First, thanks to community member @亮亮rayne for providing the images, so I didn't need to look for any.

Qwen-Image-Edit-2511-Lightning is a series of models optimized for image editing tasks, leveraging step distillation and quantization techniques to achieve high-efficiency inference performance.

Usage Instructions This model suite supports two mainstream frameworks. Detailed guides are provided below:

1. Qwen-Image-Lightning Framework For complete documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipeline, and customization), please refer to: Qwen-Image-Lightning GitHub Repository https://github.com/ModelTC/Qwen-Image-Lightning/

2. LightX2V Framework These models are fully compatible with LightX2V, a lightweight video/image generation inference framework. For step-by-step usage examples, configuration templates, and performance optimization tips, please refer to: LightX2V Qwen Image Editing Documentation https://github.com/ModelTC/LightX2V/blob/main/examples/qwen_image/README.md

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

Support For technical issues, feature requests, or integration problems:

Please submit an issue in the Qwen-Image-Lightning repository (for Qwen framework-related issues) https://github.com/ModelTC/Qwen-Image-Lightning/issues

Submit an issue in the LightX2V repository (for LightX2V integration issues) https://github.com/ModelTC/LightX2V/issues