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
| from typing import Any, Callable, Mapping | |
| DEFAULT_BOOL = ("BOOLEAN", {"default": False}) | |
| BOOL_UNARY_OPERATIONS: Mapping[str, Callable[[bool], bool]] = { | |
| "Not": lambda a: not a, | |
| } | |
| BOOL_BINARY_OPERATIONS: Mapping[str, Callable[[bool, bool], bool]] = { | |
| "Nor": lambda a, b: not (a or b), | |
| "Xor": lambda a, b: a ^ b, | |
| "Nand": lambda a, b: not (a and b), | |
| "And": lambda a, b: a and b, | |
| "Xnor": lambda a, b: not (a ^ b), | |
| "Or": lambda a, b: a or b, | |
| "Eq": lambda a, b: a == b, | |
| "Neq": lambda a, b: a != b, | |
| } | |
| class BoolUnaryOperation: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": {"op": (list(BOOL_UNARY_OPERATIONS.keys()),), "a": DEFAULT_BOOL} | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/bool" | |
| def op(self, op: str, a: bool) -> tuple[bool]: | |
| return (BOOL_UNARY_OPERATIONS[op](a),) | |
| class BoolBinaryOperation: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(BOOL_BINARY_OPERATIONS.keys()),), | |
| "a": DEFAULT_BOOL, | |
| "b": DEFAULT_BOOL, | |
| } | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/bool" | |
| def op(self, op: str, a: bool, b: bool) -> tuple[bool]: | |
| return (BOOL_BINARY_OPERATIONS[op](a, b),) | |
| NODE_CLASS_MAPPINGS = { | |
| "CM_BoolUnaryOperation": BoolUnaryOperation, | |
| "CM_BoolBinaryOperation": BoolBinaryOperation, | |
| } | |