Instructions to use BingoG/SpatialAV2AV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use BingoG/SpatialAV2AV with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download BingoG/SpatialAV2AV --local-dir models/SpatialAV2AV hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/SpatialAV2AV/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/SpatialAV2AV/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/SpatialAV2AV/<checkpoint>.safetensors \ --distilled-lora models/SpatialAV2AV/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/SpatialAV2AV/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
test_set_media_1000 — self-contained media for 1000 test samples
Full media for the first 1,000 test samples (pan/rotate trajectories) of the
SpatialAV2AV held-out test split, so the test set is directly usable without the
BingoG/LTX dataset repo.
Why this exists
The test manifests in test_set/ list 11,616 samples drawn from the 116k
all.list split. The dataset repo BingoG/LTX only carries a 4,400-sample debug subset,
so it does not cover this test set. This folder ships real, self-contained media for a
usable 1,000-sample slice (pan/rotate — the trajectories the I3 counterfactual evaluation
needs).
Contents (4,251 files, ~2.24 GB)
| Folder | Count | What |
|---|---|---|
final_json/ |
1000 | metadata JSON per sample (relative media paths inside) |
final_source/ |
1000 | source (condition) video, mp4 with embedded stereo |
final_edit/ |
1000 | edit (target) video, mp4 with embedded stereo |
final_audio_edit/ |
1000 | target stereo audio, wav (used for tILD / rank GT) |
final_audio_src/ |
250 | source stereo audio, wav (shared per origin) |
samples_1000.relative.list |
1 | the 1,000 sample json paths (relative) |
Layout mirrors the speakervid dataset root, so each json's relative paths
(final_source/<clip>.mp4, etc.) resolve against this folder directly.
Usage
from huggingface_hub import snapshot_download
root = snapshot_download("BingoG/SpatialAV2AV", allow_patterns="test_set_media_1000/*")
base = f"{root}/test_set_media_1000"
# each final_json/<clip>.json has relative paths (final_source/…, final_edit/…,
# final_audio_edit/…) that resolve under `base` — point the evaluator there.
Scope note
- These 1,000 are a subset of the full 11,616-sample test set (
test_set/test.list). They are all pan/rotate samples. The remaining test samples (other pan/rotate + fixed/push_in/pull_out) are not included here yet. - Media is real 2-channel/44.1 kHz stereo embedded in the mp4s, plus explicit stereo wavs.