Diffusion Models Already Have A Semantic Latent Space

Mingi Kwon, Jaeseok Jeong, Youngjung Uh

Keywords: diffusion models, semantic image editing

Abstract: Diffusion models achieve outstanding generative performance in various domains. Despite their great success, they lack semantic latent space which is essential for controlling the generative process. To address the problem, we propose asymmetric reverse process (Asyrp) which discovers the semantic latent space in frozen pretrained diffusion models. Our semantic latent space, named h-space, has nice properties for accommodating semantic image manipulation: homogeneity, linearity, robustness, and consistency across timesteps. In addition, we measure editing strength and quality deficiency of a generative process at timesteps to provide a principled design of the process for versatility and quality improvements. Our method is applicable to various architectures (DDPM++, iDDPM, and ADM) and datasets (CelebA-HQ, AFHQ-dog, LSUN-church, LSUN-bedroom, and METFACES).

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TL;DR: We discover the semantic latent space of pretrained diffusion models by introducing asymmetric reverse process.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.10960/code)