IC-Light (V2): Image Lighting Control Utility, Control Image Light and Background

General Introduction

IC-Light is a project for image lighting control that aims to manipulate the lighting effects of images through advanced AI models. The project, developed by Lvmin Zhang et al, provides two main models: a text-conditional relighting model and a background-conditional model. Users can adjust the illumination of the foreground image to match the background ambient lighting with simple text prompts or background images to achieve high-quality image compositing and lighting effects.

IC-Light V2 (demo) It is a series based on Flux The IC-Light model of the SD1.5 benefits from a 16-channel VAE, native high resolution and better training methods for far greater detail retention than the SD1.5.

Online experience: https://huggingface.co/spaces/lllyasviel/IC-Light

IC-Light V2 for FLUX Models

Function List

• Textual Conditional Relighting Model: Control the lighting effect of an image by entering a text prompt.
• background conditions model: Automatically adjusts the illumination of the foreground image based on the background image.
• Automatic Model Download: There is no need to download the model manually, the system will handle it automatically.
• Multiple light preference settings: Users can choose different light directions and intensities.
• High Resolution Support: Supports high-resolution image processing, preserving image detail.
• uniform light: Ensures consistent blending of different light sources for HDR spaces.

Using Help

Installation process

1. cloning project::

   git clone https://github.com/lllyasviel/IC-Light.git
   cd IC-Light
   

2. Create and activate a virtual environment::

   conda create -n iclight python=3.10
   conda activate iclight
   

3. Installation of dependencies::

   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
   pip install -r requirements.txt
   

Relighting models using text conditions

1. Run the sample script::

   python gradio_demo.py
   

2. input prompt: Enter text prompts in the Gradio interface, such as "beautiful woman, detailed face, warm atmosphere, at home, bedroom".
3. Selecting Light Preferences: Select the direction of light as required, e.g. "Left".
4. Generating images: Click the Generate button to view the effect of the adjusted image.

Use of background condition models

1. Run the sample script::

   python gradio_demo_bg.py
   

2. Upload background image: Upload a background image in the Gradio interface.
3. Input foreground prompts: Enter a description of the foreground image, e.g. "handsome man, cinematic lighting".
4. Generating images: Click the Generate button to view the effect of the adjusted image.

Advanced Features

• photometric uniformity: IC-Light ensures consistency of light in the HDR space, resulting in a natural blend of different light sources.
• Detail retention: Maintains high detail and resolution of images using 16-channel VAE models.
• style: Support for processing stylized images, maintaining the original artistic style.

common problems

• How do you handle low light images? Using the background condition model, enter a description of the low-light image and the system will automatically adjust the lighting.
• Does it support commercial use? The current version is for non-commercial use only. For commercial use, please contact the development team for a license.

IC-Light one-click installer unpacking password and colab deployment:

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