Ready to use implementations of state-of-the-art generative models in TensorFlow 2
Project description
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 for the pip package while, to enable GPU support, the use Docker container is recommended. Alternatively, GPU-enabled TensorFlow can be easily installed using the tensorflow-gpu package on conda-forge.
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:
pip install tf-gen-models
Available generative models
| Generative models | Implementation | Notebooks | Trends |
|---|---|---|---|
| GAN | ✔️ | 🛠️ | |
| VAE | ❌ | ❌ | |
| Norm Flow | ❌ | ❌ |
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.
Variational Autoencoder
Planned for release v0.2.0
Normalizing Flows
Planned for release v0.2.0
Jupyter notebooks
License
Release history Release notifications | RSS feed
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Source Distributions
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