From 1d77c36308e307d3cab8bd2d9adb32faf1b150a1 Mon Sep 17 00:00:00 2001 From: Talmaj Marinc Date: Wed, 27 May 2026 15:23:30 +0200 Subject: [PATCH] Replace em dashes with - --- comfy_extras/nodes_depth_anything_3.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/comfy_extras/nodes_depth_anything_3.py b/comfy_extras/nodes_depth_anything_3.py index c5e4bebbc..72483d40f 100644 --- a/comfy_extras/nodes_depth_anything_3.py +++ b/comfy_extras/nodes_depth_anything_3.py @@ -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): # -# 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) # "image": torch.Tensor (B, H, W, 3) -- source image in [0, 1], CPU (always present) # "mode": str -- "mono" or "multiview" (always present) # "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) # -# 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 # "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: """Return pixel-space K for batch element b, falling back to a default estimate.""" 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() logging.getLogger("comfy").warning( "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" "- lower_bound_resize: scale so the shortest side = process_res (preserves more detail on tall/wide images, uses more memory)."), 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.", options=[ io.DynamicCombo.Option("mono", []), @@ -259,7 +259,7 @@ class DA3Inference(io.ComfyNode): "first", "middle"], default="saddle_balanced", 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" "- first / middle: fixed positional picks."), io.Combo.Input("pose_method", @@ -406,7 +406,7 @@ class DA3Render(io.ComfyNode): default="v2_style", 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" - "- 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, tooltip="Clip sky-region depth to the 99th percentile of foreground depth before " "normalisation. Requires a 'sky' tensor in the da3_geometry input" @@ -563,7 +563,7 @@ class DA3GeometryToMesh(io.ComfyNode): if n_bad: logging.getLogger("comfy").warning( 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 logging.getLogger("comfy").debug(