Upload 34 files

Fooocus-API/.dockerignore

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+__pycache__
+.DS_Store
+*.ckpt
+*.safetensors
+*.pth
+*.pt
+*.bin
+*.patch
+*.backup
+*.corrupted
+sorted_styles.json
+/language/default.json
+lena.png
+lena_result.png
+lena_test.py
+config.txt
+config_modification_tutorial.txt
+user_path_config.txt
+user_path_config-deprecated.txt
+build_chb.py
+experiment.py
+/modules/*.png
+/repositories
+/venv
+/tmp
+/ui-config.json
+/outputs
+/config.json
+/log
+/webui.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/webui-user.bat
+/webui-user.sh
+/interrogate
+/user.css
+/.idea
+/notification.ogg
+/notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*
+/auth.json

Fooocus-API/.github/workflows/docker-image.yml

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+name: Docker Image CI
+
+on:
+  push:
+    tags:
+      - v*
+
+jobs:
+
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+      -
+        name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+      -
+        name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      -
+        name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      -
+        name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          push: true
+          tags: konieshadow/fooocus-api:latest,konieshadow/fooocus-api:${{ github.ref_name }}

Fooocus-API/.gitignore

@@ -0,0 +1,56 @@
+__pycache__
+.DS_Store
+*.ckpt
+*.safetensors
+*.pth
+*.pt
+*.bin
+*.patch
+*.backup
+*.corrupted
+sorted_styles.json
+/language/default.json
+lena.png
+lena_result.png
+lena_test.py
+config.txt
+config_modification_tutorial.txt
+user_path_config.txt
+user_path_config-deprecated.txt
+build_chb.py
+experiment.py
+/modules/*.png
+/repositories
+/venv
+/tmp
+/ui-config.json
+/outputs
+/config.json
+/log
+/webui.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/webui-user.bat
+/webui-user.sh
+/interrogate
+/user.css
+/.idea
+/notification.ogg
+/notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*
+/auth.json
+.cog/
+/presets
+*.db

Fooocus-API/Dockerfile

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+FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04
+
+ARG DEBIAN_FRONTEND=noninteractive
+ENV TZ=Asia/Shanghai
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends -y python3 python3-pip python3-virtualenv && \
+    apt-get install --no-install-recommends -y libopencv-dev python3-opencv && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV VIRTUAL_ENV=/opt/venv
+RUN virtualenv $VIRTUAL_ENV
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+RUN pip install packaging
+
+WORKDIR /app
+
+COPY . /app/
+
+CMD python3 main.py --host 0.0.0.0 --port 8888

Fooocus-API/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+free software which everyone can redistribute and change under these terms.
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
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+
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+<https://www.gnu.org/licenses/>.
+
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+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

Fooocus-API/README.md

@@ -0,0 +1,234 @@
+[![Docker Image CI](https://github.com/konieshadow/Fooocus-API/actions/workflows/docker-image.yml/badge.svg?branch=main)](https://github.com/konieshadow/Fooocus-API/actions/workflows/docker-image.yml)
+
+[ English | [中文](/README_zh.md) ]
+
+- [Introduction](#introduction)
+  - [Fooocus](#fooocus)
+  - [Fooocus-API](#fooocus-api)
+- [Get-Start](#get-start)
+  - [Run with Replicate](#run-with-replicate)
+  - [Self hosted](#self-hosted)
+    - [conda](#conda)
+    - [venv](#venv)
+    - [predownload and install](#predownload-and-install)
+    - [already exist Fooocus](#already-exist-fooocus)
+  - [Start with docker](#start-with-docker)
+- [cmd flags](#cmd-flags)
+- [Change log](#change-log)
+- [Apis](#apis)
+- [License](#license)
+- [Thanks :purple\_heart:](#thanks-purple_heart)
+
+
+# Introduction
+
+FastAPI powered API for [Fooocus](https://github.com/lllyasviel/Fooocus).
+
+Currently loaded Fooocus version: [2.1.860](https://github.com/lllyasviel/Fooocus/blob/main/update_log.md).
+
+## Fooocus
+
+This part from [Fooocus](https://github.com/lllyasviel/Fooocus) project.
+
+Fooocus is an image generating software (based on [Gradio](https://www.gradio.app/)).
+
+Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs:
+
+- Learned from Stable Diffusion, the software is offline, open source, and free.
+
+- Learned from Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images.
+
+Fooocus has included and automated lots of inner optimizations and quality improvements. Users can forget all those difficult technical parameters, and just enjoy the interaction between human and computer to "explore new mediums of thought and expanding the imaginative powers of the human species"
+
+## Fooocus-API
+
+I think you must have tried to use [Gradio client](https://www.gradio.app/docs/client) to call Fooocus, which was a terrible experience for me. 
+
+Fooocus API uses [FastAPI](https://fastapi.tiangolo.com/)  provides the `REST` API for using Fooocus. Now, you can use Fooocus's powerful ability in any language you like. 
+
+In addition, we also provide detailed [documentation](/docs/api_doc_en.md) and [sample code](/examples)
+
+# Get-Start
+
+## Run with Replicate
+
+Now you can use Fooocus-API by Replicate, the model is on [konieshadow/fooocus-api](https://replicate.com/konieshadow/fooocus-api).
+
+With preset:
+
+- [konieshadow/fooocus-api-anime](https://replicate.com/konieshadow/fooocus-api-anime)
+- [konieshadow/fooocus-api-realistic](https://replicate.com/konieshadow/fooocus-api-realistic)
+
+I believe this is the easiest way to generate image with Fooocus's power.
+
+## Self hosted
+
+You need python version >= 3.10, or use conda to create a new env.
+
+The hardware requirements are what Fooocus needs. You can find detail [here](https://github.com/lllyasviel/Fooocus#minimal-requirement)
+
+### conda
+
+You can easily start app follow this step use conda:
+
+```shell
+conda env create -f environment.yaml
+conda activate fooocus-api
+```
+
+and then, run `python main.py` to start app, default, server is listening on `http://127.0.0.1:8888`
+
+> If you are running the project for the first time, you may have to wait for a while, during which time the program will complete the rest of the installation and download the necessary models. You can also do these steps manually, which I'll mention later.
+
+### venv
+
+Similar to using conda, create a virtual environment, and then start and wait for a while
+
+```powershell
+# windows
+python -m venv venv
+.\venv\Scripts\Activate
+```
+
+```shell
+# linux
+python -m venv venv
+source venv/bin/activate
+```
+and then, run `python main.py`
+
+### predownload and install
+
+If you want to deal with environmental problems manually and download the model in advance, you can refer to the following steps
+
+After creating a complete environment using conda or venv, you can manually complete the installation of the subsequent environment, just follow
+
+first, install requirements `pip install -r requirements.txt`
+
+then, pytorch with cuda `pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121` , you can find more info about this [here](https://pytorch.org/get-started/previous-versions/),
+
+> It is important to note that for pytorch and cuda versions, the recommended version of Fooocus is used, which is currently pytorch2.1.0+cuda12.1. If you insist, you can also use other versions, but you need to add `--skip-pip` when you start app, otherwise the recommended version will be installed automatically
+
+next, make a dir named `repositories` and clone `https://github.com/lllyasviel/Fooocus` in to it. You must be use `git clone` but not download zip. If you have an existing Fooocus, please see [here](#already-exist-fooocus)
+
+last, you can download models and put it into `repositories\Fooocus\models`
+
+here is a list need to download for startup (for different [startup params](#cmd-flags) maybe difference):
+
+- checkpoint:  path to `repositories\Fooocus\models\checkpoints`
+    + [juggernautXL_version6Rundiffusion.safetensors](https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors)
+
+- vae_approx: path to `repositories\Fooocus\models\vae_approx`
+    + [xlvaeapp.pth](https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth')
+    + [vaeapp_sd15.pth](https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt)
+    + [xl-to-v1_interposer-v3.1.safetensors](https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors)
+
+- lora: path to `repositories\Fooocus\models\loras`
+    + [sd_xl_offset_example-lora_1.0.safetensors](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/raw/main/sd_xl_offset_example-lora_1.0.safetensors)
+
+> I've uploaded the model I'm using, which contains almost all the base models that Fooocus will use! I put it [here](https://www.123pan.com/s/dF5A-SIQsh.html) 提取码: `D4Mk`
+
+### already exist Fooocus
+
+If you already have Fooocus installed, and it is work well, The recommended way is to reuse models, you just simple copy `config.txt` file from your local Fooocus folder to Fooocus-API's root folder. See [Customization](https://github.com/lllyasviel/Fooocus#customization) for details.
+
+Use this method you will have both Fooocus and Fooocus-API running at the same time. And they operate independently and do not interfere with each other.
+
+> It is not recommended to copy an existing Fooocus installation directly to the repositories directory. If you insist on doing this, please make sure that the Fooocus directory is a Git repository, otherwise the program will not start properly
+
+## Start with docker
+
+Before use docker with GPU, you should [install NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) first.
+
+Run
+
+```shell
+docker run -d --gpus=all \
+    -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
+    -e NVIDIA_VISIBLE_DEVICES=all \
+    -p 8888:8888 konieshadow/fooocus-api
+```
+
+For a more complex usage:
+
+```shell
+mkdir ~/repositories
+mkdir -p ~/.cache/pip
+
+docker run -d --gpus=all \
+    -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
+    -e NVIDIA_VISIBLE_DEVICES=all \
+    -v ~/repositories:/app/repositories \
+    -v ~/.cache/pip:/root/.cache/pip \
+    -p 8888:8888 konieshadow/fooocus-api
+```
+
+It will persistent the dependent repositories and pip cache.
+
+You can add `-e PIP_INDEX_URL={pypi-mirror-url}` to docker run command to change pip index url.
+
+# cmd flags
+
+- `-h, --help` show this help message and exit
+- `--port PORT` Set the listen port, default: 8888
+- `--host HOST` Set the listen host, default: 127.0.0.1
+- `--base-url BASE_URL` Set base url for outside visit, default is http://host:port
+- `--log-level LOG_LEVEL` Log info for Uvicorn, default: info
+- `--sync-repo SYNC_REPO` Sync dependent git repositories to local, 'skip' for skip sync action, 'only' for only do the sync action and not launch app
+- `--skip-pip` Skip automatic pip install when setup
+- `--preload-pipeline` Preload pipeline before start http server
+- `--queue-size QUEUE_SIZE` Working queue size, default: 3, generation requests exceeding working queue size will return failure
+- `--queue-history QUEUE_HISTORY` Finished jobs reserve size, tasks exceeding the limit will be deleted, including output image files, default: 0, means no limit
+- `--webhook-url WEBHOOK_URL` Webhook url for notify generation result, default: None
+- `--presistent` Store history to db
+
+Since v0.3.25, added CMD flags support of Fooocus. You can pass any argument which Fooocus supported.
+
+For example, to startup image generation (need more vRAM):
+
+```
+python main.py --all-in-fp16 --always-gpu
+```
+
+For Fooocus CMD flags, see [here](https://github.com/lllyasviel/Fooocus?tab=readme-ov-file#all-cmd-flags).
+
+
+# Change log
+
+**[24/01/10] v0.3.29** : support for store history to db
+
+**[24/01/09] v0.3.29** : Image Prompt Mixing requirements implemented, With this implementation, you can send image prompts, and perform inpainting or upscaling with a single request.
+
+**[24/01/04] v0.3.29** : Merged Fooocus v2.1.860
+
+**[24/01/03] v0.3.28** : add text-to-image-with-ip interface
+
+**[23/12/29] v0.3.27** : Add describe interface，now you can get prompt from image
+
+**[23/12/29] v0.3.27** : Add query job hitory api. Add webhook_url support for each generation request.
+
+**[23/12/28] v0.3.26** : **Break Change**: Add web-hook cmd flag for notify generation result. Change async job id to uuid to avoid conflict between each startup.
+
+**[23/12/22] v0.3.25** : Add CMD flags support of Fooocus. **Break Change**: Removed cli argument `disable-private-log`. You can use Fooocus's `--disable-image-log` for the same purpose.
+
+**[23/12/19] v0.3.24** : Merge for Fooocus v2.1.852. This version merged Fooocus v2.1.839, which include a seed breaking change. Details for [2.1.839](https://github.com/lllyasviel/Fooocus/blob/main/update_log.md#21839).
+
+**[23/12/14] v0.3.23** : Merge for Fooocus v2.1.837.
+
+**[23/11/30] v0.3.22** : Add upscale custom support. You can pass param `upscale_value` for upsacle api to override upscale value.
+
+**[23/11/28] v0.3.21** : Add custom size support for outpaint. Thanks to [freek99](https://github.com/freek99).  Delete output files when exceeding task queue history limit.  Remove restrictions on input resolution. Now you can use any combination of `width*height` for `aspect_ratios_selection`. Change type of `seed` field from generation result to String to avoid numerical overflow.
+
+older change history you can find in [release page](https://github.com/konieshadow/Fooocus-API/releases)
+
+
+# Apis
+
+you can find all api detail [here](/docs/api_doc_en.md)
+
+# License
+
+
+# Thanks :purple_heart:
+
+Thanks for all your contributions and efforts towards improving the Fooocus API. We thank you for being part of our :sparkles: community :sparkles:!

Fooocus-API/README_zh.md

@@ -0,0 +1,234 @@
+[![Docker Image CI](https://github.com/konieshadow/Fooocus-API/actions/workflows/docker-image.yml/badge.svg?branch=main)](https://github.com/konieshadow/Fooocus-API/actions/workflows/docker-image.yml)
+
+[ [English](/README.md) | 中文 ]
+
+- [简介](#简介)
+  - [Fooocus](#fooocus)
+  - [Fooocus-API](#fooocus-api)
+- [开始](#开始)
+  - [在 Replicate 上运行](#在-replicate-上运行)
+  - [自托管](#自托管)
+    - [conda](#conda)
+    - [venv](#venv)
+    - [预下载及安装](#预下载及安装)
+    - [已经有安装好的 Fooocus](#已经有安装好的-fooocus)
+  - [使用Docker启动](#使用docker启动)
+- [命令行参数](#命令行参数)
+- [更新日志](#更新日志)
+- [Apis](#apis)
+- [License](#license)
+- [感谢 :purple\_heart:](#感谢-purple_heart)
+
+
+# 简介
+
+使用 FastAPI 构建的 [Fooocus](https://github.com/lllyasviel/Fooocus) 的 API。
+
+当前支持的 Fooocus 版本: [2.1.860](https://github.com/lllyasviel/Fooocus/blob/main/update_log.md)。
+
+## Fooocus
+
+该部分出自 [Fooocus](https://github.com/lllyasviel/Fooocus) 项目。
+
+Fooocus 是一个图像生成软件 (基于 [Gradio](https://www.gradio.app/))。
+
+Fooocus 是对于 Stable Diffusion 和 Midjourney 的重新思考以及设计：
+
+- 我们学习了 Stable Diffusion 的开源、免费、离线运行。
+
+- 我们学习了 Midjourney 的专注，不需要手动调整，专注于描述词以及图像。
+
+Fooocus 包含了许多内部优化以及质量改进。 忘记那些复杂困难的技术参数，享受人机交互带来的想象力的突破以及探索新的思维
+
+## Fooocus-API
+
+可能你已经尝试过使用 [Gradio client](https://www.gradio.app/docs/client) 来调用 Fooocus，对我来说可真是不咋地
+
+Fooocus API 使用 [FastAPI](https://fastapi.tiangolo.com/)  构建了一系列 `REST` API 来使用 Fooocus。现在，你可以用任何你喜欢的编程语言来调用 Fooocus 的强大能力。
+
+此外，我们还提供了详细的 [文档](/docs/api_doc_zh.md) 和 [示例代码](/examples)
+
+# 开始
+
+## 在 Replicate 上运行
+
+现在你可以在 Replicate 上使用 Fooocus-API，在这儿： [konieshadow/fooocus-api](https://replicate.com/konieshadow/fooocus-api).
+
+使用预先调整参数的:
+
+- [konieshadow/fooocus-api-anime](https://replicate.com/konieshadow/fooocus-api-anime)
+- [konieshadow/fooocus-api-realistic](https://replicate.com/konieshadow/fooocus-api-realistic)
+
+我认为这是更简单的体验 Fooocus's 强大的方法
+
+## 自托管
+
+需要 Python >= 3.10，或者使用 conda、venv 创建一个新的环境
+
+硬件需求来源于 Fooocus。 详细要求可以看[这里](https://github.com/lllyasviel/Fooocus#minimal-requirement)
+
+### conda
+
+按照下面的步骤启动一个 app：
+
+```shell
+conda env create -f environment.yaml
+conda activate fooocus-api
+```
+
+然后，执行 `python main.py` 启动 app ，默认情况下会监听在 `http://127.0.0.1:8888`
+
+> 如果是第一次运行，程序会自动处理完成剩余的环境配置、模型下载等工作，因此会等待一段时间。也可以预先配置好环境、下载模型，后面会提到。
+
+### venv
+
+和使用 conda 差不多，创建虚拟环境，启动 app ，等待程序完成环境安装、模型下载
+
+```powershell
+# windows
+python -m venv venv
+.\venv\Scripts\Activate
+```
+
+```shell
+# linux
+python -m venv venv
+source venv/bin/activate
+```
+然后执行 `python main.py`
+
+### 预下载及安装
+
+如果想要手动配置环境以及放置模型，可以参考下面的步骤
+
+在创建完 conda 或者 venv 环境之后，按照下面的步骤手动配置环境、下载模型
+
+首先，安装 requirements： `pip install -r requirements.txt`
+
+然后安装 pytorch+cuda： `pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121` 更多安装信息在[这儿](https://pytorch.org/get-started/previous-versions/),
+
+> 关于 pytorch 和 cuda 的版本，Fooocus API 使用的是 Fooocus 推荐的版本，目前是 pytorch2.1.0+cuda12.1。如果你是个"犟种"非要用其他版本，我测试过也是可以的，不过启动的时候记得加上 `--skip-pip`，否则程序会自动替换为推荐版本。
+
+然后创建一个名为 `repositories` 的目录，将 `https://github.com/lllyasviel/Fooocus` 克隆到其中。注意必须使用 `git clone`，`download zip`下载解压不包含Git信息，无法正常运行。如果你有一个已经安装完成的 Fooocus，查看[这里](#已经有安装好的-fooocus)
+
+最后，把下载的模型放到这个目录 `repositories\Fooocus\models`
+
+这里是一个启动必须下载的模型列表 (也可能不一样如果 [启动参数](#命令行参数) 不同的话):
+
+- checkpoint: 放到 `repositories\Fooocus\models\checkpoints`
+    + [juggernautXL_version6Rundiffusion.safetensors](https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors)
+
+- vae_approx: 放到 `repositories\Fooocus\models\vae_approx`
+    + [xlvaeapp.pth](https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth')
+    + [vaeapp_sd15.pth](https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt)
+    + [xl-to-v1_interposer-v3.1.safetensors](https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors)
+
+- lora: 放到 `repositories\Fooocus\models\loras`
+    + [sd_xl_offset_example-lora_1.0.safetensors](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/raw/main/sd_xl_offset_example-lora_1.0.safetensors)
+
+> 国内不好下的到 [这儿](https://www.123pan.com/s/dF5A-SIQsh.html)下载， 提取码: `D4Mk`
+
+### 已经有安装好的 Fooocus
+
+如果你已经有一个安装好的且运行正常的 Fooocus， 推荐的方式是复用模型, 只需要将 Fooocus 根目录下的 `config.txt` 文件复制到 Fooocus API 的根目录即可。 查看 [Customization](https://github.com/lllyasviel/Fooocus#customization) 获取更多细节.
+
+使用这种方法 Fooocus 和 Fooocus API 会同时存在，独立运行互不干扰。
+
+> 除非你能确保已安装的 Fooocus 目录是一个 Git 仓库，否则不推荐直接将其复制到 repositories 目录。
+
+## 使用Docker启动
+
+开始之前，先安装 [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)，这是 Docker 可以使用 GPU 的前提。
+
+运行
+
+```shell
+docker run -d --gpus=all \
+    -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
+    -e NVIDIA_VISIBLE_DEVICES=all \
+    -p 8888:8888 konieshadow/fooocus-api
+```
+
+一个更实用的例子:
+
+```shell
+mkdir ~/repositories
+mkdir -p ~/.cache/pip
+
+docker run -d --gpus=all \
+    -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
+    -e NVIDIA_VISIBLE_DEVICES=all \
+    -v ~/repositories:/app/repositories \
+    -v ~/.cache/pip:/root/.cache/pip \
+    -p 8888:8888 konieshadow/fooocus-api
+```
+
+这里把 `repositories` 和 `pip cache` 映射到了本地
+
+你还可以添加 `-e PIP_INDEX_URL={pypi-mirror-url}` 选项来更换 pip 源
+
+# 命令行参数
+
+- `-h, --help` 显示本帮助并退出
+- `--port PORT` 设置监听端口，默认：8888
+- `--host HOST` 设置监听地址，默认：127.0.0.1
+- `--base-url BASE_URL` 设置返回结果中的地址，默认是： http://host:port
+- `--log-level LOG_LEVEL` Uvicorn 中的日志等级，默认：info
+- `--sync-repo SYNC_REPO` 同步 Fooocus 仓库到本地，`skip` 用于在启动时跳过同步，`only` 只同步不启动程序
+- `--skip-pip` 跳过启动时的 pip 安装
+- `--preload-pipeline` 启动 http server 之前加载 pipeline
+- `--queue-size QUEUE_SIZE` 工作队列大小，默认是 3 ，超过队列的请求会返回失败
+- `--queue-history QUEUE_HISTORY` 保留的作业历史，默认 0 即无限制，超过会被删除，包括生成的图像
+- `--webhook-url WEBHOOK_URL` 通知生成结果的 webhook 地址，默认为 None
+- `--presistent` 持久化历史记录到SQLite数据库，默认关闭
+
+从 v0.3.25 开始, Fooocus 的命令行选项也被支持，你可以在启动时加上 Fooocus 支持的选项
+
+比如(需要更大的显存):
+
+```
+python main.py --all-in-fp16 --always-gpu
+```
+
+完成的 Fooocus 命令行选项可以在[这儿](https://github.com/lllyasviel/Fooocus?tab=readme-ov-file#all-cmd-flags)找到。
+
+
+# 更新日志
+
+**[24/01/10] v0.3.29** : 支持将历史生成数据持久化到数据库，并且支持从数据库中读取历史数据
+
+**[24/01/09] v0.3.29** : Image Prompt Mixing requirements implemented, With this implementation, you can send image prompts, and perform inpainting or upscaling with a single request.
+
+**[24/01/04] v0.3.29** : 合并了 Fooocus v2.1.860
+
+**[24/01/03] v0.3.28** : 增加 text-to-image-with-ip 接口
+
+**[23/12/29] v0.3.27** : 增加 describe 接口，现在你可以使用图像反推提示词了
+
+**[23/12/29] v0.3.27** : 增加查询历史 API。增加 webhook_url 对所有请求的支持
+
+**[23/12/28] v0.3.26** : **重大变更**: 添加 webhook 选项以支持生成完毕后的事件通知。将 async 的任务 ID 由数字改为 UUID 来避免应用重启后造成的混乱
+
+**[23/12/22] v0.3.25** : 增加对 Fooocus 命令行选项的支持 **重大变更**: 移除`disable-private-log` 选项，你可以使用 Fooocus 原生的 `--disable-image-log` 来达到同样的效果
+
+**[23/12/19] v0.3.24** : 该版本合并了 Fooocus v2.1.839, 包含一个对于 seed 的重大变更，详情参考：[2.1.839](https://github.com/lllyasviel/Fooocus/blob/main/update_log.md#21839).
+
+**[23/12/14] v0.3.23** : 合并 Fooocus v2.1.837.
+
+**[23/11/30] v0.3.22** : 支持自定义 upscale， 通过传递 `upscale_value` 给 upsacle api 来重写 upscale 值
+
+**[23/11/28] v0.3.21** : 增加 outpaint 自定义大小，感谢 [freek99](https://github.com/freek99) 提供的代码。当超出队列历史限制时，删除生成的图像。删除对输入分辨率的限制。现在你可以通过 `width*height` 给 `aspect_ratios_selection` 来指定任意分辨率。将 `seed` 字段的类型从 `generation result` 更改为字符串，以避免数字溢出。
+
+更早的日志可以在 [release page](https://github.com/konieshadow/Fooocus-API/releases) 找到
+
+
+# Apis
+
+你可以在[这里](/docs/api_doc_zh.md)找到所有的 API 细节
+
+# License
+
+
+# 感谢 :purple_heart:
+
+感谢所有为改进 Fooocus API 做出贡献和努力的人。再次感谢 :sparkles: 社区万岁 :sparkles:!

