Realistic image super-resolution (Real-ISR) aims to reproduce perceptually realistic image details from a low-quality input. The commonly used adversarial training based Real-ISR methods often introduce unnatural visual artifacts and fail to generate realistic textures for natural scene images. The recently developed generative stable diffusion models provide a potential solution to Real-ISR with pre-learned strong image priors. However, the existing methods along this line either fail to keep faithful pixel-wise image structures or resort to extra skipped connections to reproduce details, which requires additional training in image space and limits their extension to other related tasks in latent space such as image stylization. In this work, we propose a pixel-aware stable diffusion (PASD) network to achieve robust Real-ISR as well as personalized stylization. In specific, a pixel-aware cross attention module is introduced to enable diffusion models perceiving image local structures in pixel-wise level, while a degradation removal module is used to extract degradation insensitive features to guide the diffusion process together with image high level information. By simply replacing the base diffusion model with a personalized one, our method can generate diverse stylized images without the need to collect pairwise training data. PASD can be easily integrated into existing diffusion models such as Stable Diffusion. Experiments on Real-ISR and personalized stylization demonstrate the effectiveness of our proposed approach. The source code and models can be found at \url{https://github.com/yangxy/PASD}.

提出了一个像素感知的稳定扩散 (PASD) 网络，可实现强大的逼真图像超分辨率和个性化风格化。引入的像素感知交叉注意力模块使扩散模型能够感知像素级的图像局部结构，同时采用降级去除模块提取对降级不敏感的特征，与图像高级信息一起引导扩散过程。通过将基础扩散模型替换为个性化模型，我们的方法可以生成不同风格的图像，无需收集成对的训练数据。实验证明了我们提出方法的有效性。

面向像素感知的稳定扩散技术用于真实图像超分辨率和个性化风格化