cl4ysg5/s3-upload Docker 镜像 - 轩辕镜像 | Docker 镜像高效稳定拉取服务

Model

The model(s) for inference will be loaded from a RunPod Network Volume.

Testing

1. Local Testing
2. RunPod Testing

Installing, Building and Deploying the Serverless Worker

1. Install ComfyUI on your Network Volume
2. Building the Docker image
3. Deploying on RunPod Serveless

RunPod API Endpoint

You can send requests to your RunPod API Endpoint using the /run or /runsync endpoints.

Requests sent to the /run endpoint will be handled asynchronously, and are non-blocking operations. Your first response status will always be IN_QUEUE. You need to send subsequent requests to the /status endpoint to get further status updates, and eventually the COMPLETED status will be returned if your request is successful.

Requests sent to the /runsync endpoint will be handled synchronously and are blocking operations. If they are processed by a worker within 90 seconds, the result will be returned in the response, but if the processing time exceeds 90 seconds, you will need to handle the response and request status updates from the /status endpoint until you receive the COMPLETED status which indicates that your request was successful.

Optional Webhook Callbacks

You can optionally Enable a Webhook.

Endpoint Status Codes

Serverless Handler

The serverless handler (rp_handler.py) is a Python script that handles the API requests to your Endpoint using the runpod Python library. It defines a function handler(event) that takes an API request (event), runs the inference using the model(s) from your Network Volume with the input, and returns the output in the JSON response.

Acknowledgements

• ComfyUI
• Generative Labs *** Tutorials

Additional Resources

• Postman Collection for this Worker
• Generative Labs *** Tutorials
• Getting Started With RunPod Serverless
• Serverless | Create a Custom Basic API

Community and Contributing

Pull requests and issues on GitHub are welcome. Bug fixes and new features are encouraged.

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