||||
def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=False):
|
||||
# load models for each process
|
||||
for pi in range(accelerator.state.num_processes):
|
||||
@@ -3980,6 +3879,8 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio
|
||||
torch.cuda.empty_cache()
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
text_encoder, unet = transform_if_model_is_DDP(text_encoder, unet)
|
||||
|
||||
return text_encoder, vae, unet, load_stable_diffusion_format
|
||||
|
||||
|
||||
@@ -4091,7 +3992,6 @@ def get_hidden_states_sdxl(
|
||||
text_encoder1: CLIPTextModel,
|
||||
text_encoder2: CLIPTextModelWithProjection,
|
||||
weight_dtype: Optional[str] = None,
|
||||
accelerator: Optional[Accelerator] = None,
|
||||
):
|
||||
# input_ids: b,n,77 -> b*n, 77
|
||||
b_size = input_ids1.size()[0]
|
||||
@@ -4107,8 +4007,7 @@ def get_hidden_states_sdxl(
|
||||
hidden_states2 = enc_out["hidden_states"][-2] # penuultimate layer
|
||||
|
||||
# pool2 = enc_out["text_embeds"]
|
||||
unwrapped_text_encoder2 = text_encoder2 if accelerator is None else accelerator.unwrap_model(text_encoder2)
|
||||
pool2 = pool_workaround(unwrapped_text_encoder2, enc_out["last_hidden_state"], input_ids2, tokenizer2.eos_token_id)
|
||||
pool2 = pool_workaround(text_encoder2, enc_out["last_hidden_state"], input_ids2, tokenizer2.eos_token_id)
|
||||
|
||||
# b*n, 77, 768 or 1280 -> b, n*77, 768 or 1280
|
||||
n_size = 1 if max_token_length is None else max_token_length // 75
|
||||
@@ -4467,29 +4366,6 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
|
||||
return noise, noisy_latents, timesteps
|
||||
|
||||
|
||||
def append_lr_to_logs(logs, lr_scheduler, optimizer_type, including_unet=True):
|
||||
names = []
|
||||
if including_unet:
|
||||
names.append("unet")
|
||||
names.append("text_encoder1")
|
||||
names.append("text_encoder2")
|
||||
|
||||
append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names)
|
||||
|
||||
|
||||
def append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names):
|
||||
lrs = lr_scheduler.get_last_lr()
|
||||
|
||||
for lr_index in range(len(lrs)):
|
||||
name = names[lr_index]
|
||||
logs["lr/" + name] = float(lrs[lr_index])
|
||||
|
||||
if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower() == "Prodigy".lower():
|
||||
logs["lr/d*lr/" + name] = (
|
||||
lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["lr"]
|
||||
)
|
||||
|
||||
|
||||
# scheduler:
|
||||
SCHEDULER_LINEAR_START = 0.00085
|
||||
SCHEDULER_LINEAR_END = 0.0120
|
||||
@@ -4497,119 +4373,13 @@ SCHEDULER_TIMESTEPS = 1000
|
||||
SCHEDLER_SCHEDULE = "scaled_linear"
|
||||
|
||||
|
||||
def get_my_scheduler(
|
||||
*,
|
||||
sample_sampler: str,
|
||||
v_parameterization: bool,
|
||||
):
|
||||
sched_init_args = {}
|
||||
if sample_sampler == "ddim":
|
||||
scheduler_cls = DDIMScheduler
|
||||
elif sample_sampler == "ddpm": # ddpmはおかしくなるのでoptionから外してある
|
||||
scheduler_cls = DDPMScheduler
|
||||
elif sample_sampler == "pndm":
|
||||
scheduler_cls = PNDMScheduler
|
||||
elif sample_sampler == "lms" or sample_sampler == "k_lms":
|
||||
scheduler_cls = LMSDiscreteScheduler
|
||||
elif sample_sampler == "euler" or sample_sampler == "k_euler":
|
||||
scheduler_cls = EulerDiscreteScheduler
|
||||
elif sample_sampler == "euler_a" or sample_sampler == "k_euler_a":
|
||||
scheduler_cls = EulerAncestralDiscreteScheduler
|
||||
elif sample_sampler == "dpmsolver" or sample_sampler == "dpmsolver++":
|
||||
scheduler_cls = DPMSolverMultistepScheduler
|
||||
sched_init_args["algorithm_type"] = sample_sampler
|
||||
elif sample_sampler == "dpmsingle":
|
||||
scheduler_cls = DPMSolverSinglestepScheduler
|
||||
elif sample_sampler == "heun":
|
||||
scheduler_cls = HeunDiscreteScheduler
|
||||
elif sample_sampler == "dpm_2" or sample_sampler == "k_dpm_2":
|
||||
scheduler_cls = KDPM2DiscreteScheduler
|
||||
elif sample_sampler == "dpm_2_a" or sample_sampler == "k_dpm_2_a":
|
||||
scheduler_cls = KDPM2AncestralDiscreteScheduler
|
||||
else:
|
||||
scheduler_cls = DDIMScheduler
|
||||
|
||||
if v_parameterization:
|
||||
sched_init_args["prediction_type"] = "v_prediction"
|
||||
|
||||
scheduler = scheduler_cls(
|
||||
num_train_timesteps=SCHEDULER_TIMESTEPS,
|
||||
beta_start=SCHEDULER_LINEAR_START,
|
||||
beta_end=SCHEDULER_LINEAR_END,
|
||||
beta_schedule=SCHEDLER_SCHEDULE,
|
||||
**sched_init_args,
|
||||
)
|
||||
|
||||
# clip_sample=Trueにする
|
||||
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
|
||||
# print("set clip_sample to True")
|
||||
scheduler.config.clip_sample = True
|
||||
|
||||
return scheduler
|
||||
|
||||
|
||||
def sample_images(*args, **kwargs):
|
||||
return sample_images_common(StableDiffusionLongPromptWeightingPipeline, *args, **kwargs)
|
||||
|
||||
|
||||
def line_to_prompt_dict(line: str) -> dict:
|
||||
# subset of gen_img_diffusers
|
||||
prompt_args = line.split(" --")
|
||||
prompt_dict = {}
|
||||
prompt_dict["prompt"] = prompt_args[0]
|
||||
|
||||
for parg in prompt_args:
|
||||
try:
|
||||
m = re.match(r"w (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
prompt_dict["width"] = int(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"h (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
prompt_dict["height"] = int(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"d (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
prompt_dict["seed"] = int(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"s (\d+)", parg, re.IGNORECASE)
|
||||
if m: # steps
|
||||
prompt_dict["sample_steps"] = max(1, min(1000, int(m.group(1))))
|
||||
continue
|
||||
|
||||
m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # scale
|
||||
prompt_dict["scale"] = float(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"n (.+)", parg, re.IGNORECASE)
|
||||
if m: # negative prompt
|
||||
prompt_dict["negative_prompt"] = m.group(1)
|
||||
continue
|
||||
|
||||
m = re.match(r"ss (.+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
prompt_dict["sample_sampler"] = m.group(1)
|
||||
continue
|
||||
|
||||
m = re.match(r"cn (.+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
prompt_dict["controlnet_image"] = m.group(1)
|
||||
continue
|
||||
|
||||
except ValueError as ex:
|
||||
print(f"Exception in parsing / 解析エラー: {parg}")
|
||||
print(ex)
|
||||
|
||||
return prompt_dict
|
||||
|
||||
|
||||
def sample_images_common(
|
||||
pipe_class,
|
||||
accelerator: Accelerator,
|
||||
accelerator,
|
||||
args: argparse.Namespace,
|
||||
epoch,
|
||||
steps,
|
||||
@@ -4624,19 +4394,15 @@ def sample_images_common(
|
||||
"""
|
||||
StableDiffusionLongPromptWeightingPipelineの改造版を使うようにしたので、clip skipおよびプロンプトの重みづけに対応した
|
||||
"""
|
||||
if steps == 0:
|
||||
if not args.sample_at_first:
|
||||
if args.sample_every_n_steps is None and args.sample_every_n_epochs is None:
|
||||
return
|
||||
if args.sample_every_n_epochs is not None:
|
||||
# sample_every_n_steps は無視する
|
||||
if epoch is None or epoch % args.sample_every_n_epochs != 0:
|
||||
return
|
||||
else:
|
||||
if args.sample_every_n_steps is None and args.sample_every_n_epochs is None:
|
||||
if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch
|
||||
return
|
||||
if args.sample_every_n_epochs is not None:
|
||||
# sample_every_n_steps は無視する
|
||||
if epoch is None or epoch % args.sample_every_n_epochs != 0:
|
||||
return
|
||||
else:
|
||||
if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch
|
||||
return
|
||||
|
||||
print(f"\ngenerating sample images at step / サンプル画像生成 ステップ: {steps}")
|
||||
if not os.path.isfile(args.sample_prompts):
|
||||
@@ -4646,13 +4412,6 @@ def sample_images_common(
|
||||
org_vae_device = vae.device # CPUにいるはず
|
||||
vae.to(device)
|
||||
|
||||
# unwrap unet and text_encoder(s)
|
||||
unet = accelerator.unwrap_model(unet)
|
||||
if isinstance(text_encoder, (list, tuple)):
|
||||
text_encoder = [accelerator.unwrap_model(te) for te in text_encoder]
|
||||
else:
|
||||
text_encoder = accelerator.unwrap_model(text_encoder)
|
||||
|
||||
# read prompts
|
||||
|
||||
# with open(args.sample_prompts, "rt", encoding="utf-8") as f:
|
||||
@@ -4670,19 +4429,56 @@ def sample_images_common(
|
||||
with open(args.sample_prompts, "r", encoding="utf-8") as f:
|
||||
prompts = json.load(f)
|
||||
|
||||
schedulers: dict = {}
|
||||
default_scheduler = get_my_scheduler(
|
||||
sample_sampler=args.sample_sampler,
|
||||
v_parameterization=args.v_parameterization,
|
||||
# schedulerを用意する
|
||||
sched_init_args = {}
|
||||
if args.sample_sampler == "ddim":
|
||||
scheduler_cls = DDIMScheduler
|
||||
elif args.sample_sampler == "ddpm": # ddpmはおかしくなるのでoptionから外してある
|
||||
scheduler_cls = DDPMScheduler
|
||||
elif args.sample_sampler == "pndm":
|
||||
scheduler_cls = PNDMScheduler
|
||||
elif args.sample_sampler == "lms" or args.sample_sampler == "k_lms":
|
||||
scheduler_cls = LMSDiscreteScheduler
|
||||
elif args.sample_sampler == "euler" or args.sample_sampler == "k_euler":
|
||||
scheduler_cls = EulerDiscreteScheduler
|
||||
elif args.sample_sampler == "euler_a" or args.sample_sampler == "k_euler_a":
|
||||
scheduler_cls = EulerAncestralDiscreteScheduler
|
||||
elif args.sample_sampler == "dpmsolver" or args.sample_sampler == "dpmsolver++":
|
||||
scheduler_cls = DPMSolverMultistepScheduler
|
||||
sched_init_args["algorithm_type"] = args.sample_sampler
|
||||
elif args.sample_sampler == "dpmsingle":
|
||||
scheduler_cls = DPMSolverSinglestepScheduler
|
||||
elif args.sample_sampler == "heun":
|
||||
scheduler_cls = HeunDiscreteScheduler
|
||||
elif args.sample_sampler == "dpm_2" or args.sample_sampler == "k_dpm_2":
|
||||
scheduler_cls = KDPM2DiscreteScheduler
|
||||
elif args.sample_sampler == "dpm_2_a" or args.sample_sampler == "k_dpm_2_a":
|
||||
scheduler_cls = KDPM2AncestralDiscreteScheduler
|
||||
else:
|
||||
scheduler_cls = DDIMScheduler
|
||||
|
||||
if args.v_parameterization:
|
||||
sched_init_args["prediction_type"] = "v_prediction"
|
||||
|
||||
scheduler = scheduler_cls(
|
||||
num_train_timesteps=SCHEDULER_TIMESTEPS,
|
||||
beta_start=SCHEDULER_LINEAR_START,
|
||||
beta_end=SCHEDULER_LINEAR_END,
|
||||
beta_schedule=SCHEDLER_SCHEDULE,
|
||||
**sched_init_args,
|
||||
)
|
||||
schedulers[args.sample_sampler] = default_scheduler
|
||||
|
||||
# clip_sample=Trueにする
|
||||
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
|
||||
# print("set clip_sample to True")
|
||||
scheduler.config.clip_sample = True
|
||||
|
||||
pipeline = pipe_class(
|
||||
text_encoder=text_encoder,
|
||||
vae=vae,
|
||||
unet=unet,
|
||||
tokenizer=tokenizer,
|
||||
scheduler=default_scheduler,
|
||||
scheduler=scheduler,
|
||||
safety_checker=None,
|
||||
feature_extractor=None,
|
||||
requires_safety_checker=False,
|
||||
@@ -4698,37 +4494,78 @@ def sample_images_common(
|
||||
|
||||
with torch.no_grad():
|
||||
# with accelerator.autocast():
|
||||
for i, prompt_dict in enumerate(prompts):
|
||||
for i, prompt in enumerate(prompts):
|
||||
if not accelerator.is_main_process:
|
||||
continue
|
||||
|
||||
if isinstance(prompt_dict, str):
|
||||
prompt_dict = line_to_prompt_dict(prompt_dict)
|
||||
if isinstance(prompt, dict):
|
||||
negative_prompt = prompt.get("negative_prompt")
|
||||
sample_steps = prompt.get("sample_steps", 30)
|
||||
width = prompt.get("width", 512)
|
||||
height = prompt.get("height", 512)
|
||||
scale = prompt.get("scale", 7.5)
|
||||
seed = prompt.get("seed")
|
||||
controlnet_image = prompt.get("controlnet_image")
|
||||
prompt = prompt.get("prompt")
|
||||
else:
|
||||
# prompt = prompt.strip()
|
||||
# if len(prompt) == 0 or prompt[0] == "#":
|
||||
# continue
|
||||
|
||||
assert isinstance(prompt_dict, dict)
|
||||
negative_prompt = prompt_dict.get("negative_prompt")
|
||||
sample_steps = prompt_dict.get("sample_steps", 30)
|
||||
width = prompt_dict.get("width", 512)
|
||||
height = prompt_dict.get("height", 512)
|
||||
scale = prompt_dict.get("scale", 7.5)
|
||||
seed = prompt_dict.get("seed")
|
||||
controlnet_image = prompt_dict.get("controlnet_image")
|
||||
prompt: str = prompt_dict.get("prompt", "")
|
||||
sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler)
|
||||
# subset of gen_img_diffusers
|
||||
prompt_args = prompt.split(" --")
|
||||
prompt = prompt_args[0]
|
||||
negative_prompt = None
|
||||
sample_steps = 30
|
||||
width = height = 512
|
||||
scale = 7.5
|
||||
seed = None
|
||||
controlnet_image = None
|
||||
for parg in prompt_args:
|
||||
try:
|
||||
m = re.match(r"w (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
width = int(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"h (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
height = int(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"d (\d+)", parg, re.IGNORECASE)
|
||||
if m:
|
||||
seed = int(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"s (\d+)", parg, re.IGNORECASE)
|
||||
if m: # steps
|
||||
sample_steps = max(1, min(1000, int(m.group(1))))
|
||||
continue
|
||||
|
||||
m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # scale
|
||||
scale = float(m.group(1))
|
||||
continue
|
||||
|
||||
m = re.match(r"n (.+)", parg, re.IGNORECASE)
|
||||
if m: # negative prompt
|
||||
negative_prompt = m.group(1)
|
||||
continue
|
||||
|
||||
m = re.match(r"cn (.+)", parg, re.IGNORECASE)
|
||||
if m: # negative prompt
|
||||
controlnet_image = m.group(1)
|
||||
continue
|
||||
|
||||
except ValueError as ex:
|
||||
print(f"Exception in parsing / 解析エラー: {parg}")
|
||||
print(ex)
|
||||
|
||||
if seed is not None:
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed(seed)
|
||||
|
||||