Fooocus-API/cog.yaml

@@ -0,0 +1,44 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+  cuda: "12.1"
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.11' or '3.11.4'
+  python_version: "3.11"
+
+  # a list of packages in the format <package-name>==<version>
+  python_packages:
+    - "torchsde==0.2.5"
+    - "einops==0.4.1"
+    - "transformers==4.30.2"
+    - "safetensors==0.3.1"
+    - "accelerate==0.21.0"
+    - "pyyaml==6.0"
+    - "Pillow==9.2.0"
+    - "scipy==1.9.3"
+    - "tqdm==4.64.1"
+    - "psutil==5.9.5"
+    - "pytorch_lightning==1.9.4"
+    - "omegaconf==2.2.3"
+    - "pygit2==1.12.2"
+    - "opencv-contrib-python==4.8.0.74"
+    - "torch==2.1.0"
+    - "torchvision==0.16.0"
+
+  # commands run after the environment is setup
+  # run:
+  #   - "echo env is ready!"
+  #   - "echo another command if needed"
+
+image: "r8.im/konieshadow/fooocus-api"
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

Fooocus-API/docs/api_doc_en.md

@@ -0,0 +1,971 @@
+- [Introduction](#introduction)
+- [Fooocus capability related interfaces](#fooocus-capability-related-interfaces)
+  - [text-to-image](#text-to-image)
+  - [image-upscale-vary](#image-upscale-vary)
+  - [image-inpaint-outpaint](#image-inpaint-outpaint)
+  - [image-prompt](#image-prompt)
+  - [text-to-image-with-imageprompt](#text-to-image-with-imageprompt)
+  - [describe](#describe)
+  - [all-models](#all-models)
+  - [refresh-models](#refresh-models)
+  - [styles](#styles)
+- [Fooocus API task related interfaces](#fooocus-api-task-related-interfaces)
+  - [job-queue](#job-queue)
+  - [query-job](#query-job)
+  - [job-history](#job-history)
+  - [stop](#stop)
+  - [ping](#ping)
+- [webhook](#webhook)
+- [public requests body](#public-requests-params)
+  - [AdvanceParams](#advanceparams)
+  - [lora](#lora)
+  - [response](#response)
+
+
+
+# Introduction
+
+Fooocus API are provided more than a dozen REST interfaces now, I roughly divide it into two categories, the first is the ability to call Fooocus, such as generating images, refreshing models, and so on, and the second is related to Fooocus API itself, mainly related to task queries. I will try to illustrate their role and usage and provide examples in the following content.
+
+> Almost all interface parameters have default values, which means you only need to send the parameters you are interested in. The complete parameters and default values can be viewed in the table.
+
+# Fooocus capability related interfaces
+
+## text-to-image
+
+Corresponding to the function of text to image in Fooocus
+
+**base info：**
+
+```yaml
+EndPoint: /v1/generation/text-to-image
+Method: Post
+DataType: json
+```
+**requests params：**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| prompt | string | prompt, default to empty string |
+| negative_prompt | string | negative_prompt |
+| style_selections | List[str] | list of style, must be supported style, you can get all supported [style](#styles) here |
+| performance_selection | Enum | performance_selection, must be one of `Speed`, `Quality`, `Extreme Speed` default to `Speed`|
+| aspect_ratios_selection | str | resolution, default to  `1152*896` |
+| image_number | int | the num of image to generate, default to 1 , max num is 32, note: Not a parallel interface |
+| image_seed | int | seed, default to -1, meant random |
+| sharpness | float | sharpness, default to 2.0 , 0-30 |
+| guidance_scale | float | guidance scale, default to 4.0 , 1-30 |
+| base_model_name | str | base model, default to `juggernautXL_version6Rundiffusion.safetensors` |
+| refiner_model_name | str | refiner model, default to `None` |
+| refiner_switch | float | refiner switch, default to 0.5 |
+| loras | List[Lora] | lora list, include conf, lora: [Lora](#lora) |
+| advanced_params | AdvacedParams | Adavanced params, [AdvancedParams](#advanceparams) |
+| require_base64 | bool | require base64, default to False |
+| async_process | bool | is async, default to False |
+| webhook_url | str | after async task completed, address for callback, default to None, refer to [webhook](#webhook) |
+
+**response params：**
+
+Most response have the same structure, but different parts will be specifically explained 
+
+This interface returns a universal response structure, refer to [response](#response)
+
+**request example：**
+
+```python
+host = "http://127.0.0.1:8888"
+
+def text2img(params: dict) -> dict:
+    """
+    text to image
+    """
+    result = requests.post(url=f"{host}/v1/generation/text-to-image",
+                           data=json.dumps(params),
+                           headers={"Content-Type": "application/json"})
+    return result.json()
+
+result =text2img({
+    "prompt": "1girl sitting on the ground",
+    "async_process": True})
+print(result)
+```
+
+## image-upscale-vary
+
+Corresponding to the function of Upscale or Variation in Fooocus
+
+the requests body for this interface based on [text-to-image](#text-to-image), so i will only list the difference with [text-to-image](#text-to-image)
+
+In addition, the interface provides two versions, and there is no functional difference between the two versions, mainly due to slight differences in request methods
+
+**base info：**
+
+```yaml
+EndPoint_V1: /v1/generation/image-upscale-vary
+EndPoint_V2: /v2/generation/image-upscale-vary
+Method: Post
+DataType: form|json
+```
+
+### V1
+
+**requests params**
+
+| Name | Type | Description             |
+| ---- | ---- |---------------------------|
+| input_image | string($binary) | binary imagge                                                                                                                             |
+| uov_method | Enum | 'Vary (Subtle)','Vary (Strong)','Upscale (1.5x)','Upscale (2x)','Upscale (Fast 2x)','Upscale (Custom)'                                    |
+| upscale_value | float | default to None , 1.0-5.0, magnification, only for uov_method is 'Upscale (Custom)'                                                       |
+| style_selections | List[str] | list Fooocus style seg with comma                                                                                                         |
+| loras | str(List[Lora]) | list for lora, with configure, lora: [Lora](#lora), example: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}] |
+| advanced_params | str(AdvacedParams) | AdvancedParams, AdvancedParams: [AdvancedParams](#advanceparams), send with str, None is available                                        |
+
+**response params：**
+
+This interface returns a universal response structure, refer to [response](#response)
+
+**requests example：**
+
+```python
+# headers should not contain {"Content-Type": "application/json"}
+
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def upscale_vary(image, params: dict) -> dict:
+    """
+    Upscale or Vary
+    """
+    response = requests.post(url=f"{host}/v1/generation/image-upscale-vary",
+                        data=params,
+                        files={"input_image": image})
+    return response.json()
+
+result =upscale_vary(image=image,
+                     params={
+                         "uov_method": "Upscale (2x)",
+                         "async_process": True
+                     })
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+### V2
+
+**requests params**
+
+| Name | Type | Description                                                                                                                           |
+| ---- | ---- |---------------------------------------------------------------------------------------------------------------------------------------|
+| uov_method | UpscaleOrVaryMethod | Enum type, value should one of 'Vary (Subtle)','Vary (Strong)','Upscale (1.5x)','Upscale (2x)','Upscale (Fast 2x)','Upscale (Custom)' |
+| upscale_value | float | default to None , 1.0-5.0, magnification, only for uov_method is 'Upscale (Custom)'                                                   |
+| input_image | str | input image, base64 str, or a URL                                                                                                               |
+
+**response params：**
+
+This interface returns a universal response structure, refer to [response](#response)
+
+**requests params：**
+
+```python
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def upscale_vary(image, params: dict) -> dict:
+    """
+    Upscale or Vary
+    """
+    params["input_image"] = base64.b64encode(image).decode('utf-8') 
+    response = requests.post(url=f"{host}/v2/generation/image-upscale-vary",
+                        data=json.dumps(params),
+                        headers={"Content-Type": "application/json"},
+                        timeout=300)
+    return response.json()
+
+result =upscale_vary(image=image,
+                     params={
+                         "uov_method": "Upscale (2x)",
+                         "async_process": True
+                     })
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## image-inpaint-outpaint
+
+**base info：**
+
+```yaml
+EndPoint_V1: /v1/generation/image-inpait-outpaint
+EndPoint_V2: /v2/generation/image-inpait-outpaint
+Method: Post
+DataType: form|json
+```
+
+### V1
+
+**requests params**
+
+| Name | Type | Description                                                                                                               |
+| ---- | ---- |---------------------------------------------------------------------------------------------------------------------------|
+| input_image | string($binary) | binary imagge                                                                                                             |
+| input_mask | string($binary) | binary imagge                                                                                                             |
+| inpaint_additional_prompt | string | additional_prompt                                                                                                         |
+| outpaint_selections | str | Image extension direction , 'Left', 'Right', 'Top', 'Bottom' seg with comma                                               |
+| outpaint_distance_left | int | Image extension distance, default to 0                                                                                    |
+| outpaint_distance_right | int | Image extension distance, default to 0                                                                                                             |
+| outpaint_distance_top | int | Image extension distance, default to 0                                                                                                             |
+| outpaint_distance_bottom | int | Image extension distance, default to 0                                                                                                             |
+| style_selections | List[str] | list Fooocus style seg with comma                                                                                                       |
+| loras | str(List[Lora]) | list for lora, with configure, lora: Lora, example: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}] |
+| advanced_params | str(AdvacedParams) | AdvancedParams, AdvancedParams: AdvancedParams, send with str, None is available                                                  |
+
+**response params：**
+
+This interface returns a universal response structure, refer to [response](#response)
+
+**requests example：**
+
+```python
+# example for inpaint outpaint v1
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def inpaint_outpaint(params: dict, input_image: bytes, input_mask: bytes = None) -> dict:
+    """
+    example for inpaint outpaint v1
+    """
+    response = requests.post(url=f"{host}/v1/generation/image-inpait-outpaint",
+                        data=params,
+                        files={"input_image": input_image,
+                               "input_mask": input_mask})
+    return response.json()
+
+# image extension example
+result = inpaint_outpaint(params={
+                            "outpaint_selections": "Left,Right",
+                            "async_process": True},
+                          input_image=image,
+                          input_mask=None)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# image inpaint example
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+result = inpaint_outpaint(params={
+                            "prompt": "a cat",
+                            "async_process": True},
+                          input_image=source,
+                          input_mask=mask)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+### V2
+
+**requests params**
+
+| Name | Type | Description                                                                     |
+| ---- | ---- |---------------------------------------------------------------------------------|
+| input_image | str | input image, base64 str, or a URL                                                         |
+| input_mask | str | input mask, base64 str, or a URL                                                          |
+| inpaint_additional_prompt | str | additional prompt                                                               |
+| outpaint_selections | List[OutpaintExpansion] | OutpaintExpansion is Enum, value shoule one of "Left", "Right", "Top", "Bottom" |
+| outpaint_distance_left | int | Image extension distance, default to 0                                                                    |
+| outpaint_distance_right | int | Image extension distance, default to 0                                                                    |
+| outpaint_distance_top | int | Image extension distance, default to 0                                                                    |
+| outpaint_distance_bottom | int | Image extension distance, default to 0                                                                    |
+
+**response params：**
+
+This interface returns a universal response structure, refer to [response](#response)[response params](#response)
+
+**requests example：**
+
+```python
+# example for inpaint outpaint v2
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def inpaint_outpaint(params: dict) -> dict:
+    """
+    example for inpaint outpaint v2
+    """
+    response = requests.post(url=f"{host}/v2/generation/image-inpait-outpaint",
+                        data=json.dumps(params),
+                        headers={"Content-Type": "application/json"})
+    return response.json()
+
+# image extension example
+result = inpaint_outpaint(params={
+                            "input_image": base64.b64encode(image).decode('utf-8'),
+                            "input_mask": None,
+                            "outpaint_selections": ["Left", "Right"],
+                            "async_process": True})
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# image inpaint example
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+result = inpaint_outpaint(params={
+                            "prompt": "a cat",
+                            "input_image": base64.b64encode(source).decode('utf-8'),
+                            "input_mask": base64.b64encode(mask).decode('utf-8'),
+                            "async_process": True})
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## image-prompt
+
+`v0.3.27` has a break change. Interface based on change to [inpaint-outpaint](#image-inpaint-outpaint)
+
+after v0.3.27, this interface implements the functions of `inpaint_outpaint` and `image-prompt`.
+
+>  Multi-function interface, which does not implement the functions of `inpaint_outpaint` and `image-prompt` at the same time in the same request
+
+**base info：**
+
+```yaml
+EndPoint_V1: /v1/generation/image-prompt
+EndPoint_V2: /v2/generation/image-prompt
+Method: Post
+DataType: form|json
+```
+
+### V1
+
+**requests params**
+
+| Name | Type | Description                    |
+| ---- | ---- |--------------------------------|
+| input_image | Bytes | binary image, use for inpaint    |
+| input_mask | Bytes | binary image mask, use for inpaint |
+| inpaint_additional_prompt | str | inpaint additional prompt |
+| outpaint_selections | str | Image extension direction , 'Left', 'Right', 'Top', 'Bottom' seg with comma |
+| outpaint_distance_left | int | Image extension distance, default to 0 |
+| outpaint_distance_right | int | Image extension distance, default to 0 |
+| outpaint_distance_top | int | Image extension distance, default to 0 |
+| outpaint_distance_bottom | int | Image extension distance, default to 0 |
+| cn_img1 | string($binary) | binary image                                                                                                                    |
+| cn_stop1 | float | default to 0.6                                                                                                                  |
+| cn_weight1 | float | default to 0.6                                                                                                                  |
+| cn_type1 | Emum | should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"                                                                             |
+| cn_img2 | string($binary) | binary image                                                                                                                    |
+| cn_stop2 | float | default to 0.6                                                                                                                  |
+| cn_weight2 | float | default to 0.6                                                                                                                  |
+| cn_type2 | Emum | should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"                                                                             |
+| cn_img3 | string($binary) | binary image                                                                                                                    |
+| cn_stop3 | float | default to 0.6                                                                                                                  |
+| cn_weight3 | float | default to 0.6                                                                                                                  |
+| cn_type3 | Emum | should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"                                                                             |
+| cn_img4 | string($binary) | binary image                                                                                                                    |
+| cn_stop4 | float | default to 0.6                                                                                                                  |
+| cn_weight4 | float | default to 0.6                                                                                                                  |
+| cn_type4 | Emum | should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS"                                                                |
+| style_selections | List[str] | list Fooocus style seg with comma                                                                                               |
+| loras | str(List[Lora]) | list for lora, with configure, lora: Lora, example: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}] |
+| advanced_params | str(AdvacedParams) | AdvancedParams, AdvancedParams: AdvancedParams, send with str, None is available                                                |
+
+**response params：**
+
+This interface returns a universal response structure, refer to [response](#response)[response params](#response)
+
+**requests example：**
+
+```python
+# image_prompt v1 example
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+
+def image_prompt(params: dict,
+                 input_iamge: bytes=None,
+                 input_mask: bytes=None,
+                 cn_img1: bytes=None,
+                 cn_img2: bytes=None,
+                 cn_img3: bytes=None,
+                 cn_img4: bytes=None,) -> dict:
+    """
+    image prompt
+    """
+    response = requests.post(url=f"{host}/v1/generation/image-prompt",
+                             data=params,
+                             files={
+                                 "input_image": input_iamge,
+                                 "input_mask": input_mask,
+                                 "cn_img1": cn_img1,
+                                 "cn_img2": cn_img2,
+                                 "cn_img3": cn_img3,
+                                 "cn_img4": cn_img4,
+                              })
+    return response.json()
+
+# image extend
+params = {
+    "outpaint_selections": ["Left", "Right"],
+    "image_prompts": [] # required, can be empty list
+}
+result = image_prompt(params=params, input_iamge=image)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# inpaint
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "image_prompts": [], # required, can be empty list
+    "async_process": True
+}
+result = image_prompt(params=params, input_iamge=source, input_mask=mask)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# image prompt
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "image_prompts": [
+        {
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        },{
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        }]
+    }
+result = image_prompt(params=params, cn_img1=image, cn_img2=source)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+### V2
+
+**requests params**
+
+| Name | Type | Description                                     |
+| ---- | ---- |-------------------------------------------------|
+| input_image | str | base64 image, or a URL, use for inpaint    |
+| input_mask | str | base64 image mask, or a URL, use for inpaint |
+| inpaint_additional_prompt | str | inpaint additional prompt |
+| outpaint_selections | List[] | Image extension direction , 'Left', 'Right', 'Top', 'Bottom' seg with comma |
+| outpaint_distance_left | int | Image extension distance, default to 0 |
+| outpaint_distance_right | int | Image extension distance, default to 0 |
+| outpaint_distance_top | int | Image extension distance, default to 0 |
+| outpaint_distance_bottom | int | Image extension distance, default to 0 |
+| image_prompts | List[ImagePrompt] | image list, include config, ImagePrompt struct： |
+
+**ImagePrompt**
+
+| Name | Type | Description                                                                         |
+| ---- | ---- |-------------------------------------------------------------------------------------|
+| cn_img | str | input image, base64 str, or a URL                                                             |
+| cn_stop | float | 0-1, default to 0.5                                                                 |
+| cn_weight | float | weight, 0-2, default to 1.0                                                         |
+| cn_type | ControlNetType | ControlNetType Enum, should one of "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS" |
+
+**response params：**
+
+This interface returns a universal response structure, refer to [response](#response)[response params](#response)
+
+**requests example：**
+
+```python
+# image_prompt v2 example
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+
+def image_prompt(params: dict) -> dict:
+    """
+    image prompt
+    """
+    response = requests.post(url=f"{host}/v2/generation/image-prompt",
+                             data=json.dumps(params),
+                             headers={"Content-Type": "application/json"})
+    return response.json()
+
+# image extend
+params = {
+    "input_image": base64.b64encode(image).decode('utf-8'),
+    "outpaint_selections": ["Left", "Right"],
+    "image_prompts": [] # required, can be empty list
+}
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# inpaint
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "input_image": base64.b64encode(source).decode('utf-8'),
+    "input_mask": base64.b64encode(mask).decode('utf-8'),
+    "image_prompts": [], # required, can be empty list
+    "async_process": True
+}
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# image prompt
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "image_prompts": [
+        {
+            "cn_img": base64.b64encode(source).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        },{
+            "cn_img": base64.b64encode(image).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        }]
+    }
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## text to image with imageprompt
+
+this interface only provides v2 version
+
+**base info:**
+
+```yaml
+EndPoint: /v2/generation/text-to-image-with-ip
+Method: Post
+DataType: json
+```
+
+**requests params**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| image_prompts | List[ImagePrompt] | Image list |
+
+**requests example**:
+
+```python
+# text to image with imageprompt example
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+source = open("./examples/imgs/s.jpg", "rb").read()
+def image_prompt(params: dict) -> dict:
+    """
+    image prompt
+    """
+    response = requests.post(url=f"{host}/v2/generation/text-to-image-with-ip",
+                             data=json.dumps(params),
+                             headers={"Content-Type": "application/json"})
+    return response.json()
+
+params = {
+    "prompt": "A bear",
+    "image_prompts": [
+        {
+            "cn_img": base64.b64encode(source).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        },{
+            "cn_img": base64.b64encode(image).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        }
+    ]
+}
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## describe
+
+**base info：**
+
+```yaml
+EndPoint: /v1/tools/describe-image
+Method: Post
+DataType: form
+```
+
+**requests params**
+
+| Name | Type | Description                              |
+|------|------|------------------------------------------|
+| type | Enum | type, should be one of "Photo", "Anime"  |
+
+**requests example**:
+
+```python
+def describe_image(image: bytes,
+                   params: dict = {"type": "Photo"}) -> dict:
+    """
+    describe-image
+    """
+    response = requests.post(url="http://127.0.0.1:8888/v1/tools/describe-image",
+                        files={
+                            "image": image
+                        },
+                        timeout=30)
+    return response.json()
+```
+
+**response example**:
+
+```python
+{
+  "describe": "a young woman posing with her hands behind her head"
+}
+```
+
+--------------------------------------------
+
+## all-models
+
+**base info：**
+
+```yaml
+EndPoint: /v1/engines/all-models
+Method: Get
+```
+
+**requests example**:
+
+```python
+def all_models() -> dict:
+    """
+    all-models
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/engines/all-models",
+                        timeout=30)
+    return response.json()
+```
+
+**response params**:
+
+```python
+{
+  "model_filenames": [
+    "juggernautXL_version6Rundiffusion.safetensors",
+    "sd_xl_base_1.0_0.9vae.safetensors",
+    "sd_xl_refiner_1.0_0.9vae.safetensors"
+  ],
+  "lora_filenames": [
+    "sd_xl_offset_example-lora_1.0.safetensors"
+  ]
+}
+```
+
+## refresh-models
+
+**base info：**
+
+```yaml
+EndPoint: /v1/engines/refresh-models
+Method: Post
+```
+
+**requests example**
+```python
+def refresh() -> dict:
+    """
+    refresh-models
+    """
+    response = requests.post(url="http://127.0.0.1:8888/v1/engines/refresh-models",
+                        timeout=30)
+    return response.json()
+```
+
+**response params**
+```python
+{
+  "model_filenames": [
+    "juggernautXL_version6Rundiffusion.safetensors",
+    "sd_xl_base_1.0_0.9vae.safetensors",
+    "sd_xl_refiner_1.0_0.9vae.safetensors"
+  ],
+  "lora_filenames": [
+    "sd_xl_offset_example-lora_1.0.safetensors"
+  ]
+}
+```
+
+## styles
+
+**base info：**
+
+```yaml
+EndPoint: /v1/engines/styles
+Method: Get
+```
+
+**requests example**:
+
+```python
+def styles() -> dict:
+    """
+    styles
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/engines/styles",
+                        timeout=30)
+    return response.json()
+```
+
+**response params**:
+
+```python
+[
+  "Fooocus V2",
+  "Fooocus Enhance",
+  ...
+  "Watercolor 2",
+  "Whimsical And Playful"
+]
+```
+
+# Fooocus API task related interfaces
+
+## job-queue
+
+**base info：**
+
+```yaml
+EndPoint: /v1/engines/job-queue
+Method: Get
+```
+
+**requests example**:
+
+```python
+def job_queue() -> dict:
+    """
+    job-queue
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/generation/job-queue",
+                        timeout=30)
+    return response.json()
+```
+
+**response params**:
+
+```python
+{
+  "running_size": 0,
+  "finished_size": 1,
+  "last_job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4"
+}
+```
+
+## query-job
+
+**base info：**
+
+```yaml
+EndPoint: /v1/generation/query-job
+Method: Get
+```
+
+**requests example**:
+```python
+def taskResult(task_id: str) -> dict:
+    # get task status
+    task_status = requests.get(url="http://127.0.0.1:8888/v1/generation/query-job",
+                               params={"job_id": task_id,
+                                       "require_step_preivew": False},
+                               timeout=30)
+
+    return task_status.json()
+```
+
+**response params**:
+```python
+{
+  "job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4",
+  "job_type": "Text to Image",
+  "job_stage": "SUCCESS",
+  "job_progress": 100,
+  "job_status": "Finished",
+  "job_step_preview": null,
+  "job_result": [
+    {
+      "base64": null,
+      "url": "http://127.0.0.1:8888/files/2023-11-27/b928e50e-3c09-4187-a3f9-1c12280bfd95.png",
+      "seed": 8228839561385006000,
+      "finish_reason": "SUCCESS"
+    }
+  ]
+}
+```
+
+## job-history
+
+**base info：**
+
+```yaml
+EndPoint: /v1/generation/job-history
+Method: get
+```
+
+**requests example**:
+
+```python
+def job-history() -> dict:
+    """
+    job-history
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/generation/job-history",
+                        timeout=30)
+    return response.json()
+```
+
+**response params**:
+
+```python
+{
+  "queue": [],
+  "history": [
+    "job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4",
+    "is_finished": True
+  ]
+}
+```
+
+## stop
+
+**base info：**
+
+```yaml
+EndPoint: /v1/generation/stop
+Method: post
+```
+
+**requests example**:
+
+```python
+def stop() -> dict:
+    """
+    stop
+    """
+    response = requests.post(url="http://127.0.0.1:8888/v1/generation/stop",
+                        timeout=30)
+    return response.json()
+```
+
+**response params**:
+
+```python
+{
+  "msg": "success"
+}
+```
+
+## ping
+
+**base info：**
+
+```yaml
+EndPoint: /ping
+Method: get
+```
+
+pong
+
+# webhook
+
+You can specify an address through '--webhook_url' on the command line so that you can receive notifications after asynchronous tasks are completed
+
+Here is a simple example to demonstrate how 'webhook' works
+
+First，start a simple server using the following code:
+
+```python
+from fastapi import FastAPI
+import uvicorn
+
+app = FastAPI()
+
+@app.post("/status")
+async def status(requests: dict):
+    print(requests)
+
+uvicorn.run(app, host="0.0.0.0", port=8000)
+```
+
+Then, start Fooocus API with `--webhook-url http://host:8000/status`
+
+Submit a task in any way, and after completion, you will see the task completion information in the background of this simple server：
+
+```python
+{'job_id': '717ec0b5-85df-4174-80d6-bddf93cd8248', 'job_result': [{'url': 'http://127.0.0.1:8888/files/2023-12-29/f1eca704-718e-4781-9d5f-82d41aa799d7.png', 'seed': '3283449865282320931'}]}
+```
+
+# public requests params
+
+## AdvanceParams
+
+| Name | Type | Description                                                                      |
+| ---- | ---- |----------------------------------------------------------------------------------|
+| disable_preview | bool | disable preview, default to False                                                |
+| adm_scaler_positive | float | ADM Guidance Scaler, default to 1.5, range 0.1-3.0                               |
+| adm_scaler_negative | float | negative ADM Guidance Scaler, default to 0.8, range 0.1-3.0                      |
+| adm_scaler_end | float | ADM Guidance Scaler end value, default to 0.5, range 0.0-1.0                     |
+| refiner_swap_method | str | refiner model swap method, default to `joint`                                    |
+| adaptive_cfg | float | CFG Mimicking from TSNR, default to 7.0, range 1.0-30.0                          |
+| sampler_name | str | sampler, default to `default_sampler`                                            |
+| scheduler_name | str | scheduler, default to `default_scheduler`                                        |
+| overwrite_step | int | Forced Overwrite of Sampling Step, default to -1, range -1-200                   |
+| overwrite_switch | int | Forced Overwrite of Refiner Switch Step, default to -1, range -1-200             |
+| overwrite_width | int | Forced Overwrite of Generating Width, default to -1, range -1-2048               |
+| overwrite_height | int | Forced Overwrite of Generating Height, default to -1, range -1-2048              |
+| overwrite_vary_strength | float | Forced Overwrite of Denoising Strength of "Vary", default to -1, range -1-1.0    |
+| overwrite_upscale_strength | float | Forced Overwrite of Denoising Strength of "Upscale", default to -1, range -1-1.0 |
+| mixing_image_prompt_and_vary_upscale | bool | Mixing Image Prompt and Vary/Upscale, default to False                           |
+| mixing_image_prompt_and_inpaint | bool | Mixing Image Prompt and Inpaint, default to False                                |
+| debugging_cn_preprocessor | bool | Debug Preprocessors, default to False                                            |
+| skipping_cn_preprocessor | bool | Skip Preprocessors, default to False                                             |
+| controlnet_softness | float | Softness of ControlNet, default to 0.25, range 0.0-1.0                           |
+| canny_low_threshold | int | Canny Low Threshold, default to 64, range 1-255                                  |
+| canny_high_threshold | int | Canny High Threshold, default to 128, range 1-255                                |
+| freeu_enabled | bool | FreeU enabled, default to False                                                  |
+| freeu_b1 | float | FreeU B1, default to 1.01                                                        |
+| freeu_b2 | float | FreeU B2, default to 1.02                                                        |
+| freeu_s1 | float | FreeU B3, default to 0.99                                                        |
+| freeu_s2 | float | FreeU B4, default to 0.95                                                        |
+| debugging_inpaint_preprocessor | bool | Debug Inpaint Preprocessing, default to False                                    |
+| inpaint_disable_initial_latent | bool | Disable initial latent in inpaint, default to False                              |
+| inpaint_engine | str | Inpaint Engine, default to `v1`                                                  |
+| inpaint_strength | float | Inpaint Denoising Strength, default to 1.0, range 0.0-1.0                        |
+| inpaint_respective_field | float | Inpaint Respective Field, default to 1.0, range 0.0-1.0                          |
+
+## lora
+
+| Name | Type | Description            |
+| ---- | ---- |------------------------|
+| model_name | str | model name             |
+| weight | float | weight, default to 0.5 |
+
+## response
+
+success response：
+
+**async_process: True**
+
+| Name | Type | Description  |
+| ---- | ---- |--------------|
+| job_id | int | job ID       |
+| job_type | str | job type     |
+| job_stage | str | job stage    |
+| job_progress | float | job progress |
+| job_status | str | job status   |
+| job_step_preview | str | job previes  |
+| job_result | str | job result   |
+
+**async_process: False**
+
+| Name | Type | Description                                                                      |
+| ---- | ---- |----------------------------------------------------------------------------------|
+| base64 | str | base64 image, according to `require_base64` params determines whether it is null |
+| url | str | result image url                                                                 |
+| seed | int | image seed                                                                       |
+| finish_reason | str | finish reason                                                                    |
+
+fail response：

