101 citations · 135 across the 9 of their papers we have counts for
9 papers
DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow
Kyungmin Lee, Kihyuk Sohn, Jinwoo Shin
Recent progress in text-to-3D generation has been achieved through the utilization of score distillation methods: they make use of the pre-trained text-to-image (T2I) diffusion mod…
Instruct-Imagen: Image Generation with Multi-modal Instruction
Hexiang Hu, Kelvin C. K. Chan, Yu-Chuan Su +9
This paper presents instruct-imagen, a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image…
Label Budget Allocation in Multi-Task Learning
Ximeng Sun, Kihyuk Sohn, Kate Saenko +2
The cost of labeling data often limits the performance of machine learning systems. In multi-task learning, related tasks provide information to each other and improve overall perf…
StyleDrop: Text-to-Image Generation in Any Style
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee +11
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distributi…
Learning Disentangled Prompts for Compositional Image Synthesis
Kihyuk Sohn, Albert Shaw, Yuan Hao +5
We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to…
Video Probabilistic Diffusion Models in Projected Latent Space
Sihyun Yu, Kihyuk Sohn, Subin Kim +1
Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality an…