11 citations · 15 across the 11 of their papers we have counts for
6 papers · 1 filter
Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation
Junhyuk So, Hyunho Kook, Chaeyeon Jang +1
Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrained by the slow inference speed o…
Grouped Speculative Decoding for Autoregressive Image Generation
Junhyuk So, Juncheol Shin, Hyunho Kook +1
Recently, autoregressive (AR) image models have demonstrated remarkable generative capabilities, positioning themselves as a compelling alternative to diffusion models. However, th…
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models
Junhyuk So, Jiwoong Shin, Chaeyeon Jang +1
Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes.…
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models
Junhyuk So, Jungwon Lee, Eunhyeok Park
The substantial computational costs of diffusion models, especially due to the repeated denoising steps necessary for high-quality image generation, present a major obstacle to the…
Temporal Dynamic Quantization for Diffusion Models
Junhyuk So, Jungwon Lee, Daehyun Ahn +2
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility. However, high storage and computation demands, result…
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Changdae Oh, Junhyuk So, Hoyoon Byun +4
Pre-trained multi-modal models, such as CLIP, provide transferable embeddings and show promising results in diverse applications. However, the analysis of learned multi-modal embed…