Fooocus-API/docs/api_doc_zh.md

@@ -0,0 +1,973 @@
+- [简介](#简介)
+- [Fooocus 能力相关接口](#fooocus-能力相关接口)
+  - [文生图 | text-to-image](#文生图--text-to-image)
+  - [图像放大 | image-upscale-vary](#图像放大--image-upscale-vary)
+  - [局部重绘 | image-inpaint-outpaint](#局部重绘--image-inpaint-outpaint)
+  - [图生图 | image-prompt](#图生图--image-prompt)
+  - [text-to-image-with-imageprompt](#text-to-image-with-imageprompt)
+  - [图像反推 | describe](#图像反推--describe)
+  - [列出模型 | all-models](#列出模型--all-models)
+  - [刷新模型 | refresh-models](#刷新模型--refresh-models)
+  - [样式 | styles](#样式--styles)
+- [Fooocus API 任务相关接口](#fooocus-api-任务相关接口)
+  - [任务队列 | job-queue](#任务队列--job-queue)
+  - [查询任务 | query-job](#查询任务--query-job)
+  - [查询任务历史 | job-history](#查询任务历史--job-history)
+  - [停止任务 | stop](#停止任务--stop)
+  - [ping](#ping)
+- [webhook](#webhook)
+- [公共请求体](#公共请求体)
+  - [高级参数 | AdvanceParams](#高级参数--advanceparams)
+  - [lora](#lora)
+  - [响应参数 | response](#响应参数--response)
+
+
+
+# 简介
+
+Fooocus API 目前提供了十多个 REST 接口, 我大致将其分为两类, 第一类用来调用 Fooocus 的能力, 比如生成图像、刷新模型之类的, 第二类为 Fooocus API 自身相关的, 主要是任务查询相关。我会在接下来的内容中尝试说明它们的作用以及用法并提供示例。
+
+> 几乎所有的接口参数都有默认值，这意味着你只需要发送你感兴趣的参数即可。完整的参数以及默认值可以通过表格查看
+
+# Fooocus 能力相关接口
+
+## 文生图 | text-to-image
+
+对应 Fooocus 中的文生图功能
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/generation/text-to-image
+Method: Post
+DataType: json
+```
+**请求参数：**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| prompt | string | 描述词, 默认为空字符串 |
+| negative_prompt | string | 描述词, 反向描述词 |
+| style_selections | List[str] | 风格列表, 需要是受支持的风格, 可以通过 [样式接口](#样式--styles) 获取所有支持的样式 |
+| performance_selection | Enum | 性能选择, `Speed`, `Quality`, `Extreme Speed` 中的一个, 默认 `Speed`|
+| aspect_ratios_selection | str | 分辨率, 默认 '1152*896' |
+| image_number | int | 生成图片数量, 默认 1 , 最大32, 注: 非并行接口 |
+| image_seed | int | 图片种子, 默认 -1, 即随机生成 |
+| sharpness | float | 锐度, 默认 2.0 , 0-30 |
+| guidance_scale | float | 引导比例, 默认 4.0 , 1-30 |
+| base_model_name | str | 基础模型, 默认 `juggernautXL_version6Rundiffusion.safetensors` |
+| refiner_model_name | str | 优化模型, 默认 `None` |
+| refiner_switch | float | 优化模型切换时机, 默认 0.5 |
+| loras | List[Lora] | lora 模型列表, 包含配置, lora 结构: [Lora](#lora) |
+| advanced_params | AdvacedParams | 高级参数, AdvancedParams 结构 [AdvancedParams](#高级参数--advanceparams) |
+| require_base64 | bool | 是否返回base64编码, 默认 False |
+| async_process | bool | 是否异步处理, 默认 False |
+| webhook_url | str | 异步处理完成后, 触发的 webhook 地址, 参考[webhook](#webhook) |
+
+**响应参数：**
+
+多数响应结构式相同的, 不同的部分会进行特别说明.
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+host = "http://127.0.0.1:8888"
+
+def text2img(params: dict) -> dict:
+    """
+    文生图
+    """
+    result = requests.post(url=f"{host}/v1/generation/text-to-image",
+                           data=json.dumps(params),
+                           headers={"Content-Type": "application/json"})
+    return result.json()
+
+result =text2img({
+    "prompt": "1girl sitting on the ground",
+    "async_process": True})
+print(result)
+```
+
+## 图像放大 | image-upscale-vary
+
+该接口对应 Fooocus 中的 Upscale or Variation 功能
+
+该接口参数继承自[文生图](#文生图--text-to-image), 因此后面只会列出和[文生图](#文生图--text-to-image)请求参数差异部分
+
+此外, 该接口提供了两个版本, 两个版本并无功能上的差异, 主要是请求方式略有区别
+
+**基础信息：**
+
+```yaml
+EndPoint_V1: /v1/generation/image-upscale-vary
+EndPoint_V2: /v2/generation/image-upscale-vary
+Method: Post
+DataType: form|json
+```
+
+### V1
+
+**请求参数**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| input_image | string($binary) | 二进制 str 图像 |
+| uov_method | Enum | 'Vary (Subtle)','Vary (Strong)','Upscale (1.5x)','Upscale (2x)','Upscale (Fast 2x)','Upscale (Custom)' |
+| upscale_value | float | 默认为 None , 1.0-5.0, 放大倍数, 仅在 'Upscale (Custom)' 中有效 |
+| style_selections | List[str] | 以逗号分割的 Fooocus 风格列表 |
+| loras | str(List[Lora]) | lora 模型列表, 包含配置, lora 结���: [Lora](#lora), 比如: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}] |
+| advanced_params | str(AdvacedParams) | 高级参数, AdvancedParams 结构 [AdvancedParams](#高级参数--advanceparams), 以字符串形式发送, 可以为空 |
+
+**响应参数：**
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+# 不要加 {"Content-Type": "application/json"} 这个 header
+
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def upscale_vary(image, params: dict) -> dict:
+    """
+    Upscale or Vary
+    """
+    response = requests.post(url=f"{host}/v1/generation/image-upscale-vary",
+                        data=params,
+                        files={"input_image": image})
+    return response.json()
+
+result =upscale_vary(image=image,
+                     params={
+                         "uov_method": "Upscale (2x)",
+                         "async_process": True
+                     })
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+### V2
+
+**请求参数**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| uov_method | UpscaleOrVaryMethod | 是个枚举类型, 包括 'Vary (Subtle)','Vary (Strong)','Upscale (1.5x)','Upscale (2x)','Upscale (Fast 2x)','Upscale (Custom)' |
+| upscale_value | float | 默认为 None , 1.0-5.0, 放大倍数, 仅在 'Upscale (Custom)' 中有效 |
+| input_image | str | 输入图像, base64 格式, 或者一个URL |
+
+**响应参数：**
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def upscale_vary(image, params: dict) -> dict:
+    """
+    Upscale or Vary
+    """
+    params["input_image"] = base64.b64encode(image).decode('utf-8') 
+    response = requests.post(url=f"{host}/v2/generation/image-upscale-vary",
+                        data=json.dumps(params),
+                        headers={"Content-Type": "application/json"},
+                        timeout=300)
+    return response.json()
+
+result =upscale_vary(image=image,
+                     params={
+                         "uov_method": "Upscale (2x)",
+                         "async_process": True
+                     })
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## 局部重绘 | image-inpaint-outpaint
+
+**基础信息：**
+
+```yaml
+EndPoint_V1: /v1/generation/image-inpait-outpaint
+EndPoint_V2: /v2/generation/image-inpait-outpaint
+Method: Post
+DataType: form|json
+```
+
+### V1
+
+**请求参数**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| input_image | string($binary) | 二进制 str 图像 |
+| input_mask | string($binary) | 二进制 str 图像 |
+| inpaint_additional_prompt | string | 附加描述 |
+| outpaint_selections | str | 图像扩展方向, 逗号分割的 'Left', 'Right', 'Top', 'Bottom' |
+| outpaint_distance_left | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_right | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_top | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_bottom | int | 图像扩展距离, 默认 0 |
+| style_selections | List[str] | 以逗号分割的 Fooocus 风格列表 |
+| loras | str(List[Lora]) | lora 模型列表, 包含配置, lora 结构: [Lora](#lora), 比如: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}] |
+| advanced_params | str(AdvacedParams) | 高级参数, AdvancedParams 结构 [AdvancedParams](#高级参数--advanceparams), 以字符串形式发送 |
+
+**响应参数：**
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+# 局部重绘 v1 接口示例
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def inpaint_outpaint(params: dict, input_image: bytes, input_mask: bytes = None) -> dict:
+    """
+    局部重绘 v1 接口示例
+    """
+    response = requests.post(url=f"{host}/v1/generation/image-inpait-outpaint",
+                        data=params,
+                        files={"input_image": input_image,
+                               "input_mask": input_mask})
+    return response.json()
+
+# 图片扩展示例
+result = inpaint_outpaint(params={
+                            "outpaint_selections": "Left,Right",
+                            "async_process": True},
+                          input_image=image,
+                          input_mask=None)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# 局部重绘示例
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+result = inpaint_outpaint(params={
+                            "prompt": "a cat",
+                            "async_process": True},
+                          input_image=source,
+                          input_mask=mask)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+### V2
+
+**请求参数**
+
+| Name | Type | Description                                                     |
+| ---- | ---- |-----------------------------------------------------------------|
+| input_image | str | 输入图像, base64 格式, 或者一个URL                                                 |
+| input_mask | str | 输入遮罩, base64 格式, 或者一个URL                                                 |
+| inpaint_additional_prompt | str | 附加描述词                                                           |
+| outpaint_selections | List[OutpaintExpansion] | OutpaintExpansion 是一个枚举类型, 值包括 "Left", "Right", "Top", "Bottom" |
+| outpaint_distance_left | int | 图像扩展距离, 默认 0                                                    |
+| outpaint_distance_right | int | 图像扩展距离, 默认 0                                                    |
+| outpaint_distance_top | int | 图像扩展距离, 默认 0                                                    |
+| outpaint_distance_bottom | int | 图像扩展距离, 默认 0                                                    |
+
+**响应参数：**
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+# 局部重绘 v2 接口示例
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+
+def inpaint_outpaint(params: dict) -> dict:
+    """
+    局部重绘 v1 接口示例
+    """
+    response = requests.post(url=f"{host}/v2/generation/image-inpait-outpaint",
+                        data=json.dumps(params),
+                        headers={"Content-Type": "application/json"})
+    return response.json()
+
+# 图像扩展示例
+result = inpaint_outpaint(params={
+                            "input_image": base64.b64encode(image).decode('utf-8'),
+                            "input_mask": None,
+                            "outpaint_selections": ["Left", "Right"],
+                            "async_process": True})
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# 局部重绘示例
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+result = inpaint_outpaint(params={
+                            "prompt": "a cat",
+                            "input_image": base64.b64encode(source).decode('utf-8'),
+                            "input_mask": base64.b64encode(mask).decode('utf-8'),
+                            "async_process": True})
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## 图生图 | image-prompt
+
+该接口更新自 `v0.3.27` 后有重大更新。从继承自 [文生图](#文生图--text-to-image) 更改为继承自 [局部重绘](#局部重绘--image-inpaint-outpaint)
+
+该版本之后可以通过该接口实现 `inpaint_outpaint` 以及 `image-prompt` 接口的功能
+
+> 多功能接口，并非可以同时实现 `inpaint_outpaint` 以及 `image-prompt` 接口的功能
+
+**基础信息：**
+
+```yaml
+EndPoint_V1: /v1/generation/image-prompt
+EndPoint_V2: /v2/generation/image-prompt
+Method: Post
+DataType: form|json
+```
+
+### V1
+
+**请求参数**
+
+> 注意: 虽然接口更改为继承自[局部重绘](#局部重绘--image-inpaint-outpaint), 但下方表格展示的仍然继承自[文生图](#文生图--text-to-image), 但参数是完整的
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| input_image | Bytes | 二进制图像, 用于局部重绘 |
+| input_mask | Bytes | 二进制图像遮罩, 用于局部重绘 |
+| inpaint_additional_prompt | str | inpaint 附加提示词 |
+| outpaint_selections | str | 图像扩展选项, 逗号分割的 "Left", "Right", "Top", "Bottom" |
+| outpaint_distance_left | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_right | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_top | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_bottom | int | 图像扩展距离, 默认 0 |
+| cn_img1 | string($binary) | 二进制 str 图像 |
+| cn_stop1 | float | 默认 0.6 |
+| cn_weight1 | float | 默认 0.6 |
+| cn_type1 | Emum | "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS" 中的一个 |
+| cn_img2 | string($binary) | 二进制 str 图像 |
+| cn_stop2 | float | 默认 0.6 |
+| cn_weight2 | float | 默认 0.6 |
+| cn_type2 | Emum | "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS" 中的一个 |
+| cn_img3 | string($binary) | 二进制 str 图像 |
+| cn_stop3 | float | 默认 0.6 |
+| cn_weight3 | float | 默认 0.6 |
+| cn_type3 | Emum | "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS" 中的一个 |
+| cn_img4 | string($binary) | 二进制 str 图像 |
+| cn_stop4 | float | 默认 0.6 |
+| cn_weight4 | float | 默认 0.6 |
+| cn_type4 | Emum | "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS" 中的一个 |
+| style_selections | List[str] | 以逗号分割的 Fooocus 风格列表 |
+| loras | str(List[Lora]) | lora 模型列表, 包含配置, lora 结构: [Lora](#lora), 比如: [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}] |
+| advanced_params | str(AdvacedParams) | 高级参数, AdvancedParams 结构 [AdvancedParams](#高级参数--advanceparams), 以字符串形式发送 |
+
+**响应参数：**
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+# image_prompt v1 接口示例
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+
+def image_prompt(params: dict,
+                 input_iamge: bytes=None,
+                 input_mask: bytes=None,
+                 cn_img1: bytes=None,
+                 cn_img2: bytes=None,
+                 cn_img3: bytes=None,
+                 cn_img4: bytes=None,) -> dict:
+    """
+    image prompt
+    """
+    response = requests.post(url=f"{host}/v1/generation/image-prompt",
+                             data=params,
+                             files={
+                                 "input_image": input_iamge,
+                                 "input_mask": input_mask,
+                                 "cn_img1": cn_img1,
+                                 "cn_img2": cn_img2,
+                                 "cn_img3": cn_img3,
+                                 "cn_img4": cn_img4,
+                              })
+    return response.json()
+
+# 图像扩展
+params = {
+    "outpaint_selections": ["Left", "Right"],
+    "image_prompts": [] # 必传参数，可以为空列表
+}
+result = image_prompt(params=params, input_iamge=image)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# 局部重绘
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "image_prompts": [], # 必传参数，可以为空列表
+    "async_process": True
+}
+result = image_prompt(params=params, input_iamge=source, input_mask=mask)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# image prompt
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "image_prompts": [
+        {
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        },{
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        }]
+    }
+result = image_prompt(params=params, cn_img1=image, cn_img2=source)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+### V2
+
+**请求参数**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| input_image | str | base64 图像, 或者一个URL, 用于局部重绘 |
+| input_mask | str | base64 图像遮罩, 或者一个URL, 用于局部重绘 |
+| inpaint_additional_prompt | str | inpaint 附加提示词 |
+| outpaint_selections | List[OutpaintExpansion] | 图像扩展选项, 逗号分割的 "Left", "Right", "Top", "Bottom" |
+| outpaint_distance_left | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_right | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_top | int | 图像扩展距离, 默认 0 |
+| outpaint_distance_bottom | int | 图像扩展距离, 默认 0 |
+| image_prompts | List[ImagePrompt] | 图像列表, 包含配置, ImagePrompt 结构如下： |
+
+**ImagePrompt**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| cn_img | str | 输入图像, base64 编码, 或者一个URL |
+| cn_stop | float | 停止位置, 范围 0-1, 默认 0.5 |
+| cn_weight | float | 权重, 范围 0-2, 默认 1.0 |
+| cn_type | ControlNetType | 控制网络类型, 是一个枚举类型, 包括: "ImagePrompt", "FaceSwap", "PyraCanny", "CPDS" |
+
+**响应参数：**
+
+该接口返回通用响应结构, 参考[响应参数](#响应参数--response)
+
+**请求示例：**
+
+```python
+# image_prompt v2 接口示例
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+source = open("./examples/imgs/s.jpg", "rb").read()
+mask = open("./examples/imgs/m.png", "rb").read()
+
+def image_prompt(params: dict) -> dict:
+    """
+    image prompt
+    """
+    response = requests.post(url=f"{host}/v2/generation/image-prompt",
+                             data=json.dumps(params),
+                             headers={"Content-Type": "application/json"})
+    return response.json()
+
+# 图像扩展
+params = {
+    "input_image": base64.b64encode(image).decode('utf-8'),
+    "outpaint_selections": ["Left", "Right"],
+    "image_prompts": [] # 必传参数，可以为空列表
+}
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# 局部重绘
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "input_image": base64.b64encode(source).decode('utf-8'),
+    "input_mask": base64.b64encode(mask).decode('utf-8'),
+    "image_prompts": [], # 必传参数，可以为空列表
+    "async_process": True
+}
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+
+# image prompt
+
+params = {
+    "prompt": "1girl sitting on the chair",
+    "image_prompts": [
+        {
+            "cn_img": base64.b64encode(source).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        },{
+            "cn_img": base64.b64encode(image).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        }]
+    }
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## text to image with imageprompt
+
+该接口暂无 v1 版本
+
+**基础信息：**
+
+```yaml
+EndPoint: /v2/generation/text-to-image-with-ip
+Method: Post
+DataType: json
+```
+
+**请求参数**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| image_prompts | List[ImagePrompt] | 图像列表 |
+
+**请求示例**:
+
+```python
+# text to image with imageprompt 示例
+host = "http://127.0.0.1:8888"
+image = open("./examples/imgs/bear.jpg", "rb").read()
+source = open("./examples/imgs/s.jpg", "rb").read()
+def image_prompt(params: dict) -> dict:
+    """
+    image prompt
+    """
+    response = requests.post(url=f"{host}/v2/generation/text-to-image-with-ip",
+                             data=json.dumps(params),
+                             headers={"Content-Type": "application/json"})
+    return response.json()
+
+params = {
+    "prompt": "A bear",
+    "image_prompts": [
+        {
+            "cn_img": base64.b64encode(source).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        },{
+            "cn_img": base64.b64encode(image).decode('utf-8'),
+            "cn_stop": 0.6,
+            "cn_weight": 0.6,
+            "cn_type": "ImagePrompt"
+        }
+    ]
+}
+result = image_prompt(params)
+print(json.dumps(result, indent=4, ensure_ascii=False))
+```
+
+## 图像反推 | describe
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/tools/describe-image
+Method: Post
+DataType: form
+```
+
+**请求参数**
+
+| Name | Type | Description                 |
+|------|------|-----------------------------|
+| type | Enum | 反推类型, "Photo", "Anime" 中的一个 |
+
+**请求示例**:
+
+```python
+def describe_image(image: bytes,
+                   params: dict = {"type": "Photo"}) -> dict:
+    """
+    describe-image
+    """
+    response = requests.post(url="http://127.0.0.1:8888/v1/tools/describe-image",
+                        files={
+                            "image": image
+                        },
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**:
+
+```python
+{
+  "describe": "a young woman posing with her hands behind her head"
+}
+```
+
+--------------------------------------------
+
+## 列出模型 | all-models
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/engines/all-models
+Method: Get
+```
+
+**请求示例**:
+
+```python
+def all_models() -> dict:
+    """
+    all-models
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/engines/all-models",
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**:
+
+```python
+{
+  "model_filenames": [
+    "juggernautXL_version6Rundiffusion.safetensors",
+    "sd_xl_base_1.0_0.9vae.safetensors",
+    "sd_xl_refiner_1.0_0.9vae.safetensors"
+  ],
+  "lora_filenames": [
+    "sd_xl_offset_example-lora_1.0.safetensors"
+  ]
+}
+```
+
+## 刷新模型 | refresh-models
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/engines/refresh-models
+Method: Post
+```
+
+**请求示例**
+```python
+def refresh() -> dict:
+    """
+    refresh-models
+    """
+    response = requests.post(url="http://127.0.0.1:8888/v1/engines/refresh-models",
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**
+```python
+{
+  "model_filenames": [
+    "juggernautXL_version6Rundiffusion.safetensors",
+    "sd_xl_base_1.0_0.9vae.safetensors",
+    "sd_xl_refiner_1.0_0.9vae.safetensors"
+  ],
+  "lora_filenames": [
+    "sd_xl_offset_example-lora_1.0.safetensors"
+  ]
+}
+```
+
+## 样式 | styles
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/engines/styles
+Method: Get
+```
+
+**请求示例**:
+
+```python
+def styles() -> dict:
+    """
+    styles
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/engines/styles",
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**:
+
+```python
+[
+  "Fooocus V2",
+  "Fooocus Enhance",
+  ...
+  "Watercolor 2",
+  "Whimsical And Playful"
+]
+```
+
+# Fooocus API 任务相关接口
+
+## 任务队列 | job-queue
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/engines/job-queue
+Method: Get
+```
+
+**请求示例**:
+
+```python
+def job_queue() -> dict:
+    """
+    job-queue
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/generation/job-queue",
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**:
+
+```python
+{
+  "running_size": 0,
+  "finished_size": 1,
+  "last_job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4"
+}
+```
+
+## 查询任务 | query-job
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/generation/query-job
+Method: Get
+```
+
+**请求示例**:
+```python
+def taskResult(task_id: str) -> dict:
+    # 获取任务状态
+    task_status = requests.get(url="http://127.0.0.1:8888/v1/generation/query-job",
+                               params={"job_id": task_id,
+                                       "require_step_preivew": False},
+                               timeout=30)
+
+    return task_status.json()
+```
+
+**响应示例**:
+```python
+{
+  "job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4",
+  "job_type": "Text to Image",
+  "job_stage": "SUCCESS",
+  "job_progress": 100,
+  "job_status": "Finished",
+  "job_step_preview": null,
+  "job_result": [
+    {
+      "base64": null,
+      "url": "http://127.0.0.1:8888/files/2023-11-27/b928e50e-3c09-4187-a3f9-1c12280bfd95.png",
+      "seed": 8228839561385006000,
+      "finish_reason": "SUCCESS"
+    }
+  ]
+}
+```
+
+## 查询任务历史 | job-history
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/generation/job-history
+Method: get
+```
+
+**请求示例**:
+
+```python
+def job-history() -> dict:
+    """
+    job-history
+    """
+    response = requests.get(url="http://127.0.0.1:8888/v1/generation/job-history",
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**:
+
+```python
+{
+  "queue": [],
+  "history": [
+    "job_id": "cac3914a-926d-4b6f-a46a-83794a0ce1d4",
+    "is_finished": True
+  ]
+}
+```
+
+## 停止任务 | stop
+
+**基础信息：**
+
+```yaml
+EndPoint: /v1/generation/stop
+Method: post
+```
+
+**请求示例**:
+
+```python
+def stop() -> dict:
+    """
+    stop
+    """
+    response = requests.post(url="http://127.0.0.1:8888/v1/generation/stop",
+                        timeout=30)
+    return response.json()
+```
+
+**响应示例**:
+
+```python
+{
+  "msg": "success"
+}
+```
+
+## ping
+
+**基础信息：**
+
+```yaml
+EndPoint: /ping
+Method: get
+```
+
+pong
+
+# webhook
+
+你可以在命令行通过 `--webhook-url` 指定一个地址，以便异步任务完成之后可以收到通知
+
+下面是一个简单的示例来展示 `webhook` 是如何工作的
+
+首先，使用下面的代码启动一个简易服务器:
+
+```python
+from fastapi import FastAPI
+import uvicorn
+
+app = FastAPI()
+
+@app.post("/status")
+async def status(requests: dict):
+    print(requests)
+
+uvicorn.run(app, host="0.0.0.0", port=8000)
+```
+
+然后, 在启动 Fooocus API 时添加 `--webhook-url http://host:8000/status`
+
+通过任意方式提交一个任务, 等完成后你会在这个简易服务器的后台看到任务结束信息：
+
+```python
+{'job_id': '717ec0b5-85df-4174-80d6-bddf93cd8248', 'job_result': [{'url': 'http://127.0.0.1:8888/files/2023-12-29/f1eca704-718e-4781-9d5f-82d41aa799d7.png', 'seed': '3283449865282320931'}]}
+```
+
+# 公共请求体
+
+## 高级参数 | AdvanceParams
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| disable_preview | bool | 是否禁用预览, 默认 False |
+| adm_scaler_positive | float | 正 ADM Guidance Scaler, 默认 1.5, 范围 0.1-3.0 |
+| adm_scaler_negative | float | 负 ADM Guidance Scaler, 默认 0.8, 范围 0.1-3.0 |
+| adm_scaler_end | float | ADM Guidance Scaler 结束值, 默认 0.5, 范围 0.0-1.0 |
+| refiner_swap_method | str | 优化模型交换方法, 默认 `joint` |
+| adaptive_cfg | float | CFG Mimicking from TSNR, 默认 7.0, 范围 1.0-30.0 |
+| sampler_name | str | 采样器, 默认 `default_sampler` |
+| scheduler_name | str | 调度器, 默认 `default_scheduler` |
+| overwrite_step | int | Forced Overwrite of Sampling Step, 默认 -1, 范围 -1-200 |
+| overwrite_switch | int | Forced Overwrite of Refiner Switch Step, 默认 -1, 范围 -1-200 |
+| overwrite_width | int | Forced Overwrite of Generating Width, 默认 -1, 范围 -1-2048 |
+| overwrite_height | int | Forced Overwrite of Generating Height, 默认 -1, 范围 -1-2048 |
+| overwrite_vary_strength | float | Forced Overwrite of Denoising Strength of "Vary", 默认 -1, 范围 -1-1.0 |
+| overwrite_upscale_strength | float | Forced Overwrite of Denoising Strength of "Upscale", 默认 -1, 范围 -1-1.0 |
+| mixing_image_prompt_and_vary_upscale | bool | Mixing Image Prompt and Vary/Upscale, 默认 False |
+| mixing_image_prompt_and_inpaint | bool | Mixing Image Prompt and Inpaint, 默认 False |
+| debugging_cn_preprocessor | bool | Debug Preprocessors, 默认 False |
+| skipping_cn_preprocessor | bool | Skip Preprocessors, 默认 False |
+| controlnet_softness | float | Softness of ControlNet, 默认 0.25, 范围 0.0-1.0 |
+| canny_low_threshold | int | Canny Low Threshold, 默认 64, 范围 1-255 |
+| canny_high_threshold | int | Canny High Threshold, 默认 128, 范围 1-255 |
+| freeu_enabled | bool | FreeU enabled, 默认 False |
+| freeu_b1 | float | FreeU B1, 默认 1.01 |
+| freeu_b2 | float | FreeU B2, 默认 1.02 |
+| freeu_s1 | float | FreeU B3, 默认 0.99 |
+| freeu_s2 | float | FreeU B4, 默认 0.95 |
+| debugging_inpaint_preprocessor | bool | Debug Inpaint Preprocessing, 默认 False |
+| inpaint_disable_initial_latent | bool | Disable initial latent in inpaint, 默认 False |
+| inpaint_engine | str | Inpaint Engine, 默认 `v1` |
+| inpaint_strength | float | Inpaint Denoising Strength, 默认 1.0, 范围 0.0-1.0 |
+| inpaint_respective_field | float | Inpaint Respective Field, 默认 1.0, 范围 0.0-1.0 |
+
+## lora
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| model_name | str | 模型名称 |
+| weight | float | 权重, 默认 0.5 |
+
+## 响应参数 | response
+
+成功响应：
+
+**async_process: True**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| job_id | int | 任务ID |
+| job_type | str | 任务类型 |
+| job_stage | str | 任务阶段 |
+| job_progress | float | 任务进度 |
+| job_status | str | 任务状态 |
+| job_step_preview | str | 任务预览 |
+| job_result | str | 任务结果 |
+
+**async_process: False**
+
+| Name | Type | Description |
+| ---- | ---- | ----------- |
+| base64 | str | 图片base64编码, 根据 `require_base64` 参数决定是否为 null |
+| url | str | 图片url |
+| seed | int | 图片种子 |
+| finish_reason | str | 任务结束原因 |
+
+失败响应：

Fooocus-API/docs/openapi.json

@@ -0,0 +1 @@
+{"openapi":"3.1.0","info":{"title":"FastAPI","version":"0.1.0"},"paths":{"/":{"get":{"summary":"Home","operationId":"home__get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/ping":{"get":{"summary":"Ping","description":"Returns a simple 'pong' response","operationId":"ping_ping_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/v1/generation/text-to-image":{"post":{"summary":"Text2Img Generation","operationId":"text2img_generation_v1_generation_text_to_image_post","parameters":[{"name":"accept","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Parameter to overvide 'Accept' header, 'image/png' for output bytes","title":"Accept"},"description":"Parameter to overvide 'Accept' header, 'image/png' for output bytes"},{"name":"accept","in":"header","required":false,"schema":{"type":"string","title":"Accept"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/Text2ImgRequest"}}}},"responses":{"200":{"description":"PNG bytes if request's 'Accept' header is 'image/png', otherwise JSON","content":{"application/json":{"schema":{"anyOf":[{"type":"array","items":{"$ref":"#/components/schemas/GeneratedImageResult"}},{"$ref":"#/components/schemas/AsyncJobResponse"}],"title":"Response Text2Img Generation V1 Generation Text To Image Post"},"example":[{"base64":"...very long string...","seed":"1050625087","finish_reason":"SUCCESS"}]},"application/json async":{"example":{"job_id":1,"job_type":"Text to Image"}},"image/png":{"example":"PNG bytes, what did you expect?"}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/v2/generation/text-to-image-with-ip":{"post":{"summary":"Text To Img With Ip","operationId":"text_to_img_with_ip_v2_generation_text_to_image_with_ip_post","parameters":[{"name":"accept","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Parameter to overvide 'Accept' header, 'image/png' for output bytes","title":"Accept"},"description":"Parameter to overvide 'Accept' header, 'image/png' for output bytes"},{"name":"accept","in":"header","required":false,"schema":{"type":"string","title":"Accept"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/Text2ImgRequestWithPrompt"}}}},"responses":{"200":{"description":"PNG bytes if request's 'Accept' header is 'image/png', otherwise 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expect?"}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/v2/generation/image-upscale-vary":{"post":{"summary":"Img Upscale Or Vary V2","operationId":"img_upscale_or_vary_v2_v2_generation_image_upscale_vary_post","parameters":[{"name":"accept","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Parameter to overvide 'Accept' header, 'image/png' for output bytes","title":"Accept"},"description":"Parameter to overvide 'Accept' header, 'image/png' for output bytes"},{"name":"accept","in":"header","required":false,"schema":{"type":"string","title":"Accept"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImgUpscaleOrVaryRequestJson"}}}},"responses":{"200":{"description":"PNG bytes if request's 'Accept' header is 'image/png', otherwise 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Fooocus-API/environment.yaml

@@ -0,0 +1,7 @@
+name: fooocus-api
+channels:
+  - defaults
+dependencies:
+  - python=3.10
+  - pip=23.0
+  - packaging

