InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
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.
68c76c47ee0eed1697d6b662
Tao Han
68c76c47ee0eed1697d6b663
Wanghan Xu
claimed_verified
2025-09-15T15:07:54.551Z
65f3f43fc9940817ca9a427b
https://cdn-avatars.huggingface.co/v1/production/uploads/65f3f43fc9940817ca9a427b/02NN3XjSsbgWDhjrJWtVL.jpeg
Wanghan Xu
user
CoCoOne
68c76c47ee0eed1697d6b664
Junchao Gong
68c76c47ee0eed1697d6b665
Xiaoyu Yue
claimed_verified
2025-09-16T09:43:17.062Z
6662a450b1fff5575fdf0fbd
/avatars/2a6065269f1980213625a9cfd8d42fbd.svg
Xiaoyu Yue
user
YueXY233
68c76c47ee0eed1697d6b666
Song Guo
68c76c47ee0eed1697d6b667
Luping Zhou
68c76c47ee0eed1697d6b668
Lei Bai
InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
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.
InfGen, a one-step generator replacing the VAE decoder, enables arbitrary high-resolution image generation from a fixed-size latent, significantly reducing computational complexity and generation time.
diffusion models latent diffusion models VAE decoder one-step generator arbitrary resolution computational complexity image generation time