Qwen Image Edit 2511 Lightning
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...
Source du modèle
- Type
- Other
- Taille du fichier
- 155 MB–19,2 GB
- Modèle de base
- Qwen/Qwen-Image-Edit-2511
- Version
- vRevision d74eba1
- Mis à jour
- 5 oct. 2026
- Éditeur
- @lightx2v
- Téléchargements
- 348,8 k
- Tâches
- Édition d'image, Image en image, Texte en image
- Source
- Hugging Face
Description de la source
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 Qwen-Image-Lightning repo (for Qwen framework-specific questions)
- Open an issue in the LightX2V repo (for LightX2V integration questions)
Ressources connexes
3 connexions
Ressources connexes
3 connexionsSources
1 source
Sources
1 sourceVérifié 5 oct.
Artefacts du modèle
64 artefacts
Artefacts du modèle
64 artefactsqwen_image_edit_2511_fp8_e4m3fn_scaled_lightning_4steps_v1.0.safetensors
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TéléchargerExtraits de sources
3 extraits
Extraits de sources
3 extraits--- 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](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 Edit Documentation](https://github.com/ModelTC/LightX2V/blob/main/examples/qwen_image/README.md) ## 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 integrat...
Source context: 348817 downloads · 533 likes · Pipeline image-to-image · Library diffusers · Repo lightx2v/Qwen-Image-Edit-2511-Lightning
Source context: 315772 downloads · 486 likes · Pipeline image-to-image · Library diffusers · Repo lightx2v/Qwen-Image-Edit-2511-Lightning
