InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks.

Release

• [2024/04/03] ? We release our recent work InstantStyle for style transfer, compatible with InstantID!
• [2024/02/01] ? We have supported LCM acceleration and Multi-ControlNets on our Huggingface Spaces Demo! Our depth estimator is supported by Depth-Anything.
• [2024/01/31] ? OneDiff now supports accelerated inference for InstantID, check this for details!
• [2024/01/23] ? Our pipeline has been merged into diffusers!
• [2024/01/22] ? We release the pre-trained checkpoints, inference code and gradio demo!
• [2024/01/15] ? We release the technical report.
• [2023/12/11] ? We launch the project page.

Demos

Stylized Synthesis

Comparison with Previous Works

Comparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better).

Comparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training.

Comparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background.

Download

You can directly download the model from Huggingface. You also can download the model in python script:

```python from huggingfacehub import hfhubdownload hfhubdownload(repoid="InstantX/InstantID", filename="ControlNetModel/config.json", loc