updating examples

app.py

@@ -21,14 +21,19 @@ torch.manual_seed(1)
 counter = 0
 
 #Getting Lora fine-tuned weights
-def monkeypatching(alpha, in_prompt): #, prompt, pipe): finetuned_lora_weights
+def monkeypatching(alpha, in_prompt, example_wt): #, prompt, pipe): finetuned_lora_weights
     print("****** inside monkeypatching *******")
     print(f"in_prompt is - {str(in_prompt)}")
     global counter
     if counter == 0 :
-        monkeypatch_lora(pipe.unet, torch.load("./output_example/lora_weight.pt")) #finetuned_lora_weights
-        tune_lora_scale(pipe.unet, alpha) #1.00)
-        counter +=1
+        if example_wt is None : 
+            monkeypatch_lora(pipe.unet, torch.load("./output_example/lora_weight.pt")) #finetuned_lora_weights
+            tune_lora_scale(pipe.unet, alpha) #1.00)
+            counter +=1
+        else:
+            monkeypatch_lora(pipe.unet, torch.load(example_wt)) #finetuned_lora_weights
+            tune_lora_scale(pipe.unet, alpha) #1.00)
+            counter +=1
     else :
         tune_lora_scale(pipe.unet, alpha) #1.00)
     prompt = "style of hclu, " + str(in_prompt)   #"baby lion"
@@ -77,9 +82,16 @@ with gr.Blocks() as demo:
                 in_steps = gr.Number(label="Enter the number of training steps", value = 4000)
                 in_alpha = gr.Slider(0.1,1.0, step=0.01, label="Set Alpha level", value=0.5)
             out_file = gr.File(label="Lora trained model weights", )
+
+    gr.Examples(
+        examples=[[0.65, "lion", "./lora_playgroundai_wt.pt" ]],
+        inputs=[in_alpha, in_prompt, example_wt],
+        outputs=out_image,
+        fn=monkeypatching,
+        cache_examples=True,)
         
     b1.click(fn = accelerate_train_lora, inputs=in_steps, outputs=out_file) 
-    b2.click(fn = monkeypatching, inputs=[in_alpha, in_prompt], outputs=out_image)
+    b2.click(fn = monkeypatching, inputs=[in_alpha, in_prompt, example_wt], outputs=out_image)
 
 demo.queue(concurrency_count=3) 
 demo.launch(debug=True, show_error=True)