Fooocus-API/examples/examples.ipynb

@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# text to image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "\n",
+    "# Vincent diagram example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "\n",
+    "def text2img(params: dict) -> dict:\n",
+    "    \"\"\"\n",
+    "    Vincentian picture\n",
+    "    \"\"\"\n",
+    "    result = requests.post(url=f\"{host}/v1/generation/text-to-image\",\n",
+    "                           data=json.dumps(params),\n",
+    "                           headers={\"Content-Type\": \"application/json\"})\n",
+    "    return result.json()\n",
+    "\n",
+    "result =text2img(\n",
+    "        {\"prompt\": \"1girl sitting on the ground\",\n",
+    "         \"async_process\": True}\n",
+    "    )\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# upscale or vary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "\n",
+    "\n",
+    "# upscale or vary v1 Interface example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "\n",
+    "def upscale_vary(image, params: dict) -> dict:\n",
+    "    \"\"\"\n",
+    "    Upscale or Vary\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v1/generation/image-upscale-vary\",\n",
+    "                        data=params,\n",
+    "                        files={\"input_image\": image})\n",
+    "    return response.json()\n",
+    "\n",
+    "result =upscale_vary(image=image,\n",
+    "                     params={\n",
+    "                         \"uov_method\": \"Upscale (2x)\",\n",
+    "                         \"async_process\": True\n",
+    "                     })\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import base64\n",
+    "\n",
+    "\n",
+    "# upscale or vary v2 Interface example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "\n",
+    "def upscale_vary(params: dict) -> dict:\n",
+    "    \"\"\"\n",
+    "    Upscale or Vary\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v2/generation/image-upscale-vary\",\n",
+    "                        data=json.dumps(params),\n",
+    "                        headers={\"Content-Type\": \"application/json\"},\n",
+    "                        timeout=300)\n",
+    "    return response.json()\n",
+    "\n",
+    "result =upscale_vary(params={\n",
+    "                         \"input_image\": base64.b64encode(image).decode('utf-8'),\n",
+    "                         \"uov_method\": \"Upscale (2x)\",\n",
+    "                         \"async_process\": True\n",
+    "                     })\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# inpaint or outpaint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "\n",
+    "# Partial redraw v1 interface example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "\n",
+    "def inpaint_outpaint(params: dict, input_image: bytes, input_mask: bytes = None) -> dict:\n",
+    "    \"\"\"\n",
+    "    Partial redraw v1 interface example\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v1/generation/image-inpait-outpaint\",\n",
+    "                        data=params,\n",
+    "                        files={\"input_image\": input_image,\n",
+    "                               \"input_mask\": input_mask})\n",
+    "    return response.json()\n",
+    "\n",
+    "\n",
+    "# Image extension example\n",
+    "result = inpaint_outpaint(params={\n",
+    "                            \"outpaint_selections\": \"Left,Right\",\n",
+    "                            \"async_process\": True},\n",
+    "                          input_image=image,\n",
+    "                          input_mask=None)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Partial redraw example\n",
+    "source = open(\"./imgs/s.jpg\", \"rb\").read()\n",
+    "mask = open(\"./imgs/m.png\", \"rb\").read()\n",
+    "result = inpaint_outpaint(params={\n",
+    "                            \"prompt\": \"a cat\",\n",
+    "                            \"async_process\": True},\n",
+    "                          input_image=source,\n",
+    "                          input_mask=mask)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import base64\n",
+    "\n",
+    "\n",
+    "# Partial redraw v2 interface example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "\n",
+    "def inpaint_outpaint(params: dict) -> dict:\n",
+    "    \"\"\"\n",
+    "    Partial redraw v2 interface example\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v2/generation/image-inpait-outpaint\",\n",
+    "                        data=json.dumps(params),\n",
+    "                        headers={\"Content-Type\": \"application/json\"})\n",
+    "    return response.json()\n",
+    "\n",
+    "# Image extension example\n",
+    "result = inpaint_outpaint(params={\n",
+    "                            \"input_image\": base64.b64encode(image).decode('utf-8'),\n",
+    "                            \"input_mask\": None,\n",
+    "                            \"outpaint_selections\": [\"Left\", \"Right\"],\n",
+    "                            \"async_process\": True})\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Partial redraw example\n",
+    "source = open(\"./imgs/s.jpg\", \"rb\").read()\n",
+    "mask = open(\"./imgs/m.png\", \"rb\").read()\n",
+    "result = inpaint_outpaint(params={\n",
+    "                            \"prompt\": \"a cat\",\n",
+    "                            \"input_image\": base64.b64encode(source).decode('utf-8'),\n",
+    "                            \"input_mask\": base64.b64encode(mask).decode('utf-8'),\n",
+    "                            \"async_process\": True})\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# image prompts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "\n",
+    "\n",
+    "# image_prompt v1 Interface example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "source = open(\"./imgs/s.jpg\", \"rb\").read()\n",
+    "mask = open(\"./imgs/m.png\", \"rb\").read()\n",
+    "\n",
+    "def image_prompt(params: dict,\n",
+    "                 input_iamge: bytes=None,\n",
+    "                 input_mask: bytes=None,\n",
+    "                 cn_img1: bytes=None,\n",
+    "                 cn_img2: bytes=None,\n",
+    "                 cn_img3: bytes=None,\n",
+    "                 cn_img4: bytes=None,) -> dict:\n",
+    "    \"\"\"\n",
+    "    image prompt\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v1/generation/image-prompt\",\n",
+    "                             data=params,\n",
+    "                             files={\n",
+    "                                 \"input_image\": input_iamge,\n",
+    "                                 \"input_mask\": input_mask,\n",
+    "                                 \"cn_img1\": cn_img1,\n",
+    "                                 \"cn_img2\": cn_img2,\n",
+    "                                 \"cn_img3\": cn_img3,\n",
+    "                                 \"cn_img4\": cn_img4,\n",
+    "                              })\n",
+    "    return response.json()\n",
+    "\n",
+    "# image extension\n",
+    "params = {\n",
+    "    \"outpaint_selections\": [\"Left\", \"Right\"],\n",
+    "    \"image_prompts\": [] # Required parameters, can be an empty list\n",
+    "}\n",
+    "result = image_prompt(params=params, input_iamge=image)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# partial redraw\n",
+    "\n",
+    "params = {\n",
+    "    \"prompt\": \"1girl sitting on the chair\",\n",
+    "    \"image_prompts\": [], # Required parameters, can be an empty list\n",
+    "    \"async_process\": True\n",
+    "}\n",
+    "result = image_prompt(params=params, input_iamge=source, input_mask=mask)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# image prompt\n",
+    "\n",
+    "params = {\n",
+    "    \"prompt\": \"1girl sitting on the chair\",\n",
+    "    \"image_prompts\": [\n",
+    "        {\n",
+    "            \"cn_stop\": 0.6,\n",
+    "            \"cn_weight\": 0.6,\n",
+    "            \"cn_type\": \"ImagePrompt\"\n",
+    "        },{\n",
+    "            \"cn_stop\": 0.6,\n",
+    "            \"cn_weight\": 0.6,\n",
+    "            \"cn_type\": \"ImagePrompt\"\n",
+    "        }]\n",
+    "    }\n",
+    "result = image_prompt(params=params, cn_img1=image, cn_img2=source)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import base64\n",
+    "\n",
+    "# image_prompt v2 Interface example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "source = open(\"./imgs/s.jpg\", \"rb\").read()\n",
+    "mask = open(\"./imgs/m.png\", \"rb\").read()\n",
+    "\n",
+    "def image_prompt(params: dict) -> dict:\n",
+    "    \"\"\"\n",
+    "    image prompt\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v2/generation/image-prompt\",\n",
+    "                             data=json.dumps(params),\n",
+    "                             headers={\"Content-Type\": \"application/json\"})\n",
+    "    return response.json()\n",
+    "\n",
+    "# image extension\n",
+    "params = {\n",
+    "    \"input_image\": base64.b64encode(image).decode('utf-8'),\n",
+    "    \"outpaint_selections\": [\"Left\", \"Right\"],\n",
+    "    \"image_prompts\": [] # Required parameters, can be an empty list\n",
+    "}\n",
+    "result = image_prompt(params)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# partial redraw\n",
+    "\n",
+    "params = {\n",
+    "    \"prompt\": \"1girl sitting on the chair\",\n",
+    "    \"input_image\": base64.b64encode(source).decode('utf-8'),\n",
+    "    \"input_mask\": base64.b64encode(mask).decode('utf-8'),\n",
+    "    \"image_prompts\": [], # Required parameters, can be an empty list\n",
+    "    \"async_process\": True\n",
+    "}\n",
+    "result = image_prompt(params)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# image prompt\n",
+    "\n",
+    "params = {\n",
+    "    \"prompt\": \"1girl sitting on the chair\",\n",
+    "    \"image_prompts\": [\n",
+    "        {\n",
+    "            \"cn_img\": base64.b64encode(source).decode('utf-8'),\n",
+    "            \"cn_stop\": 0.6,\n",
+    "            \"cn_weight\": 0.6,\n",
+    "            \"cn_type\": \"ImagePrompt\"\n",
+    "        },{\n",
+    "            \"cn_img\": base64.b64encode(image).decode('utf-8'),\n",
+    "            \"cn_stop\": 0.6,\n",
+    "            \"cn_weight\": 0.6,\n",
+    "            \"cn_type\": \"ImagePrompt\"\n",
+    "        }]\n",
+    "    }\n",
+    "result = image_prompt(params)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# text to image with imageprompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import base64\n",
+    "\n",
+    "# text to image with imageprompt Example\n",
+    "host = \"http://127.0.0.1:8888\"\n",
+    "image = open(\"./imgs/bear.jpg\", \"rb\").read()\n",
+    "source = open(\"./imgs/s.jpg\", \"rb\").read()\n",
+    "def image_prompt(params: dict) -> dict:\n",
+    "    \"\"\"\n",
+    "    image prompt\n",
+    "    \"\"\"\n",
+    "    response = requests.post(url=f\"{host}/v2/generation/text-to-image-with-ip\",\n",
+    "                             data=json.dumps(params),\n",
+    "                             headers={\"Content-Type\": \"application/json\"})\n",
+    "    return response.json()\n",
+    "\n",
+    "params = {\n",
+    "    \"prompt\": \"A bear\",\n",
+    "    \"image_prompts\": [\n",
+    "        {\n",
+    "            \"cn_img\": base64.b64encode(source).decode('utf-8'),\n",
+    "            \"cn_stop\": 0.6,\n",
+    "            \"cn_weight\": 0.6,\n",
+    "            \"cn_type\": \"ImagePrompt\"\n",
+    "        },{\n",
+    "            \"cn_img\": base64.b64encode(image).decode('utf-8'),\n",
+    "            \"cn_stop\": 0.6,\n",
+    "            \"cn_weight\": 0.6,\n",
+    "            \"cn_type\": \"ImagePrompt\"\n",
+    "        }\n",
+    "    ]\n",
+    "}\n",
+    "result = image_prompt(params)\n",
+    "print(json.dumps(result, indent=4, ensure_ascii=False))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Fooocus-API/examples/examples.py

@@ -0,0 +1,135 @@
+import json
+import os
+import requests
+import base64
+
+inpaint_engine = 'v1'
+
+
+class Config():
+    fooocus_host = 'http://127.0.0.1:8888'
+
+    text2img = '/v1/generation/text-to-image'
+    img_upscale = '/v2/generation/image-upscale-vary'
+    img_upscale1 = '/v1/generation/image-upscale-vary'
+    inpaint_outpaint = '/v2/generation/image-inpait-outpaint'
+    inpaint_outpaint1 = '/v1/generation/image-inpait-outpaint'
+    img_prompt = '/v2/generation/image-prompt'
+    img_prompt1 = '/v1/generation/image-prompt'
+
+    job_queue = '/v1/generation/job-queue'
+    query_job = '/v1/generation/query-job'
+
+    res_path = '/v1/generation/temp'
+
+
+cfg = Config()
+
+upscale_params = {
+    "uov_method": "Upscale (Custom)",
+    "upscale_value": 3,
+    "input_image": ""
+}
+
+inpaint_params = {
+    "input_image": "",
+    "input_mask": None,
+    "inpaint_additional_prompt": None,
+}
+
+img_prompt_params = {
+    "image_prompts": []
+}
+
+headers = {
+    "accept": "application/json"
+}
+
+imgs_base_path = os.path.join(os.path.dirname(__file__), 'imgs')
+
+with open(os.path.join(imgs_base_path, "bear.jpg"), "rb") as f:
+    img1 = f.read()
+    image_base64 = base64.b64encode(img1).decode('utf-8')
+    f.close()
+
+with open(os.path.join(imgs_base_path, "s.jpg"), "rb") as f:
+    s = f.read()
+    s_base64 = base64.b64encode(s).decode('utf-8')
+    f.close()
+
+with open(os.path.join(imgs_base_path, "m.png"), "rb") as f:
+    m = f.read()
+    m_base64 = base64.b64encode(m).decode('utf-8')
+    f.close()
+
+
+def upscale_vary(image, params=upscale_params) -> dict:
+    """
+    Upscale or Vary
+    """
+    params["input_image"] = image
+    data = json.dumps(params)
+    response = requests.post(url=f"{cfg.fooocus_host}{cfg.img_upscale}",
+                             data=data,
+                             headers=headers,
+                             timeout=300)
+    return response.json()
+
+
+def inpaint_outpaint(input_image: str, input_mask: str = None, params=inpaint_params) -> dict:
+    """
+    Inpaint or Outpaint
+    """
+    params["input_image"] = input_image
+    params["input_mask"] = input_mask
+    params["outpaint_selections"] = ["Left", "Right"]
+    params["prompt"] = "cat"
+    data = json.dumps(params)
+    response = requests.post(url=f"{cfg.fooocus_host}{cfg.inpaint_outpaint}",
+                             data=data,
+                             headers=headers,
+                             timeout=300)
+    return response.json()
+
+
+def image_prompt(img_prompt: list, params: dict) -> dict:
+    """
+    Image Prompt
+    """
+    params["image_prompts"] = img_prompt
+    data = json.dumps(params)
+    response = requests.post(url=f"{cfg.fooocus_host}{cfg.img_prompt}",
+                             data=data,
+                             headers=headers,
+                             timeout=300)
+    return response.json()
+
+
+def image_prompt_with_inpaint(img_prompt: list, input_image: str, input_mask: str, params: dict) -> dict:
+    """
+    Image Prompt
+    """
+    params["image_prompts"] = img_prompt
+    params["input_image"] = input_image
+    params["input_mask"] = input_mask
+    params["outpaint_selections"] = ["Left", "Right"]
+    data = json.dumps(params)
+    response = requests.post(url=f"{cfg.fooocus_host}{cfg.img_prompt}",
+                             data=data,
+                             headers=headers,
+                             timeout=300)
+    return response.json()
+
+
+img_prompt = [
+    {
+        "cn_img": image_base64,
+        "cn_stop": 0.6,
+        "cn_weight": 0.6,
+        "cn_type": "ImagePrompt"
+    }
+]
+# print(upscale_vary(image=image_base64))
+# print(inpaint_outpaint(input_image=s_base64, input_mask=m_base64))
+# print(image_prompt(img_prompt=img_prompt, params=img_prompt_params))
+print(image_prompt_with_inpaint(img_prompt=img_prompt, input_image=s_base64, input_mask=m_base64, params=img_prompt_params))

Fooocus-API/fooocus_api_version.py

@@ -0,0 +1 @@
+version = '0.3.29'

Fooocus-API/fooocusapi/api.py

@@ -0,0 +1,390 @@
+import uvicorn
+
+from typing import List, Optional
+from fastapi.responses import JSONResponse
+from fastapi import Depends, FastAPI, Header, Query, Response, UploadFile
+from fastapi.params import File
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+
+from fooocusapi.args import args
+from fooocusapi.sql_client import query_history
+from fooocusapi.models import *
+from fooocusapi.api_utils import generation_output, req_to_params
+import fooocusapi.file_utils as file_utils
+from fooocusapi.parameters import GenerationFinishReason, ImageGenerationResult
+from fooocusapi.task_queue import TaskType
+from fooocusapi.worker import process_generate, task_queue, process_top
+from fooocusapi.models_v2 import *
+from fooocusapi.img_utils import base64_to_stream, read_input_image
+
+from concurrent.futures import ThreadPoolExecutor
+from modules.util import HWC3
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow access from all sources
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all HTTP methods
+    allow_headers=["*"],  # Allow all request headers
+)
+
+work_executor = ThreadPoolExecutor(
+    max_workers=task_queue.queue_size * 2, thread_name_prefix="worker_")
+
+img_generate_responses = {
+    "200": {
+        "description": "PNG bytes if request's 'Accept' header is 'image/png', otherwise JSON",
+        "content": {
+            "application/json": {
+                "example": [{
+                    "base64": "...very long string...",
+                    "seed": "1050625087",
+                    "finish_reason": "SUCCESS"
+                }]
+            },
+            "application/json async": {
+                "example": {
+                    "job_id": 1,
+                    "job_type": "Text to Image"
+                }
+            },
+            "image/png": {
+                "example": "PNG bytes, what did you expect?"
+            }
+        }
+    }
+}
+
+
+def call_worker(req: Text2ImgRequest, accept: str):
+    task_type = TaskType.text_2_img
+    if isinstance(req, ImgUpscaleOrVaryRequest) or isinstance(req, ImgUpscaleOrVaryRequestJson):
+        task_type = TaskType.img_uov
+    elif isinstance(req, ImgPromptRequest) or isinstance(req, ImgPromptRequestJson):
+        task_type = TaskType.img_prompt
+    elif isinstance(req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson):
+        task_type = TaskType.img_inpaint_outpaint
+
+    params = req_to_params(req)
+    queue_task = task_queue.add_task(
+        task_type, {'params': params.__dict__, 'accept': accept, 'require_base64': req.require_base64},
+        webhook_url=req.webhook_url)
+
+    if queue_task is None:
+        print("[Task Queue] The task queue has reached limit")
+        results = [ImageGenerationResult(im=None, seed=0,
+                                         finish_reason=GenerationFinishReason.queue_is_full)]
+    elif req.async_process:
+        work_executor.submit(process_generate, queue_task, params)
+        results = queue_task
+    else:
+        results = process_generate(queue_task, params)
+
+    return results
+
+
+def stop_worker():
+    process_top()
+
+
+@app.get("/")
+def home():
+    return Response(content='Swagger-UI to: <a href="/docs">/docs</a>', media_type="text/html")
+
+
+@app.get("/ping", description="Returns a simple 'pong' response")
+def ping():
+    return Response(content='pong', media_type="text/html")
+
+
+@app.post("/v1/generation/text-to-image", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def text2img_generation(req: Text2ImgRequest, accept: str = Header(None),
+                        accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v2/generation/text-to-image-with-ip", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def text_to_img_with_ip(req: Text2ImgRequestWithPrompt,
+                        accept: str = Header(None),
+                        accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    default_image_promt = ImagePrompt(cn_img=None)
+    image_prompts_files: List[ImagePrompt] = []
+    for img_prompt in req.image_prompts:
+        img_prompt.cn_img = base64_to_stream(img_prompt.cn_img)
+        image = ImagePrompt(cn_img=img_prompt.cn_img,
+                            cn_stop=img_prompt.cn_stop,
+                            cn_weight=img_prompt.cn_weight,
+                            cn_type=img_prompt.cn_type)
+        image_prompts_files.append(image)
+
+    while len(image_prompts_files) <= 4:
+        image_prompts_files.append(default_image_promt)
+
+    req.image_prompts = image_prompts_files
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v1/generation/image-upscale-vary", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def img_upscale_or_vary(input_image: UploadFile, req: ImgUpscaleOrVaryRequest = Depends(ImgUpscaleOrVaryRequest.as_form),
+                        accept: str = Header(None),
+                        accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v2/generation/image-upscale-vary", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def img_upscale_or_vary_v2(req: ImgUpscaleOrVaryRequestJson,
+                           accept: str = Header(None),
+                           accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+    req.input_image = base64_to_stream(req.input_image)
+
+    default_image_promt = ImagePrompt(cn_img=None)
+    image_prompts_files: List[ImagePrompt] = []
+    for img_prompt in req.image_prompts:
+        img_prompt.cn_img = base64_to_stream(img_prompt.cn_img)
+        image = ImagePrompt(cn_img=img_prompt.cn_img,
+                            cn_stop=img_prompt.cn_stop,
+                            cn_weight=img_prompt.cn_weight,
+                            cn_type=img_prompt.cn_type)
+        image_prompts_files.append(image)
+    while len(image_prompts_files) <= 4:
+        image_prompts_files.append(default_image_promt)
+    req.image_prompts = image_prompts_files
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v1/generation/image-inpait-outpaint", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def img_inpaint_or_outpaint(input_image: UploadFile, req: ImgInpaintOrOutpaintRequest = Depends(ImgInpaintOrOutpaintRequest.as_form),
+                            accept: str = Header(None),
+                            accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v2/generation/image-inpait-outpaint", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def img_inpaint_or_outpaint_v2(req: ImgInpaintOrOutpaintRequestJson,
+                               accept: str = Header(None),
+                               accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    req.input_image = base64_to_stream(req.input_image)
+    if req.input_mask is not None:
+        req.input_mask = base64_to_stream(req.input_mask)
+    default_image_promt = ImagePrompt(cn_img=None)
+    image_prompts_files: List[ImagePrompt] = []
+    for img_prompt in req.image_prompts:
+        img_prompt.cn_img = base64_to_stream(img_prompt.cn_img)
+        image = ImagePrompt(cn_img=img_prompt.cn_img,
+                            cn_stop=img_prompt.cn_stop,
+                            cn_weight=img_prompt.cn_weight,
+                            cn_type=img_prompt.cn_type)
+        image_prompts_files.append(image)
+    while len(image_prompts_files) <= 4:
+        image_prompts_files.append(default_image_promt)
+    req.image_prompts = image_prompts_files
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v1/generation/image-prompt", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def img_prompt(cn_img1: Optional[UploadFile] = File(None),
+               req: ImgPromptRequest = Depends(ImgPromptRequest.as_form),
+               accept: str = Header(None),
+               accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.post("/v2/generation/image-prompt", response_model=List[GeneratedImageResult] | AsyncJobResponse, responses=img_generate_responses)
+def img_prompt(req: ImgPromptRequestJson,
+               accept: str = Header(None),
+               accept_query: str | None = Query(None, alias='accept', description="Parameter to overvide 'Accept' header, 'image/png' for output bytes")):
+    if accept_query is not None and len(accept_query) > 0:
+        accept = accept_query
+
+    if accept == 'image/png':
+        streaming_output = True
+        # image_number auto set to 1 in streaming mode
+        req.image_number = 1
+    else:
+        streaming_output = False
+
+    if req.input_image is not None:
+        req.input_image = base64_to_stream(req.input_image)
+    if req.input_mask is not None:
+        req.input_mask = base64_to_stream(req.input_mask)
+
+    default_image_promt = ImagePrompt(cn_img=None)
+    image_prompts_files: List[ImagePrompt] = []
+    for img_prompt in req.image_prompts:
+        img_prompt.cn_img = base64_to_stream(img_prompt.cn_img)
+        image = ImagePrompt(cn_img=img_prompt.cn_img,
+                            cn_stop=img_prompt.cn_stop,
+                            cn_weight=img_prompt.cn_weight,
+                            cn_type=img_prompt.cn_type)
+        image_prompts_files.append(image)
+
+    while len(image_prompts_files) <= 4:
+        image_prompts_files.append(default_image_promt)
+
+    req.image_prompts = image_prompts_files
+
+    results = call_worker(req, accept)
+    return generation_output(results, streaming_output, req.require_base64)
+
+
+@app.get("/v1/generation/query-job", response_model=AsyncJobResponse, description="Query async generation job")
+def query_job(req: QueryJobRequest = Depends()):
+    queue_task = task_queue.get_task(req.job_id, True)
+    if queue_task is None:
+        return JSONResponse(content=AsyncJobResponse(job_id="",
+                                                     job_type="Not Found",
+                                                     job_stage="ERROR",
+                                                     job_progress=0,
+                                                     job_status="Job not found"), status_code=404)
+
+    return generation_output(queue_task, streaming_output=False, require_base64=False,
+                             require_step_preivew=req.require_step_preivew)
+
+
+@app.get("/v1/generation/job-queue", response_model=JobQueueInfo, description="Query job queue info")
+def job_queue():
+    return JobQueueInfo(running_size=len(task_queue.queue), finished_size=len(task_queue.history), last_job_id=task_queue.last_job_id)
+
+
+@app.get("/v1/generation/job-history", response_model=JobHistoryResponse | dict, description="Query historical job data")
+def get_history(job_id: str = None, page: int = 0, page_size: int = 20):
+    # Fetch and return the historical tasks
+    queue = [JobHistoryInfo(job_id=item.job_id, is_finished=item.is_finished) for item in task_queue.queue]
+    if not args.presistent:
+        history = [JobHistoryInfo(job_id=item.job_id, is_finished=item.is_finished) for item in task_queue.history]
+        return JobHistoryResponse(history=history, queue=queue)
+    else:
+        history = query_history(task_id=job_id, page=page, page_size=page_size)
+        return {
+            "history": history,
+            "queue": queue
+        }
+
+
+@app.post("/v1/generation/stop", response_model=StopResponse, description="Job stoping")
+def stop():
+    stop_worker()
+    return StopResponse(msg="success")
+
+
+@app.post("/v1/tools/describe-image", response_model=DescribeImageResponse)
+def describe_image(image: UploadFile, type: DescribeImageType = Query(DescribeImageType.photo, description="Image type, 'Photo' or 'Anime'")):
+    if type == DescribeImageType.photo:
+        from extras.interrogate import default_interrogator as default_interrogator_photo
+        interrogator = default_interrogator_photo
+    else:
+        from extras.wd14tagger import default_interrogator as default_interrogator_anime
+        interrogator = default_interrogator_anime
+    img = HWC3(read_input_image(image))
+    result = interrogator(img)
+    return DescribeImageResponse(describe=result)
+
+
+@app.get("/v1/engines/all-models", response_model=AllModelNamesResponse, description="Get all filenames of base model and lora")
+def all_models():
+    import modules.config as config
+    return AllModelNamesResponse(model_filenames=config.model_filenames, lora_filenames=config.lora_filenames)
+
+
+@app.post("/v1/engines/refresh-models", response_model=AllModelNamesResponse, description="Refresh local files and get all filenames of base model and lora")
+def refresh_models():
+    import modules.config as config
+    config.update_all_model_names()
+    return AllModelNamesResponse(model_filenames=config.model_filenames, lora_filenames=config.lora_filenames)
+
+
+@app.get("/v1/engines/styles", response_model=List[str], description="Get all legal Fooocus styles")
+def all_styles():
+    from modules.sdxl_styles import legal_style_names
+    return legal_style_names
+
+
+app.mount("/files", StaticFiles(directory=file_utils.output_dir), name="files")
+
+
+def start_app(args):
+    file_utils.static_serve_base_url = args.base_url + "/files/"
+    uvicorn.run("fooocusapi.api:app", host=args.host,
+                port=args.port, log_level=args.log_level)

