Update app.py

app.py

@@ -7,25 +7,26 @@ model_id = "hsuwill000/Fluently-v4-LCM-openvino"
 HIGH = 1024
 WIDTH = 512
 
-batch_size = -1
+batch_size = None  # Or set it to a specific positive integer if needed
 
 pipe = OVStableDiffusionPipeline.from_pretrained(
     model_id,
     compile=False,
     ov_config={"CACHE_DIR": ""},
-    torch_dtype=torch.int8,  # fast
+    torch_dtype=torch.float16,  # More standard dtype for speed
     safety_checker=None,
     use_safetensors=False,
 )
 print(pipe.scheduler.compatibles)
 
-pipe.reshape(batch_size=-1, height=HIGH, width=WIDTH, num_images_per_prompt=1)
+pipe.reshape(batch_size=batch_size, height=HIGH, width=WIDTH, num_images_per_prompt=1)
 
 pipe.compile()
 
 prompt = ""
 negative_prompt = "EasyNegative, "
 num_inference_steps = 4
+
 def infer(prompt, negative_prompt, num_inference_steps):
     image = pipe(
         prompt=prompt,
@@ -71,20 +72,20 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )         
-            run_button = gr.Button("Run", scale=0)
+            run_button = gr.Button("Run", scale=1)
         
         result = gr.Image(label="Result", show_label=False)
 
         gr.Examples(
             examples=examples,
             fn=infer,
-            inputs=[prompt],
+            inputs=[prompt, negative_prompt, num_inference_steps],
             outputs=[result]
         )
 
     run_button.click(
         fn=infer,
-        inputs=[prompt],
+        inputs=[prompt, negative_prompt, num_inference_steps],
         outputs=[result]
     )