Create app.py

app.py

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+import torch
+import matplotlib.pyplot as plt
+import torchvision
+import gradio as gr
+use_gpu = True if torch.cuda.is_available() else False
+
+model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub',
+                       'PGAN', model_name='celebAHQ-512',
+                       pretrained=True, useGPU=use_gpu)
+
+
+
+def pggan(num_images):
+  noise, _ = model.buildNoiseData(int(num_images))
+  with torch.no_grad():
+      generated_images = model.test(noise)
+
+  grid = torchvision.utils.make_grid(generated_images.clamp(min=-1, max=1), scale_each=True, normalize=True)
+  plt.axis("off")
+  plt.imshow(grid.permute(1, 2, 0).cpu().numpy())
+  return plt
+
+
+inputs = gr.inputs.Number(label="number of images")
+outputs = gr.outputs.Image(label="Output Image")
+
+title = "Progressive Growing of GANs"
+description = "Gradio demo for Progressive Growing of GANs (PGAN). To use it, simply add the number of images to generate or click on the examples. Read more below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1710.10196'>Progressive Growing of GANs for Improved Quality, Stability, and Variation</a> | <a href='https://github.com/facebookresearch/pytorch_GAN_zoo/blob/master/models/progressive_gan.py'>Github Repo</a></p>"
+examples = [
+            [1], 
+            [2],
+            [3],
+            [4]
+]
+
+
+gr.Interface(pggan, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False, examples=examples).launch(debug=True)