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20242026
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cs.CV2026

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices

Dongting Hu, Aarush Gupta, Magzhan Gabidolla +12

Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and mem…

cs.CV2025

MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input

Zhenchen Wan, Yanwu xu, Dongting Hu +6

Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-image (T2I) diffusion models. Ho…

cs.CV2024

SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training

Dongting Hu, Jierun Chen, Xijie Huang +16

Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to…

cs.CV2024

AniFaceDiff: Animating Stylized Avatars via Parametric Conditioned Diffusion Models

Ken Chen, Sachith Seneviratne, Wei Wang +7

Animating stylized avatars with dynamic poses and expressions has attracted increasing attention for its broad range of applications. Previous research has made significant progres…

cs.CV2024

STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario

Renyang Liu, Kwok-Yan Lam, Wei Zhou +4

Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existin…