Learning Visual Generative Priors without Text
Journal:
arXiv
Published Date:
Dec 10, 2024
Abstract
Although text-to-image (T2I) models have recently thrived as visual
generative priors, their reliance on high-quality text-image pairs makes
scaling up expensive. We argue that grasping the cross-modality alignment is
not a necessity for a sound visual generative prior, whose focus should be on
texture modeling. Such a philosophy inspires us to study image-to-image (I2I)
generation, where models can learn from in-the-wild images in a self-supervised
manner. We first develop a pure vision-based training framework, Lumos, and
confirm the feasibility and the scalability of learning I2I models. We then
find that, as an upstream task of T2I, our I2I model serves as a more
foundational visual prior and achieves on-par or better performance than
existing T2I models using only 1/10 text-image pairs for fine-tuning. We
further demonstrate the superiority of I2I priors over T2I priors on some
text-irrelevant visual generative tasks, like image-to-3D and image-to-video.
Our project page is available at https://ant-research.github.io/lumos.
