Recent advances in large-scale pretraining have yielded visual foundation models with
strong capabilities. Not only can recent models generalize to arbitrary images for their …

Score distillation sampling (SDS) and its variants have greatly boosted the development of
text-to-3D generation but are vulnerable to geometry collapse and poor textures yet. To …

Generative models can produce impressively realistic images. This paper demonstrates that
generated images have geometric features different from those of real images. We build a …

We introduce beta diffusion, a novel generative modeling method that integrates demasking
and denoising to generate data within bounded ranges. Using scaled and shifted beta …

Generative models excel at mimicking real scenes, suggesting they might inherently encode
important intrinsic scene properties. In this paper, we aim to explore the following key …

Image editing plays a vital role in computer vision field, aiming to realistically manipulate
images while ensuring seamless integration. It finds numerous applications across various …

Diffusion models have recently brought a powerful revolution in image generation. Despite
showing impressive generative capabilities most of these models rely on the current sample …

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

We study text-based image editing (TBIE) of a single image by counterfactual inference
because it is an elegant formulation to precisely address the requirement: the edited image …

Training diffusion models is always a computation-intensive task. In this paper, we introduce
a novel speed-up method for diffusion model training, called, which is based on a closer …