Update app.py

app.py

@@ -52,8 +52,6 @@ def style_aligned_controlnet(ref_style_prompt, depth_map, ref_image, img_generat
         depth_image = pipeline_calls.get_depth_map(image, feature_processor, depth_estimator)
     else:
         depth_image = load_image(ref_image).resize((1024, 1024))
-    #reference_prompt = ref_style_prompt #"a poster in minimalist origami style"
-    #target_prompts = img_generation_prompt #["mona lisa"] #, "gal gadot"]
     controlnet_conditioning_scale = 0.8
     num_images_per_prompt = 3 # adjust according to VRAM size
     latents = torch.randn(1 + num_images_per_prompt, 4, 128, 128).to(pipeline.unet.dtype)
@@ -103,4 +101,19 @@ with gr.Blocks() as demo:
               api_name="style_aligned_controlnet")
 
 
+
+    gr.Examples(
+      examples=[
+        ['A poster in a papercut art style.', True, 'example_image/A.png', 'A village in a papercut art style.'],
+        ['A poster in a papercut art style.', True, 'example_image/camel.jpg', 'A village in a papercut art style.'],
+        ['A poster in a papercut art style.', False, 'example_image/train.jpg', 'A village in a papercut art style.'],
+        ['A poster in a papercut art style.', False, 'example_image/sun.png', 'A village in a papercut art style.'],
+        ['A poster in a papercut art style.', True, 'example_image/whale.png', 'A village in a papercut art style.'],
+      ],
+      inputs=[ref_style_prompt, depth_map, ref_image, img_generation_prompt], 
+      outputs=[gallery, ref_style_image], 
+      fn=style_aligned_controlnet,
+      )
+
+    
 demo.launch()