Latent diffusion models (LDMs), such as Stable Diffusion, often experience significant structural distortions when directly generating high-resolution (HR) images that exceed their original training resolutions. A straightforward and cost-effective solution is to adapt pre-trained LDMs for HR image generation; however, existing methods often suffer from poor image quality and long inference time. In this paper, we propose an Attentive and Progressive LDM (AP-LDM), a novel, training-free framework aimed at enhancing HR image quality while accelerating the generation process. AP-LDM decomposes the denoising process of LDMs into two stages: (i) attentive training-resolution denoising, and (ii) progressive high-resolution denoising. The first stage generates a latent representation of a higher-quality training-resolution image through the proposed attentive guidance, which utilizes a novel parameter-free self-attention mechanism to enhance the structural consistency. The second stage progressively performs upsampling in pixel space, alleviating the severe artifacts caused by latent space upsampling. Leveraging the effective initialization from the first stage enables denoising at higher resolutions with significantly fewer steps, enhancing overall efficiency. Extensive experimental results demonstrate that AP-LDM significantly outperforms state-of-the-art methods, delivering up to a 5x speedup in HR image generation, thereby highlighting its substantial advantages for real-world applications. Code is available at https://github.com/kmittle/AP-LDM.

本研究解决了现有潜在扩散模型在生成高分辨率图像时质量和效率不足的问题。提出的注意力和渐进性潜在扩散模型（AP-LDM）通过分阶段的去噪过程显著提高图像质量，并加快生成速度。实验表明，AP-LDM在高分辨率图像生成方面比当前最先进方法快5倍，具有重要的实际应用价值。

AP-LDM：注意力和渐进性潜在扩散模型，用于无训练的高分辨率图像生成