Qwen Image Edit 2511 Lightning

Providerlightx2v
Categoryimage-to-image
Licenseapache-2.0
Downloads24.7K
Stars72

Overview

Qwen Image Edit 2511 Lightning is a specialized image-to-image model designed for high-speed visual manipulation and refinement. Unlike general-purpose diffusion models, this iteration prioritizes low-latency inference, making it suitable for real-time applications or iterative design workflows where rapid prototyping is essential. Developers can integrate it into pipelines requiring precise local edits, style transfers, or attribute modifications without the computational overhead of larger frameworks. Operating under the Apache-2.0 license, it offers significant flexibility for commercial deployment and customization. It bridges the gap between high-fidelity image generation and the operational efficiency needed for production-grade AI tools.

Highlights

  • Optimized for low-latency, high-speed image-to-image transformations
  • Permissive Apache-2.0 license for commercial scalability
  • Ideal for real-time editing and iterative design workflows
  • Efficient integration into existing visual production pipelines

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("lightx2v/Qwen-Image-Edit-2511-Lightning")
tokenizer = AutoTokenizer.from_pretrained("lightx2v/Qwen-Image-Edit-2511-Lightning")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download lightx2v/Qwen-Image-Edit-2511-Lightning

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download lightx2v/Qwen-Image-Edit-2511-Lightning config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('lightx2v/Qwen-Image-Edit-2511-Lightning')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('lightx2v/Qwen-Image-Edit-2511-Lightning')
tokenizer = AutoTokenizer.from_pretrained('lightx2v/Qwen-Image-Edit-2511-Lightning')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model lightx2v/Qwen-Image-Edit-2511-Lightning

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model lightx2v/Qwen-Image-Edit-2511-Lightning README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('lightx2v/Qwen-Image-Edit-2511-Lightning')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/lightx2v/Qwen-Image-Edit-2511-Lightning.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/lightx2v/Qwen-Image-Edit-2511-Lightning.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'lightx2v/Qwen-Image-Edit-2511-Lightning')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
tags:

  • diffusion-single-file

  • comfyui

  • distillation

  • LoRA

  • lora

  • Qwen-Image

  • Qwen-Image-Edit

base_model:
  • Qwen/Qwen-Image-Edit-2511

pipeline_tags:
  • image-to-image

  • text-to-image

library_name: diffusers
pipeline_tag: image-to-image
---

Qwen-Image-Edit-2511-Lightning

Model Overview

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:

| Model File Name | Type | Key Features |
|-----------------|------|--------------|
| Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors | 4-step Distilled LoRA | BF16 precision, lightweight, 4-step inference |
| Qwen-Image-Edit-2511-Lightning-4steps-V1.0-fp32.safetensors | 4-step Distilled LoRA | FP32 precision, high accuracy, 4-step inference |
| qwen_image_edit_2511_fp8_e4m3fn_scaled_lightning.safetensors | FP8 Quantized | FP8 (e4m3fn scaled) precision, fused with 4-step distilled LoRA, optimized for low-memory deployment |

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

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 LightX2V repo (for LightX2V integration questions)
View official source
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