1 citations · 1 across the 5 of their papers we have counts for
5 papers
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks
Bhishma Dedhia, David Bourgin, Krishna Kumar Singh +5
Diffusion Transformers (DiTs) can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video remains computationall…
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Alireza Ganjdanesh, Yan Kang, Yuchen Liu +3
Diffusion probabilistic models can generate high-quality samples. Yet, their sampling process requires numerous denoising steps, making it slow and computationally intensive. We pr…
SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
Zhengang Li, Yan Kang, Yuchen Liu +4
While AI-generated content has garnered significant attention, achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion…
Personalized Residuals for Concept-Driven Text-to-Image Generation
Cusuh Ham, Matthew Fisher, James Hays +4
We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models. Our method first represents…
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models
Hongjie Wang, Difan Liu, Yan Kang +4
Diffusion Models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive archi…