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most citedRepresentation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

4 citations · 6 across the 8 of their papers we have counts for

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cs.CV2025

FontAdapter: Instant Font Adaptation in Visual Text Generation

Myungkyu Koo, Subin Kim, Sangkyung Kwak +3

Text-to-image diffusion models have significantly improved the seamless integration of visual text into diverse image contexts. Recent approaches further improve control over font…

cs.CV2024

Controllable Human Image Generation with Personalized Multi-Garments

Yisol Choi, Sangkyung Kwak, Sihyun Yu +2

We present BootComp, a novel framework based on text-to-image diffusion models for controllable human image generation with multiple reference garments. Here, the main bottleneck i…

cs.CV2024★ 4 cited

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Sihyun Yu, Sangkyung Kwak, Huiwon Jang +4

Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of…

cs.CV2024★ 2 cited

Improving Diffusion Models for Authentic Virtual Try-on in the Wild

Yisol Choi, Sangkyung Kwak, Kyungmin Lee +2

This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment, given a pair of images depicting the person and the garment, respecti…

cs.CV2024

Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models

Kyungmin Lee, Sangkyung Kwak, Kihyuk Sohn +1

Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency. However, such fine-tuned models are not robu…