Update app.py

app.py

@@ -87,7 +87,16 @@ def generate():
         prompt = data.get('prompt', '')
         ratio = data.get('ratio', '1:1')
         num_images = min(int(data.get('num_images', 1)), 4)
-        guidance_scale = float(min(max(data.get('guidance_scale', 7.5), 1.0), 20.0))  # Clamp guidance scale
+        
+        # Handle guidance_scale with explicit type conversion
+        guidance_scale_raw = data.get('guidance_scale', 7.5)
+        logger.info(f"Raw guidance_scale: {guidance_scale_raw} (type: {type(guidance_scale_raw)})")
+        try:
+            guidance_scale = float(guidance_scale_raw)
+            guidance_scale = min(max(guidance_scale, 1.0), 20.0)  # Clamp between 1.0 and 20.0
+        except (ValueError, TypeError):
+            logger.error(f"Invalid guidance_scale value: {guidance_scale_raw}")
+            return jsonify({"error": "guidance_scale must be a valid number"}), 400
 
         # Log input parameters
         logger.info(f"Generating with model: {model_id}, prompt: {prompt}, ratio: {ratio}, num_images: {num_images}, guidance_scale: {guidance_scale}")
@@ -114,7 +123,7 @@ def generate():
         pipe.to(torch.device("cpu"))  # Change to "cuda" if GPU is available
 
         images = []
-        num_inference_steps = 30 if model_id == 'ssd-1b' else 40  # Unified steps for stability
+        num_inference_steps = 30 if model_id == 'ssd-1b' else 30  # Unified steps for stability
         try:
             for _ in range(num_images):
                 image = pipe(