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: 922 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 | import { app } from "../../../scripts/app.js";
const id = "pysssss.UseNumberInputPrompt";
const ext = {
name: id,
async setup(app) {
const prompt = LGraphCanvas.prototype.prompt;
const setting = app.ui.settings.addSetting({
id,
name: "🐍 Use number input on value entry",
defaultValue: false,
type: "boolean",
});
LGraphCanvas.prototype.prompt = function () {
const dialog = prompt.apply(this, arguments);
if (setting.value && typeof arguments[1] === "number") {
// If this should be a number then update the imput
const input = dialog.querySelector("input");
input.type = "number";
// Add constraints
const widget = app.canvas.node_widget?.[1];
if (widget?.options) {
for (const prop of ["min", "max", "step"]) {
if (widget.options[prop]) input[prop] = widget.options[prop];
}
}
}
return dialog;
};
},
};
app.registerExtension(ext);
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