Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models

Recent research highlights the potential of Vision Foundation Models to serve as effective tokenizers for Latent Diffusion Models, enhancing their overall performance. This development addresses a significant issue in current methodologies, which tend to weaken the alignment with original models and cause semantic deviations when distribution shifts occur. By leveraging Vision Foundation Models, these challenges can be mitigated, leading to improved semantic consistency and robustness in Latent Diffusion Models. The findings underscore the importance of refining tokenization processes to maintain fidelity to the original data representations. This advancement could have broad implications for applications relying on diffusion models in computer vision tasks. The study, published on arXiv in November 2025, contributes to ongoing efforts to optimize AI model architectures for better accuracy and reliability.