Fix: Force EngineFast for GPU T4 small environment

- Remove CPU fallback logic (not needed on GPU tier)
- Force use of EngineFast which has t2t() method
- Resolves AttributeError: 'Engine' object has no attribute 't2t'

Requirements: HF Space must use GPU hardware (T4 small or better)

code/demo.py

@@ -78,21 +78,11 @@ def get_gpt_engine_cached():
         shape_ckpt_path = 'model_weights/save_shape_cars_whole_p_rot_scratch_4mask_randp.safetensors'
         save_gpt_ckpt_path = 'model_weights/save_shape_cars_whole_p_rot_scratch_4mask_randp.safetensors'
 
-    # Select device with safe CPU fallback to avoid "no NVIDIA driver" errors on basic HF instances
-    device_override = os.getenv("GPT_DEVICE")
-    if device_override:
-        device = torch.device(device_override)
-        print(f"🔧 GPT device override via GPT_DEVICE={device_override}")
-    else:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    engine_cls = EngineFast if device.type == "cuda" else Engine
-    if device.type == "cuda":
-        print("✅ GPT engine will use CUDA (EngineFast)")
-    else:
-        print("ℹ️  GPU not available; using CPU Engine (slower, no CUDA graphs)")
+    # GPU T4 small environment: Use EngineFast with CUDA (required for t2t method)
+    device = torch.device("cuda")
+    print("✅ Using EngineFast on GPU (T4 small)")
 
-    engine = engine_cls(
+    engine = EngineFast(
         config_path, gpt_ckpt_path, shape_ckpt_path, save_gpt_ckpt_path,
         device=device,
         mode='test'