diff --git a/README-ja.md b/README-ja.md index 29c33a659..4ae6b2334 100644 --- a/README-ja.md +++ b/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。 @@ -21,6 +17,7 @@ GUIやPowerShellスクリプトなど、より使いやすくする機能が[bma * [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) +* [SDXL学習](./docs/train_SDXL-en.md) （英語版） * [DreamBoothの学習について](./docs/train_db_README-ja.md) * [fine-tuningのガイド](./docs/fine_tune_README_ja.md): * [LoRAの学習について](./docs/train_network_README-ja.md) @@ -44,9 +41,7 @@ PowerShellを使う場合、venvを使えるようにするためには以下の ## Windows環境でのインストール -スクリプトはPyTorch 2.0.1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。 +スクリプトはPyTorch 2.1.2でテストしています。PyTorch 2.0.1、1.12.1でも動作すると思われます。 （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。） @@ -59,20 +54,20 @@ 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==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118 pip install --upgrade -r requirements.txt -pip install xformers==0.0.20 +pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118 accelerate config ``` コマンドプロンプトでも同一です。 -（注:``python -m venv venv`` のほうが ``python -m venv --system-site-packages venv`` より安全そうなため書き換えました。globalなpythonにパッケージがインストールしてあると、後者だといろいろと問題が起きます。） +注：`bitsandbytes==0.43.0`、`prodigyopt==1.0`、`lion-pytorch==0.0.6` は `requirements.txt` に含まれるようになりました。他のバージョンを使う場合は適宜インストールしてください。 -accelerate configの質問には以下のように答えてください。（bf16で学習する場合、最後の質問にはbf16と答えてください。） +この例では PyTorch および xfomers は2.1.2／CUDA 11.8版をインストールします。CUDA 12.1版やPyTorch 1.12.1を使う場合は適宜書き換えください。たとえば CUDA 12.1版の場合は `pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121` および `pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu121` としてください。 -※0.15.0から日本語環境では選択のためにカーソルキーを押すと落ちます（……）。数字キーの0、1、2……で選択できますので、そちらを使ってください。 +accelerate configの質問には以下のように答えてください。（bf16で学習する場合、最後の質問にはbf16と答えてください。） ```txt - This machine @@ -87,41 +82,6 @@ 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 - -`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）。 - -Windowsでは0.35.0または0.41.1を推奨します。 - -- `bitsandbytes` 0.35.0: 安定しているとみられるバージョンです。AdamW8bitは使用できますが、他のいくつかの8bit optimizer、学習時の`full_bf16`オプションは使用できません。 -- `bitsandbytes` 0.41.1: Lion8bit、PagedAdamW8bit、PagedLion8bitをサポートします。`full_bf16`が使用できます。 - -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。 - -```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 -``` - -### 0.41.1を使う場合 - -jllllll氏の配布されている[こちら](https://github.com/jllllll/bitsandbytes-windows-webui) または他の場所から、Windows用のwhlファイルをインストールしてください。 - -```powershell -python -m pip install bitsandbytes==0.41.1 --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui -``` - ## アップグレード 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。 @@ -151,4 +111,47 @@ Conv2d 3x3への拡大は [cloneofsimo氏](https://github.com/cloneofsimo/lora) [BLIP](https://github.com/salesforce/BLIP): BSD-3-Clause +## その他の情報 + +### LoRAの名称について + +`train_network.py` がサポートするLoRAについて、混乱を避けるため名前を付けました。ドキュメントは更新済みです。以下は当リポジトリ内の独自の名称です。 + +1. __LoRA-LierLa__ : (LoRA for __Li__ n __e__ a __r__ __La__ yers、リエラと読みます) + + Linear 層およびカーネルサイズ 1x1 の Conv2d 層に適用されるLoRA + +2. __LoRA-C3Lier__ : (LoRA for __C__ olutional layers with __3__ x3 Kernel and __Li__ n __e__ a __r__ layers、セリアと読みます) + + 1.に加え、カーネルサイズ 3x3 の Conv2d 層に適用されるLoRA + +デフォルトではLoRA-LierLaが使われます。LoRA-C3Lierを使う場合は `--network_args` に `conv_dim` を指定してください。 + + + +### 学習中のサンプル画像生成 + +プロンプトファイルは例えば以下のようになります。 + +``` +# prompt 1 +masterpiece, best quality, (1girl), in white shirts, upper body, looking at viewer, simple background --n low quality, worst quality, bad anatomy,bad composition, poor, low effort --w 768 --h 768 --d 1 --l 7.5 --s 28 + +# prompt 2 +masterpiece, best quality, 1boy, in business suit, standing at street, looking back --n (low quality, worst quality), bad anatomy,bad composition, poor, low effort --w 576 --h 832 --d 2 --l 5.5 --s 40 +``` + + `#` で始まる行はコメントになります。`--n` のように「ハイフン二個＋英小文字」の形でオプションを指定できます。以下が使用可能できます。 + + * `--n` Negative prompt up to the next option. + * `--w` Specifies the width of the generated image. + * `--h` Specifies the height of the generated image. + * `--d` Specifies the seed of the generated image. + * `--l` Specifies the CFG scale of the generated image. + * `--s` Specifies the number of steps in the generation. + `( )` や `[ ]` などの重みづけも動作します。 diff --git a/README.md b/README.md index 001446b7c..1ca699be5 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -__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. @@ -20,9 +18,9 @@ This repository contains the scripts for: ## 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 file does not contain requirements for PyTorch. Because the version of PyTorch depends on the environment, it is not included in the file. Please install PyTorch first according to the environment. See installation instructions below. -The scripts are tested with Pytorch 2.0.1. 1.12.1 is not tested but should work. +The scripts are tested with Pytorch 2.1.2. 2.0.1 and 1.12.1 is not tested but should work. ## Links to usage documentation @@ -32,11 +30,13 @@ Most of the documents are written in Japanese. * [Training guide - common](./docs/train_README-ja.md) : data preparation, options etc... * [Chinese version](./docs/train_README-zh.md) +* [SDXL training](./docs/train_SDXL-en.md) (English version) * [Dataset config](./docs/config_README-ja.md) + * [English version](./docs/config_README-en.md) * [DreamBooth training guide](./docs/train_db_README-ja.md) * [Step by Step fine-tuning guide](./docs/fine_tune_README_ja.md): -* [training LoRA](./docs/train_network_README-ja.md) -* [training Textual Inversion](./docs/train_ti_README-ja.md) +* [Training LoRA](./docs/train_network_README-ja.md) +* [Training Textual Inversion](./docs/train_ti_README-ja.md) * [Image generation](./docs/gen_img_README-ja.md) * note.com [Model conversion](https://note.com/kohya_ss/n/n374f316fe4ad) @@ -64,14 +64,18 @@ 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==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118 pip install --upgrade -r requirements.txt -pip install xformers==0.0.20 +pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118 accelerate config ``` -__Note:__ Now bitsandbytes is optional. Please install any version of bitsandbytes as needed. Installation instructions are in the following section. +If `python -m venv` shows only `python`, change `python` to `py`. + +__Note:__ Now `bitsandbytes==0.43.0`, `prodigyopt==1.0` and `lion-pytorch==0.0.6` are included in the requirements.txt. If you'd like to use the another version, please install it manually. + +This installation is for CUDA 11.8. If you use a different version of CUDA, please install the appropriate version of PyTorch and xformers. For example, if you use CUDA 12, please install `pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121` and `pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu121`. -## Sample image generation during training +### Sample image generation during training A prompt file might look like this, for example ``` @@ -431,26 +345,3 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b * `--s` Specifies the number of steps in the generation. The prompt weighting such as `( )` and `[ ]` are working. - -## サンプル画像生成 -プロンプトファイルは例えば以下のようになります。 - -``` -# prompt 1 -masterpiece, best quality, (1girl), in white shirts, upper body, looking at viewer, simple background --n low quality, worst quality, bad anatomy,bad composition, poor, low effort --w 768 --h 768 --d 1 --l 7.5 --s 28 - -# prompt 2 -masterpiece, best quality, 1boy, in business suit, standing at street, looking back --n (low quality, worst quality), bad anatomy,bad composition, poor, low effort --w 576 --h 832 --d 2 --l 5.5 --s 40 -``` - - `#` で始まる行はコメントになります。`--n` のように「ハイフン二個＋英小文字」の形でオプションを指定できます。以下が使用可能できます。 - - * `--n` Negative prompt up to the next option. - * `--w` Specifies the width of the generated image. - * `--h` Specifies the height of the generated image. - * `--d` Specifies the seed of the generated image. - * `--l` Specifies the CFG scale of the generated image. - * `--s` Specifies the number of steps in the generation. - - `( )` や `[ ]` などの重みづけも動作します。 - diff --git a/docs/config_README-en.md b/docs/config_README-en.md new file mode 100644 index 000000000..83bea329b --- /dev/null +++ b/docs/config_README-en.md @@ -0,0 +1,384 @@ +Original Source by kohya-ss + +First version: +A.I Translation by Model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO, editing by Darkstorm2150 + +Some parts are manually added. + +# Config Readme + +This README is about the configuration files that can be passed with the `--dataset_config` option. + +## Overview + +By passing a configuration file, users can make detailed settings. + +* Multiple datasets can be configured + * For example, by setting `resolution` for each dataset, they can be mixed and trained. + * In training methods that support both the DreamBooth approach and the fine-tuning approach, datasets of the DreamBooth method and the fine-tuning method can be mixed. +* Settings can be changed for each subset + * A subset is a partition of the dataset by image directory or metadata. Several subsets make up a dataset. + * Options such as `keep_tokens` and `flip_aug` can be set for each subset. On the other hand, options such as `resolution` and `batch_size` can be set for each dataset, and their values are common among subsets belonging to the same dataset. More details will be provided later. + +The configuration file format can be JSON or TOML. Considering the ease of writing, it is recommended to use [TOML](https://toml.io/ja/v1.0.0-rc.2). The following explanation assumes the use of TOML. + + +Here is an example of a configuration file written in TOML. + +```toml +[general] +shuffle_caption = true +caption_extension = '.txt' +keep_tokens = 1 + +# This is a DreamBooth-style dataset +[[datasets]] +resolution = 512 +batch_size = 4 +keep_tokens = 2 + + [[datasets.subsets]] + image_dir = 'C:\hoge' + class_tokens = 'hoge girl' + # This subset uses keep_tokens = 2 (the value of the parent datasets) + + [[datasets.subsets]] + image_dir = 'C:\fuga' + class_tokens = 'fuga boy' + keep_tokens = 3 + + [[datasets.subsets]] + is_reg = true + image_dir = 'C:\reg' + class_tokens = 'human' + keep_tokens = 1 + +# This is a fine-tuning dataset +[[datasets]] +resolution = [768, 768] +batch_size = 2 + + [[datasets.subsets]] + image_dir = 'C:\piyo' + metadata_file = 'C:\piyo\piyo_md.json' + # This subset uses keep_tokens = 1 (the value of [general]) +``` + +In this example, three directories are trained as a DreamBooth-style dataset at 512x512 (batch size 4), and one directory is trained as a fine-tuning dataset at 768x768 (batch size 2). + +## Settings for datasets and subsets + +Settings for datasets and subsets are divided into several registration locations. + +* `[general]` + * This is where options that apply to all datasets or all subsets are specified. + * If there are options with the same name in the dataset-specific or subset-specific settings, the dataset-specific or subset-specific settings take precedence. +* `[[datasets]]` + * `datasets` is where settings for datasets are registered. This is where options that apply individually to each dataset are specified. + * If there are subset-specific settings, the subset-specific settings take precedence. +* `[[datasets.subsets]]` + * `datasets.subsets` is where settings for subsets are registered. This is where options that apply individually to each subset are specified. + +Here is an image showing the correspondence between image directories and registration locations in the previous example. + +``` +C:\ +├─ hoge -> [[datasets.subsets]] No.1 ┐ ┐ +├─ fuga -> [[datasets.subsets]] No.2 |-> [[datasets]] No.1 |-> [general] +├─ reg -> [[datasets.subsets]] No.3 ┘ | +└─ piyo -> [[datasets.subsets]] No.4 --> [[datasets]] No.2 ┘ +``` + +The image directory corresponds to each `[[datasets.subsets]]`. Then, multiple `[[datasets.subsets]]` are combined to form one `[[datasets]]`. All `[[datasets]]` and `[[datasets.subsets]]` belong to `[general]`. + +The available options for each registration location may differ, but if the same option is specified, the value in the lower registration location will take precedence. You can check how the `keep_tokens` option is handled in the previous example for better understanding. + +Additionally, the available options may vary depending on the method that the learning approach supports. + +* Options specific to the DreamBooth method +* Options specific to the fine-tuning method +* Options available when using the caption dropout technique + +When using both the DreamBooth method and the fine-tuning method, they can be used together with a learning approach that supports both. +When using them together, a point to note is that the method is determined based on the dataset, so it is not possible to mix DreamBooth method subsets and fine-tuning method subsets within the same dataset. +In other words, if you want to use both methods together, you need to set up subsets of different methods belonging to different datasets. + +In terms of program behavior, if the `metadata_file` option exists, it is determined to be a subset of fine-tuning. Therefore, for subsets belonging to the same dataset, as long as they are either "all have the `metadata_file` option" or "all have no `metadata_file` option," there is no problem. + +Below, the available options will be explained. For options with the same name as the command-line argument, the explanation will be omitted in principle. Please refer to other READMEs. + +### Common options for all learning methods + +These are options that can be specified regardless of the learning method. + +#### Data set specific options + +These are options related to the configuration of the data set. They cannot be described in `datasets.subsets`. + + +| Option Name | Example Setting | `[general]` | `[[datasets]]` | +| ---- | ---- | ---- | ---- | +| `batch_size` | `1` | o | o | +| `bucket_no_upscale` | `true` | o | o | +| `bucket_reso_steps` | `64` | o | o | +| `enable_bucket` | `true` | o | o | +| `max_bucket_reso` | `1024` | o | o | +| `min_bucket_reso` | `128` | o | o | +| `resolution` | `256`, `[512, 512]` | o | o | + +* `batch_size` + * This corresponds to the command-line argument `--train_batch_size`. + +These settings are fixed per dataset. That means that subsets belonging to the same dataset will share these settings. For example, if you want to prepare datasets with different resolutions, you can define them as separate datasets as shown in the example above, and set different resolutions for each. + +#### Options for Subsets + +These options are related to subset configuration. + +| Option Name | Example | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` | +| ---- | ---- | ---- | ---- | ---- | +| `color_aug` | `false` | o | o | o | +| `face_crop_aug_range` | `[1.0, 3.0]` | o | o | o | +| `flip_aug` | `true` | o | o | o | +| `keep_tokens` | `2` | o | o | o | +| `num_repeats` | `10` | o | o | o | +| `random_crop` | `false` | o | o | o | +| `shuffle_caption` | `true` | o | o | o | +| `caption_prefix` | `"masterpiece, best quality, "` | o | o | o | +| `caption_suffix` | `", from side"` | o | o | o | +| `caption_separator` | (not specified) | o | o | o | +| `keep_tokens_separator` | `“|||”` | o | o | o | +| `secondary_separator` | `“;;;”` | o | o | o | +| `enable_wildcard` | `true` | o | o | o | + +* `num_repeats` + * Specifies the number of repeats for images in a subset. This is equivalent to `--dataset_repeats` in fine-tuning but can be specified for any training method. +* `caption_prefix`, `caption_suffix` + * Specifies the prefix and suffix strings to be appended to the captions. Shuffling is performed with these strings included. Be cautious when using `keep_tokens`. +* `caption_separator` + * Specifies the string to separate the tags. The default is `,`. This option is usually not necessary to set. +* `keep_tokens_separator` + * Specifies the string to separate the parts to be fixed in the caption. For example, if you specify `aaa, bbb ||| ccc, ddd, eee, fff ||| ggg, hhh`, the parts `aaa, bbb` and `ggg, hhh` will remain, and the rest will be shuffled and dropped. The comma in between is not necessary. As a result, the prompt will be `aaa, bbb, eee, ccc, fff, ggg, hhh` or `aaa, bbb, fff, ccc, eee, ggg, hhh`, etc. +* `secondary_separator` + * Specifies an additional separator. The part separated by this separator is treated as one tag and is shuffled and dropped. It is then replaced by `caption_separator`. For example, if you specify `aaa;;;bbb;;;ccc`, it will be replaced by `aaa,bbb,ccc` or dropped together. +* `enable_wildcard` + * Enables wildcard notation. This will be explained later. + +### DreamBooth-specific options + +DreamBooth-specific options only exist as subsets-specific options. + +#### Subset-specific options + +Options related to the configuration of DreamBooth subsets. + +| Option Name | Example Setting | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` | +| ---- | ---- | ---- | ---- | ---- | +| `image_dir` | `'C:\hoge'` | - | - | o (required) | +| `caption_extension` | `".txt"` | o | o | o | +| `class_tokens` | `"sks girl"` | - | - | o | +| `cache_info` | `false` | o | o | o | +| `is_reg` | `false` | - | - | o | + +Firstly, note that for `image_dir`, the path to the image files must be specified as being directly in the directory. Unlike the previous DreamBooth method, where images had to be placed in subdirectories, this is not compatible with that specification. Also, even if you name the folder something like "5_cat", the number of repeats of the image and the class name will not be reflected. If you want to set these individually, you will need to explicitly specify them using `num_repeats` and `class_tokens`. + +* `image_dir` + * Specifies the path to the image directory. This is a required option. + * Images must be placed directly under the directory. +* `class_tokens` + * Sets the class tokens. + * Only used during training when a corresponding caption file does not exist. The determination of whether or not to use it is made on a per-image basis. If `class_tokens` is not specified and a caption file is not found, an error will occur. +* `cache_info` + * Specifies whether to cache the image size and caption. If not specified, it is set to `false`. The cache is saved in `metadata_cache.json` in `image_dir`. + * Caching speeds up the loading of the dataset after the first time. It is effective when dealing with thousands of images or more. +* `is_reg` + * Specifies whether the subset images are for normalization. If not specified, it is set to `false`, meaning that the images are not for normalization. + +### Fine-tuning method specific options + +The options for the fine-tuning method only exist for subset-specific options. + +#### Subset-specific options + +These options are related to the configuration of the fine-tuning method's subsets. + +| Option name | Example setting | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` | +| ---- | ---- | ---- | ---- | ---- | +| `image_dir` | `'C:\hoge'` | - | - | o | +| `metadata_file` | `'C:\piyo\piyo_md.json'` | - | - | o (required) | + +* `image_dir` + * Specify the path to the image directory. Unlike the DreamBooth method, specifying it is not mandatory, but it is recommended to do so. + * The case where it is not necessary to specify is when the `--full_path` is added to the command line when generating the metadata file. + * The images must be placed directly under the directory. +* `metadata_file` + * Specify the path to the metadata file used for the subset. This is a required option. + * It is equivalent to the command-line argument `--in_json`. + * Due to the specification that a metadata file must be specified for each subset, it is recommended to avoid creating a metadata file with images from different directories as a single metadata file. It is strongly recommended to prepare a separate metadata file for each image directory and register them as separate subsets. + +### Options available when caption dropout method can be used + +The options available when the caption dropout method can be used exist only for subsets. Regardless of whether it's the DreamBooth method or fine-tuning method, if it supports caption dropout, it can be specified. + +#### Subset-specific options + +Options related to the setting of subsets that caption dropout can be used for. + +| Option Name | `[general]` | `[[datasets]]` | `[[dataset.subsets]]` | +| ---- | ---- | ---- | ---- | +| `caption_dropout_every_n_epochs` | o | o | o | +| `caption_dropout_rate` | o | o | o | +| `caption_tag_dropout_rate` | o | o | o | + +## Behavior when there are duplicate subsets + +In the case of the DreamBooth dataset, if there are multiple `image_dir` directories with the same content, they are considered to be duplicate subsets. For the fine-tuning dataset, if there are multiple `metadata_file` files with the same content, they are considered to be duplicate subsets. If duplicate subsets exist in the dataset, subsequent subsets will be ignored. + +However, if they belong to different datasets, they are not considered duplicates. For example, if you have subsets with the same `image_dir` in different datasets, they will not be considered duplicates. This is useful when you want to train with the same image but with different resolutions. + +```toml +# If data sets exist separately, they are not considered duplicates and are both used for training. + +[[datasets]] +resolution = 512 + + [[datasets.subsets]] + image_dir = 'C:\hoge' + +[[datasets]] +resolution = 768 + + [[datasets.subsets]] + image_dir = 'C:\hoge' +``` + +## Command Line Argument and Configuration File + +There are options in the configuration file that have overlapping roles with command line argument options. + +The following command line argument options are ignored if a configuration file is passed: + +* `--train_data_dir` +* `--reg_data_dir` +* `--in_json` + +The following command line argument options are given priority over the configuration file options if both are specified simultaneously. In most cases, they have the same names as the corresponding options in the configuration file. + +| Command Line Argument Option | Prioritized Configuration File Option | +| ------------------------------- | ------------------------------------- | +| `--bucket_no_upscale` | | +| `--bucket_reso_steps` | | +| `--caption_dropout_every_n_epochs` | | +| `--caption_dropout_rate` | | +| `--caption_extension` | | +| `--caption_tag_dropout_rate` | | +| `--color_aug` | | +| `--dataset_repeats` | `num_repeats` | +| `--enable_bucket` | | +| `--face_crop_aug_range` | | +| `--flip_aug` | | +| `--keep_tokens` | | +| `--min_bucket_reso` | | +| `--random_crop` | | +| `--resolution` | | +| `--shuffle_caption` | | +| `--train_batch_size` | `batch_size` | + +## Error Guide + +Currently, we are using an external library to check if the configuration file is written correctly, but the development has not been completed, and there is a problem that the error message is not clear. In the future, we plan to improve this problem. + +As a temporary measure, we will list common errors and their solutions. If you encounter an error even though it should be correct or if the error content is not understandable, please contact us as it may be a bug. + +* `voluptuous.error.MultipleInvalid: required key not provided @ ...`: This error occurs when a required option is not provided. It is highly likely that you forgot to specify the option or misspelled the option name. + * The error location is indicated by `...` in the error message. For example, if you encounter an error like `voluptuous.error.MultipleInvalid: required key not provided @ data['datasets'][0]['subsets'][0]['image_dir']`, it means that the `image_dir` option does not exist in the 0th `subsets` of the 0th `datasets` setting. +* `voluptuous.error.MultipleInvalid: expected int for dictionary value @ ...`: This error occurs when the specified value format is incorrect. It is highly likely that the value format is incorrect. The `int` part changes depending on the target option. The example configurations in this README may be helpful. +* `voluptuous.error.MultipleInvalid: extra keys not allowed @ ...`: This error occurs when there is an option name that is not supported. It is highly likely that you misspelled the option name or mistakenly included it. + +## Miscellaneous + +### Multi-line captions + +By setting `enable_wildcard = true`, multiple-line captions are also enabled. If the caption file consists of multiple lines, one line is randomly selected as the caption. + +```txt +1girl, hatsune miku, vocaloid, upper body, looking at viewer, microphone, stage +a girl with a microphone standing on a stage +detailed digital art of a girl with a microphone on a stage +``` + +It can be combined with wildcard notation. + +In metadata files, you can also specify multiple-line captions. In the `.json` metadata file, use `\n` to represent a line break. If the caption file consists of multiple lines, `merge_captions_to_metadata.py` will create a metadata file in this format. + +The tags in the metadata (`tags`) are added to each line of the caption. + +```json +{ + "/path/to/image.png": { + "caption": "a cartoon of a frog with the word frog on it\ntest multiline caption1\ntest multiline caption2", + "tags": "open mouth, simple background, standing, no humans, animal, black background, frog, animal costume, animal focus" + }, + ... +} +``` + +In this case, the actual caption will be `a cartoon of a frog with the word frog on it, open mouth, simple background ...`, `test multiline caption1, open mouth, simple background ...`, `test multiline caption2, open mouth, simple background ...`, etc. + +### Example of configuration file : `secondary_separator`, wildcard notation, `keep_tokens_separator`, etc. + +```toml +[general] +flip_aug = true +color_aug = false +resolution = [1024, 1024] + +[[datasets]] +batch_size = 6 +enable_bucket = true +bucket_no_upscale = true +caption_extension = ".txt" +keep_tokens_separator= "|||" +shuffle_caption = true +caption_tag_dropout_rate = 0.1 +secondary_separator = ";;;" # subset 側に書くこともできます / can be written in the subset side +enable_wildcard = true # 同上 / same as above + + [[datasets.subsets]] + image_dir = "/path/to/image_dir" + num_repeats = 1 + + # ||| の前後はカンマは不要です（自動的に追加されます） / No comma is required before and after ||| (it is added automatically) + caption_prefix = "1girl, hatsune miku, vocaloid |||" + + # ||| の後はシャッフル、drop されず残ります / After |||, it is not shuffled or dropped and remains + # 単純に文字列として連結されるので、カンマなどは自分で入れる必要があります / It is simply concatenated as a string, so you need to put commas yourself + caption_suffix = ", anime screencap ||| masterpiece, rating: general" +``` + +### Example of caption, secondary_separator notation: `secondary_separator = ";;;"` + +```txt +1girl, hatsune miku, vocaloid, upper body, looking at viewer, sky;;;cloud;;;day, outdoors +``` +The part `sky;;;cloud;;;day` is replaced with `sky,cloud,day` without shuffling or dropping. When shuffling and dropping are enabled, it is processed as a whole (as one tag). For example, it becomes `vocaloid, 1girl, upper body, sky,cloud,day, outdoors, hatsune miku` (shuffled) or `vocaloid, 1girl, outdoors, looking at viewer, upper body, hatsune miku` (dropped). + +### Example of caption, enable_wildcard notation: `enable_wildcard = true` + +```txt +1girl, hatsune miku, vocaloid, upper body, looking at viewer, {simple|white} background +``` +`simple` or `white` is randomly selected, and it becomes `simple background` or `white background`. + +```txt +1girl, hatsune miku, vocaloid, {{retro style}} +``` +If you want to include `{` or `}` in the tag string, double them like `{{` or `}}` (in this example, the actual caption used for training is `{retro style}`). + +### Example of caption, `keep_tokens_separator` notation: `keep_tokens_separator = "|||"` + +```txt +1girl, hatsune miku, vocaloid ||| stage, microphone, white shirt, smile ||| best quality, rating: general +``` +It becomes `1girl, hatsune miku, vocaloid, microphone, stage, white shirt, best quality, rating: general` or `1girl, hatsune miku, vocaloid, white shirt, smile, stage, microphone, best quality, rating: general` etc. + diff --git a/docs/config_README-ja.md b/docs/config_README-ja.md index 69a03f6cf..cc74c341b 100644 --- a/docs/config_README-ja.md +++ b/docs/config_README-ja.md @@ -1,5 +1,3 @@ -For non-Japanese speakers: this README is provided only in Japanese in the current state. Sorry for inconvenience. We will provide English version in the near future. - `--dataset_config` で渡すことができる設定ファイルに関する説明です。 ## 概要 @@ -140,12 +138,28 @@ DreamBooth の手法と fine tuning の手法の両方とも利用可能な学 | `shuffle_caption` | `true` | o | o | o | | `caption_prefix` | `“masterpiece, best quality, ”` | o | o | o | | `caption_suffix` | `“, from side”` | o | o | o | +| `caption_separator` | （通常は設定しません） | o | o | o | +| `keep_tokens_separator` | `“|||”` | o | o | o | +| `secondary_separator` | `“;;;”` | o | o | o | +| `enable_wildcard` | `true` | o | o | o | * `num_repeats` * サブセットの画像の繰り返し回数を指定します。fine tuning における `--dataset_repeats` に相当しますが、`num_repeats` はどの学習方法でも指定可能です。 * `caption_prefix`, `caption_suffix` * キャプションの前、後に付与する文字列を指定します。シャッフルはこれらの文字列を含めた状態で行われます。`keep_tokens` を指定する場合には注意してください。 +* `caption_separator` + * タグを区切る文字列を指定します。デフォルトは `,` です。このオプションは通常は設定する必要はありません。 + +* `keep_tokens_separator` + * キャプションで固定したい部分を区切る文字列を指定します。たとえば `aaa, bbb ||| ccc, ddd, eee, fff ||| ggg, hhh` のように指定すると、`aaa, bbb` と `ggg, hhh` の部分はシャッフル、drop されず残ります。間のカンマは不要です。結果としてプロンプトは `aaa, bbb, eee, ccc, fff, ggg, hhh` や `aaa, bbb, fff, ccc, eee, ggg, hhh` などになります。 + +* `secondary_separator` + * 追加の区切り文字を指定します。この区切り文字で区切られた部分は一つのタグとして扱われ、シャッフル、drop されます。その後、`caption_separator` に置き換えられます。たとえば `aaa;;;bbb;;;ccc` のように指定すると、`aaa,bbb,ccc` に置き換えられるか、まとめて drop されます。 + +* `enable_wildcard` + * ワイルドカード記法および複数行キャプションを有効にします。ワイルドカード記法、複数行キャプションについては後述します。 + ### DreamBooth 方式専用のオプション DreamBooth 方式のオプションは、サブセット向けオプションのみ存在します。 @@ -159,6 +173,7 @@ DreamBooth 方式のサブセットの設定に関わるオプションです。 | `image_dir` | `‘C:\hoge’` | - | - | o（必須） | | `caption_extension` | `".txt"` | o | o | o | | `class_tokens` | `“sks girl”` | - | - | o | +| `cache_info` | `false` | o | o | o | | `is_reg` | `false` | - | - | o | まず注意点として、 `image_dir` には画像ファイルが直下に置かれているパスを指定する必要があります。従来の DreamBooth の手法ではサブディレクトリに画像を置く必要がありましたが、そちらとは仕様に互換性がありません。また、`5_cat` のようなフォルダ名にしても、画像の繰り返し回数とクラス名は反映されません。これらを個別に設定したい場合、`num_repeats` と `class_tokens` で明示的に指定する必要があることに注意してください。 @@ -169,6 +184,9 @@ DreamBooth 方式のサブセットの設定に関わるオプションです。 * `class_tokens` * クラストークンを設定します。 * 画像に対応する caption ファイルが存在しない場合にのみ学習時に利用されます。利用するかどうかの判定は画像ごとに行います。`class_tokens` を指定しなかった場合に caption ファイルも見つからなかった場合にはエラーになります。 +* `cache_info` + * 画像サイズ、キャプションをキャッシュするかどうかを指定します。指定しなかった場合は `false` になります。キャッシュは `image_dir` に `metadata_cache.json` というファイル名で保存されます。 + * キャッシュを行うと、二回目以降のデータセット読み込みが高速化されます。数千枚以上の画像を扱う場合には有効です。 * `is_reg` * サブセットの画像が正規化用かどうかを指定します。指定しなかった場合は `false` として、つまり正規化画像ではないとして扱います。 @@ -280,4 +298,89 @@ resolution = 768 * `voluptuous.error.MultipleInvalid: expected int for dictionary value @ ...`: 指定する値の形式が不正というエラーです。値の形式が間違っている可能性が高いです。`int` の部分は対象となるオプションによって変わります。この README に載っているオプションの「設定例」が役立つかもしれません。 * `voluptuous.error.MultipleInvalid: extra keys not allowed @ ...`: 対応していないオプション名が存在している場合に発生するエラーです。オプション名を間違って記述しているか、誤って紛れ込んでいる可能性が高いです。 +## その他 + +### 複数行キャプション + +`enable_wildcard = true` を設定することで、複数行キャプションも同時に有効になります。キャプションファイルが複数の行からなる場合、ランダムに一つの行が選ばれてキャプションとして利用されます。 + +```txt +1girl, hatsune miku, vocaloid, upper body, looking at viewer, microphone, stage +a girl with a microphone standing on a stage +detailed digital art of a girl with a microphone on a stage +``` + +ワイルドカード記法と組み合わせることも可能です。 + +メタデータファイルでも同様に複数行キャプションを指定することができます。メタデータの .json 内には、`\n` を使って改行を表現してください。キャプションファイルが複数行からなる場合、`merge_captions_to_metadata.py` を使うと、この形式でメタデータファイルが作成されます。 + +メタデータのタグ (`tags`) は、キャプションの各行に追加されます。 + +```json +{ + "/path/to/image.png": { + "caption": "a cartoon of a frog with the word frog on it\ntest multiline caption1\ntest multiline caption2", + "tags": "open mouth, simple background, standing, no humans, animal, black background, frog, animal costume, animal focus" + }, + ... +} +``` +この場合、実際のキャプションは `a cartoon of a frog with the word frog on it, open mouth, simple background ...` または `test multiline caption1, open mouth, simple background ...`、 `test multiline caption2, open mouth, simple background ...` 等になります。 + +### 設定ファイルの記述例：追加の区切り文字、ワイルドカード記法、`keep_tokens_separator` 等 + +```toml +[general] +flip_aug = true +color_aug = false +resolution = [1024, 1024] + +[[datasets]] +batch_size = 6 +enable_bucket = true +bucket_no_upscale = true +caption_extension = ".txt" +keep_tokens_separator= "|||" +shuffle_caption = true +caption_tag_dropout_rate = 0.1 +secondary_separator = ";;;" # subset 側に書くこともできます / can be written in the subset side +enable_wildcard = true # 同上 / same as above + + [[datasets.subsets]] + image_dir = "/path/to/image_dir" + num_repeats = 1 + + # ||| の前後はカンマは不要です（自動的に追加されます） / No comma is required before and after ||| (it is added automatically) + caption_prefix = "1girl, hatsune miku, vocaloid |||" + + # ||| の後はシャッフル、drop されず残ります / After |||, it is not shuffled or dropped and remains + # 単純に文字列として連結されるので、カンマなどは自分で入れる必要があります / It is simply concatenated as a string, so you need to put commas yourself + caption_suffix = ", anime screencap ||| masterpiece, rating: general" +``` + +### キャプション記述例、secondary_separator 記法：`secondary_separator = ";;;"` の場合 + +```txt +1girl, hatsune miku, vocaloid, upper body, looking at viewer, sky;;;cloud;;;day, outdoors +``` +`sky;;;cloud;;;day` の部分はシャッフル、drop されず `sky,cloud,day` に置換されます。シャッフル、drop が有効な場合、まとめて（一つのタグとして）処理されます。つまり `vocaloid, 1girl, upper body, sky,cloud,day, outdoors, hatsune miku` （シャッフル）や `vocaloid, 1girl, outdoors, looking at viewer, upper body, hatsune miku` （drop されたケース）などになります。 + +### キャプション記述例、ワイルドカード記法： `enable_wildcard = true` の場合 + +```txt +1girl, hatsune miku, vocaloid, upper body, looking at viewer, {simple|white} background +``` +ランダムに `simple` または `white` が選ばれ、`simple background` または `white background` になります。 + +```txt +1girl, hatsune miku, vocaloid, {{retro style}} +``` +タグ文字列に `{` や `}` そのものを含めたい場合は `{{` や `}}` のように二つ重ねてください（この例では実際に学習に用いられるキャプションは `{retro style}` になります）。 + +### キャプション記述例、`keep_tokens_separator` 記法： `keep_tokens_separator = "|||"` の場合 + +```txt +1girl, hatsune miku, vocaloid ||| stage, microphone, white shirt, smile ||| best quality, rating: general +``` +`1girl, hatsune miku, vocaloid, microphone, stage, white shirt, best quality, rating: general` や `1girl, hatsune miku, vocaloid, white shirt, smile, stage, microphone, best quality, rating: general` などになります。 diff --git a/docs/train_SDXL-en.md b/docs/train_SDXL-en.md new file mode 100644 index 000000000..a4c55b3fd --- /dev/null +++ b/docs/train_SDXL-en.md @@ -0,0 +1,84 @@ +## SDXL training + +The documentation will be moved to the training documentation in the future. The following is a brief explanation of the training scripts for SDXL. + +### 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. + diff --git a/docs/train_lllite_README-ja.md b/docs/train_lllite_README-ja.md index dbdc1fea2..1f6a78d5c 100644 --- a/docs/train_lllite_README-ja.md +++ b/docs/train_lllite_README-ja.md @@ -21,9 +21,13 @@ ComfyUIのカスタムノードを用意しています。: https://github.com/k ## モデルの学習 ### データセットの準備 -通常のdatasetに加え、`conditioning_data_dir` で指定したディレクトリにconditioning imageを格納してください。conditioning imageは学習用画像と同じbasenameを持つ必要があります。また、conditioning imageは学習用画像と同じサイズに自動的にリサイズされます。conditioning imageにはキャプションファイルは不要です。 +DreamBooth 方式の dataset で、`conditioning_data_dir` で指定したディレクトリにconditioning imageを格納してください。 -たとえば DreamBooth 方式でキャプションファイルを用いる場合の設定ファイルは以下のようになります。 +（finetuning 方式の dataset はサポートしていません。） + +conditioning imageは学習用画像と同じbasenameを持つ必要があります。また、conditioning imageは学習用画像と同じサイズに自動的にリサイズされます。conditioning imageにはキャプションファイルは不要です。 + +たとえば、キャプションにフォルダ名ではなくキャプションファイルを用いる場合の設定ファイルは以下のようになります。 ```toml [[datasets.subsets]] diff --git a/docs/train_lllite_README.md b/docs/train_lllite_README.md index 04dc12da2..a05f87f5f 100644 --- a/docs/train_lllite_README.md +++ b/docs/train_lllite_README.md @@ -26,7 +26,9 @@ Due to the limitations of the inference environment, only CrossAttention (attn1 ### Preparing the dataset -In addition to the normal dataset, please store the conditioning image in the directory specified by `conditioning_data_dir`. The conditioning image must have the same basename as the training image. The conditioning image will be automatically resized to the same size as the training image. The conditioning image does not require a caption file. +In addition to the normal DreamBooth method dataset, please store the conditioning image in the directory specified by `conditioning_data_dir`. The conditioning image must have the same basename as the training image. The conditioning image will be automatically resized to the same size as the training image. The conditioning image does not require a caption file. + +(We do not support the finetuning method dataset.) ```toml [[datasets.subsets]] diff --git a/docs/wd14_tagger_README-en.md b/docs/wd14_tagger_README-en.md new file mode 100644 index 000000000..34f448823 --- /dev/null +++ b/docs/wd14_tagger_README-en.md @@ -0,0 +1,88 @@ +# Image Tagging using WD14Tagger + +This document is based on the information from this github page (https://github.com/toriato/stable-diffusion-webui-wd14-tagger#mrsmilingwolfs-model-aka-waifu-diffusion-14-tagger). + +Using onnx for inference is recommended. Please install onnx with the following command: + +```powershell +pip install onnx==1.15.0 onnxruntime-gpu==1.17.1 +``` + +The model weights will be automatically downloaded from Hugging Face. + +# Usage + +Run the script to perform tagging. + +```powershell +python finetune/tag_images_by_wd14_tagger.py --onnx --repo_id --batch_size +``` + +For example, if using the repository `SmilingWolf/wd-swinv2-tagger-v3` with a batch size of 4, and the training data is located in the parent folder `train_data`, it would be: + +```powershell +python tag_images_by_wd14_tagger.py --onnx --repo_id SmilingWolf/wd-swinv2-tagger-v3 --batch_size 4 ..