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---
library_name: tf-keras
tags:
- gan
- dcgan
- huggan
- tensorflow
- unconditional-image-generation
---
## Model description
Simple DCGAN implementation in TensorFlow to generate CryptoPunks.
## Generated samples
<img src="https://github.com/dimitreOliveira/cryptogans/raw/main/assets/gen_samples.png" width="350" height="350">
Project repository: [CryptoGANs](https://github.com/dimitreOliveira/cryptogans).
## Usage
You can play with the HuggingFace [space demo](https://huggingface.co/spaces/huggan/crypto-gan).
Or try it yourself
```python
import tensorflow as tf
import matplotlib.pyplot as plt
from huggingface_hub import from_pretrained_keras
seed = 42
n_images = 36
codings_size = 100
generator = from_pretrained_keras("huggan/crypto-gan")
def generate(generator, seed):
noise = tf.random.normal(shape=[n_images, codings_size], seed=seed)
generated_images = generator(noise, training=False)
fig = plt.figure(figsize=(10, 10))
for i in range(generated_images.shape[0]):
plt.subplot(6, 6, i+1)
plt.imshow(generated_images[i, :, :, :])
plt.axis('off')
plt.savefig("samples.png")
generate(generator, seed)
```
## Training data
For training, I used the 10000 CryptoPunks images.
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |