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import gradio as gr | |
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'))) | |
def predict(seed, num_punks): | |
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' | |
gr.Interface( | |
predict, | |
inputs=[ | |
gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42), | |
gr.inputs.Slider(label='Number of Punks', minimum=4, maximum=64, step=1, default=10), | |
], | |
outputs="image", | |
title="Cryptopunks GAN", | |
description="These CryptoPunks do not exist. Generate random punks with an initial seed!", | |
article="<p style='text-align: center'><a href='https://arxiv.org/pdf/1511.06434.pdf'>Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a> | <a href='https://github.com/teddykoker/cryptopunks-gan'>Github Repo</a></p>", | |
examples=[[123, 15], [42, 29], [456, 8], [1337, 35]], | |
css=".footer{display:none !important}", | |
).launch(cache_examples=True) | |