4 citations · 5 across the 6 of their papers we have counts for
6 papers · 1 filter
Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation
Xiao He, Yang Li, Peizhen Zhang +3
Diffusion models exhibit remarkable generative capability, but their high latency limits practical deployment. Many studies have attempted to reduce sampling steps to accelerate in…
Mixture of Ranks with Degradation-Aware Routing for One-Step Real-World Image Super-Resolution
Xiao He, Zhijun Tu, Kun Cheng +4
The demonstrated success of sparsely-gated Mixture-of-Experts (MoE) architectures, exemplified by models such as DeepSeek and Grok, has motivated researchers to investigate their a…
Effective Diffusion Transformer Architecture for Image Super-Resolution
Kun Cheng, Lei Yu, Zhijun Tu +7
Recent advances indicate that diffusion models hold great promise in image super-resolution. While the latest methods are primarily based on latent diffusion models with convolutio…
One Step Diffusion-based Super-Resolution with Time-Aware Distillation
Xiao He, Huaao Tang, Zhijun Tu +8
Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these a…
Diff-Privacy: Diffusion-based Face Privacy Protection
Xiao He, Mingrui Zhu, Dongxin Chen +2
Privacy protection has become a top priority as the proliferation of AI techniques has led to widespread collection and misuse of personal data. Anonymization and visual identity i…
Few-shot Font Generation by Learning Style Difference and Similarity
Xiao He, Mingrui Zhu, Nannan Wang +2
Few-shot font generation (FFG) aims to preserve the underlying global structure of the original character while generating target fonts by referring to a few samples. It has been a…