\train_data +``` + +On the first run, the model files will be automatically downloaded to the `wd14_tagger_model` folder (the folder can be changed with an option). + +Tag files will be created in the same directory as the training data images, with the same filename and a `.txt` extension. + +![Generated tag files](https://user-images.githubusercontent.com/52813779/208910534-ea514373-1185-4b7d-9ae3-61eb50bc294e.png) + +![Tags and image](https://user-images.githubusercontent.com/52813779/208910599-29070c15-7639-474f-b3e4-06bd5a3df29e.png) + +## Example + +To output in the Animagine XL 3.1 format, it would be as follows (enter on a single line in practice): + +``` +python tag_images_by_wd14_tagger.py --onnx --repo_id SmilingWolf/wd-swinv2-tagger-v3 + --batch_size 4 --remove_underscore --undesired_tags "PUT,YOUR,UNDESIRED,TAGS" --recursive + --use_rating_tags_as_last_tag --character_tags_first --character_tag_expand + --always_first_tags "1girl,1boy" ..\train_data +``` + +## Available Repository IDs + +[SmilingWolf's V2 and V3 models](https://huggingface.co/SmilingWolf) are available for use. Specify them in the format like `SmilingWolf/wd-vit-tagger-v3`. The default when omitted is `SmilingWolf/wd-v1-4-convnext-tagger-v2`. + +# Options + +## General Options + +- `--onnx`: Use ONNX for inference. If not specified, TensorFlow will be used. If using TensorFlow, please install TensorFlow separately. +- `--batch_size`: Number of images to process at once. Default is 1. Adjust according to VRAM capacity. +- `--caption_extension`: File extension for caption files. Default is `.txt`. +- `--max_data_loader_n_workers`: Maximum number of workers for DataLoader. Specifying a value of 1 or more will use DataLoader to speed up image loading. If unspecified, DataLoader will not be used. +- `--thresh`: Confidence threshold for outputting tags. Default is 0.35. Lowering the value will assign more tags but accuracy will decrease. +- `--general_threshold`: Confidence threshold for general tags. If omitted, same as `--thresh`. +- `--character_threshold`: Confidence threshold for character tags. If omitted, same as `--thresh`. +- `--recursive`: If specified, subfolders within the specified folder will also be processed recursively. +- `--append_tags`: Append tags to existing tag files. +- `--frequency_tags`: Output tag frequencies. +- `--debug`: Debug mode. Outputs debug information if specified. + +## Model Download + +- `--model_dir`: Folder to save model files. Default is `wd14_tagger_model`. +- `--force_download`: Re-download model files if specified. + +## Tag Editing + +- `--remove_underscore`: Remove underscores from output tags. +- `--undesired_tags`: Specify tags not to output. Multiple tags can be specified, separated by commas. For example, `black eyes,black hair`. +- `--use_rating_tags`: Output rating tags at the beginning of the tags. +- `--use_rating_tags_as_last_tag`: Add rating tags at the end of the tags. +- `--character_tags_first`: Output character tags first. +- `--character_tag_expand`: Expand character tag series names. For example, split the tag `chara_name_(series)` into `chara_name, series`. +- `--always_first_tags`: Specify tags to always output first when a certain tag appears in an image. Multiple tags can be specified, separated by commas. For example, `1girl,1boy`. +- `--caption_separator`: Separate tags with this string in the output file. Default is `, `. +- `--tag_replacement`: Perform tag replacement. Specify in the format `tag1,tag2;tag3,tag4`. If using `,` and `;`, escape them with `\`. \ + For example, specify `aira tsubase,aira tsubase (uniform)` (when you want to train a specific costume), `aira tsubase,aira tsubase\, heir of shadows` (when the series name is not included in the tag). + +When using `tag_replacement`, it is applied after `character_tag_expand`. + +When specifying `remove_underscore`, specify `undesired_tags`, `always_first_tags`, and `tag_replacement` without including underscores. + +When specifying `caption_separator`, separate `undesired_tags` and `always_first_tags` with `caption_separator`. Always separate `tag_replacement` with `,`. diff --git a/docs/wd14_tagger_README-ja.md b/docs/wd14_tagger_README-ja.md new file mode 100644 index 000000000..58e9ede95 --- /dev/null +++ b/docs/wd14_tagger_README-ja.md @@ -0,0 +1,88 @@ +# WD14Taggerによるタグ付け + +こちらのgithubページ（https://github.com/toriato/stable-diffusion-webui-wd14-tagger#mrsmilingwolfs-model-aka-waifu-diffusion-14-tagger ）の情報を参考にさせていただきました。 + +onnx を用いた推論を推奨します。以下のコマンドで onnx をインストールしてください。 + +```powershell +pip install onnx==1.15.0 onnxruntime-gpu==1.17.1 +``` + +モデルの重みはHugging Faceから自動的にダウンロードしてきます。 + +# 使い方 + +スクリプトを実行してタグ付けを行います。 +``` +python fintune/tag_images_by_wd14_tagger.py --onnx --repo_id <モデルのrepo id> --batch_size <バッチサイズ> <教師データフォルダ> +``` + +レポジトリに `SmilingWolf/wd-swinv2-tagger-v3` を使用し、バッチサイズを4にして、教師データを親フォルダの `train_data`に置いた場合、以下のようになります。 + +``` +python tag_images_by_wd14_tagger.py --onnx --repo_id SmilingWolf/wd-swinv2-tagger-v3 --batch_size 4 ..\train_data +``` + +初回起動時にはモデルファイルが `wd14_tagger_model` フォルダに自動的にダウンロードされます（フォルダはオプションで変えられます）。 + +タグファイルが教師データ画像と同じディレクトリに、同じファイル名、拡張子.txtで作成されます。 + +![生成されたタグファイル](https://user-images.githubusercontent.com/52813779/208910534-ea514373-1185-4b7d-9ae3-61eb50bc294e.png) + +![タグと画像](https://user-images.githubusercontent.com/52813779/208910599-29070c15-7639-474f-b3e4-06bd5a3df29e.png) + +## 記述例 + +Animagine XL 3.1 方式で出力する場合、以下のようになります（実際には 1 行で入力してください）。 + +``` +python tag_images_by_wd14_tagger.py --onnx --repo_id SmilingWolf/wd-swinv2-tagger-v3 + --batch_size 4 --remove_underscore --undesired_tags "PUT,YOUR,UNDESIRED,TAGS" --recursive + --use_rating_tags_as_last_tag --character_tags_first --character_tag_expand + --always_first_tags "1girl,1boy" ..\train_data +``` + +## 使用可能なリポジトリID + +[SmilingWolf 氏の V2、V3 のモデル](https://huggingface.co/SmilingWolf)が使用可能です。`SmilingWolf/wd-vit-tagger-v3` のように指定してください。省略時のデフォルトは `SmilingWolf/wd-v1-4-convnext-tagger-v2` です。 + +# オプション + +## 一般オプション + +- `--onnx` : ONNX を使用して推論します。指定しない場合は TensorFlow を使用します。TensorFlow 使用時は別途 TensorFlow をインストールしてください。 +- `--batch_size` : 一度に処理する画像の数。デフォルトは1です。VRAMの容量に応じて増減してください。 +- `--caption_extension` : キャプションファイルの拡張子。デフォルトは `.txt` です。 +- `--max_data_loader_n_workers` : DataLoader の最大ワーカー数です。このオプションに 1 以上の数値を指定すると、DataLoader を用いて画像読み込みを高速化します。未指定時は DataLoader を用いません。 +- `--thresh` : 出力するタグの信頼度の閾値。デフォルトは0.35です。値を下げるとより多くのタグが付与されますが、精度は下がります。 +- `--general_threshold` : 一般タグの信頼度の閾値。省略時は `--thresh` と同じです。 +- `--character_threshold` : キャラクタータグの信頼度の閾値。省略時は `--thresh` と同じです。 +- `--recursive` : 指定すると、指定したフォルダ内のサブフォルダも再帰的に処理します。 +- `--append_tags` : 既存のタグファイルにタグを追加します。 +- `--frequency_tags` : タグの頻度を出力します。 +- `--debug` : デバッグモード。指定するとデバッグ情報を出力します。 + +## モデルのダウンロード + +- `--model_dir` : モデルファイルの保存先フォルダ。デフォルトは `wd14_tagger_model` です。 +- `--force_download` : 指定するとモデルファイルを再ダウンロードします。 + +## タグ編集関連 + +- `--remove_underscore` : 出力するタグからアンダースコアを削除します。 +- `--undesired_tags` : 出力しないタグを指定します。カンマ区切りで複数指定できます。たとえば `black eyes,black hair` のように指定します。 +- `--use_rating_tags` : タグの最初にレーティングタグを出力します。 +- `--use_rating_tags_as_last_tag` : タグの最後にレーティングタグを追加します。 +- `--character_tags_first` : キャラクタータグを最初に出力します。 +- `--character_tag_expand` : キャラクタータグのシリーズ名を展開します。たとえば `chara_name_(series)` のタグを `chara_name, series` に分割します。 +- `--always_first_tags` : あるタグが画像に出力されたとき、そのタグを最初に出力するタグを指定します。カンマ区切りで複数指定できます。たとえば `1girl,1boy` のように指定します。 +- `--caption_separator` : 出力するファイルでタグをこの文字列で区切ります。デフォルトは `, ` です。 +- `--tag_replacement` : タグの置換を行います。`tag1,tag2;tag3,tag4` のように指定します。`,` および `;` を使う場合は `\` でエスケープしてください。\ + たとえば `aira tsubase,aira tsubase (uniform)` （特定の衣装を学習させたいとき）、`aira tsubase,aira tsubase\, heir of shadows` （シリーズ名がタグに含まれないとき）のように指定します。 + +`tag_replacement` は `character_tag_expand` の後に適用されます。 + +`remove_underscore` 指定時は、`undesired_tags`、`always_first_tags`、`tag_replacement` はアンダースコアを含めずに指定してください。 + +`caption_separator` 指定時は、`undesired_tags`、`always_first_tags` は `caption_separator` で区切ってください。`tag_replacement` は必ず `,` で区切ってください。 + diff --git a/fine_tune.py b/fine_tune.py index 875a91951..c7e6bbd2e 100644 --- a/fine_tune.py +++ b/fine_tune.py @@ -10,7 +10,9 @@ from tqdm import tqdm import torch +from library import deepspeed_utils from library.device_utils import init_ipex, clean_memory_on_device + init_ipex() from accelerate.utils import set_seed @@ -42,6 +44,7 @@ def train(args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) + deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) cache_latents = args.cache_latents @@ -108,6 +111,7 @@ 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) @@ -158,7 +162,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): # 学習を準備する if cache_latents: - vae.to(accelerator.device, dtype=weight_dtype) + vae.to(accelerator.device, dtype=vae_dtype) vae.requires_grad_(False) vae.eval() with torch.no_grad(): @@ -191,7 +195,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): if not cache_latents: vae.requires_grad_(False) vae.eval() - vae.to(accelerator.device, dtype=weight_dtype) + vae.to(accelerator.device, dtype=vae_dtype) for m in training_models: m.requires_grad_(True) @@ -246,13 +250,23 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): unet.to(weight_dtype) text_encoder.to(weight_dtype) - # acceleratorがなんかよろしくやってくれるらしい - if args.train_text_encoder: - unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, text_encoder, optimizer, train_dataloader, lr_scheduler + if args.deepspeed: + if args.train_text_encoder: + ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet, text_encoder=text_encoder) + else: + ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet) + ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + ds_model, optimizer, train_dataloader, lr_scheduler ) + training_models = [ds_model] else: - unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) + # acceleratorがなんかよろしくやってくれるらしい + if args.train_text_encoder: + unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, text_encoder, optimizer, train_dataloader, lr_scheduler + ) + else: + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする if args.full_fp16: @@ -311,13 +325,13 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): 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 + with accelerator.accumulate(*training_models): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) # .to(dtype=weight_dtype) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 - latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample() + latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(weight_dtype) latents = latents * 0.18215 b_size = latents.shape[0] @@ -340,7 +354,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) # Predict the noise residual with accelerator.autocast(): @@ -354,7 +368,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred or args.debiased_estimation_loss: # do not mean over batch dimension for snr weight or scale v-pred loss - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) loss = loss.mean([1, 2, 3]) if args.min_snr_gamma: @@ -366,7 +380,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): loss = loss.mean() # mean over batch dimension else: - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c) accelerator.backward(loss) if accelerator.sync_gradients and args.max_grad_norm != 0.0: @@ -457,7 +471,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): accelerator.end_training() - if args.save_state and is_main_process: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) del accelerator # この後メモリを使うのでこれは消す @@ -477,6 +491,7 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, False, True, True) train_util.add_training_arguments(parser, False) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_sd_saving_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) @@ -492,6 +507,11 @@ def setup_parser() -> argparse.ArgumentParser: 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_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 @@ -500,6 +520,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args) diff --git a/finetune/blip/blip.py b/finetune/blip/blip.py index 7d192cb26..13b69ffd3 100644 --- a/finetune/blip/blip.py +++ b/finetune/blip/blip.py @@ -134,8 +134,9 @@ def forward(self, image, caption): def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): image_embeds = self.visual_encoder(image) - if not sample: - image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) + # recent version of transformers seems to do repeat_interleave automatically + # if not sample: + # image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} diff --git a/finetune/merge_captions_to_metadata.py b/finetune/merge_captions_to_metadata.py index 60765b863..89f717473 100644 --- a/finetune/merge_captions_to_metadata.py +++ b/finetune/merge_captions_to_metadata.py @@ -6,75 +6,95 @@ import library.train_util as train_util import os from library.utils import setup_logging + setup_logging() import logging + logger = logging.getLogger(__name__) + def main(args): - assert not args.recursive or (args.recursive and args.full_path), "recursive requires full_path / recursiveはfull_pathと同時に指定してください" + assert not args.recursive or ( + args.recursive and args.full_path + ), "recursive requires full_path / recursiveはfull_pathと同時に指定してください" - train_data_dir_path = Path(args.train_data_dir) - image_paths: List[Path] = train_util.glob_images_pathlib(train_data_dir_path, args.recursive) - logger.info(f"found {len(image_paths)} images.") + train_data_dir_path = Path(args.train_data_dir) + image_paths: List[Path] = train_util.glob_images_pathlib(train_data_dir_path, args.recursive) + logger.info(f"found {len(image_paths)} images.") - if args.in_json is None and Path(args.out_json).is_file(): - args.in_json = args.out_json + if args.in_json is None and Path(args.out_json).is_file(): + args.in_json = args.out_json - if args.in_json is not None: - logger.info(f"loading existing metadata: {args.in_json}") - metadata = json.loads(Path(args.in_json).read_text(encoding='utf-8')) - logger.warning("captions for existing images will be overwritten / 既存の画像のキャプションは上書きされます") - else: - logger.info("new metadata will be created / 新しいメタデータファイルが作成されます") - metadata = {} + if args.in_json is not None: + logger.info(f"loading existing metadata: {args.in_json}") + metadata = json.loads(Path(args.in_json).read_text(encoding="utf-8")) + logger.warning("captions for existing images will be overwritten / 既存の画像のキャプションは上書きされます") + else: + logger.info("new metadata will be created / 新しいメタデータファイルが作成されます") + metadata = {} - logger.info("merge caption texts to metadata json.") - for image_path in tqdm(image_paths): - caption_path = image_path.with_suffix(args.caption_extension) - caption = caption_path.read_text(encoding='utf-8').strip() + logger.info("merge caption texts to metadata json.") + for image_path in tqdm(image_paths): + caption_path = image_path.with_suffix(args.caption_extension) + caption = caption_path.read_text(encoding="utf-8").strip() - if not os.path.exists(caption_path): - caption_path = os.path.join(image_path, args.caption_extension) + if not os.path.exists(caption_path): + caption_path = os.path.join(image_path, args.caption_extension) - image_key = str(image_path) if args.full_path else image_path.stem - if image_key not in metadata: - metadata[image_key] = {} + image_key = str(image_path) if args.full_path else image_path.stem + if image_key not in metadata: + metadata[image_key] = {} - metadata[image_key]['caption'] = caption - if args.debug: - logger.info(f"{image_key} {caption}") + metadata[image_key]["caption"] = caption + if args.debug: + logger.info(f"{image_key} {caption}") - # metadataを書き出して終わり - logger.info(f"writing metadata: {args.out_json}") - Path(args.out_json).write_text(json.dumps(metadata, indent=2), encoding='utf-8') - logger.info("done!") + # metadataを書き出して終わり + logger.info(f"writing metadata: {args.out_json}") + Path(args.out_json).write_text(json.dumps(metadata, indent=2), encoding="utf-8") + logger.info("done!") def setup_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser() - parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ") - parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先") - parser.add_argument("--in_json", type=str, - help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）") - parser.add_argument("--caption_extention", type=str, default=None, - help="extension of caption file (for backward compatibility) / 読み込むキャプションファイルの拡張子（スペルミスしていたのを残してあります）") - parser.add_argument("--caption_extension", type=str, default=".caption", help="extension of caption file / 読み込むキャプションファイルの拡張子") - parser.add_argument("--full_path", action="store_true", - help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）") - parser.add_argument("--recursive", action="store_true", - help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す") - parser.add_argument("--debug", action="store_true", help="debug mode") - - 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 - if args.caption_extention is not None: - args.caption_extension = args.caption_extention - - main(args) + parser = argparse.ArgumentParser() + parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ") + parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先") + parser.add_argument( + "--in_json", + type=str, + help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）", + ) + parser.add_argument( + "--caption_extention", + type=str, + default=None, + help="extension of caption file (for backward compatibility) / 読み込むキャプションファイルの拡張子（スペルミスしていたのを残してあります）", + ) + parser.add_argument( + "--caption_extension", type=str, default=".caption", help="extension of caption file / 読み込むキャプションファイルの拡張子" + ) + parser.add_argument( + "--full_path", + action="store_true", + help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）", + ) + parser.add_argument( + "--recursive", + action="store_true", + help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す", + ) + parser.add_argument("--debug", action="store_true", help="debug mode") + + 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 + if args.caption_extention is not None: + args.caption_extension = args.caption_extention + + main(args) diff --git a/finetune/merge_dd_tags_to_metadata.py b/finetune/merge_dd_tags_to_metadata.py index 9ef8f14b0..ce22d990e 100644 --- a/finetune/merge_dd_tags_to_metadata.py +++ b/finetune/merge_dd_tags_to_metadata.py @@ -6,70 +6,88 @@ import library.train_util as train_util import os from library.utils import setup_logging + setup_logging() import logging + logger = logging.getLogger(__name__) + def main(args): - assert not args.recursive or (args.recursive and args.full_path), "recursive requires full_path / recursiveはfull_pathと同時に指定してください" + assert not args.recursive or ( + args.recursive and args.full_path + ), "recursive requires full_path / recursiveはfull_pathと同時に指定してください" - train_data_dir_path = Path(args.train_data_dir) - image_paths: List[Path] = train_util.glob_images_pathlib(train_data_dir_path, args.recursive) - logger.info(f"found {len(image_paths)} images.") + train_data_dir_path = Path(args.train_data_dir) + image_paths: