Latent Diffusion Models: High-Res Image Synthesis

Diffusion Models (DMs) excel at image synthesis but are computationally intensive, often requiring hundreds of GPU days for training. This limits accessibility and incurs significant carbon footprints, making them impractical for many researchers and applications.

The core challenge with DMs lies in their operation directly in pixel space, leading to high-dimensional data processing. This results in slow inference due to sequential evaluations and expensive gradient computations during training.

Latent Diffusion Models (LDMs) address this by performing diffusion processes in a compressed latent space. This space is learned by a powerful autoencoder, which significantly reduces computational complexity while preserving visual fidelity.

This two-stage approach separates perceptual compression from generative learning. The autoencoder handles high-frequency details, allowing the DM to focus on semantic and conceptual composition in a much lower-dimensional space.

A key advantage of LDMs is their efficiency. Training is substantially cheaper, and inference speed is dramatically increased with minimal impact on synthesis quality. This democratizes access to high-resolution image generation.

LDMs introduce cross-attention layers into their architecture, enabling flexible conditioning mechanisms. This allows for diverse inputs like text prompts or bounding boxes to guide the image generation process effectively.

This innovative conditioning makes LDMs powerful generators for various tasks, including text-to-image synthesis, image inpainting, and super-resolution, achieving state-of-the-art results across multiple benchmarks.

The convolutional nature of LDMs allows for high-resolution synthesis, even up to megapixel images, by applying the model in a convolutional manner. This is crucial for tasks like semantic synthesis and large-scale image generation.

LDMs demonstrate superior performance in unconditional image generation, outperforming previous likelihood-based models and GANs in FID scores, precision, and recall, indicating better mode coverage and sample quality.

For text-to-image synthesis, LDMs, particularly with classifier-free guidance, achieve competitive results with significantly fewer parameters compared to other state-of-the-art models, proving their efficiency and effectiveness.

The research also highlights the reusability of the pretrained autoencoding stage. This universal component can be leveraged for multiple DM trainings or entirely different downstream applications, fostering broader research and development.

While LDMs offer significant advancements, their sequential sampling is still slower than GANs. Also, the slight loss of fidelity from compression might be a bottleneck for tasks requiring extreme pixel-level accuracy.