:tada: init

README.md

@@ -1,6 +1,6 @@
 ---
 title: Cryptopunks Generator
-emoji: 🦀
+emoji: 🧠➡️🙍‍♀️
 colorFrom: red
 colorTo: indigo
 sdk: gradio

app.py

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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(text):
+    z = torch.randn(64, 100, 1, 1)
+    punks = model(z)
+    save_image(punks, "punks.png", normalize=True)
+    return 'punks.png'
+
+
+gr.Interface(
+    predict,
+    inputs="text",
+    outputs="image",
+    title="InfiniPunks",
+    description="These CryptoPunks do not exist.",
+    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>",
+).launch()

requirements.txt

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+gradio
+torch
+torchvision