Nexmoe

Hello everyone, I am the daydreamer Nexmoe. Recently, I have open-sourced a ComfyUI elastic Serverless application packaged with Docker, which features a complete front-end and back-end separation architecture and a user-friendly interface.

After completing the development of the ComfyUI workflow, deploying it to a production environment can be quite tricky. Therefore, I have open-sourced a set of examples for everyone to learn from.

Demo: https://hadoop.nexmoe.com/
Open source address: https://github.com/nexmoe/serverless-comfyui

Project Features#

• 🐳 Complete Docker deployment solution
• 🎨 Modern front-end interface
• 🔌 Modular back-end architecture
• 🛠 Simple configuration and usage

Architecture Diagram#

Project Structure#

comfy-docker/
├── frontend/           # Next.js front-end project
│   ├── src/           # Source code
│   └── .env          # Environment configuration
├── backend/           # ComfyUI back-end
│   ├── checkpoints/   # Model checkpoints
│   ├── controlnet/    # ControlNet models
│   ├── custom_nodes/  # Custom nodes
│   └── loras/        # LoRA models
└── bruno/            # API test files

The directory structure of frontend/ is as follows, models and custom nodes need to be downloaded and installed manually.

.
├── Dockerfile
├── checkpoints
│   └── dreamshaperXL_sfwV2TurboDPMSDE.safetensors
├── controlnet
│   ├── sai_xl_canny_256lora.safetensors
│   └── sai_xl_depth_256lora.safetensors
├── custom_nodes
│   ├── ComfyUI-Custom-Scripts
│   ├── ComfyUI-WD14-Tagger
│   ├── ComfyUI_Comfyroll_CustomNodes
│   ├── comfyui-art-venture
│   └── comfyui_controlnet_aux
├── docker-compose.yml
├── loras
│   └── StudioGhibli.Redmond-StdGBRRedmAF-StudioGhibli.safetensors
├── provisioning.sh  // Custom script
└── sanhua.json  // Workflow

Environment Requirements#

• Docker & Docker Compose
• NVIDIA GPU (Currently, the demo workflow requires more than 12G of VRAM)
• Sufficient disk space (100G~200G) for storing models

Quick Start#

Local Testing of Backend#

1. Navigate to the backend Dockerfile directory

cd backend

2. Download model files

Please refer to: https://gongjiyun.com/docs/tutorials/comfyui.html#%E4%B8%8B%E8%BD%BD%E6%A8%A1%E5%9E%8B%E5%92%8C%E6%89%A9%E5%B1%95

3. Build the Docker image

docker build -t gongji/comfyui:0.1 .

4. Run the Docker container

docker run -it --rm --gpus all -p 3000:3000 -p 8188:8188 --name comfyui gongji/comfyui:0.1

After the container starts, you can access:

• ComfyUI interface: http://localhost:8188
• API interface: http://localhost:3000/docs

Local Testing of Frontend#

1. Navigate to the frontend directory

cd frontend

2. Configure environment variables

cp .env.example .env
# Edit the .env file to configure necessary environment variables

3. Install dependencies and start

pnpm install
pnpm dev

Deploying ComfyUI Docker to Serverless Elastic Platform#

Please refer to Gongji Technology's ComfyUI deployment documentation

API Documentation#

The project uses Bruno for API testing and documentation management, with related files located in the bruno/ directory.

Example of ComfyUI API Call#

Here is an example code for calling the ComfyUI API (refer to frontend/src/app/api/route.ts):

async function generateImage(imageUrl: string) {
    // 1. Prepare prompt data
    const promptData = { ...promptob };  // Import base prompt from JSON file
    promptData.prompt["30"].inputs.image = imageUrl;  // Modify input image

    // 2. Set request options
    const url = `${process.env.GONGJI_ENDPOINT}/prompt`;
    const options = {
        method: 'POST',
        headers: { 'content-type': 'application/json' },
        body: JSON.stringify(promptData)
    };

    // 3. Send request
    const response = await fetch(url, options);
    const data = await response.json();

    // 4. Error handling
    if (response.status !== 200) {
        throw new Error(response.statusText);
    }

    // 5. Process returned image data
    if (data.images && data.images.length > 0) {
        return data.images[0];  // Return base64 format image data
    } else {
        throw new Error('No valid image data returned');
    }
}

Main steps explanation:

1. Prepare Prompt:

   • Import base prompt configuration from JSON file
   • Modify parameters in the prompt as needed (e.g., input image)

2. Send Request:

   • Use POST method
   • Set Content-Type to application/json
   • Request body is serialized prompt data

3. Handle Response:

   • Check response status code
   • Parse returned JSON data
   • Extract generated image (base64 format)

4. Error Handling:

   • Log error messages
   • Throw appropriate error messages

Environment Variable Configuration#

Before using the API, ensure the following environment variables are configured:

GONGJI_ENDPOINT=your-comfyui-api-endpoint  # ComfyUI API endpoint

S3 Configuration Instructions#

The project's image upload feature requires configuring S3 storage services. You can use AWS S3 or other object storage services compatible with the S3 protocol (like MinIO).

In the frontend/.env file, configure the following environment variables:

S3_ENDPOINT=your-s3-endpoint
S3_ACCESS_KEY=your-access-key
S3_SECRET_KEY=your-secret-key
S3_BUCKET=your-bucket-name
S3_REGION=your-region

Note:

• Ensure the created bucket has appropriate access permissions
• If using MinIO, the endpoint should be a complete URL (e.g., http://localhost:9000)
• When using AWS S3, you can omit the endpoint configuration

Contribution Guidelines#

Feel free to submit Issues and Pull Requests!

License#

MIT License