4 papers
Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models
Luca Eyring, Shyamgopal Karthik, Alexey Dosovitskiy +2
The new paradigm of test-time scaling has yielded remarkable breakthroughs in Large Language Models (LLMs) (e.g. reasoning models) and in generative vision models, allowing models…
: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark
Siwei Yang, Mude Hui, Bingchen Zhao +3
We introduce , a comprehensive benchmark designed to systematically evaluate instruction-based image editing models across instructions of varying complexity…
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition
Rupayan Mallick, Sibo Dong, Nataniel Ruiz +1
Applications of diffusion models for visual tasks have been quite noteworthy. This paper targets making classification models more robust to occlusions for the task of object recog…
M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
Sucheng Ren, Yaodong Yu, Nataniel Ruiz +3
There exists recent work in computer vision, named VAR, that proposes a new autoregressive paradigm for image generation. Diverging from the vanilla next-token prediction, VAR stru…