InstantID

InstantID: Zero-shot Identity-Preserving Generation in Seconds

Qixun Wang12 · Xu Bai12 · Haofan Wang12* · Zekui Qin12 · Anthony Chen123

Huaxia Li2 · Xu Tang2 · Yao Hu2

1InstantX Team · 2Xiaohongshu Inc · 3Peking University

*corresponding authors

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
- [2024/07/18] 🔥 We are training InstantID for Kolors. The weight requires significant computational power, which is currently in the process of iteration. After the model training is completed, it will be open-sourced. The latest checkpoint results are referenced in Kolors Version. 
- [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.

Kolors Version

We have adapted InstantID for Kolors. Leveraging Kolors' robust text generation capabilities 👍👍👍, InstantID can be integrated with Kolors to simultaneously generate ID and text.

| demo | demo | demo |

Download

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

python
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")

Or run the following command to download all models:

python
pip install -r gradio_demo/requirements.txt
python gradio_demo/download_models.py

If you cannot access to Huggingface, you can use hf-mirror to download models.
python
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download InstantX/InstantID --local-dir checkpoints --local-dir-use-symlinks False

For face encoder, you need to manually download via this URL to models/antelopev2 as the default link is invalid. Once you have prepared all models, the folder tree should be like:

  .
  ├── models
  ├── check

……（后略，见原文）