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NewsByteDance Releases InfiniteYou: Flexible Photo Recrafting While Preserving User Identity

ByteDance Releases InfiniteYou: Flexible Photo Recrafting While Preserving User Identity

ByteDance’s Intelligent Creation team has recently released InfiniteYou (InfU), a new image generation framework that can flexibly generate diverse, high-quality images based on text prompts while maintaining the user’s identity.

Technical Breakthrough

Achieving flexible and high-fidelity image generation while preserving personal identity remains a challenging task. InfiniteYou is an innovative framework based on advanced Diffusion Transformers (DiTs), particularly the FLUX model, designed to address limitations in existing methods regarding identity similarity, text-image alignment, and generation quality.

InfiniteYou Examples

Core Advantages

InfiniteYou demonstrates several advantages over existing identity-preserving image generation methods:

  • High Identity Similarity: The InfuseNet component directly injects identity features via residual connections, ensuring precise preservation of user identity
  • Superior Text-Image Alignment: A multi-stage training strategy significantly improves the alignment between generated images and text descriptions
  • High Generation Quality and Aesthetics: Generated images surpass existing baseline models in terms of detail, clarity, and aesthetic appeal
  • Alleviates Face Copy-Pasting: Better generates realistic facial features based on text prompts, reducing direct face copy-pasting phenomena
  • Plug-and-Play Property: Features an ideal plug-and-play design compatible with various existing methods and tools

Practical Applications

InfiniteYou technology can be applied to various creative scenarios:

  • Personalized avatar creation
  • Diverse representation for content creators
  • Virtual try-on and image transformation
  • Personalized advertising and marketing content creation

Open Source and Availability

ByteDance has open-sourced InfiniteYou’s code and models on GitHub. Developers and researchers can use its code under the Apache 2.0 license, while the models are available under the Creative Commons Attribution-NonCommercial 4.0 International Public License for academic research purposes only.