desc

app.py

@@ -13,6 +13,7 @@ from PIL import Image
 from huggingface_hub import from_pretrained_keras
 from math import sqrt, ceil
 import numpy as np
+import pandas as pd
 import gradio as gr
 
 modelieo=[
@@ -196,11 +197,21 @@ def TextToImage(Prompt,inference_steps, model):
 
   return [ai_gen, another_one]
 
+df = pd.DataFrame({
+    "Model"                 : ['MNIST-diffusion', 'MNIST-diffusion-oneImage', 'MNIST-GAN', 'MNIST-GAN-noDropout'], 
+    "Class (Architecture)"  : ['UNet2DModel', 'UNet2DModel', 'Sequential', 'Sequential'], 
+    "Dataset Examples"      : [60000, 1, 60000, 60000], 
+    "Training Loops"        : [300, 100000, 90000, 90000], 
+    "Notes"                 : ['Similar architecture as Stable Diffusion, different training data', 'Toy model, purposed to store protected content', 'GANs are not as likely to store protected content', 'Attempting to increase copying']
+}) 
+
+# Applying style to highlight the maximum value in each row
+styler = df#.style.highlight_max(color = 'lightgreen', axis = 0)
 
 with gr.Blocks() as app:
   
   interface = gr.Interface(fn=TextToImage, 
-                          inputs=[gr.Textbox(show_label=True, label='How many seconds to hunt for copies?',), gr.Slider(1, 1000, label='Inference Steps', value=100, step=1), gr.Dropdown(modelieo)],
+                          inputs=[gr.Textbox(show_label=True, label='How many seconds to hunt for copies?',), gr.Slider(1, 1000, label='Inference Steps (leave unchanged for default, best is 1000 but it is slow!)', value=10, step=1), gr.Dropdown(modelieo)],
                           outputs=gr.Gallery(label="Generated image", show_label=True, elem_id="gallery", columns=[2], rows=[1], object_fit="contain", height="auto"), 
                           # css="#output_image{width: 256px !important; height: 256px !important;}",
                           title='Unconditional Image Generation',
@@ -209,11 +220,13 @@ with gr.Blocks() as app:
       "<hr>"
       "<h1><center>Do machine learing models store protected content?</center></h1>" +
       "<p><center><span style='color: red;'>Enter a time to hunt for copies (seconds), select a model, and hit submit!</center></p>" +
-      "<p><center><strong>These image generation models will give you a 'bespoke' generation ❤ of an MNIST hand-drawn digit</p>" +
+      "<p><center><strong>These image generation models will give you a 'bespoke' generation ❤ of an <a href='https://paperswithcode.com/dataset/mnist'>MNIST hand-drawn digit<a></p> " +
       "<p><center>then the program will search in training data (for <i>n</i> seconds) to find similar images: <a href='https://medium.com/@mygreatlearning/rmse-what-does-it-mean-2d446c0b1d0e'>RMSE<a>, lower is more similar</p>" +
-      "<p><a href='https://nathanreitinger.umiacs.io'>@nathanReitinger<a></p>"
+      "<p><a href='https://nathanreitinger.umiacs.io'>@nathanReitinger</a></p>"
   )
 
+  gr.Dataframe(styler)
+
 app.queue().launch()
 # interface.launch(share=True)
 

requirements.txt

@@ -6,6 +6,7 @@ huggingface-hub==0.22.2
 image-similarity-measures==0.3.6
 matplotlib==3.8.4
 numpy==1.25.2
+pandas==2.2.2
 pillow==10.3.0
 requests==2.31.0
 tensorflow==2.11.0