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: 3,429 Bytes
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from typing import Any, Callable, Mapping
DEFAULT_INT = ("INT", {"default": 0})
INT_UNARY_OPERATIONS: Mapping[str, Callable[[int], int]] = {
"Abs": lambda a: abs(a),
"Neg": lambda a: -a,
"Inc": lambda a: a + 1,
"Dec": lambda a: a - 1,
"Sqr": lambda a: a * a,
"Cube": lambda a: a * a * a,
"Not": lambda a: ~a,
"Factorial": lambda a: math.factorial(a),
}
INT_UNARY_CONDITIONS: Mapping[str, Callable[[int], bool]] = {
"IsZero": lambda a: a == 0,
"IsNonZero": lambda a: a != 0,
"IsPositive": lambda a: a > 0,
"IsNegative": lambda a: a < 0,
"IsEven": lambda a: a % 2 == 0,
"IsOdd": lambda a: a % 2 == 1,
}
INT_BINARY_OPERATIONS: Mapping[str, Callable[[int, int], int]] = {
"Add": lambda a, b: a + b,
"Sub": lambda a, b: a - b,
"Mul": lambda a, b: a * b,
"Div": lambda a, b: a // b,
"Mod": lambda a, b: a % b,
"Pow": lambda a, b: a**b,
"And": lambda a, b: a & b,
"Nand": lambda a, b: ~a & b,
"Or": lambda a, b: a | b,
"Nor": lambda a, b: ~a & b,
"Xor": lambda a, b: a ^ b,
"Xnor": lambda a, b: ~a ^ b,
"Shl": lambda a, b: a << b,
"Shr": lambda a, b: a >> b,
"Max": lambda a, b: max(a, b),
"Min": lambda a, b: min(a, b),
}
INT_BINARY_CONDITIONS: Mapping[str, Callable[[int, int], bool]] = {
"Eq": lambda a, b: a == b,
"Neq": lambda a, b: a != b,
"Gt": lambda a, b: a > b,
"Lt": lambda a, b: a < b,
"Geq": lambda a, b: a >= b,
"Leq": lambda a, b: a <= b,
}
class IntUnaryOperation:
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {
"required": {"op": (list(INT_UNARY_OPERATIONS.keys()),), "a": DEFAULT_INT}
}
RETURN_TYPES = ("INT",)
FUNCTION = "op"
CATEGORY = "math/int"
def op(self, op: str, a: int) -> tuple[int]:
return (INT_UNARY_OPERATIONS[op](a),)
class IntUnaryCondition:
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {
"required": {"op": (list(INT_UNARY_CONDITIONS.keys()),), "a": DEFAULT_INT}
}
RETURN_TYPES = ("BOOL",)
FUNCTION = "op"
CATEGORY = "math/int"
def op(self, op: str, a: int) -> tuple[bool]:
return (INT_UNARY_CONDITIONS[op](a),)
class IntBinaryOperation:
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {
"required": {
"op": (list(INT_BINARY_OPERATIONS.keys()),),
"a": DEFAULT_INT,
"b": DEFAULT_INT,
}
}
RETURN_TYPES = ("INT",)
FUNCTION = "op"
CATEGORY = "math/int"
def op(self, op: str, a: int, b: int) -> tuple[int]:
return (INT_BINARY_OPERATIONS[op](a, b),)
class IntBinaryCondition:
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {
"required": {
"op": (list(INT_BINARY_CONDITIONS.keys()),),
"a": DEFAULT_INT,
"b": DEFAULT_INT,
}
}
RETURN_TYPES = ("BOOL",)
FUNCTION = "op"
CATEGORY = "math/int"
def op(self, op: str, a: int, b: int) -> tuple[bool]:
return (INT_BINARY_CONDITIONS[op](a, b),)
NODE_CLASS_MAPPINGS = {
"CM_IntUnaryOperation": IntUnaryOperation,
"CM_IntUnaryCondition": IntUnaryCondition,
"CM_IntBinaryOperation": IntBinaryOperation,
"CM_IntBinaryCondition": IntBinaryCondition,
}
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