Alumni
Jiyeon Han
Ph.D., 2025.09
- BS in Computer Science Engineering from UNIST, 2016
5 notes
Research
2025
Creative Concept Catalyst
C3 selectively amplifies features during denoising to improve creativity in Stable Diffusion-based models without additional training.
2025
Enhancing Creative Generation on Stable Diffusion-based Models
Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their creative capability remains constrained, as including `creative' in prompts seldom yields the desired results. In this paper, we introduce C3 (Creative Concept Catalyst), a training-free approach designed to enhance creativity in Stable Diffusion-based models. C3 selectively amplifies features during the denoising process to foster more creative outputs. We offer practical guidelines for choosing amplification factors based on two main aspects of creativity. C3 is the first study to enhance creativity in diffusion models without extensive computational costs. We demonstrate its effectiveness across various Stable Diffusion-based models.
2025
Diverse Rare Image Generation
Latent optimization and feature-space density estimation generate rare images with controllable rarity, diversity, and reference similarity.
2025
Diverse Rare Sample Generation with Pretrained GANs
This study proposes a novel approach for generating diverse rare samples from high-resolution image datasets with pretrained GANs. Our method employs gradient-based optimization of latent vectors within a multi-objective framework and utilizes normalizing flows for density estimation on the feature space. This enables the generation of diverse rare images, with controllable parameters for rarity, diversity, and similarity to a reference image. We demonstrate the effectiveness of our approach both qualitatively and quantitatively across various datasets and GANs without retraining or fine-tuning the pretrained GANs.
2023
Rarity Score: A New Metric to Evaluate the Uncommonness of Synthesized Images
In this work, we propose a new evaluation metric, called ‘rarity score’, to measure the individual rarity of each image synthesized by generative models. We first show empirical observation that common samples are close to each other and rare samples are far from each other in nearest-neighbor distances of feature space. We then use our metric to demonstrate that the extent to which different generative models produce rare images can be effectively compared. We also propose a method to compare rarities between datasets that share the same concept such as CelebA-HQ and FFHQ.
7 papers