1 citations · 1 across the 4 of their papers we have counts for
5 papers
Vitality-Aware Compression for Efficient Image-to-Shape Diffusion Transformers
Jaeah Lee, Hyunjin Kim, Jaewoong Cho +1
We propose the first compression approach for image-to-shape Diffusion Transformers (DiTs) that substantially reduces model size while preserving geometric fidelity. Despite remark…
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens
Jaehyeon Kim, Taehong Moon, Keon Lee +1
We introduce ResGen, an efficient Residual Vector Quantization (RVQ)-based generative model for high-fidelity generation with fast sampling. RVQ improves data fidelity by increasin…
Fast and Accurate Neural Rendering Using Semi-Gradients
In-Young Cho, Jaewoong Cho
We propose a simple yet effective neural network-based framework for global illumination rendering. Recently, rendering techniques that learn neural radiance caches by minimizing t…
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
Dongmin Park, Sebin Kim, Taehong Moon +3
State-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that t…
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models
Taehong Moon, Moonseok Choi, EungGu Yun +4
Diffusion models have shown remarkable performance in generation problems over various domains including images, videos, text, and audio. A practical bottleneck of diffusion models…