Practical overview
When it fits
Use Fast Srgan when you want a Python and command-line repository that supports pretrained image inference as well as configurable training, rather than a hosted media-enhancement service.
Who it is for
- Developers building self-hosted image or video enhancement tools.
- Teams creating batch image super-resolution pipelines.
- Researchers and practitioners experimenting with SR-GAN training and model configuration.
- Python users who need a repository-based starting point for local super-resolution workflows.
Documented uses
- Upscale a directory of low-resolution images with the provided pretrained generator.
- Create a local workflow for enhancing low-resolution video footage.
- Train a super-resolution model with a custom image dataset.
- Adjust model and training parameters through YAML configuration or Hydra command-line arguments.
- Inspect checkpoints and training summaries with TensorBoard.
Requirements & limits
The repository is public, open source and MIT-licensed. Its documented setup uses Python 3.10 and Pipenv-based dependency installation. The implementation is available for Linux, macOS and Windows command-line or self-hosted workflows.
- A pretrained generator is included, so image-directory inference does not require starting with a training run.
- Training settings can be edited in configs/config.yaml or supplied through Hydra command-line parameters.
- The model uses pixel shuffle and an SR-GAN design with residual blocks.
- The README reports approximately 27 fps for 720p output in its tested MPS MacBook M1 Pro setup, while noting 82 fps for the smaller 360p output benchmark.
- The documented inference example targets an image directory; a complete video input and output procedure is not provided.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run the pretrained model on an image directory
Set up the repository dependencies and generate super-resolution images from a directory using the supplied pretrained generator.
Before you start
- Python 3.10, as specified by the repository’s tested setup.
- The repository’s Pipfile and a directory containing input images.
- An output directory path for the generated images.
Steps
Install Pipenv and install the repository dependencies with the documented Pipenv commands.
Use the pretrained generator included in the repository’s models directory.
Run inference.py with image_dir set to the input image directory and output_dir set to the destination directory.
Expected outcome
The inference workflow writes super-resolution images to the specified output directory.
Guide limitations
- The documented inference command covers image-directory input; it does not document a complete video-processing command.
- The published speed figures are measurements from an MPS-equipped MacBook M1 Pro GPU and do not guarantee performance on other systems.
Configure a custom training run
Edit the training configuration and launch Fast Srgan training with repository-supported settings.
Before you start
- Python 3.10 and the dependencies installed through the repository’s Pipfile.
- An image dataset directory.
- Access to configs/config.yaml for configuration changes.
Steps
Edit configs/config.yaml with the desired training settings.
Launch training with python train.py, or pass documented Hydra parameters on the command line.
For the documented example, set the dataset path, batch size to 32 and generator layers to 12.
Point TensorBoard at the outputs directory to monitor the saved training summaries.
Expected outcome
The repository starts a configured training run and saves model checkpoints and training summaries in the outputs directory.
Guide limitations
- The documentation describes configuration and launch steps but does not specify training duration or resulting model quality.
- The example settings are repository parameters, not a guarantee that they are suitable for every dataset or hardware setup.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 4 October 2026
- Original website title
- HasnainRaz/Fast-SRGAN · Official homepage title · retained in its source language