A survey on deep generative 3d-aware image synthesis
Recent years have seen remarkable progress in deep learning powered visual content
creation. This includes deep generative 3D-aware image synthesis, which produces high …
creation. This includes deep generative 3D-aware image synthesis, which produces high …
Concept sliders: Lora adaptors for precise control in diffusion models
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 …
attributes in image generations from diffusion models. Our approach identifies a low-rank …
A neural space-time representation for text-to-image personalization
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 …
concept is represented within the generative process. This choice greatly affects the visual …
Understanding the latent space of diffusion models through the lens of riemannian geometry
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 …
their latent space. To understand the latent space $\mathbf {x} _t\in\mathcal {X} $, we …
Self-discovering interpretable diffusion latent directions for responsible text-to-image generation
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 …
to their exceptional image-generation capabilities. A risk with these models is the potential …
Concept algebra for (score-based) text-controlled generative models
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 …
models, focusing on score-based models. A key property of such models is that they can …
Noiseclr: A contrastive learning approach for unsupervised discovery of interpretable directions in diffusion models
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 …
capabilities. GAN-based models are highly regarded for their disentangled latent space …
Loosecontrol: Lifting controlnet for generalized depth conditioning
We present LooseControl to allow generalized depth conditioning for diffusion-based image
generation. ControlNet, the SOTA for depth conditioned image generation, produces …
generation. ControlNet, the SOTA for depth conditioned image generation, produces …
Exploring low-dimensional subspaces in diffusion models for controllable image editing
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 …
their success, there is still limited understanding of their semantic spaces. This makes it …
Emergence of hidden capabilities: Exploring learning dynamics in concept space
Modern generative models demonstrate impressive capabilities, likely stemming from an
ability to identify and manipulate abstract concepts underlying their training data. However …
ability to identify and manipulate abstract concepts underlying their training data. However …