readme: ---
  license: afl-3.0
  tags:
  - PyTorch 
  - huggan
  - gan
  - unconditional-image-generation
  ---
  
  ## Model Description 
  
  Generate Art using PyTorch and [DCGAN](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html).
  
  ## 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)
  
  ```
  
  ## Generate Image 
  
  ![Example Image](example.png)
  