init

app.py

@@ -199,7 +199,9 @@ class ControlNetDepthDesignModelMulti:
                                   weight_name="ip-adapter_sd15.bin")
         self.pipe.set_ip_adapter_scale(0.4)
         self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
+        print(self.pipe.device)
         self.pipe = self.pipe.to(device)
+        print(self.pipe.device)
         self.guide_pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B",
                                                                     torch_dtype=dtype, use_safetensors=True, variant="fp16")
         self.guide_pipe = self.guide_pipe.to(device)
@@ -213,6 +215,17 @@ class ControlNetDepthDesignModelMulti:
         self.depth_feature_extractor, self.depth_estimator = get_depth_pipeline()
         self.depth_estimator = self.depth_estimator.to(device)
         
+        if torch.cuda.is_available():
+            # Print the number of available GPUs
+            print("Available GPU devices:")
+            for i in range(torch.cuda.device_count()):
+                print(f"Device {i}: {torch.cuda.get_device_name(i)}")
+        else:
+            print("No GPU devices available. Using CPU.")
+    
+        print(self.depth_estimator.device)
+        print(self.pipe.device)
+        
     @spaces.GPU
     def generate_design(self, empty_room_image: Image, prompt: str, guidance_scale: int = 10, num_steps: int = 50, strength: float =0.9, img_size: int = 640) -> Image:
         """
@@ -226,16 +239,6 @@ class ControlNetDepthDesignModelMulti:
                            If the size is not the same the submission will fail.
         """
         print(prompt)
-        if torch.cuda.is_available():
-            # Print the number of available GPUs
-            print("Available GPU devices:")
-            for i in range(torch.cuda.device_count()):
-                print(f"Device {i}: {torch.cuda.get_device_name(i)}")
-        else:
-            print("No GPU devices available. Using CPU.")
-    
-        print(self.depth_estimator.device)
-        print(self.pipe.device)
         flush()
         self.generator = torch.Generator(device=device).manual_seed(self.seed)