Model Description

Generate Art using PyTorch and DCGAN.

How To Use

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