List[Path] = train_util.glob_images_pathlib(train_data_dir_path, args.recursive) + logger.info(f"found {len(image_paths)} images.") - if args.in_json is None and Path(args.out_json).is_file(): - args.in_json = args.out_json + if args.in_json is None and Path(args.out_json).is_file(): + args.in_json = args.out_json - if args.in_json is not None: - logger.info(f"loading existing metadata: {args.in_json}") - metadata = json.loads(Path(args.in_json).read_text(encoding='utf-8')) - logger.warning("tags data for existing images will be overwritten / 既存の画像のタグは上書きされます") - else: - logger.info("new metadata will be created / 新しいメタデータファイルが作成されます") - metadata = {} + if args.in_json is not None: + logger.info(f"loading existing metadata: {args.in_json}") + metadata = json.loads(Path(args.in_json).read_text(encoding="utf-8")) + logger.warning("tags data for existing images will be overwritten / 既存の画像のタグは上書きされます") + else: + logger.info("new metadata will be created / 新しいメタデータファイルが作成されます") + metadata = {} - logger.info("merge tags to metadata json.") - for image_path in tqdm(image_paths): - tags_path = image_path.with_suffix(args.caption_extension) - tags = tags_path.read_text(encoding='utf-8').strip() + logger.info("merge tags to metadata json.") + for image_path in tqdm(image_paths): + tags_path = image_path.with_suffix(args.caption_extension) + tags = tags_path.read_text(encoding="utf-8").strip() - if not os.path.exists(tags_path): - tags_path = os.path.join(image_path, args.caption_extension) + if not os.path.exists(tags_path): + tags_path = os.path.join(image_path, args.caption_extension) - image_key = str(image_path) if args.full_path else image_path.stem - if image_key not in metadata: - metadata[image_key] = {} + image_key = str(image_path) if args.full_path else image_path.stem + if image_key not in metadata: + metadata[image_key] = {} - metadata[image_key]['tags'] = tags - if args.debug: - logger.info(f"{image_key} {tags}") + metadata[image_key]["tags"] = tags + if args.debug: + logger.info(f"{image_key} {tags}") - # metadataを書き出して終わり - logger.info(f"writing metadata: {args.out_json}") - Path(args.out_json).write_text(json.dumps(metadata, indent=2), encoding='utf-8') + # metadataを書き出して終わり + logger.info(f"writing metadata: {args.out_json}") + Path(args.out_json).write_text(json.dumps(metadata, indent=2), encoding="utf-8") - logger.info("done!") + logger.info("done!") def setup_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser() - parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ") - parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先") - parser.add_argument("--in_json", type=str, - help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）") - parser.add_argument("--full_path", action="store_true", - help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）") - parser.add_argument("--recursive", action="store_true", - help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す") - parser.add_argument("--caption_extension", type=str, default=".txt", - help="extension of caption (tag) file / 読み込むキャプション（タグ）ファイルの拡張子") - parser.add_argument("--debug", action="store_true", help="debug mode, print tags") - - return parser - - -if __name__ == '__main__': - parser = setup_parser() - - args = parser.parse_args() - main(args) + parser = argparse.ArgumentParser() + parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ") + parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先") + parser.add_argument( + "--in_json", + type=str, + help="metadata file to input (if omitted and out_json exists, existing out_json is read) / 読み込むメタデータファイル（省略時、out_jsonが存在すればそれを読み込む）", + ) + parser.add_argument( + "--full_path", + action="store_true", + help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）", + ) + parser.add_argument( + "--recursive", + action="store_true", + help="recursively look for training tags in all child folders of train_data_dir / train_data_dirのすべての子フォルダにある学習タグを再帰的に探す", + ) + parser.add_argument( + "--caption_extension", + type=str, + default=".txt", + help="extension of caption (tag) file / 読み込むキャプション（タグ）ファイルの拡張子", + ) + parser.add_argument("--debug", action="store_true", help="debug mode, print tags") + + return parser + + +if __name__ == "__main__": + parser = setup_parser() + + args = parser.parse_args() + main(args) diff --git a/finetune/tag_images_by_wd14_tagger.py b/finetune/tag_images_by_wd14_tagger.py index b56d921a3..a327bbd61 100644 --- a/finetune/tag_images_by_wd14_tagger.py +++ b/finetune/tag_images_by_wd14_tagger.py @@ -12,8 +12,10 @@ import library.train_util as train_util from library.utils import setup_logging + setup_logging() import logging + logger = logging.getLogger(__name__) # from wd14 tagger @@ -60,12 +62,12 @@ def __getitem__(self, idx): try: image = Image.open(img_path).convert("RGB") image = preprocess_image(image) - tensor = torch.tensor(image) + # tensor = torch.tensor(image) # これ Tensor に変換する必要ないな……(;･∀･) except Exception as e: logger.error(f"Could not load image path / 画像を読み込めません: {img_path}, error: {e}") return None - return (tensor, img_path) + return (image, img_path) def collate_fn_remove_corrupted(batch): @@ -79,34 +81,42 @@ def collate_fn_remove_corrupted(batch): def main(args): + # model location is model_dir + repo_id + # repo id may be like "user/repo" or "user/repo/branch", so we need to remove slash + model_location = os.path.join(args.model_dir, args.repo_id.replace("/", "_")) + # hf_hub_downloadをそのまま使うとsymlink関係で問題があるらしいので、キャッシュディレクトリとforce_filenameを指定してなんとかする # depreacatedの警告が出るけどなくなったらその時 # https://github.com/toriato/stable-diffusion-webui-wd14-tagger/issues/22 - if not os.path.exists(args.model_dir) or args.force_download: + if not os.path.exists(model_location) or args.force_download: + os.makedirs(args.model_dir, exist_ok=True) logger.info(f"downloading wd14 tagger model from hf_hub. id: {args.repo_id}") files = FILES if args.onnx: + files = ["selected_tags.csv"] files += FILES_ONNX + else: + for file in SUB_DIR_FILES: + hf_hub_download( + args.repo_id, + file, + subfolder=SUB_DIR, + cache_dir=os.path.join(model_location, SUB_DIR), + force_download=True, + force_filename=file, + ) 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( - args.repo_id, - file, - subfolder=SUB_DIR, - cache_dir=os.path.join(args.model_dir, SUB_DIR), - force_download=True, - force_filename=file, - ) + hf_hub_download(args.repo_id, file, cache_dir=model_location, force_download=True, force_filename=file) else: logger.info("using existing wd14 tagger model") - # 画像を読み込む + # モデルを読み込む if args.onnx: + import torch import onnx import onnxruntime as ort - onnx_path = f"{args.model_dir}/model.onnx" + onnx_path = f"{model_location}/model.onnx" logger.info("Running wd14 tagger with onnx") logger.info(f"loading onnx model: {onnx_path}") @@ -120,10 +130,10 @@ def main(args): input_name = model.graph.input[0].name try: batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_value - except: + except Exception: 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: + if args.batch_size != batch_size and not isinstance(batch_size, str) and batch_size > 0: # some rebatch model may use 'N' as dynamic axes logger.warning( f"Batch size {args.batch_size} doesn't match onnx model batch size {batch_size}, use model batch size {batch_size}" @@ -132,32 +142,79 @@ def main(args): del model - ort_sess = ort.InferenceSession( - onnx_path, - providers=["CUDAExecutionProvider"] - if "CUDAExecutionProvider" in ort.get_available_providers() - else ["CPUExecutionProvider"], - ) + if "OpenVINOExecutionProvider" in ort.get_available_providers(): + # requires provider options for gpu support + # fp16 causes nonsense outputs + ort_sess = ort.InferenceSession( + onnx_path, + providers=(["OpenVINOExecutionProvider"]), + provider_options=[{'device_type' : "GPU_FP32"}], + ) + else: + ort_sess = ort.InferenceSession( + onnx_path, + providers=( + ["CUDAExecutionProvider"] if "CUDAExecutionProvider" in ort.get_available_providers() else + ["ROCMExecutionProvider"] if "ROCMExecutionProvider" 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(f"{model_location}") # 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 - with open(os.path.join(args.model_dir, CSV_FILE), "r", encoding="utf-8") as f: + with open(os.path.join(model_location, CSV_FILE), "r", encoding="utf-8") as f: reader = csv.reader(f) - l = [row for row in reader] - header = l[0] # tag_id,name,category,count - rows = l[1:] + line = [row for row in reader] + header = line[0] # tag_id,name,category,count + rows = line[1:] assert header[0] == "tag_id" and header[1] == "name" and header[2] == "category", f"unexpected csv format: {header}" - general_tags = [row[1] for row in rows[1:] if row[2] == "0"] - character_tags = [row[1] for row in rows[1:] if row[2] == "4"] + rating_tags = [row[1] for row in rows[0:] if row[2] == "9"] + general_tags = [row[1] for row in rows[0:] if row[2] == "0"] + character_tags = [row[1] for row in rows[0:] if row[2] == "4"] + + # preprocess tags in advance + if args.character_tag_expand: + for i, tag in enumerate(character_tags): + if tag.endswith(")"): + # chara_name_(series) -> chara_name, series + # chara_name_(costume)_(series) -> chara_name_(costume), series + tags = tag.split("(") + character_tag = "(".join(tags[:-1]) + if character_tag.endswith("_"): + character_tag = character_tag[:-1] + series_tag = tags[-1].replace(")", "") + character_tags[i] = character_tag + args.caption_separator + series_tag + + if args.remove_underscore: + rating_tags = [tag.replace("_", " ") if len(tag) > 3 else tag for tag in rating_tags] + general_tags = [tag.replace("_", " ") if len(tag) > 3 else tag for tag in general_tags] + character_tags = [tag.replace("_", " ") if len(tag) > 3 else tag for tag in character_tags] + + if args.tag_replacement is not None: + # escape , and ; in tag_replacement: wd14 tag names may contain , and ; + escaped_tag_replacements = args.tag_replacement.replace("\\,", "@@@@").replace("\\;", "####") + tag_replacements = escaped_tag_replacements.split(";") + for tag_replacement in tag_replacements: + tags = tag_replacement.split(",") # source, target + assert len(tags) == 2, f"tag replacement must be in the format of `source,target` / タグの置換は `置換元,置換先` の形式で指定してください: {args.tag_replacement}" + + source, target = [tag.replace("@@@@", ",").replace("####", ";") for tag in tags] + logger.info(f"replacing tag: {source} -> {target}") + + if source in general_tags: + general_tags[general_tags.index(source)] = target + elif source in character_tags: + character_tags[character_tags.index(source)] = target + elif source in rating_tags: + rating_tags[rating_tags.index(source)] = target # 画像を読み込む - train_data_dir_path = Path(args.train_data_dir) image_paths = train_util.glob_images_pathlib(train_data_dir_path, args.recursive) logger.info(f"found {len(image_paths)} images.") @@ -166,14 +223,19 @@ def main(args): caption_separator = args.caption_separator stripped_caption_separator = caption_separator.strip() - undesired_tags = set(args.undesired_tags.split(stripped_caption_separator)) + undesired_tags = args.undesired_tags.split(stripped_caption_separator) + undesired_tags = set([tag.strip() for tag in undesired_tags if tag.strip() != ""]) + + always_first_tags = None + if args.always_first_tags is not None: + always_first_tags = [tag for tag in args.always_first_tags.split(stripped_caption_separator) if tag.strip() != ""] 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) + # 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: @@ -181,22 +243,16 @@ def run_batch(path_imgs): 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 - # # First 4 labels are actually ratings: pick one with argmax - # ratings_names = label_names[:4] - # rating_index = ratings_names["probs"].argmax() - # found_rating = ratings_names[rating_index: rating_index + 1][["name", "probs"]] - - # それ以降はタグなのでconfidenceがthresholdより高いものを追加する - # Everything else is tags: pick any where prediction confidence > threshold combined_tags = [] - general_tag_text = "" + rating_tag_text = "" character_tag_text = "" + general_tag_text = "" + + # 最初の4つ以降はタグなのでconfidenceがthreshold以上のものを追加する + # First 4 labels are ratings, the rest are tags: pick any where prediction confidence >= threshold for i, p in enumerate(prob[4:]): if i < len(general_tags) and p >= args.general_threshold: tag_name = general_tags[i] - if args.remove_underscore and len(tag_name) > 3: # ignore emoji tags like >_< and ^_^ - tag_name = tag_name.replace("_", " ") if tag_name not in undesired_tags: tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1 @@ -204,13 +260,37 @@ def run_batch(path_imgs): combined_tags.append(tag_name) elif i >= len(general_tags) and p >= args.character_threshold: tag_name = character_tags[i - len(general_tags)] - if args.remove_underscore and len(tag_name) > 3: - tag_name = tag_name.replace("_", " ") 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 - combined_tags.append(tag_name) + if args.character_tags_first: # insert to the beginning + combined_tags.insert(0, tag_name) + else: + combined_tags.append(tag_name) + + # 最初の4つはratingなのでargmaxで選ぶ + # First 4 labels are actually ratings: pick one with argmax + if args.use_rating_tags or args.use_rating_tags_as_last_tag: + ratings_probs = prob[:4] + rating_index = ratings_probs.argmax() + found_rating = rating_tags[rating_index] + + if found_rating not in undesired_tags: + tag_freq[found_rating] = tag_freq.get(found_rating, 0) + 1 + rating_tag_text = found_rating + if args.use_rating_tags: + combined_tags.insert(0, found_rating) # insert to the beginning + else: + combined_tags.append(found_rating) + + # 一番最初に置くタグを指定する + # Always put some tags at the beginning + if always_first_tags is not None: + for tag in always_first_tags: + if tag in combined_tags: + combined_tags.remove(tag) + combined_tags.insert(0, tag) # 先頭のカンマを取る if len(general_tag_text) > 0: @@ -243,6 +323,7 @@ def run_batch(path_imgs): if args.debug: logger.info("") logger.info(f"{image_path}:") + logger.info(f"\tRating tags: {rating_tag_text}") logger.info(f"\tCharacter tags: {character_tag_text}") logger.info(f"\tGeneral tags: {general_tag_text}") @@ -267,9 +348,7 @@ def run_batch(path_imgs): continue image, image_path = data - if image is not None: - image = image.detach().numpy() - else: + if image is None: try: image = Image.open(image_path) if image.mode != "RGB": @@ -300,7 +379,9 @@ def run_batch(path_imgs): def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() - parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ") + parser.add_argument( + "train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ" + ) parser.add_argument( "--repo_id", type=str, @@ -314,9 +395,13 @@ def setup_parser() -> argparse.ArgumentParser: help="directory to store wd14 tagger model / wd14 taggerのモデルを格納するディレクトリ", ) parser.add_argument( - "--force_download", action="store_true", help="force downloading wd14 tagger models / wd14 taggerのモデルを再ダウンロードします" + "--force_download", + action="store_true", + help="force downloading wd14 tagger models / wd14 taggerのモデルを再ダウンロードします", + ) + parser.add_argument( + "--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ" ) - parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ") parser.add_argument( "--max_data_loader_n_workers", type=int, @@ -329,8 +414,12 @@ def setup_parser() -> argparse.ArgumentParser: default=None, help="extension of caption file (for backward compatibility) / 出力されるキャプションファイルの拡張子（スペルミスしていたのを残してあります）", ) - parser.add_argument("--caption_extension", type=str, default=".txt", help="extension of caption file / 出力されるキャプションファイルの拡張子") - parser.add_argument("--thresh", type=float, default=0.35, help="threshold of confidence to add a tag / タグを追加するか判定する閾値") + parser.add_argument( + "--caption_extension", type=str, default=".txt", help="extension of caption file / 出力されるキャプションファイルの拡張子" + ) + parser.add_argument( + "--thresh", type=float, default=0.35, help="threshold of confidence to add a tag / タグを追加するか判定する閾値" + ) parser.add_argument( "--general_threshold", type=float, @@ -343,28 +432,67 @@ def setup_parser() -> argparse.ArgumentParser: default=None, help="threshold of confidence to add a tag for character category, same as --thres if omitted / characterカテゴリのタグを追加するための確信度の閾値、省略時は --thresh と同じ", ) - parser.add_argument("--recursive", action="store_true", help="search for images in subfolders recursively / サブフォルダを再帰的に検索する") + parser.add_argument( + "--recursive", action="store_true", help="search for images in subfolders recursively / サブフォルダを再帰的に検索する" + ) parser.add_argument( "--remove_underscore", action="store_true", help="replace underscores with spaces in the output tags / 出力されるタグのアンダースコアをスペースに置き換える", ) - parser.add_argument("--debug", action="store_true", help="debug mode") + parser.add_argument( + "--debug", action="store_true", help="debug mode" + ) parser.add_argument( "--undesired_tags", type=str, default="", 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( + "--frequency_tags", action="store_true", help="Show frequency of tags for images / 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( + "--use_rating_tags", action="store_true", help="Adds rating tags as the first tag / レーティングタグを最初のタグとして追加する", + ) + parser.add_argument( + "--use_rating_tags_as_last_tag", action="store_true", help="Adds rating tags as the last tag / レーティングタグを最後のタグとして追加する", + ) + parser.add_argument( + "--character_tags_first", action="store_true", help="Always inserts character tags before the general tags / characterタグを常にgeneralタグの前に出力する", + ) + parser.add_argument( + "--always_first_tags", + type=str, + default=None, + help="comma-separated list of tags to always put at the beginning, e.g. `1girl,1boy`" + + " / 必ず先頭に置くタグのカンマ区切りリスト、例 : `1girl,1boy`", + ) 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", ) + parser.add_argument( + "--tag_replacement", + type=str, + default=None, + help="tag replacement in the format of `source1,target1;source2,target2; ...`. Escape `,` and `;` with `\`. e.g. `tag1,tag2;tag3,tag4`" + + " / タグの置換を `置換元1,置換先1;置換元2,置換先2; ...`で指定する。`\` で `,` と `;` をエスケープできる。例: `tag1,tag2;tag3,tag4`", + ) + parser.add_argument( + "--character_tag_expand", + action="store_true", + help="expand tag tail parenthesis to another tag for character tags. `chara_name_(series)` becomes `chara_name, series`" + + " / キャラクタタグの末尾の括弧を別のタグに展開する。`chara_name_(series)` は `chara_name, series` になる", + ) return parser diff --git a/library/config_util.py b/library/config_util.py index fc4b36175..d75d03b03 100644 --- a/library/config_util.py +++ b/library/config_util.py @@ -41,12 +41,17 @@ DatasetGroup, ) from .utils import setup_logging + setup_logging() import logging + logger = logging.getLogger(__name__) + def add_config_arguments(parser: argparse.ArgumentParser): - parser.add_argument("--dataset_config", type=Path, default=None, help="config file for detail settings / 詳細な設定用の設定ファイル") + parser.add_argument( + "--dataset_config", type=Path, default=None, help="config file for detail settings / 詳細な設定用の設定ファイル" + ) # TODO: inherit Params class in Subset, Dataset @@ -60,6 +65,8 @@ class BaseSubsetParams: caption_separator: str = (",",) keep_tokens: int = 0 keep_tokens_separator: str = (None,) + secondary_separator: Optional[str] = None + enable_wildcard: bool = False color_aug: bool = False flip_aug: bool = False face_crop_aug_range: Optional[Tuple[float, float]] = None @@ -78,6 +85,7 @@ class DreamBoothSubsetParams(BaseSubsetParams): is_reg: bool = False class_tokens: Optional[str] = None caption_extension: str = ".caption" + cache_info: bool = False @dataclass @@ -89,6 +97,7 @@ class FineTuningSubsetParams(BaseSubsetParams): class ControlNetSubsetParams(BaseSubsetParams): conditioning_data_dir: str = None caption_extension: str = ".caption" + cache_info: bool = False @dataclass @@ -181,6 +190,8 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence] "shuffle_caption": bool, "keep_tokens": int, "keep_tokens_separator": str, + "secondary_separator": str, + "enable_wildcard": bool, "token_warmup_min": int, "token_warmup_step": Any(float, int), "caption_prefix": str, @@ -196,6 +207,7 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence] DB_SUBSET_ASCENDABLE_SCHEMA = { "caption_extension": str, "class_tokens": str, + "cache_info": bool, } DB_SUBSET_DISTINCT_SCHEMA = { Required("image_dir"): str, @@ -208,6 +220,7 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence] } CN_SUBSET_ASCENDABLE_SCHEMA = { "caption_extension": str, + "cache_info": bool, } CN_SUBSET_DISTINCT_SCHEMA = { Required("image_dir"): str, @@ -244,9 +257,10 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence] } def __init__(self, support_dreambooth: bool, support_finetuning: bool, support_controlnet: bool, support_dropout: bool) -> None: - assert ( - support_dreambooth or support_finetuning or support_controlnet - ), "Neither DreamBooth mode nor fine tuning mode specified. Please specify one mode or more. / DreamBooth モードか fine tuning モードのどちらも指定されていません。1つ以上指定してください。" + assert support_dreambooth or support_finetuning or support_controlnet, ( + "Neither DreamBooth mode nor fine tuning mode nor controlnet mode specified. Please specify one mode or more." + + " / DreamBooth モードか fine tuning モードか controlnet モードのどれも指定されていません。1つ以上指定してください。" + ) self.db_subset_schema = self.__merge_dict( self.SUBSET_ASCENDABLE_SCHEMA, @@ -313,7 +327,10 @@ def validate_flex_dataset(dataset_config: dict): self.dataset_schema = validate_flex_dataset elif support_dreambooth: - self.dataset_schema = self.db_dataset_schema + if support_controlnet: + self.dataset_schema = self.cn_dataset_schema + else: + self.dataset_schema = self.db_dataset_schema elif support_finetuning: self.dataset_schema = self.ft_dataset_schema elif support_controlnet: @@ -358,7 +375,9 @@ def sanitize_argparse_namespace(self, argparse_namespace: argparse.Namespace) -> return self.argparse_config_validator(argparse_namespace) except MultipleInvalid: # XXX: this should be a bug - logger.error("Invalid cmdline parsed arguments. This should be a bug. / コマンドラインのパース結果が正しくないようです。プログラムのバグの可能性が高いです。") + logger.error( + "Invalid cmdline parsed arguments. This should be a bug. / コマンドラインのパース結果が正しくないようです。プログラムのバグの可能性が高いです。" + ) raise # NOTE: value would be overwritten by latter dict if there is already the same key @@ -504,6 +523,8 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu shuffle_caption: {subset.shuffle_caption} keep_tokens: {subset.keep_tokens} keep_tokens_separator: {subset.keep_tokens_separator} + secondary_separator: {subset.secondary_separator} + enable_wildcard: {subset.enable_wildcard} 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} @@ -541,11 +562,11 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu " ", ) - logger.info(f'{info}') + logger.info(f"{info}") # make buckets first because it determines the length of dataset # and set the same seed for all datasets - seed = random.randint(0, 2**31) # actual seed is seed + epoch_no + seed = random.randint(0, 2**31) # actual seed is seed + epoch_no for i, dataset in enumerate(datasets): logger.info(f"[Dataset {i}]") dataset.make_buckets() @@ -632,13 +653,17 @@ def load_user_config(file: str) -> dict: with open(file, "r") as f: config = json.load(f) except Exception: - logger.error(f"Error on parsing JSON config file. Please check the format. / JSON 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}") + logger.error( + f"Error on parsing JSON config file. Please check the format. / JSON 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}" + ) raise elif file.name.lower().endswith(".toml"): try: config = toml.load(file) except Exception: - logger.error(f"Error on parsing TOML config file. Please check the format. / TOML 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}") + logger.error( + f"Error on parsing TOML config file. Please check the format. / TOML 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}" + ) raise else: raise ValueError(f"not supported config file format / 対応していない設定ファイルの形式です: {file}") @@ -665,13 +690,13 @@ def load_user_config(file: str) -> dict: train_util.prepare_dataset_args(argparse_namespace, config_args.support_finetuning) logger.info("[argparse_namespace]") - logger.info(f'{vars(argparse_namespace)}') + logger.info(f"{vars(argparse_namespace)}") user_config = load_user_config(config_args.dataset_config) logger.info("") logger.info("[user_config]") - logger.info(f'{user_config}') + logger.info(f"{user_config}") sanitizer = ConfigSanitizer( config_args.support_dreambooth, config_args.support_finetuning, config_args.support_controlnet, config_args.support_dropout @@ -680,10 +705,10 @@ def load_user_config(file: str) -> dict: logger.info("") logger.info("[sanitized_user_config]") - logger.info(f'{sanitized_user_config}') + logger.info(f"{sanitized_user_config}") blueprint = BlueprintGenerator(sanitizer).generate(user_config, argparse_namespace) logger.info("") logger.info("[blueprint]") - logger.info(f'{blueprint}') + logger.info(f"{blueprint}") diff --git a/library/custom_train_functions.py b/library/custom_train_functions.py index a56474622..406e0e36e 100644 --- a/library/custom_train_functions.py +++ b/library/custom_train_functions.py @@ -3,11 +3,14 @@ import random import re from typing import List, Optional, Union -from .utils import setup_logging +from .utils import setup_logging + setup_logging() -import logging +import logging + logger = logging.getLogger(__name__) + def prepare_scheduler_for_custom_training(noise_scheduler, device): if hasattr(noise_scheduler, "all_snr"): return @@ -64,7 +67,7 @@ def apply_snr_weight(loss, timesteps, noise_scheduler, gamma, v_prediction=False 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) + 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) loss = loss * snr_weight @@ -92,13 +95,15 @@ 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) + 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 @@ -474,6 +479,17 @@ def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale): return noise +def apply_masked_loss(loss, batch): + # mask image is -1 to 1. we need to convert it to 0 to 1 + mask_image = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1) # use R channel + + # resize to the same size as the loss + mask_image = torch.nn.functional.interpolate(mask_image, size=loss.shape[2:], mode="area") + mask_image = mask_image / 2 + 0.5 + loss = loss * mask_image + return loss + + """ ########################################## # Perlin Noise diff --git a/library/deepspeed_utils.py b/library/deepspeed_utils.py new file mode 100644 index 000000000..99a7b2b3b --- /dev/null +++ b/library/deepspeed_utils.py @@ -0,0 +1,139 @@ +import os +import argparse +import torch +from accelerate import DeepSpeedPlugin, Accelerator + +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +def add_deepspeed_arguments(parser: argparse.ArgumentParser): + # DeepSpeed Arguments. https://huggingface.co/docs/accelerate/usage_guides/deepspeed + parser.add_argument("--deepspeed", action="store_true", help="enable deepspeed training") + parser.add_argument("--zero_stage", type=int, default=2, choices=[0, 1, 2, 3], help="Possible options are 0,1,2,3.") + parser.add_argument( + "--offload_optimizer_device", + type=str, + default=None, + choices=[None, "cpu", "nvme"], + help="Possible options are none|cpu|nvme. Only applicable with ZeRO Stages 2 and 3.", + ) + parser.add_argument( + "--offload_optimizer_nvme_path", + type=str, + default=None, + help="Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3.", + ) + parser.add_argument( + "--offload_param_device", + type=str, + default=None, + choices=[None, "cpu", "nvme"], + help="Possible options are none|cpu|nvme. Only applicable with ZeRO Stage 3.", + ) + parser.add_argument( + "--offload_param_nvme_path", + type=str, + default=None, + help="Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3.", + ) + parser.add_argument( + "--zero3_init_flag", + action="store_true", + help="Flag to indicate whether to enable `deepspeed.zero.Init` for constructing massive models." + "Only applicable with ZeRO Stage-3.", + ) + parser.add_argument( + "--zero3_save_16bit_model", + action="store_true", + help="Flag to indicate whether to save 16-bit model. Only applicable with ZeRO Stage-3.", + ) + parser.add_argument( + "--fp16_master_weights_and_gradients", + action="store_true", + help="fp16_master_and_gradients requires optimizer to support keeping fp16 master and gradients while keeping the optimizer states in fp32.", + ) + + +def prepare_deepspeed_args(args: argparse.Namespace): + if not args.deepspeed: + return + + # To avoid RuntimeError: DataLoader worker exited unexpectedly with exit code 1. + args.max_data_loader_n_workers = 1 + + +def prepare_deepspeed_plugin(args: argparse.Namespace): + if not args.deepspeed: + return None + + try: + import deepspeed + except ImportError as e: + logger.error( + "deepspeed is not installed. please install deepspeed in your environment with following command. DS_BUILD_OPS=0 pip install deepspeed" + ) + exit(1) + + deepspeed_plugin = DeepSpeedPlugin( + zero_stage=args.zero_stage, + gradient_accumulation_steps=args.gradient_accumulation_steps, + gradient_clipping=args.max_grad_norm, + offload_optimizer_device=args.offload_optimizer_device, + offload_optimizer_nvme_path=args.offload_optimizer_nvme_path, + offload_param_device=args.offload_param_device, + offload_param_nvme_path=args.offload_param_nvme_path, + zero3_init_flag=args.zero3_init_flag, + zero3_save_16bit_model=args.zero3_save_16bit_model, + ) + deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] = args.train_batch_size + deepspeed_plugin.deepspeed_config["train_batch_size"] = ( + args.train_batch_size * args.gradient_accumulation_steps * int(os.environ["WORLD_SIZE"]) + ) + deepspeed_plugin.set_mixed_precision(args.mixed_precision) + if args.mixed_precision.lower() == "fp16": + deepspeed_plugin.deepspeed_config["fp16"]["initial_scale_power"] = 0 # preventing overflow. + if args.full_fp16 or args.fp16_master_weights_and_gradients: + if args.offload_optimizer_device == "cpu" and args.zero_stage == 2: + deepspeed_plugin.deepspeed_config["fp16"]["fp16_master_weights_and_grads"] = True + logger.info("[DeepSpeed] full fp16 enable.") + else: + logger.info( + "[DeepSpeed]full fp16, fp16_master_weights_and_grads currently only supported using ZeRO-Offload with DeepSpeedCPUAdam on ZeRO-2 stage." + ) + + if args.offload_optimizer_device is not None: + logger.info("[DeepSpeed] start to manually build cpu_adam.") + deepspeed.ops.op_builder.CPUAdamBuilder().load() + logger.info("[DeepSpeed] building cpu_adam done.") + + return deepspeed_plugin + + +# Accelerate library does not support multiple models for deepspeed. So, we need to wrap multiple models into a single model. +def prepare_deepspeed_model(args: argparse.Namespace, **models): + # remove None from models + models = {k: v for k, v in models.items() if v is not None} + + class DeepSpeedWrapper(torch.nn.Module): + def __init__(self, **kw_models) -> None: + super().__init__() + self.models = torch.nn.ModuleDict() + + for key, model in kw_models.items(): + if isinstance(model, list): + model = torch.nn.ModuleList(model) + assert isinstance( + model, torch.nn.Module + ), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}" + self.models.update(torch.nn.ModuleDict({key: model})) + + def get_models(self): + return self.models + + ds_model = DeepSpeedWrapper(**models) + return ds_model diff --git a/library/ipex/__init__.py b/library/ipex/__init__.py index 972a3bf63..e5aba693c 100644 --- a/library/ipex/__init__.py +++ b/library/ipex/__init__.py @@ -32,6 +32,7 @@ def ipex_init(): # pylint: disable=too-many-statements torch.cuda.FloatTensor = torch.xpu.FloatTensor torch.Tensor.cuda = torch.Tensor.xpu torch.Tensor.is_cuda = torch.Tensor.is_xpu + torch.nn.Module.cuda = torch.nn.Module.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 @@ -147,9 +148,9 @@ def ipex_init(): # pylint: disable=too-many-statements # C torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream - ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_eu_count - ipex._C._DeviceProperties.major = 2023 - ipex._C._DeviceProperties.minor = 2 + ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_subslice_count + ipex._C._DeviceProperties.major = 2024 + ipex._C._DeviceProperties.minor = 0 # 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] diff --git a/library/ipex/attention.py b/library/ipex/attention.py index 8253c5b17..d989ad53d 100644 --- a/library/ipex/attention.py +++ b/library/ipex/attention.py @@ -122,15 +122,15 @@ def torch_bmm_32_bit(input, mat2, *, out=None): mat2[start_idx:end_idx], out=out ) + torch.xpu.synchronize(input.device) else: return original_torch_bmm(input, mat2, out=out) - torch.xpu.synchronize(input.device) 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): +def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs): if query.device.type != "xpu": - return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal) + return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_sdpa_slice_sizes(query.shape, query.element_size()) # Slice SDPA @@ -153,7 +153,7 @@ def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropo 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 + dropout_p=dropout_p, is_causal=is_causal, **kwargs ) else: hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention( @@ -161,7 +161,7 @@ def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropo 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 + dropout_p=dropout_p, is_causal=is_causal, **kwargs ) else: hidden_states[start_idx:end_idx] = original_scaled_dot_product_attention( @@ -169,9 +169,9 @@ def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropo 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 + dropout_p=dropout_p, is_causal=is_causal, **kwargs ) + torch.xpu.synchronize(query.device) else: - return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal) - torch.xpu.synchronize(query.device) + return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs) return hidden_states diff --git a/library/ipex/hijacks.py b/library/ipex/hijacks.py index b1b9ccf0e..d3cef8276 100644 --- a/library/ipex/hijacks.py +++ b/library/ipex/hijacks.py @@ -12,7 +12,7 @@ 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: - logger.error("IPEX backend doesn't support DataParallel on multiple XPU devices") + 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 @@ -42,7 +42,7 @@ def autocast_init(self, device_type, dtype=None, enabled=True, cache_enabled=Non original_interpolate = torch.nn.functional.interpolate @wraps(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: + if antialias or align_corners is not None or mode == 'bicubic': 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, @@ -190,6 +190,16 @@ def Tensor_cuda(self, device=None, *args, **kwargs): else: return original_Tensor_cuda(self, device, *args, **kwargs) +original_Tensor_pin_memory = torch.Tensor.pin_memory +@wraps(torch.Tensor.pin_memory) +def Tensor_pin_memory(self, device=None, *args, **kwargs): + if device is None: + device = "xpu" + if check_device(device): + return original_Tensor_pin_memory(self, return_xpu(device), *args, **kwargs) + else: + return original_Tensor_pin_memory(self, device, *args, **kwargs) + original_UntypedStorage_init = torch.UntypedStorage.__init__ @wraps(torch.UntypedStorage.__init__) def UntypedStorage_init(*args, device=None, **kwargs): @@ -216,7 +226,9 @@ def torch_empty(*args, device=None, **kwargs): original_torch_randn = torch.randn @wraps(torch.randn) -def torch_randn(*args, device=None, **kwargs): +def torch_randn(*args, device=None, dtype=None, **kwargs): + if dtype == bytes: + dtype = None if check_device(device): return original_torch_randn(*args, device=return_xpu(device), **kwargs) else: @@ -256,11 +268,13 @@ def torch_Generator(device=None): original_torch_load = torch.load @wraps(torch.load) -def torch_load(f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs): +def torch_load(f, map_location=None, *args, **kwargs): + if map_location is None: + map_location = "xpu" if check_device(map_location): - return original_torch_load(f, map_location=return_xpu(map_location), pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs) + return original_torch_load(f, *args, map_location=return_xpu(map_location), **kwargs) else: - return original_torch_load(f, map_location=map_location, pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs) + return original_torch_load(f, *args, map_location=map_location, **kwargs) # Hijack Functions: @@ -268,6 +282,7 @@ def ipex_hijacks(): torch.tensor = torch_tensor torch.Tensor.to = Tensor_to torch.Tensor.cuda = Tensor_cuda + torch.Tensor.pin_memory = Tensor_pin_memory torch.UntypedStorage.