From 3f51595a2811dd2add586aa293156b6b4e342bd7 Mon Sep 17 00:00:00 2001
From: Talmaj Marinc <talmaj@comfy.org>
Date: Tue, 19 May 2026 20:47:28 +0200
Subject: [PATCH] Fix no clamping when normalization is raw and refactor
 reusable code to _apply_sky_clip and _depth_to_image

---
 comfy_extras/nodes_depth_anything_3.py | 68 ++++++++++++++------------
 1 file changed, 37 insertions(+), 31 deletions(-)

diff --git a/comfy_extras/nodes_depth_anything_3.py b/comfy_extras/nodes_depth_anything_3.py
index 999613276..9f41022a2 100644
--- a/comfy_extras/nodes_depth_anything_3.py
+++ b/comfy_extras/nodes_depth_anything_3.py
@@ -296,29 +296,48 @@ class DepthAnything3(io.ComfyNode):
             ref_view_strategy, pose_method,
         )
 
-    @classmethod
-    def _execute_mono(cls, model, image, process_res, resize_method,
-                      normalization, apply_sky_clip) -> io.NodeOutput:
-        depth, confidence, sky = _run_da3(model, image, process_res, method=resize_method)
+    @staticmethod
+    def _apply_sky_clip(depth: torch.Tensor, sky: torch.Tensor) -> torch.Tensor:
+        return torch.stack([
+            da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
+            for i in range(depth.shape[0])
+        ], dim=0)
 
-        if apply_sky_clip and sky is not None:
-            depth = torch.stack([
-                da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
-                for i in range(depth.shape[0])
-            ], dim=0)
+    @staticmethod
+    def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None,
+                        normalization: str) -> torch.Tensor:
+        """Normalise depth and pack as an (N,H,W,3) image tensor.
 
+        Preserves metric units when normalization is 'raw' (no clamping).
+        """
+        N = depth.shape[0]
         if normalization == "v2_style":
             norm = torch.stack([
                 da3_preprocess.normalize_depth_v2_style(
-                    depth[i], sky[i] if sky is not None else None)
-                for i in range(depth.shape[0])
+                    depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
+                for i in range(N)
             ], dim=0)
         elif normalization == "min_max":
             norm = da3_preprocess.normalize_depth_min_max(depth)
         else:
             norm = depth
 
-        out_image = norm.unsqueeze(-1).repeat(1, 1, 1, 3).clamp(0.0, 1.0).contiguous()
+        # Preserve metric units when normalization is raw.
+        out = norm.unsqueeze(-1).repeat(1, 1, 1, 3)
+        if normalization != "raw":
+            out = out.clamp(0.0, 1.0)
+        return out.contiguous()
+
+    @classmethod
+    def _execute_mono(cls, model, image, process_res, resize_method,
+                      normalization, apply_sky_clip) -> io.NodeOutput:
+        depth, confidence, sky = _run_da3(model, image, process_res, method=resize_method)
+
+        if apply_sky_clip and sky is not None:
+            depth = cls._apply_sky_clip(depth, sky)
+
+        out_image = cls._depth_to_image(depth, sky, normalization)
+
         sky_mask = sky if sky is not None else torch.zeros_like(depth)
         conf_mask = (_normalize_confidence(confidence)
                      if confidence is not None else torch.zeros_like(depth))
@@ -367,18 +386,15 @@ class DepthAnything3(io.ComfyNode):
 
         conf_mask = _normalize_confidence(conf_raw) if conf_raw.any() else conf_raw
 
-        sky = torch.zeros_like(depth)
+        sky = None
         if "sky" in out:
             sky = torch.nn.functional.interpolate(
                 out["sky"].unsqueeze(1).float(), size=(H, W),
                 mode="bilinear", align_corners=False,
             ).squeeze(1).cpu()
 
-        if apply_sky_clip and "sky" in out:
-            depth = torch.stack([
-                da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
-                for i in range(S)
-            ], dim=0)
+        if apply_sky_clip and sky is not None:
+            depth = cls._apply_sky_clip(depth, sky)
 
         if "extrinsics" in out and "intrinsics" in out:
             extrinsics = out["extrinsics"].float().cpu()
@@ -388,19 +404,9 @@ class DepthAnything3(io.ComfyNode):
             intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone()
 
         sky_for_norm = sky if diffusion.has_sky else None
-        if normalization == "v2_style":
-            norm = torch.stack([
-                da3_preprocess.normalize_depth_v2_style(
-                    depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
-                for i in range(S)
-            ], dim=0)
-        elif normalization == "min_max":
-            norm = da3_preprocess.normalize_depth_min_max(depth)
-        else:
-            norm = depth
-
-        depth_image = norm.unsqueeze(-1).repeat(1, 1, 1, 3).clamp(0.0, 1.0).contiguous()
+        depth_image = cls._depth_to_image(depth, sky_for_norm, normalization)
 
+        sky_mask = sky if sky is not None else torch.zeros_like(depth)
         camera_latent = {
             "samples": depth.unsqueeze(0).unsqueeze(2).contiguous(),  # (1, S, 1, H, W)
             "type": "da3_multiview",
@@ -411,7 +417,7 @@ class DepthAnything3(io.ComfyNode):
         }
         return io.NodeOutput(
             depth_image,
-            sky.contiguous(),
+            sky_mask.contiguous(),
             conf_mask.contiguous(),
             camera_latent,
         )