---
title: Nunchaku-Qwen-Image-EDIT-2511
canonical_url: "https://www.modelscope.cn/models/QuantFunc/Nunchaku-Qwen-Image-EDIT-2511"
md_url: "https://www.modelscope.cn/models/QuantFunc/Nunchaku-Qwen-Image-EDIT-2511.md"
repository: QuantFunc/Nunchaku-Qwen-Image-EDIT-2511
last_updated: 2026-06-19
license: apache-2.0
pipeline_tag: image-to-image
tasks:
  - image-to-image
base_model:
  - Qwen/Qwen-Image-Edit-2511
base_model_relation: quantized
parameters: 55.5B
tensor_type:
  - BF16
  - I32
  - F8_E4M3
  - I8
library_name:
  - safetensors
language:
  - en
  - zh
downloads: 2787
stars: 9
tags:
  - image-generation
  - multimodal
  - qwen
  - quantized
  - nunchaku
  - comfyui
---

# Nunchaku-Qwen-Image-EDIT-2511

> Nunchaku-Qwen-Image-EDIT-2511 - QuantFunc 在 ModelScope 开源的模型。License & Attribution

QuantFunc/Nunchaku-Qwen-Image-EDIT-2511 是 ModelScope 魔搭社区上的 55.5B 参数image-to-image模型，采用 apache-2.0 许可，基于 Qwen/Qwen-Image-Edit-2511 构建。

- **Repository**: QuantFunc/Nunchaku-Qwen-Image-EDIT-2511
- **License**: apache-2.0
- **Tasks**: image-to-image
- **Parameters**: 55.5B
- **Base model**: Qwen/Qwen-Image-Edit-2511
- **Tags**: image-generation, multimodal, qwen, quantized, nunchaku, comfyui
- **Downloads**: 2787
- **Stars**: 9
- **Last updated**: 2026-06-19

Source: https://www.modelscope.cn/models/QuantFunc/Nunchaku-Qwen-Image-EDIT-2511

---

<!-- QF-LICENSE-BLOCK:START -->
## License & Attribution

These are **quantized derivative weights** of [`Qwen/Qwen-Image-Edit-2511`](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) (**Qwen-Image-Edit-2511**).

- **Modifications:** the original weights were quantized (e.g. W4A4 / FP4 / INT4 / FP8) and
  repackaged for the QuantFunc inference engine — a "modification" under Apache-2.0 §4(b).
- **License:** Apache License 2.0 (inherited from the base model), included as [`LICENSE`](./LICENSE). Upstream copyright and attribution notices are retained.
- This repository is **not affiliated with or endorsed by** the upstream model authors.
- **Text/vision encoder:** the bundled **Qwen2.5-VL-7B** text/vision encoder is licensed under the **Apache License 2.0** (as distributed with [`Qwen/Qwen-Image-Edit-2511`](https://huggingface.co/Qwen/Qwen-Image-Edit-2511)).

> **Disclaimer:** "Nunchaku" / "SVDQuant" name the *quantization method/format* (the open-source SVDQuant work by MIT HAN Lab, Apache-2.0). This repository is an independent re-quantization and is **not affiliated with, sponsored by, or endorsed by MIT HAN Lab or the Nunchaku project**. Official Nunchaku releases are under the `nunchaku-ai` / `nunchaku-tech` namespaces.
<!-- QF-LICENSE-BLOCK:END -->

# QuantFunc

<div align="center" style="margin-top: 50px;">
  <img src="assets/logo.webp" width="300" alt="Logo">
</div>

<p align="center">
  🤗 <a href="https://huggingface.co/QuantFunc">Hugging Face</a> &nbsp;|&nbsp;
  🤖 <a href="https://www.modelscope.cn/profile/QuantFunc">ModelScope</a> &nbsp;|&nbsp;
  💻 <a href="https://github.com/RealJonathanYip/ComfyUI-QuantFunc">GitHub</a> &nbsp;|&nbsp;
  💬 <a href="#wechat">WeChat (微信)</a> &nbsp;|&nbsp;
  🎮 <a href="https://discord.gg/jCp9TpFWcn">Discord</a>
</p>

> ⚡ **Qwen-Image-Edit-2511 — SVDQ (Nunchaku) pre-quantized image editing.** Reference-based edits at **2x–11x** with the QuantFunc plugin.

Offline-quantized **Qwen-Image-Edit-2511** image-editing checkpoints in **best-quality / balanced / ultimate-speed** variants (INT4 & FP4), with a 4-step LoRA workflow for ComfyUI.

**Powered by the [QuantFunc ComfyUI plugin](https://github.com/RealJonathanYip/ComfyUI-QuantFunc) — the fastest diffusion inference engine:**

- 🚀 **2x–11x speedup** over standard BF16/FP16 Python pipelines (pre-exported → even faster loading).
- ⚙️ **Native C++/CUDA** (`libquantfunc.so` / `quantfunc.dll`) with **zero Python model dependencies**.
- 🧩 **Dual engine** (SVDQ offline + Lighting runtime 4-bit), **zero-cost LoRA stacking**, reference-image editing & inpainting.
- 🟢 **Full GPU coverage** — RTX 20/30/40/50 · A100/H100/H200/B100/B200/GB300 · RTX 6000 Ada / PRO Blackwell (CUDA 12 & 13); native **FP4** on Blackwell.

👉 **Install the plugin:** **https://github.com/RealJonathanYip/ComfyUI-QuantFunc**

## Introduction

We are excited to share our latest model series based on <strong>nunchaku + qwen-image-edit-2511</strong> quantization. These models are carefully optimized to maintain high-quality output while significantly improving inference speed and efficiency. <strong>All models are 100% compatible with the nunchaku-comfyui plugin</strong> and can be used directly in ComfyUI.


## Model Checkpoints

| Name | low_rank | Notes |
|:---|:---:|:---|
| nunchaku_qwen_image_edit_2511_best_quality_fp4 | 256 | Best quality model, suitable for scenarios with extremely high quality requirements |
| nunchaku_qwen_image_edit_2511_best_quality_int4 | 256 | Best quality model, suitable for scenarios with extremely high quality requirements |
| nunchaku_qwen_image_edit_2511_ultimate_speed_int4 | 32 | Ultimate speed model, prioritizing inference speed |
| nunchaku_qwen_image_edit_2511_ultimate_speed_fp4 | 32 | Ultimate speed model, prioritizing inference speed |
| nunchaku_qwen_image_edit_2511_balance_int4 | 128 | Balanced model, achieving the best balance between quality and speed |
| nunchaku_qwen_image_edit_2511_balance_fp4 | 128 | Balanced model, achieving the best balance between quality and speed |


## 4 steps workflow

Here’s a <a href="https://huggingface.co/QuantFunc/Nunchaku-Qwen-Image-EDIT-2511/tree/main">workflow</a> example of integrating 4-step LoRA in ComfyUI. If you don’t need 4-step LoRA, simply remove the LoRA node.
<img src="assets/workflow_demo_edit.png" alt="work flow" style="max-width: 800px;">

## Coming Soon

If you encounter any issues during use, feel free to join our community for feedback:
- Join our [Discord server](https://discord.gg/jCp9TpFWcn)
- Scan the QR code below to join our WeChat group

We will add support for build-in lora and wan 2.2 next step, stay turned.

<div align="center" id="wechat">
  <img src="https://raw.githubusercontent.com/RealJonathanYip/ComfyUI-QuantFunc/main/assets/WeChat.jpg" alt="WeChat Group" style="max-width: 300px;">
</div>