convert from numpy

baseline.py

@@ -40,12 +40,22 @@ def run(prompt, scheduler_key='dpm_scheduler', bg_seed=1, num_inference_steps=20
         model_dict, latents, input_embeddings, num_inference_steps,  
         guidance_scale=guidance_scale, scheduler_key=scheduler_key
     )
+
+    # Convert to PIL Image
+    import PIL.Image
+    image = PIL.Image.fromarray(images[0])
+    
+    # Save as PNG in memory
+    buffer = BytesIO()
+    image.save(buffer, format='PNG')
+    
+    # Encode PNG to base64
+    png_bytes = buffer.getvalue()
+    base64_string = base64.b64encode(png_bytes).decode('utf-8')
+
     
     gc.collect()
     torch.cuda.empty_cache()
 
-    with BytesIO() as buffer:
-        np.save(buffer, images[0])
-        img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
 
-    return images[0], img_str
+    return images[0], base64_string