Improving the Stability of Diffusion Models for Content Consistent Super-Resolution

• URL: http://arxiv.org/abs/2401.00877v1
• Date: Sat, 30 Dec 2023 10:22:59 GMT
• Title: Improving the Stability of Diffusion Models for Content Consistent Super-Resolution
• Authors: Lingchen Sun, Rongyuan Wu, Zhengqiang Zhang, Hongwei Yong, Lei Zhang
• Abstract summary: generative priors of pre-trained latent diffusion models have demonstrated great potential to enhance the perceptual quality of image super-resolution (SR) results. We propose to employ the diffusion models to refine image structures, while employing the generative adversarial training to enhance image fine details. Specifically, we propose a non-uniform timestep learning strategy to train a compact diffusion network, which has high efficiency and stability to reproduce the image main structures.
• Score: 17.2713480052151
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generative priors of pre-trained latent diffusion models have demonstrated great potential to enhance the perceptual quality of image super-resolution (SR) results. Unfortunately, the existing diffusion prior-based SR methods encounter a common problem, i.e., they tend to generate rather different outputs for the same low-resolution image with different noise samples. Such stochasticity is desired for text-to-image generation tasks but problematic for SR tasks, where the image contents are expected to be well preserved. To improve the stability of diffusion prior-based SR, we propose to employ the diffusion models to refine image structures, while employing the generative adversarial training to enhance image fine details. Specifically, we propose a non-uniform timestep learning strategy to train a compact diffusion network, which has high efficiency and stability to reproduce the image main structures, and finetune the pre-trained decoder of variational auto-encoder (VAE) by adversarial training for detail enhancement. Extensive experiments show that our proposed method, namely content consistent super-resolution (CCSR), can significantly reduce the stochasticity of diffusion prior-based SR, improving the content consistency of SR outputs and speeding up the image generation process. Codes and models can be found at {https://github.com/csslc/CCSR}.

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