File size: 3,417 Bytes
eeaa5e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
---
license: cc-by-nc-sa-4.0
pipeline_tag: image-to-image
---
# 🪶 MagicQuill V2: Precise and Interactive Image Editing with Layered Visual Cues
- **Paper:** [MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues](https://huggingface.co/papers/2512.03046)
- **Project Page:** https://magicquill.art/v2/
- **Code Repository:** https://github.com/zliucz/MagicQuillV2
- **Hugging Face Spaces Demo:** https://huggingface.co/spaces/AI4Editing/MagicQuillV2
<br>
<div align="center">
<video src="https://github.com/user-attachments/assets/58079152-7729-48ed-9bb4-0ddfd1873dd0" width="100%" controls autoplay muted loop></video>
</div>
<br>
**TLDR:** MagicQuill V2 introduces a layered composition paradigm to generative image editing, disentangling creative intent into controllable visual cues (Content, Spatial, Structural, Color) for precise and intuitive control.
## Hardware Requirements
Our model is based on Flux Kontext, which is large and computationally intensive.
- **VRAM**: Approximately **40GB** of VRAM is required for inference.
- **Speed**: It takes about **30 seconds** to generate a single image.
> **Important**: This is a research project focused on pushing the boundaries of interactive image editing. If you do not have sufficient GPU memory, we recommend checking out our [**MagicQuill V1**](https://github.com/ant-research/MagicQuill) or trying the online demo on [**Hugging Face Spaces**](https://huggingface.co/spaces/AI4Editing/MagicQuillV2).
## Setup
1. **Clone the repository**
```bash
git clone https://github.com/magic-quill/MagicQuillV2.git
cd MagicQuillV2
```
2. **Create environment**
```bash
conda create -n MagicQuillV2 python=3.10 -y
conda activate MagicQuillV2
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Download models**
Download the models from [Hugging Face](https://huggingface.co/LiuZichen/MagicQuillV2-models) and place them in the `models/` directory.
```bash
huggingface-cli download LiuZichen/MagicQuillV2-models --local-dir models
```
5. **Run the demo**
```bash
python app.py
```
## System Overview
The MagicQuill V2 interactive system is designed to unify our layered composition framework.
<div align="center">
<img src="https://github.com/zliucz/MagicQuillV2/raw/main/assets/V2_UI.png" alt="MagicQuill V2 UI" width="100%">
</div>
### Key Upgrades from V1
1. **Toolbar (A)**: Features a new **Local Edit Brush** for defining the target editing area, along with tools for sketching edges and applying color.
2. **Visual Cue Manager (B)**: Holds all content layer visual cues (**foreground props**) that users can drag onto the canvas to define what to generate.
3. **Image Segmentation Panel (C)**: Accessed via the segment icon, this panel allows precise object extraction using SAM (Segment Anything Model) with positive/negative dots or bounding boxes.
## Citation
If you find MagicQuill V2 useful for your research, please cite our paper:
```bibtex
@article{liu2025magicquillv2,
title={MagicQuill V2: Precise and Interactive Image Editing with Layered Visual Cues},
author={Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Shuailei Ma, Ka Leong Cheng, Wen Wang, Qingyan Bai, Yuxuan Zhang, Yanhong Zeng, Yixuan Li, Xing Zhu, Yujun Shen, Qifeng Chen},
journal={arXiv:2512.03046},
year={2025}
}
``` |