Update marigold_depth_estimation_lcm.py

marigold_depth_estimation_lcm.py

@@ -283,6 +283,7 @@ class MarigoldDepthConsistencyPipeline(DiffusionPipeline):
         """
         Encode text embedding for empty prompt.
         """
+        print("_encode_empty_text")
         prompt = ""
         text_inputs = self.tokenizer(
             prompt,
@@ -291,8 +292,11 @@ class MarigoldDepthConsistencyPipeline(DiffusionPipeline):
             truncation=True,
             return_tensors="pt",
         )
+        print(f"{self.text_encoder.device=}")
         text_input_ids = text_inputs.input_ids.to(self.text_encoder.device)
+        print(f"{text_input_ids.device=}")
         self.empty_text_embed = self.text_encoder(text_input_ids)[0].to(self.dtype)
+        print(f"{self.empty_text_embed.device=}", f"{self.empty_text_embed.dtype=}")
 
     @torch.no_grad()
     def single_infer(
@@ -358,7 +362,10 @@ class MarigoldDepthConsistencyPipeline(DiffusionPipeline):
 
         # Batched empty text embedding
         if self.empty_text_embed is None:
+            print("self.empty_text_embed is None")
             self._encode_empty_text()
+        else:
+            print("self.empty_text_embed is not None")
         batch_empty_text_embed = self.empty_text_embed.repeat(
             (rgb_latent.shape[0], 1, 1)
         )  # [B, 2, 1024]