158 citations · 190 across the 14 of their papers we have counts for
12 papers · 1 filter
FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing
Jeongsol Kim, Yeobin Hong, Jonghyun Park +1
Recent inversion-free, flow-based image editing methods such as FlowEdit leverages a pre-trained noise-to-image flow model such as Stable Diffusion 3, enabling text-driven manipula…
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
Bryan Sangwoo Kim, Jeongsol Kim, Jong Chul Ye
Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond tha…
FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems
Jeongsol Kim, Bryan Sangwoo Kim, Jong Chul Ye
Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides…
Aligning Text to Image in Diffusion Models is Easier Than You Think
Jaa-Yeon Lee, Byunghee Cha, Jeongsol Kim +1
While recent advancements in generative modeling have significantly improved text-image alignment, some residual misalignment between text and image representations still remains.…
Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation
Jeongsol Kim, Beomsu Kim, Jong Chul Ye
Diffusion models (DMs), which enable both image generation from noise and inversion from data, have inspired powerful unpaired image-to-image (I2I) translation algorithms. However,…
CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models
Hyungjin Chung, Jeongsol Kim, Geon Yeong Park +2
Classifier-free guidance (CFG) is a fundamental tool in modern diffusion models for text-guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with C…
Generalized Consistency Trajectory Models for Image Manipulation
Beomsu Kim, Jaemin Kim, Jeongsol Kim +1
Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. The success of DMs lies in the iterative nature of diffu…
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
Jeongsol Kim, Geon Yeong Park, Jong Chul Ye
Reverse sampling and score-distillation have emerged as main workhorses in recent years for image manipulation using latent diffusion models (LDMs). While reverse diffusion samplin…
Regularization by Texts for Latent Diffusion Inverse Solvers
Jeongsol Kim, Geon Yeong Park, Hyungjin Chung +1
The recent development of diffusion models has led to significant progress in solving inverse problems by leveraging these models as powerful generative priors. However, challenges…
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Geon Yeong Park, Jeongsol Kim, Beomsu Kim +2
Despite the remarkable performance of text-to-image diffusion models in image generation tasks, recent studies have raised the issue that generated images sometimes cannot capture…
Direct Diffusion Bridge using Data Consistency for Inverse Problems
Hyungjin Chung, Jeongsol Kim, Jong Chul Ye
Diffusion model-based inverse problem solvers have shown impressive performance, but are limited in speed, mostly as they require reverse diffusion sampling starting from noise. Se…
Parallel Diffusion Models of Operator and Image for Blind Inverse Problems
Hyungjin Chung, Jeongsol Kim, Sehui Kim +1
Diffusion model-based inverse problem solvers have demonstrated state-of-the-art performance in cases where the forward operator is known (i.e. non-blind). However, the applicabili…