CVPR 2025 "denoising process" Papers

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FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute

Sotiris Anagnostidis, Gregor Bachmann, Yeongmin Kim et al.

CVPR 2025highlightarXiv:2502.20126
5
citations

Make It Count: Text-to-Image Generation with an Accurate Number of Objects

Lital Binyamin, Yoad Tewel, Hilit Segev et al.

CVPR 2025posterarXiv:2406.10210
32
citations

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability

Lei Wang, Senmao Li, Fei Yang et al.

CVPR 2025posterarXiv:2505.03097
2
citations

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning

Maosen Zhao, Pengtao Chen, Chong Yu et al.

CVPR 2025posterarXiv:2505.21591
3
citations

VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide

Dohun Lee, Bryan Sangwoo Kim, Geon Yeong Park et al.

CVPR 2025posterarXiv:2410.04364
2
citations