AnirudAggarwal
[x: 0, y: 0]Computer Vision Researcher @ Stealth Startup
// research_interests
I research efficient methods for vision generation and understanding.
While I'm currently developing AI-native video infrastructure, my past work has included efficient image generation and lightweight upsampling methods.
View all research →// featured_work
2 detectionsEvolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Anirud Aggarwal, Abhinav Shrivastava, Matthew Gwilliam
We introduce ECAD, an evolutionary algorithm to automatically discover efficient caching schedules for accelerating diffusion-based image generation models. ECAD achieves faster than state-of-the-art speed and higher quality among training-free methods and generalizes across models and resolutions.
UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders
Matthew Walmer, Saksham Suri, Anirud Aggarwal, Abhinav Shrivastava
We introduce UPLiFT, a lightweight, iterative feature upsampler that converts coarse ViT and VAE features into pixel-dense representations using a fully local attention operator. It achieves state-of-the-art performance on segmentation and depth tasks while scaling linearly in visual tokens, and extends naturally to generative tasks for efficient image upscaling.
conf: 0.96
