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This commit is contained in:
Talmaj Marinc 2026-05-27 15:23:30 +02:00
parent 479de20d84
commit 1d77c36308
1 changed files with 7 additions and 7 deletions
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14
comfy_extras/nodes_depth_anything_3.py
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@ -30,14 +30,14 @@ DA3PointCloud = io.Custom("DA3_POINT_CLOUD")
# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them): # DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them):
# #
# Per-frame tensors — B = batch size in mono mode; B = S (number of views) in multi-view mode. # Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode.
# "depth": torch.Tensor (B, H, W) -- raw model depth (always present; matches MoGe convention) # "depth": torch.Tensor (B, H, W) -- raw model depth (always present; matches MoGe convention)
# "image": torch.Tensor (B, H, W, 3) -- source image in [0, 1], CPU (always present) # "image": torch.Tensor (B, H, W, 3) -- source image in [0, 1], CPU (always present)
# "mode": str -- "mono" or "multiview" (always present) # "mode": str -- "mono" or "multiview" (always present)
# "sky": torch.Tensor (B, H, W) -- sky probability in [0, 1] (Mono/Metric variants only) # "sky": torch.Tensor (B, H, W) -- sky probability in [0, 1] (Mono/Metric variants only)
# "confidence": torch.Tensor (B, H, W) -- raw model confidence output (Small/Base variants only) # "confidence": torch.Tensor (B, H, W) -- raw model confidence output (Small/Base variants only)
# #
# Multi-view only — S = number of views; the leading 1 is the scene dimension from the model. # Multi-view only - S = number of views; the leading 1 is the scene dimension from the model.
# "extrinsics": torch.Tensor (1, S, 3, 4) -- world-to-camera [R|t] matrices # "extrinsics": torch.Tensor (1, S, 3, 4) -- world-to-camera [R|t] matrices
# "intrinsics": torch.Tensor (1, S, 3, 3) -- pixel-space intrinsics # "intrinsics": torch.Tensor (1, S, 3, 3) -- pixel-space intrinsics
# #
@ -69,7 +69,7 @@ def _da3_default_K(H: int, W: int) -> torch.Tensor:
def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor: def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor:
"""Return pixel-space K for batch element b, falling back to a default estimate.""" """Return pixel-space K for batch element b, falling back to a default estimate."""
if "intrinsics" in geometry: if "intrinsics" in geometry:
# shape (1, S, 3, 3) — leading scene dimension from the multiview head # shape (1, S, 3, 3) - leading scene dimension from the multiview head
return geometry["intrinsics"][0, b].float() return geometry["intrinsics"][0, b].float()
logging.getLogger("comfy").warning( logging.getLogger("comfy").warning(
"DA3_GEOMETRY has no intrinsics (mono-mode model). " "DA3_GEOMETRY has no intrinsics (mono-mode model). "
@ -249,7 +249,7 @@ class DA3Inference(io.ComfyNode):
tooltip="- upper_bound_resize: scale so the longest side = process_res (caps memory, default).\n" tooltip="- upper_bound_resize: scale so the longest side = process_res (caps memory, default).\n"
"- lower_bound_resize: scale so the shortest side = process_res (preserves more detail on tall/wide images, uses more memory)."), "- lower_bound_resize: scale so the shortest side = process_res (preserves more detail on tall/wide images, uses more memory)."),
io.DynamicCombo.Input("mode", io.DynamicCombo.Input("mode",
tooltip="- mono: single view image — works with any model variant.\n" tooltip="- mono: single view image - works with any model variant.\n"
"- multiview: all images processed together for geometric consistency + camera pose, for Small/Base models only.", "- multiview: all images processed together for geometric consistency + camera pose, for Small/Base models only.",
options=[ options=[
io.DynamicCombo.Option("mono", []), io.DynamicCombo.Option("mono", []),
@ -259,7 +259,7 @@ class DA3Inference(io.ComfyNode):
"first", "middle"], "first", "middle"],
default="saddle_balanced", default="saddle_balanced",
tooltip="Which view acts as the geometric anchor (only when S >= 3 and no extrinsics provided).\n" tooltip="Which view acts as the geometric anchor (only when S >= 3 and no extrinsics provided).\n"
"- saddle_balanced: the view most 'average' across all others — best general choice.\n" "- saddle_balanced: the view most 'average' across all others - best general choice.\n"
"- saddle_sim_range: the view most visually distinct from the others.\n" "- saddle_sim_range: the view most visually distinct from the others.\n"
"- first / middle: fixed positional picks."), "- first / middle: fixed positional picks."),
io.Combo.Input("pose_method", io.Combo.Input("pose_method",
@ -406,7 +406,7 @@ class DA3Render(io.ComfyNode):
default="v2_style", default="v2_style",
tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n" tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
"- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n" "- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
"- raw: no scaling — preserves metric units for Metric model."), "- raw: no scaling - preserves metric units for Metric model."),
io.Boolean.Input("apply_sky_clip", default=False, io.Boolean.Input("apply_sky_clip", default=False,
tooltip="Clip sky-region depth to the 99th percentile of foreground depth before " tooltip="Clip sky-region depth to the 99th percentile of foreground depth before "
"normalisation. Requires a 'sky' tensor in the da3_geometry input" "normalisation. Requires a 'sky' tensor in the da3_geometry input"
@ -563,7 +563,7 @@ class DA3GeometryToMesh(io.ComfyNode):
if n_bad: if n_bad:
logging.getLogger("comfy").warning( logging.getLogger("comfy").warning(
f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels " f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels "
f"({100*n_bad/(H*W):.1f}%) — zeroed before unproject." f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject."
) )
depth[~torch.isfinite(depth)] = 0.0 depth[~torch.isfinite(depth)] = 0.0
logging.getLogger("comfy").debug( logging.getLogger("comfy").debug(