104 citations · 195 across the 18 of their papers we have counts for
17 papers · 1 filter
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…
FlowLong: Inference-time Long Video Generation via Manifold-constrained Tweedie Matching
Jangho Park, Geon Yeong Park, Gihyun Kwon +1
Extending the generation horizon of video diffusion models to long sequences remains a long-standing and important challenge. Existing training-free approaches fall into two catego…
TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation
Gihyun Kwon, Jong Chul Ye
Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts re…
Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection
Gihyun Kwon, Jangho Park, Jong Chul Ye
While text-to-image models have achieved impressive capabilities in image generation and editing, their application across various modalities often necessitates training separate m…
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models
Gihyun Kwon, Simon Jenni, Dingzeyu Li +3
While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this…
Patch-wise Graph Contrastive Learning for Image Translation
Chanyong Jung, Gihyun Kwon, Jong Chul Ye
Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input and output images. To further ex…
Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
Hyelin Nam, Gihyun Kwon, Geon Yeong Park +1
With the remarkable advent of text-to-image diffusion models, image editing methods have become more diverse and continue to evolve. A promising recent approach in this realm is De…
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF
Jangho Park, Gihyun Kwon, Jong Chul Ye
Recently, there has been a significant advancement in text-to-image diffusion models, leading to groundbreaking performance in 2D image generation. These advancements have been ext…
Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance
Gihyun Kwon, Jong Chul Ye
Diffusion models have shown significant progress in image translation tasks recently. However, due to their stochastic nature, there's often a trade-off between style transformatio…
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Beomsu Kim, Gihyun Kwon, Kwanyoung Kim +1
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved…
Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models
Hyeonho Jeong, Gihyun Kwon, Jong Chul Ye
Recent advancements in large scale text-to-image models have opened new possibilities for guiding the creation of images through human-devised natural language. However, while prio…
Diffusion-based Image Translation using Disentangled Style and Content Representation
Gihyun Kwon, Jong Chul Ye
Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains. Unfortunately,…
Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks
Chanyong Jung, Gihyun Kwon, Jong Chul Ye
Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the m…
One-Shot Adaptation of GAN in Just One CLIP
Gihyun Kwon, Jong Chul Ye
There are many recent research efforts to fine-tune a pre-trained generator with a few target images to generate images of a novel domain. Unfortunately, these methods often suffer…
CLIPstyler: Image Style Transfer with a Single Text Condition
Gihyun Kwon, Jong Chul Ye
Existing neural style transfer methods require reference style images to transfer texture information of style images to content images. However, in many practical situations, user…
Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation
Gihyun Kwon, Jong Chul Ye
One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content…
Progressive Face Super-Resolution via Attention to Facial Landmark
Deokyun Kim, Minseon Kim, Gihyun Kwon +1
Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images. The main challenge of face SR is to restore essential facial…