High-Resolution Image Synthesis with Latent Diffusion Models

TL;DR

This paper introduces latent diffusion models that generate high-resolution images efficiently by operating in a compressed latent space, enabling flexible conditioning and achieving state-of-the-art results with reduced computational costs.

Contribution

The authors propose a novel latent space diffusion approach that improves visual fidelity, reduces training complexity, and enhances conditioning capabilities compared to previous pixel-based models.

Findings

Achieved state-of-the-art in image inpainting.

Performed competitively in image synthesis and super-resolution.

Reduced computational requirements significantly.

Abstract

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion .