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
File size: 310 Bytes
797378a | 1 2 3 4 5 | 6cb4b2292f7fac4fb1020ea5eb2e09faee2d125777433a165e0669dbbcfe5248 train.list
0f56416ce6890c5857145d7c74c77269a738784d1d6b16b891b4abcdac3d47d1 val.list
8bc3e0322d70823ea3cf19533cd756ba4f3c7caf19270eb473aae333e3abef88 test.list
f6ee06b26c9d18e03e3b8a0724e993a8d0dae27470ebd8c9271368dc99de33b0 <input>all.list
|