Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 from optimum.intel.openvino import OVStableDiffusionPipeline
 from diffusers.training_utils import set_seed
 
+import time
+
 pipe_fp32 = OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-pokemons-fp32", compile=False)
 pipe_fp32.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
 pipe_fp32.compile()
@@ -24,8 +26,10 @@ pipes = {
 
 def generate(image, option):
     pipe = pipes[option]
+    start_time = time.time()
     output = pipe(prompt, num_inference_steps=50, output_type="pil")
-    return output.images[0]
+    elapsed_time = time.time() - start_time
+    return (output.images[0], f"Inference time: {elapsed_time}")
 
 examples = ["cartoon bird",
             "a drawing of a green pokemon with red eyes",
@@ -36,8 +40,8 @@ gr.Interface(
     inputs=[gr.inputs.Textbox(placeholder="cartoon bird", label="Prompt", lines=1),
             gr.inputs.Dropdown(choices=[option for option in pipes.keys()], default="Merged and quantized", label="Model version"),
            ],
-    outputs=gr.outputs.Image(type="pil", label="Generated Image"),
+    outputs=[gr.outputs.Image(type="pil", label="Generated Image"), "text"],
     title="OpenVINO-optimized Stable Diffusion",
-    description="This is the Optimum-based demo for optimized Stable Diffusion pipeline trained on Pokemon dataset and running with OpenVINO",
+    description="This is the Optimum-based demo for NNCF-optimized Stable Diffusion pipeline trained on 'lambdalabs/pokemon-blip-captions' dataset and running with OpenVINO",
     theme="huggingface",
 ).launch()