Compatibility of VFM-derived representations with large-scale high-resolution text-to-image diffusion training

Ascertain whether Visual Foundation Model-derived representations are inherently compatible with large-scale, high-resolution text-to-image diffusion training required for real-world applications.

Background

Prior works have shown promising results in class-conditioned ImageNet generation using VFM features, but systematic large-scale text-to-image training has been largely absent. The authors stress the need to verify feasibility and scaling behavior in realistic, high-resolution T2I scenarios.

This question directly impacts the practical deployment of representation-driven diffusion systems and whether VFM features can serve as an effective latent manifold for real-world text-to-image generation.

References

However, two key challenges remain unresolved: Are VFM-derived representations inherently compatible with large-scale, high-resolution text-to-image diffusion training, which is essential for real-world applications?

— SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder  (2512.11749 - Shi et al., 12 Dec 2025) in Section 1 (Introduction)

We remain behind PixelGen-XL, which reaches 1.83 at 160 epochs in a similar parameter range; we attribute this to its use of frozen VGG~\citep{simonyan2014very} supervision alongside DINO, and leave the combination of the two signals to future work.

— Dino Forcing Flow Models: Do not denoise what you can predict  (2610.11751 - Ghosh et al., 8 Oct 2026) in Section 4, subsection 'Pixel Space Setting', paragraph 'Results with guidance'