Update app.py

app.py

@@ -17,7 +17,8 @@ pipe = StableDiffusionPipeline.from_pretrained(
     torch_dtype=torch.float32,
     cache_dir=CACHE_DIR,
     low_cpu_mem_usage=True,
-    safety_checker=None
+    safety_checker=None,
+    use_safetensors=True  # More reliable on CPU
 )
 
 DISCLAIMER = """
@@ -40,18 +41,27 @@ def generate_image(prompt, width, height, num_inference_steps=20, guidance_scale
         height = height - (height % 8)
         
         with torch.inference_mode():
+            # Generate with explicit parameter casting
             result = pipe(
-                prompt=prompt,
-                width=width,
-                height=height,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale
-            ).images[0]
+                prompt=str(prompt),
+                width=int(width),
+                height=int(height),
+                num_inference_steps=int(num_inference_steps),
+                guidance_scale=float(guidance_scale),
+                generator=None  # Avoid seed-related issues
+            )
             
-            if result is None:
-                return None, "Error: Failed to generate image"
+            # Strict output validation
+            if not hasattr(result, "images") or not isinstance(result.images, list):
+                return None, f"Invalid pipeline output format: {type(result)}"
+                
+            if len(result.images) == 0:
+                return None, "Pipeline returned empty image list"
+                
+            if result.images[0] is None:
+                return None, "Pipeline returned None image"
                 
-            return result.convert("RGB"), f"Success ({width}x{height})"
+            return result.images[0].convert("RGB"), f"Success ({width}x{height})"
             
     except Exception as e:
         return None, f"Generation Error: {str(e)}"