diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..491c850
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,22 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    # if this is a forked repository. Skipping the workflow.
+    if: github.event.repository.fork == false
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
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+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
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+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
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+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..39a8c6b
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..fbebf1e
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,391 @@
+import math
+import json
+
+import numpy as np
+import torch
+from comfy_api.latest import io
+
+
+# ---------------------------------------------------------------------------
+# Constants (matching LTXVDrawTracks rendering)
+# ---------------------------------------------------------------------------
+_MIN_RADIUS = 2
+_MAX_RADIUS = 8
+_MAX_TRAIL = 50
+_REF_SHORT_SIDE = 1080
+
+
+# ---------------------------------------------------------------------------
+# Swing pattern functions
+# ---------------------------------------------------------------------------
+
+def _pendulum(phase: torch.Tensor, size: int, rate: float) -> torch.Tensor:
+    """Sinusoidal swing: offset = sin(phase) * size * rate / 2"""
+    return torch.sin(phase) * size * rate / 2
+
+
+def _bounce(phase: torch.Tensor, size: int, rate: float) -> torch.Tensor:
+    """Parabolic bounce arches with phase shift so offset=0 at phase=0.
+
+    offset = (0.5 - t^2) * size * rate
+    where t = (((phase + phi) / pi) % 2) - 1
+    phi = pi * (1 + 1/sqrt(2))
+    """
+    phi = math.pi * (1 + 1 / math.sqrt(2))
+    t = ((phase + phi) / math.pi % 2) - 1
+    return (0.5 - t ** 2) * size * rate
+
+
+def _linear(phase: torch.Tensor, size: int, rate: float) -> torch.Tensor:
+    """Triangle wave: offset = trianglewave(phase) * size * rate / 2
+
+    trianglewave(phase) = (2/pi) * arcsin(sin(phase))
+    """
+    return (2 / math.pi) * torch.arcsin(torch.sin(phase)) * size * rate / 2
+
+
+_PATTERNS = {
+    "pendulum": _pendulum,
+    "bounce": _bounce,
+    "linear": _linear,
+}
+
+
+# ---------------------------------------------------------------------------
+# Rendering helpers (ported from LTXVDrawTracks)
+# ---------------------------------------------------------------------------
+
+def _render_resolution(width: int, height: int, reference_short_side: int):
+    """Compute the higher render resolution that preserves aspect ratio."""
+    if height <= width:
+        rw = int(width * reference_short_side / height)
+        rh = reference_short_side
+    else:
+        rw = reference_short_side
+        rh = int(height * reference_short_side / width)
+    scale_x = rw / width
+    scale_y = rh / height
+    return rw, rh, scale_x, scale_y
+
+
+def _age_color_batch(ratios: torch.Tensor, device: torch.device) -> torch.Tensor:
+    """Vectorised age-ratio -> RGB [0..1] mapping on GPU.
+
+    Gradient: blue -> green -> yellow -> red.
+    """
+    colors = torch.zeros(ratios.shape[0], 3, device=device)
+
+    m1 = ratios <= 1 / 3
+    tr1 = ratios[m1] * 3
+    colors[m1, 1] = tr1
+    colors[m1, 2] = 1 - tr1
+
+    m2 = (ratios > 1 / 3) & (ratios <= 2 / 3)
+    tr2 = (ratios[m2] - 1 / 3) * 3
+    colors[m2, 0] = tr2
+    colors[m2, 1] = 1
+
+    m3 = ratios > 2 / 3
+    tr3 = (ratios[m3] - 2 / 3) * 3
+    colors[m3, 0] = 1
+    colors[m3, 1] = 1 - tr3
+
+    return colors
+
+
+def _rasterise_circles(
+    frame: torch.Tensor,
+    pts: torch.Tensor,
+    radii: torch.Tensor,
+    colors: torch.Tensor,
+    template_dist_sq: torch.Tensor,
+    half_d: int,
+    max_d: int,
+    H: int,
+    W: int,
+) -> None:
+    """Stamp filled circles onto *frame* fully on-device."""