scheduler = schedulers.get(sampler_name)
|
||||
if scheduler is None:
|
||||
scheduler = get_my_scheduler(
|
||||
sample_sampler=sampler_name,
|
||||
v_parameterization=args.v_parameterization,
|
||||
)
|
||||
schedulers[sampler_name] = scheduler
|
||||
pipeline.scheduler = scheduler
|
||||
|
||||
if prompt_replacement is not None:
|
||||
prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
|
||||
if negative_prompt is not None:
|
||||
@@ -4746,9 +4583,6 @@ def sample_images_common(
|
||||
print(f"width: {width}")
|
||||
print(f"sample_steps: {sample_steps}")
|
||||
print(f"scale: {scale}")
|
||||
print(f"sample_sampler: {sampler_name}")
|
||||
if seed is not None:
|
||||
print(f"seed: {seed}")
|
||||
with accelerator.autocast():
|
||||
latents = pipeline(
|
||||
prompt=prompt,
|
||||
@@ -4824,7 +4658,7 @@ class ImageLoadingDataset(torch.utils.data.Dataset):
|
||||
|
||||
|
||||
# collate_fn用 epoch,stepはmultiprocessing.Value
|
||||
class collator_class:
|
||||
class collater_class:
|
||||
def __init__(self, epoch, step, dataset):
|
||||
self.current_epoch = epoch
|
||||
self.current_step = step
|
||||
@@ -4842,21 +4676,3 @@ class collator_class:
|
||||
dataset.set_current_epoch(self.current_epoch.value)
|
||||
dataset.set_current_step(self.current_step.value)
|
||||
return examples[0]
|
||||
|
||||
|
||||
class LossRecorder:
|
||||
def __init__(self):
|
||||
self.loss_list: List[float] = []
|
||||
self.loss_total: float = 0.0
|
||||
|
||||
def add(self, *, epoch: int, step: int, loss: float) -> None:
|
||||
if epoch == 0:
|
||||
self.loss_list.append(loss)
|
||||
else:
|
||||
self.loss_total -= self.loss_list[step]
|
||||
self.loss_list[step] = loss
|
||||
self.loss_total += loss
|
||||
|
||||
@property
|
||||
def moving_average(self) -> float:
|
||||
return self.loss_total / len(self.loss_list)
|
||||
|
||||
@@ -13,8 +13,8 @@ from library import sai_model_spec, model_util, sdxl_model_util
|
||||
import lora
|
||||
|
||||
|
||||
# CLAMP_QUANTILE = 0.99
|
||||
# MIN_DIFF = 1e-1
|
||||
CLAMP_QUANTILE = 0.99
|
||||
MIN_DIFF = 1e-4
|
||||
|
||||
|
||||
def save_to_file(file_name, model, state_dict, dtype):
|
||||
@@ -29,21 +29,7 @@ def save_to_file(file_name, model, state_dict, dtype):
|
||||
torch.save(model, file_name)
|
||||
|
||||
|
||||
def svd(
|
||||
model_org=None,
|
||||
model_tuned=None,
|
||||
save_to=None,
|
||||
dim=4,
|
||||
v2=None,
|
||||
sdxl=None,
|
||||
conv_dim=None,
|
||||
v_parameterization=None,
|
||||
device=None,
|
||||
save_precision=None,
|
||||
clamp_quantile=0.99,
|
||||
min_diff=0.01,
|
||||
no_metadata=False,
|
||||
):
|
||||
def svd(args):
|
||||
def str_to_dtype(p):
|
||||
if p == "float":
|
||||
return torch.float
|
||||
@@ -53,42 +39,44 @@ def svd(
|
||||
return torch.bfloat16
|
||||
return None
|
||||
|
||||
assert v2 != sdxl or (not v2 and not sdxl), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません"
|
||||
if v_parameterization is None:
|
||||
v_parameterization = v2
|
||||
assert args.v2 != args.sdxl or (
|
||||
not args.v2 and not args.sdxl
|
||||
), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません"
|
||||
if args.v_parameterization is None:
|
||||
args.v_parameterization = args.v2
|
||||
|
||||
save_dtype = str_to_dtype(save_precision)
|
||||
save_dtype = str_to_dtype(args.save_precision)
|
||||
|
||||
# load models
|
||||
if not sdxl:
|
||||
print(f"loading original SD model : {model_org}")
|
||||
text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_org)
|
||||
if not args.sdxl:
|
||||
print(f"loading original SD model : {args.model_org}")
|
||||
text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_org)
|
||||
text_encoders_o = [text_encoder_o]
|
||||
print(f"loading tuned SD model : {model_tuned}")
|
||||
text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_tuned)
|
||||
print(f"loading tuned SD model : {args.model_tuned}")
|
||||
text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned)
|
||||
text_encoders_t = [text_encoder_t]
|
||||
model_version = model_util.get_model_version_str_for_sd1_sd2(v2, v_parameterization)
|
||||
model_version = model_util.get_model_version_str_for_sd1_sd2(args.v2, args.v_parameterization)
|
||||
else:
|
||||
print(f"loading original SDXL model : {model_org}")
|
||||
print(f"loading original SDXL model : {args.model_org}")
|
||||
text_encoder_o1, text_encoder_o2, _, unet_o, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
|
||||
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_org, "cpu"
|
||||
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_org, "cpu"
|
||||
)
|
||||
text_encoders_o = [text_encoder_o1, text_encoder_o2]
|
||||
print(f"loading original SDXL model : {model_tuned}")
|
||||
print(f"loading original SDXL model : {args.model_tuned}")
|
||||
text_encoder_t1, text_encoder_t2, _, unet_t, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
|
||||
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_tuned, "cpu"
|
||||
sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, args.model_tuned, "cpu"
|
||||
)
|
||||
text_encoders_t = [text_encoder_t1, text_encoder_t2]
|
||||
model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0
|
||||
|
||||
# create LoRA network to extract weights: Use dim (rank) as alpha
|
||||
if conv_dim is None:
|
||||
if args.conv_dim is None:
|
||||
kwargs = {}
|
||||
else:
|
||||
kwargs = {"conv_dim": conv_dim, "conv_alpha": conv_dim}
|
||||
kwargs = {"conv_dim": args.conv_dim, "conv_alpha": args.conv_dim}
|
||||
|
||||
lora_network_o = lora.create_network(1.0, dim, dim, None, text_encoders_o, unet_o, **kwargs)
|
||||
lora_network_t = lora.create_network(1.0, dim, dim, None, text_encoders_t, unet_t, **kwargs)
|
||||
lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoders_o, unet_o, **kwargs)
|
||||
lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoders_t, unet_t, **kwargs)
|
||||
assert len(lora_network_o.text_encoder_loras) == len(
|
||||
lora_network_t.text_encoder_loras
|
||||
), f"model version is different (SD1.x vs SD2.x) / それぞれのモデルのバージョンが違います（SD1.xベースとSD2.xベース） "
|
||||
@@ -103,9 +91,9 @@ def svd(
|
||||
diff = module_t.weight - module_o.weight
|
||||
|
||||
# Text Encoder might be same
|
||||
if not text_encoder_different and torch.max(torch.abs(diff)) > min_diff:
|
||||
if not text_encoder_different and torch.max(torch.abs(diff)) > MIN_DIFF:
|
||||
text_encoder_different = True
|
||||
print(f"Text encoder is different. {torch.max(torch.abs(diff))} > {min_diff}")
|
||||
print(f"Text encoder is different. {torch.max(torch.abs(diff))} > {MIN_DIFF}")
|
||||
|
||||
diff = diff.float()
|
||||
diffs[lora_name] = diff
|
||||
@@ -132,16 +120,16 @@ def svd(
|
||||
lora_weights = {}
|
||||
with torch.no_grad():
|
||||
for lora_name, mat in tqdm(list(diffs.items())):
|
||||
# if conv_dim is None, diffs do not include LoRAs for conv2d-3x3
|
||||
# if args.conv_dim is None, diffs do not include LoRAs for conv2d-3x3
|
||||
conv2d = len(mat.size()) == 4
|
||||
kernel_size = None if not conv2d else mat.size()[2:4]
|
||||
conv2d_3x3 = conv2d and kernel_size != (1, 1)
|
||||
|
||||
rank = dim if not conv2d_3x3 or conv_dim is None else conv_dim
|
||||
rank = args.dim if not conv2d_3x3 or args.conv_dim is None else args.conv_dim
|
||||
out_dim, in_dim = mat.size()[0:2]
|
||||
|
||||
if device:
|
||||
mat = mat.to(device)
|
||||
if args.device:
|
||||
mat = mat.to(args.device)
|
||||
|
||||
# print(lora_name, mat.size(), mat.device, rank, in_dim, out_dim)
|
||||
rank = min(rank, in_dim, out_dim) # LoRA rank cannot exceed the original dim
|
||||
@@ -161,7 +149,7 @@ def svd(
|
||||
Vh = Vh[:rank, :]
|
||||
|
||||
dist = torch.cat([U.flatten(), Vh.flatten()])
|
||||
hi_val = torch.quantile(dist, clamp_quantile)
|
||||
hi_val = torch.quantile(dist, CLAMP_QUANTILE)
|
||||
low_val = -hi_val
|
||||
|
||||
U = U.clamp(low_val, hi_val)
|
||||
@@ -190,32 +178,34 @@ def svd(
|
||||
info = lora_network_save.load_state_dict(lora_sd)
|
||||
print(f"Loading extracted LoRA weights: {info}")
|
||||
|
||||
dir_name = os.path.dirname(save_to)
|
||||
dir_name = os.path.dirname(args.save_to)
|
||||
if dir_name and not os.path.exists(dir_name):
|
||||
os.makedirs(dir_name, exist_ok=True)
|
||||
|
||||
# minimum metadata
|
||||
net_kwargs = {}
|
||||
if conv_dim is not None:
|
||||
net_kwargs["conv_dim"] = str(conv_dim)
|
||||
net_kwargs["conv_alpha"] = str(float(conv_dim))
|
||||
if args.conv_dim is not None:
|
||||
net_kwargs["conv_dim"] = args.conv_dim
|
||||
net_kwargs["conv_alpha"] = args.conv_dim
|
||||
|
||||
metadata = {
|
||||
"ss_v2": str(v2),
|
||||
"ss_v2": str(args.v2),
|
||||
"ss_base_model_version": model_version,
|
||||
"ss_network_module": "networks.lora",
|
||||
"ss_network_dim": str(dim),
|
||||
"ss_network_alpha": str(float(dim)),
|
||||
"ss_network_dim": str(args.dim),
|
||||
"ss_network_alpha": str(args.dim),
|
||||
"ss_network_args": json.dumps(net_kwargs),
|
||||
}
|
||||
|
||||
if not no_metadata:
|
||||
title = os.path.splitext(os.path.basename(save_to))[0]
|
||||
sai_metadata = sai_model_spec.build_metadata(None, v2, v_parameterization, sdxl, True, False, time.time(), title=title)
|
||||
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
|
||||
)
|
||||
metadata.update(sai_metadata)
|
||||
|
||||
lora_network_save.save_weights(save_to, save_dtype, metadata)
|
||||
print(f"LoRA weights are saved to: {save_to}")
|
||||
lora_network_save.save_weights(args.save_to, save_dtype, metadata)
|
||||
print(f"LoRA weights are saved to: {args.save_to}")
|
||||
|
||||
|
||||
def setup_parser() -> argparse.ArgumentParser:
|
||||
@@ -223,7 +213,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
parser.add_argument("--v2", action="store_true", help="load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む")
|
||||
parser.add_argument(
|
||||
"--v_parameterization",
|
||||
action="store_true",
|
||||
type=bool,
|
||||
default=None,
|
||||
help="make LoRA metadata for v-parameterization (default is same to v2) / 作成するLoRAのメタデータにv-parameterization用と設定する（省略時はv2と同じ）",
|
||||
)
|
||||
@@ -241,22 +231,16 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
"--model_org",
|
||||
type=str,
|
||||
default=None,
|
||||
required=True,
|
||||
help="Stable Diffusion original model: ckpt or safetensors file / 元モデル、ckptまたはsafetensors",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model_tuned",
|
||||
type=str,
|
||||
default=None,
|
||||
required=True,
|
||||
help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_to",
|
||||
type=str,
|
||||
default=None,
|
||||
required=True,
|
||||
help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors",
|
||||
"--save_to", type=str, default=None, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
|
||||
)
|
||||
parser.add_argument("--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）")
|
||||
parser.add_argument(
|
||||
@@ -266,19 +250,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
help="dimension (rank) of LoRA for Conv2d-3x3 (default None, disabled) / LoRAのConv2d-3x3の次元数（rank）（デフォルトNone、適用なし）",
|
||||
)
|
||||
parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
|
||||
parser.add_argument(
|
||||
"--clamp_quantile",
|
||||
type=float,
|
||||
default=0.99,
|
||||
help="Quantile clamping value, float, (0-1). Default = 0.99 / 値をクランプするための分位点、float、(0-1)。デフォルトは0.99",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min_diff",
|
||||
type=float,
|
||||
default=0.01,
|
||||
help="Minimum difference between finetuned model and base to consider them different enough to extract, float, (0-1). Default = 0.01 /"
|
||||
+ "LoRAを抽出するために元モデルと派生モデルの差分の最小値、float、(0-1)。デフォルトは0.01",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no_metadata",
|
||||
action="store_true",
|
||||
@@ -293,4 +264,4 @@ if __name__ == "__main__":
|
||||
parser = setup_parser()
|
||||
|
||||
args = parser.parse_args()
|
||||
svd(**vars(args))
|
||||
svd(args)
|
||||
|
||||
@@ -117,7 +117,7 @@ class LoRAModule(torch.nn.Module):
|
||||
super().__init__()
|
||||
self.lora_name = lora_name
|
||||
|
||||
if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
|
||||
if org_module.__class__.__name__ == "Conv2d":
|
||||
in_dim = org_module.in_channels
|
||||
out_dim = org_module.out_channels
|
||||
else:
|
||||
@@ -126,7 +126,7 @@ class LoRAModule(torch.nn.Module):
|
||||
|
||||
self.lora_dim = lora_dim
|
||||
|
||||
if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
|
||||
if org_module.__class__.__name__ == "Conv2d":
|
||||
kernel_size = org_module.kernel_size
|
||||
stride = org_module.stride
|
||||
padding = org_module.padding
|
||||
@@ -166,8 +166,7 @@ class LoRAModule(torch.nn.Module):
|
||||
self.org_module[0].forward = self.org_forward
|
||||
|
||||
# forward with lora
|
||||
# scale is used LoRACompatibleConv, but we ignore it because we have multiplier
|
||||
def forward(self, x, scale=1.0):
|
||||
def forward(self, x):
|
||||
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
|
||||
@@ -319,12 +318,8 @@ class LoRANetwork(torch.nn.Module):
|
||||
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" or child_module.__class__.__name__ == "LoRACompatibleLinear"
|
||||
)
|
||||
is_conv2d = (
|
||||
child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv"
|
||||
)
|
||||
is_linear = child_module.__class__.__name__ == "Linear"
|
||||
is_conv2d = child_module.__class__.__name__ == "Conv2d"
|
||||
|
||||
if is_linear or is_conv2d:
|
||||
lora_name = prefix + "." + name + "." + child_name
|
||||
@@ -364,7 +359,7 @@ class LoRANetwork(torch.nn.Module):
|
||||
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 for text encoder.")
