  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 .
