1 citations · 2 across the 7 of their papers we have counts for
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Orthogonal Negative Guidance in Attention Feature Space for Text-to-Image Generation
Jungmin Ko, Jungwon Park, Jimyeong Kim +3
Text-to-image (T2I) models have become increasingly capable of generating high-quality images. Yet, enforcing the explicit absence of a specified object or attribute remains a fund…
Selective Aggregation of Attention Maps Improves Diffusion-Based Visual Interpretation
Jungwon Park, Jungmin Ko, Dongnam Byun +1
Numerous studies on text-to-image (T2I) generative models have utilized cross-attention maps to boost application performance and interpret model behavior. However, the distinct ch…
DOS: Directional Object Separation in Text Embeddings for Multi-Object Image Generation
Dongnam Byun, Jungwon Park, Jungmin Ko +2
Recent progress in text-to-image (T2I) generative models has led to significant improvements in generating high-quality images aligned with text prompts. However, these models stil…
ReFlex: Text-Guided Editing of Real Images in Rectified Flow via Mid-Step Feature Extraction and Attention Adaptation
Jimyeong Kim, Jungwon Park, Yeji Song +2
Rectified Flow text-to-image models surpass diffusion models in image quality and text alignment, but adapting ReFlow for real-image editing remains challenging. We propose a new r…
Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models
Jungwon Park, Jungmin Ko, Dongnam Byun +2
Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understandin…
Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization
Jimyeong Kim, Jungwon Park, Wonjong Rhee
In text-to-image personalization, a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images. We initiate our stud…