Practical overview
When it fits
Use it when you need an implementation-level PyTorch starting point for rectified-flow experiments and local image-generation training.
Who it is for
- PyTorch developers building generative-model prototypes
- Researchers comparing rectified-flow and flow-matching methods
- Learners studying implementation-level generative-model workflows
Documented uses
- Prototype rectified-flow generative models
- Experiment with flow-matching algorithms in PyTorch
- Train image-generation models from a local image folder
- Compare rectified-flow improvements in research notebooks
Requirements & limits
The repository is public and MIT-licensed. The documented installation uses pip; the examples are designed for local Python/PyTorch use rather than a hosted service.
- The package exposes RectifiedFlow, Reflow, Unet, ImageDataset and Trainer examples.
- The image training workflow uses an Accelerate-based Trainer.
- The repository includes an Oxford Flowers quick-test path and a tutorial link.
- The implementation is Python/PyTorch and intended for self-hosted development.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install and run the documented rectified-flow example
Set up the package, calculate a flow loss from image tensors and request a sample.
Before you start
- Python environment with PyTorch
- The rectified-flow-pytorch package
Steps
Install the package with pip install rectified-flow-pytorch.
Import torch, RectifiedFlow and Unet, then create an Unet and wrap it with RectifiedFlow.
Create image tensors, evaluate the flow loss and call backward().
Call rectified_flow.sample() to obtain a generated tensor.
Expected outcome
A local PyTorch workflow containing a training loss, gradient step and sampled tensor.
Guide limitations
- The example documents the API sequence but does not provide a trained checkpoint or guarantee a particular output quality.
- The sample shape assertion in the example checks tensor dimensions only.
Configure the documented image-folder training example
Prepare the repository’s Trainer workflow for training on images stored in a local folder.
Before you start
- Python environment with PyTorch
- The package and its Accelerate-based training components
- A local image folder
- An output directory for periodic samples
Steps
Create a Unet and RectifiedFlow instance.
Create ImageDataset with the local image-folder path and an image size.
Create Trainer with the flow object, dataset, training-step count and results folder.
Invoke the Trainer as shown in the README.
Expected outcome
A configured local training workflow that saves periodic samples to the specified results folder.
Guide limitations
- The README does not specify hardware requirements, training time or expected image quality.
- The example documents configuration and invocation, not a verified training run.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 4 October 2026
- Original website title
- lucidrains/rectified-flow-pytorch · Official homepage title · retained in its source language