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
File size: 1,533 Bytes
46dc982 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | 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:
@classmethod
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:
@classmethod
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,
}
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