Update app.py

app.py

@@ -1,16 +1,39 @@
+"""See https://huggingface.co/fusing/latent-diffusion-text2im-large."""
+import gradio as gr
+import PIL
 from diffusers import DiffusionPipeline
 
 ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
 
 generator = torch.manual_seed(42)
 
-prompt = "A painting of a squirrel eating a burger"
-image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)
+examples = ["A street sign that reads Huggingface", "A painting of a squirrel eating a burger"]
 
-image_processed = image.cpu().permute(0, 2, 3, 1)
-image_processed = image_processed  * 255.
-image_processed = image_processed.numpy().astype(np.uint8)
-image_pil = PIL.Image.fromarray(image_processed[0])
+prompt_ = "A painting of a squirrel eating a burger"
 
-# save image
-image_pil.save("test.png")
+def fn(prompt=prompt_):
+  image = ldm(
+    [prompt], 
+    generator=generator, 
+    eta=0.3, 
+    guidance_scale=6.0, 
+    num_inference_steps=50,
+  )
+  
+  image_processed = image.cpu().permute(0, 2, 3, 1)
+  image_processed = image_processed  * 255.
+  image_processed = image_processed.numpy().astype(np.uint8)
+  image_pil = PIL.Image.fromarray(image_processed[0])
+  
+  # save image
+  # image_pil.save("test.png")
+  return image_pil 
+  
+iface = gr.Interface(
+  fn=fn, 
+  inputs="text", 
+  outputs="image",
+  examples=examples,
+  live=True,
+)
+iface.launch()