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https://github.com/mbarbetti/tf-gen-models
:package: Ready to use implementations of state-of-the-art generative models in TensorFlow 2
https://github.com/mbarbetti/tf-gen-models
deep-learning gan generative-models python tensorflow
Last synced: 4 months ago
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:package: Ready to use implementations of state-of-the-art generative models in TensorFlow 2
- Host: GitHub
- URL: https://github.com/mbarbetti/tf-gen-models
- Owner: mbarbetti
- License: mit
- Created: 2022-01-23T16:19:37.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-09-30T09:51:09.000Z (over 2 years ago)
- Last Synced: 2024-04-29T18:22:36.478Z (10 months ago)
- Topics: deep-learning, gan, generative-models, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 14.2 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Ready to use implementations of state-of-the-art generative models in TensorFlow 2
## Installation
### Dependencies
tf-gen-models requires:
* Python (>= 3.7, < 3.10)
* TensorFlow (>= 2.5)
* Matplotlib (>= 3.4)
* Pillow (>= 8.0)
- - -
The `tf-gen-models` package is built upon TensorFlow 2. See the [TensorFlow install guide](https://www.tensorflow.org/install) for the [pip package](https://www.tensorflow.org/install/pip) while, to enable GPU support, the use [Docker container](https://www.tensorflow.org/install/docker) is recommended. Alternatively, GPU-enabled TensorFlow can be easily installed using the `tensorflow-gpu` package on [conda-forge](https://conda-forge.org/blog/posts/2021-11-03-tensorflow-gpu/).
### User installation
If you already have a working installation of TensorFlow 2 (preferably with the GPU support enabled), the easiest way to install tf-gen-models is using `pip`:
```shell
pip install tf-gen-models
```
## Available generative models
| Generative models | Implementation | Notebooks | Trends |
| :-----------------: |:--------------:|:---------:|:------:|
| GAN | ✔️ | 🛠️ | |
| VAE | ❌ | ❌ | |
| Norm Flow | ❌ | ❌ | |
| Diffusion | ❌ | ❌ | |
### Generative Adversarial Networks
| Algorithms | Implementation | Conditioning*| Notebooks | Paper |
|:----------:|:--------------:|:------------:|:---------:|:---------------------------------------------------------------:|
| `GAN` | ✔️ | 🛠️ | ✔️ | arXiv:1406.2661 |
| `BceGAN` | ✔️ | ❌ | ✔️ | |
| `WGAN` | ✔️ | ❌ | ✔️ | arXiv:1701.07875 |
| `WGAN_GP` | ✔️ | ❌ | ✔️ | arXiv:1704.00028 |
| `CramerGAN`| ✔️ | ❌ | ✔️ | arXiv:1705.10743 |
| `WGAN_ALP` | ✔️ | ❌ | 🛠️ | arXiv:1907.05681 |
*Referring to the **conditional version** of GANs proposed in [arXiv:1411.1784](https://arxiv.org/abs/1411.1784).
### Variational Autoencoders
_Planned for release v0.1.0_
### Normalizing Flows
_Planned for release v0.2.0_
### Diffusion Models
_Planned for release v0.2.0_
## Jupyter notebooks
* MNIST generation with GANs [[GAN](https://github.com/mbarbetti/tf-gen-models/blob/main/notebooks/gan/0_MNIST_gen_DC-GAN.ipynb)] [[BceGAN](https://github.com/mbarbetti/tf-gen-models/blob/main/notebooks/gan/0_MNIST_gen_DC-BceGAN.ipynb)] [[WGAN](https://github.com/mbarbetti/tf-gen-models/blob/main/notebooks/gan/0_MNIST_gen_DC-WGAN.ipynb)] [[WGAN-GP](https://github.com/mbarbetti/tf-gen-models/blob/main/notebooks/gan/0_MNIST_gen_DC-WGAN-GP.ipynb)] [[CramerGAN](https://github.com/mbarbetti/tf-gen-models/blob/main/notebooks/gan/0_MNIST_gen_DC-CramerGAN.ipynb)]
## License
[MIT License](LICENSE)