Recent years have seen remarkable progress in deep learning powered visual content
creation. This includes deep generative 3D-aware image synthesis, which produces high …

We present a method to create interpretable concept sliders that enable precise control over
attributes in image generations from diffusion models. Our approach identifies a low-rank …

A key aspect of text-to-image personalization methods is the manner in which the target
concept is represented within the generative process. This choice greatly affects the visual …

Despite the success of diffusion models (DMs), we still lack a thorough understanding of
their latent space. To understand the latent space $\mathbf {x} _t\in\mathcal {X} $, we …

Diffusion-based models have gained significant popularity for text-to-image generation due
to their exceptional image-generation capabilities. A risk with these models is the potential …

We present LooseControl to allow generalized depth conditioning for diffusion-based image
generation. ControlNet, the SOTA for depth conditioned image generation, produces …

This paper concerns the structure of learned representations in text-guided generative
models, focusing on score-based models. A key property of such models is that they can …

Generative models have been very popular in the recent years for their image generation
capabilities. GAN-based models are highly regarded for their disentangled latent space …

Recently, diffusion models have emerged as a powerful class of generative models. Despite
their success, there is still limited understanding of their semantic spaces. This makes it …

Modern generative models demonstrate impressive capabilities, likely stemming from an
ability to identify and manipulate abstract concepts underlying their training data. However …