diff --git a/node_wrappers/depth_anything.py b/node_wrappers/depth_anything.py
index 97ed5b3..5557726 100644
--- a/node_wrappers/depth_anything.py
+++ b/node_wrappers/depth_anything.py
@@ -1,55 +1,88 @@
-from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION
 import comfy.model_management as model_management
 
+class Depth_Anything_Loader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ckpt_name": (["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"], {"default": "depth_anything_vitl14.pth"}) }}
+    RETURN_TYPES = ("DEPTH_MODEL",)
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "ControlNet Preprocessors/Depth Loader"
+
+    def load_checkpoint(self, ckpt_name):
+        from custom_controlnet_aux.depth_anything import DepthAnythingDetector
+        model = DepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
+        return (model, )
+
 class Depth_Anything_Preprocessor:
     @classmethod
     def INPUT_TYPES(s):
-        return define_preprocessor_inputs(
-            ckpt_name=INPUT.COMBO(
-                ["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"]
-            ),
-            resolution=INPUT.RESOLUTION()
-        )
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "model": ("DEPTH_MODEL",)
+            },
+            "optional": {
+                "resolution": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64})
+            }
+        }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "execute"
 
     CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
 
-    def execute(self, image, ckpt_name="depth_anything_vitl14.pth", resolution=512, **kwargs):
-        from custom_controlnet_aux.depth_anything import DepthAnythingDetector
-
-        model = DepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
+    def execute(self, image, model, resolution=512, **kwargs):
         out = common_annotator_call(model, image, resolution=resolution)
-        del model
         return (out, )
 
+class Zoe_Depth_Anything_Loader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "environment": (["indoor", "outdoor"], {"default": "indoor"})}}
+    RETURN_TYPES = ("ZOEDEPTH_MODEL",)
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "ControlNet Preprocessors/Depth Loader"
+
+    def load_checkpoint(self, environment):
+        from custom_controlnet_aux.zoe import ZoeDepthAnythingDetector
+        ckpt_name = "depth_anything_metric_depth_indoor.pt" if environment == "indoor" else "depth_anything_metric_depth_outdoor.pt"
+        model = ZoeDepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
+        return (model, )
+        
 class Zoe_Depth_Anything_Preprocessor:
     @classmethod
     def INPUT_TYPES(s):
-        return define_preprocessor_inputs(
-            environment=INPUT.COMBO(["indoor", "outdoor"]),
-            resolution=INPUT.RESOLUTION()
-        )
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "model": ("ZOEDEPTH_MODEL",)
+            },
+            "optional": {
+                "resolution": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64})
+            }
+        }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "execute"
 
     CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
 
-    def execute(self, image, environment="indoor", resolution=512, **kwargs):
-        from custom_controlnet_aux.zoe import ZoeDepthAnythingDetector
-        ckpt_name = "depth_anything_metric_depth_indoor.pt" if environment == "indoor" else "depth_anything_metric_depth_outdoor.pt"
-        model = ZoeDepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
+    def execute(self, image, model, resolution=512, **kwargs):
         out = common_annotator_call(model, image, resolution=resolution)
-        del model
         return (out, )
 
 NODE_CLASS_MAPPINGS = {
+    "DepthAnythingLoader": Depth_Anything_Loader,
     "DepthAnythingPreprocessor": Depth_Anything_Preprocessor,
+    "Zoe_DepthAnythingLoader": Zoe_Depth_Anything_Loader,
     "Zoe_DepthAnythingPreprocessor": Zoe_Depth_Anything_Preprocessor
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
+    "DepthAnythingLoader": "Depth Anything Loader",
     "DepthAnythingPreprocessor": "Depth Anything",
+    "Zoe_DepthAnythingLoader": "Zoe Depth Anything Loader",
     "Zoe_DepthAnythingPreprocessor": "Zoe Depth Anything"
 }
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