Qwen-Image-Edit-2511-Lightning is a collection of optimized models tailored for image editing tasks, leveraging step distillation and quantization techniques to deliver high-efficiency inference performance. This repository hosts three core model files with distinct characteristics:

This model suite supports two mainstream usage frameworks, with detailed guides provided below:

1. Qwen-Image-Lightning Framework

For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository

2. LightX2V Framework

The models are fully compatible with the LightX2V lightweight video/image generation inference framework. For step-by-step usage examples, configuration templates, and performance optimization tips, see: LightX2V Qwen Image Edit Documentation

Key Optimizations

Step Distillation: The LoRA models reduce the original inference steps to just 4 steps, achieving significant speedup (≈10x faster than standard 40-step inference) while preserving image editing quality.
FP8 Quantization: The quantized base model balances performance and resource efficiency, reducing GPU memory usage by ~50% compared to FP32 while maintaining editing fidelity.

Support

For technical issues, feature requests, or integration questions:

Open an issue in the Qwen-Image-Lightning repo (for Qwen framework-specific questions)
Open an issue in the LightX2V repo (for LightX2V integration questions

lightx2v-Qwen-Image-Edit-2511-Lightning - 4step_V1.0