Update app.py

app.py

@@ -85,7 +85,7 @@ model_config.update({
     'class_cond': False,
     'diffusion_steps': 1000,
     'rescale_timesteps': True,
-    'timestep_respacing': '90',  # Modify this value to decrease the number of
+    'timestep_respacing': '300',  # Modify this value to decrease the number of
                                    # timesteps.
     'image_size': 256,
     'learn_sigma': True,
@@ -217,6 +217,6 @@ def inference(text, init_image, skip_timesteps):
 title = "CLIP Guided Diffusion HQ"
 description = "Gradio demo for CLIP Guided Diffusion. To use it, simply add 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/12a_Wrfi2_gwwAuN3VvMTwVMz9TfqctNj' target='_blank'>Colab</a></p>"
-iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=45, step=1, default=0, label="skip_timesteps")], outputs=["image","video"], title=title, description=description, article=article, examples=[["coral reef city by artistation artists"]],
+iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=150, step=1, default=0, label="skip_timesteps")], outputs=["image","video"], title=title, description=description, article=article, examples=[["coral reef city by artistation artists"]],
     enable_queue=True)
 iface.launch()