InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
Authors: name: Tao Han, name: Wanghan Xu, name: Junchao Gong, name: Xiaoyu Yue, name: Song Guo, name: Luping Zhou, name: Lei Bai

Arbitrary resolution image generation provides a consistent visual experience
across devices, having extensive applications for producers and consumers.
Current diffusion models increase computational demand quadratically with
resolution, causing 4K image generation delays over 100 seconds. To solve this,
we explore the second generation upon the latent diffusion models, where the
fixed latent generated by diffusion models is regarded as the content
representation and we propose to decode arbitrary resolution images with a
compact generated latent using a one-step generator. Thus, we present the
InfGen, replacing the VAE decoder with the new generator, for
generating images at any resolution from a fixed-size latent without retraining
the diffusion models, which simplifies the process, reducing computational
complexity and can be applied to any model using the same latent space.
Experiments show InfGen is capable of improving many models into the arbitrary
high-resolution era while cutting 4K image generation time to under 10 seconds.