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: 1,265 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 | # Hack: string type that is always equal in not equal comparisons
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
# Our any instance wants to be a wildcard string
any = AnyType("*")
class ReroutePrimitive:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {"value": (any, )},
}
@classmethod
def VALIDATE_INPUTS(s, **kwargs):
return True
RETURN_TYPES = (any,)
FUNCTION = "route"
CATEGORY = "__hidden__"
def route(self, value):
return (value,)
class MultiPrimitive:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {},
"optional": {"value": (any, )},
}
@classmethod
def VALIDATE_INPUTS(s, **kwargs):
return True
RETURN_TYPES = (any,)
FUNCTION = "listify"
CATEGORY = "utils"
OUTPUT_IS_LIST = (True,)
def listify(self, **kwargs):
return (list(kwargs.values()),)
NODE_CLASS_MAPPINGS = {
"ReroutePrimitive|pysssss": ReroutePrimitive,
# "MultiPrimitive|pysssss": MultiPrimitive,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ReroutePrimitive|pysssss": "Reroute Primitive 🐍",
# "MultiPrimitive|pysssss": "Multi Primitive 🐍",
}
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