DPM-Solver provides a fast, high-order ODE solver specifically designed for diffusion probabilistic models, enabling high-quality sample generation in as few as 10-20 steps. It supports both discrete-time and continuous-time diffusion models without requiring additional training, making it a versatile tool for researchers and practitioners in generative AI.

How It Works

DPM-Solver implements a family of solvers for the underlying ODEs of diffusion models. It offers both single-step (Runge-Kutta-like) and multi-step (Adams-Bashforth-like) methods with convergence orders up to 3. The improved DPM-Solver++ variant incorporates dynamic thresholding for enhanced sample quality in pixel-space models. The library provides a model_wrapper to seamlessly integrate various diffusion model types (noise, data, velocity, score prediction) and sampling strategies (unconditional, classifier, classifier-free guidance).

Quick Start & Requirements

• Install: Typically integrated via libraries like diffusers. For direct use, copy dpm_solver_pytorch.py or dpm_solver_jax.py.
• Prerequisites: PyTorch or JAX. GPU recommended for performance.
• Resources: Integration with diffusers simplifies setup. Direct usage requires defining noise schedules and model wrappers.
• Docs: Official Documentation

Highlighted Details

• Achieves high-quality samples in 10-20 steps, significantly faster than traditional methods.
• Supports multiple diffusion model types (noise, x_start, v, score prediction).
• Offers both single-step and multi-step solvers with varying orders (1-3).
• DPM-Solver++ includes dynamic thresholding for improved pixel-space image quality.
• Integrates with popular libraries like diffusers, Stable-Diffusion, and DeepFloyd-IF.

Maintenance & Community

The project is associated with academic research from Tsinghua University. It has seen significant adoption and integration into major diffusion model libraries.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

The README suggests that DPM-Solver cannot improve sample quality if the base diffusion model performs poorly with 1000-step DDIM. Dynamic thresholding is noted as only valid for pixel-space diffusion models.