DPM++ SDE (Karras)

Here are some key points about the DPM++ SDE sampler developed by Karras:

• DPM refers to Diffusion Probabilistic Models, powerful generative models that can create high-quality synthetic data like images.
• DPMs model data generation as a noisy diffusion process which is reversed via sampling to get clean outputs.

Karras introduced several optimizations to the sampling process:

• Predictor Preprocessing - trains an autoencoder to preprocess samples before feeding to main predictor network.
• Improved Timestep Scaling - dynamically scales timestep sizes during sampling for higher quality results.
• Corrector Network - helps reverse diffusion process by bringing samples closer to data distribution.
• Classifier Guidance - uses a classifier network to guide sampling and improve sample quality.
• Additional tricks like higher order diffusion and zero initialization.

Together these advances enable generating photorealistic images with unprecedented quality and coherence.

Karras demonstrated capabilities by creating human faces, cats, landscapes that are essentially indistinguishable from real images.

• Established new state-of-the-art in image generation quality using diffusion models.

The optimized DPM++ sampler addresses issues like color deterioration and delivers excellent sample fidelity.

DPM++ incorporates predictor preprocessing, improved timestep scaling, corrector networks and other innovations to achieve much more stable and higher-quality sampling from diffusion models.