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Mina Kang

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2 papers

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NeurIPS Conference 2025 Conference Paper

Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models

Byeonghu Na
Minsang Park
Gyuwon Sim
Donghyeok Shin
HeeSun Bae
Mina Kang
Se Jung Kwon
Wanmo Kang

Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptability to the generative process. We propose Diffusion Adaptive Text Embedding (DATE), which dynamically updates text embeddings at each diffusion timestep based on intermediate perturbed data. We formulate an optimization problem and derive an update rule that refines the text embeddings at each sampling step to improve alignment and preference between the mean predicted image and the text. This allows DATE to dynamically adapts the text conditions to the reverse-diffused images throughout diffusion sampling without requiring additional model training. Through theoretical analysis and empirical results, we show that DATE maintains the generative capability of the model while providing superior text-image alignment over fixed text embeddings across various tasks, including multi-concept generation and text-guided image editing. Our code is available at https: //github. com/aailab-kaist/DATE.

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NeurIPS Conference 2025 Conference Paper

Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion Models

Byeonghu Na
Mina Kang
Jiseok Kwak
Minsang Park
Jiwoo Shin
SeJoon Jun
Gayoung Lee
Jin-Hwa Kim

Text-to-image models have recently made significant advances in generating realistic and semantically coherent images, driven by advanced diffusion models and large-scale web-crawled datasets. However, these datasets often contain inappropriate or biased content, raising concerns about the generation of harmful outputs when provided with malicious text prompts. We propose Safe Text embedding Guidance (STG), a training-free approach to improve the safety of diffusion models by guiding the text embeddings during sampling. STG adjusts the text embeddings based on a safety function evaluated on the expected final denoised image, allowing the model to generate safer outputs without additional training. Theoretically, we show that STG aligns the underlying model distribution with safety constraints, thereby achieving safer outputs while minimally affecting generation quality. Experiments on various safety scenarios, including nudity, violence, and artist-style removal, show that STG consistently outperforms both training-based and training-free baselines in removing unsafe content while preserving the core semantic intent of input prompts. Our code is available at https: //github. com/aailab-kaist/STG.

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