Update README.md

README.md

@@ -14,3 +14,46 @@ Check out the code by Teddy Koker [here](https://github.com/teddykoker/cryptopun
 Here are some punks generated by this model:
 
 ![](fake_samples_epoch_999.png)
+
+## Usage
+
+You can try it out yourself, or you can play with the [demo](https://huggingface.co/spaces/nateraw/cryptopunks-generator).
+
+To use it yourself - make sure you have `torch`, `torchvision`, and `huggingface_hub` installed. Then, run the following to generate a grid of 64 random punks:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from torch import nn
+from torchvision.utils import save_image
+
+
+class Generator(nn.Module):
+    def __init__(self, nc=4, nz=100, ngf=64):
+        super(Generator, self).__init__()
+        self.network = nn.Sequential(
+            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),
+            nn.BatchNorm2d(ngf * 4),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(ngf * 2),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),
+            nn.BatchNorm2d(ngf),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
+            nn.Tanh(),
+        )
+
+    def forward(self, input):
+        output = self.network(input)
+        return output
+
+
+model = Generator()
+weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
+model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
+
+out = model(torch.randn(64, 100, 1, 1))
+save_image(out, "punks.png", normalize=True)
+```