app.py

from torch import nn

class Generator(nn.Module):
    # Refer to the link below for explanations about nc, nz, and ngf
    # https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html#inputs
    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

from huggingface_hub import hf_hub_download
import torch

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'))) # Use 'cuda' if you have a GPU available

from torchvision.utils import save_image

def predict(seed):
    num_punks = 4
    torch.manual_seed(seed)
    z = torch.randn(num_punks, 100, 1, 1)
    punks = model(z)
    save_image(punks, "punks.png", normalize=True)
    return 'punks.png'

import gradio as gr

gr.Interface(
    predict,
    inputs=[
        gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
    ],
    outputs="image",
    css=".footer{display:none !important}",
).launch()