__init__ = UntypedStorage_init torch.UntypedStorage.cuda = UntypedStorage_cuda torch.empty = torch_empty diff --git a/library/sdxl_train_util.py b/library/sdxl_train_util.py index 1932bf881..a29013e34 100644 --- a/library/sdxl_train_util.py +++ b/library/sdxl_train_util.py @@ -24,7 +24,6 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype): - # load models for each process model_dtype = match_mixed_precision(args, weight_dtype) # prepare fp16/bf16 for pi in range(accelerator.state.num_processes): if pi == accelerator.state.local_process_index: diff --git a/library/train_util.py b/library/train_util.py index d2b69edb5..9ce129bd9 100644 --- a/library/train_util.py +++ b/library/train_util.py @@ -63,12 +63,14 @@ from huggingface_hub import hf_hub_download import numpy as np from PIL import Image +import imagesize import cv2 import safetensors.torch from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline import library.model_util as model_util import library.huggingface_util as huggingface_util import library.sai_model_spec as sai_model_spec +import library.deepspeed_utils as deepspeed_utils from library.utils import setup_logging setup_logging() @@ -364,6 +366,8 @@ def __init__( caption_separator: str, keep_tokens: int, keep_tokens_separator: str, + secondary_separator: Optional[str], + enable_wildcard: bool, color_aug: bool, flip_aug: bool, face_crop_aug_range: Optional[Tuple[float, float]], @@ -382,6 +386,8 @@ def __init__( self.caption_separator = caption_separator self.keep_tokens = keep_tokens self.keep_tokens_separator = keep_tokens_separator + self.secondary_separator = secondary_separator + self.enable_wildcard = enable_wildcard self.color_aug = color_aug self.flip_aug = flip_aug self.face_crop_aug_range = face_crop_aug_range @@ -405,11 +411,14 @@ def __init__( is_reg: bool, class_tokens: Optional[str], caption_extension: str, + cache_info: bool, num_repeats, shuffle_caption, caption_separator: str, keep_tokens, keep_tokens_separator, + secondary_separator, + enable_wildcard, color_aug, flip_aug, face_crop_aug_range, @@ -431,6 +440,8 @@ def __init__( caption_separator, keep_tokens, keep_tokens_separator, + secondary_separator, + enable_wildcard, color_aug, flip_aug, face_crop_aug_range, @@ -449,6 +460,7 @@ def __init__( self.caption_extension = caption_extension if self.caption_extension and not self.caption_extension.startswith("."): self.caption_extension = "." + self.caption_extension + self.cache_info = cache_info def __eq__(self, other) -> bool: if not isinstance(other, DreamBoothSubset): @@ -466,6 +478,8 @@ def __init__( caption_separator, keep_tokens, keep_tokens_separator, + secondary_separator, + enable_wildcard, color_aug, flip_aug, face_crop_aug_range, @@ -487,6 +501,8 @@ def __init__( caption_separator, keep_tokens, keep_tokens_separator, + secondary_separator, + enable_wildcard, color_aug, flip_aug, face_crop_aug_range, @@ -514,11 +530,14 @@ def __init__( image_dir: str, conditioning_data_dir: str, caption_extension: str, + cache_info: bool, num_repeats, shuffle_caption, caption_separator, keep_tokens, keep_tokens_separator, + secondary_separator, + enable_wildcard, color_aug, flip_aug, face_crop_aug_range, @@ -540,6 +559,8 @@ def __init__( caption_separator, keep_tokens, keep_tokens_separator, + secondary_separator, + enable_wildcard, color_aug, flip_aug, face_crop_aug_range, @@ -557,6 +578,7 @@ def __init__( self.caption_extension = caption_extension if self.caption_extension and not self.caption_extension.startswith("."): self.caption_extension = "." + self.caption_extension + self.cache_info = cache_info def __eq__(self, other) -> bool: if not isinstance(other, ControlNetSubset): @@ -675,15 +697,48 @@ def process_caption(self, subset: BaseSubset, caption): if is_drop_out: caption = "" else: + # process wildcards + if subset.enable_wildcard: + # if caption is multiline, random choice one line + if "\n" in caption: + caption = random.choice(caption.split("\n")) + + # wildcard is like '{aaa|bbb|ccc...}' + # escape the curly braces like {{ or }} + replacer1 = "⦅" + replacer2 = "⦆" + while replacer1 in caption or replacer2 in caption: + replacer1 += "⦅" + replacer2 += "⦆" + + caption = caption.replace("{{", replacer1).replace("}}", replacer2) + + # replace the wildcard + def replace_wildcard(match): + return random.choice(match.group(1).split("|")) + + caption = re.sub(r"\{([^}]+)\}", replace_wildcard, caption) + + # unescape the curly braces + caption = caption.replace(replacer1, "{").replace(replacer2, "}") + else: + # if caption is multiline, use the first line + caption = caption.split("\n")[0] + if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0: fixed_tokens = [] flex_tokens = [] + fixed_suffix_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) + if subset.keep_tokens_separator in flex_part: + flex_part, fixed_suffix_part = flex_part.split(subset.keep_tokens_separator, 1) + fixed_suffix_tokens = [t.strip() for t in fixed_suffix_part.split(subset.caption_separator) if t.strip()] + 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: @@ -718,7 +773,11 @@ def dropout_tags(tokens): flex_tokens = dropout_tags(flex_tokens) - caption = ", ".join(fixed_tokens + flex_tokens) + caption = ", ".join(fixed_tokens + flex_tokens + fixed_suffix_tokens) + + # process secondary separator + if subset.secondary_separator: + caption = caption.replace(subset.secondary_separator, subset.caption_separator) # textual inversion対応 for str_from, str_to in self.replacements.items(): @@ -1027,8 +1086,7 @@ def cache_text_encoder_outputs( ) def get_image_size(self, image_path): - image = Image.open(image_path) - return image.size + return imagesize.get(image_path) def load_image_with_face_info(self, subset: BaseSubset, image_path: str): img = load_image(image_path) @@ -1357,6 +1415,8 @@ def get_item_for_caching(self, bucket, bucket_batch_size, image_index): class DreamBoothDataset(BaseDataset): + IMAGE_INFO_CACHE_FILE = "metadata_cache.json" + def __init__( self, subsets: Sequence[DreamBoothSubset], @@ -1400,7 +1460,7 @@ def __init__( self.bucket_reso_steps = None # この情報は使われない self.bucket_no_upscale = False - def read_caption(img_path, caption_extension): + def read_caption(img_path, caption_extension, enable_wildcard): # captionの候補ファイル名を作る base_name = os.path.splitext(img_path)[0] base_name_face_det = base_name @@ -1419,7 +1479,10 @@ def read_caption(img_path, caption_extension): logger.error(f"illegal char in file (not UTF-8) / ファイルにUTF-8以外の文字があります: {cap_path}") raise e assert len(lines) > 0, f"caption file is empty / キャプションファイルが空です: {cap_path}" - caption = lines[0].strip() + if enable_wildcard: + caption = "\n".join([line.strip() for line in lines if line.strip() != ""]) # 空行を除く、改行で連結 + else: + caption = lines[0].strip() break return caption @@ -1428,26 +1491,54 @@ def load_dreambooth_dir(subset: DreamBoothSubset): logger.warning(f"not directory: {subset.image_dir}") return [], [] - img_paths = glob_images(subset.image_dir, "*") - logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files") - - # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う - captions = [] - missing_captions = [] - for img_path in img_paths: - cap_for_img = read_caption(img_path, subset.caption_extension) - if cap_for_img is None and subset.class_tokens is None: + info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE) + use_cached_info_for_subset = subset.cache_info + if use_cached_info_for_subset: + logger.info( + f"using cached image info for this subset / このサブセットで、キャッシュされた画像情報を使います: {info_cache_file}" + ) + if not os.path.isfile(info_cache_file): logger.warning( - f"neither caption file nor class tokens are found. use empty caption for {img_path} / キャプションファイルもclass tokenも見つかりませんでした。空のキャプションを使用します: {img_path}" + f"image info file not found. You can ignore this warning if this is the first time to use this subset" + + " / キャッシュファイルが見つかりませんでした。初回実行時はこの警告を無視してください: {metadata_file}" ) - captions.append("") - missing_captions.append(img_path) - else: - if cap_for_img is None: - captions.append(subset.class_tokens) + use_cached_info_for_subset = False + + if use_cached_info_for_subset: + # json: {`img_path`:{"caption": "caption...", "resolution": [width, height]}, ...} + with open(info_cache_file, "r", encoding="utf-8") as f: + metas = json.load(f) + img_paths = list(metas.keys()) + sizes = [meta["resolution"] for meta in metas.values()] + + # we may need to check image size and existence of image files, but it takes time, so user should check it before training + else: + img_paths = glob_images(subset.image_dir, "*") + sizes = [None] * len(img_paths) + + logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files") + + if use_cached_info_for_subset: + captions = [meta["caption"] for meta in metas.values()] + missing_captions = [img_path for img_path, caption in zip(img_paths, captions) if caption is None or caption == ""] + else: + # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う + captions = [] + missing_captions = [] + for img_path in img_paths: + cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard) + if cap_for_img is None and subset.class_tokens is None: + logger.warning( + f"neither caption file nor class tokens are found. use empty caption for {img_path} / キャプションファイルもclass tokenも見つかりませんでした。空のキャプションを使用します: {img_path}" + ) + captions.append("") missing_captions.append(img_path) else: - captions.append(cap_for_img) + if cap_for_img is None: + captions.append(subset.class_tokens) + missing_captions.append(img_path) + else: + captions.append(cap_for_img) self.set_tag_frequency(os.path.basename(subset.image_dir), captions) # タグ頻度を記録 @@ -1464,12 +1555,24 @@ def load_dreambooth_dir(subset: DreamBoothSubset): logger.warning(missing_caption + f"... and {remaining_missing_captions} more") break logger.warning(missing_caption) - return img_paths, captions + + if not use_cached_info_for_subset and subset.cache_info: + logger.info(f"cache image info for / 画像情報をキャッシュします : {info_cache_file}") + sizes = [self.get_image_size(img_path) for img_path in tqdm(img_paths, desc="get image size")] + matas = {} + for img_path, caption, size in zip(img_paths, captions, sizes): + matas[img_path] = {"caption": caption, "resolution": list(size)} + with open(info_cache_file, "w", encoding="utf-8") as f: + json.dump(matas, f, ensure_ascii=False, indent=2) + logger.info(f"cache image info done for / 画像情報を出力しました : {info_cache_file}") + + # if sizes are not set, image size will be read in make_buckets + return img_paths, captions, sizes logger.info("prepare images.") num_train_images = 0 num_reg_images = 0 - reg_infos: List[ImageInfo] = [] + reg_infos: List[Tuple[ImageInfo, DreamBoothSubset]] = [] for subset in subsets: if subset.num_repeats < 1: logger.warning( @@ -1483,7 +1586,7 @@ def load_dreambooth_dir(subset: DreamBoothSubset): ) continue - img_paths, captions = load_dreambooth_dir(subset) + img_paths, captions, sizes = load_dreambooth_dir(subset) if len(img_paths) < 1: logger.warning( f"ignore subset with image_dir='{subset.image_dir}': no images found / 画像が見つからないためサブセットを無視します" @@ -1495,10 +1598,12 @@ def load_dreambooth_dir(subset: DreamBoothSubset): else: num_train_images += subset.num_repeats * len(img_paths) - for img_path, caption in zip(img_paths, captions): + for img_path, caption, size in zip(img_paths, captions, sizes): info = ImageInfo(img_path, subset.num_repeats, caption, subset.is_reg, img_path) + if size is not None: + info.image_size = size if subset.is_reg: - reg_infos.append(info) + reg_infos.append((info, subset)) else: self.register_image(info, subset) @@ -1519,7 +1624,7 @@ def load_dreambooth_dir(subset: DreamBoothSubset): n = 0 first_loop = True while n < num_train_images: - for info in reg_infos: + for info, subset in reg_infos: if first_loop: self.register_image(info, subset) n += info.num_repeats @@ -1611,10 +1716,24 @@ def __init__( caption = img_md.get("caption") tags = img_md.get("tags") if caption is None: - caption = tags - elif tags is not None and len(tags) > 0: - caption = caption + ", " + tags - tags_list.append(tags) + caption = tags # could be multiline + tags = None + + if subset.enable_wildcard: + # tags must be single line + if tags is not None: + tags = tags.replace("\n", subset.caption_separator) + + # add tags to each line of caption + if caption is not None and tags is not None: + caption = "\n".join( + [f"{line}{subset.caption_separator}{tags}" for line in caption.split("\n") if line.strip() != ""] + ) + else: + # use as is + if tags is not None and len(tags) > 0: + caption = caption + subset.caption_separator + tags + tags_list.append(tags) if caption is None: caption = "" @@ -1764,16 +1883,22 @@ def __init__( db_subsets = [] for subset in subsets: + assert ( + not subset.random_crop + ), "random_crop is not supported in ControlNetDataset / random_cropはControlNetDatasetではサポートされていません" db_subset = DreamBoothSubset( subset.image_dir, False, None, subset.caption_extension, + subset.cache_info, subset.num_repeats, subset.shuffle_caption, subset.caption_separator, subset.keep_tokens, subset.keep_tokens_separator, + subset.secondary_separator, + subset.enable_wildcard, subset.color_aug, subset.flip_aug, subset.face_crop_aug_range, @@ -1812,7 +1937,7 @@ def __init__( # assert all conditioning data exists missing_imgs = [] - cond_imgs_with_img = set() + cond_imgs_with_pair = set() for image_key, info in self.dreambooth_dataset_delegate.image_data.items(): db_subset = self.dreambooth_dataset_delegate.image_to_subset[image_key] subset = None @@ -1826,23 +1951,29 @@ def __init__( logger.warning(f"not directory: {subset.conditioning_data_dir}") continue - img_basename = os.path.basename(info.absolute_path) - ctrl_img_path = os.path.join(subset.conditioning_data_dir, img_basename) - if not os.path.exists(ctrl_img_path): + img_basename = os.path.splitext(os.path.basename(info.absolute_path))[0] + ctrl_img_path = glob_images(subset.conditioning_data_dir, img_basename) + if len(ctrl_img_path) < 1: missing_imgs.append(img_basename) + continue + ctrl_img_path = ctrl_img_path[0] + ctrl_img_path = os.path.abspath(ctrl_img_path) # normalize path info.cond_img_path = ctrl_img_path - cond_imgs_with_img.add(ctrl_img_path) + cond_imgs_with_pair.add(os.path.splitext(ctrl_img_path)[0]) # remove extension because Windows is case insensitive extra_imgs = [] for subset in subsets: conditioning_img_paths = glob_images(subset.conditioning_data_dir, "*") - extra_imgs.extend( - [cond_img_path for cond_img_path in conditioning_img_paths if cond_img_path not in cond_imgs_with_img] - ) + conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths] # normalize path + extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair]) - assert len(missing_imgs) == 0, f"missing conditioning data for {len(missing_imgs)} images: {missing_imgs}" - assert len(extra_imgs) == 0, f"extra conditioning data for {len(extra_imgs)} images: {extra_imgs}" + assert ( + len(missing_imgs) == 0 + ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}" + assert ( + len(extra_imgs) == 0 + ), f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}" self.conditioning_image_transforms = IMAGE_TRANSFORMS @@ -2888,7 +3019,12 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: parser.add_argument( "--save_state", action="store_true", - help="save training state additionally (including optimizer states etc.) / optimizerなど学習状態も含めたstateを追加で保存する", + help="save training state additionally (including optimizer states etc.) when saving model / optimizerなど学習状態も含めたstateをモデル保存時に追加で保存する", + ) + parser.add_argument( + "--save_state_on_train_end", + action="store_true", + help="save training state (including optimizer states etc.) on train end / optimizerなど学習状態も含めたstateを学習完了時に保存する", ) parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate") @@ -2971,6 +3107,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: "--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("--fp8_base", action="store_true", help="use fp8 for base model / base modelにfp8を使う") + parser.add_argument( "--ddp_timeout", type=int, @@ -3033,12 +3170,18 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: default=None, help="specify WandB API key to log in before starting training (optional). / WandB APIキーを指定して学習開始前にログインする（オプション）", ) + parser.add_argument( "--noise_offset", type=float, default=None, help="enable noise offset with this value (if enabled, around 0.1 is recommended) / Noise offsetを有効にしてこの値を設定する（有効にする場合は0.1程度を推奨）", ) + parser.add_argument( + "--noise_offset_random_strength", + action="store_true", + help="use random strength between 0~noise_offset for noise offset. / noise offsetにおいて、0からnoise_offsetの間でランダムな強度を使用します。", + ) parser.add_argument( "--multires_noise_iterations", type=int, @@ -3052,6 +3195,12 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) " + "/ input perturbation noiseを有効にする。正則化に使用される。推奨値: 0.1程度 (arxiv.org/abs/2301.11706 より)", ) + parser.add_argument( + "--ip_noise_gamma_random_strength", + action="store_true", + help="Use random strength between 0~ip_noise_gamma for input perturbation noise." + + "/ input perturbation noiseにおいて、0からip_noise_gammaの間でランダムな強度を使用します。", + ) # parser.add_argument( # "--perlin_noise", # type=int, @@ -3087,6 +3236,27 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: default=None, help="set maximum time step for U-Net training (1~1000, default is 1000) / U-Net学習時のtime stepの最大値を設定する（1~1000で指定、省略時はデフォルト値(1000)）", ) + parser.add_argument( + "--loss_type", + type=str, + default="l2", + choices=["l2", "huber", "smooth_l1"], + help="The type of loss function to use (L2, Huber, or smooth L1), default is L2 / 使用する損失関数の種類（L2、Huber、またはsmooth L1）、デフォルトはL2", + ) + parser.add_argument( + "--huber_schedule", + type=str, + default="exponential", + choices=["constant", "exponential", "snr"], + help="The scheduling method for Huber loss (constant, exponential, or SNR-based). Only used when loss_type is 'huber' or 'smooth_l1'. default is exponential" + + " / Huber損失のスケジューリング方法（constant、exponential、またはSNRベース）。loss_typeが'huber'または'smooth_l1'の場合に有効、デフォルトはexponential", + ) + parser.add_argument( + "--huber_c", + type=float, + default=0.1, + help="The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 0.1 / Huber損失のパラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは0.1", + ) parser.add_argument( "--lowram", @@ -3195,6 +3365,74 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: ) +def add_masked_loss_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--conditioning_data_dir", + type=str, + default=None, + help="conditioning data directory / 条件付けデータのディレクトリ", + ) + parser.add_argument( + "--masked_loss", + action="store_true", + help="apply mask for calculating loss. conditioning_data_dir is required for dataset. / 損失計算時にマスクを適用する。datasetにはconditioning_data_dirが必要", + ) + + +# verify command line args for training +def verify_command_line_training_args(args: argparse.Namespace): + # if wandb is enabled, the command line is exposed to the public + # check whether sensitive options are included in the command line arguments + # if so, warn or inform the user to move them to the configuration file + # wandbが有効な場合、コマンドラインが公開される + # 学習用のコマンドライン引数に敏感なオプションが含まれているかどうかを確認し、 + # 含まれている場合は設定ファイルに移動するようにユーザーに警告または通知する + + wandb_enabled = args.log_with is not None and args.log_with != "tensorboard" # "all" or "wandb" + if not wandb_enabled: + return + + sensitive_args = ["wandb_api_key", "huggingface_token"] + sensitive_path_args = [ + "pretrained_model_name_or_path", + "vae", + "tokenizer_cache_dir", + "train_data_dir", + "conditioning_data_dir", + "reg_data_dir", + "output_dir", + "logging_dir", + ] + + for arg in sensitive_args: + if getattr(args, arg, None) is not None: + logger.warning( + f"wandb is enabled, but option `{arg}` is included in the command line. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file." + + f" / wandbが有効で、かつオプション `{arg}` がコマンドラインに含まれています。コマンドラインは公開されるため、`.toml`ファイルに移動することをお勧めします。" + ) + + # if path is absolute, it may include sensitive information + for arg in sensitive_path_args: + if getattr(args, arg, None) is not None and os.path.isabs(getattr(args, arg)): + logger.info( + f"wandb is enabled, but option `{arg}` is included in the command line and it is an absolute path. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file or use relative path." + + f" / wandbが有効で、かつオプション `{arg}` がコマンドラインに含まれており、絶対パスです。コマンドラインは公開されるため、`.toml`ファイルに移動するか、相対パスを使用することをお勧めします。" + ) + + if getattr(args, "config_file", None) is not None: + logger.info( + f"wandb is enabled, but option `config_file` is included in the command line. Because the command line is exposed to the public, please be careful about the information included in the path." + + f" / wandbが有効で、かつオプション `config_file` がコマンドラインに含まれています。