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Denoising Diffusion Pytorch

Denoising Diffusion Pytorch is a Python library implementing denoising diffusion probabilistic models in PyTorch. It provides model and diffusion components, training helpers, and sampling workflows for image and 1D sequence data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Denoising Diffusion Pytorch — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a modifiable PyTorch implementation and are prepared to provide data and run model training locally or through your own compute setup.

Who it is for

  • PyTorch developers building generative-model prototypes with their own data.
  • Researchers exploring diffusion training, score matching and sampling.
  • Learners seeking code examples for image or sequence diffusion workflows.

Documented uses

  • Configure and train an image diffusion model on a local image folder.
  • Compute diffusion loss and sample images with a U-Net and GaussianDiffusion.
  • Adapt the provided 1D U-Net workflow to sequence-shaped data.
  • Run the Trainer through Hugging Face Accelerate for multi-GPU training.
Before you choose

Requirements & limits

The repository identifies the project as MIT-licensed open-source software.

  • The package supplies implementation code rather than a hosted generation API or pretrained model.
  • The image Trainer accepts a folder path and exposes batch size, learning rate, training steps, gradient accumulation, EMA, mixed precision and optional FID calculation.
  • The documented image example uses normalized image tensors with values from 0 to 1.
  • Trainer1D does not evaluate generated samples; the README suggests adding a suitable metric to the training loop.

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 up image-model training with the Trainer

Install the package and configure the documented image-training workflow.

Before you start

  • Python environment with PyTorch available.
  • An image folder for training.

Steps

  1. Install the package with pip install denoising_diffusion_pytorch.

  2. Create a U-Net and GaussianDiffusion instance, setting image dimensions and diffusion timesteps.

  3. Pass the diffusion object and image-folder path to Trainer, then set the documented training parameters.

  4. Call trainer.train().

Expected outcome

The README says samples and model checkpoints are logged periodically to ./results.

Guide limitations

  • The README's example uses 700,000 training steps; it does not specify a training duration or hardware requirement.
  • The recipe describes the documented workflow and is not a report of an executed training run.
Documentation 1 ↗
Source-based practical guide

Configure the documented multi-GPU launch

Set up an Accelerate configuration and launch a training script from the project root.

Before you start

  • The project's Trainer and Hugging Face Accelerate.
  • A training script in the project root directory.

Steps

  1. From the project root, run accelerate config.

  2. In the same directory, run accelerate launch train.py.

Expected outcome

The README documents this as the two-step multi-GPU training launch using Accelerate.

Guide limitations

  • The README does not provide hardware-specific configuration guidance or report a verified launch.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
lucidrains/denoising-diffusion-pytorch · Official homepage title · retained in its source language
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