Update app.py

app.py

@@ -220,10 +220,10 @@ with gr.Blocks() as demo:
                 num_tokens = gr.Number(value="5", label="num tokens to represent each object", interactive= True)
                 num_tokens_global = num_tokens
                 embedding_learning_rate = gr.Textbox(value="0.00025", label="Embedding optimization: Learning rate", interactive= True )
-                max_emb_train_steps =  gr.Number(value="6", maximum="100", label="embedding optimization: Training steps", interactive= True )
+                max_emb_train_steps =  gr.Number(value="6", label="embedding optimization: Training steps", interactive= True )
                 
                 diffusion_model_learning_rate = gr.Textbox(value="0.0002", label="UNet Optimization: Learning rate", interactive= True )
-                max_diffusion_train_steps = gr.Number(value="28", maximum="200", label="UNet Optimization: Learning rate: Training steps", interactive= True )
+                max_diffusion_train_steps = gr.Number(value="28", label="UNet Optimization: Learning rate: Training steps", interactive= True )
                 
                 train_batch_size = gr.Number(value="20", label="Batch size", interactive= True )
                 gradient_accumulation_steps=gr.Number(value="2", label="Gradient accumulation", interactive= True )
@@ -249,9 +249,9 @@ with gr.Blocks() as demo:
                         image_gt=np.array(image),
                         num_tokens=int(num_tokens),
                         embedding_learning_rate = float(embedding_learning_rate), 
-                        max_emb_train_steps = int(max_emb_train_steps), 
+                        max_emb_train_steps = min(int(max_emb_train_steps),50), 
                         diffusion_model_learning_rate= float(diffusion_model_learning_rate), 
-                        max_diffusion_train_steps = int(max_diffusion_train_steps),
+                        max_diffusion_train_steps = min(int(max_diffusion_train_steps),100),
                         train_batch_size=int(train_batch_size),
                         gradient_accumulation_steps=int(gradient_accumulation_steps)
                     )