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Apply PR comments, reduce verbosity of docstrings, unify model and image/frame naming convention.
This commit is contained in:
Talmaj Marinc
parent
aefc61f42d
commit
f674c44e58
@ -1,16 +1,5 @@
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"""ComfyUI nodes for Depth Anything 3.
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Adds these nodes:
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* ``LoadDA3Model`` -- load a DA3 ``.safetensors`` file from the
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``models/geometry_estimation/`` folder.
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* ``DA3Inference`` -- unified depth estimation node supporting both mono and
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multi-view modes via a DynamicCombo selector. Returns a DA3_GEOMETRY dict of
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raw tensors (depth, sky, confidence, camera). Feed into ``DA3Render``
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to produce display images, or directly into ``MoGeRender`` for depth / mask views.
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* ``DA3Render`` -- post-processes a DA3_GEOMETRY dict: applies optional
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sky clipping, normalises depth and confidence, and returns display images.
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Model capability matrix
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-----------------------
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Variant head_type has_sky has_conf cam_dec
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@ -18,10 +7,6 @@ Model capability matrix
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DA3-Base dualdpt False True yes
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DA3-Mono-Large dpt True False no
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DA3-Metric-Large dpt True False no (raw output is metres)
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The node raises a ``ValueError`` at execution time when the selected
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parameters conflict with the loaded model's capabilities (e.g.
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``apply_sky_clip=True`` on a model with no sky head).
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"""
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from __future__ import annotations
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@ -241,17 +226,6 @@ def _run_da3(model_patcher, image: torch.Tensor, process_res: int,
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class DA3Inference(io.ComfyNode):
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"""Raw Depth Anything 3 inference node.
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Outputs a DA3_GEOMETRY dict of raw tensors. All display normalization
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(sky clipping, depth scaling, confidence normalisation) is handled by
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the companion ``DA3Render`` node.
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Mono mode: each batch element is processed independently.
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Multi-view mode: all frames share a single forward pass with cross-view
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attention; adds ``extrinsics`` and ``intrinsics`` to the geometry dict.
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"""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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@ -259,12 +233,11 @@ class DA3Inference(io.ComfyNode):
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search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"],
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display_name="Run Depth Anything 3",
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category="image/geometry_estimation",
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description="Run Depth Anything 3 on an image or image batch. In multi-view mode each frame is treated as a separate view of the same scene.",
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description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.",
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inputs=[
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DA3ModelType.Input("da3_model"),
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io.Image.Input("image",
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tooltip="Single image or image batch. "
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"In multi-view mode each frame is treated as "
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tooltip="In multi-view mode each image is treated as "
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"a separate view of the same scene."),
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io.Int.Input("process_res", default=504, min=140, max=2520, step=14,
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tooltip="Resolution the model runs at (longest side, multiple of 14). "
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@ -276,8 +249,8 @@ class DA3Inference(io.ComfyNode):
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tooltip="- upper_bound_resize: scale so the longest side = process_res (caps memory, default).\n"
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"- lower_bound_resize: scale so the shortest side = process_res (preserves more detail on tall/wide images, uses more memory)."),
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io.DynamicCombo.Input("mode",
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tooltip="- mono: single image or independent batch — works with any model variant.\n"
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"- multiview: all frames processed together for geometric consistency + camera pose — requires DA3-Small or DA3-Base (DA3-Mono-Large / DA3-Metric-Large do NOT support this mode).",
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tooltip="- mono: single view image — works with any model variant.\n"
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"- multiview: all images processed together for geometric consistency + camera pose, for Small/Base models only.",
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options=[
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io.DynamicCombo.Option("mono", []),
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io.DynamicCombo.Option("multiview", [
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@ -292,17 +265,17 @@ class DA3Inference(io.ComfyNode):
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io.Combo.Input("pose_method",
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options=["cam_dec", "ray_pose"],
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default="cam_dec",
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tooltip="- cam_dec: small MLP on the final camera token — works on DA3-Small and DA3-Base.\n"
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"- ray_pose: RANSAC over the DualDPT ray output — works on DA3-Small and DA3-Base.\n"
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"Both methods require DA3-Small or DA3-Base; this setting is ignored on Mono/Metric-Large."),
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tooltip="This seeting is ignored for Mono/Metric models."
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"- cam_dec: small MLP on the final camera token.\n"
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"- ray_pose: RANSAC over the DualDPT ray output."),
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]),
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]),
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],
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outputs=[
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DA3Geometry.Output("geometry",
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tooltip="DA3_GEOMETRY dict of raw tensors.\n"
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"- Always: 'depth' (B,H,W), 'image', 'mode'.\n"
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"- Optional: 'sky' + 'mask' (Mono/Metric), 'confidence' raw (Small/Base), 'extrinsics' + 'intrinsics' (multi-view)."),
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DA3Geometry.Output("da3_geometry",
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tooltip="Dictionary of non-normalized tensors.\n"
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"- Always: 'depth', 'image', 'mode'.\n"
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"- Optional: 'sky' (Mono/Metric), 'confidence' (Small/Base), 'extrinsics' + 'intrinsics' (multi-view)."),
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],
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)
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@ -323,10 +296,10 @@ class DA3Inference(io.ComfyNode):
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if not has_cam_dec and not has_dualdpt:
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raise ValueError(
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"multiview mode requires DA3-Small or DA3-Base — the loaded model "
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"multiview mode requires Small or Base model. The loaded model "
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f"(head_type='{diffusion.head_type}') does not support cross-view "
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"attention or camera pose estimation. Switch mode to 'mono', or "
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"load DA3-Small / DA3-Base for multiview."