Fooocus-API/fooocusapi/api_utils.py

@@ -0,0 +1,213 @@
+from typing import List
+
+from fastapi import Response
+from fooocusapi.file_utils import get_file_serve_url, output_file_to_base64img, output_file_to_bytesimg
+from fooocusapi.img_utils import read_input_image
+from fooocusapi.models import AsyncJobResponse, AsyncJobStage, GeneratedImageResult, GenerationFinishReason, ImgInpaintOrOutpaintRequest, ImgPromptRequest, ImgUpscaleOrVaryRequest, Text2ImgRequest
+from fooocusapi.models_v2 import *
+from fooocusapi.parameters import ImageGenerationParams, ImageGenerationResult, default_inpaint_engine_version, default_sampler, default_scheduler, default_base_model_name, default_refiner_model_name
+from fooocusapi.task_queue import QueueTask
+
+from modules import flags
+from modules import config
+from modules.sdxl_styles import legal_style_names
+
+
+def req_to_params(req: Text2ImgRequest) -> ImageGenerationParams:
+    if req.base_model_name is not None:
+        if req.base_model_name not in config.model_filenames:
+            print(f"[Warning] Wrong base_model_name input: {req.base_model_name}, using default")
+            req.base_model_name = default_base_model_name
+
+    if req.refiner_model_name is not None and req.refiner_model_name != 'None':
+        if req.refiner_model_name not in config.model_filenames:
+            print(f"[Warning] Wrong refiner_model_name input: {req.refiner_model_name}, using default")
+            req.refiner_model_name = default_refiner_model_name
+
+    for l in req.loras:
+        if l.model_name != 'None' and l.model_name not in config.lora_filenames:
+            print(f"[Warning] Wrong lora model_name input: {l.model_name}, using 'None'")
+            l.model_name = 'None'
+
+    prompt = req.prompt
+    negative_prompt = req.negative_prompt
+    style_selections = [
+        s for s in req.style_selections if s in legal_style_names]
+    performance_selection = req.performance_selection.value
+    aspect_ratios_selection = req.aspect_ratios_selection
+    image_number = req.image_number
+    image_seed = None if req.image_seed == -1 else req.image_seed
+    sharpness = req.sharpness
+    guidance_scale = req.guidance_scale
+    base_model_name = req.base_model_name
+    refiner_model_name = req.refiner_model_name
+    refiner_switch = req.refiner_switch
+    loras = [(lora.model_name, lora.weight) for lora in req.loras]
+    uov_input_image = None
+    if not isinstance(req, Text2ImgRequestWithPrompt):
+        if isinstance(req, ImgUpscaleOrVaryRequest) or isinstance(req, ImgUpscaleOrVaryRequestJson):
+            uov_input_image = read_input_image(req.input_image)
+    uov_method = flags.disabled if not (isinstance(
+        req, ImgUpscaleOrVaryRequest) or isinstance(req, ImgUpscaleOrVaryRequestJson)) else req.uov_method.value
+    upscale_value = None if not (isinstance(
+                req, ImgUpscaleOrVaryRequest) or isinstance(req, ImgUpscaleOrVaryRequestJson)) else req.upscale_value
+    outpaint_selections = [] if not (isinstance(
+        req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson)) else [
+        s.value for s in req.outpaint_selections]
+    outpaint_distance_left = None if not (isinstance(
+        req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson)) else req.outpaint_distance_left
+    outpaint_distance_right = None if not (isinstance(
+        req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson)) else req.outpaint_distance_right
+    outpaint_distance_top = None if not (isinstance(
+        req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson)) else req.outpaint_distance_top
+    outpaint_distance_bottom = None if not (isinstance(
+        req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson)) else req.outpaint_distance_bottom
+
+    if refiner_model_name == '':
+        refiner_model_name = 'None'
+
+    inpaint_input_image = None
+    inpaint_additional_prompt = None
+    if (isinstance(req, ImgInpaintOrOutpaintRequest) or isinstance(req, ImgInpaintOrOutpaintRequestJson)) and req.input_image is not None:
+        inpaint_additional_prompt = req.inpaint_additional_prompt
+        input_image = read_input_image(req.input_image)
+        input_mask = None
+        if req.input_mask is not None:
+            input_mask = read_input_image(req.input_mask)
+        inpaint_input_image = {
+            'image': input_image,
+            'mask': input_mask
+        }
+
+    image_prompts = []
+    if isinstance(req, ImgPromptRequest) or isinstance(req, ImgPromptRequestJson) or isinstance(req, Text2ImgRequestWithPrompt) or isinstance(req, ImgUpscaleOrVaryRequestJson) or isinstance(req, ImgInpaintOrOutpaintRequestJson):
+        # Auto set mixing_image_prompt_and_inpaint to True
+        if len(req.image_prompts) > 0 and uov_input_image is not None:
+            print("[INFO] Mixing image prompt and vary upscale is set to True")
+            req.advanced_params.mixing_image_prompt_and_vary_upscale = True
+        elif len(req.image_prompts) > 0 and not isinstance(req, Text2ImgRequestWithPrompt) and req.input_image is not None and req.advanced_params is not None:
+            print("[INFO] Mixing image prompt and inpaint is set to True")
+            req.advanced_params.mixing_image_prompt_and_inpaint = True
+
+        for img_prompt in req.image_prompts:
+            if img_prompt.cn_img is not None:
+                cn_img = read_input_image(img_prompt.cn_img)
+                if img_prompt.cn_stop is None or img_prompt.cn_stop == 0:
+                    img_prompt.cn_stop = flags.default_parameters[img_prompt.cn_type.value][0]
+                if img_prompt.cn_weight is None or img_prompt.cn_weight == 0:
+                    img_prompt.cn_weight = flags.default_parameters[img_prompt.cn_type.value][1]
+                image_prompts.append(
+                    (cn_img, img_prompt.cn_stop, img_prompt.cn_weight, img_prompt.cn_type.value))
+                
+    advanced_params = None
+    if req.advanced_params is not None:
+        adp = req.advanced_params
+
+        if adp.refiner_swap_method not in ['joint', 'separate', 'vae']:
+            print(f"[Warning] Wrong refiner_swap_method input: {adp.refiner_swap_method}, using default")
+            adp.refiner_swap_method = 'joint'
+
+        if adp.sampler_name not in flags.sampler_list:
+            print(f"[Warning] Wrong sampler_name input: {adp.sampler_name}, using default")
+            adp.sampler_name = default_sampler
+
+        if adp.scheduler_name not in flags.scheduler_list:
+            print(f"[Warning] Wrong scheduler_name input: {adp.scheduler_name}, using default")
+            adp.scheduler_name = default_scheduler
+
+        if adp.inpaint_engine not in flags.inpaint_engine_versions:
+            print(f"[Warning] Wrong inpaint_engine input: {adp.inpaint_engine}, using default")
+            adp.inpaint_engine = default_inpaint_engine_version
+        
+        advanced_params = [
+            adp.disable_preview, adp.adm_scaler_positive, adp.adm_scaler_negative, adp.adm_scaler_end, adp.adaptive_cfg, adp.sampler_name, \
+            adp.scheduler_name, False, adp.overwrite_step, adp.overwrite_switch, adp.overwrite_width, adp.overwrite_height, \
+            adp.overwrite_vary_strength, adp.overwrite_upscale_strength, \
+            adp.mixing_image_prompt_and_vary_upscale, adp.mixing_image_prompt_and_inpaint, \
+            adp.debugging_cn_preprocessor, adp.skipping_cn_preprocessor, adp.controlnet_softness, adp.canny_low_threshold, adp.canny_high_threshold, \
+            adp.refiner_swap_method, \
+            adp.freeu_enabled, adp.freeu_b1, adp.freeu_b2, adp.freeu_s1, adp.freeu_s2, \
+            adp.debugging_inpaint_preprocessor, adp.inpaint_disable_initial_latent, adp.inpaint_engine, adp.inpaint_strength, adp.inpaint_respective_field, \
+            False, adp.invert_mask_checkbox, adp.inpaint_erode_or_dilate
+        ]
+
+    return ImageGenerationParams(prompt=prompt,
+                                 negative_prompt=negative_prompt,
+                                 style_selections=style_selections,
+                                 performance_selection=performance_selection,
+                                 aspect_ratios_selection=aspect_ratios_selection,
+                                 image_number=image_number,
+                                 image_seed=image_seed,
+                                 sharpness=sharpness,
+                                 guidance_scale=guidance_scale,
+                                 base_model_name=base_model_name,
+                                 refiner_model_name=refiner_model_name,
+                                 refiner_switch=refiner_switch,
+                                 loras=loras,
+                                 uov_input_image=uov_input_image,
+                                 uov_method=uov_method,
+                                 upscale_value=upscale_value,
+                                 outpaint_selections=outpaint_selections,
+                                 outpaint_distance_left=outpaint_distance_left,
+                                 outpaint_distance_right=outpaint_distance_right,
+                                 outpaint_distance_top=outpaint_distance_top,
+                                 outpaint_distance_bottom=outpaint_distance_bottom,
+                                 inpaint_input_image=inpaint_input_image,
+                                 inpaint_additional_prompt=inpaint_additional_prompt,
+                                 image_prompts=image_prompts,
+                                 advanced_params=advanced_params,
+                                 )
+
+
+def generation_output(results: QueueTask | List[ImageGenerationResult], streaming_output: bool, require_base64: bool, require_step_preivew: bool=False) -> Response | List[GeneratedImageResult] | AsyncJobResponse:
+    if isinstance(results, QueueTask):
+        task = results
+        job_stage = AsyncJobStage.running
+        job_result = None
+        if task.start_millis == 0:
+            job_stage = AsyncJobStage.waiting
+        if task.is_finished:
+            if task.finish_with_error:
+                job_stage = AsyncJobStage.error
+            else:
+                if task.task_result != None:
+                    job_stage = AsyncJobStage.success
+                    task_result_require_base64 = False
+                    if 'require_base64' in task.req_param and task.req_param['require_base64']:
+                        task_result_require_base64 = True
+
+                    job_result = generation_output(task.task_result, False, task_result_require_base64)
+        job_step_preview = None if not require_step_preivew else task.task_step_preview
+        return AsyncJobResponse(job_id=task.job_id,
+                                job_type=task.type,
+                                job_stage=job_stage,
+                                job_progress=task.finish_progress,
+                                job_status=task.task_status,
+                                job_step_preview=job_step_preview,
+                                job_result=job_result)
+
+    if streaming_output:
+        if len(results) == 0:
+            return Response(status_code=500)
+        result = results[0]
+        if result.finish_reason == GenerationFinishReason.queue_is_full:
+            return Response(status_code=409, content=result.finish_reason.value)
+        elif result.finish_reason == GenerationFinishReason.user_cancel:
+            return Response(status_code=400, content=result.finish_reason.value)
+        elif result.finish_reason == GenerationFinishReason.error:
+            return Response(status_code=500, content=result.finish_reason.value)
+        
+        bytes = output_file_to_bytesimg(results[0].im)
+        return Response(bytes, media_type='image/png')
+    else:
+        results = [GeneratedImageResult(
+            base64=output_file_to_base64img(
+                item.im) if require_base64 else None,
+            url=get_file_serve_url(item.im),
+            seed=item.seed,
+            finish_reason=item.finish_reason) for item in results]
+        return results
+
+
+class QueueReachLimitException(Exception):
+    pass

Fooocus-API/fooocusapi/args.py

@@ -0,0 +1,17 @@
+from fooocusapi.base_args import add_base_args
+import ldm_patched.modules.args_parser as args_parser
+
+# Add Fooocus-API args to parser
+add_base_args(args_parser.parser, False)
+
+# Apply Fooocus's args
+from args_manager import args_parser
+
+# Override the port default value
+args_parser.parser.set_defaults(
+    port=8888
+)
+
+# Execute args parse again
+args_parser.args = args_parser.parser.parse_args()
+args = args_parser.args

Fooocus-API/fooocusapi/base_args.py

@@ -0,0 +1,17 @@
+from argparse import ArgumentParser
+
+
+def add_base_args(parser: ArgumentParser, before_prepared: bool):
+    if before_prepared:
+        parser.add_argument("--port", type=int, default=8888, help="Set the listen port, default: 8888")
+        
+    parser.add_argument("--host", type=str, default='127.0.0.1', help="Set the listen host, default: 127.0.0.1")
+    parser.add_argument("--base-url", type=str, default=None, help="Set base url for outside visit, default is http://host:port")
+    parser.add_argument("--log-level", type=str, default='info', help="Log info for Uvicorn, default: info")
+    parser.add_argument("--sync-repo", default=None, help="Sync dependent git repositories to local, 'skip' for skip sync action, 'only' for only do the sync action and not launch app")
+    parser.add_argument("--skip-pip", default=False, action="store_true", help="Skip automatic pip install when setup")
+    parser.add_argument("--preload-pipeline", default=False, action="store_true", help="Preload pipeline before start http server")
+    parser.add_argument("--queue-size", type=int, default=3, help="Working queue size, default: 3, generation requests exceeding working queue size will return failure")
+    parser.add_argument("--queue-history", type=int, default=0, help="Finished jobs reserve size, tasks exceeding the limit will be deleted, including output image files, default: 0, means no limit")
+    parser.add_argument('--webhook-url', type=str, default=None, help='The URL to send a POST request when a job is finished')
+    parser.add_argument('--presistent', default=False, action="store_true", help="Store history to db")

Fooocus-API/fooocusapi/file_utils.py

@@ -0,0 +1,70 @@
+import base64
+import datetime
+from io import BytesIO
+import os
+import numpy as np
+from PIL import Image
+import uuid
+
+output_dir = os.path.abspath(os.path.join(
+    os.path.dirname(__file__), '..', 'outputs', 'files'))
+os.makedirs(output_dir, exist_ok=True)
+
+static_serve_base_url = 'http://127.0.0.1:8888/files/'
+
+
+def save_output_file(img: np.ndarray) -> str:
+    current_time = datetime.datetime.now()
+    date_string = current_time.strftime("%Y-%m-%d")
+
+    filename = os.path.join(date_string, str(uuid.uuid4()) + '.png')
+    file_path = os.path.join(output_dir, filename)
+
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    Image.fromarray(img).save(file_path)
+    return filename
+
+
+def delete_output_file(filename: str):
+    file_path = os.path.join(output_dir, filename)
+    if not os.path.exists(file_path) or not os.path.isfile(file_path):
+        return
+    try:
+        os.remove(file_path)
+    except OSError:
+        print(f"Delete output file failed: {filename}")
+
+
+def output_file_to_base64img(filename: str | None) -> str | None:
+    if filename is None:
+        return None
+    file_path = os.path.join(output_dir, filename)
+    if not os.path.exists(file_path) or not os.path.isfile(file_path):
+        return None
+
+    img = Image.open(file_path)
+    output_buffer = BytesIO()
+    img.save(output_buffer, format='PNG')
+    byte_data = output_buffer.getvalue()
+    base64_str = base64.b64encode(byte_data)
+    return base64_str
+
+
+def output_file_to_bytesimg(filename: str | None) -> bytes | None:
+    if filename is None:
+        return None
+    file_path = os.path.join(output_dir, filename)
+    if not os.path.exists(file_path) or not os.path.isfile(file_path):
+        return None
+
+    img = Image.open(file_path)
+    output_buffer = BytesIO()
+    img.save(output_buffer, format='PNG')
+    byte_data = output_buffer.getvalue()
+    return byte_data
+
+
+def get_file_serve_url(filename: str | None) -> str | None:
+    if filename is None:
+        return None
+    return static_serve_base_url + filename.replace('\\', '/')

Fooocus-API/fooocusapi/img_utils.py

@@ -0,0 +1,70 @@
+import base64
+import requests
+import numpy as np
+
+from io import BytesIO
+from fastapi import UploadFile
+from PIL import Image
+
+
+def narray_to_base64img(narray: np.ndarray) -> str:
+    if narray is None:
+        return None
+
+    img = Image.fromarray(narray)
+    output_buffer = BytesIO()
+    img.save(output_buffer, format='PNG')
+    byte_data = output_buffer.getvalue()
+    base64_str = base64.b64encode(byte_data)
+    return base64_str
+
+
+def narray_to_bytesimg(narray) -> bytes:
+    if narray is None:
+        return None
+
+    img = Image.fromarray(narray)
+    output_buffer = BytesIO()
+    img.save(output_buffer, format='PNG')
+    byte_data = output_buffer.getvalue()
+    return byte_data
+
+
+def read_input_image(input_image: UploadFile | None) -> np.ndarray | None:
+    if input_image is None:
+        return None
+    input_image_bytes = input_image.file.read()
+    pil_image = Image.open(BytesIO(input_image_bytes))
+    image = np.array(pil_image)
+    return image
+
+def base64_to_stream(image: str) -> UploadFile | None:
+    if image == '':
+        return None
+    if image.startswith('http'):
+        return get_check_image(url=image)
+    if image.startswith('data:image'):
+        image = image.split(sep=',', maxsplit=1)[1]
+    image_bytes = base64.b64decode(image)
+    byte_stream = BytesIO()
+    byte_stream.write(image_bytes)
+    byte_stream.seek(0)
+    return UploadFile(file=byte_stream)
+
+def get_check_image(url: str) -> UploadFile | None:
+    if url == '':
+        return None
+    try:
+        response = requests.get(url, timeout=10)
+        binary_image = response.content
+    except:
+        return None
+    try:
+        buffer = BytesIO(binary_image)
+        Image.open(buffer)
+    except:
+        return None
+    byte_stream = BytesIO()
+    byte_stream.write(binary_image)
+    byte_stream.seek(0)
+    return UploadFile(file=byte_stream)

Fooocus-API/fooocusapi/models.py

@@ -0,0 +1,449 @@
+from fastapi import Form, UploadFile
+from fastapi.params import File
+from fastapi.exceptions import RequestValidationError
+
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, ValidationError
+from pydantic_core import InitErrorDetails
+
+from typing import List, Tuple
+from enum import Enum
+
+from fooocusapi.parameters import (GenerationFinishReason,
+                                   default_styles,
+                                   default_base_model_name,
+                                   default_refiner_model_name,
+                                   default_refiner_switch,
+                                   default_loras,
+                                   default_cfg_scale,
+                                   default_prompt_negative,
+                                   default_aspect_ratio,
+                                   default_sampler,
+                                   default_scheduler)
+
+from fooocusapi.task_queue import TaskType
+
+
+class Lora(BaseModel):
+    model_name: str
+    weight: float = Field(default=0.5, ge=-2, le=2)
+
+    model_config = ConfigDict(
+        protected_namespaces=('protect_me_', 'also_protect_')
+    )
+
+
+LoraList = TypeAdapter(List[Lora])
+default_loras_model = [Lora(model_name=lora[0], weight=lora[1]) for lora in default_loras if lora[0] != 'None']
+default_loras_json = LoraList.dump_json(default_loras_model)
+
+
+class PerfomanceSelection(str, Enum):
+    speed = 'Speed'
+    quality = 'Quality'
+    extreme_speed = 'Extreme Speed'
+    
+
+class UpscaleOrVaryMethod(str, Enum):
+    subtle_variation = 'Vary (Subtle)'
+    strong_variation = 'Vary (Strong)'
+    upscale_15 = 'Upscale (1.5x)'
+    upscale_2 = 'Upscale (2x)'
+    upscale_fast = 'Upscale (Fast 2x)'
+    upscale_custom = 'Upscale (Custom)'
+
+class OutpaintExpansion(str, Enum):
+    left = 'Left'
+    right = 'Right'
+    top = 'Top'
+    bottom = 'Bottom'
+
+
+class ControlNetType(str, Enum):
+    cn_ip = "ImagePrompt"
+    cn_ip_face = "FaceSwap"
+    cn_canny = "PyraCanny"
+    cn_cpds = "CPDS"
+
+
+class ImagePrompt(BaseModel):
+    cn_img: UploadFile | None = Field(default=None)
+    cn_stop: float | None = Field(default=None, ge=0, le=1)
+    cn_weight: float | None = Field(default=None, ge=0, le=2, description="None for default value")
+    cn_type: ControlNetType = Field(default=ControlNetType.cn_ip)
+
+
+class AdvancedParams(BaseModel):
+    disable_preview: bool = Field(False, description="Disable preview during generation")
+    adm_scaler_positive: float = Field(1.5, description="Positive ADM Guidance Scaler", ge=0.1, le=3.0)
+    adm_scaler_negative: float = Field(0.8, description="Negative ADM Guidance Scaler", ge=0.1, le=3.0)
+    adm_scaler_end: float = Field(0.3, description="ADM Guidance End At Step", ge=0.0, le=1.0)
+    refiner_swap_method: str = Field('joint', description="Refiner swap method")
+    adaptive_cfg: float = Field(7.0, description="CFG Mimicking from TSNR", ge=1.0, le=30.0)
+    sampler_name: str = Field(default_sampler, description="Sampler")
+    scheduler_name: str = Field(default_scheduler, description="Scheduler")
+    overwrite_step: int = Field(-1, description="Forced Overwrite of Sampling Step", ge=-1, le=200)
+    overwrite_switch: int = Field(-1, description="Forced Overwrite of Refiner Switch Step", ge=-1, le=200)
+    overwrite_width: int = Field(-1, description="Forced Overwrite of Generating Width", ge=-1, le=2048)
+    overwrite_height: int = Field(-1, description="Forced Overwrite of Generating Height", ge=-1, le=2048)
+    overwrite_vary_strength: float = Field(-1, description='Forced Overwrite of Denoising Strength of "Vary"', ge=-1, le=1.0)
+    overwrite_upscale_strength: float = Field(-1, description='Forced Overwrite of Denoising Strength of "Upscale"', ge=-1, le=1.0)
+    mixing_image_prompt_and_vary_upscale: bool = Field(False, description="Mixing Image Prompt and Vary/Upscale")
+    mixing_image_prompt_and_inpaint: bool = Field(False, description="Mixing Image Prompt and Inpaint")
+    debugging_cn_preprocessor: bool = Field(False, description="Debug Preprocessors")
+    skipping_cn_preprocessor: bool = Field(False, description="Skip Preprocessors")
+    controlnet_softness: float = Field(0.25, description="Softness of ControlNet", ge=0.0, le=1.0)
+    canny_low_threshold: int = Field(64, description="Canny Low Threshold", ge=1, le=255)
+    canny_high_threshold: int = Field(128, description="Canny High Threshold", ge=1, le=255)
+    freeu_enabled: bool = Field(False, description="FreeU enabled")
+    freeu_b1: float = Field(1.01, description="FreeU B1")
+    freeu_b2: float = Field(1.02, description="FreeU B2")
+    freeu_s1: float = Field(0.99, description="FreeU B3")
+    freeu_s2: float = Field(0.95, description="FreeU B4")
+    debugging_inpaint_preprocessor: bool = Field(False, description="Debug Inpaint Preprocessing")
+    inpaint_disable_initial_latent: bool = Field(False, description="Disable initial latent in inpaint")
+    inpaint_engine: str = Field('v1', description="Inpaint Engine")
+    inpaint_strength: float = Field(1.0, description="Inpaint Denoising Strength", ge=0.0, le=1.0)
+    inpaint_respective_field: float = Field(1.0, description="Inpaint Respective Field", ge=0.0, le=1.0)
+    invert_mask_checkbox: bool = Field(False, description="Invert Mask")
+    inpaint_erode_or_dilate: int = Field(0, description="Mask Erode or Dilate", ge=-64, le=64)
+
+
+class Text2ImgRequest(BaseModel):
+    prompt: str = ''
+    negative_prompt: str = default_prompt_negative
+    style_selections: List[str] = default_styles
+    performance_selection: PerfomanceSelection = PerfomanceSelection.speed
+    aspect_ratios_selection: str = default_aspect_ratio
+    image_number: int = Field(default=1, description="Image number", ge=1, le=32)
+    image_seed: int = Field(default=-1, description="Seed to generate image, -1 for random")
+    sharpness: float = Field(default=2.0, ge=0.0, le=30.0)
+    guidance_scale: float = Field(default=default_cfg_scale, ge=1.0, le=30.0)
+    base_model_name: str = default_base_model_name
+    refiner_model_name: str = default_refiner_model_name
+    refiner_switch: float = Field(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0)
+    loras: List[Lora] = Field(default=default_loras_model)
+    advanced_params: AdvancedParams | None = AdvancedParams()
+    require_base64: bool = Field(default=False, description="Return base64 data of generated image")
+    async_process: bool = Field(default=False, description="Set to true will run async and return job info for retrieve generataion result later")
+    webhook_url: str | None = Field(default=None, description="Optional URL for a webhook callback. If provided, the system will send a POST request to this URL upon task completion or failure."
+                                                              " This allows for asynchronous notification of task status.")
+
+def style_selection_parser(style_selections: str) -> List[str]:
+    style_selection_arr: List[str] = []
+    if style_selections is None or len(style_selections) == 0:
+        return []
+    for part in style_selections:
+        if len(part) > 0:
+            for s in part.split(','):
+                style = s.strip()
+                style_selection_arr.append(style)
+    return style_selection_arr
+
+def lora_parser(loras: str) -> List[Lora]:
+    loras_model: List[Lora] = []
+    if loras is None or len(loras) == 0:
+        return []
+    try:
+        loras_model = LoraList.validate_json(loras)
+        return loras_model
+    except ValidationError as ve:
+        errs = ve.errors()
+        raise RequestValidationError(errors=[errs])
+
+def advanced_params_parser(advanced_params: str | None) -> AdvancedParams:
+    advanced_params_obj = None
+    if advanced_params is not None and len(advanced_params) > 0:
+        try:
+            advanced_params_obj = AdvancedParams.__pydantic_validator__.validate_json(advanced_params)
+            return advanced_params_obj
+        except ValidationError as ve:
+            errs = ve.errors()
+            raise RequestValidationError(errors=[errs])
+    return advanced_params_obj
+
+def oupaint_selections_parser(outpaint_selections: str) -> List[OutpaintExpansion]:
+    outpaint_selections_arr: List[OutpaintExpansion] = []
+    if outpaint_selections is None or len(outpaint_selections) == 0:
+        return []
+    for part in outpaint_selections:
+        if len(part) > 0:
+            for s in part.split(','):
+                try:
+                    expansion = OutpaintExpansion(s)
+                    outpaint_selections_arr.append(expansion)
+                except ValueError as ve:
+                    err = InitErrorDetails(type='enum', loc=['outpaint_selections'],
+                                           input=outpaint_selections,
+                                           ctx={
+                                               'expected': "Literal 'Left', 'Right', 'Top', 'Bottom' seperated by comma"
+                                            })
+                    raise RequestValidationError(errors=[err])
+    return outpaint_selections_arr
+
+def image_prompt_parser(image_prompts_config: List[Tuple]) -> List[ImagePrompt]:
+    image_prompts: List[ImagePrompt] = []
+    if image_prompts_config is None or len(image_prompts_config) == 0:
+        return []
+    for config in image_prompts_config:
+        cn_img, cn_stop, cn_weight, cn_type = config
+        image_prompts.append(ImagePrompt(cn_img=cn_img, cn_stop=cn_stop,
+                                         cn_weight=cn_weight, cn_type=cn_type))
+    return image_prompts
+
+
+class ImgUpscaleOrVaryRequest(Text2ImgRequest):
+    input_image: UploadFile
+    uov_method: UpscaleOrVaryMethod
+    upscale_value: float | None
+
+    @classmethod
+    def as_form(cls, input_image: UploadFile = Form(description="Init image for upsacale or outpaint"),
+                uov_method: UpscaleOrVaryMethod = Form(),
+                upscale_value: float | None = Form(None, description="Upscale custom value, None for default value", ge=1.0, le=5.0),
+                prompt: str = Form(''),
+                negative_prompt: str = Form(default_prompt_negative),
+                style_selections: List[str] = Form(default_styles, description="Fooocus style selections, seperated by comma"),
+                performance_selection: PerfomanceSelection = Form(PerfomanceSelection.speed, description="Performance Selection, one of 'Speed','Quality','Extreme Speed'"),
+                aspect_ratios_selection: str = Form(default_aspect_ratio, description="Aspect Ratios Selection, default 1152*896"),
+                image_number: int = Form(default=1, description="Image number", ge=1, le=32),
+                image_seed: int = Form(default=-1, description="Seed to generate image, -1 for random"),
+                sharpness: float = Form(default=2.0, ge=0.0, le=30.0),
+                guidance_scale: float = Form(default=default_cfg_scale, ge=1.0, le=30.0),
+                base_model_name: str = Form(default_base_model_name, description="checkpoint file name"),
+                refiner_model_name: str = Form(default_refiner_model_name, description="refiner file name"),
+                refiner_switch: float = Form(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0),
+                loras: str | None = Form(default=default_loras_json, description='Lora config in JSON. Format as [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]'),
+                advanced_params: str | None = Form(default=None, description="Advanced parameters in JSON"),
+                require_base64: bool = Form(default=False, description="Return base64 data of generated image"),
+                async_process: bool = Form(default=False, description="Set to true will run async and return job info for retrieve generataion result later"),
+                ):
+        style_selection_arr = style_selection_parser(style_selections)
+        loras_model = lora_parser(loras)
+        advanced_params_obj = advanced_params_parser(advanced_params)
+
+        return cls(input_image=input_image, uov_method=uov_method,upscale_value=upscale_value,
+                   prompt=prompt, negative_prompt=negative_prompt, style_selections=style_selection_arr,
+                   performance_selection=performance_selection, aspect_ratios_selection=aspect_ratios_selection,
+                   image_number=image_number, image_seed=image_seed, sharpness=sharpness, guidance_scale=guidance_scale,
+                   base_model_name=base_model_name, refiner_model_name=refiner_model_name, refiner_switch=refiner_switch,
+                   loras=loras_model, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
+
+
+class ImgInpaintOrOutpaintRequest(Text2ImgRequest):
+    input_image: UploadFile | None
+    input_mask: UploadFile | None
+    inpaint_additional_prompt: str | None
+    outpaint_selections: List[OutpaintExpansion]
+    outpaint_distance_left: int
+    outpaint_distance_right: int
+    outpaint_distance_top: int
+    outpaint_distance_bottom: int
+
+    @classmethod
+    def as_form(cls, input_image: UploadFile = Form(description="Init image for inpaint or outpaint"),
+                input_mask: UploadFile = Form(File(None), description="Inpaint or outpaint mask"),
+                inpaint_additional_prompt: str | None = Form(None, description="Describe what you want to inpaint"),
+                outpaint_selections: List[str] = Form([], description="Outpaint expansion selections, literal 'Left', 'Right', 'Top', 'Bottom' seperated by comma"),
+                outpaint_distance_left: int = Form(default=0, description="Set outpaint left distance, -1 for default"),
+                outpaint_distance_right: int = Form(default=0, description="Set outpaint right distance, -1 for default"),
+                outpaint_distance_top: int = Form(default=0, description="Set outpaint top distance, -1 for default"),
+                outpaint_distance_bottom: int = Form(default=0, description="Set outpaint bottom distance, -1 for default"),
+                prompt: str = Form(''),
+                negative_prompt: str = Form(default_prompt_negative),
+                style_selections: List[str] = Form(default_styles, description="Fooocus style selections, seperated by comma"),
+                performance_selection: PerfomanceSelection = Form(PerfomanceSelection.speed, description="Performance Selection, one of 'Speed','Quality','Extreme Speed'"),
+                aspect_ratios_selection: str = Form(default_aspect_ratio, description="Aspect Ratios Selection, default 1152*896"),
+                image_number: int = Form(default=1, description="Image number", ge=1, le=32),
+                image_seed: int = Form(default=-1, description="Seed to generate image, -1 for random"),
+                sharpness: float = Form(default=2.0, ge=0.0, le=30.0),
+                guidance_scale: float = Form(default=default_cfg_scale, ge=1.0, le=30.0),
+                base_model_name: str = Form(default_base_model_name),
+                refiner_model_name: str = Form(default_refiner_model_name),
+                refiner_switch: float = Form(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0),
+                loras: str | None = Form(default=default_loras_json, description='Lora config in JSON. Format as [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]'),
+                advanced_params: str| None = Form(default=None, description="Advanced parameters in JSON"),
+                require_base64: bool = Form(default=False, description="Return base64 data of generated image"),
+                async_process: bool = Form(default=False, description="Set to true will run async and return job info for retrieve generataion result later"),
+                ):
+
+        if isinstance(input_mask, File):
+            input_mask = None
+        
+        outpaint_selections_arr = oupaint_selections_parser(outpaint_selections)
+        style_selection_arr = style_selection_parser(style_selections)
+        loras_model = lora_parser(loras)
+        advanced_params_obj = advanced_params_parser(advanced_params)
+
+        return cls(input_image=input_image, input_mask=input_mask, inpaint_additional_prompt=inpaint_additional_prompt,
+                   outpaint_selections=outpaint_selections_arr,outpaint_distance_left=outpaint_distance_left,
+                   outpaint_distance_right=outpaint_distance_right, outpaint_distance_top=outpaint_distance_top,
+                   outpaint_distance_bottom=outpaint_distance_bottom, prompt=prompt, negative_prompt=negative_prompt, style_selections=style_selection_arr,
+                   performance_selection=performance_selection, aspect_ratios_selection=aspect_ratios_selection,
+                   image_number=image_number, image_seed=image_seed, sharpness=sharpness, guidance_scale=guidance_scale,
+                   base_model_name=base_model_name, refiner_model_name=refiner_model_name, refiner_switch=refiner_switch,
+                   loras=loras_model, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
+
+
+class ImgPromptRequest(ImgInpaintOrOutpaintRequest):
+    image_prompts: List[ImagePrompt]
+
+    @classmethod
+    def as_form(cls, input_image: UploadFile = Form(File(None), description="Init image for inpaint or outpaint"),
+                input_mask: UploadFile = Form(File(None), description="Inpaint or outpaint mask"),
+                inpaint_additional_prompt: str | None = Form(None, description="Describe what you want to inpaint"),
+                outpaint_selections: List[str] = Form([], description="Outpaint expansion selections, literal 'Left', 'Right', 'Top', 'Bottom' seperated by comma"),
+                outpaint_distance_left: int = Form(default=0, description="Set outpaint left distance, 0 for default"),
+                outpaint_distance_right: int = Form(default=0, description="Set outpaint right distance, 0 for default"),
+                outpaint_distance_top: int = Form(default=0, description="Set outpaint top distance, 0 for default"),
+                outpaint_distance_bottom: int = Form(default=0, description="Set outpaint bottom distance, 0 for default"),
+                cn_img1: UploadFile = Form(File(None), description="Input image for image prompt"),
+                cn_stop1: float | None = Form(
+                    default=None, ge=0, le=1, description="Stop at for image prompt, None for default value"),
+                cn_weight1: float | None = Form(
+                    default=None, ge=0, le=2, description="Weight for image prompt, None for default value"),
+                cn_type1: ControlNetType = Form(
+                    default=ControlNetType.cn_ip, description="ControlNet type for image prompt"),
+                cn_img2: UploadFile = Form(
+                    File(None), description="Input image for image prompt"),
+                cn_stop2: float | None = Form(
+                    default=None, ge=0, le=1, description="Stop at for image prompt, None for default value"),
+                cn_weight2: float | None = Form(
+                    default=None, ge=0, le=2, description="Weight for image prompt, None for default value"),
+                cn_type2: ControlNetType = Form(
+                    default=ControlNetType.cn_ip, description="ControlNet type for image prompt"),
+                cn_img3: UploadFile = Form(
+                    File(None), description="Input image for image prompt"),
+                cn_stop3: float | None = Form(
+                    default=None, ge=0, le=1, description="Stop at for image prompt, None for default value"),
+                cn_weight3: float | None = Form(
+                    default=None, ge=0, le=2, description="Weight for image prompt, None for default value"),
+                cn_type3: ControlNetType = Form(
+                    default=ControlNetType.cn_ip, description="ControlNet type for image prompt"),
+                cn_img4: UploadFile = Form(
+                    File(None), description="Input image for image prompt"),
+                cn_stop4: float | None = Form(
+                    default=None, ge=0, le=1, description="Stop at for image prompt, None for default value"),
+                cn_weight4: float | None = Form(
+                    default=None, ge=0, le=2, description="Weight for image prompt, None for default value"),
+                cn_type4: ControlNetType = Form(
+                    default=ControlNetType.cn_ip, description="ControlNet type for image prompt"),
+                prompt: str = Form(''),
+                negative_prompt: str = Form(default_prompt_negative),
+                style_selections: List[str] = Form(default_styles, description="Fooocus style selections, seperated by comma"),
+                performance_selection: PerfomanceSelection = Form(
+                    PerfomanceSelection.speed),
+                aspect_ratios_selection: str = Form(default_aspect_ratio),
+                image_number: int = Form(
+                    default=1, description="Image number", ge=1, le=32),
+                image_seed: int = Form(default=-1, description="Seed to generate image, -1 for random"),
+                sharpness: float = Form(default=2.0, ge=0.0, le=30.0),
+                guidance_scale: float = Form(default=default_cfg_scale, ge=1.0, le=30.0),
+                base_model_name: str = Form(default_base_model_name),
+                refiner_model_name: str = Form(default_refiner_model_name),
+                refiner_switch: float = Form(default=default_refiner_switch, description="Refiner Switch At", ge=0.1, le=1.0),
+                loras: str | None = Form(default=default_loras_json, description='Lora config in JSON. Format as [{"model_name": "sd_xl_offset_example-lora_1.0.safetensors", "weight": 0.5}]'),
+                advanced_params: str| None = Form(default=None, description="Advanced parameters in JSON"),
+                require_base64: bool = Form(default=False, description="Return base64 data of generated image"),
+                async_process: bool = Form(default=False, description="Set to true will run async and return job info for retrieve generataion result later"),
+                ):
+        if isinstance(input_image, File):
+            input_image = None
+        if isinstance(input_mask, File):
+            input_mask = None
+        if isinstance(cn_img1, File):
+            cn_img1 = None
+        if isinstance(cn_img2, File):
+            cn_img2 = None
+        if isinstance(cn_img3, File):
+            cn_img3 = None
+        if isinstance(cn_img4, File):
+            cn_img4 = None
+
+        outpaint_selections_arr = oupaint_selections_parser(outpaint_selections)
+
+        image_prompt_config = [(cn_img1, cn_stop1, cn_weight1, cn_type1),
+                               (cn_img2, cn_stop2, cn_weight2, cn_type2),
+                               (cn_img3, cn_stop3, cn_weight3, cn_type3),
+                               (cn_img4, cn_stop4, cn_weight4, cn_type4)]
+        image_prompts = image_prompt_parser(image_prompt_config)
+        style_selection_arr = style_selection_parser(style_selections)
+        loras_model = lora_parser(loras)
+        advanced_params_obj = advanced_params_parser(advanced_params)
+
+        return cls(input_image=input_image, input_mask=input_mask, inpaint_additional_prompt=inpaint_additional_prompt, outpaint_selections=outpaint_selections_arr,
+                   outpaint_distance_left=outpaint_distance_left, outpaint_distance_right=outpaint_distance_right, outpaint_distance_top=outpaint_distance_top, outpaint_distance_bottom=outpaint_distance_bottom,
+                   image_prompts=image_prompts, prompt=prompt, negative_prompt=negative_prompt, style_selections=style_selection_arr,
+                   performance_selection=performance_selection, aspect_ratios_selection=aspect_ratios_selection,
+                   image_number=image_number, image_seed=image_seed, sharpness=sharpness, guidance_scale=guidance_scale,
+                   base_model_name=base_model_name, refiner_model_name=refiner_model_name, refiner_switch=refiner_switch,
+                   loras=loras_model, advanced_params=advanced_params_obj, require_base64=require_base64, async_process=async_process)
+
+
+class GeneratedImageResult(BaseModel):
+    base64: str | None = Field(
+        description="Image encoded in base64, or null if finishReasen is not 'SUCCESS', only return when request require base64")
+    url: str | None = Field(description="Image file static serve url, or null if finishReasen is not 'SUCCESS'")
+    seed: str = Field(description="The seed associated with this image")
+    finish_reason: GenerationFinishReason
+
+
+class DescribeImageType(str, Enum):
+    photo = 'Photo'
+    anime = 'Anime'
+
+
+class DescribeImageResponse(BaseModel):
+    describe: str
+
+
+class AsyncJobStage(str, Enum):
+    waiting = 'WAITING'
+    running = 'RUNNING'
+    success = 'SUCCESS'
+    error = 'ERROR'
+
+
+class QueryJobRequest(BaseModel):
+    job_id: str = Field(description="Job ID to query")
+    require_step_preivew: bool = Field(False, description="Set to true will return preview image of generation steps at current time")
+
+
+class AsyncJobResponse(BaseModel):
+    job_id: str = Field(description="Job ID")
+    job_type: TaskType = Field(description="Job type")
+    job_stage: AsyncJobStage = Field(description="Job running stage")
+    job_progress: int = Field(description="Job running progress, 100 is for finished.")
+    job_status: str | None = Field(None, description="Job running status in text")
+    job_step_preview: str | None = Field(None, description="Preview image of generation steps at current time, as base64 image")
+    job_result: List[GeneratedImageResult] | None = Field(None, description="Job generation result")
+
+
+class JobQueueInfo(BaseModel):
+    running_size: int = Field(description="The current running and waiting job count")
+    finished_size: int = Field(description="Finished job cound (after auto clean)")
+    last_job_id: str = Field(description="Last submit generation job id")
+
+
+# TODO May need more detail fields, will add later when someone need
+class JobHistoryInfo(BaseModel):
+    job_id: str
+    is_finished: bool = False
+
+
+# Response model for the historical tasks
+class JobHistoryResponse(BaseModel):
+    queue: List[JobHistoryInfo] = []
+    history: List[JobHistoryInfo] = []
+
+
+class AllModelNamesResponse(BaseModel):
+    model_filenames: List[str] = Field(description="All available model filenames")
+    lora_filenames: List[str] = Field(description="All available lora filenames")
+
+    model_config = ConfigDict(
+        protected_namespaces=('protect_me_', 'also_protect_')
+    )
+
+    
+class StopResponse(BaseModel):
+    msg: str

