Summary of Instantid: Zero-shot Identity-preserving Generation in Seconds, by Qixun Wang et al.
InstantID: Zero-shot Identity-Preserving Generation in Seconds
by Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, Anthony Chen, Huaxia Li, Xu Tang, Yao Hu
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents InstantID, a novel diffusion model-based solution for personalized image synthesis. Building on existing methods like Textual Inversion and DreamBooth, InstantID addresses limitations in ID embedding-based approaches by requiring only a single facial image input while maintaining high face fidelity. The authors introduce IdentityNet, a plug-and-play module that integrates facial and landmark images with textual prompts to steer image generation. This allows for exceptional performance and efficiency in real-world applications where identity preservation is crucial. InstantID seamlessly integrates with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving as an adaptable plugin. The authors will make their codes and pre-trained checkpoints available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easy to create personalized images using just one photo of a person’s face! It solves some big problems in current methods by requiring only one image instead of many references. The new model, called InstantID, works well with popular models like SD1.5 and SDXL. It’s really good at keeping the person’s face looking the same as the original picture. This is important for real-life uses where people want to keep their identity private. |
Keywords
» Artificial intelligence » Diffusion model » Embedding » Image generation » Image synthesis
