Skip-BART

Skip-BART is an end-to-end generative model designed for Automatic Stage Lighting Control (ASLC). Unlike traditional rule-based methods, Skip-BART conceptualizes lighting control as a generative task, learning directly from professional lighting engineers to predict vivid, human-like lighting sequences synchronized with music.

This model was presented in the paper Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?.

• Repository: https://github.com/RS2002/Skip-BART
• Dataset: RS2002/RPMC-L2

Model Details

• Model Type: Transformer-based model (BART architecture) with skip connections.
• Task: Stage lighting sequence generation (predicting light hue and intensity).
• Architecture: BART-based structure enhanced with a novel skip-connection mechanism to strengthen the relationship between musical frames and lighting states.
• Input Format: Encoder input (batch_size, length, 512) for audio features; Decoder input (batch_size, length, 2) for lighting parameters.
• Output Format: Hidden states representing lighting control parameters (dimension 1024).

Training Data

The model was trained on the RPMC-L2 dataset, a self-collected dataset containing music and corresponding stage lighting data synchronized within a frame grid.

Usage

Installation

git clone https://huggingface.co/RS2002/Skip-BART

Example Code

The following snippet demonstrates how to load the model and perform a forward pass (requires model.py from the official repository).

import torch
from model import Skip_BART

# Load the model
model = Skip_BART.from_pretrained("RS2002/Skip-BART")

# Example input
x_encoder = torch.rand((2, 1024, 512))
x_decoder = torch.randint(0, 10, (2, 1024, 2))
encoder_attention_mask = torch.zeros((2, 1024))
decoder_attention_mask = torch.zeros((2, 1024))

# Forward pass
output = model(x_encoder, x_decoder, encoder_attention_mask, decoder_attention_mask)
print(output.size())  # Output: [2, 1024, 1024]

Citation

@article{zhao2025automatic,
  title={Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?},
  author={Zhao, Zijian and Jin, Dian and Zhou, Zijing and Zhang, Xiaoyu},
  journal={arXiv preprint arXiv:2506.01482},
  year={2025}
}

Contact

Zijian Zhao: zzhaock@connect.ust.hk