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Anirud Aggarwal

2 accepted papers

2026

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model

ICLR 2026poster

Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to mitigate this by caching and reusing features within diffusion transformers across inference steps. These methods, howe…

Cited by 0SourcecodeScholar
2026

UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders

CVPR 2026

The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act as a shortcut to achieve dense features for a fraction of the cost by learning to map low-resolution features to high-r

Cited by 0SourcecodeScholar