The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

Text-guided generative diffusion models unlock powerful image creation and editing tools.
Recent approaches that edit the content of footage while retaining structure require …

To replicate the success of text-to-image (T2I) generation, recent works employ large-scale
video datasets to train a text-to-video (T2V) generator. Despite their promising results, such …

Despite tremendous progress in generating high-quality images using diffusion models,
synthesizing a sequence of animated frames that are both photorealistic and temporally …

Significant advancements have been achieved in the realm of large-scale pre-trained text-to-
video Diffusion Models (VDMs). However, previous methods either rely solely on pixel …

Video-P2P is the first framework for real-world video editing with cross-attention control.
While attention control has proven effective for image editing with pre-trained image …

Diffusion-based methods can generate realistic images and videos, but they struggle to edit
existing objects in a video while preserving their geometry over time. This prevents diffusion …

Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Denoising diffusion models (DDMs) have led to staggering performance leaps in image
generation, editing and restoration. However, existing DDMs use very large datasets for …