Image-to-Image
Diffusers
Safetensors
English
QuantFuncPipeline
custom_qwen_image
image-editing
diffusion
quantized
quantfunc
Instructions to use QuantFunc/Qwen-Image-Edit-Series with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QuantFunc/Qwen-Image-Edit-Series with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QuantFunc/Qwen-Image-Edit-Series", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
docs: point GitHub/QR links to RealJonathanYip (repo migrated)
Browse files
README.md
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@@ -37,7 +37,7 @@ These are **quantized derivative weights** of [`Qwen/Qwen-Image-Edit-2511`](http
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<p align="center">
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🤗 <a href="https://huggingface.co/QuantFunc">Hugging Face</a> |
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🤖 <a href="https://www.modelscope.cn/profile/QuantFunc">ModelScope</a> |
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💻 <a href="https://github.com/
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💬 <a href="#wechat">WeChat (微信)</a> |
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🎮 <a href="https://discord.gg/jCp9TpFWcn">Discord</a>
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</p>
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Pre-quantized **Qwen-Image-Edit-2511** for the **Lighting** engine — edit input images from text instructions (e.g. a 1K edit in ~1s on RTX 4090), for **RTX 30-series and above**.
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**Powered by the [QuantFunc ComfyUI plugin](https://github.com/
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- 🚀 **2x–11x speedup** over standard BF16/FP16 Python pipelines (pre-exported → even faster loading).
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- ⚙️ **Native C++/CUDA** (`libquantfunc.so` / `quantfunc.dll`) with **zero Python model dependencies**.
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- 🧩 **Dual engine** (SVDQ offline + Lighting runtime 4-bit), **zero-cost LoRA stacking**, reference-image editing & inpainting.
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- 🟢 **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.
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👉 **Install the plugin:** **https://github.com/
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# Qwen-Image-Edit-Series
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Pre-quantized **Qwen-Image-Edit-2511** image editing model series by [QuantFunc](https://github.com/
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## Overview
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- 💬 Scan the QR code below to join our WeChat group:
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<div align="center" id="wechat">
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<img src="https://raw.githubusercontent.com/
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</div>
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<p align="center">
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🤗 <a href="https://huggingface.co/QuantFunc">Hugging Face</a> |
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🤖 <a href="https://www.modelscope.cn/profile/QuantFunc">ModelScope</a> |
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💻 <a href="https://github.com/RealJonathanYip/ComfyUI-QuantFunc">GitHub</a> |
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💬 <a href="#wechat">WeChat (微信)</a> |
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🎮 <a href="https://discord.gg/jCp9TpFWcn">Discord</a>
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</p>
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Pre-quantized **Qwen-Image-Edit-2511** for the **Lighting** engine — edit input images from text instructions (e.g. a 1K edit in ~1s on RTX 4090), for **RTX 30-series and above**.
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**Powered by the [QuantFunc ComfyUI plugin](https://github.com/RealJonathanYip/ComfyUI-QuantFunc) — the fastest diffusion inference engine:**
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- 🚀 **2x–11x speedup** over standard BF16/FP16 Python pipelines (pre-exported → even faster loading).
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- ⚙️ **Native C++/CUDA** (`libquantfunc.so` / `quantfunc.dll`) with **zero Python model dependencies**.
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- 🧩 **Dual engine** (SVDQ offline + Lighting runtime 4-bit), **zero-cost LoRA stacking**, reference-image editing & inpainting.
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- 🟢 **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.
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👉 **Install the plugin:** **https://github.com/RealJonathanYip/ComfyUI-QuantFunc**
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# Qwen-Image-Edit-Series
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Pre-quantized **Qwen-Image-Edit-2511** image editing model series by [QuantFunc](https://github.com/RealJonathanYip/ComfyUI-QuantFunc), with both Lighting and SVDQ backend inference support.
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## Overview
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- 💬 Scan the QR code below to join our WeChat group:
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<div align="center" id="wechat">
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<img src="https://raw.githubusercontent.com/RealJonathanYip/ComfyUI-QuantFunc/main/assets/WeChat.jpg" alt="WeChat Group" width="300">
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</div>
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