Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution (2401.00877v2)

Abstract: The generative priors of pre-trained latent diffusion models (DMs) have demonstrated great potential to enhance the visual quality of image super-resolution (SR) results. However, the noise sampling process in DMs introduces randomness in the SR outputs, and the generated contents can differ a lot with different noise samples. The multi-step diffusion process can be accelerated by distilling methods, but the generative capacity is difficult to control. To address these issues, we analyze the respective advantages of DMs and generative adversarial networks (GANs) and propose to partition the generative SR process into two stages, where the DM is employed for reconstructing image structures and the GAN is employed for improving fine-grained details. Specifically, we propose a non-uniform timestep sampling strategy in the first stage. A single timestep sampling is first applied to extract the coarse information from the input image, then a few reverse steps are used to reconstruct the main structures. In the second stage, we finetune the decoder of the pre-trained variational auto-encoder by adversarial GAN training for deterministic detail enhancement. Once trained, our proposed method, namely content consistent super-resolution (CCSR),allows flexible use of different diffusion steps in the inference stage without re-training. Extensive experiments show that with 2 or even 1 diffusion step, CCSR can significantly improve the content consistency of SR outputs while keeping high perceptual quality. Codes and models can be found at \href{https://github.com/csslc/CCSR}{https://github.com/csslc/CCSR}.

• The paper presents a novel CCSR framework that integrates non-uniform timestep learning with diffusion models to stabilize content generation.
• The method refines image detail via adversarial training of a pre-trained VAE decoder without adding extra computational cost.
• Experimental results demonstrate enhanced output consistency measured by new stability metrics (G-STD and L-STD), ensuring deterministic high-resolution recovery.

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

The paper "Improving the Stability of Diffusion Models for Content Consistent Super-Resolution" addresses a critical challenge in image super-resolution (SR) using diffusion models—stability and content consistency in the generated outputs. While diffusion models have demonstrated remarkable potential in enhancing perceptual quality, their inherent stochastic nature often leads to diverse and inconsistent outputs for the same low-resolution (LR) input. This is particularly undesirable in SR tasks where deterministic recovery of high-resolution (HR) content is preferred.

Methodology Overview

The proposed Content Consistent Super-Resolution (CCSR) framework is primarily designed to mitigate the instability in diffusion model-based SR by refining the image structure with diffusion techniques and enhancing details through adversarial training. The authors introduce a non-uniform timestep learning strategy to train a diffusion network that stabilizes the generation of primary image structures. Meanwhile, the detail enhancement is achieved by finetuning a pre-trained variational auto-encoder (VAE) decoder using adversarial methods.

Diffusion Model Enhancements

The paper identifies a key observation: while diffusion models excel in generating realistic textures, they introduce variability due to their stochastic sampling processes. By introducing a non-uniform timestep learning strategy, the authors aim to optimize the diffusion process specifically for SR tasks by adjusting the sampling density. This approach is grounded in the insight that significant structure can be derived quickly from LR inputs, and only a few steps are necessary for structure generation, reducing computation time and enhancing stability.

Adversarial Detail Enhancement

Beyond structures, the CCSR employs adversarial training to refine image details. Rather than introducing an additional generative adversarial network (GAN), the method optimizes the already present VAE decoder for detail enhancement. This approach adds no extra computational burden, retaining efficiency while enhancing perceptual output quality.

Experimental Results and Stability Measures

The paper provides extensive quantitative and qualitative experiments that demonstrate the superiority of CCSR over existing diffusion-based methods. Notably, the introduction of new stability metrics, G-STD and L-STD, offers a robust measure of variance in output consistency across multiple runs, highlighting CCSR's capability in maintaining both global and local consistency.

Practical and Theoretical Implications

The reduction in stochasticity aligns diffusion models more closely with the deterministic goals of SR, potentially opening avenues for their application in other image restoration tasks where consistency is critical. The authors' successful integration of diffusion and adversarial strategies paves the way for future exploration into hybrid models that leverage the strengths of different generative approaches.

Future Directions

In future developments, further refinement in timestep strategies and decoder finetuning could push SR performance boundaries. Additionally, exploration into more complex real-world degradations could be beneficial. Applying similar stability improvements to other applications of diffusion models in AI, such as text-to-image tasks, could also yield interesting outcomes.

In summary, the proposed CCSR framework represents a significant advancement in reducing variability in SR outputs using diffusion models, balancing the need for high perceptual quality with deterministic content reproduction. The method's efficiency and effectiveness make it a valuable addition to the toolkit of diffusion-based generative models in image processing.