Extensions/ComfyUI-PulseOfMotion
ComfyUI Extension

ComfyUI-PulseOfMotion

Predicts Physical FPS (PhyFPS) from video using the Visual Chronometer model from the Pulse of Motion paper. Includes SDPA-optimized attention, device selection, progress tracking, and auto-downloads the model from HuggingFace.

By akashzeno·Created 5 months ago·Updated 5 months ago· 2
akashzeno/ComfyUI-PulseOfMotion
Nodes3
On cloudLocal install
CategoryPulse of Motion
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Updated5 months ago

Nodes (3)

Load Visual Chronometer (Pulse of Motion)

It downloads its own model so you don't have to

Pulse of Motion
Predict PhyFPS

A node that reads how fast your video 'really' moves

Pulse of Motion
Predict PhyFPS (Batch Details)

Every clip's PhyFPS, not just the average

Pulse of Motion
Readme

ComfyUI-PulseOfMotion

ComfyUI nodes for predicting Physical FPS (PhyFPS) from video using the Visual Chronometer model.

Based on the paper "The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics" by the TACO Group.

PhyFPS measures the true temporal resolution of a video from its visual motion dynamics — independent of the container frame rate. This is useful for detecting AI-generated videos, evaluating video quality, and understanding temporal characteristics.

Nodes

| Node | Description | |------|-------------| | Load Visual Chronometer | Loads the VC model checkpoint. Auto-downloads from HuggingFace on first use. Supports device selection (auto/cpu/cuda). | | Predict PhyFPS | Predicts average PhyFPS from video frames using a sliding window. Returns a float and a detailed report. | | Predict PhyFPS (Batch) | Same as above but also returns a per-segment FPS list for analysis. |

Installation

Via ComfyUI-Manager (recommended)

Search for "Pulse of Motion" in ComfyUI-Manager and click Install.

Manual

cd ComfyUI/custom_nodes
git clone https://github.com/akashzeno/ComfyUI-PulseOfMotion.git
cd ComfyUI-PulseOfMotion
pip install -r requirements.txt

Restart ComfyUI after installation.

Model

The checkpoint (vc_common_10_60fps.ckpt) is automatically downloaded from HuggingFace on first use and saved to ComfyUI/models/pulse_of_motion/.

Usage

  1. Add a Load Video (Upload) node (from VideoHelperSuite) to load your video
  2. Add a Load Visual Chronometer node — select the checkpoint and device
  3. Add a Predict PhyFPS node — connect model from the loader and IMAGE from the video loader to images
  4. Add two Preview as Text nodes:
    • Connect phyfps to one for the average FPS value
    • Connect report to another for the detailed per-segment breakdown
  5. Adjust clip_length (default 30) and stride (default 4) as needed, then queue the prompt

Parameters

  • clip_length — Number of frames per analysis clip (default: 30, trained on 30-frame clips)
  • stride — Step size between clips (default: 4). Lower = more clips = smoother average but slower

Example Workflow

Download the example workflow JSON and drag it into ComfyUI to get started.

Optimizations

This implementation uses PyTorch SDPA (scaled_dot_product_attention) for the spatial and cross-attention modules, which automatically dispatches to Flash Attention 2 or memory-efficient attention depending on your GPU. This provides identical accuracy with better speed and memory efficiency compared to the original manual attention implementation.

Credits

License

This project wraps the Visual Chronometer model for ComfyUI. Please refer to the original repository for licensing of the model and weights.