cryptopunks-gan

A DCGAN trained to generate novel Cryptopunks.

Check out the code by Teddy Koker here.

Generated Punks

Here are some punks generated by this model:

Usage

You can try it out yourself, or you can play with the demo.

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:

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)
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no pipeline_tag.

Spaces using nateraw/cryptopunks-gan 5