Update app.py

app.py

@@ -8,25 +8,37 @@ import traceback
 # Load the model once outside of the function
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
+# Create a thread-local storage for step indices
+scheduler_step_storage = threading.local()
+
 def generate_image(prompt):
     try:
-        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
-        print(f"Model output: {output}")
+        # Initialize step index per thread if not already set
+        if not hasattr(scheduler_step_storage, 'step'):
+            scheduler_step_storage.step = 0
+        
+        # Use the thread-local step index
+        output = model(
+            prompt=prompt, 
+            num_inference_steps=1,  # Add a sensible default for inference steps
+            guidance_scale=0.0, 
+            output_type="pil"  # Directly get PIL Image objects
+        )
 
-        # Check if the model returned images
-        if isinstance(output.images, list) and len(output.images) > 0:
+        # Increment the step index after generating the image
+        scheduler_step_storage.step += 1  
+        
+        # Check for output validity and return
+        if output.images:
             return output.images[0]
         else:
             raise Exception("No images returned by the model.")
-            
-    except IndexError as e:
-        print(f"Index error during image generation: {e}")
-        traceback.print_exc()
-        return None
+    
     except Exception as e:
         print(f"Error generating image: {e}")
         traceback.print_exc()
-        return None
+        return None  # Return None on error to handle it gracefully in the UI
+
 
 def inference(sentence_mapping, character_dict, selected_style):
     images = []
@@ -62,7 +74,6 @@ gradio_interface = gr.Interface(
         gr.Dropdown(["oil painting", "sketch", "watercolor"], label="Selected Style")
     ],
     outputs=gr.Gallery(label="Generated Images")
-).queue(default_concurrency_limit=5)
 
 if __name__ == "__main__":
     gradio_interface.launch()