コマンドラインは公開されるため、パスに含まれる情報にご注意ください。" + ) + + # other sensitive options + if args.huggingface_repo_id is not None and args.huggingface_repo_visibility != "public": + logger.info( + f"wandb is enabled, but option huggingface_repo_id is included in the command line and huggingface_repo_visibility is not 'public'. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file." + + f" / wandbが有効で、かつオプション huggingface_repo_id がコマンドラインに含まれており、huggingface_repo_visibility が 'public' ではありません。コマンドラインは公開されるため、`.toml`ファイルに移動することをお勧めします。" + ) + + def verify_training_args(args: argparse.Namespace): r""" Verify training arguments. Also reflect highvram option to global variable @@ -3250,6 +3488,18 @@ def verify_training_args(args: argparse.Namespace): + " / zero_terminal_snrが有効ですが、v_parameterizationが有効ではありません。学習結果は想定外になる可能性があります" ) + if args.sample_every_n_epochs is not None and args.sample_every_n_epochs <= 0: + logger.warning( + "sample_every_n_epochs is less than or equal to 0, so it will be disabled / sample_every_n_epochsに0以下の値が指定されたため無効になります" + ) + args.sample_every_n_epochs = None + + if args.sample_every_n_steps is not None and args.sample_every_n_steps <= 0: + logger.warning( + "sample_every_n_steps is less than or equal to 0, so it will be disabled / sample_every_n_stepsに0以下の値が指定されたため無効になります" + ) + args.sample_every_n_steps = None + def add_dataset_arguments( parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool, support_caption_dropout: bool @@ -3258,6 +3508,12 @@ def add_dataset_arguments( 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( + "--cache_info", + action="store_true", + help="cache meta information (caption and image size) for faster dataset loading. only available for DreamBooth" + + " / メタ情報（キャプションとサイズ）をキャッシュしてデータセット読み込みを高速化する。DreamBooth方式のみ有効", + ) 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" ) @@ -3284,6 +3540,18 @@ def add_dataset_arguments( 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( + "--secondary_separator", + type=str, + default=None, + help="a secondary separator for caption. This separator is replaced to caption_separator after dropping/shuffling caption" + + " / captionのセカンダリ区切り文字。この区切り文字はcaptionのドロップやシャッフル後にcaption_separatorに置き換えられる", + ) + parser.add_argument( + "--enable_wildcard", + action="store_true", + help="enable wildcard for caption (e.g. '{image|picture|rendition}') / captionのワイルドカードを有効にする（例：'{image|picture|rendition}'）", + ) parser.add_argument( "--caption_prefix", type=str, @@ -3474,7 +3742,7 @@ def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentPar exit(1) logger.info(f"Loading settings from {config_path}...") - with open(config_path, "r") as f: + with open(config_path, "r", encoding="utf-8") as f: config_dict = toml.load(f) # combine all sections into one @@ -3983,6 +4251,10 @@ def load_tokenizer(args: argparse.Namespace): def prepare_accelerator(args: argparse.Namespace): + """ + this function also prepares deepspeed plugin + """ + if args.logging_dir is None: logging_dir = None else: @@ -4028,6 +4300,8 @@ def prepare_accelerator(args: argparse.Namespace): ), ) kwargs_handlers = list(filter(lambda x: x is not None, kwargs_handlers)) + deepspeed_plugin = deepspeed_utils.prepare_deepspeed_plugin(args) + accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, @@ -4035,6 +4309,7 @@ def prepare_accelerator(args: argparse.Namespace): project_dir=logging_dir, kwargs_handlers=kwargs_handlers, dynamo_backend=dynamo_backend, + deepspeed_plugin=deepspeed_plugin, ) print("accelerator device:", accelerator.device) return accelerator @@ -4105,7 +4380,6 @@ def _load_target_model(args: argparse.Namespace, weight_dtype, device="cpu", une 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): if pi == accelerator.state.local_process_index: logger.info(f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}") @@ -4116,7 +4390,6 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio accelerator.device if args.lowram else "cpu", unet_use_linear_projection_in_v2=unet_use_linear_projection_in_v2, ) - # work on low-ram device if args.lowram: text_encoder.to(accelerator.device) @@ -4125,7 +4398,6 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio clean_memory_on_device(accelerator.device) accelerator.wait_for_everyone() - return text_encoder, vae, unet, load_stable_diffusion_format @@ -4592,11 +4864,47 @@ def save_sd_model_on_train_end_common( huggingface_util.upload(args, out_dir, "/" + model_name, force_sync_upload=True) +def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device): + + # TODO: if a huber loss is selected, it will use constant timesteps for each batch + # as. In the future there may be a smarter way + + if args.loss_type == "huber" or args.loss_type == "smooth_l1": + timesteps = torch.randint(min_timestep, max_timestep, (1,), device="cpu") + timestep = timesteps.item() + + if args.huber_schedule == "exponential": + alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps + huber_c = math.exp(-alpha * timestep) + elif args.huber_schedule == "snr": + alphas_cumprod = noise_scheduler.alphas_cumprod[timestep] + sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5 + huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c + elif args.huber_schedule == "constant": + huber_c = args.huber_c + else: + raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!") + + timesteps = timesteps.repeat(b_size).to(device) + elif args.loss_type == "l2": + timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=device) + huber_c = 1 # may be anything, as it's not used + else: + raise NotImplementedError(f"Unknown loss type {args.loss_type}") + timesteps = timesteps.long() + + return timesteps, huber_c + + def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents): # Sample noise that we'll add to the latents noise = torch.randn_like(latents, device=latents.device) if args.noise_offset: - noise = custom_train_functions.apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale) + if args.noise_offset_random_strength: + noise_offset = torch.rand(1, device=latents.device) * args.noise_offset + else: + noise_offset = args.noise_offset + noise = custom_train_functions.apply_noise_offset(latents, noise, noise_offset, args.adaptive_noise_scale) if args.multires_noise_iterations: noise = custom_train_functions.pyramid_noise_like( noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount @@ -4607,17 +4915,44 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents): min_timestep = 0 if args.min_timestep is None else args.min_timestep max_timestep = noise_scheduler.config.num_train_timesteps if args.max_timestep is None else args.max_timestep - timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=latents.device) - timesteps = timesteps.long() + timesteps, huber_c = get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, latents.device) # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) if args.ip_noise_gamma: - noisy_latents = noise_scheduler.add_noise(latents, noise + args.ip_noise_gamma * torch.randn_like(latents), timesteps) + if args.ip_noise_gamma_random_strength: + strength = torch.rand(1, device=latents.device) * args.ip_noise_gamma + else: + strength = args.ip_noise_gamma + noisy_latents = noise_scheduler.add_noise(latents, noise + strength * torch.randn_like(latents), timesteps) else: noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - return noise, noisy_latents, timesteps + return noise, noisy_latents, timesteps, huber_c + + +# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already +def conditional_loss( + model_pred: torch.Tensor, target: torch.Tensor, reduction: str = "mean", loss_type: str = "l2", huber_c: float = 0.1 +): + + if loss_type == "l2": + loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction) + elif loss_type == "huber": + loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c) + if reduction == "mean": + loss = torch.mean(loss) + elif reduction == "sum": + loss = torch.sum(loss) + elif loss_type == "smooth_l1": + loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c) + if reduction == "mean": + loss = torch.mean(loss) + elif reduction == "sum": + loss = torch.sum(loss) + else: + raise NotImplementedError(f"Unsupported Loss Type {loss_type}") + return loss def append_lr_to_logs(logs, lr_scheduler, optimizer_type, including_unet=True): diff --git a/networks/lora.py b/networks/lora.py index 948b30b0e..d1208040f 100644 --- a/networks/lora.py +++ b/networks/lora.py @@ -247,14 +247,13 @@ def get_mask_for_x(self, x): area = x.size()[1] mask = self.network.mask_dic.get(area, None) - if mask is None: - # raise ValueError(f"mask is None for resolution {area}") + if mask is None or len(x.size()) == 2: # emb_layers in SDXL doesn't have mask # if "emb" not in self.lora_name: # print(f"mask is None for resolution {self.lora_name}, {area}, {x.size()}") mask_size = (1, x.size()[1]) if len(x.size()) == 2 else (1, *x.size()[1:-1], 1) return torch.ones(mask_size, dtype=x.dtype, device=x.device) / self.network.num_sub_prompts - if len(x.size()) != 4: + if len(x.size()) == 3: mask = torch.reshape(mask, (1, -1, 1)) return mask diff --git a/requirements.txt b/requirements.txt index 279de350c..e99775b8a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,8 +6,10 @@ ftfy==6.1.1 opencv-python==4.7.0.68 einops==0.7.0 pytorch-lightning==1.9.0 -# bitsandbytes==0.39.1 -tensorboard==2.10.1 +bitsandbytes==0.43.0 +prodigyopt==1.0 +lion-pytorch==0.0.6 +tensorboard safetensors==0.4.2 # gradio==3.16.2 altair==4.2.2 @@ -15,6 +17,8 @@ easygui==0.98.3 toml==0.10.2 voluptuous==0.13.1 huggingface-hub==0.20.1 +# for Image utils +imagesize==1.4.1 # for BLIP captioning # requests==2.28.2 # timm==0.6.12 @@ -22,13 +26,16 @@ huggingface-hub==0.20.1 # for WD14 captioning (tensorflow) # tensorflow==2.10.1 # for WD14 captioning (onnx) -# onnx==1.14.1 -# onnxruntime-gpu==1.16.0 -# onnxruntime==1.16.0 +# onnx==1.15.0 +# onnxruntime-gpu==1.17.1 +# onnxruntime==1.17.1 +# for cuda 12.1(default 11.8) +# onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ + # this is for onnx: # protobuf==3.20.3 # open clip for SDXL -open-clip-torch==2.20.0 +# open-clip-torch==2.20.0 # For logging rich==13.7.0 # for kohya_ss library diff --git a/sdxl_minimal_inference.py b/sdxl_minimal_inference.py index 084735665..a1e93b7f0 100644 --- a/sdxl_minimal_inference.py +++ b/sdxl_minimal_inference.py @@ -11,20 +11,24 @@ import torch from library.device_utils import init_ipex, get_preferred_device + init_ipex() from tqdm import tqdm from transformers import CLIPTokenizer from diffusers import EulerDiscreteScheduler from PIL import Image -import open_clip + +# import open_clip from safetensors.torch import load_file from library import model_util, sdxl_model_util import networks.lora as lora from library.utils import setup_logging + setup_logging() import logging + logger = logging.getLogger(__name__) # scheduler: このあたりの設定はSD1/2と同じでいいらしい @@ -154,12 +158,13 @@ def get_timestep_embedding(x, outdim): text_model2.eval() unet.set_use_memory_efficient_attention(True, False) - if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える + if torch.__version__ >= "2.0.0": # PyTorch 2.0.0 以上対応のxformersなら以下が使える vae.set_use_memory_efficient_attention_xformers(True) # Tokenizers tokenizer1 = CLIPTokenizer.from_pretrained(text_encoder_1_name) - tokenizer2 = lambda x: open_clip.tokenize(x, context_length=77) + # tokenizer2 = lambda x: open_clip.tokenize(x, context_length=77) + tokenizer2 = CLIPTokenizer.from_pretrained(text_encoder_2_name) # LoRA for weights_file in args.lora_weights: @@ -192,7 +197,9 @@ def generate_image(prompt, prompt2, negative_prompt, seed=None): emb3 = get_timestep_embedding(torch.FloatTensor([target_height, target_width]).unsqueeze(0), 256) # logger.info("emb1", emb1.shape) c_vector = torch.cat([emb1, emb2, emb3], dim=1).to(DEVICE, dtype=DTYPE) - uc_vector = c_vector.clone().to(DEVICE, dtype=DTYPE) # ちょっとここ正しいかどうかわからない I'm not sure if this is right + uc_vector = c_vector.clone().to( + DEVICE, dtype=DTYPE + ) # ちょっとここ正しいかどうかわからない I'm not sure if this is right # crossattn @@ -215,13 +222,22 @@ def call_text_encoder(text, text2): # text_embedding = pipe.text_encoder.text_model.final_layer_norm(text_embedding) # layer normは通さないらしい # text encoder 2 - with torch.no_grad(): - tokens = tokenizer2(text2).to(DEVICE) + # tokens = tokenizer2(text2).to(DEVICE) + tokens = tokenizer2( + text, + truncation=True, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(DEVICE) + with torch.no_grad(): enc_out = text_model2(tokens, output_hidden_states=True, return_dict=True) text_embedding2_penu = enc_out["hidden_states"][-2] # logger.info("hidden_states2", text_embedding2_penu.shape) - text_embedding2_pool = enc_out["text_embeds"] # do not support Textual Inversion + text_embedding2_pool = enc_out["text_embeds"] # do not support Textual Inversion # 連結して終了 concat and finish text_embedding = torch.cat([text_embedding1, text_embedding2_penu], dim=2) diff --git a/sdxl_train.py b/sdxl_train.py index e0df263d6..46d7860be 100644 --- a/sdxl_train.py +++ b/sdxl_train.py @@ -11,11 +11,13 @@ import torch from library.device_utils import init_ipex, clean_memory_on_device + + init_ipex() from accelerate.utils import set_seed from diffusers import DDPMScheduler -from library import sdxl_model_util +from library import deepspeed_utils, sdxl_model_util import library.train_util as train_util @@ -39,6 +41,7 @@ scale_v_prediction_loss_like_noise_prediction, add_v_prediction_like_loss, apply_debiased_estimation, + apply_masked_loss, ) from library.sdxl_original_unet import SdxlUNet2DConditionModel @@ -97,6 +100,7 @@ def train(args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) sdxl_train_util.verify_sdxl_training_args(args) + deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) assert ( @@ -124,7 +128,7 @@ def train(args): # データセットを準備する if args.dataset_class is None: - blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, True)) + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) if args.dataset_config is not None: logger.info(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) @@ -398,18 +402,33 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): text_encoder1.to(weight_dtype) 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) + # freeze last layer and final_layer_norm in te1 since we use the output of the penultimate layer 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) - optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) + if args.deepspeed: + ds_model = deepspeed_utils.prepare_deepspeed_model( + args, + unet=unet if train_unet else None, + text_encoder1=text_encoder1 if train_text_encoder1 else None, + text_encoder2=text_encoder2 if train_text_encoder2 else None, + ) + # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007 + ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + ds_model, optimizer, train_dataloader, lr_scheduler + ) + training_models = [ds_model] + + else: + # accelerator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: + text_encoder1 = accelerator.prepare(text_encoder1) + if train_text_encoder2: + text_encoder2 = accelerator.prepare(text_encoder2) + optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) # 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: @@ -424,6 +443,8 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする if args.full_fp16: + # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do. + # -> But we think it's ok to patch accelerator even if deepspeed is enabled. train_util.patch_accelerator_for_fp16_training(accelerator) # resumeする @@ -561,7 +582,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype @@ -576,9 +597,12 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): or args.scale_v_pred_loss_like_noise_pred or args.v_pred_like_loss or args.debiased_estimation_loss + or args.masked_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 = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) + if args.masked_loss: + loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) if args.min_snr_gamma: @@ -592,7 +616,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): loss = loss.mean() # mean over batch dimension else: - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c) accelerator.backward(loss) if accelerator.sync_gradients and args.max_grad_norm != 0.0: @@ -712,7 +736,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): accelerator.end_training() - if args.save_state: # and is_main_process: + if args.save_state or args.save_state_on_train_end: train_util.save_state_on_train_end(args, accelerator) del accelerator # この後メモリを使うのでこれは消す @@ -744,6 +768,8 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, True, True, True) train_util.add_training_arguments(parser, False) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_sd_saving_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) @@ -779,7 +805,6 @@ def setup_parser() -> argparse.ArgumentParser: help=f"learning rates for each block of U-Net, comma-separated, {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / " + f"U-Netの各ブロックの学習率、カンマ区切り、{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値", ) - return parser @@ -787,6 +812,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args) diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py index 1e5f92349..f89c3628f 100644 --- a/sdxl_train_control_net_lllite.py +++ b/sdxl_train_control_net_lllite.py @@ -22,7 +22,7 @@ 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 +from library import deepspeed_utils, sai_model_spec, sdxl_model_util, sdxl_original_unet, sdxl_train_util import library.model_util as model_util import library.train_util as train_util @@ -394,10 +394,10 @@ def remove_model(old_ckpt_name): with accelerator.accumulate(unet): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 - latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample() + latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype) # NaNが含まれていれば警告を表示し0に置き換える if torch.any(torch.isnan(latents)): @@ -439,7 +439,7 @@ def remove_model(old_ckpt_name): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype @@ -458,7 +458,7 @@ def remove_model(old_ckpt_name): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -549,7 +549,7 @@ def remove_model(old_ckpt_name): accelerator.end_training() - if is_main_process and args.save_state: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) if is_main_process: @@ -566,6 +566,7 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, False, True, True) train_util.add_training_arguments(parser, False) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser) @@ -611,6 +612,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args) diff --git a/sdxl_train_control_net_lllite_old.py b/sdxl_train_control_net_lllite_old.py index dac56eedd..e85e978c1 100644 --- a/sdxl_train_control_net_lllite_old.py +++ b/sdxl_train_control_net_lllite_old.py @@ -18,7 +18,7 @@ from accelerate.utils import set_seed 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 +from library import deepspeed_utils, sai_model_spec, sdxl_model_util, sdxl_original_unet, sdxl_train_util import library.model_util as model_util import library.train_util as train_util @@ -361,10 +361,10 @@ def