158 citations · 190 across the 14 of their papers we have counts for
19 papers
Adjoint Guidance Flow: Amortized Critic Guidance for VLA Policies
Jeongsol Kim, Youngjun Jun, Kyumin Choi +6
Flow-based Vision-Language-Action (VLA) policies are typically trained by behavior cloning and thus do not explicitly optimize long-term task return. Critic guidance steers generat…
Gradient-Free Noise Optimization for Reward Alignment in Generative Models
Jeongsol Kim, Hongeun Kim, Jian Wang +1
Existing reward alignment methods for diffusion and flow models rely on multi-step stochastic trajectories, making them difficult to extend to deterministic generators. A natural a…
Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
Jeongjae Lee, Jinho Chang, Jeongsol Kim +1
Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model. Although existing…
Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Jeongsol Kim, Chanseok Lee, Jongin You +2
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a nove…
Diffusion models for inverse problems
Hyungjin Chung, Jeongsol Kim, Jong Chul Ye
Using diffusion priors to solve inverse problems in imaging have significantly matured over the years. In this chapter, we review the various different approaches that were propose…
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…
Diffusion Posterior Sampling for General Noisy Inverse Problems
Hyungjin Chung, Jeongsol Kim, Michael T. Mccann +2
Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative…
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
Sangjoon Park, Gwanghyun Kim, Jeongsol Kim +2
Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentr…