Sehwan Park — accepted AI-conference papers

2025

Random Conditioning for Diffusion Model Compression with Distillation

Dohyun Kim, Sehwan Park, Geonhee Han, Seung Wook Kim, Paul Hongsuck Seo

CVPR 2025accepted

Diffusion models generate high-quality images through progressive denoising but are computationally intensive due to large model sizes and repeated sampling. Knowledge distillation--transferring knowledge from a complex teacher to a simpler student model--has been widely studied in recognition tasks…

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2025

Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression

Dohyun Kim, Sehwan Park, GeonHee Han, Seung Wook Kim, Paul Hongsuck Seo

CVPR 2025poster

Diffusion models have emerged as a cornerstone of generative modeling, capable of producing high-quality images through a progressive denoising process. However, their remarkable performance comes with substantial computational costs, driven by large model sizes and the need for multiple sampling st…

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