2 citations · 2 across the 2 of their papers we have counts for
4 papers
Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training
Qiaosi Yi, Shuai Li, Rongyuan Wu +3
Impressive results on real-world image super-resolution (Real-ISR) have been achieved by employing pre-trained stable diffusion (SD) models. However, one critical issue of such met…
Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data
Zhiyuan Ma, Xinyue Liang, Rongyuan Wu +3
It is highly desirable to obtain a model that can generate high-quality 3D meshes from text prompts in just seconds. While recent attempts have adapted pre-trained text-to-image di…
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
Lingchen Sun, Rongyuan Wu, Zhiyuan Ma +3
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR obj…
Adversarial Diffusion Compression for Real-World Image Super-Resolution
Bin Chen, Gehui Li, Rongyuan Wu +4
Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution images from low-resolution inputs degraded by complex, unknown processes. While many Stable Diffusi…