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"load Small or Base model for multiview."
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)
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if pose_method == "cam_dec" and not has_cam_dec:
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@ -424,39 +397,33 @@ class DA3Inference(io.ComfyNode):
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class DA3Render(io.ComfyNode):
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"""Visualise a DA3_GEOMETRY packet as a single image.
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Mirrors the MoGeRender interface: one ``output`` selector, one IMAGE out.
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Use multiple nodes in parallel to get depth + sky + confidence simultaneously.
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"""
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_DEPTH_RENDER_INPUTS = [
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io.Combo.Input("normalization",
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options=["v2_style", "min_max", "raw"],
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default="v2_style",
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tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
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"- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
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"- raw: no scaling — preserves metric units for DA3-Metric-Large."),
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"- raw: no scaling — preserves metric units for Metric model."),
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io.Boolean.Input("apply_sky_clip", default=False,
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tooltip="Clip sky-region depth to the 99th percentile of foreground depth before "
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"normalisation. Requires a 'sky' tensor in the geometry "
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"(DA3-Mono-Large or DA3-Metric-Large); raises an error otherwise."),
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"normalisation. Requires a 'sky' tensor in the da3_geometry input"
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"provided by Mono/Metric models; raises an error otherwise."),
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]
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="DA3Render",
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display_name="Depth Anything 3 Render",
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display_name="Render Depth Anything 3",
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category="image/geometry_estimation",
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description="Visualise a DA3_GEOMETRY packet. Drop multiple nodes to get different views simultaneously.",
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description="Render a depth map, confidence map, or sky mask from DA3 geometry data.",
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inputs=[
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DA3Geometry.Input("geometry"),
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DA3Geometry.Input("da3_geometry"),
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io.DynamicCombo.Input("output",
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tooltip="- depth: normalised greyscale depth image.\n"
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"- depth_colored: depth mapped through the Turbo colormap.\n"
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"- sky_mask: sky probability in [0, 1] (Mono/Metric variants only).\n"
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"- confidence: normalised depth confidence (Small/Base variants only).",
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"- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n"
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"- confidence: normalised depth confidence (for Small/Base models only).",
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options=[
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io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS),
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io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS),
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@ -476,8 +443,8 @@ class DA3Render(io.ComfyNode):
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apply_sky_clip = output["apply_sky_clip"]
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if apply_sky_clip and "sky" not in geometry:
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raise ValueError(
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"apply_sky_clip=True requires a sky tensor in the geometry, but none is present. "
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"Run with DA3-Mono-Large or DA3-Metric-Large, or set apply_sky_clip=False."
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"apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. "
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"Run with Mono/Metric models or set apply_sky_clip=False."
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)
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depth = geometry["depth"]
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sky = geometry.get("sky")
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@ -491,13 +458,13 @@ class DA3Render(io.ComfyNode):
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elif output_val == "sky_mask":
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if "sky" not in geometry:
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raise ValueError("geometry has no sky output; run with DA3-Mono-Large or DA3-Metric-Large.")
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raise ValueError("geometry has no sky output; run with Mono/Metric models.")
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sky = geometry["sky"]
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result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous()
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elif output_val == "confidence":
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if "confidence" not in geometry:
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raise ValueError("geometry has no confidence output; run with DA3-Small or DA3-Base.")
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raise ValueError("geometry has no confidence output; run with Small/Base models.")
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result = _normalize_confidence(geometry["confidence"])
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result = result.unsqueeze(-1).expand(*result.shape, 3).contiguous()
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@ -538,14 +505,14 @@ class DA3GeometryToMesh(io.ComfyNode):
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return io.Schema(
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node_id="DA3GeometryToMesh",
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search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"],
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display_name="DA3 Geometry to Mesh",
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display_name="Convert DA3 Geometry to Mesh",
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category="image/geometry_estimation",
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description="Convert a DA3_GEOMETRY depth map into a triangulated 3D mesh (Types.MESH).",
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description="Convert a depth map into a triangulated 3D mesh.",
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inputs=[
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DA3Geometry.Input("da3_geometry"),
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io.Int.Input("batch_index", default=0, min=0, max=4096,
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tooltip="Which frame of a batched DA3_GEOMETRY to mesh. "
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"Per-frame vertex counts differ so batches cannot be stacked."),
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tooltip="Which image of a batched DA3_GEOMETRY to mesh. "
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"Per-image vertex counts differ so batches cannot be stacked."),
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io.Int.Input("decimation", default=1, min=1, max=8,
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tooltip="Vertex stride; 1 = full resolution, 2 = half, etc."),
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io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01,
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@ -640,13 +607,13 @@ class DA3GeometryToPointCloud(io.ComfyNode):
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return io.Schema(
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node_id="DA3GeometryToPointCloud",
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search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"],
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display_name="DA3 Geometry to Point Cloud",
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display_name="Convert DA3 Geometry to Point Cloud",
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category="image/geometry_estimation",
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description="Unproject a DA3_GEOMETRY depth map into a 3D point cloud (DA3_POINT_CLOUD).",
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inputs=[
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DA3Geometry.Input("da3_geometry"),
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io.Int.Input("batch_index", default=0, min=0, max=4096,
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tooltip="Which frame of a batched DA3_GEOMETRY to convert."),
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tooltip="Which image of a batched DA3_GEOMETRY to convert."),
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io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
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tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). "
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"Ignored when the geometry has no confidence map."),
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