AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image Generation

• URL: http://arxiv.org/abs/2410.06055v1
• Date: Tue, 8 Oct 2024 13:56:28 GMT
• Title: AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image Generation
• Authors: Boyuan Cao, Jiaxin Ye, Yujie Wei, Hongming Shan,
• Abstract summary: Latent diffusion models (LDMs) often experience significant structural distortions when directly generating high-resolution (HR) images. We propose an Attentive and Progressive LDM (AP-LDM) 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.
• Score: 12.564266865237343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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