Summary of Upsample Guidance: Scale Up Diffusion Models Without Training, by Juno Hwang et al.
Upsample Guidance: Scale Up Diffusion Models without Trainingby Juno Hwang, Yong-Hyun Park, Junghyo JoFirst submitted…
Upsample Guidance: Scale Up Diffusion Models without Trainingby Juno Hwang, Yong-Hyun Park, Junghyo JoFirst submitted…
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-Onby Xu Yang, Changxing Ding, Zhibin Hong, Junhao Huang,…
Condition-Aware Neural Network for Controlled Image Generationby Han Cai, Muyang Li, Zhuoyang Zhang, Qinsheng Zhang,…
Towards Realistic Scene Generation with LiDAR Diffusion Modelsby Haoxi Ran, Vitor Guizilini, Yue WangFirst submitted…
QNCD: Quantization Noise Correction for Diffusion Modelsby Huanpeng Chu, Wei Wu, Chengjie Zang, Kun YuanFirst…
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Predictionby Yui Lo, Yuqian Chen,…
AID: Attention Interpolation of Text-to-Image Diffusionby Qiyuan He, Jinghao Wang, Ziwei Liu, Angela YaoFirst submitted…
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactmentby Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis…
Improving Diffusion Models’s Data-Corruption Resistance using Scheduled Pseudo-Huber Lossby Artem Khrapov, Vadim Popov, Tasnima Sadekova,…
Refining Text-to-Image Generation: Towards Accurate Training-Free Glyph-Enhanced Image Generationby Sanyam Lakhanpal, Shivang Chopra, Vinija Jain,…