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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+demo.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
-license: apache-2.0
+library_name: pytorch
+tags:
+  - pytorch
+  - implicit-neural-representation
+  - coordinate-network
+  - multimodal
+  - video
+  - audio
 ---
+
+# 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/YOUR_USERNAME/YOUR_REPOSITORY/resolve/main/demo.mp4" type="video/mp4">
+  Your browser does not support embedded video.
+</video>
+
+[Download or open the generated video](./demo.mp4)
+
+## Run
+
+Install the Python requirements and make sure `ffmpeg` is available on your
+system. Then render the checkpoint:
+
+```bash
+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
+
+| Property | Value |
+|---|---:|
+| Architecture | Unified coordinate neural field (`unified-v3`) |
+| Trainable parameters | 1,111,571 |
+| Training video resolution | 192 x 144 |
+| Rendered resolution at default 3x scale | 576 x 432 |
+| Frames | 6,572 |
+| Frame rate | 30 FPS |
+| Duration | About 3 minutes 39 seconds |
+| Generated audio | 16 kHz stereo |
+| Best checkpoint step | 59,000 / 80,000 |
+| Video pixel accuracy | 99.6% |
+| Silhouette IoU | 0.991 |
+
+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
+
+| File | Purpose |
+|---|---|
+| `model.pt` | Unified video-and-audio checkpoint |
+| `bad_apple_nn.py` | Model definition and renderer |
+| `requirements.txt` | Python dependencies |
+| `demo.mp4` | Example generated result |
+
+## 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.

demo.mp4

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+oid sha256:710bdf4bfebdbb4e610a6201a73f7d0e20fb082d39794bbceed9ae7cde085828
+size 27383988

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0eeb84cb010b6cd1722eb1a2e1050586cddf1af2c6b27f9ae99295f894f17889
+size 8918518

requirements.txt

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+numpy
+opencv-python
+torch