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:tada: init

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Files changed (3) hide show
  1. README.md +1 -1
  2. app.py +49 -0
  3. requirements.txt +3 -0
README.md CHANGED
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  ---
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  title: Cryptopunks Generator
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- emoji: πŸ¦€
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  colorFrom: red
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  colorTo: indigo
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  sdk: gradio
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  ---
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  title: Cryptopunks Generator
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+ emoji: πŸ§ βž‘οΈπŸ™β€β™€οΈ
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  colorFrom: red
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  colorTo: indigo
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  sdk: gradio
app.py ADDED
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+ import gradio as gr
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+ import torch
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+ from huggingface_hub import hf_hub_download
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+ from torch import nn
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+ from torchvision.utils import save_image
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+
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+
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+ class Generator(nn.Module):
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+ def __init__(self, nc=4, nz=100, ngf=64):
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+ super(Generator, self).__init__()
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+ self.network = nn.Sequential(
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+ nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),
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+ nn.BatchNorm2d(ngf * 4),
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+ nn.ReLU(True),
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+ nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),
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+ nn.BatchNorm2d(ngf * 2),
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+ nn.ReLU(True),
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+ nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),
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+ nn.BatchNorm2d(ngf),
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+ nn.ReLU(True),
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+ nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
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+ nn.Tanh(),
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+ )
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+
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+ def forward(self, input):
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+ output = self.network(input)
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+ return output
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+
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+
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+ model = Generator()
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+ weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
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+ model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
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+
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+
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+ def predict(text):
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+ z = torch.randn(64, 100, 1, 1)
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+ punks = model(z)
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+ save_image(punks, "punks.png", normalize=True)
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+ return 'punks.png'
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+
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+
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+ gr.Interface(
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+ predict,
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+ inputs="text",
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+ outputs="image",
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+ title="InfiniPunks",
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+ description="These CryptoPunks do not exist.",
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+ 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>",
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+ ).launch()
requirements.txt ADDED
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+ gradio
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+ torch
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+ torchvision