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• Introduction
  • Fooocus
  • Fooocus-API
• Get-Start
  • Run with Replicate
  • Self-hosted
    • conda
    • venv
    • predownload and install
    • already exist Fooocus
  • Start with docker
• cmd flags
• Change log
• Apis
• License
• Thanks 💜

Note:

Although I tested it, I still suggest you test it again before the official update

Fooocus 2.5 includes a significant update, with most dependencies upgraded. Therefore, after updating, do not use --skip-pip unless you have already performed a manual update.

Additionally, groundingdino-py may encounter installation errors, especially in Chinese Windows environments. The solution can be found in the following issue.

GenerateMask is same as DescribeImage, It is not process as a task, result will directly return

Below are examples of parameters that include the main parameters required for ImageEnhance. The V1 interface adopts a form-like approach similar to ImagePrompt to break down the enhance controller.

{
  "enhance_input_image": "",
  "enhance_checkbox": true,
  "enhance_uov_method": "Vary (Strong)",
  "enhance_uov_processing_order": "Before First Enhancement",
  "enhance_uov_prompt_type": "Original Prompts",
  "save_final_enhanced_image_only": true,
  "enhance_ctrlnets": [
    {
      "enhance_enabled": false,
      "enhance_mask_dino_prompt": "face",
      "enhance_prompt": "",
      "enhance_negative_prompt": "",
      "enhance_mask_model": "sam",
      "enhance_mask_cloth_category": "full",
      "enhance_mask_sam_model": "vit_b",
      "enhance_mask_text_threshold": 0.25,
      "enhance_mask_box_threshold": 0.3,
      "enhance_mask_sam_max_detections": 0,
      "enhance_inpaint_disable_initial_latent": false,
      "enhance_inpaint_engine": "v2.6",
      "enhance_inpaint_strength": 1,
      "enhance_inpaint_respective_field": 0.618,
      "enhance_inpaint_erode_or_dilate": 0,
      "enhance_mask_invert": false
    }
  ]
}

• enhance_input_image: The image to be enhanced, which is required and can be provided as an image URL for the V2 interface.
• enhance_checkbox: A toggle switch that must be set to true if you want to use the enhance image feature.
• save_final_enhanced_image_only: Since image enhancement is a pipeline operation, it can produce multiple result images. This parameter allows you to only return the final enhanced image.

There are three parameters related to UpscaleVary, which are used to perform Upscale or Vary before or after enhancement.

• enhance_uov_method: Similar to the UpscaleOrVary interface, Disabled turns it off.
• enhance_uov_processing_order: Determines whether to process the image before or after enhancement.
• enhance_uov_prompt_type: I'm not sure about the specific function; you might want to research it based on the WebUI.

The enhance_ctrlnets element is a list of ImageEnhance controller objects, with a maximum of three elements in the list, any additional elements will be discarded. The parameters correspond roughly to the WebUI, and the notable parameters are:

• enhance_enabled: This parameter controls whether the enhance controller is active. If there are no enabled enhance controllers, the task will be skipped.
• enhance_mask_dino_prompt: This parameter is required and indicates the area to be enhanced. If it is empty, even if the enhance controller is enabled, the task will be skipped.

FastAPI powered API for Fooocus.

Currently loaded Fooocus version: 2.3.0.

This part from Fooocus project.

Fooocus is an image generating software (based on Gradio).

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"

I think you must have tried to use Gradio client to call Fooocus, which was a terrible experience for me.

Fooocus API uses FastAPI 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 and sample code

Now you can use Fooocus-API by Replicate, the model is on konieshadow/fooocus-api.

With preset:

• konieshadow/fooocus-api-anime
• konieshadow/fooocus-api-realistic

I believe this is the easiest way to generate image with Fooocus's power.

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

You can easily start app follow this step use conda:

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.

Similar to using conda, create a virtual environment, and then start and wait for a while

# windows
python -m venv venv
.\venv\Scripts\Activate

# linux
python -m venv venv
source venv/bin/activate

and then, run python main.py

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,

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

Go to the repositories directories, download models and put it into repositories\Fooocus\models

If you have Fooocus installed, see already-exist-fooocus

here is a list need to download for startup (for different startup params maybe difference):

• checkpoint: path to repositories\Fooocus\models\checkpoints

  • juggernautXL_version6Rundiffusion.safetensors

• vae_approx: path to repositories\Fooocus\models\vae_approx

  • xlvaeapp.pth
  • vaeapp_sd15.pth
  • xl-to-v1_interposer-v3.1.safetensors

• lora: path to repositories\Fooocus\models\loras

  • 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 提取码: D4Mk

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 root folder. See 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.

Do not copy Fooocus to repositories directory

Before use docker with GPU, you should install NVIDIA Container Toolkit first.

Run

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:

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 be 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.

From version 0.4.0.0, Full environment include in docker image, mapping models or project root if you needed For example:

docker run -d --gpus all \
    -v /Fooocus-API:/app \
    -p 8888:8888 konieshadow/fooocus-api

• -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
• --skip-pip Skip automatic pip install when setup
• --preload-pipeline Preload pipeline before start http server
• --queue-size QUEUE_SIZE Working queue size, default: 100, 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
• --persistent Store history to db
• --apikey APIKEY Set apikey to enable secure api, default: None

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.

CHANGELOG

older change history you can find in release page

you can find all api detail here

This repository is licensed under the GUN General Public License v3.0

The default checkpoint is published by RunDiffusion, is licensed under the CreativeML Open RAIL-M.

or, you can find it here

Thanks for all your contributions and efforts towards improving the Fooocus API. We thank you for being part of our ✨ community ✨!