Create app.py

app.py

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+from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, truncated_noise_sample,
+                                       save_as_images, display_in_terminal)
+initial_archi = 'biggan-deep-128' #@param ['biggan-deep-128', 'biggan-deep-256', 'biggan-deep-512'] {allow-input: true}
+
+gan_model = BigGAN.from_pretrained(initial_archi).cuda().eval()
+
+# Prepare a input
+truncation = 0.4
+class_vector = one_hot_from_names(initial_class, batch_size=1)
+noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1)
+
+# All in tensors
+noise_vector = torch.from_numpy(noise_vector)
+class_vector = torch.from_numpy(class_vector)
+
+# If you have a GPU, put everything on cuda
+noise_vector = noise_vector.to('cuda')
+class_vector = class_vector.to('cuda')
+gan_model.to('cuda')
+
+# Generate an image
+with torch.no_grad():
+    output = gan_model(noise_vector, class_vector, truncation)
+
+# If you have a GPU put back on CPU
+output = output.to('cpu')
+
+# If you have a sixtel compatible terminal you can display the images in the terminal
+# (see https://github.com/saitoha/libsixel for details)
+#display_in_terminal(output)
+
+# Save results as png images
+save_as_images(output)