|
||||
print(f"skipped {len(skipped_te)} modules because of missing weight.")
|
||||
|
||||
# extend U-Net target modules to include Conv2d 3x3
|
||||
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
|
||||
@@ -373,7 +368,7 @@ class LoRANetwork(torch.nn.Module):
|
||||
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 for U-Net.")
|
||||
print(f"skipped {len(skipped_un)} modules because of missing weight.")
|
||||
|
||||
# assertion
|
||||
names = set()
|
||||
|
||||
@@ -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, concat=False, shuffle=False):
|
||||
def merge_lora_models(models, ratios, merge_dtype):
|
||||
base_alphas = {} # alpha for merged model
|
||||
base_dims = {}
|
||||
|
||||
@@ -158,12 +158,6 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
|
||||
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_")]
|
||||
|
||||
@@ -171,16 +165,12 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
|
||||
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() or concat_dim is not None
|
||||
merged_sd[key].size() == lora_sd[key].size()
|
||||
), 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"
|
||||
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
|
||||
merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
|
||||
else:
|
||||
merged_sd[key] = lora_sd[key] * scale
|
||||
|
||||
@@ -188,13 +178,6 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
|
||||
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()))}")
|
||||
@@ -273,7 +256,7 @@ def merge(args):
|
||||
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, args.concat, args.shuffle)
|
||||
state_dict, metadata, v2 = merge_lora_models(args.models, args.ratios, merge_dtype)
|
||||
|
||||
print(f"calculating hashes and creating metadata...")
|
||||
|
||||
@@ -334,19 +317,7 @@ 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
|
||||
|
||||
|
||||
|
||||
430
networks/oft.py
430
networks/oft.py
@@ -1,430 +0,0 @@
|
||||
# OFT network module
|
||||
|
||||
import math
|
||||
import os
|
||||
from typing import Dict, List, Optional, Tuple, Type, Union
|
||||
from diffusers import AutoencoderKL
|
||||
from transformers import CLIPTextModel
|
||||
import numpy as np
|
||||
import torch
|
||||
import re
|
||||
|
||||
|
||||
RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
|
||||
|
||||
|
||||
class OFTModule(torch.nn.Module):
|
||||
"""
|
||||
replaces forward method of the original Linear, instead of replacing the original Linear module.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
oft_name,
|
||||
org_module: torch.nn.Module,
|
||||
multiplier=1.0,
|
||||
dim=4,
|
||||
alpha=1,
|
||||
):
|
||||
"""
|
||||
dim -> num blocks
|
||||
alpha -> constraint
|
||||
"""
|
||||
super().__init__()
|
||||
self.oft_name = oft_name
|
||||
|
||||
self.num_blocks = dim
|
||||
|
||||
if "Linear" in org_module.__class__.__name__:
|
||||
out_dim = org_module.out_features
|
||||
elif "Conv" in org_module.__class__.__name__:
|
||||
out_dim = org_module.out_channels
|
||||
|
||||
if type(alpha) == torch.Tensor:
|
||||
alpha = alpha.detach().numpy()
|
||||
self.constraint = alpha * out_dim
|
||||
self.register_buffer("alpha", torch.tensor(alpha))
|
||||
|
||||
self.block_size = out_dim // self.num_blocks
|
||||
self.oft_blocks = torch.nn.Parameter(torch.zeros(self.num_blocks, self.block_size, self.block_size))
|
||||
|
||||
self.out_dim = out_dim
|
||||
self.shape = org_module.weight.shape
|
||||
|
||||
self.multiplier = multiplier
|
||||
self.org_module = [org_module] # moduleにならないようにlistに入れる
|
||||
|
||||
def apply_to(self):
|
||||
self.org_forward = self.org_module[0].forward
|
||||
self.org_module[0].forward = self.forward
|
||||
|
||||
def get_weight(self, multiplier=None):
|
||||
if multiplier is None:
|
||||
multiplier = self.multiplier
|
||||
|
||||
block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
|
||||
norm_Q = torch.norm(block_Q.flatten())
|
||||
new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
|
||||
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
|
||||
I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
|
||||
block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
|
||||
|
||||
block_R_weighted = self.multiplier * block_R + (1 - self.multiplier) * I
|
||||
R = torch.block_diag(*block_R_weighted)
|
||||
|
||||
return R
|
||||
|
||||
def forward(self, x, scale=None):
|
||||
x = self.org_forward(x)
|
||||
if self.multiplier == 0.0:
|
||||
return x
|
||||
|
||||
R = self.get_weight().to(x.device, dtype=x.dtype)
|
||||
if x.dim() == 4:
|
||||
x = x.permute(0, 2, 3, 1)
|
||||
x = torch.matmul(x, R)
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
else:
|
||||
x = torch.matmul(x, R)
|
||||
return x
|
||||
|
||||
|
||||
class OFTInfModule(OFTModule):
|
||||
def __init__(
|
||||
self,
|
||||
oft_name,
|
||||
org_module: torch.nn.Module,
|
||||
multiplier=1.0,
|
||||
dim=4,
|
||||
alpha=1,
|
||||
**kwargs,
|
||||
):
|
||||
# no dropout for inference
|
||||
super().__init__(oft_name, org_module, multiplier, dim, alpha)
|
||||
self.enabled = True
|
||||
self.network: OFTNetwork = None
|
||||
|
||||
def set_network(self, network):
|
||||
self.network = network
|
||||
|
||||
def forward(self, x, scale=None):
|
||||
if not self.enabled:
|
||||
return self.org_forward(x)
|
||||
return super().forward(x, scale)
|
||||
|
||||
def merge_to(self, multiplier=None, sign=1):
|
||||
R = self.get_weight(multiplier) * sign
|
||||
|
||||
# get org weight
|
||||
org_sd = self.org_module[0].state_dict()
|
||||
org_weight = org_sd["weight"]
|
||||
R = R.to(org_weight.device, dtype=org_weight.dtype)
|
||||
|
||||
if org_weight.dim() == 4:
|
||||
weight = torch.einsum("oihw, op -> pihw", org_weight, R)
|
||||
else:
|
||||
weight = torch.einsum("oi, op -> pi", org_weight, R)
|
||||
|
||||
# set weight to org_module
|
||||
org_sd["weight"] = weight
|
||||
self.org_module[0].load_state_dict(org_sd)
|
||||
|
||||
|
||||
def create_network(
|
||||
multiplier: float,
|
||||
network_dim: Optional[int],
|
||||
network_alpha: Optional[float],
|
||||
vae: AutoencoderKL,
|
||||
text_encoder: Union[CLIPTextModel, List[CLIPTextModel]],
|
||||
unet,
|
||||
neuron_dropout: Optional[float] = None,
|
||||
**kwargs,
|
||||
):
|
||||
if network_dim is None:
|
||||
network_dim = 4 # default
|
||||
if network_alpha is None:
|
||||
network_alpha = 1.0
|
||||
|
||||
enable_all_linear = kwargs.get("enable_all_linear", None)
|
||||
enable_conv = kwargs.get("enable_conv", None)
|
||||
if enable_all_linear is not None:
|
||||
enable_all_linear = bool(enable_all_linear)
|
||||
if enable_conv is not None:
|
||||
enable_conv = bool(enable_conv)
|
||||
|
||||
network = OFTNetwork(
|
||||
text_encoder,
|
||||
unet,
|
||||
multiplier=multiplier,
|
||||
dim=network_dim,
|
||||
alpha=network_alpha,
|
||||
enable_all_linear=enable_all_linear,
|
||||
enable_conv=enable_conv,
|
||||
varbose=True,
|
||||
)
|
||||
return network
|
||||
|
||||
|
||||
# Create network from weights for inference, weights are not loaded here (because can be merged)
|
||||
def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs):
|
||||
if weights_sd is None:
|
||||
if os.path.splitext(file)[1] == ".safetensors":
|
||||
from safetensors.torch import load_file, safe_open
|
||||
|
||||
weights_sd = load_file(file)
|
||||
else:
|
||||
weights_sd = torch.load(file, map_location="cpu")
|
||||
|
||||
# check dim, alpha and if weights have for conv2d
|
||||
dim = None
|
||||
alpha = None
|
||||
has_conv2d = None
|
||||
all_linear = None
|
||||
for name, param in weights_sd.items():
|
||||
if name.endswith(".alpha"):
|
||||
if alpha is None:
|
||||
alpha = param.item()
|
||||
else:
|
||||
if dim is None:
|
||||
dim = param.size()[0]
|
||||
if has_conv2d is None and param.dim() == 4:
|
||||
has_conv2d = True
|
||||
if all_linear is None:
|
||||
if param.dim() == 3 and "attn" not in name:
|
||||
all_linear = True
|
||||
if dim is not None and alpha is not None and has_conv2d is not None:
|
||||
break
|
||||
if has_conv2d is None:
|
||||
has_conv2d = False
|
||||
if all_linear is None:
|
||||
all_linear = False
|
||||
|
||||
module_class = OFTInfModule if for_inference else OFTModule
|
||||
network = OFTNetwork(
|
||||
text_encoder,
|
||||
unet,
|
||||
multiplier=multiplier,
|
||||
dim=dim,
|
||||
alpha=alpha,
|
||||
enable_all_linear=all_linear,
|
||||
enable_conv=has_conv2d,
|
||||
module_class=module_class,
|
||||
)
|
||||
return network, weights_sd
|
||||
|
||||
|
||||
class OFTNetwork(torch.nn.Module):
|
||||
UNET_TARGET_REPLACE_MODULE_ATTN_ONLY = ["CrossAttention"]
|
||||
UNET_TARGET_REPLACE_MODULE_ALL_LINEAR = ["Transformer2DModel"]
|
||||
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
|
||||
OFT_PREFIX_UNET = "oft_unet" # これ変えないほうがいいかな
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
|
||||
unet,
|
||||
multiplier: float = 1.0,
|
||||
dim: int = 4,
|
||||
alpha: float = 1,
|
||||
enable_all_linear: Optional[bool] = False,
|
||||
enable_conv: Optional[bool] = False,
|
||||
module_class: Type[object] = OFTModule,
|
||||
varbose: Optional[bool] = False,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.multiplier = multiplier
|
||||
|
||||
self.dim = dim
|
||||
self.alpha = alpha
|
||||
|
||||
print(
|
||||
f"create OFT network. num blocks: {self.dim}, constraint: {self.alpha}, multiplier: {self.multiplier}, enable_conv: {enable_conv}"
|
||||
)
|
||||
|
||||
# create module instances
|
||||
def create_modules(
|
||||
root_module: torch.nn.Module,
|
||||
target_replace_modules: List[torch.nn.Module],
|
||||
) -> List[OFTModule]:
|
||||
prefix = self.OFT_PREFIX_UNET
|
||||
ofts = []
|
||||
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 = "Linear" in child_module.__class__.__name__
|
||||
is_conv2d = "Conv2d" in child_module.__class__.__name__
|
||||
is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
|
||||
|
||||
if is_linear or is_conv2d_1x1 or (is_conv2d and enable_conv):
|
||||
oft_name = prefix + "." + name + "." + child_name
|
||||
oft_name = oft_name.replace(".", "_")
|
||||
# print(oft_name)
|
||||
|
||||
oft = module_class(
|
||||
oft_name,
|
||||
child_module,
|
||||
self.multiplier,
|
||||
dim,
|
||||
alpha,
|
||||
)
|
||||
ofts.append(oft)
|
||||
return ofts
|
||||
|
||||
# extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
|
||||
if enable_all_linear:
|
||||
target_modules = OFTNetwork.UNET_TARGET_REPLACE_MODULE_ALL_LINEAR
|
||||
else:
|
||||
target_modules = OFTNetwork.UNET_TARGET_REPLACE_MODULE_ATTN_ONLY
|
||||
if enable_conv:
|
||||
target_modules += OFTNetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
|
||||
|
||||
self.unet_ofts: List[OFTModule] = create_modules(unet, target_modules)
|
||||
print(f"create OFT for U-Net: {len(self.unet_ofts)} modules.")
