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Dpm Solver

Dpm Solver is a Python implementation of DPM-Solver and DPM-Solver++, high-order methods for accelerating sampling in diffusion probabilistic models by solving their sampling dynamics with fewer function evaluations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Dpm Solver — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use it when building or modifying a diffusion-model pipeline and you want to investigate faster sampling without retraining the model.

Who it is for

  • Developers integrating or adapting diffusion-model generation pipelines.
  • Researchers working with guided or unguided diffusion sampling.

Documented uses

  • Replace or configure a diffusion pipeline's sampling scheduler.
  • Reduce the number of function evaluations needed to generate samples.
  • Explore solver options for guided sampling, including DPM-Solver++.
Before you choose

Requirements & limits

The public GitHub repository specifies an MIT license.

  • DPM-Solver and DPM-Solver++ serve as sampling solvers, not as diffusion models.
  • The README includes a Diffusers scheduler example for Stable Diffusion and recommendations for different DeepFloyd-IF stages.
  • The reported 10–20 function evaluations are project-reported experimental results, not a performance guarantee.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set DPM-Solver++ as a Stable Diffusion scheduler in Diffusers

Adapt a Diffusers Stable Diffusion pipeline to use the multistep DPM-Solver++ scheduler.

Before you start

  • A Python environment with PyTorch and Hugging Face Diffusers.
  • Access to the stabilityai/stable-diffusion-2-1 model used in the repository example.

Steps

  1. Load StableDiffusionPipeline from the documented model identifier.

  2. Create DPMSolverMultistepScheduler from the pipeline's current scheduler configuration.

  3. Assign the new scheduler to the pipeline.

  4. Move the pipeline to CUDA, provide a prompt, and call the pipeline to generate an image.

Expected outcome

A Stable Diffusion pipeline configured to sample with the multistep DPM-Solver++ scheduler.

Guide limitations

  • The example uses CUDA and half-precision weights.
  • The repository's example is not a guarantee of performance or compatibility in other environments.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
LuChengTHU/dpm-solver · Official homepage title · retained in its source language
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