Update app.py

app.py

@@ -31,7 +31,7 @@ model_config.update({
     'class_cond': False,
     'diffusion_steps': 1000,
     'rescale_timesteps': False,
-    'timestep_respacing': '200',
+    'timestep_respacing': '300',
     'image_size': 256,
     'learn_sigma': True,
     'noise_schedule': 'linear',
@@ -111,5 +111,11 @@ def inference(text):
                 tqdm.write(f'Step {i}, output {j}:')
                 #display.display(display.Image(filename))
     return 'progress_00000.png'
-iface = gr.Interface(inference, inputs="text", outputs="image")
+    
+title = "CLIP guided Diffusion"
+description = "Gradio demo for CLIP guided Diffusion. To use it, simply upload your text, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'>By Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses OpenAI's 256x256 unconditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images. | <a href='https://colab.research.google.com/drive/1ED6_MYVXTApBHzQObUPaaMolgf9hZOOF' target='_blank'>Colab</a></p>"
+
+iface = gr.Interface(inference, inputs="text", outputs="image", title=title, description=description, article=article, examples=[["coral reef city by artistation artists"]],
+    enable_queue=True)
 iface.launch()