+    M = pts.shape[0]
+    if M == 0:
+        return
+    device = pts.device
+
+    radii_sq = (radii * radii).view(M, 1, 1)
+    circle_masks = template_dist_sq.unsqueeze(0) <= radii_sq
+
+    cx = pts[:, 0].round().long().view(M, 1, 1)
+    cy = pts[:, 1].round().long().view(M, 1, 1)
+    offsets_y = torch.arange(max_d, device=device).sub(half_d).view(1, max_d, 1)
+    offsets_x = torch.arange(max_d, device=device).sub(half_d).view(1, 1, max_d)
+    fy = (cy + offsets_y).expand(M, max_d, max_d)
+    fx = (cx + offsets_x).expand(M, max_d, max_d)
+
+    valid = circle_masks & (fy >= 0) & (fy < H) & (fx >= 0) & (fx < W)
+
+    flat_fy = fy[valid]
+    flat_fx = fx[valid]
+    flat_lin = (flat_fy * W + flat_fx).long()
+
+    j_map = torch.arange(M, device=device, dtype=torch.float32).view(M, 1, 1)
+    j_map = j_map.expand_as(valid)
+    flat_j = j_map[valid]
+
+    priority = torch.full((H * W,), -1.0, device=device)
+    priority.scatter_reduce_(0, flat_lin, flat_j, reduce="amax", include_self=False)
+    priority = priority.view(H, W).long()
+
+    has_circle = priority >= 0
+    frame[has_circle] = colors[priority[has_circle]]
+
+
+# ---------------------------------------------------------------------------
+# DrawRhythmTracks Node
+# ---------------------------------------------------------------------------
+
+class DrawRhythmTracks(io.ComfyNode):
+    """Generate BPM-synced sparse track point images."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DrawRhythmTracks",
+            category="rhythm/motion_tracking",
+            display_name="Draw Rhythm Tracks",
+            description=(
+                "Generate BPM-synced sparse track point images. "
+                "A single point swings to the rhythm with independent "
+                "X/Y patterns and timing controls."
+            ),
+            inputs=[
+                io.Float.Input(
+                    "bpm", default=140.0, min=30.0, max=300.0, step=0.1,
+                    tooltip="Tempo in beats per minute. One beat = one swing cycle.",
+                ),
+                io.Float.Input(
+                    "fps", default=24.0, min=1.0, max=120.0, step=0.1,
+                    tooltip="Output frame rate.",
+                ),
+                io.Float.Input(
+                    "length", default=5.0, min=0.1, max=60.0, step=0.1,
+                    tooltip="Output duration in seconds.",
+                ),
+                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.",
+                ),
+                io.Int.Input(
+                    "start_x", default=256, min=0, max=8192,
+                    tooltip="Swing center point X coordinate.",
+                ),
+                io.Int.Input(
+                    "start_y", default=128, min=0, max=8192,
+                    tooltip="Swing center point Y coordinate.",
+                ),
+                io.Combo.Input(
+                    "pattern_x", options=["pendulum", "bounce", "linear"],
+                    default="pendulum",
+                    tooltip="X-axis swing pattern.",
+                ),
+                io.Combo.Input(
+                    "pattern_y", options=["pendulum", "bounce", "linear"],
+                    default="bounce",
+                    tooltip="Y-axis swing pattern.",
+                ),
+                io.Float.Input(
+                    "rate_x", default=0.0, min=0.0, max=1.0, step=0.01,
+                    tooltip="Horizontal swing width as ratio of image width.",
+                ),
+                io.Float.Input(
+                    "rate_y", default=0.25, min=0.0, max=1.0, step=0.01,
+                    tooltip="Vertical swing width as ratio of image height.",
+                ),
+                io.Float.Input(
+                    "rhythm_x", default=0.5, min=0.01, max=10.0, step=0.01,
+                    tooltip="X-axis BPM multiplier. Effective BPM = bpm * rhythm_x.",
+                ),
+                io.Float.Input(
+                    "rhythm_y", default=1.0, min=0.01, max=10.0, step=0.01,
+                    tooltip="Y-axis BPM multiplier. Effective BPM = bpm * rhythm_y.",
+                ),
+                io.Float.Input(
+                    "delay", default=0.0, min=-10.0, max=10.0, step=0.01,
+                    tooltip="Time offset in seconds. Negative = earlier, positive = later.",
+                ),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        bpm: float,
+        fps: float,
+        length: float,
+        width: int,
+        height: int,
+        start_x: int,
+        start_y: int,
+        pattern_x: str,
+        pattern_y: str,
+        rate_x: float,
+        rate_y: float,
+        rhythm_x: float,
+        rhythm_y: float,
+        delay: float,
+    ) -> io.NodeOutput:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        num_frames = math.ceil(length * fps)
+        beat_period = 60.0 / bpm
+
+        # Compute per-frame coordinates
+        frame_indices = torch.arange(num_frames, device=device, dtype=torch.float32)
+        frame_times = frame_indices / fps + delay
+
+        phase_x = (frame_times / beat_period * rhythm_x) * 2 * math.pi
+        phase_y = (frame_times / beat_period * rhythm_y) * 2 * math.pi
+
+        pattern_fn_x = _PATTERNS[pattern_x]
