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 dataclasses import dataclass | |
| from typing import Any, Callable, Mapping | |
| from .float import ( | |
| FLOAT_UNARY_OPERATIONS, | |
| FLOAT_UNARY_CONDITIONS, | |
| FLOAT_BINARY_OPERATIONS, | |
| FLOAT_BINARY_CONDITIONS, | |
| ) | |
| from .types import Number | |
| DEFAULT_NUMBER = ("NUMBER", {"default": 0.0}) | |
| class NumberUnaryOperation: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_UNARY_OPERATIONS.keys()),), | |
| "a": DEFAULT_NUMBER, | |
| } | |
| } | |
| RETURN_TYPES = ("NUMBER",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/number" | |
| def op(self, op: str, a: Number) -> tuple[float]: | |
| return (FLOAT_UNARY_OPERATIONS[op](float(a)),) | |
| class NumberUnaryCondition: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_UNARY_CONDITIONS.keys()),), | |
| "a": DEFAULT_NUMBER, | |
| } | |
| } | |
| RETURN_TYPES = ("BOOL",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/Number" | |
| def op(self, op: str, a: Number) -> tuple[bool]: | |
| return (FLOAT_UNARY_CONDITIONS[op](float(a)),) | |
| class NumberBinaryOperation: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_BINARY_OPERATIONS.keys()),), | |
| "a": DEFAULT_NUMBER, | |
| "b": DEFAULT_NUMBER, | |
| } | |
| } | |
| RETURN_TYPES = ("NUMBER",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/number" | |
| def op(self, op: str, a: Number, b: Number) -> tuple[float]: | |
| return (FLOAT_BINARY_OPERATIONS[op](float(a), float(b)),) | |
| class NumberBinaryCondition: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_BINARY_CONDITIONS.keys()),), | |
| "a": DEFAULT_NUMBER, | |
| "b": DEFAULT_NUMBER, | |
| } | |
| } | |
| RETURN_TYPES = ("BOOL",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/float" | |
| def op(self, op: str, a: Number, b: Number) -> tuple[bool]: | |
| return (FLOAT_BINARY_CONDITIONS[op](float(a), float(b)),) | |
| NODE_CLASS_MAPPINGS = { | |
| "CM_NumberUnaryOperation": NumberUnaryOperation, | |
| "CM_NumberUnaryCondition": NumberUnaryCondition, | |
| "CM_NumberBinaryOperation": NumberBinaryOperation, | |
| "CM_NumberBinaryCondition": NumberBinaryCondition, | |
| } | |