DPM Solver (Stability AI)

DPM Solver is an improved sampling algorithm for diffusion probabilistic models developed by Stability AI:

• DPMs are deep generative models that can create high-quality synthetic data like images, audio, etc.
• Sampling involves reversing the noising process over multiple timesteps to recover the original signal.
• DPM Solver optimizes sampling using a LSTM parameterization of the noise schedule and classifier guidance.
• The LSTM takes in sampling coordinates and outputs timestep noise levels dynamically based on context.
• A classifier network provides guidance to enhance sample quality and coherence during the process.
• Noise levels are adjusted based on classifier uncertainty to keep samples on the data manifold.
• This improves sample fidelity and coherence compared to traditional fixed schedule sampling.
• Enables creating samples with 512x512 resolution and minimal artifacts at scale.
• DPM Solver was used in Stable Diffusion - an open source text-to-image generation model capable of photorealistic quality.
• Allows high-resolution image generation with consistency and controllability from text prompts.
• DPM Solver addresses problems like overshooting and color deterioration during sampling.

DPM Solver dynamically adjusts diffusion timesteps using LSTM-based scheduling and classifier guidance to achieve state-of-the-art sample quality and stability in generative models.