+        pattern_fn_y = _PATTERNS[pattern_y]
+
+        offset_x = pattern_fn_x(phase_x, width, rate_x)
+        offset_y = pattern_fn_y(phase_y, height, rate_y)
+
+        coords_x = start_x + offset_x
+        coords_y = start_y + offset_y
+
+        # Build track data: [1 track, num_frames, 2]
+        point_xy = torch.stack([coords_x, coords_y], dim=-1).unsqueeze(0)
+        vis = torch.ones(1, num_frames, dtype=torch.bool, device=device)
+
+        # Rendering (ported from LTXVDrawTracks)
+        rw, rh, sx, sy = _render_resolution(width, height, _REF_SHORT_SIDE)
+        scaled_xy = point_xy.clone()
+        scaled_xy[..., 0] *= sx
+        scaled_xy[..., 1] *= sy
+
+        max_d = 2 * _MAX_RADIUS + 3
+        half_d = max_d // 2
+        offsets = torch.arange(max_d, device=device) - half_d
+        oy, ox = torch.meshgrid(offsets, offsets, indexing="ij")
+        template_dist_sq = oy.float().square() + ox.float().square()
+
+        render_frames = torch.zeros(num_frames, rh, rw, 3, device=device)
+
+        for t in range(num_frames):
+            tau_min = max(0, t - _MAX_TRAIL)
+            window = t - tau_min + 1
+
+            active_xy = scaled_xy[:, tau_min:t + 1]
+            active_vis = vis[:, tau_min:t + 1]
+
+            ages = torch.arange(
+                window - 1, -1, -1, device=device, dtype=torch.float32
+            )
+            ratios = 1.0 - ages / _MAX_TRAIL
+            radii = _MIN_RADIUS + (_MAX_RADIUS - _MIN_RADIUS) * ratios
+            colors = _age_color_batch(ratios, device)
+
+            flat_xy = active_xy.reshape(-1, 2)
+            flat_vis = active_vis.reshape(-1)
+            flat_radii = radii.unsqueeze(0).expand(1, -1).reshape(-1)
+            flat_colors = colors.unsqueeze(0).expand(1, -1, -1).reshape(-1, 3)
+
+            idx = flat_vis.nonzero(as_tuple=True)[0]
+            if idx.shape[0] == 0:
+                continue
+
+            pts = flat_xy[idx]
+            r = flat_radii[idx]
+            c = flat_colors[idx]
+
+            flat_ages = ages.unsqueeze(0).expand(1, -1).reshape(-1)
+            sort_order = flat_ages[idx].argsort(descending=True)
+            pts = pts[sort_order]
+            r = r[sort_order]
+            c = c[sort_order]
+
+            _rasterise_circles(
+                render_frames[t], pts, r, c,
+                template_dist_sq, half_d, max_d, rh, rw,
+            )
+
+        # Downscale to target resolution
+        out = torch.nn.functional.interpolate(
+            render_frames.permute(0, 3, 1, 2),
+            size=(height, width),
+            mode="bilinear",
+            align_corners=False,
+        ).permute(0, 2, 3, 1)
+
+        # RGB -> BGR to match IC-LoRA training data format
+        out = out[..., [2, 1, 0]]
+        return io.NodeOutput(out.half())
+
+
+# ---------------------------------------------------------------------------
+# BPMDetector Node
+# ---------------------------------------------------------------------------
+
+class BPMDetector(io.ComfyNode):
+    """Detect BPM from audio input."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BPMDetector",
+            category="rhythm/audio",
+            display_name="BPM Detector",
+            description="Detect BPM (tempo) from audio input using beat tracking.",
+            inputs=[
+                io.Audio.Input(
+                    "audio",
+                    tooltip="Audio input from Load Audio node.",
+                ),
+            ],
+            outputs=[
+                io.Float.Output("bpm"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, audio) -> io.NodeOutput:
+        import librosa
+
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        # Convert to mono numpy array
+        # waveform shape: [1, channels, samples]
+        audio_np = waveform.squeeze(0).mean(dim=0).cpu().numpy()
+
+        # Detect BPM
+        tempo, _ = librosa.beat.beat_track(y=audio_np, sr=sample_rate)
+
+        # librosa may return an array; extract scalar
+        if hasattr(tempo, "__len__"):
+            bpm_value = float(tempo[0]) if len(tempo) > 0 else 120.0
+        else:
+            bpm_value = float(tempo)
+
+        return io.NodeOutput(bpm_value)
+
+
+# ---------------------------------------------------------------------------
+# Node Registration
+# ---------------------------------------------------------------------------
+
+NODE_CLASS_MAPPINGS = {
+    "DrawRhythmTracks": DrawRhythmTracks,
+    "BPMDetector": BPMDetector,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DrawRhythmTracks": "Draw Rhythm Tracks",
+    "BPMDetector": "BPM Detector",
+}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9668f46
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,14 @@
+[project]
+name = "ComfyUI-Rhythm-Tracks"
+description = "BPM-synced sparse track point generator and BPM detector for ComfyUI"
+version = "0.1.0"
+license = {file = "LICENSE"}
+dependencies = ["torch", "numpy", "librosa"]
+
+[project.urls]
+Repository = "https://github.com/grmchn/ComfyUI-Rhythm-Tracks.git"
+
+[tool.comfy]
+PublisherId = "gearmachine"
+DisplayName = "ComfyUI-Rhythm-Tracks"
+Icon = ""
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..36bd426
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+librosa