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Bit Diffusion

Bit Diffusion is a Python and PyTorch implementation of discrete denoising diffusion. The repository supplies a U-Net, a BitDiffusion model, and a trainer for learning from image folders and sampling generated images.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bit Diffusion — 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 want Python code for hands-on training or sampling experiments with discrete denoising diffusion, rather than a hosted image-generation interface.

Who it is for

  • Researchers and developers experimenting with discrete diffusion for image generation.
  • PyTorch users who want to train a model on an image-folder dataset or explore sampling and loss calculations directly.

Documented uses

  • Configure and train a discrete diffusion image model on a folder of images.
  • Save training results and periodically sample images through the Trainer.
  • Calculate model loss from image tensors, backpropagate, and request generated image batches.
  • Experiment with timestep settings and the documented DDIM option.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. Installation is documented with pip.

  • The README shows both a Trainer-based workflow and direct use of the model classes.
  • The Trainer example accepts an image-folder path and exposes training and sampling settings.
  • The shown Trainer model is moved to CUDA; the README does not describe a CPU alternative.
  • The direct example expects image tensors normalized from 0 to 1.

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 trainer for an image folder.

Before you start

  • Python environment with PyTorch
  • A folder of images
  • A supported CUDA setup for the README's shown model configuration

Steps

  1. Install the package with the command below.

    Documented command
    pip install bit-diffusion

  2. Import Unet, Trainer, and BitDiffusion from bit_diffusion.

  3. Configure the U-Net and BitDiffusion model, including image size, timesteps, and whether to use DDIM.

  4. Create a Trainer with the image-folder path and settings such as results folder, batch size, learning rate, and training steps.

Expected outcome

The README's next documented action is `trainer.train()`; it says results are saved periodically in the configured results folder.

Guide limitations

  • The example uses .cuda() and does not document CPU setup or hardware requirements beyond that configuration.
  • The README does not specify image-folder preparation or supported file formats.
Documentation 1 ↗
Source-based practical guide

Calculate loss and sample with the model classes

Use the documented direct model path to calculate loss and request generated image samples.

Before you start

  • PyTorch
  • Image tensors normalized from 0 to 1

Steps

  1. Import Unet and BitDiffusion from bit_diffusion.

  2. Create a U-Net and BitDiffusion model with the desired image size and timestep setting.

  3. Pass training images to the model to calculate loss, then call loss.backward().

  4. After training, call bit_diffusion.sample(batch_size=4) to request a batch.

Expected outcome

The README shows a sampled tensor shaped `(4, 3, 128, 128)` for its example configuration.

Guide limitations

  • The README does not provide a complete training loop for this direct path; it notes sampling after training.
  • Its sample dimensions correspond to the shown configuration and are not a compatibility guarantee.
Documentation 1 ↗
Evidence and transparency

Sources & review

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