activity
20242026
collaborators

5 papers

cs.CV2026

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices

Dongting Hu, Aarush Gupta, Magzhan Gabidolla +12

Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and mem…

cs.CV2026

S2DiT: Sandwich Diffusion Transformer for Mobile Streaming Video Generation

Lin Zhao, Yushu Wu, Aleksei Lebedev +11

Diffusion Transformers (DiTs) have recently improved video generation quality. However, their heavy computational cost makes real-time or on-device generation infeasible. In this w…

cs.CV2025

Taming Diffusion Transformer for Efficient Mobile Video Generation in Seconds

Yushu Wu, Yanyu Li, Anil Kag +9

Diffusion Transformers (DiT) have shown strong performance in video generation tasks, but their high computational cost makes them impractical for resource-constrained devices like…

cs.CV2025

SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device

Yushu Wu, Zhixing Zhang, Yanyu Li +11

We have witnessed the unprecedented success of diffusion-based video generation over the past year. Recently proposed models from the community have wielded the power to generate c…

cs.CV2024

BitsFusion: 1.99 bits Weight Quantization of Diffusion Model

Yang Sui, Yanyu Li, Anil Kag +7

Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a…