InfiniteYou
| Property | Value |
|---|---|
| Developer | ByteDance Intelligent Creation |
| License | Creative Commons Attribution-NonCommercial 4.0 |
| Base Model | FLUX.1-dev |
| Paper | arXiv:2503.16418 |
What is InfiniteYou?
InfiniteYou (InfU) is a breakthrough framework that leverages Diffusion Transformers (DiTs) for identity-preserved image generation. It addresses critical challenges in existing methods, including identity similarity, text-image alignment, and generation quality. The model introduces InfuseNet, a specialized component that integrates identity features into the DiT base model through residual connections.
Implementation Details
The model implements a multi-stage training strategy, featuring pretraining and supervised fine-tuning (SFT) using synthetic single-person-multiple-sample (SPMS) data. Two primary variants are available: aes_stage2 for better text-image alignment and aesthetics, and sim_stage1 for higher identity similarity.
- InfuseNet with residual connections for identity feature injection
- Multi-stage training strategy with SPMS data
- Plug-and-play compatibility with existing methods
- Adjustable conditioning scale and guidance parameters
Core Capabilities
- High-fidelity identity preservation in generated images
- Superior text-image alignment compared to existing solutions
- Enhanced image quality and aesthetics
- Compatible with ControlNets, LoRAs, and OminiControl
- Support for multi-concept personalization
Frequently Asked Questions
Q: What makes this model unique?
InfiniteYou stands out through its InfuseNet architecture and multi-stage training approach, delivering superior identity preservation while maintaining high-quality image generation. Its plug-and-play design ensures compatibility with various existing methods, making it highly versatile.
Q: What are the recommended use cases?
The model excels in identity-preserved image generation tasks, particularly when users need to maintain personal identity while creating variations or new scenarios. It's especially useful for controlled image manipulation while preserving identity features, with additional flexibility through LoRA adaptations for realism and anti-blur effects.
