Add description

app.py

@@ -2,7 +2,6 @@ import argparse
 from pathlib import Path
 
 import gradio as gr
-print("hello")
 from icecream import ic
 import torch
 from diffusers import DiffusionPipeline
@@ -72,9 +71,12 @@ def generate_content(
         prompts[1],
         save_video_path=output_name,
     )
-    return output_name, f"sample_{size}.png", f"sample_{size}.views.png"
+    return output_name, f"sample_{size}.views.png"
 
 
+with open("description.txt") as f:
+    description = f.read()
+
 choices = list(VIEW_MAP_NAMES.keys())
 gradio_app = gr.Interface(
     fn=generate_content,
@@ -87,7 +89,8 @@ gradio_app = gr.Interface(
         gr.Number(label="Number of diffusion steps", value=75, step=1, minimum=1, maximum=300),
         gr.Number(label="Random seed", value=0, step=1, minimum=0, maximum=100000)
     ],
-    outputs=[gr.Video(label="Illusion"), gr.Image(label="Original"), gr.Image(label="Transformed")],
+    outputs=[gr.Video(label="Illusion"), gr.Image(label="Before and After")],
+    description=description,
 )
 
 

description.txt

@@ -0,0 +1,5 @@
+This application uses diffusion to create **Multi-View Optical Illusions** (or “Visual Anagrams”) - a method developed by [Daniel Geng et al. at the University of Michigan](https://dangeng.github.io/visual_anagrams/). See their original post for good examples and an overview of how it works.
+
+Their code can be found [here](https://github.com/dangeng/visual_anagrams) (along with tips for choosing prompts) and is used heavily in this app. The method is zero-shot, so this Space uses the pretrained diffusion model [DeepFloyd](https://huggingface.co/DeepFloyd), as in the original paper.
+
+Please report any issues to Callum Canavan on [Hugging Face](https://huggingface.co/callum-canavan) or [Twitter/X](https://twitter.com/CallumCanavan3).