[Update]: Enhanced app.py for improved model handling and metrics visualization 🌟

- Updated: `init_stable_diffusion` to explicitly request the `fp32` variant for consistent float32 usage.
- Improved: Metrics formatting in `process_memory_operation` to convert shape to a list for better readability.
- Enhanced: GPU image generation in `generate_art_with_gpu` to ensure the random generator is on CUDA.
- Pro Tip of the Commit: Clarity in metrics and consistency in models make for a smoother ride on the waves of innovation! 🌊✨
Aye, Aye! 🚢

app.py

@@ -67,8 +67,9 @@ def init_stable_diffusion():
             
             pipe = DiffusionPipeline.from_pretrained(
                 model_id,
-                torch_dtype=torch.float32,  # Start with float32 on CPU
-                use_safetensors=True
+                torch_dtype=torch.float32,  # Use float32 consistently
+                use_safetensors=True,
+                variant="fp32"  # Explicitly request fp32 variant
             )
             pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)
             print("✨ Stable Diffusion loaded on CPU")
@@ -790,7 +791,10 @@ def process_memory_operation(
         metrics = result["metrics"]
         metrics_str = "📊 Analysis Results:\n\n"
         for key, value in metrics.items():
-            metrics_str += f"• {key.replace('_', ' ').title()}: {value:.4f}\n"
+            if key == "shape":
+                metrics_str += f"• {key.replace('_', ' ').title()}: {list(value)}\n"  # Convert shape to list
+            else:
+                metrics_str += f"• {key.replace('_', ' ').title()}: {value:.4f}\n"
         
         metrics_str += f"\n🎭 Emotional Context:\n"
         metrics_str += f"• Valence: {result['emotion']['valence']:.2f}\n"
@@ -819,6 +823,8 @@ def process_memory_operation(
         print(f"❌ Error during processing: {e}")
         # Ensure we're back on CPU
         memory_wave.to("cpu")
+        if pipe is not None:
+            pipe.to("cpu")
         return None, None, None, None
 
 @spaces.GPU
@@ -829,14 +835,14 @@ def generate_art_with_gpu(prompt: str, seed: int = 42) -> Optional[np.ndarray]:
         
     try:
         # Move to GPU and optimize
-        pipe.to("cuda")
+        pipe.to("cuda", torch_dtype=torch.float32)  # Ensure float32 on GPU
         pipe.enable_model_cpu_offload()
         pipe.enable_vae_slicing()
         pipe.enable_vae_tiling()
         pipe.enable_attention_slicing(slice_size="max")
         
         # Generate image
-        generator = torch.Generator().manual_seed(seed)
+        generator = torch.Generator("cuda").manual_seed(seed)  # Ensure generator is on CUDA
         image = pipe(
             prompt=prompt,
             negative_prompt="text, watermark, signature, blurry, distorted",