High-Resolution Image Synthesis with Latent Diffusion Models

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve advanced synthesis results on image data and beyond. 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.

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• 1 Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining.
• 2 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.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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