|
||||
|
||||
# assertion
|
||||
names = set()
|
||||
for oft in self.unet_ofts:
|
||||
assert oft.oft_name not in names, f"duplicated oft name: {oft.oft_name}"
|
||||
names.add(oft.oft_name)
|
||||
|
||||
def set_multiplier(self, multiplier):
|
||||
self.multiplier = multiplier
|
||||
for oft in self.unet_ofts:
|
||||
oft.multiplier = self.multiplier
|
||||
|
||||
def load_weights(self, file):
|
||||
if os.path.splitext(file)[1] == ".safetensors":
|
||||
from safetensors.torch import load_file
|
||||
|
||||
weights_sd = load_file(file)
|
||||
else:
|
||||
weights_sd = torch.load(file, map_location="cpu")
|
||||
|
||||
info = self.load_state_dict(weights_sd, False)
|
||||
return info
|
||||
|
||||
def apply_to(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True):
|
||||
assert apply_unet, "apply_unet must be True"
|
||||
|
||||
for oft in self.unet_ofts:
|
||||
oft.apply_to()
|
||||
self.add_module(oft.oft_name, oft)
|
||||
|
||||
# マージできるかどうかを返す
|
||||
def is_mergeable(self):
|
||||
return True
|
||||
|
||||
# TODO refactor to common function with apply_to
|
||||
def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
|
||||
print("enable OFT for U-Net")
|
||||
|
||||
for oft in self.unet_ofts:
|
||||
sd_for_lora = {}
|
||||
for key in weights_sd.keys():
|
||||
if key.startswith(oft.oft_name):
|
||||
sd_for_lora[key[len(oft.oft_name) + 1 :]] = weights_sd[key]
|
||||
oft.load_state_dict(sd_for_lora, False)
|
||||
oft.merge_to()
|
||||
|
||||
print(f"weights are merged")
|
||||
|
||||
# 二つのText Encoderに別々の学習率を設定できるようにするといいかも
|
||||
def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
|
||||
self.requires_grad_(True)
|
||||
all_params = []
|
||||
|
||||
def enumerate_params(ofts):
|
||||
params = []
|
||||
for oft in ofts:
|
||||
params.extend(oft.parameters())
|
||||
|
||||
# print num of params
|
||||
num_params = 0
|
||||
for p in params:
|
||||
num_params += p.numel()
|
||||
print(f"OFT params: {num_params}")
|
||||
return params
|
||||
|
||||
param_data = {"params": enumerate_params(self.unet_ofts)}
|
||||
if unet_lr is not None:
|
||||
param_data["lr"] = unet_lr
|
||||
all_params.append(param_data)
|
||||
|
||||
return all_params
|
||||
|
||||
def enable_gradient_checkpointing(self):
|
||||
# not supported
|
||||
pass
|
||||
|
||||
def prepare_grad_etc(self, text_encoder, unet):
|
||||
self.requires_grad_(True)
|
||||
|
||||
def on_epoch_start(self, text_encoder, unet):
|
||||
self.train()
|
||||
|
||||
def get_trainable_params(self):
|
||||
return self.parameters()
|
||||
|
||||
def save_weights(self, file, dtype, metadata):
|
||||
if metadata is not None and len(metadata) == 0:
|
||||
metadata = None
|
||||
|
||||
state_dict = self.state_dict()
|
||||
|
||||
if dtype is not None:
|
||||
for key in list(state_dict.keys()):
|
||||
v = state_dict[key]
|
||||
v = v.detach().clone().to("cpu").to(dtype)
|
||||
state_dict[key] = v
|
||||
|
||||
if os.path.splitext(file)[1] == ".safetensors":
|
||||
from safetensors.torch import save_file
|
||||
from library import train_util
|
||||
|
||||
# Precalculate model hashes to save time on indexing
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
|
||||
metadata["sshs_model_hash"] = model_hash
|
||||
metadata["sshs_legacy_hash"] = legacy_hash
|
||||
|
||||
save_file(state_dict, file, metadata)
|
||||
else:
|
||||
torch.save(state_dict, file)
|
||||
|
||||
def backup_weights(self):
|
||||
# 重みのバックアップを行う
|
||||
ofts: List[OFTInfModule] = self.unet_ofts
|
||||
for oft in ofts:
|
||||
org_module = oft.org_module[0]
|
||||
if not hasattr(org_module, "_lora_org_weight"):
|
||||
sd = org_module.state_dict()
|
||||
org_module._lora_org_weight = sd["weight"].detach().clone()
|
||||
org_module._lora_restored = True
|
||||
|
||||
def restore_weights(self):
|
||||
# 重みのリストアを行う
|
||||
ofts: List[OFTInfModule] = self.unet_ofts
|
||||
for oft in ofts:
|
||||
org_module = oft.org_module[0]
|
||||
if not org_module._lora_restored:
|
||||
sd = org_module.state_dict()
|
||||
sd["weight"] = org_module._lora_org_weight
|
||||
org_module.load_state_dict(sd)
|
||||
org_module._lora_restored = True
|
||||
|
||||
def pre_calculation(self):
|
||||
# 事前計算を行う
|
||||
ofts: List[OFTInfModule] = self.unet_ofts
|
||||
for oft in ofts:
|
||||
org_module = oft.org_module[0]
|
||||
oft.merge_to()
|
||||
# sd = org_module.state_dict()
|
||||
# org_weight = sd["weight"]
|
||||
# lora_weight = oft.get_weight().to(org_weight.device, dtype=org_weight.dtype)
|
||||
# sd["weight"] = org_weight + lora_weight
|
||||
# assert sd["weight"].shape == org_weight.shape
|
||||
# org_module.load_state_dict(sd)
|
||||
|
||||
org_module._lora_restored = False
|
||||
oft.enabled = False
|
||||
@@ -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.rsplit('.lora_down', 1)[0]
|
||||
weight_name = key.rsplit(".", 1)[-1]
|
||||
block_down_name = key.split(".")[0]
|
||||
weight_name = key.split(".")[-1]
|
||||
lora_down_weight = value
|
||||
else:
|
||||
continue
|
||||
@@ -283,10 +283,7 @@ 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
|
||||
|
||||
@@ -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, concat=False, shuffle=False):
|
||||
def merge_lora_models(models, ratios, merge_dtype):
|
||||
base_alphas = {} # alpha for merged model
|
||||
base_dims = {}
|
||||
|
||||
@@ -161,13 +161,6 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
|
||||
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_")]
|
||||
|
||||
@@ -175,16 +168,12 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
|
||||
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() or concat_dim is not None
|
||||
merged_sd[key].size() == lora_sd[key].size()
|
||||
), 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"
|
||||
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
|
||||
merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
|
||||
else:
|
||||
merged_sd[key] = lora_sd[key] * scale
|
||||
|
||||
@@ -192,13 +181,6 @@ def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
|
||||
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()))}")
|
||||
@@ -270,7 +252,7 @@ def merge(args):
|
||||
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, args.concat, args.shuffle)
|
||||
state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype)
|
||||
|
||||
print(f"calculating hashes and creating metadata...")
|
||||
|
||||
@@ -325,18 +307,6 @@ 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
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
accelerate==0.23.0
|
||||
accelerate==0.19.0
|
||||
transformers==4.30.2
|
||||
diffusers[torch]==0.21.2
|
||||
diffusers[torch]==0.18.2
|
||||
ftfy==6.1.1
|
||||
# albumentations==1.3.0
|
||||
opencv-python==4.7.0.68
|
||||
@@ -15,18 +15,14 @@ easygui==0.98.3
|
||||
toml==0.10.2
|
||||
voluptuous==0.13.1
|
||||
huggingface-hub==0.15.1
|
||||
# for loading Diffusers' SDXL
|
||||
invisible-watermark==0.2.0
|
||||
# for BLIP captioning
|
||||
# requests==2.28.2
|
||||
# timm==0.6.12
|
||||
# fairscale==0.4.13
|
||||
# for WD14 captioning (tensorflow)
|
||||
# for WD14 captioning
|
||||
# tensorflow==2.10.1
|
||||
# for WD14 captioning (onnx)
|
||||
# onnx==1.14.1
|
||||
# onnxruntime-gpu==1.16.0
|
||||
# onnxruntime==1.16.0
|
||||
# this is for onnx:
|
||||
# protobuf==3.20.3
|
||||
# open clip for SDXL
|
||||
open-clip-torch==2.20.0
|
||||
# for kohya_ss library
|
||||
|
||||
189
sdxl_gen_img.py
189
sdxl_gen_img.py
@@ -17,16 +17,6 @@ import re
|
||||
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,
|
||||
@@ -57,7 +47,7 @@ import library.train_util as train_util
|
||||
import library.sdxl_model_util as sdxl_model_util
|
||||
import library.sdxl_train_util as sdxl_train_util
|
||||
from networks.lora import LoRANetwork
|
||||
from library.sdxl_original_unet import InferSdxlUNet2DConditionModel
|
||||
from library.sdxl_original_unet import SdxlUNet2DConditionModel
|
||||
from library.original_unet import FlashAttentionFunction
|
||||
from networks.control_net_lllite import ControlNetLLLite
|
||||
|
||||
@@ -290,7 +280,7 @@ class PipelineLike:
|
||||
vae: AutoencoderKL,
|
||||
text_encoders: List[CLIPTextModel],
|
||||
tokenizers: List[CLIPTokenizer],
|
||||
unet: InferSdxlUNet2DConditionModel,
|
||||
unet: SdxlUNet2DConditionModel,
|
||||
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
||||
clip_skip: int,
|
||||
):
|
||||
@@ -328,7 +318,7 @@ class PipelineLike:
|
||||
self.vae = vae
|
||||
self.text_encoders = text_encoders
|
||||
self.tokenizers = tokenizers
|
||||
self.unet: InferSdxlUNet2DConditionModel = unet
|
||||
self.unet: SdxlUNet2DConditionModel = unet
|
||||
self.scheduler = scheduler
|
||||
self.safety_checker = None
|
||||
|
||||
@@ -504,8 +494,7 @@ class PipelineLike:
|
||||
uncond_embeddings = tes_uncond_embs[0]
|
||||
for i in range(1, len(tes_text_embs)):
|
||||
text_embeddings = torch.cat([text_embeddings, tes_text_embs[i]], dim=2) # n,77,2048
|
||||
if do_classifier_free_guidance:
|
||||
uncond_embeddings = torch.cat([uncond_embeddings, tes_uncond_embs[i]], dim=2) # n,77,2048
|
||||
uncond_embeddings = torch.cat([uncond_embeddings, tes_uncond_embs[i]], dim=2) # n,77,2048
|
||||
|
||||
if do_classifier_free_guidance:
|
||||
if negative_scale is None:
|
||||
@@ -568,11 +557,9 @@ class PipelineLike:
|
||||
text_pool = clip_vision_embeddings # replace: same as ComfyUI (?)
|
||||
|
||||
c_vector = torch.cat([text_pool, c_vector], dim=1)
|
||||
if do_classifier_free_guidance:
|
||||
uc_vector = torch.cat([uncond_pool, uc_vector], dim=1)
|
||||
vector_embeddings = torch.cat([uc_vector, c_vector])
|
||||
else:
|
||||
vector_embeddings = c_vector
|
||||
uc_vector = torch.cat([uncond_pool, uc_vector], dim=1)
|
||||
|
||||
vector_embeddings = torch.cat([uc_vector, c_vector])
|
||||
|
||||
# set timesteps
|
||||
self.scheduler.set_timesteps(num_inference_steps, self.device)
|
||||
@@ -1374,7 +1361,6 @@ def main(args):
|
||||
(_, text_encoder1, text_encoder2, vae, unet, _, _) = sdxl_train_util._load_target_model(
|
||||
args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, dtype
|
||||
)
|
||||
unet: InferSdxlUNet2DConditionModel = InferSdxlUNet2DConditionModel(unet)
|
||||
|
||||
# xformers、Hypernetwork対応
|
||||
if not args.diffusers_xformers:
|
||||
@@ -1530,14 +1516,14 @@ def main(args):
|
||||
print("set vae_dtype to float32")
|
||||
vae_dtype = torch.float32
|
||||
vae.to(vae_dtype).to(device)
|
||||
vae.eval()
|
||||
|
||||
text_encoder1.to(dtype).to(device)
|
||||
text_encoder2.to(dtype).to(device)
|
||||
unet.to(dtype).to(device)
|
||||
text_encoder1.eval()
|
||||
text_encoder2.eval()
|
||||
unet.eval()
|
||||
|
||||
# freeU
|
||||
# unet.set_free_u_enabled(False, 1.0, 1.0, 0)
|
||||
unet.set_free_u_enabled(True, 1.1, 1.2, 0.9, 0.2)
|
||||
|
||||
# networkを組み込む
|
||||
if args.network_module:
|
||||
@@ -1545,20 +1531,12 @@ def main(args):
|
||||
network_default_muls = []
|
||||
network_pre_calc = args.network_pre_calc
|
||||
|
||||
# merge関連の引数を統合する
|
||||
if args.network_merge:
|
||||
network_merge = len(args.network_module) # all networks are merged
|
||||
elif args.network_merge_n_models:
|
||||
network_merge = args.network_merge_n_models
|
||||
else:
|
||||
network_merge = 0
|
||||
print(f"network_merge: {network_merge}")
|
||||
|
||||
for i, network_module in enumerate(args.network_module):
|
||||
print("import network module:", network_module)
|
||||
imported_module = importlib.import_module(network_module)
|
||||
|
||||
network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
|
||||
network_default_muls.append(network_mul)
|
||||
|
||||
net_kwargs = {}
|
||||
if args.network_args and i < len(args.network_args):
|
||||
@@ -1569,32 +1547,31 @@ def main(args):
|
||||
key, value = net_arg.split("=")
|
||||
net_kwargs[key] = value
|
||||
|
||||
if args.network_weights is None or len(args.network_weights) <= i:
|
||||
if args.network_weights and i < len(args.network_weights):
|
||||
network_weight = args.network_weights[i]
|
||||
print("load network weights from:", network_weight)
|
||||
|
||||
if model_util.is_safetensors(network_weight) and args.network_show_meta:
|
||||
from safetensors.torch import safe_open
|
||||
|
||||
with safe_open(network_weight, framework="pt") as f:
|
||||
metadata = f.metadata()
|
||||
if metadata is not None:
|
||||
print(f"metadata for: {network_weight}: {metadata}")
|
||||
|
||||
network, weights_sd = imported_module.create_network_from_weights(
|
||||
network_mul, network_weight, vae, [text_encoder1, text_encoder2], unet, for_inference=True, **net_kwargs
|
||||
)
|
||||
else:
|
||||
raise ValueError("No weight. Weight is required.")
|
||||
|
||||
network_weight = args.network_weights[i]
|
||||
print("load network weights from:", network_weight)
|
||||
|
||||
if model_util.is_safetensors(network_weight) and args.network_show_meta:
|
||||
from safetensors.torch import safe_open
|
||||
|
||||
with safe_open(network_weight, framework="pt") as f:
|
||||
metadata = f.metadata()
|
||||
if metadata is not None:
|
||||
print(f"metadata for: {network_weight}: {metadata}")
|
||||
|
||||
network, weights_sd = imported_module.create_network_from_weights(
|
||||
network_mul, network_weight, vae, [text_encoder1, text_encoder2], unet, for_inference=True, **net_kwargs
|
||||
)
|
||||
if network is None:
|
||||
return
|
||||
|
||||
mergeable = network.is_mergeable()
|
||||
if network_merge and not mergeable:
|
||||
if args.network_merge and not mergeable:
|
||||
print("network is not mergiable. ignore merge option.")
|
||||
|
||||
if not mergeable or i >= network_merge:
|
||||
# not merging
|
||||
if not args.network_merge or not mergeable:
|
||||
network.apply_to([text_encoder1, text_encoder2], unet)
|
||||
info = network.load_state_dict(weights_sd, False) # network.load_weightsを使うようにするとよい
|
||||
print(f"weights are loaded: {info}")
|
||||
@@ -1608,7 +1585,6 @@ def main(args):
|
||||
network.backup_weights()
|
||||
|
||||
networks.append(network)
|
||||
network_default_muls.append(network_mul)
|
||||
else:
|
||||
network.merge_to([text_encoder1, text_encoder2], unet, weights_sd, dtype, device)
|
||||
|
||||
@@ -1704,10 +1680,6 @@ def main(args):
|
||||
if args.diffusers_xformers:
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
|
||||
# Deep Shrink
|
||||
if args.ds_depth_1 is not None:
|
||||
unet.set_deep_shrink(args.ds_depth_1, args.ds_timesteps_1, args.ds_depth_2, args.ds_timesteps_2, args.ds_ratio)
|
||||
|
||||
# Textual Inversionを処理する
|
||||
if args.textual_inversion_embeddings:
|
||||
token_ids_embeds1 = []
|
||||
@@ -1889,18 +1861,9 @@ def main(args):
|
||||
|
||||
size = None
|
||||
for i, network in enumerate(networks):
|
||||
if (i < 3 and args.network_regional_mask_max_color_codes is None) or i < args.network_regional_mask_max_color_codes:
|
||||
if i < 3:
|
||||
np_mask = np.array(mask_images[0])
|
||||
|
||||
if args.network_regional_mask_max_color_codes:
|
||||
# カラーコードでマスクを指定する
|
||||
ch0 = (i + 1) & 1
|
||||
ch1 = ((i + 1) >> 1) & 1
|
||||
ch2 = ((i + 1) >> 2) & 1
|
||||
np_mask = np.all(np_mask == np.array([ch0, ch1, ch2]) * 255, axis=2)
|
||||
np_mask = np_mask.astype(np.uint8) * 255
|
||||
else:
|
||||
np_mask = np_mask[:, :, i]
|
||||
np_mask = np_mask[:, :, i]
|
||||
size = np_mask.shape
|
||||
else:
|
||||
np_mask = np.full(size, 255, dtype=np.uint8)
|
||||
@@ -2298,13 +2261,6 @@ def main(args):
|
||||
clip_prompt = None
|
||||
network_muls = None
|
||||
|
||||
# Deep Shrink
|
||||
ds_depth_1 = None # means no override
|
||||
ds_timesteps_1 = args.ds_timesteps_1
|
||||
ds_depth_2 = args.ds_depth_2
|
||||
ds_timesteps_2 = args.ds_timesteps_2
|
||||
ds_ratio = args.ds_ratio
|
||||
|
||||
prompt_args = raw_prompt.strip().split(" --")
|
||||
prompt = prompt_args[0]
|
||||
print(f"prompt {prompt_index+1}/{len(prompt_list)}: {prompt}")
|
||||
@@ -2412,51 +2368,10 @@ def main(args):
|
||||
print(f"network mul: {network_muls}")
|
||||
continue
|
||||
|
||||
# Deep Shrink
|
||||
m = re.match(r"dsd1 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink depth 1
|
||||
ds_depth_1 = int(m.group(1))
|
||||
print(f"deep shrink depth 1: {ds_depth_1}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dst1 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink timesteps 1
|
||||
ds_timesteps_1 = int(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink timesteps 1: {ds_timesteps_1}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dsd2 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink depth 2
|
||||
ds_depth_2 = int(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink depth 2: {ds_depth_2}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dst2 ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink timesteps 2
|
||||
ds_timesteps_2 = int(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink timesteps 2: {ds_timesteps_2}")
|
||||
continue
|
||||
|
||||
m = re.match(r"dsr ([\d\.]+)", parg, re.IGNORECASE)
|
||||
if m: # deep shrink ratio
|
||||
ds_ratio = float(m.group(1))
|
||||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||||
print(f"deep shrink ratio: {ds_ratio}")
|
||||
continue
|
||||
|
||||
except ValueError as ex:
|
||||
print(f"Exception in parsing / 解析エラー: {parg}")
|
||||
print(ex)
|
||||
|
||||
# override Deep Shrink
|
||||
if ds_depth_1 is not None:
|
||||
if ds_depth_1 < 0:
|
||||
ds_depth_1 = args.ds_depth_1 or 3
|
||||
unet.set_deep_shrink(ds_depth_1, ds_timesteps_1, ds_depth_2, ds_timesteps_2, ds_ratio)
|
||||
|
||||
# prepare seed
|
||||
if seeds is not None: # given in prompt
|
||||
# 数が足りないなら前のをそのまま使う
|
||||
@@ -2694,22 +2609,13 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
)
|
||||
parser.add_argument("--network_mul", type=float, default=None, nargs="*", help="additional network multiplier / 追加ネットワークの効果の倍率")
|
||||
parser.add_argument(
|
||||
"--network_args", type=str, default=None, nargs="*", help="additional arguments for network (key=value) / ネットワークへの追加の引数"
|
||||
"--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"
|
||||
)
|
||||
parser.add_argument("--network_show_meta", action="store_true", help="show metadata of network model / ネットワークモデルのメタデータを表示する")
|
||||
parser.add_argument(
|
||||
"--network_merge_n_models", type=int, default=None, help="merge this number of networks / この数だけネットワークをマージする"
|
||||
)
|
||||
parser.add_argument("--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする")
|
||||
parser.add_argument(
|
||||
"--network_pre_calc", action="store_true", help="pre-calculate network for generation / ネットワークのあらかじめ計算して生成する"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--network_regional_mask_max_color_codes",
|
||||
type=int,
|
||||
default=None,
|
||||
help="max color codes for regional mask (default is None, mask by channel) / regional maskの最大色数（デフォルトはNoneでチャンネルごとのマスク）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--textual_inversion_embeddings",
|
||||
type=str,
|
||||
@@ -2722,7 +2628,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
"--max_embeddings_multiples",
|
||||
type=int,
|
||||
default=None,
|
||||
help="max embedding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
|
||||
help="max embeding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--guide_image_path", type=str, default=None, nargs="*", help="image to CLIP guidance / CLIP guided SDでガイドに使う画像"
|
||||
@@ -2757,7 +2663,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
"--highres_fix_upscaler_args",
|
||||
type=str,
|
||||
default=None,
|
||||
help="additional arguments for upscaler (key=value) / upscalerへの追加の引数",
|
||||
help="additional argmuments for upscaler (key=value) / upscalerへの追加の引数",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--highres_fix_disable_control_net",
|
||||
@@ -2794,31 +2700,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
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",
|
||||
)
|
||||
|
||||
# Deep Shrink
|
||||
parser.add_argument(
|
||||
"--ds_depth_1",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Enable Deep Shrink with this depth 1, valid values are 0 to 8 / Deep Shrinkをこのdepthで有効にする",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ds_timesteps_1",
|
||||
type=int,
|
||||
default=650,
|
||||
help="Apply Deep Shrink depth 1 until this timesteps / Deep Shrink depth 1を適用するtimesteps",
|
||||
)
|
||||
parser.add_argument("--ds_depth_2", type=int, default=None, help="Deep Shrink depth 2 / Deep Shrinkのdepth 2")
|
||||
parser.add_argument(
|
||||
"--ds_timesteps_2",
|
||||
type=int,
|
||||
default=650,
|
||||
help="Apply Deep Shrink depth 2 until this timesteps / Deep Shrink depth 2を適用するtimesteps",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ds_ratio", type=float, default=0.5, help="Deep Shrink ratio for downsampling / Deep Shrinkのdownsampling比率"
|
||||
)
|
||||
|
||||
# # 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"
|
||||
# )
|
||||
|
||||
@@ -9,13 +9,6 @@ import random
|
||||
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
|
||||
@@ -101,7 +94,7 @@ if __name__ == "__main__":
|
||||
type=str,
|
||||
nargs="*",
|
||||
default=[],
|
||||
help="LoRA weights, only supports networks.lora, each argument is a `path;multiplier` (semi-colon separated)",
|
||||
help="LoRA weights, only supports networks.lora, each arguement is a `path;multiplier` (semi-colon separated)",
|
||||
)
|
||||
parser.add_argument("--interactive", action="store_true")
|
||||
args = parser.parse_args()
|
||||
|
||||
184
sdxl_train.py
184
sdxl_train.py
@@ -10,16 +10,6 @@ import toml
|
||||
|
||||
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
|
||||
@@ -37,7 +27,6 @@ from library.custom_train_functions import (
|
||||
prepare_scheduler_for_custom_training,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
add_v_prediction_like_loss,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
from library.sdxl_original_unet import SdxlUNet2DConditionModel
|
||||
|
||||
@@ -74,22 +63,33 @@ def get_block_params_to_optimize(unet: SdxlUNet2DConditionModel, block_lrs: List
|
||||
|
||||
|
||||
def append_block_lr_to_logs(block_lrs, logs, lr_scheduler, optimizer_type):
|
||||
names = []
|
||||
lrs = lr_scheduler.get_last_lr()
|
||||
|
||||
lr_index = 0
|
||||
block_index = 0
|
||||
while block_index < UNET_NUM_BLOCKS_FOR_BLOCK_LR + 2:
|
||||
while lr_index < len(lrs):
|
||||
if block_index < UNET_NUM_BLOCKS_FOR_BLOCK_LR:
|
||||
name = f"block{block_index}"
|
||||
if block_lrs[block_index] == 0:
|
||||
block_index += 1
|
||||
continue
|
||||
names.append(f"block{block_index}")
|
||||
elif block_index == UNET_NUM_BLOCKS_FOR_BLOCK_LR:
|
||||
names.append("text_encoder1")
|
||||
name = "text_encoder1"
|
||||
elif block_index == UNET_NUM_BLOCKS_FOR_BLOCK_LR + 1:
|
||||
names.append("text_encoder2")
|
||||
name = "text_encoder2"
|
||||
else:
|
||||
raise ValueError(f"unexpected block_index: {block_index}")
|
||||
|
||||
block_index += 1
|
||||
|
||||
train_util.append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names)
|
||||
logs["lr/" + name] = float(lrs[lr_index])
|
||||
|
||||
if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower() == "Prodigy".lower():
|
||||
logs["lr/d*lr/" + name] = (
|
||||
lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["lr"]
|
||||
)
|
||||
|
||||
lr_index += 1
|
||||
|
||||
|
||||
def train(args):
|
||||
@@ -165,8 +165,8 @@ def train(args):
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
train_dataset_group.verify_bucket_reso_steps(32)
|
||||
|
||||
@@ -264,11 +264,10 @@ def train(args):
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
# 学習を準備する：モデルを適切な状態にする
|
||||
training_models = []
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
train_unet = args.learning_rate > 0
|
||||
train_text_encoder1 = False
|
||||
train_text_encoder2 = False
|
||||
training_models.append(unet)
|
||||
|
||||
if args.train_text_encoder:
