AEmotionStudio/Visual_Chronometer

Visual Chronometer — Physical FPS Prediction

Safetensors conversion of the Visual Chronometer model for predicting the Physical Frame Rate (PhyFPS) from video motion patterns.

Model Details

• Architecture: 2+1D VAE encoder + attention-pooled probe + MLP regression head
• Training range: 10–60 FPS
• Output: log(FPS) — take exp() to get predicted PhyFPS
• Input: 30-frame clips at 216×216 resolution, normalized to [-1, 1]
• Parameters: ~170M (683 MB safetensors)

Format

This repo provides the model in safetensors format, converted from the original PyTorch Lightning checkpoint.

File	Size	Format
vc_common_10_60fps.safetensors	683 MB	safetensors

Original

• Paper: Pulse of Motion
• Original weights: xiangbog/Visual_Chronometer
• License: Apache 2.0

Usage

Used by ComfyUI-FFMPEGA as the phyfps no-LLM mode for automated video temporal quality assessment.