How to Self-Host LightX2V with Docker: Step-by-Step Setup Tutorial

In this tutorial, we walk through a complete self-hosted setup of LightX2V — the lightweight image, video, and action generation inference framework from ModelTC. By the end, you will have the official Docker image running and be ready to generate content through its unified API.

Step 1 — Prerequisites

You need a Linux server with Docker installed and at least 4 GB of RAM (8 GB recommended for video workloads) and 2+ vCPUs. A GPU is optional but greatly speeds up generation.

Step 2 — Pull and Run the Container

The official image is lightx2v/lightx2v:latest. Start it with port 8080 exposed and a persistent data volume:

docker run -d --name lightx2v \
  -p 8080:8080 \
  -v ./data/lightx2v:/data \
  lightx2v/lightx2v:latest

Step 3 — Verify the Service

Once the container is up, check the health of the inference endpoint:

curl -s http://localhost:8080/health

You should receive a JSON response confirming the service is ready. From here, the unified LightX2V API lets you switch between Open-Sora, Stable Diffusion, AnimateDiff, and CogVideoX workloads without changing your serving setup.

Step 4 — Tune for Your Hardware

For CPU-only setups, keep batch sizes small and use the optimized kernels LightX2V ships for reduced memory pressure. With a GPU, you can raise resolution and video length significantly while keeping latency low.

Wrap-Up

LightX2V delivers a genuinely lightweight path to self-hosted image, video, and action generation. The official Docker image, active maintenance, and unified API make it one of the easiest generation frameworks to operate today.