---
title: Qwen-Image-2512-Lightning
canonical_url: "https://www.modelscope.cn/models/lightx2v/Qwen-Image-2512-Lightning"
md_url: "https://www.modelscope.cn/models/lightx2v/Qwen-Image-2512-Lightning.md"
repository: lightx2v/Qwen-Image-2512-Lightning
last_updated: 2026-06-16
license: apache-2.0
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - Qwen/Qwen-Image-2512
base_model_relation: adapter
parameters: 103.9B
tensor_type:
  - F8_E4M3
  - I8
  - BF16
  - F32
  - I64
library_name:
  - lora
  - safetensors
  - pytorch
frameworks:
  - pytorch
supports_inference: txt2img
downloads: 26535
stars: 22
tags:
  - diffusion-single-file
  - comfyui
  - distillation
  - LoRA
  - lora
  - Qwen-Image
---

# Qwen-Image-2512-Lightning

> Qwen-Image-2512-Lightning - lightx2v 在 ModelScope 开源的模型。Qwen-Image-2512-Lightning

lightx2v/Qwen-Image-2512-Lightning 是 ModelScope 魔搭社区上的 103.9B 参数text-to-image-synthesis模型，采用 apache-2.0 许可，基于 Qwen/Qwen-Image-2512 构建，并支持在线推理（txt2img）。

- **Repository**: lightx2v/Qwen-Image-2512-Lightning
- **License**: apache-2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 103.9B
- **Base model**: Qwen/Qwen-Image-2512
- **Online inference**: txt2img
- **Tags**: diffusion-single-file, comfyui, distillation, LoRA, lora, Qwen-Image
- **Downloads**: 26535
- **Stars**: 22
- **Last updated**: 2026-06-16

Source: https://www.modelscope.cn/models/lightx2v/Qwen-Image-2512-Lightning

---

# Qwen-Image-2512-Lightning

## Usage Instructions

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](https://github.com/ModelTC/Qwen-Image-Lightning/)

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 Documentation](https://github.com/ModelTC/LightX2V/tree/main/examples/qwen_image)