🪶 MagicQuill V2: Precise and Interactive Image Editing with Layered Visual Cues

• Paper: MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues
• Project Page: https://magicquill.art/v2/
• Code Repository: https://github.com/zliucz/MagicQuillV2
• Hugging Face Spaces Demo: https://huggingface.co/spaces/AI4Editing/MagicQuillV2

Hardware Requirements

• 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 or trying the online demo on Hugging Face Spaces.

Setup

1. Clone the repository
   bash

   1git clone https://github.com/magic-quill/MagicQuillV2.git 2cd MagicQuillV2
2. Create environment
   bash

   1conda create -n MagicQuillV2 python=3.10 -y 2conda activate MagicQuillV2
3. Install dependencies
   pip install -r requirements.txt
4. Download models Download the models from Hugging Face and place them in the models/ directory.
   huggingface-cli download LiuZichen/MagicQuillV2-models --local-dir models
5. Run the demo
   python app.py

System Overview

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.

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