Class LTXVDrawTracks

sparse_tracks.py:209–327

GPU-accelerated sparse track renderer. Renders circles at a high reference resolution and downscales with bilinear interpolation so circle sizes match the CPU version. All work — rasterisation, compositing and resize — stays on GPU.

@comfy_node(name="LTXVDrawTracks", description="LTX Draw Sparse Tracks")
class LTXVDrawTracks(io.ComfyNode):
    """GPU-accelerated sparse track renderer.

    Renders circles at a high reference resolution and downscales with
    bilinear interpolation so circle sizes match the CPU version.
    All work — rasterisation, compositing and resize — stays on GPU.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LTXVDrawTracks",
            category="Lightricks/motion_tracking",
            description=(
                "GPU-accelerated sparse track renderer. Rasterises circles at "
                "high resolution and downscales with bilinear interpolation."
            ),
            inputs=[
                io.String.Input(
                    "tracks",
                    multiline=True,
                    tooltip="JSON string of track coordinates (list of point lists with x/y keys).",
                ),
                io.Int.Input(
                    "width",
                    default=512,
                    min=8,
                    max=8192,
                    step=8,
                    tooltip="Output image width in pixels.",
                ),
                io.Int.Input(
                    "height",
                    default=512,
                    min=8,
                    max=8192,
                    step=8,
                    tooltip="Output image height in pixels.",
                ),
            ],
            outputs=[
                io.Image.Output(),
            ],
        )

    @classmethod
    def execute(cls, tracks: str, width: int, height: int) -> io.NodeOutput:
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        parsed = _parse_tracks(tracks)
        if not parsed:
            blank = torch.zeros(1, height, width, 3, device=device, dtype=torch.half)
            return io.NodeOutput(blank)
        num_tracks = len(parsed)
        num_frames = max(len(t) for t in parsed)
        rw, rh, sx, sy = _render_resolution(width, height, _REF_SHORT_SIDE)

        point_xy = torch.zeros(num_tracks, num_frames, 2, device=device)
        vis = torch.zeros(num_tracks, num_frames, dtype=torch.bool, device=device)