Fooocus-API/fooocusapi/models_v2.py

@@ -0,0 +1,31 @@
+from fooocusapi.models import *
+
+class ImagePromptJson(BaseModel):
+    cn_img: str | None = Field(None, description="Input image for image prompt as base64")
+    cn_stop: float | None = Field(0, ge=0, le=1, description="Stop at for image prompt, 0 for default value")
+    cn_weight: float | None = Field(0, ge=0, le=2, description="Weight for image prompt, 0 for default value")
+    cn_type: ControlNetType = Field(default=ControlNetType.cn_ip, description="ControlNet type for image prompt")
+
+class ImgInpaintOrOutpaintRequestJson(Text2ImgRequest):
+    input_image: str = Field(description="Init image for inpaint or outpaint as base64")
+    input_mask: str | None = Field('', description="Inpaint or outpaint mask as base64")
+    inpaint_additional_prompt: str | None = Field('', description="Describe what you want to inpaint")
+    outpaint_selections: List[OutpaintExpansion] = []
+    outpaint_distance_left: int | None = Field(-1, description="Set outpaint left distance")
+    outpaint_distance_right: int | None = Field(-1, description="Set outpaint right distance")
+    outpaint_distance_top: int | None = Field(-1, description="Set outpaint top distance")
+    outpaint_distance_bottom: int | None = Field(-1, description="Set outpaint bottom distance")
+    image_prompts: List[ImagePromptJson | ImagePrompt] = []
+
+class ImgPromptRequestJson(ImgInpaintOrOutpaintRequestJson):
+    input_image: str | None = Field(None, description="Init image for inpaint or outpaint as base64")
+    image_prompts: List[ImagePromptJson | ImagePrompt]
+
+class Text2ImgRequestWithPrompt(Text2ImgRequest):
+    image_prompts: List[ImagePromptJson] = []
+
+class ImgUpscaleOrVaryRequestJson(Text2ImgRequest):
+    uov_method: UpscaleOrVaryMethod = "Upscale (2x)"
+    upscale_value: float | None = Field(1.0, ge=1.0, le=5.0, description="Upscale custom value, 1.0 for default value")
+    input_image: str = Field(description="Init image for upsacale or outpaint as base64")
+    image_prompts: List[ImagePromptJson | ImagePrompt] = []

Fooocus-API/fooocusapi/parameters.py

@@ -0,0 +1,186 @@
+from enum import Enum
+from typing import Dict, List, Tuple
+import numpy as np
+
+
+default_inpaint_engine_version = 'v2.6'
+
+
+default_styles = ['Fooocus V2', 'Fooocus Enhance', 'Fooocus Sharp']
+default_base_model_name = 'juggernautXL_version6Rundiffusion.safetensors'
+default_refiner_model_name = 'None'
+default_refiner_switch = 0.5
+default_loras = [['sd_xl_offset_example-lora_1.0.safetensors', 0.1]]
+default_lora_name = 'sd_xl_offset_example-lora_1.0.safetensors'
+default_lora_weight = 0.1
+default_cfg_scale = 4.0
+default_prompt_negative = ''
+default_aspect_ratio = '1152*896'
+default_sampler = 'dpmpp_2m_sde_gpu'
+default_scheduler = 'karras'
+
+
+available_aspect_ratios = [
+    '704*1408',
+    '704*1344',
+    '768*1344',
+    '768*1280',
+    '832*1216',
+    '832*1152',
+    '896*1152',
+    '896*1088',
+    '960*1088',
+    '960*1024',
+    '1024*1024',
+    '1024*960',
+    '1088*960',
+    '1088*896',
+    '1152*896',
+    '1152*832',
+    '1216*832',
+    '1280*768',
+    '1344*768',
+    '1344*704',
+    '1408*704',
+    '1472*704',
+    '1536*640',
+    '1600*640',
+    '1664*576',
+    '1728*576',
+]
+
+uov_methods = [
+    'Disabled', 'Vary (Subtle)', 'Vary (Strong)', 'Upscale (1.5x)', 'Upscale (2x)', 'Upscale (Fast 2x)', 'Upscale (Custom)'
+]
+
+
+outpaint_expansions = [
+    'Left', 'Right', 'Top', 'Bottom'
+]
+
+
+def get_aspect_ratio_value(label: str) -> str:
+    return label.split(' ')[0].replace('×', '*')
+
+
+class GenerationFinishReason(str, Enum):
+    success = 'SUCCESS'
+    queue_is_full = 'QUEUE_IS_FULL'
+    user_cancel = 'USER_CANCEL'
+    error = 'ERROR'
+
+
+class ImageGenerationResult(object):
+    def __init__(self, im: str | None, seed: str, finish_reason: GenerationFinishReason):
+        self.im = im
+        self.seed = seed
+        self.finish_reason = finish_reason
+
+
+class ImageGenerationParams(object):
+    def __init__(self, prompt: str,
+                 negative_prompt: str,
+                 style_selections: List[str],
+                 performance_selection: str,
+                 aspect_ratios_selection: str,
+                 image_number: int,
+                 image_seed: int | None,
+                 sharpness: float,
+                 guidance_scale: float,
+                 base_model_name: str,
+                 refiner_model_name: str,
+                 refiner_switch: float,
+                 loras: List[Tuple[str, float]],
+                 uov_input_image: np.ndarray | None,
+                 uov_method: str,
+                 upscale_value: float | None,
+                 outpaint_selections: List[str],
+                 outpaint_distance_left: int,
+                 outpaint_distance_right: int,
+                 outpaint_distance_top: int,
+                 outpaint_distance_bottom: int,
+                 inpaint_input_image: Dict[str, np.ndarray] | None,
+                 inpaint_additional_prompt: str | None,
+                 image_prompts: List[Tuple[np.ndarray, float, float, str]],
+                 advanced_params: List[any] | None):
+        self.prompt = prompt
+        self.negative_prompt = negative_prompt
+        self.style_selections = style_selections
+        self.performance_selection = performance_selection
+        self.aspect_ratios_selection = aspect_ratios_selection
+        self.image_number = image_number
+        self.image_seed = image_seed
+        self.sharpness = sharpness
+        self.guidance_scale = guidance_scale
+        self.base_model_name = base_model_name
+        self.refiner_model_name = refiner_model_name
+        self.refiner_switch = refiner_switch
+        self.loras = loras
+        self.uov_input_image = uov_input_image
+        self.uov_method = uov_method
+        self.upscale_value = upscale_value
+        self.outpaint_selections = outpaint_selections
+        self.outpaint_distance_left = outpaint_distance_left
+        self.outpaint_distance_right = outpaint_distance_right
+        self.outpaint_distance_top = outpaint_distance_top
+        self.outpaint_distance_bottom = outpaint_distance_bottom
+        self.inpaint_input_image = inpaint_input_image
+        self.inpaint_additional_prompt = inpaint_additional_prompt
+        self.image_prompts = image_prompts
+        
+        if advanced_params is None:
+            disable_preview = False
+            adm_scaler_positive = 1.5
+            adm_scaler_negative = 0.8
+            adm_scaler_end = 0.3
+            adaptive_cfg = 7.0
+            sampler_name = default_sampler
+            scheduler_name = default_scheduler
+            generate_image_grid = False
+            overwrite_step = -1
+            overwrite_switch = -1
+            overwrite_width = -1
+            overwrite_height = -1
+            overwrite_vary_strength = -1
+            overwrite_upscale_strength = -1
+            mixing_image_prompt_and_vary_upscale = False
+            mixing_image_prompt_and_inpaint = False
+            debugging_cn_preprocessor = False
+            skipping_cn_preprocessor = False
+            controlnet_softness = 0.25
+            canny_low_threshold = 64
+            canny_high_threshold = 128
+            refiner_swap_method = 'joint'
+            freeu_enabled = False
+            freeu_b1, freeu_b2, freeu_s1, freeu_s2 = [None] * 4
+            debugging_inpaint_preprocessor = False
+            inpaint_disable_initial_latent = False
+            inpaint_engine = default_inpaint_engine_version
+            inpaint_strength = 1.0
+            inpaint_respective_field = 0.618
+            inpaint_mask_upload_checkbox = False
+            invert_mask_checkbox = False
+            inpaint_erode_or_dilate = 0
+
+
+            # Auto set mixing_image_prompt_and_inpaint to True
+            if len(self.image_prompts) > 0 and inpaint_input_image is not None:
+                print('Mixing Image Prompts and Inpaint Enabled')
+                mixing_image_prompt_and_inpaint = True
+            if len(self.image_prompts) > 0 and uov_input_image is not None:
+                print('Mixing Image Prompts and Vary Upscale Enabled')
+                mixing_image_prompt_and_vary_upscale = True
+
+            self.advanced_params = [
+                disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \
+                scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \
+                overwrite_vary_strength, overwrite_upscale_strength, \
+                mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, \
+                debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \
+                refiner_swap_method, \
+                freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
+                debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
+                inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
+            ]
+        else:
+            self.advanced_params = advanced_params

Fooocus-API/fooocusapi/repositories_versions.py

@@ -0,0 +1,5 @@
+import os
+
+fooocus_version = '2.1.860'
+fooocus_commit_hash = os.environ.get(
+    'FOOOCUS_COMMIT_HASH', "624f74a1ed78ea09467c856cef35aeee0af863f6")

Fooocus-API/fooocusapi/sql_client.py

@@ -0,0 +1,205 @@
+import os
+import time
+import platform
+from datetime import datetime
+from typing import Optional
+
+from sqlalchemy import Integer, Float,VARCHAR, Boolean, JSON, Text, create_engine
+from sqlalchemy.orm import declarative_base, Session, Mapped, mapped_column
+
+
+Base = declarative_base()
+
+adv_params_keys = [
+    "disable_preview",
+    "adm_scaler_positive",
+    "adm_scaler_negative",
+    "adm_scaler_end",
+    "adaptive_cfg",
+    "sampler_name",
+    "scheduler_name",
+    "generate_image_grid",
+    "overwrite_step",
+    "overwrite_switch",
+    "overwrite_width",
+    "overwrite_height",
+    "overwrite_vary_strength",
+    "overwrite_upscale_strength",
+    "mixing_image_prompt_and_vary_upscale",
+    "mixing_image_prompt_and_inpaint",
+    "debugging_cn_preprocessor",
+    "skipping_cn_preprocessor",
+    "controlnet_softness",
+    "canny_low_threshold",
+    "canny_high_threshold",
+    "refiner_swap_method",
+    "freeu_enabled",
+    "freeu_b1",
+    "freeu_b2",
+    "freeu_s1",
+    "freeu_s2",
+    "debugging_inpaint_preprocessor",
+    "inpaint_disable_initial_latent",
+    "inpaint_engine",
+    "inpaint_strength",
+    "inpaint_respective_field",
+    "inpaint_mask_upload_checkbox",
+    "invert_mask_checkbox",
+    "inpaint_erode_or_dilate"
+]
+
+if platform.system().lower() == 'windows':
+    default_sqlite_db_path = os.path.join(os.path.dirname(__file__), "../database.db").replace("\\", "/")
+else:
+    default_sqlite_db_path = os.path.join(os.path.dirname(__file__), "../database.db")
+
+connection_uri = os.environ.get("FOOOCUS_DB_CONF", f"sqlite:///{default_sqlite_db_path}")
+
+
+class GenerateRecord(Base):
+    __tablename__ = 'generate_record'
+
+    task_id: Mapped[str] = mapped_column(VARCHAR(255), nullable=False, primary_key=True)
+    task_type: Mapped[str] = mapped_column(Text, nullable=False)
+    result_url: Mapped[str] = mapped_column(Text, nullable=True)
+    finish_reason: Mapped[str] = mapped_column(Text, nullable=True)
+    date_time: Mapped[int] = mapped_column(Integer, nullable=False)
+
+    prompt: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    negative_prompt: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    style_selections: Mapped[Optional[list]] = mapped_column(JSON, nullable=True)
+    performance_selection: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    aspect_ratios_selection: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    base_model_name: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    refiner_model_name: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    refiner_switch: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    loras: Mapped[Optional[list]] = mapped_column(JSON, nullable=True)
+    image_number: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    image_seed: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    sharpness: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    guidance_scale: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    advanced_params: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
+
+    input_image: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    input_mask: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    image_prompts: Mapped[Optional[list]] = mapped_column(JSON, nullable=True)
+    inpaint_additional_prompt: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    outpaint_selections: Mapped[Optional[list]] = mapped_column(JSON, nullable=True)
+    outpaint_distance_left: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    outpaint_distance_right: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    outpaint_distance_top: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    outpaint_distance_bottom: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    uov_method: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    upscale_value: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+
+    webhook_url: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    require_base64: Mapped[Optional[bool]] = mapped_column(Boolean, nullable=True)
+    async_process: Mapped[Optional[bool]] = mapped_column(Boolean, nullable=True)
+
+    def __repr__(self) -> str:
+        return f"GenerateRecord(task_id={self.task_id!r}, task_type={self.task_type!r}, \
+                result_url={self.result_url!r}, finish_reason={self.finish_reason!r}, date_time={self.date_time!r}, \
+                prompt={self.prompt!r}, negative_prompt={self.negative_prompt!r}, style_selections={self.style_selections!r}, performance_selection={self.performance_selection!r}, \
+                aspect_ratios_selection={self.aspect_ratios_selection!r}, base_model_name={self.base_model_name!r}, \
+                refiner_model_name={self.refiner_model_name!r}, refiner_switch={self.refiner_switch!r}, loras={self.loras!r}, \
+                image_number={self.image_number!r}, image_seed={self.image_seed!r}, sharpness={self.sharpness!r}, \
+                guidance_scale={self.guidance_scale!r}, advanced_params={self.advanced_params!r}, input_image={self.input_image!r}, \
+                input_mask={self.input_mask!r}, image_prompts={self.image_prompts!r}, inpaint_additional_prompt={self.inpaint_additional_prompt!r}, \
+                outpaint_selections={self.outpaint_selections!r}, outpaint_distance_left={self.outpaint_distance_left!r}, outpaint_distance_right={self.outpaint_distance_right!r}, \
+                outpaint_distance_top={self.outpaint_distance_top!r}, outpaint_distance_bottom={self.outpaint_distance_bottom!r}, uov_method={self.uov_method!r}, \
+                upscale_value={self.upscale_value!r}, webhook_url={self.webhook_url!r}, require_base64={self.require_base64!r}, \
+                async_process={self.async_process!r})"
+
+engine = create_engine(connection_uri)
+
+session = Session(engine)
+Base.metadata.create_all(engine, checkfirst=True)
+session.close()
+
+
+def convert_to_dict_list(obj_list: list[object]) -> dict:
+    dict_list = []
+    for obj in obj_list:
+        # 将对象属性转化为字典键值对
+        dict_obj = {}
+        for attr, value in vars(obj).items():
+            if not callable(value) and not attr.startswith("__") and not attr.startswith("_"):
+                dict_obj[attr] = value
+        task_info = {
+            "task_id": obj.task_id,
+            "task_type": obj.task_type,
+            "result_url": obj.result_url,
+            "finish_reason": obj.finish_reason,
+            "date_time": datetime.fromtimestamp(obj.date_time).strftime("%Y-%m-%d %H:%M:%S"),
+        }
+        del dict_obj['task_id']
+        del dict_obj['task_type']
+        del dict_obj['result_url']
+        del dict_obj['finish_reason']
+        del dict_obj['date_time']
+        dict_list.append({"params": dict_obj, "task_info": task_info})
+    return dict_list
+
+
+
+class MysqlSQLAlchemy:
+    def __init__(self, connection_uri: str):
+        # 'mysql+pymysql://{username}:{password}@{host}:{port}/{database}'
+        self.engine = create_engine(connection_uri)
+        self.session = Session(self.engine)
+
+    def store_history(self, record: dict) -> None:
+        """
+        Store history to database
+        :param record:
+        :return:
+        """
+        self.session.add_all([GenerateRecord(**record)])
+        self.session.commit()
+    
+    def get_history(self, task_id: str=None, page: int=0, page_size: int=20,
+                    order_by: str='date_time') -> list:
+        """
+        Get history from database
+        :param task_id:
+        :return:
+        """
+        if task_id is not None:
+            res = self.session.query(GenerateRecord).filter(GenerateRecord.task_id == task_id).all()
+            if len(res) == 0:
+                return []
+            return convert_to_dict_list(res)
+        
+        res = self.session.query(GenerateRecord).order_by(getattr(GenerateRecord, order_by).desc()).offset(page * page_size).limit(page_size).all()
+        if len(res) == 0:
+            return []
+        return convert_to_dict_list(res)
+
+
+db = MysqlSQLAlchemy(connection_uri=connection_uri)
+def req_to_dict(req: dict) -> dict:
+    req["loras"] = [{"model_name": lora[0], "weight": lora[1]} for lora in req["loras"]]
+    req["advanced_params"] = dict(zip(adv_params_keys, req["advanced_params"]))
+    req["image_prompts"] = [{
+        "cn_img": "",
+        "cn_stop": image[1],
+        "cn_weight": image[2],
+        "cn_type": image[3]
+    } for image in req["image_prompts"]]
+    del req["inpaint_input_image"]
+    del req["uov_input_image"]
+    return req
+
+def add_history(params: dict, task_type: str, task_id: str, result_url: str, finish_reason: str) -> None:
+    params = req_to_dict(params["params"])
+    params["date_time"] = int(time.time())
+    params["task_type"] = task_type
+    params["task_id"] = task_id
+    params["result_url"] = result_url
+    params["finish_reason"] = finish_reason
+
+    db.store_history(params)
+
+
+def query_history(task_id: str=None, page: int=0, page_size: int=20, order_by: str="date_time") -> list:
+    return db.get_history(task_id=task_id, page=page, page_size=page_size, order_by=order_by)

