Update README.md

Added "How to Use"

README.md

@@ -4,3 +4,54 @@ tags:
 - PyTorch 
 - huggan
 ---
+
+## How To Use
+
+```python
+
+from huggingface_hub import hf_hub_download
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+from torch import nn
+
+class Generator(nn.Module):
+    def __init__(self):
+        super(Generator, self).__init__()
+        self.main = nn.Sequential(
+            nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(64 * 8),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 4),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 2),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
+            nn.Tanh()
+        )
+
+    def forward(self, input):
+        return self.main(input)
+
+path = hf_hub_download('huggan/ArtGAN', 'ArtGAN.pt')
+model = torch.load(path, map_location=torch.device('cpu'))
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def generate(seed):
+    with torch.no_grad():
+        noise = torch.randn(seed, 100, 1, 1, device=device)
+    with torch.no_grad():
+        art = model(noise).detach().cpu()
+    gen = np.transpose(art[-1], (1, 2, 0))
+    fig = plt.figure(figsize=(5, 5))
+    plt.imshow(gen)
+    plt.axis('off')
+
+generate(25)
+
+```