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Matthew Zheng

3 accepted papers

2025

LoRAverse: A Submodular Framework to Retrieve Diverse Adapters for Diffusion Models

ICCV 2025poster

Low-rank Adaptation (LoRA) models have revolutionized the personalization of pre-trained diffusion models by enabling fine-tuning through low-rank, factorized weight matrices specifically optimized for attention layers. These models facilitate the generation of highly customized content across a var…

Cited by 0SourcePDFScholar
2025

Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization

NeurIPS 2025poster

Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this work, we present the first framework for pers…

Cited by 0SourceScholar
2024

Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models

NeurIPS 2024poster

Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation. These models have been widely employed across various domains, particularly in art generation, where they facilitate a bro…