Instructions to use Murphyyyy/RIGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX-2
How to use Murphyyyy/RIGen with LTX-2:
# 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 Murphyyyy/RIGen --local-dir models/RIGen 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/RIGen/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/RIGen/<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/RIGen/<checkpoint>.safetensors \ --distilled-lora models/RIGen/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/RIGen/<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
RIGen
RIGen checkpoint for autoregressive video generation.
Files
weights/model.safetensors: BF16 inference checkpoint.weights/model_fp32.safetensors: FP32 checkpoint.configs/eval_config.yaml: model and inference configuration.
The checkpoint uses the LTX-2.3 model architecture and the RIGen Stage-2 AR training setup.
License
The model is distributed under the LTX-2.x Community License Agreement in LICENSE.
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