remove_model(old_ckpt_name): with accelerator.accumulate(network): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 - latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample() + latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype) # NaNが含まれていれば警告を表示し0に置き換える if torch.any(torch.isnan(latents)): @@ -406,7 +406,7 @@ def remove_model(old_ckpt_name): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype @@ -426,7 +426,7 @@ def remove_model(old_ckpt_name): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -534,6 +534,7 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, False, True, True) train_util.add_training_arguments(parser, False) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser) @@ -579,6 +580,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args) diff --git a/sdxl_train_network.py b/sdxl_train_network.py index d33239d92..83969bb1d 100644 --- a/sdxl_train_network.py +++ b/sdxl_train_network.py @@ -178,6 +178,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) trainer = SdxlNetworkTrainer() diff --git a/sdxl_train_textual_inversion.py b/sdxl_train_textual_inversion.py index b9a948bb2..5df739e28 100644 --- a/sdxl_train_textual_inversion.py +++ b/sdxl_train_textual_inversion.py @@ -7,7 +7,6 @@ from library.device_utils import init_ipex init_ipex() -import open_clip from library import sdxl_model_util, sdxl_train_util, train_util import train_textual_inversion @@ -132,6 +131,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) trainer = SdxlTextualInversionTrainer() diff --git a/train_controlnet.py b/train_controlnet.py index dc73a91c8..f4c94e8d9 100644 --- a/train_controlnet.py +++ b/train_controlnet.py @@ -11,6 +11,7 @@ from tqdm import tqdm import torch +from library import deepspeed_utils from library.device_utils import init_ipex, clean_memory_on_device init_ipex() @@ -396,7 +397,7 @@ def remove_model(old_ckpt_name): with accelerator.accumulate(controlnet): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample() @@ -419,13 +420,8 @@ def remove_model(old_ckpt_name): ) # Sample a random timestep for each image - timesteps = torch.randint( - 0, - noise_scheduler.config.num_train_timesteps, - (b_size,), - device=latents.device, - ) - timesteps = timesteps.long() + timesteps, huber_c = train_util.get_timesteps_and_huber_c(args, 0, noise_scheduler.config.num_train_timesteps, noise_scheduler, b_size, latents.device) + # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) @@ -456,7 +452,7 @@ def remove_model(old_ckpt_name): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -565,7 +561,7 @@ def remove_model(old_ckpt_name): accelerator.end_training() - if is_main_process and args.save_state: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) # del accelerator # この後メモリを使うのでこれは消す→printで使うので消さずにおく @@ -584,6 +580,7 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, False, True, True) train_util.add_training_arguments(parser, False) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser) @@ -615,6 +612,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args) diff --git a/train_db.py b/train_db.py index 8d36097a5..1de504ed8 100644 --- a/train_db.py +++ b/train_db.py @@ -11,7 +11,10 @@ from tqdm import tqdm import torch +from library import deepspeed_utils from library.device_utils import init_ipex, clean_memory_on_device + + init_ipex() from accelerate.utils import set_seed @@ -32,6 +35,7 @@ apply_noise_offset, scale_v_prediction_loss_like_noise_prediction, apply_debiased_estimation, + apply_masked_loss, ) from library.utils import setup_logging, add_logging_arguments @@ -46,6 +50,7 @@ def train(args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, False) + deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) cache_latents = args.cache_latents @@ -57,7 +62,7 @@ def train(args): # データセットを準備する if args.dataset_class is None: - blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, False, True)) + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, args.masked_loss, True)) if args.dataset_config is not None: logger.info(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) @@ -219,12 +224,25 @@ def train(args): text_encoder.to(weight_dtype) # acceleratorがなんかよろしくやってくれるらしい - if train_text_encoder: - unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, text_encoder, optimizer, train_dataloader, lr_scheduler + if args.deepspeed: + if args.train_text_encoder: + ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet, text_encoder=text_encoder) + else: + ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet) + ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + ds_model, optimizer, train_dataloader, lr_scheduler ) + training_models = [ds_model] + else: - unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) + if train_text_encoder: + unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, text_encoder, optimizer, train_dataloader, lr_scheduler + ) + training_models = [unet, text_encoder] + else: + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) + training_models = [unet] if not train_text_encoder: text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error @@ -296,12 +314,14 @@ def train(args): if not args.gradient_checkpointing: text_encoder.train(False) text_encoder.requires_grad_(False) + if len(training_models) == 2: + training_models = training_models[0] # remove text_encoder from training_models - with accelerator.accumulate(unet): + with accelerator.accumulate(*training_models): with torch.no_grad(): # latentに変換 if cache_latents: - latents = batch["latents"].to(accelerator.device) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample() latents = latents * 0.18215 @@ -326,7 +346,7 @@ def train(args): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) # Predict the noise residual with accelerator.autocast(): @@ -338,7 +358,9 @@ def train(args): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) + if args.masked_loss: + loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -444,7 +466,7 @@ def train(args): accelerator.end_training() - if args.save_state and is_main_process: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) del accelerator # この後メモリを使うのでこれは消す @@ -464,6 +486,8 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, True, False, True) train_util.add_training_arguments(parser, True) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_sd_saving_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) @@ -499,6 +523,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args) diff --git a/train_network.py b/train_network.py index e0fa69458..c99d37247 100644 --- a/train_network.py +++ b/train_network.py @@ -13,18 +13,15 @@ import torch from library.device_utils import init_ipex, clean_memory_on_device -init_ipex() -from torch.nn.parallel import DistributedDataParallel as DDP +init_ipex() from accelerate.utils import set_seed from diffusers import DDPMScheduler -from library import model_util +from library import deepspeed_utils, model_util import library.train_util as train_util -from library.train_util import ( - DreamBoothDataset, -) +from library.train_util import DreamBoothDataset import library.config_util as config_util from library.config_util import ( ConfigSanitizer, @@ -39,6 +36,7 @@ scale_v_prediction_loss_like_noise_prediction, add_v_prediction_like_loss, apply_debiased_estimation, + apply_masked_loss, ) from library.utils import setup_logging, add_logging_arguments @@ -141,6 +139,7 @@ def train(self, args): training_started_at = time.time() train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) + deepspeed_utils.prepare_deepspeed_args(args) setup_logging(args, reset=True) cache_latents = args.cache_latents @@ -157,7 +156,7 @@ def train(self, args): # データセットを準備する if args.dataset_class is None: - blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, True)) + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) if use_user_config: logger.info(f"Loading dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) @@ -413,20 +412,36 @@ def train(self, args): t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) # acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good - if train_unet: - unet = accelerator.prepare(unet) + if args.deepspeed: + ds_model = deepspeed_utils.prepare_deepspeed_model( + args, + unet=unet if train_unet else None, + text_encoder1=text_encoders[0] if train_text_encoder else None, + text_encoder2=text_encoders[1] if train_text_encoder and len(text_encoders) > 1 else None, + network=network, + ) + ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + ds_model, optimizer, train_dataloader, lr_scheduler + ) + training_model = ds_model else: - unet.to(accelerator.device, dtype=unet_weight_dtype) # move to device because unet is not prepared by accelerator - if train_text_encoder: - if len(text_encoders) > 1: - text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders] + if train_unet: + unet = accelerator.prepare(unet) else: - text_encoder = accelerator.prepare(text_encoder) - text_encoders = [text_encoder] - else: - pass # if text_encoder is not trained, no need to prepare. and device and dtype are already set + unet.to(accelerator.device, dtype=unet_weight_dtype) # move to device because unet is not prepared by accelerator + if train_text_encoder: + if len(text_encoders) > 1: + text_encoder = text_encoders = [accelerator.prepare(t_enc) for t_enc in text_encoders] + else: + text_encoder = accelerator.prepare(text_encoder) + text_encoders = [text_encoder] + else: + pass # if text_encoder is not trained, no need to prepare. and device and dtype are already set - 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 + ) + training_model = network if args.gradient_checkpointing: # according to TI example in Diffusers, train is required @@ -456,6 +471,31 @@ def train(self, args): if args.full_fp16: train_util.patch_accelerator_for_fp16_training(accelerator) + # before resuming make hook for saving/loading to save/load the network weights only + def save_model_hook(models, weights, output_dir): + # pop weights of other models than network to save only network weights + if accelerator.is_main_process: + remove_indices = [] + for i, model in enumerate(models): + if not isinstance(model, type(accelerator.unwrap_model(network))): + remove_indices.append(i) + for i in reversed(remove_indices): + weights.pop(i) + # print(f"save model hook: {len(weights)} weights will be saved") + + def load_model_hook(models, input_dir): + # remove models except network + remove_indices = [] + for i, model in enumerate(models): + if not isinstance(model, type(accelerator.unwrap_model(network))): + remove_indices.append(i) + for i in reversed(remove_indices): + models.pop(i) + # print(f"load model hook: {len(models)} models will be loaded") + + accelerator.register_save_state_pre_hook(save_model_hook) + accelerator.register_load_state_pre_hook(load_model_hook) + # resumeする train_util.resume_from_local_or_hf_if_specified(accelerator, args) @@ -529,6 +569,11 @@ def train(self, args): "ss_scale_weight_norms": args.scale_weight_norms, "ss_ip_noise_gamma": args.ip_noise_gamma, "ss_debiased_estimation": bool(args.debiased_estimation_loss), + "ss_noise_offset_random_strength": args.noise_offset_random_strength, + "ss_ip_noise_gamma_random_strength": args.ip_noise_gamma_random_strength, + "ss_loss_type": args.loss_type, + "ss_huber_schedule": args.huber_schedule, + "ss_huber_c": args.huber_c, } if use_user_config: @@ -564,6 +609,11 @@ def train(self, args): "random_crop": bool(subset.random_crop), "shuffle_caption": bool(subset.shuffle_caption), "keep_tokens": subset.keep_tokens, + "keep_tokens_separator": subset.keep_tokens_separator, + "secondary_separator": subset.secondary_separator, + "enable_wildcard": bool(subset.enable_wildcard), + "caption_prefix": subset.caption_prefix, + "caption_suffix": subset.caption_suffix, } image_dir_or_metadata_file = None @@ -753,21 +803,21 @@ def remove_model(old_ckpt_name): for step, batch in enumerate(train_dataloader): current_step.value = global_step - with accelerator.accumulate(network): + with accelerator.accumulate(training_model): on_step_start(text_encoder, unet) - with torch.no_grad(): - if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) - else: + if "latents" in batch and batch["latents"] is not None: + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) + else: + with torch.no_grad(): # latentに変換 - latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample() + latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype) # NaNが含まれていれば警告を表示し0に置き換える if torch.any(torch.isnan(latents)): accelerator.print("NaN found in latents, replacing with zeros") latents = torch.nan_to_num(latents, 0, out=latents) - latents = latents * self.vae_scale_factor + latents = latents * self.vae_scale_factor # get multiplier for each sample if network_has_multiplier: @@ -798,7 +848,7 @@ def remove_model(old_ckpt_name): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps( + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps( args, noise_scheduler, latents ) @@ -828,7 +878,11 @@ def remove_model(old_ckpt_name): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss( + noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c + ) + if args.masked_loss: + loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -935,7 +989,7 @@ def remove_model(old_ckpt_name): accelerator.end_training() - if is_main_process and args.save_state: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) if is_main_process: @@ -952,6 +1006,8 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, True, True, True) train_util.add_training_arguments(parser, True) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser) @@ -1052,6 +1108,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) trainer = NetworkTrainer() diff --git a/train_textual_inversion.py b/train_textual_inversion.py index df1d8485a..10fce2677 100644 --- a/train_textual_inversion.py +++ b/train_textual_inversion.py @@ -8,12 +8,14 @@ import torch from library.device_utils import init_ipex, clean_memory_on_device + + init_ipex() from accelerate.utils import set_seed from diffusers import DDPMScheduler from transformers import CLIPTokenizer -from library import model_util +from library import deepspeed_utils, model_util import library.train_util as train_util import library.huggingface_util as huggingface_util @@ -29,6 +31,7 @@ scale_v_prediction_loss_like_noise_prediction, add_v_prediction_like_loss, apply_debiased_estimation, + apply_masked_loss, ) from library.utils import setup_logging, add_logging_arguments @@ -268,7 +271,7 @@ def train(self, args): # データセットを準備する if args.dataset_class is None: - blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, False)) + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, False)) if args.dataset_config is not None: accelerator.print(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) @@ -558,10 +561,10 @@ def remove_model(old_ckpt_name): with accelerator.accumulate(text_encoders[0]): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 - latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample() + latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype) latents = latents * self.vae_scale_factor # Get the text embedding for conditioning @@ -569,7 +572,7 @@ def remove_model(old_ckpt_name): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps( + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps( args, noise_scheduler, latents ) @@ -585,7 +588,9 @@ def remove_model(old_ckpt_name): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) + if args.masked_loss: + loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -732,7 +737,7 @@ def remove_model(old_ckpt_name): accelerator.end_training() - if args.save_state and is_main_process: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) if is_main_process: @@ -749,6 +754,8 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, True, True, False) train_util.add_training_arguments(parser, True) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser, False) @@ -799,6 +806,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) trainer = TextualInversionTrainer() diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py index 695fad2a8..ddd03d532 100644 --- a/train_textual_inversion_XTI.py +++ b/train_textual_inversion_XTI.py @@ -8,7 +8,9 @@ from tqdm import tqdm import torch +from library import deepspeed_utils from library.device_utils import init_ipex, clean_memory_on_device + init_ipex() from accelerate.utils import set_seed @@ -31,6 +33,7 @@ apply_noise_offset, scale_v_prediction_loss_like_noise_prediction, apply_debiased_estimation, + apply_masked_loss, ) import library.original_unet as original_unet from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI @@ -200,7 +203,7 @@ def train(args): logger.info(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}") # データセットを準備する - blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, False)) + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, False)) if args.dataset_config is not None: logger.info(f"Load dataset config from {args.dataset_config}") user_config = config_util.load_user_config(args.dataset_config) @@ -439,7 +442,7 @@ def remove_model(old_ckpt_name): with accelerator.accumulate(text_encoder): with torch.no_grad(): if "latents" in batch and batch["latents"] is not None: - latents = batch["latents"].to(accelerator.device) + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) else: # latentに変換 latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample() @@ -458,7 +461,7 @@ def remove_model(old_ckpt_name): # Sample noise, sample a random timestep for each image, and add noise to the latents, # with noise offset and/or multires noise if specified - noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents) # Predict the noise residual with accelerator.autocast(): @@ -470,7 +473,9 @@ def remove_model(old_ckpt_name): else: target = noise - loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") + loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c) + if args.masked_loss: + loss = apply_masked_loss(loss, batch) loss = loss.mean([1, 2, 3]) loss_weights = batch["loss_weights"] # 各sampleごとのweight @@ -586,7 +591,7 @@ def remove_model(old_ckpt_name): accelerator.end_training() - if args.save_state and is_main_process: + if is_main_process and (args.save_state or args.save_state_on_train_end): train_util.save_state_on_train_end(args, accelerator) updated_embs = text_encoder.get_input_embeddings().weight[token_ids_XTI].data.detach().clone() @@ -662,6 +667,8 @@ def setup_parser() -> argparse.ArgumentParser: train_util.add_sd_models_arguments(parser) train_util.add_dataset_arguments(parser, True, True, False) train_util.add_training_arguments(parser, True) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) train_util.add_optimizer_arguments(parser) config_util.add_config_arguments(parser) custom_train_functions.add_custom_train_arguments(parser, False) @@ -707,6 +714,7 @@ def setup_parser() -> argparse.ArgumentParser: parser = setup_parser() args = parser.parse_args() + train_util.verify_command_line_training_args(args) args = train_util.read_config_from_file(args, parser) train(args)