Instructions to use hgjc/ltx-ugc-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use hgjc/ltx-ugc-bundle 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 hgjc/ltx-ugc-bundle --local-dir models/ltx-ugc-bundle 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/ltx-ugc-bundle/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/ltx-ugc-bundle/<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/ltx-ugc-bundle/<checkpoint>.safetensors \ --distilled-lora models/ltx-ugc-bundle/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/ltx-ugc-bundle/<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
| # Hack: string type that is always equal in not equal comparisons | |
| class AnyType(str): | |
| def __ne__(self, __value: object) -> bool: | |
| return False | |
| # Our any instance wants to be a wildcard string | |
| any = AnyType("*") | |
| class ReroutePrimitive: | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": {"value": (any, )}, | |
| } | |
| def VALIDATE_INPUTS(s, **kwargs): | |
| return True | |
| RETURN_TYPES = (any,) | |
| FUNCTION = "route" | |
| CATEGORY = "__hidden__" | |
| def route(self, value): | |
| return (value,) | |
| class MultiPrimitive: | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": {}, | |
| "optional": {"value": (any, )}, | |
| } | |
| def VALIDATE_INPUTS(s, **kwargs): | |
| return True | |
| RETURN_TYPES = (any,) | |
| FUNCTION = "listify" | |
| CATEGORY = "utils" | |
| OUTPUT_IS_LIST = (True,) | |
| def listify(self, **kwargs): | |
| return (list(kwargs.values()),) | |
| NODE_CLASS_MAPPINGS = { | |
| "ReroutePrimitive|pysssss": ReroutePrimitive, | |
| # "MultiPrimitive|pysssss": MultiPrimitive, | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "ReroutePrimitive|pysssss": "Reroute Primitive 🐍", | |
| # "MultiPrimitive|pysssss": "Multi Primitive 🐍", | |
| } | |