1 citations · 1 across the 14 of their papers we have counts for
14 papers
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…
LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
Jeongchan Kim, Yunkyung Ko, Jong Chul Ye
We study the application of recent Edit Flows for inference-time reward control for DNA sequence generation. Unlike most reward-guided DNA generation frameworks, which operate on f…
Geometric 4D Stitching for Grounded 4D Generation
Sunwoo Park, Taesung Kwon, Jong Chul Ye
Recent 4D generation methods complete scene-level missing information using generative models and reconstruct the scene into radiance-based representations. However, these pipeline…
Understanding and Accelerating the Training of Masked Diffusion Language Models
Chunsan Hong, Sanghyun Lee, Chieh-Hsin Lai +5
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known to learn substantially more sl…
CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation
Seonghyun Jin, Youngmin Kim, Sunwoo Park +1
Video world models should predict future appearance in a way that remains consistent with 3D scene structure, camera motion, and lens geometry. Existing attention-level camera enco…
AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization
Hyunmin Hwang, Jaemin Kim, Choonghan Kim +2
Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore diverse reasoning paths. However,…
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…
MotionCFG: Boosting Motion Dynamics via Stochastic Concept Perturbation
Byungjun Kim, Soobin Um, Jong Chul Ye
Despite recent advances in Text-to-Video (T2V) synthesis, generating high-fidelity and dynamic motion remains a significant challenge. Existing methods primarily rely on Classifier…
Diverse Text-to-Image Generation via Contrastive Noise Optimization
Byungjun Kim, Soobin Um, Jong Chul Ye
Text-to-image (T2I) diffusion models have demonstrated impressive performance in generating high-fidelity images, largely enabled by text-guided inference. However, this advantage…
Align Your Tangent: Training Better Consistency Models via Manifold-Aligned Tangents
Beomsu Kim, Byunghee Cha, Jong Chul Ye
With diffusion and flow matching models achieving state-of-the-art generating performance, the interest of the community now turned to reducing the inference time without sacrifici…
Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models
Dongjin Seo, Soobin Um, Sangbin Lee +2
Designing free-form photonic devices is fundamentally challenging due to the vast number of possible geometries and the complex requirements of fabrication constraints. Traditional…
Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation
Soobin Um, Beomsu Kim, Jong Chul Ye
Minority samples are underrepresented instances located in low-density regions of a data manifold, and are valuable in many generative AI applications, such as data augmentation, c…
Minority-Focused Text-to-Image Generation via Prompt Optimization
Soobin Um, Jong Chul Ye
We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models. Minority instances, in the context of T2I generation, can be defined…
Self-Guided Generation of Minority Samples Using Diffusion Models
Soobin Um, Jong Chul Ye
We present a novel approach for generating minority samples that live on low-density regions of a data manifold. Our framework is built upon diffusion models, leveraging the princi…