NTIRE 2023 challenge on efficient super-resolution: Methods and results

Y Li, Y Zhang, R Timofte, L Van Gool… - Proceedings of the …, 2023 - openaccess.thecvf.com
This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …

Patch diffusion: Faster and more data-efficient training of diffusion models

Z Wang, Y Jiang, H Zheng, P Wang… - Advances in neural …, 2024 - proceedings.neurips.cc
Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …

Efficient deep models for real-time 4k image super-resolution. NTIRE 2023 benchmark and report

MV Conde, E Zamfir, R Timofte… - Proceedings of the …, 2023 - openaccess.thecvf.com
This paper introduces a novel benchmark for efficient upscaling as part of the NTIRE 2023
Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images …

Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup

M Wang, Y Zhao, J Liu, J Chen, C Zhuang, J Gu… - arXiv preprint arXiv …, 2023 - arxiv.org
The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly
advanced multimodal tasks in payment, security, and advertising, notably enhancing …

Large Multimodal Model Compression via Iterative Efficient Pruning and Distillation

M Wang, Y Zhao, J Liu, J Chen, C Zhuang… - … Proceedings of the …, 2024 - dl.acm.org
The deployment of Large Multimodal Models (LMMs) within Ant Group has significantly
advanced multimodal tasks in payment, security, and advertising, notably enhancing …

[PDF][PDF] Structural Pruning for Diffusion Models—Supplementary Materials—

GFXMX Wang - proceedings.neurips.cc
This section provides further insights into the coupled structures present in U-Net, which
function as denoisers in diffusion models. In the context of structural pruning, it is crucial to …