9 citations · 13 across the 7 of their papers we have counts for
7 papers
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…
Training-Free Reward-Guided Image Editing via Trajectory Optimal Control
Jinho Chang, Jaemin Kim, Jong Chul Ye
Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A prominent line of research involves reward-g…
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
Jinho Chang, Changsun Lee, Hyungjin Chung +1
As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negat…
LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models
Jinho Chang, Jong Chul Ye
With the emergence of diffusion models as a frontline generative model, many researchers have proposed molecule generation techniques with conditional diffusion models. However, th…
Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing
Hangeol Chang, Jinho Chang, Jong Chul Ye
Despite recent advancements in text-to-image diffusion models facilitating various image editing techniques, complex text prompts often lead to an oversight of some requests due to…
DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing
Hyeonho Jeong, Jinho Chang, Geon Yeong Park +1
Text-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion. Unlike existing video editing app…
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
Suhyeon Lee, Won Jun Kim, Jinho Chang +1
Following the impressive development of LLMs, vision-language alignment in LLMs is actively being researched to enable multimodal reasoning and visual IO. This direction of researc…