4 citations · 6 across the 18 of their papers we have counts for
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OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows
Shufan Li, Konstantinos Kallidromitis, Akash Gokul +4
We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the r…
PopAlign: Population-Level Alignment for Fair Text-to-Image Generation
Shufan Li, Harkanwar Singh, Aditya Grover
Text-to-image (T2I) models achieve high-fidelity generation through extensive training on large datasets. However, these models may unintentionally pick up undesirable biases of th…
Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
Shufan Li, Harkanwar Singh, Aditya Grover
In recent years, Transformers have become the de-facto architecture for sequence modeling on text and a variety of multi-dimensional data, such as images and video. However, the us…