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Using Golang, convert the video capabilities of the LumaAI Dream Machine into an API format for calls, making it easier to integrate quickly into third-party systems.

• This project is released on GitHub under the MIT license, free and open-source for educational purposes.

I provide the Luma AI API, no deployment is required, no subscription to Luma is required. Lower price, more convenient to use the Luma API.

• Luma API supports video generation and allows uploading reference images.
• Users can upload reference images in advance or provide third-party image links or base64 encoded images when submitting video tasks.
• API interface security verification.
• Simplified deployment process, supporting both docker-compose and docker.
• Provides standardized services compatible with OpenAI's interface format, supporting both streaming and non-streaming output.
• Supports customization of the OpenAI Chat response format based on Go Template syntax.
• Compatible with frontend projects like chat-next-web.
• Automatic keep-alive (max keep-alive 7 days).
• [] Adapts to intermediary projects like New API.
• [] Pre-detection of sensitive words.

Video generation, supports uploading two images (start and end). [x] /generations Video extension, extends by 5 seconds each time. [x] /generations/:task_id/extend Query single video task. [x] /generations/:task_id Pre-upload images. [x] /generations/file_upload Get download URL without watermark. [x] /generations/:task_id/download_video_url Get account subscription information (balance, etc.). [x] /subscription/usage Get account information. [x] /users/me

https://github.com/Dooy/chatgpt-web-midjourney-proxy
Online
https://api-chat.gptbest.vip

http://localhost:8000/swagger/index.html

curl --location 'http://localhost:8000/luma/generations/' \
--header 'Authorization: Bearer {SECRET_TOKEN}' \
--header 'Content-Type: application/json' \
--data '{
    "user_prompt": "Increase dynamic",
    "aspect_ratio": "16:9",
    "expand_prompt": true,
    "image_url": "https://api.bltcy.ai/logo.png" 
}'

{
    "created_at": "2024-06-18T14:47:43.318498Z",
    "estimate_wait_seconds": null,
    "id": "a4a8f1ff-e59b-4969-abcb-98b3e16f003e",
    "liked": null,
    "prompt": "Increase dynamic",
    "state": "pending",
    "video": null
}

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Step-by-step guide on how to run a Docker container with specific environment variables and port mappings. Sensitive details like SQL names, passwords, and IP addresses are replaced with placeholders for this guide.

docker run --name luma-api -d -p 8000:8000 \
-e COOKIE=xxxx  \
 lumaapi/luma-api

docker-compose deployment

docker-compose pull && docker-compose up -d

docker-compose.yml

version: '3.2'

services:
  sunoapi:
    image: lumaapi/luma-api:latest
    container_name: luma-api
    restart: always
    ports:
      - "8000:8000"
    volumes:
      - ./logs:/logs
    environment:
      - COOKIE=xxxx

• Golang 1.20+

git clone https://github.com/LumaAI-API/Luma-API.git
cd Luma-API
go mod tidy

MIT © Luma API

This project is open-source on GitHub under the MIT license and is free of charge. If you find this project helpful, please give it a star and share it. Thank you for your support!