Bad-Apple_Unified-Model-Test

Bad Apple Unified Neural Representation

This is a compact coordinate-based neural representation of the complete Bad Apple!! shadow video and its stereo audio.

The checkpoint does not store ordinary video frames or compressed audio. Given a normalized time and pixel coordinate, the network predicts the image brightness. Given a normalized time coordinate with audio conditioning, the same network predicts the stereo waveform.

Generated Result

<!-- Upload your rendered MP4 as demo.mp4, then replace YOUR_USERNAME and YOUR_REPOSITORY below. --> <video controls width="100%"> <source src="https://huggingface.co/Ma7ee7/Bad-Apple_Unified-Model-Test/resolve/main/demo.mp4" type="video/mp4"> Your browser does not support embedded video. </video>

Download or open the generated video

Run

Install the Python requirements and make sure ffmpeg is available on your system. Then render the checkpoint:

python bad_apple_nn.py render model.pt --audio-source generated

The generated MP4 is written to outputs/.

What The Model Is

This is one unified multimodal model, not two independently trained models.

Video and audio share:

• one normalized time representation;
• Fourier coordinate features;
• an interpolated learned time-memory;
• modality conditioning; and
• one residual neural-network trunk.

The shared trunk ends in two small task-specific output heads:

• a video head that predicts grayscale pixel brightness;
• an audio head that predicts two stereo waveform channels.

Separate output heads are necessary because pixels and audio samples have different output shapes, but the representation and main network are shared and trained together in one checkpoint.

Model Details

The inference weights occupy approximately 4.45 MB in FP32. The uploaded training checkpoint may be larger because it contains both regular and exponential-moving-average weights. Rendering uses the EMA weights by default.

Files

Notes

• This is a custom PyTorch model, not a Transformers model.
• It is an implicit neural representation specialized to one video.
• The checkpoint generates both the frames and audio.
• ffmpeg is required to encode and combine the rendered streams.
• This repository is an experimental and educational demonstration. Rights to the original music and animation remain with their respective owners.