toandev / Qwen-Image-Edit-2511-4bit

This is a 4-bit (NF4) quantized version of Qwen/Qwen-Image-Edit-2511 . It enables running this massive pipeline on consumer GPUs (16GB-24GB VRAM) with minimal quality loss.

• Smart Quantization: Critical layers ( img_mod , proj_out ) are kept in bfloat16 to prevent artifacts.
• Optimized: Both Text Encoder (Qwen2.5-VL) and Transformer are compressed using bitsandbytes NF4.

How to Run

import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline

pipeline = QwenImageEditPlusPipeline.from_pretrained("toandev/Qwen-Image-Edit-2511-4bit", torch_dtype=torch.bfloat16)
print("pipeline loaded")

# pipeline.to('cuda')
pipe.enable_model_cpu_offload() # Automatically offloads model components to CPU. Required for GPUs < 40GB VRAM.
pipeline.set_progress_bar_config(disable=None)
image1 = Image.open("input1.png")
image2 = Image.open("input2.png")
prompt = "The magician bear is on the left, the alchemist bear is on the right, facing each other in the central park square."
inputs = {
    "image": [image1, image2],
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 40,
    "guidance_scale": 1.0,
    "num_images_per_prompt": 1,
}
with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_image.save("output_image_edit_2511.png")
    print("image saved at", os.path.abspath("output_image_edit_2511.png"))

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Introduction

We are excited to introduce Qwen-Image-Edit-2511, an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. To try out the latest model, please visit Qwen Chat and select the Image Editing feature.

Key enhancements in Qwen-Image-Edit-2511 include: mitigate image drift, improved character consistency，integrated LoRA capabilities， enhanced industrial design generation, and strengthened geometric reasoning ability.

Quick Start

Install the latest version of diffusers

pip install git+https://github.com/huggingface/diffusers

The following contains a code snippet illustrating how to use Qwen-Image-Edit-2511 :

import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline

pipeline = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511", torch_dtype=torch.bfloat16)
print("pipeline loaded")

pipeline.to('cuda')
pipeline.set_progress_bar_config(disable=None)
image1 = Image.open("input1.png")
image2 = Image.open("input2.png")
prompt = "The magician bear is on the left, the alchemist bear is on the right, facing each other in the central park square."
inputs = {
    "image": [image1, image2],
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 40,
    "guidance_scale": 1.0,
    "num_images_per_prompt": 1,
}
with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_image.save("output_image_edit_2511.png")
    print("image saved at", os.path.abspath("output_image_edit_2511.png"))

Showcase

Qwen-Image-Edit-2511 Enhances Character Consistency In Qwen-Image-Edit-2511, character consistency has been significantly improved. The model can perform imaginative edits based on an input portrait while preserving the identity and visual characteristics of the subject.

Improved Multi-Person Consistency While Qwen-Image-Edit-2509 already improved consistency for single-subject editing, Qwen-Image-Edit-2511 further enhances consistency in multi-person group photos—enabling high-fidelity fusion of two separate person images into a coherent group shot:

Built-in Support for Community-Created LoRAs Since Qwen-Image-Edit’s release, the community has developed many creative and high-quality LoRAs—greatly expanding its expressive potential. Qwen-Image-Edit-2511 integrates selected popular LoRAs directly into the base model, unlocking their effects without extra tuning.

For example, Lighting Enhancement LoRA Realistic lighting control is now achievable out-of-the-box:

Another example, generating new viewpoints can now be done directly with the base model:

Industrial Design Applications

We’ve paid special attention to practical engineering scenarios—for instance, batch industrial product design:

…and material replacement for industrial components:

Enhanced Geometric Reasoning Qwen-Image-Edit-2511 introduces stronger geometric reasoning capability—e.g., directly generating auxiliary construction lines for design or annotation purposes:

That wraps up the major updates in Qwen-Image-Edit-2511. Enjoy exploring the new capabilities! 🎉

License Agreement

Qwen-Image is licensed under Apache 2.0.

Citation

We kindly encourage citation of our work if you find it useful.

@misc{wu2025qwenimagetechnicalreport,
      title={Qwen-Image Technical Report}, 
      author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},
      year={2025},
      eprint={2508.02324},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.02324}, 
}