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: 2,450 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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | 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:
@classmethod
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:
@classmethod
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:
@classmethod
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:
@classmethod
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,
}
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