7 papers · 1 filter
Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers
Kanghyun Baek, Jaihyun Lew, Chaehun Shin +2
Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-to-image generation, yet they frequently suffer from concept omission, where specified objects…
TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment
Kanghyun Baek, Sangyub Lee, Jin Young Choi +6
Despite recent advances, diffusion-based text-to-image models still struggle with accurate text rendering. Several studies have proposed fine-tuning or training-free refinement met…
Negative-Guided Subject Fidelity Optimization for Zero-Shot Subject-Driven Generation
Chaehun Shin, Jooyoung Choi, Johan Barthelemy +2
We present Subject Fidelity Optimization (SFO), a novel comparative learning framework for zero-shot subject-driven generation that enhances subject fidelity. Existing supervised f…
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator
Chaehun Shin, Jooyoung Choi, Heeseung Kim +1
Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and…
Style-Friendly SNR Sampler for Style-Driven Generation
Jooyoung Choi, Chaehun Shin, Yeongtak Oh +3
Recent text-to-image diffusion models generate high-quality images but struggle to learn new, personalized styles, which limits the creation of unique style templates. In style-dri…
Disentangled Motion Modeling for Video Frame Interpolation
Jaihyun Lew, Jooyoung Choi, Chaehun Shin +2
Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on th…