Ready to use implementations of state-of-the-art generative models in TensorFlow
Project description
Generative Models in TensorFlow
Ready to use implementations of state-of-the-art generative models in TensorFlow.
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 | Trend |
---|---|---|---|
GAN | :heavy_check_mark: | :hammer_and_wrench: | |
VAE | :x: | :x: | |
Norm Flow | :x: | :x: |
Generative Adversarial Networks
Algorithms | Implementation | Notebooks | Paper |
---|---|---|---|
GAN |
:heavy_check_mark: | :heavy_check_mark: | arXiv:1406.2661 |
BceGAN |
:hammer_and_wrench: | :x: | |
WGAN |
:heavy_check_mark: | :hammer_and_wrench: | arXiv:1701.07875 |
WGAN_GP |
:heavy_check_mark: | :hammer_and_wrench: | arXiv:1704.00028 |
CramerGAN |
:hammer_and_wrench: | :x: | arXiv:1705.10743 |
Variational Autoencoder
Planned for release v0.2.0
Normalizing Flows
Planned for release v0.2.0
Jupyter notebooks
License
Project details
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