5 papers
Rethinking the Use of Vision Transformers for AI-Generated Image Detection
NaHyeon Park, Kunhee Kim, Junsuk Choe +1
Rich feature representations derived from CLIP-ViT have been widely utilized in AI-generated image detection. While most existing methods primarily leverage features from the final…
Directional Textual Inversion for Personalized Text-to-Image Generation
Kunhee Kim, NaHyeon Park, Kibeom Hong +1
Textual Inversion (TI) is an efficient approach to text-to-image personalization but often fails on complex prompts. We trace these failures to embedding norm inflation: learned to…
Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models
NaHyeon Park, Na Min An, Kunhee Kim +3
Text-to-image (T2I) systems increasingly rely on Large Language Model (LLM)-based text conditioning to interpret and expand user prompts. While this improves prompt understanding a…
TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation
NaHyeon Park, Kunhee Kim, Hyunjung Shim
In this paper, we introduce TextBoost, an efficient one-shot personalization approach for text-to-image diffusion models. Traditional personalization methods typically involve fine…
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
Namhyuk Ahn, Junsoo Lee, Chunggi Lee +4
Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristic…