FJDiffusion

Implementation and Pretrained Diffusion Models in JAX

Description

This project provides an implementation of diffusion models in JAX, along with pretrained models. Diffusion models are a class of generative models that can be used for tasks such as image generation, inpainting, denoising, and super-resolution. This project aims to make it easy for researchers and developers to use diffusion models in their own projects.

Features

• Implementation of diffusion models in JAX
• Pretrained models for various tasks
• Easy-to-use API for training and inference
• Support for distributed training on multiple GPUs or TPUs
• Extensive documentation and examples

Installation

To install the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/erfanzar/FJDiffusion.git

2. Change into the project directory:

   cd FJDiffusion

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To use the project, follow these steps:

1. Import the necessary modules:

   TODO

2. Load a pretrained model:

   TODO

3. Generate samples from the model:

   TODO

4. Perform inference on an input:

   TODO

For more detailed usage instructions and examples, please refer to the documentation.

Getting PartitionRules

here's how you can get partition rules of each model in order to use them for pjit and fsdp

Unet2D

from FJDiffusion.moonwalker.configs import Unet2DConfig

partition_rules = Unet2DConfig.get_partition_rules(fully_fsdp=True)

VAE

from FJDiffusion.moonwalker.configs import AutoencoderKlConfig

partition_rules = AutoencoderKlConfig.get_partition_rules(fully_fsdp=True)

CLIPTextModel

from FJDiffusion.moonwalker.configs import get_clip_partition_rules

partition_rules = get_clip_partition_rules(fully_fsdp=True)

Contributing

Contributions to this project are welcome! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request.

Please make sure to follow the code style and conventions used in the project.

License

This project is licensed under the Apache v2.0 License. See the LICENSE file for more information.

Acknowledgements

This project is built upon the work of only one researcher / developer. I would like to say if there's any problem in open-source implementations and pretrained models after final releases please let me know <3.

Contact

If you have any questions or suggestions regarding this project, please feel free to contact me at erfanzare810@gmail.com