|
||||
# TODO each option for two text encoders?
|
||||
@@ -276,23 +275,10 @@ def train(args):
|
||||
if args.gradient_checkpointing:
|
||||
text_encoder1.gradient_checkpointing_enable()
|
||||
text_encoder2.gradient_checkpointing_enable()
|
||||
lr_te1 = args.learning_rate_te1 if args.learning_rate_te1 is not None else args.learning_rate # 0 means not train
|
||||
lr_te2 = args.learning_rate_te2 if args.learning_rate_te2 is not None else args.learning_rate # 0 means not train
|
||||
train_text_encoder1 = lr_te1 > 0
|
||||
train_text_encoder2 = lr_te2 > 0
|
||||
|
||||
# caching one text encoder output is not supported
|
||||
if not train_text_encoder1:
|
||||
text_encoder1.to(weight_dtype)
|
||||
if not train_text_encoder2:
|
||||
text_encoder2.to(weight_dtype)
|
||||
text_encoder1.requires_grad_(train_text_encoder1)
|
||||
text_encoder2.requires_grad_(train_text_encoder2)
|
||||
text_encoder1.train(train_text_encoder1)
|
||||
text_encoder2.train(train_text_encoder2)
|
||||
training_models.append(text_encoder1)
|
||||
training_models.append(text_encoder2)
|
||||
# set require_grad=True later
|
||||
else:
|
||||
text_encoder1.to(weight_dtype)
|
||||
text_encoder2.to(weight_dtype)
|
||||
text_encoder1.requires_grad_(False)
|
||||
text_encoder2.requires_grad_(False)
|
||||
text_encoder1.eval()
|
||||
@@ -301,7 +287,7 @@ def train(args):
|
||||
# TextEncoderの出力をキャッシュする
|
||||
if args.cache_text_encoder_outputs:
|
||||
# Text Encodes are eval and no grad
|
||||
with torch.no_grad(), accelerator.autocast():
|
||||
with torch.no_grad():
|
||||
train_dataset_group.cache_text_encoder_outputs(
|
||||
(tokenizer1, tokenizer2),
|
||||
(text_encoder1, text_encoder2),
|
||||
@@ -317,33 +303,30 @@ def train(args):
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=vae_dtype)
|
||||
|
||||
unet.requires_grad_(train_unet)
|
||||
if not train_unet:
|
||||
unet.to(accelerator.device, dtype=weight_dtype) # because of unet is not prepared
|
||||
for m in training_models:
|
||||
m.requires_grad_(True)
|
||||
|
||||
training_models = []
|
||||
params_to_optimize = []
|
||||
if train_unet:
|
||||
training_models.append(unet)
|
||||
if block_lrs is None:
|
||||
params_to_optimize.append({"params": list(unet.parameters()), "lr": args.learning_rate})
|
||||
else:
|
||||
params_to_optimize.extend(get_block_params_to_optimize(unet, block_lrs))
|
||||
if block_lrs is None:
|
||||
params = []
|
||||
for m in training_models:
|
||||
params.extend(m.parameters())
|
||||
params_to_optimize = params
|
||||
|
||||
if train_text_encoder1:
|
||||
training_models.append(text_encoder1)
|
||||
params_to_optimize.append({"params": list(text_encoder1.parameters()), "lr": args.learning_rate_te1 or args.learning_rate})
|
||||
if train_text_encoder2:
|
||||
training_models.append(text_encoder2)
|
||||
params_to_optimize.append({"params": list(text_encoder2.parameters()), "lr": args.learning_rate_te2 or args.learning_rate})
|
||||
|
||||
# calculate number of trainable parameters
|
||||
n_params = 0
|
||||
for params in params_to_optimize:
|
||||
for p in params["params"]:
|
||||
# calculate number of trainable parameters
|
||||
n_params = 0
|
||||
for p in params:
|
||||
n_params += p.numel()
|
||||
else:
|
||||
params_to_optimize = get_block_params_to_optimize(training_models[0], block_lrs) # U-Net
|
||||
for m in training_models[1:]: # Text Encoders if exists
|
||||
params_to_optimize.append({"params": m.parameters(), "lr": args.learning_rate})
|
||||
|
||||
# calculate number of trainable parameters
|
||||
n_params = 0
|
||||
for params in params_to_optimize:
|
||||
for p in params["params"]:
|
||||
n_params += p.numel()
|
||||
|
||||
accelerator.print(f"train unet: {train_unet}, text_encoder1: {train_text_encoder1}, text_encoder2: {train_text_encoder2}")
|
||||
accelerator.print(f"number of models: {len(training_models)}")
|
||||
accelerator.print(f"number of trainable parameters: {n_params}")
|
||||
|
||||
@@ -358,7 +341,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -395,17 +378,18 @@ def train(args):
|
||||
text_encoder2.to(weight_dtype)
|
||||
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
if train_unet:
|
||||
unet = accelerator.prepare(unet)
|
||||
if train_text_encoder1:
|
||||
# freeze last layer and final_layer_norm in te1 since we use the output of the penultimate layer
|
||||
text_encoder1.text_model.encoder.layers[-1].requires_grad_(False)
|
||||
text_encoder1.text_model.final_layer_norm.requires_grad_(False)
|
||||
text_encoder1 = accelerator.prepare(text_encoder1)
|
||||
if train_text_encoder2:
|
||||
text_encoder2 = accelerator.prepare(text_encoder2)
|
||||
if args.train_text_encoder:
|
||||
unet, text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
|
||||
optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
|
||||
# transform DDP after prepare
|
||||
text_encoder1, text_encoder2, unet = train_util.transform_models_if_DDP([text_encoder1, text_encoder2, unet])
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
(unet,) = train_util.transform_models_if_DDP([unet])
|
||||
text_encoder1.to(weight_dtype)
|
||||
text_encoder2.to(weight_dtype)
|
||||
|
||||
# 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:
|
||||
@@ -461,12 +445,6 @@ def train(args):
|
||||
init_kwargs = toml.load(args.log_tracker_config)
|
||||
accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
|
||||
|
||||
# For --sample_at_first
|
||||
sdxl_train_util.sample_images(
|
||||
accelerator, args, 0, global_step, accelerator.device, vae, [tokenizer1, tokenizer2], [text_encoder1, text_encoder2], unet
|
||||
)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
for epoch in range(num_train_epochs):
|
||||
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
||||
current_epoch.value = epoch + 1
|
||||
@@ -474,9 +452,10 @@ def train(args):
|
||||
for m in training_models:
|
||||
m.train()
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
current_step.value = global_step
|
||||
with accelerator.accumulate(*training_models):
|
||||
with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
|
||||
else:
|
||||
@@ -487,7 +466,7 @@ def train(args):
|
||||
# NaNが含まれていれば警告を表示し0に置き換える
|
||||
if torch.any(torch.isnan(latents)):
|
||||
accelerator.print("NaN found in latents, replacing with zeros")
|
||||
latents = torch.nan_to_num(latents, 0, out=latents)
|
||||
latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
|
||||
latents = latents * sdxl_model_util.VAE_SCALE_FACTOR
|
||||
|
||||
if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
|
||||
@@ -508,7 +487,6 @@ def train(args):
|
||||
# else:
|
||||
input_ids1 = input_ids1.to(accelerator.device)
|
||||
input_ids2 = input_ids2.to(accelerator.device)
|
||||
# unwrap_model is fine for models not wrapped by accelerator
|
||||
encoder_hidden_states1, encoder_hidden_states2, pool2 = train_util.get_hidden_states_sdxl(
|
||||
args.max_token_length,
|
||||
input_ids1,
|
||||
@@ -518,7 +496,6 @@ def train(args):
|
||||
text_encoder1,
|
||||
text_encoder2,
|
||||
None if not args.full_fp16 else weight_dtype,
|
||||
accelerator=accelerator,
|
||||
)
|
||||
else:
|
||||
encoder_hidden_states1 = batch["text_encoder_outputs1_list"].to(accelerator.device).to(weight_dtype)
|
||||
@@ -564,12 +541,7 @@ def train(args):
|
||||
|
||||
target = noise
|
||||
|
||||
if (
|
||||
args.min_snr_gamma
|
||||
or args.scale_v_pred_loss_like_noise_pred
|
||||
or args.v_pred_like_loss
|
||||
or args.debiased_estimation_loss
|
||||
):
|
||||
if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred or args.v_pred_like_loss:
|
||||
# do not mean over batch dimension for snr weight or scale v-pred loss
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||||
loss = loss.mean([1, 2, 3])
|
||||
@@ -580,8 +552,6 @@ def train(args):
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.v_pred_like_loss:
|
||||
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # mean over batch dimension
|
||||
else:
|
||||
@@ -643,22 +613,29 @@ def train(args):
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss}
|
||||
if block_lrs is None:
|
||||
train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=train_unet)
|
||||
logs["lr"] = float(lr_scheduler.get_last_lr()[0])
|
||||
if (
|
||||
args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
|
||||
): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
else:
|
||||
append_block_lr_to_logs(block_lrs, logs, lr_scheduler, args.optimizer_type) # U-Net is included in block_lrs
|
||||
append_block_lr_to_logs(block_lrs, logs, lr_scheduler, args.optimizer_type)
|
||||
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
# TODO moving averageにする
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step + 1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
@@ -742,19 +719,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
custom_train_functions.add_custom_train_arguments(parser)
|
||||
sdxl_train_util.add_sdxl_training_arguments(parser)
|
||||
|
||||
parser.add_argument(
|
||||
"--learning_rate_te1",
|
||||
type=float,
|
||||
default=None,
|
||||
help="learning rate for text encoder 1 (ViT-L) / text encoder 1 (ViT-L)の学習率",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--learning_rate_te2",
|
||||
type=float,
|
||||
default=None,
|
||||
help="learning rate for text encoder 2 (BiG-G) / text encoder 2 (BiG-G)の学習率",
|
||||
)
|
||||
|
||||
parser.add_argument("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する")
|
||||
parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
|
||||
parser.add_argument(
|
||||
|
||||
@@ -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
|
||||
@@ -14,16 +11,8 @@ import toml
|
||||
|
||||
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
|
||||
@@ -44,9 +33,8 @@ from library.custom_train_functions import (
|
||||
pyramid_noise_like,
|
||||
apply_noise_offset,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
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
|
||||
@@ -107,8 +95,8 @@ def train(args):
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
train_dataset_group.verify_bucket_reso_steps(32)
|
||||
|
||||
@@ -153,6 +141,9 @@ 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)
|
||||
@@ -186,53 +177,22 @@ 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)}")
|
||||
|
||||
@@ -246,7 +206,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -265,29 +225,37 @@ 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, 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
|
||||
@@ -348,17 +316,12 @@ def train(args):
|
||||
"lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
|
||||
)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
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)
|
||||
|
||||
@@ -366,7 +329,7 @@ def train(args):
|
||||
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)
|
||||
|
||||
@@ -381,9 +344,11 @@ def train(args):
|
||||
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)
|
||||
@@ -394,7 +359,7 @@ def train(args):
|
||||
# NaNが含まれていれば警告を表示し0に置き換える
|
||||
if torch.any(torch.isnan(latents)):
|
||||
accelerator.print("NaN found in latents, replacing with zeros")
|
||||
latents = torch.nan_to_num(latents, 0, out=latents)
|
||||
latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
|
||||
latents = latents * sdxl_model_util.VAE_SCALE_FACTOR
|
||||
|
||||
if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
|
||||
@@ -440,9 +405,10 @@ def train(args):
|
||||
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
|
||||
@@ -457,19 +423,17 @@ def train(args):
|
||||
loss = loss * loss_weights
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.v_pred_like_loss:
|
||||
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = unet.get_trainable_params()
|
||||
params_to_clip = network.get_trainable_params()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
@@ -488,7 +452,7 @@ def train(args):
|
||||
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)
|
||||
@@ -499,9 +463,14 @@ def train(args):
|
||||
remove_model(remove_ckpt_name)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if args.logging_dir is not None:
|
||||
@@ -512,7 +481,7 @@ def train(args):
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
@@ -522,7 +491,7 @@ def train(args):
|
||||
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:
|
||||
@@ -537,7 +506,7 @@ def train(args):
|
||||
# end of epoch
|
||||
|
||||
if is_main_process:
|
||||
unet = accelerator.unwrap_model(unet)
|
||||
network = accelerator.unwrap_model(network)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
@@ -546,7 +515,7 @@ def train(args):
|
||||
|
||||
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.")