Fooocus-API/fooocusapi/task_queue.py

@@ -0,0 +1,171 @@
+import uuid
+import time
+import requests
+import numpy as np
+
+from enum import Enum
+from typing import List, Tuple
+
+from fooocusapi.args import args
+from fooocusapi.file_utils import delete_output_file, get_file_serve_url
+from fooocusapi.img_utils import narray_to_base64img
+from fooocusapi.sql_client import add_history
+from fooocusapi.parameters import ImageGenerationResult, GenerationFinishReason
+
+class TaskType(str, Enum):
+    text_2_img = 'Text to Image'
+    img_uov = 'Image Upscale or Variation'
+    img_inpaint_outpaint = 'Image Inpaint or Outpaint'
+    img_prompt = 'Image Prompt'
+    not_found = 'Not Found'
+
+
+class QueueTask(object):
+    job_id: str
+    is_finished: bool = False
+    finish_progress: int = 0
+    start_millis: int = 0
+    finish_millis: int = 0
+    finish_with_error: bool = False
+    task_status: str | None = None
+    task_step_preview: str | None = None
+    task_result: List[ImageGenerationResult] = None
+    error_message: str | None = None
+    webhook_url: str | None = None  # attribute for individual webhook_url
+
+    def __init__(self, job_id: str, type: TaskType, req_param: dict, in_queue_millis: int,
+                 webhook_url: str | None = None):
+        self.job_id = job_id
+        self.type = type
+        self.req_param = req_param
+        self.in_queue_millis = in_queue_millis
+        self.webhook_url = webhook_url
+
+    def set_progress(self, progress: int, status: str | None):
+        if progress > 100:
+            progress = 100
+        self.finish_progress = progress
+        self.task_status = status
+
+    def set_step_preview(self, task_step_preview: str | None):
+        self.task_step_preview = task_step_preview
+
+    def set_result(self, task_result: List[ImageGenerationResult], finish_with_error: bool, error_message: str | None = None):
+        if not finish_with_error:
+            self.finish_progress = 100
+            self.task_status = 'Finished'
+        self.task_result = task_result
+        self.finish_with_error = finish_with_error
+        self.error_message = error_message
+
+
+class TaskQueue(object):
+    queue: List[QueueTask] = []
+    history: List[QueueTask] = []
+    last_job_id = None
+    webhook_url: str | None = None
+
+    def __init__(self, queue_size: int, hisotry_size: int, webhook_url: str | None = None):
+        self.queue_size = queue_size
+        self.history_size = hisotry_size
+        self.webhook_url = webhook_url
+
+    def add_task(self, type: TaskType, req_param: dict, webhook_url: str | None = None) -> QueueTask | None:
+        """
+        Create and add task to queue
+        :returns: The created task's job_id, or None if reach the queue size limit
+        """
+        if len(self.queue) >= self.queue_size:
+            return None
+
+        job_id = str(uuid.uuid4())
+        task = QueueTask(job_id=job_id, type=type, req_param=req_param,
+                         in_queue_millis=int(round(time.time() * 1000)),
+                         webhook_url=webhook_url)
+        self.queue.append(task)
+        self.last_job_id = job_id
+        return task
+
+    def get_task(self, job_id: str, include_history: bool = False) -> QueueTask | None:
+        for task in self.queue:
+            if task.job_id == job_id:
+                return task
+
+        if include_history:
+            for task in self.history:
+                if task.job_id == job_id:
+                    return task
+
+        return None
+
+    def is_task_ready_to_start(self, job_id: str) -> bool:
+        task = self.get_task(job_id)
+        if task is None:
+            return False
+
+        return self.queue[0].job_id == job_id
+
+    def start_task(self, job_id: str):
+        task = self.get_task(job_id)
+        if task is not None:
+            task.start_millis = int(round(time.time() * 1000))
+
+    def finish_task(self, job_id: str):
+        task = self.get_task(job_id)
+        if task is not None:
+            task.is_finished = True
+            task.finish_millis = int(round(time.time() * 1000))
+
+            # Use the task's webhook_url if available, else use the default
+            webhook_url = task.webhook_url or self.webhook_url
+
+            data = { "job_id": task.job_id, "job_result": [] }
+            for item in task.task_result:
+                data["job_result"].append({
+                    "url": get_file_serve_url(item.im) if item.im else None,
+                    "seed": item.seed if item.seed else "-1",
+                })
+
+            # Send webhook
+            if task.is_finished and webhook_url:
+                try:
+                    res = requests.post(webhook_url, json=data)
+                    print(f'Call webhook response status: {res.status_code}')
+                except Exception as e:
+                    print('Call webhook error:', e)
+
+            # Move task to history
+            self.queue.remove(task)
+            self.history.append(task)
+
+            if args.presistent:
+                add_history(task.req_param, task.type, task.job_id,
+                            ','.join([job["url"] for job in data["job_result"]]),
+                            task.task_result[0].finish_reason)
+
+            # Clean history
+            if len(self.history) > self.history_size and self.history_size != 0:
+                removed_task = self.history.pop(0)
+                if isinstance(removed_task.task_result, List):
+                    for item in removed_task.task_result:
+                        if isinstance(item, ImageGenerationResult) and item.finish_reason == GenerationFinishReason.success and item.im is not None:
+                            delete_output_file(item.im)
+                print(f"Clean task history, remove task: {removed_task.job_id}")
+
+
+class TaskOutputs:
+    outputs = []
+
+    def __init__(self, task: QueueTask):
+        self.task = task
+
+    def append(self, args: List[any]):
+        self.outputs.append(args)
+        if len(args) >= 2:
+            if args[0] == 'preview' and isinstance(args[1], Tuple) and len(args[1]) >= 2:
+                number = args[1][0]
+                text = args[1][1]
+                self.task.set_progress(number, text)
+                if len(args[1]) >= 3 and isinstance(args[1][2], np.ndarray):
+                    base64_preview_img = narray_to_base64img(args[1][2])
+                    self.task.set_step_preview(base64_preview_img)

Fooocus-API/fooocusapi/worker.py

@@ -0,0 +1,867 @@
+import copy
+import random
+import time
+import numpy as np
+import torch
+import re
+import logging
+
+from typing import List
+from fooocusapi.file_utils import save_output_file
+from fooocusapi.parameters import GenerationFinishReason, ImageGenerationParams, ImageGenerationResult
+from fooocusapi.task_queue import QueueTask, TaskQueue, TaskOutputs
+
+
+task_queue = TaskQueue(queue_size=3, hisotry_size=6, webhook_url=None)
+
+
+def process_top():
+    import ldm_patched.modules.model_management
+    ldm_patched.modules.model_management.interrupt_current_processing()
+
+
+@torch.no_grad()
+@torch.inference_mode()
+def process_generate(async_task: QueueTask, params: ImageGenerationParams) -> List[ImageGenerationResult]:
+    try:
+        import modules.default_pipeline as pipeline
+    except Exception as e:
+        print('Import default pipeline error:', e)
+        if not async_task.is_finished:
+            task_queue.finish_task(async_task.job_id)
+            async_task.set_result([], True, str(e))
+            print(f"[Task Queue] Finish task with error, seq={async_task.job_id}")
+        return []
+
+    import modules.patch as patch
+    import modules.flags as flags
+    import modules.core as core
+    import modules.inpaint_worker as inpaint_worker
+    import modules.config as config
+    import modules.advanced_parameters as advanced_parameters
+    import modules.constants as constants
+    import extras.preprocessors as preprocessors
+    import extras.ip_adapter as ip_adapter
+    import extras.face_crop as face_crop
+    import ldm_patched.modules.model_management as model_management
+    from modules.util import remove_empty_str, resize_image, HWC3, set_image_shape_ceil, get_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
+    from modules.private_logger import log
+    from modules.upscaler import perform_upscale
+    from extras.expansion import safe_str
+    from modules.sdxl_styles import apply_style, fooocus_expansion, apply_wildcards
+    import fooocus_version
+
+    outputs = TaskOutputs(async_task)
+    results = []
+
+    def refresh_seed(r, seed_string):
+        if r:
+            return random.randint(constants.MIN_SEED, constants.MAX_SEED)
+        else:
+            try:
+                seed_value = int(seed_string)
+                if constants.MIN_SEED <= seed_value <= constants.MAX_SEED:
+                    return seed_value
+            except ValueError:
+                pass
+            return random.randint(constants.MIN_SEED, constants.MAX_SEED)
+        
+    def progressbar(_, number, text):
+        print(f'[Fooocus] {text}')
+        outputs.append(['preview', (number, text, None)])
+
+    def yield_result(_, imgs, tasks):
+        if not isinstance(imgs, list):
+            imgs = [imgs]
+
+        results = []
+        for i, im in enumerate(imgs):
+            seed = -1 if len(tasks) == 0 else tasks[i]['task_seed']
+            img_filename = save_output_file(im)
+            results.append(ImageGenerationResult(im=img_filename, seed=str(seed), finish_reason=GenerationFinishReason.success))
+        async_task.set_result(results, False)
+        task_queue.finish_task(async_task.job_id)
+        print(f"[Task Queue] Finish task, job_id={async_task.job_id}")
+
+        outputs.append(['results', imgs])
+        pipeline.prepare_text_encoder(async_call=True)
+        return results
+
+    try:
+        waiting_sleep_steps: int = 0
+        waiting_start_time = time.perf_counter()
+        while not task_queue.is_task_ready_to_start(async_task.job_id):
+            if waiting_sleep_steps == 0:
+                print(
+                    f"[Task Queue] Waiting for task queue become free, job_id={async_task.job_id}")
+            delay = 0.1
+            time.sleep(delay)
+            waiting_sleep_steps += 1
+            if waiting_sleep_steps % int(10 / delay) == 0:
+                waiting_time = time.perf_counter() - waiting_start_time
+                print(
+                    f"[Task Queue] Already waiting for {waiting_time}S, seq={async_task.job_id}")
+
+        print(f"[Task Queue] Task queue is free, start task, job_id={async_task.job_id}")
+
+        task_queue.start_task(async_task.job_id)
+
+        execution_start_time = time.perf_counter()
+
+        # Transform parameters
+        prompt = params.prompt
+        negative_prompt = params.negative_prompt
+        style_selections = params.style_selections
+        performance_selection = params.performance_selection
+        aspect_ratios_selection = params.aspect_ratios_selection
+        image_number = params.image_number
+        image_seed = None if params.image_seed == -1 else params.image_seed
+        sharpness = params.sharpness
+        guidance_scale = params.guidance_scale
+        base_model_name = params.base_model_name
+        refiner_model_name = params.refiner_model_name
+        refiner_switch = params.refiner_switch
+        loras = params.loras
+        input_image_checkbox = params.uov_input_image is not None or params.inpaint_input_image is not None or len(params.image_prompts) > 0
+        current_tab = 'uov' if params.uov_method != flags.disabled else 'ip' if len(params.image_prompts) > 0 else 'inpaint' if params.inpaint_input_image is not None else None
+        uov_method = params.uov_method
+        upscale_value = params.upscale_value
+        uov_input_image = params.uov_input_image
+        outpaint_selections = params.outpaint_selections
+        outpaint_distance_left = params.outpaint_distance_left
+        outpaint_distance_top = params.outpaint_distance_top
+        outpaint_distance_right = params.outpaint_distance_right
+        outpaint_distance_bottom = params.outpaint_distance_bottom
+        inpaint_input_image = params.inpaint_input_image
+        inpaint_additional_prompt = params.inpaint_additional_prompt
+        inpaint_mask_image_upload = None
+
+        if inpaint_additional_prompt is None:
+            inpaint_additional_prompt = ''
+
+        image_seed = refresh_seed(image_seed is None, image_seed)
+
+        cn_tasks = {x: [] for x in flags.ip_list}
+        for img_prompt in params.image_prompts:
+            cn_img, cn_stop, cn_weight, cn_type = img_prompt
+            cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
+
+        advanced_parameters.set_all_advanced_parameters(*params.advanced_params)
+
+        if inpaint_input_image is not None and inpaint_input_image['image'] is not None:
+            inpaint_image_size = inpaint_input_image['image'].shape[:2]
+            if inpaint_input_image['mask'] is None:
+                inpaint_input_image['mask'] = np.zeros(inpaint_image_size, dtype=np.uint8)
+            else:
+                advanced_parameters.inpaint_mask_upload_checkbox = True
+
+            inpaint_input_image['mask'] = HWC3(inpaint_input_image['mask'])
+            inpaint_mask_image_upload = inpaint_input_image['mask']
+
+        # Fooocus async_worker.py code start
+
+        outpaint_selections = [o.lower() for o in outpaint_selections]
+        base_model_additional_loras = []
+        raw_style_selections = copy.deepcopy(style_selections)
+        uov_method = uov_method.lower()
+
+        if fooocus_expansion in style_selections:
+            use_expansion = True
+            style_selections.remove(fooocus_expansion)
+        else:
+            use_expansion = False
+
+        use_style = len(style_selections) > 0
+
+        if base_model_name == refiner_model_name:
+            print(f'Refiner disabled because base model and refiner are same.')
+            refiner_model_name = 'None'
+
+        assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
+
+        steps = 30
+
+        if performance_selection == 'Speed':
+            steps = 30
+
+        if performance_selection == 'Quality':
+            steps = 60
+
+        if performance_selection == 'Extreme Speed':
+            print('Enter LCM mode.')
+            progressbar(async_task, 1, 'Downloading LCM components ...')
+            loras += [(config.downloading_sdxl_lcm_lora(), 1.0)]
+
+            if refiner_model_name != 'None':
+                print(f'Refiner disabled in LCM mode.')
+
+            refiner_model_name = 'None'
+            sampler_name = advanced_parameters.sampler_name = 'lcm'
+            scheduler_name = advanced_parameters.scheduler_name = 'lcm'
+            patch.sharpness = sharpness = 0.0
+            cfg_scale = guidance_scale = 1.0
+            patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
+            refiner_switch = 1.0
+            patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
+            patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
+            patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
+            steps = 8
+
+        patch.adaptive_cfg = advanced_parameters.adaptive_cfg
+        print(f'[Parameters] Adaptive CFG = {patch.adaptive_cfg}')
+
+        patch.sharpness = sharpness
+        print(f'[Parameters] Sharpness = {patch.sharpness}')
+
+        patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
+        patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
+        patch.adm_scaler_end = advanced_parameters.adm_scaler_end
+        print(f'[Parameters] ADM Scale = '
+              f'{patch.positive_adm_scale} : '
+              f'{patch.negative_adm_scale} : '
+              f'{patch.adm_scaler_end}')
+
+        cfg_scale = float(guidance_scale)
+        print(f'[Parameters] CFG = {cfg_scale}')
+
+        initial_latent = None
+        denoising_strength = 1.0
+        tiled = False
+
+        # Validate input format
+        if not aspect_ratios_selection.replace('*', ' ').replace(' ', '').isdigit():
+            raise ValueError("Invalid input format. Please enter aspect ratios in the form 'width*height'.")
+        width, height = aspect_ratios_selection.replace('*', '*').replace('*', ' ').split(' ')[:2]
+        # Validate width and height are integers
+        if not (width.isdigit() and height.isdigit()):
+            raise ValueError("Invalid width or height. Please enter valid integers.")
+
+        width, height = int(width), int(height)
+
+        skip_prompt_processing = False
+        refiner_swap_method = advanced_parameters.refiner_swap_method
+
+        inpaint_worker.current_task = None
+        inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
+        inpaint_image = None
+        inpaint_mask = None
+        inpaint_head_model_path = None
+
+        use_synthetic_refiner = False
+
+        controlnet_canny_path = None
+        controlnet_cpds_path = None
+        clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None
+
+        seed = int(image_seed)
+        print(f'[Parameters] Seed = {seed}')
+
+        sampler_name = advanced_parameters.sampler_name
+        scheduler_name = advanced_parameters.scheduler_name
+
+        goals = []
+        tasks = []
+
+        if input_image_checkbox:
+            if (current_tab == 'uov' or (
+                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
+                    and uov_method != flags.disabled and uov_input_image is not None:
+                uov_input_image = HWC3(uov_input_image)
+                if 'vary' in uov_method:
+                    goals.append('vary')
+                elif 'upscale' in uov_method:
+                    goals.append('upscale')
+                    if 'fast' in uov_method:
+                        skip_prompt_processing = True
+                    else:
+                        steps = 18
+
+                        if performance_selection == 'Speed':
+                            steps = 18
+
+                        if performance_selection == 'Quality':
+                            steps = 36
+
+                        if performance_selection == 'Extreme Speed':
+                            steps = 8
+
+                    progressbar(async_task, 1, 'Downloading upscale models ...')
+                    config.downloading_upscale_model()
+            if (current_tab == 'inpaint' or (
+                    current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
+                    and isinstance(inpaint_input_image, dict):
+                inpaint_image = inpaint_input_image['image']
+                inpaint_mask = inpaint_input_image['mask'][:, :, 0]
+
+                if advanced_parameters.inpaint_mask_upload_checkbox:
+                    if isinstance(inpaint_mask_image_upload, np.ndarray):
+                        if inpaint_mask_image_upload.ndim == 3:
+                            H, W, C = inpaint_image.shape
+                            inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H)
+                            inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2)
+                            inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
+                            inpaint_mask = inpaint_mask_image_upload
+
+                if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
+                    inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
+
+                if advanced_parameters.invert_mask_checkbox:
+                    inpaint_mask = 255 - inpaint_mask
+
+                inpaint_image = HWC3(inpaint_image)
+                if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
+                        and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
+                    progressbar(async_task, 1, 'Downloading upscale models ...')
+                    config.downloading_upscale_model()
+                    if inpaint_parameterized:
+                        progressbar(async_task, 1, 'Downloading inpainter ...')
+                        inpaint_head_model_path, inpaint_patch_model_path = config.downloading_inpaint_models(
+                            advanced_parameters.inpaint_engine)
+                        base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
+                        print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
+                        if refiner_model_name == 'None':
+                            use_synthetic_refiner = True
+                            refiner_switch = 0.5
+                    else:
+                        inpaint_head_model_path, inpaint_patch_model_path = None, None
+                        print(f'[Inpaint] Parameterized inpaint is disabled.')
+                    if inpaint_additional_prompt != '':
+                        if prompt == '':
+                            prompt = inpaint_additional_prompt
+                        else:
+                            prompt = inpaint_additional_prompt + '\n' + prompt
+                    goals.append('inpaint')
+            if current_tab == 'ip' or \
+                    advanced_parameters.mixing_image_prompt_and_inpaint or \
+                    advanced_parameters.mixing_image_prompt_and_vary_upscale:
+                goals.append('cn')
+                progressbar(async_task, 1, 'Downloading control models ...')
+                if len(cn_tasks[flags.cn_canny]) > 0:
+                    controlnet_canny_path = config.downloading_controlnet_canny()
+                if len(cn_tasks[flags.cn_cpds]) > 0:
+                    controlnet_cpds_path = config.downloading_controlnet_cpds()
+                if len(cn_tasks[flags.cn_ip]) > 0:
+                    clip_vision_path, ip_negative_path, ip_adapter_path = config.downloading_ip_adapters('ip')
+                if len(cn_tasks[flags.cn_ip_face]) > 0:
+                    clip_vision_path, ip_negative_path, ip_adapter_face_path = config.downloading_ip_adapters(
+                        'face')
+                progressbar(async_task, 1, 'Loading control models ...')
+
+        # Load or unload CNs
+        pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
+        ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
+        ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
+
+        switch = int(round(steps * refiner_switch))
+
+        if advanced_parameters.overwrite_step > 0:
+            steps = advanced_parameters.overwrite_step
+
+        if advanced_parameters.overwrite_switch > 0:
+            switch = advanced_parameters.overwrite_switch
+
+        if advanced_parameters.overwrite_width > 0:
+            width = advanced_parameters.overwrite_width
+
+        if advanced_parameters.overwrite_height > 0:
+            height = advanced_parameters.overwrite_height
+
+        print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
+        print(f'[Parameters] Steps = {steps} - {switch}')
+
+        progressbar(async_task, 1, 'Initializing ...')
+
+        if not skip_prompt_processing:
+
+            prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')
+            negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='')
+
+            prompt = prompts[0]
+            negative_prompt = negative_prompts[0]
+
+            if prompt == '':
+                # disable expansion when empty since it is not meaningful and influences image prompt
+                use_expansion = False
+
+            extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
+            extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
+
+            progressbar(async_task, 3, 'Loading models ...')
+            pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
+                                        loras=loras, base_model_additional_loras=base_model_additional_loras,
+                                        use_synthetic_refiner=use_synthetic_refiner)
+
+            progressbar(async_task, 3, 'Processing prompts ...')
+            tasks = []
+            for i in range(image_number):
+                task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
+                task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future
+
+                task_prompt = apply_wildcards(prompt, task_rng)
+                task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
+                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
+                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
+
+                positive_basic_workloads = []
+                negative_basic_workloads = []
+
+                if use_style:
+                    for s in style_selections:
+                        p, n = apply_style(s, positive=task_prompt)
+                        positive_basic_workloads = positive_basic_workloads + p
+                        negative_basic_workloads = negative_basic_workloads + n
+                else:
+                    positive_basic_workloads.append(task_prompt)
+
+                negative_basic_workloads.append(task_negative_prompt)  # Always use independent workload for negative.
+
+                positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts
+                negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts
+
+                positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt)
+                negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt)
+
+                tasks.append(dict(
+                    task_seed=task_seed,
+                    task_prompt=task_prompt,
+                    task_negative_prompt=task_negative_prompt,
+                    positive=positive_basic_workloads,
+                    negative=negative_basic_workloads,
+                    expansion='',
+                    c=None,
+                    uc=None,
+                    positive_top_k=len(positive_basic_workloads),
+                    negative_top_k=len(negative_basic_workloads),
+                    log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
+                    log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
+                ))
+
+            if use_expansion:
+                for i, t in enumerate(tasks):
+                    progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
+                    expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
+                    print(f'[Prompt Expansion] {expansion}')
+                    t['expansion'] = expansion
+                    t['positive'] = copy.deepcopy(t['positive']) + [expansion]  # Deep copy.
+
+            for i, t in enumerate(tasks):
+                progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
+                t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
+
+            for i, t in enumerate(tasks):
+                if abs(float(cfg_scale) - 1.0) < 1e-4:
+                    t['uc'] = pipeline.clone_cond(t['c'])
+                else:
+                    progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
+                    t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
+
+        if len(goals) > 0:
+            progressbar(async_task, 13, 'Image processing ...')
+
+        if 'vary' in goals:
+            if 'subtle' in uov_method:
+                denoising_strength = 0.5
+            if 'strong' in uov_method:
+                denoising_strength = 0.85
+            if advanced_parameters.overwrite_vary_strength > 0:
+                denoising_strength = advanced_parameters.overwrite_vary_strength
+
+            shape_ceil = get_image_shape_ceil(uov_input_image)
+            if shape_ceil < 1024:
+                print(f'[Vary] Image is resized because it is too small.')
+                shape_ceil = 1024
+            elif shape_ceil > 2048:
+                print(f'[Vary] Image is resized because it is too big.')
+                shape_ceil = 2048
+
+            uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
+
+            initial_pixels = core.numpy_to_pytorch(uov_input_image)
+            progressbar(async_task, 13, 'VAE encoding ...')
+
+            candidate_vae, _ = pipeline.get_candidate_vae(
+                steps=steps,
+                switch=switch,
+                denoise=denoising_strength,
+                refiner_swap_method=refiner_swap_method
+            )
+
+            initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels)
+            B, C, H, W = initial_latent['samples'].shape
+            width = W * 8
+            height = H * 8
+            print(f'Final resolution is {str((height, width))}.')
+
+        if 'upscale' in goals:
+            H, W, C = uov_input_image.shape
+            progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
+            uov_input_image = perform_upscale(uov_input_image)
+            print(f'Image upscaled.')
+
+            f = 1.0
+            if upscale_value is not None and upscale_value > 1.0:
+                f = upscale_value
+            else:
+                pattern = r"([0-9]+(?:\.[0-9]+)?)x"
+                matches = re.findall(pattern, uov_method)
+                if len(matches) > 0:
+                    f_tmp = float(matches[0])
+                    f = 1.0 if f_tmp < 1.0 else 5.0 if f_tmp > 5.0 else f_tmp
+
+            shape_ceil = get_shape_ceil(H * f, W * f)
+
+            if shape_ceil < 1024:
+                print(f'[Upscale] Image is resized because it is too small.')
+                uov_input_image = set_image_shape_ceil(uov_input_image, 1024)
+                shape_ceil = 1024
+            else:
+                uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f)
+
+            image_is_super_large = shape_ceil > 2800
+
+            if 'fast' in uov_method:
+                direct_return = True
+            elif image_is_super_large:
+                print('Image is too large. Directly returned the SR image. '
+                      'Usually directly return SR image at 4K resolution '
+                      'yields better results than SDXL diffusion.')
+                direct_return = True
+            else:
+                direct_return = False
+
+            if direct_return:
+                d = [('Upscale (Fast)', '2x')]
+                log(uov_input_image, d)
+                return yield_result(async_task, uov_input_image, tasks)
+
+            tiled = True
+            denoising_strength = 0.382
+
+            if advanced_parameters.overwrite_upscale_strength > 0:
+                denoising_strength = advanced_parameters.overwrite_upscale_strength
+
+            initial_pixels = core.numpy_to_pytorch(uov_input_image)
+            progressbar(async_task, 13, 'VAE encoding ...')
+
+            candidate_vae, _ = pipeline.get_candidate_vae(
+                steps=steps,
+                switch=switch,
+                denoise=denoising_strength,
+                refiner_swap_method=refiner_swap_method
+            )
+
+            initial_latent = core.encode_vae(
+                vae=candidate_vae,
+                pixels=initial_pixels, tiled=True)
+            B, C, H, W = initial_latent['samples'].shape
+            width = W * 8
+            height = H * 8
+            print(f'Final resolution is {str((height, width))}.')
+
+        if 'inpaint' in goals:
+            if len(outpaint_selections) > 0:
+                H, W, C = inpaint_image.shape
+                if 'top' in outpaint_selections:
+                    distance_top = int(H * 0.3)
+                    if outpaint_distance_top > 0:
+                        distance_top = outpaint_distance_top
+
+                    inpaint_image = np.pad(inpaint_image, [[distance_top, 0], [0, 0], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[distance_top, 0], [0, 0]], mode='constant',
+                                          constant_values=255)
+                
+                if 'bottom' in outpaint_selections:
+                    distance_bottom = int(H * 0.3)
+                    if outpaint_distance_bottom > 0:
+                        distance_bottom = outpaint_distance_bottom
+
+                    inpaint_image = np.pad(inpaint_image, [[0, distance_bottom], [0, 0], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, distance_bottom], [0, 0]], mode='constant',
+                                          constant_values=255)
+
+                H, W, C = inpaint_image.shape
+                if 'left' in outpaint_selections:
+                    distance_left = int(W * 0.3)
+                    if outpaint_distance_left > 0:
+                        distance_left = outpaint_distance_left
+                    
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [distance_left, 0], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [distance_left, 0]], mode='constant',
+                                          constant_values=255)
+                
+                if 'right' in outpaint_selections:
+                    distance_right = int(W * 0.3)
+                    if outpaint_distance_right > 0:
+                        distance_right = outpaint_distance_right
+                    
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, distance_right], [0, 0]], mode='edge')
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, distance_right]], mode='constant',
+                                          constant_values=255)
+                    
+                inpaint_image = np.ascontiguousarray(inpaint_image.copy())
+                inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
+                advanced_parameters.inpaint_strength = 1.0
+                advanced_parameters.inpaint_respective_field = 1.0
+
+            denoising_strength = advanced_parameters.inpaint_strength
+
+            inpaint_worker.current_task = inpaint_worker.InpaintWorker(
+                image=inpaint_image,
+                mask=inpaint_mask,
+                use_fill=denoising_strength > 0.99,
+                k=advanced_parameters.inpaint_respective_field
+            )
+
+            if advanced_parameters.debugging_inpaint_preprocessor:
+                return yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
+                             do_not_show_finished_images=True)
+
+            progressbar(async_task, 13, 'VAE Inpaint encoding ...')
+
+            inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
+            inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
+            inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
+
+            candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(
+                steps=steps,
+                switch=switch,
+                denoise=denoising_strength,
+                refiner_swap_method=refiner_swap_method
+            )
+
+            latent_inpaint, latent_mask = core.encode_vae_inpaint(
+                mask=inpaint_pixel_mask,
+                vae=candidate_vae,
+                pixels=inpaint_pixel_image)
+
+            latent_swap = None
+            if candidate_vae_swap is not None:
+                progressbar(async_task, 13, 'VAE SD15 encoding ...')
+                latent_swap = core.encode_vae(
+                    vae=candidate_vae_swap,
+                    pixels=inpaint_pixel_fill)['samples']
+
+            progressbar(async_task, 13, 'VAE encoding ...')
+            latent_fill = core.encode_vae(
+                vae=candidate_vae,
+                pixels=inpaint_pixel_fill)['samples']
+
+            inpaint_worker.current_task.load_latent(
+                latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)
+
+            if inpaint_parameterized:
+                pipeline.final_unet = inpaint_worker.current_task.patch(
+                    inpaint_head_model_path=inpaint_head_model_path,
+                    inpaint_latent=latent_inpaint,
+                    inpaint_latent_mask=latent_mask,
+                    model=pipeline.final_unet
+                )
+
+            if not advanced_parameters.inpaint_disable_initial_latent:
+                initial_latent = {'samples': latent_fill}
+
+            B, C, H, W = latent_fill.shape
+            height, width = H * 8, W * 8
+            final_height, final_width = inpaint_worker.current_task.image.shape[:2]
+            print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.')
+
+        if 'cn' in goals:
+            for task in cn_tasks[flags.cn_canny]:
+                cn_img, cn_stop, cn_weight = task
+                cn_img = resize_image(HWC3(cn_img), width=width, height=height)
+
+                if not advanced_parameters.skipping_cn_preprocessor:
+                    cn_img = preprocessors.canny_pyramid(cn_img)
+
+                cn_img = HWC3(cn_img)
+                task[0] = core.numpy_to_pytorch(cn_img)
+                if advanced_parameters.debugging_cn_preprocessor:
+                    return yield_result(async_task, cn_img, tasks)
+            for task in cn_tasks[flags.cn_cpds]:
+                cn_img, cn_stop, cn_weight = task
+                cn_img = resize_image(HWC3(cn_img), width=width, height=height)
+
+                if not advanced_parameters.skipping_cn_preprocessor:
+                    cn_img = preprocessors.cpds(cn_img)
+
+                cn_img = HWC3(cn_img)
+                task[0] = core.numpy_to_pytorch(cn_img)
+                if advanced_parameters.debugging_cn_preprocessor:
+                    return yield_result(async_task, cn_img, tasks)
+            for task in cn_tasks[flags.cn_ip]:
+                cn_img, cn_stop, cn_weight = task
+                cn_img = HWC3(cn_img)
+
+                # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
+                cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
+
+                task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
+                if advanced_parameters.debugging_cn_preprocessor:
+                    return yield_result(async_task, cn_img, tasks)
+            for task in cn_tasks[flags.cn_ip_face]:
+                cn_img, cn_stop, cn_weight = task
+                cn_img = HWC3(cn_img)
+
+                if not advanced_parameters.skipping_cn_preprocessor:
+                    cn_img = face_crop.crop_image(cn_img)
+
+                # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
+                cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
+
+                task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
+                if advanced_parameters.debugging_cn_preprocessor:
+                    return yield_result(async_task, cn_img, tasks)
+
+            all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
+
+            if len(all_ip_tasks) > 0:
+                pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
+
+        if advanced_parameters.freeu_enabled:
+            print(f'FreeU is enabled!')
+            pipeline.final_unet = core.apply_freeu(
+                pipeline.final_unet,
+                advanced_parameters.freeu_b1,
+                advanced_parameters.freeu_b2,
+                advanced_parameters.freeu_s1,
+                advanced_parameters.freeu_s2
+            )
+
+        all_steps = steps * image_number
+
+        print(f'[Parameters] Denoising Strength = {denoising_strength}')
+
+        if isinstance(initial_latent, dict) and 'samples' in initial_latent:
+            log_shape = initial_latent['samples'].shape
+        else:
+            log_shape = f'Image Space {(height, width)}'
+
+        print(f'[Parameters] Initial Latent shape: {log_shape}')
+
+        preparation_time = time.perf_counter() - execution_start_time
+        print(f'Preparation time: {preparation_time:.2f} seconds')
+
+        final_sampler_name = sampler_name
+        final_scheduler_name = scheduler_name
+
+        if scheduler_name == 'lcm':
+            final_scheduler_name = 'sgm_uniform'
+            if pipeline.final_unet is not None:
+                pipeline.final_unet = core.opModelSamplingDiscrete.patch(
+                    pipeline.final_unet,
+                    sampling='lcm',
+                    zsnr=False)[0]
+            if pipeline.final_refiner_unet is not None:
+                pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
+                    pipeline.final_refiner_unet,
+                    sampling='lcm',
+                    zsnr=False)[0]
+            print('Using lcm scheduler.')
+
+        outputs.append(['preview', (13, 'Moving model to GPU ...', None)])
+
+        def callback(step, x0, x, total_steps, y):
+            done_steps = current_task_id * steps + step
+            outputs.append(['preview', (
+                int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
+                f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling',
+                y)])
+
+        for current_task_id, task in enumerate(tasks):
+            execution_start_time = time.perf_counter()
+
+            try:
+                positive_cond, negative_cond = task['c'], task['uc']
+
+                if 'cn' in goals:
+                    for cn_flag, cn_path in [
+                        (flags.cn_canny, controlnet_canny_path),
+                        (flags.cn_cpds, controlnet_cpds_path)
+                    ]:
+                        for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]:
+                            positive_cond, negative_cond = core.apply_controlnet(
+                                positive_cond, negative_cond,
+                                pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop)
+
+                imgs = pipeline.process_diffusion(
+                    positive_cond=positive_cond,
+                    negative_cond=negative_cond,
+                    steps=steps,
+                    switch=switch,
+                    width=width,
+                    height=height,
+                    image_seed=task['task_seed'],
+                    callback=callback,
+                    sampler_name=final_sampler_name,
+                    scheduler_name=final_scheduler_name,
+                    latent=initial_latent,
+                    denoise=denoising_strength,
+                    tiled=tiled,
+                    cfg_scale=cfg_scale,
+                    refiner_swap_method=refiner_swap_method
+                )
+
+                del task['c'], task['uc'], positive_cond, negative_cond  # Save memory
+
+                if inpaint_worker.current_task is not None:
+                    imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
+
+                for x in imgs:
+                    d = [
+                        ('Prompt', task['log_positive_prompt']),
+                        ('Negative Prompt', task['log_negative_prompt']),
+                        ('Fooocus V2 Expansion', task['expansion']),
+                        ('Styles', str(raw_style_selections)),
+                        ('Performance', performance_selection),
+                        ('Resolution', str((width, height))),
+                        ('Sharpness', sharpness),
+                        ('Guidance Scale', guidance_scale),
+                        ('ADM Guidance', str((
+                            patch.positive_adm_scale,
+                            patch.negative_adm_scale,
+                            patch.adm_scaler_end))),
+                        ('Base Model', base_model_name),
+                        ('Refiner Model', refiner_model_name),
+                        ('Refiner Switch', refiner_switch),
+                        ('Sampler', sampler_name),
+                        ('Scheduler', scheduler_name),
+                        ('Seed', task['task_seed']),
+                    ]
+                    for n, w in loras:
+                        if n != 'None':
+                            d.append((f'LoRA', f'{n} : {w}'))
+                    d.append(('Version', 'v' + fooocus_version.version))
+                    log(x, d)
+                
+                # Fooocus async_worker.py code end
+                
+                results += imgs
+            except model_management.InterruptProcessingException as e:
+                print("User stopped")
+                results.append(ImageGenerationResult(
+                    im=None, seed=task['task_seed'], finish_reason=GenerationFinishReason.user_cancel))
+                async_task.set_result(results, True, str(e))
+                break
+            except Exception as e:
+                print('Process error:', e)
+                results.append(ImageGenerationResult(
+                    im=None, seed=task['task_seed'], finish_reason=GenerationFinishReason.error))
+                async_task.set_result(results, True, str(e))
+                break
+
+            execution_time = time.perf_counter() - execution_start_time
+            print(f'Generating and saving time: {execution_time:.2f} seconds')
+
+        if async_task.finish_with_error:
+            task_queue.finish_task(async_task.job_id)
+            return async_task.task_result
+        return yield_result(None, results, tasks)
+    except Exception as e:
+        print('Worker error:', e)
+        logging.exception(e)
+
+        if not async_task.is_finished:
+            task_queue.finish_task(async_task.job_id)
+            async_task.set_result([], True, str(e))
+            print(f"[Task Queue] Finish task with error, job_id={async_task.job_id}")
+        return []

