DriftSR uses a pretrained diffusion model as a frozen feature extractor and performs distribution drifting in its intermediate representation space.
Distribution drifting on spatial features enables efficient one-step super-resolution
DriftSR leverages pretrained diffusion priors by modeling distributions at the spatial feature level and adaptively guiding texture enhancement based on structural consistency.
Chinese Tech
Wei Zhu · Kai Zhang · Yu Zheng · Zhaopeng Yang · Lei Luo · Yong Guo · +1 more
Nanjing University of Science and Technology · Huawei · Nanjing University · South China University of Technology
Research Digest··2 min read
The authors propose DriftSR, a one-step framework for real-world image super-resolution that uses a frozen diffusion model’s intermediate features.
Why this paper
From Huawei and 3 others
In one line
DriftSR achieves one-step real-world super-resolution by drifting pretrained diffusion features without additional trainable components.
What we could check
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