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. 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).

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.

QuantFunc

Logo

🤗 Hugging Face | 🤖 ModelScope | 💻 GitHub | 💬 WeChat (微信) | 🎮 Discord

⚡ 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 — 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 nunchaku + qwen-image-edit-2511 quantization. These models are carefully optimized to maintain high-quality output while significantly improving inference speed and efficiency. All models are 100% compatible with the nunchaku-comfyui plugin 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 workflow example of integrating 4-step LoRA in ComfyUI. If you don’t need 4-step LoRA, simply remove the LoRA node.

Coming Soon

If you encounter any issues during use, feel free to join our community for feedback:

Join our Discord server
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.

shandfrank/Nunchaku-Qwen-Image-EDIT-2511

License & Attribution

QuantFunc

Introduction

Model Checkpoints

4 steps workflow

Coming Soon

shandfrank/
Nunchaku-Qwen-Image-EDIT-2511