Cache empty text embedding (#7)

* Cache empty text embedding to reduce a bit of time and avoid CLIP loading

* It is only 9MB so moving to global won't affect RAM much

* Complete

nodes.py

@@ -21,6 +21,8 @@ def colorizedepth(depth_map, colorize_method):
     depth_colored_hwc = chw2hwc(depth_colored)
     return depth_colored_hwc
 
+empty_text_embed = torch.load(os.path.join(__file__, '..', "empty_text_embed.pt"), map_location="cpu")
+
 class MarigoldDepthEstimation:
     @classmethod
     def INPUT_TYPES(s):
@@ -78,7 +80,7 @@ class MarigoldDepthEstimation:
         if checkpoint_path is None:
             raise FileNotFoundError("No checkpoint directory found.")
 
-        self.marigold_pipeline = MarigoldPipeline.from_pretrained(checkpoint_path, enable_xformers=False)
+        self.marigold_pipeline = MarigoldPipeline.from_pretrained(checkpoint_path, enable_xformers=False, empty_text_embed=empty_text_embed)
         self.marigold_pipeline = self.marigold_pipeline.to(device).half()
         self.marigold_pipeline.unet.eval()  # Set the model to evaluation mode