latest models

app.py

@@ -22,6 +22,7 @@ modelieo=[
   'nathanReitinger/MNIST-diffusion',
   'nathanReitinger/MNIST-diffusion-oneImage',
   'nathanReitinger/MNIST-diffusion-threeHundredImages',
+  'nathanReitinger/MNIST-diffusion-threeThousandImages',
   'nathanReitinger/MNIST-GAN',
   'nathanReitinger/MNIST-GAN-noDropout',
   'nathanReitinger/FASHION-diffusion',
@@ -246,15 +247,15 @@ def TextToImage(Prompt,inference_steps, model):
   ai_gen = Image.open(open(original_image, 'rb'))
   training_data = Image.open(open(the_file, 'rb'))
 
-  another_one = (training_data, "RMSE: " + str(round(the_rmse,5) ))
+  another_one = (training_data, "Training Data (RMSE: " + str(round(the_rmse,5) ) + ")")
 
   return [ai_gen, another_one]
 
 df = pd.DataFrame({
-    "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']
+    "Model"                 : ['MNIST-diffusion', 'MNIST-diffusion-oneImage', 'MNIST-diffusion-threeHundredImages', 'MNIST-diffusion-threeThousandImages', 'MNIST-GAN', 'MNIST-GAN-noDropout', 'FASHION-diffuion-oneImage', 'FASHION-diffusion'], 
+    "Class (Architecture)"  : ['UNet2DModel', 'UNet2DModel', 'UNet2DModel', 'UNet2DModel', 'Sequential', 'Sequential', 'UNet2DModel', 'UNet2DModel'], 
+    "Dataset Examples"      : [60000, 1, 300, 3000, 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', 'testing toy model with even more data', '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

requirements.txt

@@ -1,7 +1,7 @@
 # Automatically generated by https://github.com/damnever/pigar.
 
 diffusers==0.27.2
-gradio==4.28.3
+gradio==4.29.0
 huggingface-hub==0.22.2
 image-similarity-measures==0.3.6
 matplotlib==3.8.4
@@ -9,6 +9,6 @@ numpy==1.25.2
 pandas==2.2.2
 pillow==10.3.0
 requests==2.31.0
-tensorflow==2.11.0
+tensorflow==2.16.1
 torch==2.2.2
 transformers==4.40.1