models statement

app.py

@@ -21,7 +21,7 @@ import base64
 modelieo=[
   'nathanReitinger/MNIST-diffusion',
   'nathanReitinger/MNIST-diffusion-oneImage',
-  'nathanReitinger/MNIST-diffusion-threeHundredImages ',
+  'nathanReitinger/MNIST-diffusion-threeHundredImages',
   'nathanReitinger/MNIST-GAN',
   'nathanReitinger/MNIST-GAN-noDropout',
   'nathanReitinger/FASHION-diffusion',
@@ -251,10 +251,10 @@ 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', 'FASHION-diffuion-oneImage', 'FASHION-diffusion'], 
-    "Class (Architecture)"  : ['UNet2DModel', 'UNet2DModel', 'Sequential', 'Sequential', 'UNet2DModel', 'UNet2DModel'], 
-    "Dataset Examples"      : [60000, 1, 60000, 60000, 1, 60000], 
-    "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', 'less dropout, more copying?', 'same diffusion, different data (more variance in data)','larger diffusion training data, on FASHION dataset']
+    "Model"                 : ['MNIST-diffusion', 'MNIST-diffusion-oneImage', 'MNIST-diffusion-threeHundredImages', 'MNIST-GAN', 'MNIST-GAN-noDropout', 'FASHION-diffuion-oneImage', 'FASHION-diffusion'], 
+    "Class (Architecture)"  : ['UNet2DModel', 'UNet2DModel', 'UNet2DModel', 'Sequential', 'Sequential', 'UNet2DModel', 'UNet2DModel'], 
+    "Dataset Examples"      : [60000, 1, 300, 60000, 60000, 1, 60000], 
+    "Notes"                 : ['Similar architecture as Stable Diffusion, different training data', 'Toy model, purposed to store protected content', 'testing Toy model with slightly larger dataset', 'GANs are not as likely to store protected content', 'less dropout, more copying?', 'same diffusion, different data (more variance in data)','larger diffusion training data, on FASHION dataset']
 }) 
 
 # Applying style to highlight the maximum value in each row