|
||||
|
||||
|
||||
@@ -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
|
||||
@@ -11,15 +14,9 @@ import toml
|
||||
|
||||
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
|
||||
@@ -40,9 +37,8 @@ from library.custom_train_functions import (
|
||||
pyramid_noise_like,
|
||||
apply_noise_offset,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
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
|
||||
@@ -103,8 +99,8 @@ def train(args):
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
train_dataset_group.verify_bucket_reso_steps(32)
|
||||
|
||||
@@ -149,9 +145,6 @@ 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)
|
||||
@@ -185,22 +178,53 @@ 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)}")
|
||||
|
||||
@@ -214,7 +238,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -233,34 +257,32 @@ 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 = 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
|
||||
@@ -321,11 +343,18 @@ def train(args):
|
||||
"lllite_control_net_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
|
||||
)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
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)
|
||||
|
||||
@@ -333,7 +362,7 @@ def train(args):
|
||||
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)
|
||||
|
||||
@@ -348,11 +377,9 @@ def train(args):
|
||||
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)
|
||||
@@ -363,7 +390,7 @@ def train(args):
|
||||
# NaNが含まれていれば警告を表示し0に置き換える
|
||||
if torch.any(torch.isnan(latents)):
|
||||
accelerator.print("NaN found in latents, replacing with zeros")
|
||||
latents = torch.nan_to_num(latents, 0, out=latents)
|
||||
latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
|
||||
latents = latents * sdxl_model_util.VAE_SCALE_FACTOR
|
||||
|
||||
if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
|
||||
@@ -409,10 +436,9 @@ def train(args):
|
||||
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
|
||||
@@ -427,19 +453,17 @@ def train(args):
|
||||
loss = loss * loss_weights
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.v_pred_like_loss:
|
||||
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = network.get_trainable_params()
|
||||
params_to_clip = unet.get_trainable_params()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
@@ -458,7 +482,7 @@ def train(args):
|
||||
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)
|
||||
@@ -469,9 +493,14 @@ def train(args):
|
||||
remove_model(remove_ckpt_name)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if args.logging_dir is not None:
|
||||
@@ -482,7 +511,7 @@ def train(args):
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
@@ -492,7 +521,7 @@ def train(args):
|
||||
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:
|
||||
@@ -507,7 +536,7 @@ def train(args):
|
||||
# end of epoch
|
||||
|
||||
if is_main_process:
|
||||
network = accelerator.unwrap_model(network)
|
||||
unet = accelerator.unwrap_model(unet)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
@@ -516,7 +545,7 @@ def train(args):
|
||||
|
||||
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.")
|
||||
|
||||
@@ -1,15 +1,5 @@
|
||||
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
|
||||
|
||||
@@ -73,16 +63,14 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# When TE is not be trained, it will not be prepared so we need to use explicit autocast
|
||||
with accelerator.autocast():
|
||||
dataset.cache_text_encoder_outputs(
|
||||
tokenizers,
|
||||
text_encoders,
|
||||
accelerator.device,
|
||||
weight_dtype,
|
||||
args.cache_text_encoder_outputs_to_disk,
|
||||
accelerator.is_main_process,
|
||||
)
|
||||
dataset.cache_text_encoder_outputs(
|
||||
tokenizers,
|
||||
text_encoders,
|
||||
accelerator.device,
|
||||
weight_dtype,
|
||||
args.cache_text_encoder_outputs_to_disk,
|
||||
accelerator.is_main_process,
|
||||
)
|
||||
|
||||
text_encoders[0].to("cpu", dtype=torch.float32) # Text Encoder doesn't work with fp16 on CPU
|
||||
text_encoders[1].to("cpu", dtype=torch.float32)
|
||||
@@ -126,7 +114,6 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
|
||||
text_encoders[0],
|
||||
text_encoders[1],
|
||||
None if not args.full_fp16 else weight_dtype,
|
||||
accelerator=accelerator,
|
||||
)
|
||||
else:
|
||||
encoder_hidden_states1 = batch["text_encoder_outputs1_list"].to(accelerator.device).to(weight_dtype)
|
||||
|
||||
@@ -3,13 +3,6 @@ import os
|
||||
|
||||
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
|
||||
|
||||
@@ -64,7 +57,6 @@ class SdxlTextualInversionTrainer(train_textual_inversion.TextualInversionTraine
|
||||
text_encoders[0],
|
||||
text_encoders[1],
|
||||
None if not args.full_fp16 else weight_dtype,
|
||||
accelerator=accelerator,
|
||||
)
|
||||
return encoder_hidden_states1, encoder_hidden_states2, pool2
|
||||
|
||||
|
||||
@@ -86,8 +86,8 @@ def cache_to_disk(args: argparse.Namespace) -> None:
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
# acceleratorを準備する
|
||||
print("prepare accelerator")
|
||||
@@ -120,7 +120,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
|
||||
@@ -91,8 +91,8 @@ def cache_to_disk(args: argparse.Namespace) -> None:
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
# acceleratorを準備する
|
||||
print("prepare accelerator")
|
||||
@@ -125,7 +125,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
|
||||
@@ -23,7 +23,7 @@ def convert(args):
|
||||
is_load_ckpt = os.path.isfile(args.model_to_load)
|
||||
is_save_ckpt = len(os.path.splitext(args.model_to_save)[1]) > 0
|
||||
|
||||
assert not is_load_ckpt or args.v1 != args.v2, "v1 or v2 is required to load checkpoint / checkpointの読み込みにはv1/v2指定が必要です"
|
||||
assert not is_load_ckpt or args.v1 != args.v2, f"v1 or v2 is required to load checkpoint / checkpointの読み込みにはv1/v2指定が必要です"
|
||||
# assert (
|
||||
# is_save_ckpt or args.reference_model is not None
|
||||
# ), f"reference model is required to save as Diffusers / Diffusers形式での保存には参照モデルが必要です"
|
||||
@@ -34,12 +34,10 @@ def convert(args):
|
||||
|
||||
if is_load_ckpt:
|
||||
v2_model = args.v2
|
||||
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(
|
||||
v2_model, args.model_to_load, unet_use_linear_projection_in_v2=args.unet_use_linear_projection
|
||||
)
|
||||
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(v2_model, args.model_to_load, unet_use_linear_projection_in_v2=args.unet_use_linear_projection)
|
||||
else:
|
||||
pipe = StableDiffusionPipeline.from_pretrained(
|
||||
args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None, variant=args.variant
|
||||
args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None
|
||||
)
|
||||
text_encoder = pipe.text_encoder
|
||||
vae = pipe.vae
|
||||
@@ -59,26 +57,15 @@ def convert(args):
|
||||
if is_save_ckpt:
|
||||
original_model = args.model_to_load if is_load_ckpt else None
|
||||
key_count = model_util.save_stable_diffusion_checkpoint(
|
||||
v2_model,
|
||||
args.model_to_save,
|
||||
text_encoder,
|
||||
unet,
|
||||
original_model,
|
||||
args.epoch,
|
||||
args.global_step,
|
||||
None if args.metadata is None else eval(args.metadata),
|
||||
save_dtype=save_dtype,
|
||||
vae=vae,
|
||||
v2_model, args.model_to_save, text_encoder, unet, original_model, args.epoch, args.global_step, save_dtype, vae
|
||||
)
|
||||
print(f"model saved. total converted state_dict keys: {key_count}")
|
||||
else:
|
||||
print(
|
||||
f"copy scheduler/tokenizer config from: {args.reference_model if args.reference_model is not None else 'default model'}"
|
||||
)
|
||||
print(f"copy scheduler/tokenizer config from: {args.reference_model if args.reference_model is not None else 'default model'}")
|
||||
model_util.save_diffusers_checkpoint(
|
||||
v2_model, args.model_to_save, text_encoder, unet, args.reference_model, vae, args.use_safetensors
|
||||
)
|
||||
print("model saved.")
|
||||
print(f"model saved.")
|
||||
|
||||
|
||||
def setup_parser() -> argparse.ArgumentParser:
|
||||
@@ -90,9 +77,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
"--v2", action="store_true", help="load v2.0 model (v1 or v2 is required to load checkpoint) / 2.0のモデルを読み込む"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--unet_use_linear_projection",
|
||||
action="store_true",
|
||||
help="When saving v2 model as Diffusers, set U-Net config to `use_linear_projection=true` (to match stabilityai's model) / Diffusers形式でv2モデルを保存するときにU-Netの設定を`use_linear_projection=true`にする（stabilityaiのモデルと合わせる）",
|
||||
"--unet_use_linear_projection", action="store_true", help="When saving v2 model as Diffusers, set U-Net config to `use_linear_projection=true` (to match stabilityai's model) / Diffusers形式でv2モデルを保存するときにU-Netの設定を`use_linear_projection=true`にする（stabilityaiのモデルと合わせる）"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--fp16",
|
||||
@@ -114,18 +99,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
parser.add_argument(
|
||||
"--global_step", type=int, default=0, help="global_step to write to checkpoint / checkpointに記録するglobal_stepの値"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--metadata",
|
||||
type=str,
|
||||
default=None,
|
||||
help='モデルに保存されるメタデータ、Pythonの辞書形式で指定 / metadata: metadata written in to the model in Python Dictionary. Example metadata: \'{"name": "model_name", "resolution": "512x512"}\'',
|
||||
)
|
||||
parser.add_argument(
|
||||
"--variant",
|
||||
type=str,
|
||||
default=None,
|
||||
help="読む込むDiffusersのvariantを指定する、例: fp16 / variant: Diffusers variant to load. Example: fp16",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--reference_model",
|
||||
type=str,
|
||||
|
||||
@@ -11,16 +11,6 @@ import toml
|
||||
|
||||
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
|
||||
@@ -101,8 +91,8 @@ def train(args):
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group)
|
||||
@@ -248,7 +238,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -338,11 +328,10 @@ def train(args):
|
||||
init_kwargs = {}
|
||||
if args.log_tracker_config is not None:
|
||||
init_kwargs = toml.load(args.log_tracker_config)
|
||||
accelerator.init_trackers(
|
||||
"controlnet_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
|
||||
)
|
||||
accelerator.init_trackers("controlnet_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
del train_dataset_group
|
||||
|
||||
# function for saving/removing
|
||||
@@ -376,11 +365,6 @@ def train(args):
|
||||
accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
|
||||
os.remove(old_ckpt_file)
|
||||
|
||||
# For --sample_at_first
|
||||
train_util.sample_images(
|
||||
accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, controlnet=controlnet
|
||||
)
|
||||
|
||||
# training loop
|
||||
for epoch in range(num_train_epochs):
|
||||
if is_main_process:
|
||||
@@ -459,7 +443,7 @@ def train(args):
|
||||
loss = loss * loss_weights
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
@@ -509,9 +493,14 @@ def train(args):
|
||||
remove_model(remove_ckpt_name)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if args.logging_dir is not None:
|
||||
@@ -522,7 +511,7 @@ def train(args):
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
78
train_db.py
78
train_db.py
@@ -11,16 +11,6 @@ import toml
|
||||
|
||||
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
|
||||
|
||||
@@ -38,7 +28,6 @@ from library.custom_train_functions import (
|
||||
pyramid_noise_like,
|
||||
apply_noise_offset,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
|
||||
# perlin_noise,
|
||||
@@ -82,8 +71,8 @@ def train(args):
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
if args.no_token_padding:
|
||||
train_dataset_group.disable_token_padding()
|
||||
@@ -112,7 +101,6 @@ def train(args):
|
||||
|
||||
# mixed precisionに対応した型を用意しておき適宜castする
|
||||
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
||||
vae_dtype = torch.float32 if args.no_half_vae else weight_dtype
|
||||
|
||||
# モデルを読み込む
|
||||
text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype, accelerator)
|
||||
@@ -137,7 +125,7 @@ def train(args):
|
||||
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=vae_dtype)
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
@@ -168,17 +156,11 @@ def train(args):
|
||||
# 学習に必要なクラスを準備する
|
||||
accelerator.print("prepare optimizer, data loader etc.")
|
||||
if train_text_encoder:
|
||||
if args.learning_rate_te is None:
|
||||
# wightout list, adamw8bit is crashed
|
||||
trainable_params = list(itertools.chain(unet.parameters(), text_encoder.parameters()))
|
||||
else:
|
||||
trainable_params = [
|
||||
{"params": list(unet.parameters()), "lr": args.learning_rate},
|
||||
{"params": list(text_encoder.parameters()), "lr": args.learning_rate_te},
|
||||
]
|
||||
# wightout list, adamw8bit is crashed
|
||||
trainable_params = list(itertools.chain(unet.parameters(), text_encoder.parameters()))
|
||||
else:
|
||||
trainable_params = unet.parameters()
|
||||
|
||||
|
||||
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
||||
|
||||
# dataloaderを準備する
|
||||
@@ -188,7 +170,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -226,6 +208,9 @@ def train(args):
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
# transform DDP after prepare
|
||||
text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
|
||||
|
||||
if not train_text_encoder:
|
||||
text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error
|
||||
|
||||
@@ -272,10 +257,8 @@ def train(args):
|
||||
init_kwargs = toml.load(args.log_tracker_config)
|
||||
accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
|
||||
|
||||
# For --sample_at_first
|
||||
train_util.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
for epoch in range(num_train_epochs):
|
||||
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
||||
current_epoch.value = epoch + 1
|
||||
@@ -343,11 +326,9 @@ def train(args):
|
||||
loss = loss * loss_weights
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
@@ -395,20 +376,30 @@ def train(args):
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss}
|
||||
train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=True)
|
||||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||||
if (
|
||||
args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
|
||||
): # tracking d*lr value
|
||||
logs["lr/d*lr"] = (
|
||||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
|
||||
)
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
@@ -466,12 +457,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
config_util.add_config_arguments(parser)
|
||||
custom_train_functions.add_custom_train_arguments(parser)
|
||||
|
||||
parser.add_argument(
|
||||
"--learning_rate_te",
|
||||
type=float,
|
||||
default=None,
|
||||
help="learning rate for text encoder, default is same as unet / Text Encoderの学習率、デフォルトはunetと同じ",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no_token_padding",
|
||||
action="store_true",
|
||||
@@ -483,11 +468,6 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
default=None,
|
||||
help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no_half_vae",
|
||||
action="store_true",
|
||||
help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
130
train_network.py
130
train_network.py
@@ -12,17 +12,6 @@ import toml
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
|
||||
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
|
||||
@@ -44,7 +33,6 @@ from library.custom_train_functions import (
|
||||
prepare_scheduler_for_custom_training,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
add_v_prediction_like_loss,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
|
||||
|
||||
@@ -110,9 +98,6 @@ class NetworkTrainer:
|
||||
def is_text_encoder_outputs_cached(self, args):
|
||||
return False
|
||||
|
||||
def is_train_text_encoder(self, args):
|
||||
return not args.network_train_unet_only and not self.is_text_encoder_outputs_cached(args)
|
||||
|
||||
def cache_text_encoder_outputs_if_needed(
|
||||
self, args, accelerator, unet, vae, tokenizers, text_encoders, data_loader, weight_dtype
|
||||
):
|
||||
@@ -128,11 +113,6 @@ class NetworkTrainer:
|
||||
noise_pred = unet(noisy_latents, timesteps, text_conds).sample
|
||||
return noise_pred
|
||||
|
||||
def all_reduce_network(self, accelerator, network):
|
||||
for param in network.parameters():
|
||||
if param.grad is not None:
|
||||
param.grad = accelerator.reduce(param.grad, reduction="mean")
|
||||
|
||||
def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet):
|
||||
train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
@@ -202,8 +182,8 @@ class NetworkTrainer:
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
if args.debug_dataset:
|
||||
train_util.debug_dataset(train_dataset_group)
|
||||
@@ -293,10 +273,7 @@ class NetworkTrainer:
|
||||
if args.dim_from_weights:
|
||||
network, _ = network_module.create_network_from_weights(1, args.network_weights, vae, text_encoder, unet, **net_kwargs)
|
||||
else:
|
||||
if "dropout" not in net_kwargs:
|
||||
# workaround for LyCORIS (;^ω^)
|
||||
net_kwargs["dropout"] = args.network_dropout
|
||||
|
||||
# LyCORIS will work with this...
|
||||
network = network_module.create_network(
|
||||
1.0,
|
||||
args.network_dim,
|
||||
@@ -319,7 +296,7 @@ class NetworkTrainer:
|
||||
args.scale_weight_norms = False
|
||||
|
||||
train_unet = not args.network_train_text_encoder_only
|
||||
train_text_encoder = self.is_train_text_encoder(args)
|
||||
train_text_encoder = not args.network_train_unet_only and not self.is_text_encoder_outputs_cached(args)
|
||||
network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
|
||||
|
||||
if args.network_weights is not None:
|
||||
@@ -355,7 +332,7 @@ class NetworkTrainer:
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -396,20 +373,44 @@ class NetworkTrainer:
|
||||
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
# TODO めちゃくちゃ冗長なのでコードを整理する
|
||||
if train_unet:
|
||||
unet = accelerator.prepare(unet)
|
||||
else:
|
||||
unet.to(accelerator.device, dtype=weight_dtype) # move to device because unet is not prepared by accelerator
|
||||
if train_text_encoder:
|
||||
if train_unet and train_text_encoder:
|
||||
if len(text_encoders) > 1:
|
||||
text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders]
|
||||
unet, t_enc1, t_enc2, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoders[0], text_encoders[1], network, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
text_encoder = text_encoders = [t_enc1, t_enc2]
|
||||
del t_enc1, t_enc2
|
||||
else:
|
||||
text_encoder = accelerator.prepare(text_encoder)
|
||||
unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
text_encoders = [text_encoder]
|
||||
elif train_unet:
|
||||
unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, network, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
elif train_text_encoder:
|
||||
if len(text_encoders) > 1:
|
||||
t_enc1, t_enc2, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoders[0], text_encoders[1], network, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
text_encoder = text_encoders = [t_enc1, t_enc2]
|
||||
del t_enc1, t_enc2
|
||||
else:
|
||||
text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoder, network, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
text_encoders = [text_encoder]
|
||||
|
||||
unet.to(accelerator.device, dtype=weight_dtype) # move to device because unet is not prepared by accelerator
|
||||
else:
|
||||
for t_enc in text_encoders:
|
||||
t_enc.to(accelerator.device, dtype=weight_dtype)
|
||||
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler)
|
||||
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
network, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
|
||||
# transform DDP after prepare (train_network here only)
|
||||
text_encoders = train_util.transform_models_if_DDP(text_encoders)
|
||||
unet, network = train_util.transform_models_if_DDP([unet, network])
|
||||
|
||||
if args.gradient_checkpointing:
|
||||
# according to TI example in Diffusers, train is required
|
||||
@@ -418,12 +419,7 @@ class NetworkTrainer:
|
||||
t_enc.train()
|
||||
|
||||
# set top parameter requires_grad = True for gradient checkpointing works
|
||||
if train_text_encoder:
|
||||
t_enc.text_model.embeddings.requires_grad_(True)
|
||||
|
||||
# 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)
|
||||
t_enc.text_model.embeddings.requires_grad_(True)
|
||||
else:
|
||||
unet.eval()
|
||||
for t_enc in text_encoders:
|
||||
@@ -431,7 +427,7 @@ class NetworkTrainer:
|
||||
|
||||
del t_enc
|
||||
|
||||
accelerator.unwrap_model(network).prepare_grad_etc(text_encoder, unet)
|
||||
network.prepare_grad_etc(text_encoder, unet)
|
||||
|
||||
if not cache_latents: # キャッシュしない場合はVAEを使うのでVAEを準備する
|
||||
vae.requires_grad_(False)
|
||||
@@ -513,8 +509,6 @@ class NetworkTrainer:
|
||||
"ss_prior_loss_weight": args.prior_loss_weight,
|
||||
"ss_min_snr_gamma": args.min_snr_gamma,
|
||||
"ss_scale_weight_norms": args.scale_weight_norms,
|
||||
"ss_ip_noise_gamma": args.ip_noise_gamma,
|
||||
"ss_debiased_estimation": bool(args.debiased_estimation_loss),
|
||||
}
|
||||
|
||||
if use_user_config:
|
||||
@@ -684,20 +678,19 @@ class NetworkTrainer:
|
||||
|
||||
if accelerator.is_main_process:
|
||||
init_kwargs = {}
|
||||
if args.wandb_run_name:
|
||||
init_kwargs['wandb'] = {'name': args.wandb_run_name}
|
||||
if args.log_tracker_config is not None:
|
||||
init_kwargs = toml.load(args.log_tracker_config)
|
||||
accelerator.init_trackers(
|
||||
"network_train" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs
|
||||
)
|
||||
|
||||
loss_recorder = train_util.LossRecorder()
|
||||
loss_list = []
|
||||
loss_total = 0.0
|
||||
del train_dataset_group
|
||||
|
||||
# callback for step start
|
||||
if hasattr(accelerator.unwrap_model(network), "on_step_start"):
|
||||
on_step_start = accelerator.unwrap_model(network).on_step_start
|
||||
if hasattr(network, "on_step_start"):
|
||||
on_step_start = network.on_step_start
|
||||
else:
|
||||
on_step_start = lambda *args, **kwargs: None
|
||||
|
||||
@@ -725,9 +718,6 @@ class NetworkTrainer:
|
||||
accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
|
||||
os.remove(old_ckpt_file)
|
||||
|
||||
# For --sample_at_first
|
||||
self.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
|
||||
|
||||
# training loop
|
||||
for epoch in range(num_train_epochs):
|
||||
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
||||
@@ -735,7 +725,7 @@ class NetworkTrainer:
|
||||
|
||||
metadata["ss_epoch"] = str(epoch + 1)
|
||||
|
||||
accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet)
|
||||
network.on_epoch_start(text_encoder, unet)
|
||||
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
current_step.value = global_step
|
||||
@@ -752,11 +742,11 @@ class NetworkTrainer:
|
||||
# NaNが含まれていれば警告を表示し0に置き換える
|
||||
if torch.any(torch.isnan(latents)):
|
||||
accelerator.print("NaN found in latents, replacing with zeros")
|
||||
latents = torch.nan_to_num(latents, 0, out=latents)
|
||||
latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
|
||||
latents = latents * self.vae_scale_factor
|
||||
b_size = latents.shape[0]
|
||||
|
||||
with torch.set_grad_enabled(train_text_encoder), accelerator.autocast():
|
||||
with torch.set_grad_enabled(train_text_encoder):
|
||||
# Get the text embedding for conditioning
|
||||
if args.weighted_captions:
|
||||
text_encoder_conds = get_weighted_text_embeddings(
|
||||
@@ -797,20 +787,17 @@ class NetworkTrainer:
|
||||
loss = loss * loss_weights
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.v_pred_like_loss:
|
||||
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
accelerator.backward(loss)
|
||||
self.all_reduce_network(accelerator, network) # sync DDP grad manually
|
||||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||||
params_to_clip = accelerator.unwrap_model(network).get_trainable_params()
|
||||
params_to_clip = network.get_trainable_params()
|
||||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
@@ -818,7 +805,7 @@ class NetworkTrainer:
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
if args.scale_weight_norms:
|
||||
keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization(
|
||||
keys_scaled, mean_norm, maximum_norm = network.apply_max_norm_regularization(
|
||||
args.scale_weight_norms, accelerator.device
|
||||
)
|
||||
max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
|
||||
@@ -848,9 +835,14 @@ class NetworkTrainer:
|
||||
remove_model(remove_ckpt_name)
|
||||
|
||||
current_loss = loss.detach().item()
|
||||
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
|
||||
avr_loss: float = loss_recorder.moving_average
|
||||
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
if epoch == 0:
|
||||
loss_list.append(current_loss)
|
||||
else:
|
||||
loss_total -= loss_list[step]
|
||||
loss_list[step] = current_loss
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / len(loss_list)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if args.scale_weight_norms:
|
||||
@@ -864,7 +856,7 @@ class NetworkTrainer:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss/epoch": loss_recorder.moving_average}
|
||||
logs = {"loss/epoch": loss_total / len(loss_list)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
@@ -946,7 +938,7 @@ def setup_parser() -> argparse.ArgumentParser:
|
||||
help="Drops neurons out of training every step (0 or None is default behavior (no dropout), 1 would drop all neurons) / 訓練時に毎ステップでニューロンをdropする（0またはNoneはdropoutなし、1は全ニューロンをdropout）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--network_args", type=str, default=None, nargs="*", help="additional arguments for network (key=value) / ネットワークへの追加の引数"
|
||||
"--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"
|
||||
)
|
||||
parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")
|
||||
parser.add_argument(
|
||||
|
||||
@@ -7,16 +7,6 @@ import toml
|
||||
|
||||
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
|
||||
@@ -35,7 +25,6 @@ from library.custom_train_functions import (
|
||||
prepare_scheduler_for_custom_training,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
add_v_prediction_like_loss,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
|
||||
imagenet_templates_small = [
|
||||
@@ -316,8 +305,8 @@ class TextualInversionTrainer:
|
||||
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
|
||||
if use_template:
|
||||
@@ -393,7 +382,7 @@ class TextualInversionTrainer:
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -418,11 +407,15 @@ class TextualInversionTrainer:
|
||||
text_encoder_or_list, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoder_or_list, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
# transform DDP after prepare
|
||||
text_encoder_or_list, unet = train_util.transform_if_model_is_DDP(text_encoder_or_list, unet)
|
||||
|
||||
elif len(text_encoders) == 2:
|
||||
text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
text_encoders[0], text_encoders[1], optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
# transform DDP after prepare
|
||||
text_encoder1, text_encoder2, unet = train_util.transform_if_model_is_DDP(text_encoder1, text_encoder2, unet)
|
||||
|
||||
text_encoder_or_list = text_encoders = [text_encoder1, text_encoder2]
|
||||
|
||||
@@ -528,20 +521,6 @@ class TextualInversionTrainer:
|
||||
accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
|
||||
os.remove(old_ckpt_file)
|
||||
|
||||
# For --sample_at_first
|
||||
self.sample_images(
|
||||
accelerator,
|
||||
args,
|
||||
0,
|
||||
global_step,
|
||||
accelerator.device,
|
||||
vae,
|
||||
tokenizer_or_list,
|
||||
text_encoder_or_list,
|
||||
unet,
|
||||
prompt_replacement,
|
||||
)
|
||||
|
||||
# training loop
|
||||
for epoch in range(num_train_epochs):
|
||||
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
|
||||
@@ -591,13 +570,11 @@ class TextualInversionTrainer:
|
||||
loss = loss * loss_weights
|
||||
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.v_pred_like_loss:
|
||||
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
|
||||
@@ -8,13 +8,6 @@ from multiprocessing import Value
|
||||
|
||||
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
|
||||
@@ -34,7 +27,6 @@ from library.custom_train_functions import (
|
||||
pyramid_noise_like,
|
||||
apply_noise_offset,
|
||||
scale_v_prediction_loss_like_noise_prediction,
|
||||
apply_debiased_estimation,
|
||||
)
|
||||
import library.original_unet as original_unet
|
||||
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
|
||||
@@ -237,8 +229,8 @@ def train(args):
|
||||
train_dataset_group.enable_XTI(XTI_layers, token_strings=token_strings)
|
||||
current_epoch = Value("i", 0)
|
||||
current_step = Value("i", 0)
|
||||
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
|
||||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||||
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
|
||||
|
||||
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
|
||||
if use_template:
|
||||
@@ -310,7 +302,7 @@ def train(args):
|
||||
train_dataset_group,
|
||||
batch_size=1,
|
||||
shuffle=True,
|
||||
collate_fn=collator,
|
||||
collate_fn=collater,
|
||||
num_workers=n_workers,
|
||||
persistent_workers=args.persistent_data_loader_workers,
|
||||
)
|
||||
@@ -333,6 +325,9 @@ def train(args):
|
||||
text_encoder, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
|
||||
# transform DDP after prepare
|
||||
text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
|
||||
|
||||
index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
|
||||
# print(len(index_no_updates), torch.sum(index_no_updates))
|
||||
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
|
||||
@@ -466,11 +461,9 @@ def train(args):
|
||||
|
||||
loss = loss * loss_weights
|
||||
if args.min_snr_gamma:
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
|
||||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||||
if args.scale_v_pred_loss_like_noise_pred:
|
||||
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
|
||||
if args.debiased_estimation_loss:
|
||||
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
|
||||
|
||||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||||
|
||||
|
||||
Reference in New Issue
Block a user