Fooocus-API/main.py

@@ -0,0 +1,419 @@
+import argparse
+import os
+import re
+import shutil
+import subprocess
+import sys
+from importlib.util import find_spec
+from threading import Thread
+
+from fooocus_api_version import version
+from fooocusapi.repositories_versions import fooocus_commit_hash
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+
+
+print('[System ARGV] ' + str(sys.argv))
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
+
+python = sys.executable
+default_command_live = True
+index_url = os.environ.get('INDEX_URL', "")
+re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
+
+fooocus_name = 'Fooocus'
+
+fooocus_gitee_repo = 'https://gitee.com/mirrors/fooocus'
+fooocus_github_repo = 'https://github.com/lllyasviel/Fooocus'
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = modules_path
+dir_repos = "repositories"
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def onerror(func, path, exc_info):
+    import stat
+    if not os.access(path, os.W_OK):
+        os.chmod(path, stat.S_IWUSR)
+        func(path)
+    else:
+        raise 'Failed to invoke "shutil.rmtree", git management failed.'
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def git_clone(url, dir, name, hash=None):
+    import pygit2
+
+    try:
+        try:
+            repo = pygit2.Repository(dir)
+            remote_url = repo.remotes['origin'].url
+            if remote_url not in [fooocus_gitee_repo, fooocus_github_repo]:
+                print(f'{name} exists but remote URL will be updated.')
+                del repo
+                raise url
+            else:
+                print(f'{name} exists and URL is correct.')
+            url = remote_url
+        except:
+            if os.path.isdir(dir) or os.path.exists(dir):
+                print("Fooocus exists, but not a git repo. You can find how to solve this problem here: https://github.com/konieshadow/Fooocus-API#use-exist-fooocus")
+                sys.exit(1)
+            os.makedirs(dir, exist_ok=True)
+            repo = pygit2.clone_repository(url, dir)
+            print(f'{name} cloned from {url}.')
+
+        remote = repo.remotes['origin']
+        remote.fetch()
+
+        commit = repo.get(hash)
+
+        repo.checkout_tree(commit, strategy=pygit2.GIT_CHECKOUT_FORCE)
+        repo.set_head(commit.id)
+
+        print(f'{name} checkout finished for {hash}.')
+    except Exception as e:
+        print(f'Git clone failed for {name}: {str(e)}')
+        raise e
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def repo_dir(name):
+    return os.path.join(script_path, dir_repos, name)
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
+    if desc is not None:
+        print(desc)
+
+    run_kwargs = {
+        "args": command,
+        "shell": True,
+        "env": os.environ if custom_env is None else custom_env,
+        "encoding": 'utf8',
+        "errors": 'ignore',
+    }
+
+    if not live:
+        run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
+
+    result = subprocess.run(**run_kwargs)
+
+    if result.returncode != 0:
+        error_bits = [
+            f"{errdesc or 'Error running command'}.",
+            f"Command: {command}",
+            f"Error code: {result.returncode}",
+        ]
+        if result.stdout:
+            error_bits.append(f"stdout: {result.stdout}")
+        if result.stderr:
+            error_bits.append(f"stderr: {result.stderr}")
+        raise RuntimeError("\n".join(error_bits))
+
+    return result.stdout or ""
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def run_pip(command, desc=None, live=default_command_live):
+    try:
+        index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+        return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}",
+                   errdesc=f"Couldn't install {desc}", live=live)
+    except Exception as e:
+        print(e)
+        print(f'CMD Failed {desc}: {command}')
+        return None
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def requirements_met(requirements_file):
+    """
+    Does a simple parse of a requirements.txt file to determine if all requirements in it
+    are already installed. Returns True if so, False if not installed or parsing fails.
+    """
+
+    import importlib.metadata
+    import packaging.version
+
+    with open(requirements_file, "r", encoding="utf8") as file:
+        for line in file:
+            if line.strip() == "":
+                continue
+
+            m = re.match(re_requirement, line)
+            if m is None:
+                return False
+
+            package = m.group(1).strip()
+            version_required = (m.group(2) or "").strip()
+
+            if version_required == "":
+                continue
+
+            try:
+                version_installed = importlib.metadata.version(package)
+            except Exception:
+                return False
+
+            if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
+                return False
+
+    return True
+
+
+def download_repositories():
+    import pygit2
+    import requests
+
+    pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
+
+    http_proxy = os.environ.get('HTTP_PROXY')
+    https_proxy = os.environ.get('HTTPS_PROXY')
+
+    if http_proxy is not None:
+        print(f"Using http proxy for git clone: {http_proxy}")
+        os.environ['http_proxy'] = http_proxy
+
+    if https_proxy is not None:
+        print(f"Using https proxy for git clone: {https_proxy}")
+        os.environ['https_proxy'] = https_proxy
+
+    try:
+        requests.get("https://policies.google.com/privacy", timeout=5)
+        fooocus_repo_url = fooocus_github_repo
+    except:
+        fooocus_repo_url = fooocus_gitee_repo
+    fooocus_repo = os.environ.get(
+        'FOOOCUS_REPO', fooocus_repo_url)
+    git_clone(fooocus_repo, repo_dir(fooocus_name),
+              "Fooocus", fooocus_commit_hash)
+
+
+def is_installed(package):
+    try:
+        spec = find_spec(package)
+    except ModuleNotFoundError:
+        return False
+
+    return spec is not None
+
+
+def download_models():
+    vae_approx_filenames = [
+        ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'),
+        ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'),
+        ('xl-to-v1_interposer-v3.1.safetensors',
+         'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
+    ]
+
+    from modules.model_loader import load_file_from_url
+    from modules.config import (path_checkpoints as modelfile_path,
+                                path_loras as lorafile_path,
+                                path_vae_approx as vae_approx_path,
+                                path_fooocus_expansion as fooocus_expansion_path,
+                                checkpoint_downloads,
+                                path_embeddings as embeddings_path,
+                                embeddings_downloads, lora_downloads)
+
+    for file_name, url in checkpoint_downloads.items():
+        load_file_from_url(url=url, model_dir=modelfile_path, file_name=file_name)
+    for file_name, url in embeddings_downloads.items():
+        load_file_from_url(url=url, model_dir=embeddings_path, file_name=file_name)
+    for file_name, url in lora_downloads.items():
+        load_file_from_url(url=url, model_dir=lorafile_path, file_name=file_name)
+    for file_name, url in vae_approx_filenames:
+        load_file_from_url(url=url, model_dir=vae_approx_path, file_name=file_name)
+
+    load_file_from_url(
+        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
+        model_dir=fooocus_expansion_path,
+        file_name='pytorch_model.bin'
+    )
+
+
+def install_dependents(args):
+    if not args.skip_pip:
+        torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
+
+        # Check if you need pip install
+        requirements_file = 'requirements.txt'
+        if not requirements_met(requirements_file):
+            run_pip(f"install -r \"{requirements_file}\"", "requirements")
+
+        if not is_installed("torch") or not is_installed("torchvision"):
+            print(f"torch_index_url: {torch_index_url}")
+            run_pip(f"install torch==2.1.0 torchvision==0.16.0 --extra-index-url {torch_index_url}", "torch")
+
+    skip_sync_repo = False
+    if args.sync_repo is not None:
+        if args.sync_repo == 'only':
+            print("Only download and sync depent repositories")
+            download_repositories()
+            models_path = os.path.join(
+                script_path, dir_repos, fooocus_name, "models")
+            print(
+                f"Sync repositories successful. Now you can put model files in subdirectories of '{models_path}'")
+            return False
+        elif args.sync_repo == 'skip':
+            skip_sync_repo = True
+        else:
+            print(
+                f"Invalid value for argument '--sync-repo', acceptable value are 'skip' and 'only'")
+            exit(1)
+
+    if not skip_sync_repo:
+        download_repositories()
+
+    # Add dependent repositories to import path
+    sys.path.append(script_path)
+    fooocus_path = os.path.join(script_path, dir_repos, fooocus_name)
+    sys.path.append(fooocus_path)
+    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
+
+def prepare_environments(args) -> bool:
+    import fooocusapi.worker as worker
+    worker.task_queue.queue_size = args.queue_size
+    worker.task_queue.history_size = args.queue_history
+    worker.task_queue.webhook_url = args.webhook_url
+    print(f"[Fooocus-API] Task queue size: {args.queue_size}, queue history size: {args.queue_history}, webhook url: {args.webhook_url}")
+
+    if args.gpu_device_id is not None:
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
+        print("Set device to:", args.gpu_device_id)
+
+    if args.base_url is None or len(args.base_url.strip()) == 0:
+        host = args.host
+        if host == '0.0.0.0':
+            host = '127.0.0.1'
+        args.base_url = f"http://{host}:{args.port}"
+
+    sys.argv = [sys.argv[0]]
+
+    if args.preset is not None:
+        # Remove and copy preset folder
+        origin_preset_folder = os.path.abspath(os.path.join(script_path, dir_repos, fooocus_name, 'presets'))
+        preset_folder = os.path.abspath(os.path.join(script_path, 'presets'))
+        if os.path.exists(preset_folder):
+            shutil.rmtree(preset_folder)
+        shutil.copytree(origin_preset_folder, preset_folder)
+
+    import modules.config as config
+    import fooocusapi.parameters as parameters
+    parameters.default_inpaint_engine_version = config.default_inpaint_engine_version
+    parameters.default_styles = config.default_styles
+    parameters.default_base_model_name = config.default_base_model_name
+    parameters.default_refiner_model_name = config.default_refiner_model_name
+    parameters.default_refiner_switch = config.default_refiner_switch
+    parameters.default_loras = config.default_loras
+    parameters.default_cfg_scale = config.default_cfg_scale
+    parameters.default_prompt_negative = config.default_prompt_negative
+    parameters.default_aspect_ratio = parameters.get_aspect_ratio_value(config.default_aspect_ratio)
+    parameters.available_aspect_ratios = [parameters.get_aspect_ratio_value(a) for a in config.available_aspect_ratios]
+
+    ini_cbh_args()
+
+    download_models()
+
+    if args.preload_pipeline:
+        preplaod_pipeline()
+
+    return True
+
+
+def pre_setup(skip_sync_repo: bool = False,
+              disable_private_log: bool = False,
+              skip_pip=False,
+              load_all_models: bool = False,
+              preload_pipeline: bool = False,
+              always_gpu: bool = False,
+              all_in_fp16: bool = False,
+              preset: str | None = None):
+    class Args(object):
+        host = '127.0.0.1'
+        port = 8888
+        base_url = None
+        sync_repo = None
+        disable_private_log = False
+        skip_pip = False
+        preload_pipeline = False
+        queue_size = 3
+        queue_history = 0
+        preset = None
+        always_gpu = False
+        all_in_fp16 = False
+        gpu_device_id = None
+
+    print("[Pre Setup] Prepare environments")
+
+    args = Args()
+    if skip_sync_repo:
+        args.sync_repo = 'skip'
+    args.disable_private_log = disable_private_log
+    args.skip_pip = skip_pip
+    args.preload_pipeline = preload_pipeline
+    args.always_gpu = always_gpu
+    args.all_in_fp16 = all_in_fp16
+    args.preset = preset
+
+    sys.argv = [sys.argv[0]]
+    if args.preset is not None:
+        sys.argv.append('--preset')
+        sys.argv.append(args.preset)
+
+    install_dependents(args)
+
+    import fooocusapi.args as _
+    prepare_environments(args)
+
+    if load_all_models:
+        import modules.config as config
+        from fooocusapi.parameters import default_inpaint_engine_version
+        config.downloading_upscale_model()
+        config.downloading_inpaint_models(default_inpaint_engine_version)
+        config.downloading_controlnet_canny()
+        config.downloading_controlnet_cpds()
+        config.downloading_ip_adapters()
+    print("[Pre Setup] Finished")
+
+
+# This function was copied from [Fooocus](https://github.com/lllyasviel/Fooocus) repository.
+def ini_cbh_args():
+    from args_manager import args
+    return args
+
+
+def preplaod_pipeline():
+    print("Preload pipeline")
+    import modules.default_pipeline as _
+
+
+if __name__ == "__main__":
+    print(f"Python {sys.version}")
+    print(f"Fooocus-API version: {version}")
+
+    from fooocusapi.base_args import add_base_args
+
+    parser = argparse.ArgumentParser()
+    add_base_args(parser, True)
+
+    args, _ = parser.parse_known_args()
+    install_dependents(args)
+
+    from fooocusapi.args import args
+
+    if prepare_environments(args):
+        sys.argv = [sys.argv[0]]
+
+        # Load pipeline in new thread
+        t = Thread(target=preplaod_pipeline, daemon=True)
+        t.start()
+
+        # Start api server
+        from fooocusapi.api import start_app
+
+        start_app(args)

Fooocus-API/predict.py

@@ -0,0 +1,205 @@
+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+import os
+import numpy as np
+
+from PIL import Image
+from typing import List
+from cog import BasePredictor, Input, Path
+from fooocusapi.worker import process_generate, task_queue
+from fooocusapi.file_utils import output_dir
+from fooocusapi.parameters import (GenerationFinishReason,
+                                   ImageGenerationParams,
+                                   available_aspect_ratios,
+                                   uov_methods,
+                                   outpaint_expansions,
+                                   default_styles,
+                                   default_base_model_name,
+                                   default_refiner_model_name,
+                                   default_loras,
+                                   default_refiner_switch,
+                                   default_cfg_scale,
+                                   default_prompt_negative)
+from fooocusapi.task_queue import TaskType
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+        from main import pre_setup
+        pre_setup(disable_private_log=True, skip_pip=True, preload_pipeline=True, preset=None)
+
+    def predict(
+        self,
+        prompt: str = Input( default='', description="Prompt for image generation"),
+        negative_prompt: str = Input( default=default_prompt_negative, 
+                                description="Negtive prompt for image generation"),
+        style_selections: str = Input(default=','.join(default_styles),
+                                description="Fooocus styles applied for image generation, seperated by comma"),
+        performance_selection: str = Input( default='Speed', 
+                                description="Performance selection", choices=['Speed', 'Quality', 'Extreme Speed']),
+        aspect_ratios_selection: str = Input(default='1152*896', 
+                                description="The generated image's size", choices=available_aspect_ratios),
+        image_number: int = Input(default=1, 
+                                description="How many image to generate", ge=1, le=8),
+        image_seed: int = Input(default=-1, 
+                                description="Seed to generate image, -1 for random"),
+        sharpness: float = Input(default=2.0, ge=0.0, le=30.0),
+        guidance_scale: float = Input(default=default_cfg_scale, ge=1.0, le=30.0),
+        refiner_switch: float = Input(default=default_refiner_switch, ge=0.1, le=1.0),
+        uov_input_image: Path = Input(default=None, 
+                                description="Input image for upscale or variation, keep None for not upscale or variation"),
+        uov_method: str = Input(default='Disabled', choices=uov_methods),
+        uov_upscale_value: float = Input(default=0, description="Only when Upscale (Custom)"),
+        inpaint_additional_prompt: str = Input( default='', description="Prompt for image generation"),
+        inpaint_input_image: Path = Input(default=None, 
+                                description="Input image for inpaint or outpaint, keep None for not inpaint or outpaint. Please noticed, `uov_input_image` has bigger priority is not None."),
+        inpaint_input_mask: Path = Input(default=None, 
+                                description="Input mask for inpaint"),
+        outpaint_selections: str = Input(default='', 
+                                description="Outpaint expansion selections, literal 'Left', 'Right', 'Top', 'Bottom' seperated by comma"),
+        outpaint_distance_left: int = Input(default=0, 
+                                description="Outpaint expansion distance from Left of the image"),
+        outpaint_distance_top: int = Input(default=0, 
+                                description="Outpaint expansion distance from Top of the image"),
+        outpaint_distance_right: int = Input(default=0, 
+                                description="Outpaint expansion distance from Right of the image"),
+        outpaint_distance_bottom: int = Input(default=0, 
+                                description="Outpaint expansion distance from Bottom of the image"),
+        cn_img1: Path = Input(default=None, 
+                                description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."),
+        cn_stop1: float = Input(default=None, ge=0, le=1, 
+                                description="Stop at for image prompt, None for default value"),
+        cn_weight1: float = Input(default=None, ge=0, le=2, 
+                                description="Weight for image prompt, None for default value"),
+        cn_type1: str = Input(default='ImagePrompt', description="ControlNet type for image prompt", choices=[
+                              'ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS']),
+        cn_img2: Path = Input(default=None, 
+                              description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."),
+        cn_stop2: float = Input(default=None, ge=0, le=1, 
+                                description="Stop at for image prompt, None for default value"),
+        cn_weight2: float = Input(default=None, ge=0, le=2, 
+                                description="Weight for image prompt, None for default value"),
+        cn_type2: str = Input(default='ImagePrompt', description="ControlNet type for image prompt", choices=[
+                              'ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS']),
+        cn_img3: Path = Input(default=None, 
+                              description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."),
+        cn_stop3: float = Input(default=None, ge=0, le=1, 
+                                description="Stop at for image prompt, None for default value"),
+        cn_weight3: float = Input(default=None, ge=0, le=2, 
+                                description="Weight for image prompt, None for default value"),
+        cn_type3: str = Input(default='ImagePrompt', 
+                              description="ControlNet type for image prompt", choices=['ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS']),
+        cn_img4: Path = Input(default=None, 
+                              description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."),
+        cn_stop4: float = Input(default=None, ge=0, le=1, 
+                                description="Stop at for image prompt, None for default value"),
+        cn_weight4: float = Input(default=None, ge=0, le=2, 
+                                description="Weight for image prompt, None for default value"),
+        cn_type4: str = Input(default='ImagePrompt', description="ControlNet type for image prompt", choices=['ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS']),
+    ) -> List[Path]:
+        """Run a single prediction on the model"""
+        import modules.flags as flags
+        from modules.sdxl_styles import legal_style_names
+
+        base_model_name = default_base_model_name
+        refiner_model_name = default_refiner_model_name
+        loras = default_loras
+
+        style_selections_arr = []
+        for s in style_selections.strip().split(','):
+            style = s.strip()
+            if style in legal_style_names:
+                style_selections_arr.append(style)
+
+        if uov_input_image is not None:
+            im = Image.open(str(uov_input_image))
+            uov_input_image = np.array(im)
+
+        inpaint_input_image_dict = None
+        if inpaint_input_image is not None:
+            im = Image.open(str(inpaint_input_image))
+            inpaint_input_image = np.array(im)
+
+            if inpaint_input_mask is not None:
+                im = Image.open(str(inpaint_input_mask))
+                inpaint_input_mask = np.array(im)
+
+            inpaint_input_image_dict = {
+                'image': inpaint_input_image,
+                'mask': inpaint_input_mask
+            }
+
+        outpaint_selections_arr = []
+        for e in outpaint_selections.strip().split(','):
+            expansion = e.strip()
+            if expansion in outpaint_expansions:
+                outpaint_selections_arr.append(expansion)
+
+        image_prompts = []
+        image_prompt_config = [(cn_img1, cn_stop1, cn_weight1, cn_type1), (cn_img2, cn_stop2, cn_weight2, cn_type2),
+                               (cn_img3, cn_stop3, cn_weight3, cn_type3), (cn_img4, cn_stop4, cn_weight4, cn_type4)]
+        for config in image_prompt_config:
+            cn_img, cn_stop, cn_weight, cn_type = config
+            if cn_img is not None:
+                im = Image.open(str(cn_img))
+                cn_img = np.array(im)
+                if cn_stop is None:
+                    cn_stop = flags.default_parameters[cn_type][0]
+                if cn_weight is None:
+                    cn_weight = flags.default_parameters[cn_type][1]
+                image_prompts.append((cn_img, cn_stop, cn_weight, cn_type))
+
+        advanced_params = None
+
+        params = ImageGenerationParams(prompt=prompt,
+                                        negative_prompt=negative_prompt,
+                                        style_selections=style_selections_arr,
+                                        performance_selection=performance_selection,
+                                        aspect_ratios_selection=aspect_ratios_selection,
+                                        image_number=image_number,
+                                        image_seed=image_seed,
+                                        sharpness=sharpness,
+                                        guidance_scale=guidance_scale,
+                                        base_model_name=base_model_name,
+                                        refiner_model_name=refiner_model_name,
+                                        refiner_switch=refiner_switch,
+                                        loras=loras,
+                                        uov_input_image=uov_input_image,
+                                        uov_method=uov_method,
+                                        upscale_value=uov_upscale_value,
+                                        outpaint_selections=outpaint_selections_arr,
+                                        inpaint_input_image=inpaint_input_image_dict,
+                                        image_prompts=image_prompts,
+                                        advanced_params=advanced_params,
+                                        inpaint_additional_prompt=inpaint_additional_prompt,
+                                        outpaint_distance_left=outpaint_distance_left,
+                                        outpaint_distance_top=outpaint_distance_top,
+                                        outpaint_distance_right=outpaint_distance_right,
+                                        outpaint_distance_bottom=outpaint_distance_bottom
+                                       )
+
+        print(f"[Predictor Predict] Params: {params.__dict__}")
+
+        queue_task = task_queue.add_task(TaskType.text_2_img, {'params': params.__dict__, 'require_base64': False})
+        if queue_task is None:
+            print("[Task Queue] The task queue has reached limit")
+            raise Exception(
+                f"The task queue has reached limit."
+            )
+        results = process_generate(queue_task, params)
+
+        output_paths: List[Path] = []
+        for r in results:
+            if r.finish_reason == GenerationFinishReason.success and r.im is not None:
+                output_paths.append(Path(os.path.join(output_dir, r.im)))
+
+        print(f"[Predictor Predict] Finished with {len(output_paths)} images")
+
+        if len(output_paths) == 0:
+            raise Exception(
+                f"Process failed."
+            )
+
+        return output_paths

Fooocus-API/requirements.txt

@@ -0,0 +1,22 @@
+torchsde==0.2.5
+einops==0.4.1
+transformers==4.30.2
+safetensors==0.3.1
+accelerate==0.21.0
+pyyaml==6.0
+Pillow==9.2.0
+scipy==1.9.3
+tqdm==4.64.1
+psutil==5.9.5
+pytorch_lightning==1.9.4
+omegaconf==2.2.3
+pygit2==1.12.2
+opencv-contrib-python==4.8.0.74
+onnxruntime==1.16.3
+timm==0.9.2
+fastapi==0.103.1
+pydantic==2.4.2
+pydantic_core==2.10.1
+python-multipart==0.0.6
+uvicorn[standard]==0.23.2
+sqlalchemy