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: 1,599 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 | import re
class StringFunction:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"action": (["append", "replace"], {}),
"tidy_tags": (["yes", "no"], {}),
},
"optional": {
"text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"text_c": ("STRING", {"multiline": True, "dynamicPrompts": False})
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "exec"
CATEGORY = "utils"
OUTPUT_NODE = True
def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):
tidy_tags = tidy_tags == "yes"
out = ""
if action == "append":
out = (", " if tidy_tags else "").join(filter(None, [text_a, text_b, text_c]))
else:
if text_c is None:
text_c = ""
if text_b.startswith("/") and text_b.endswith("/"):
regex = text_b[1:-1]
out = re.sub(regex, text_c, text_a)
else:
out = text_a.replace(text_b, text_c)
if tidy_tags:
out = re.sub(r"\s{2,}", " ", out)
out = out.replace(" ,", ",")
out = re.sub(r",{2,}", ",", out)
out = out.strip()
return {"ui": {"text": (out,)}, "result": (out,)}
NODE_CLASS_MAPPINGS = {
"StringFunction|pysssss": StringFunction,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"StringFunction|pysssss": "String Function 🐍",
}
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