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
| import { app } from "../../../scripts/app.js"; | |
| app.registerExtension({ | |
| name: "pysssss.KSamplerAdvDenoise", | |
| async beforeRegisterNodeDef(nodeType) { | |
| // Add menu options to conver to/from widgets | |
| const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; | |
| nodeType.prototype.getExtraMenuOptions = function (_, options) { | |
| const r = origGetExtraMenuOptions?.apply?.(this, arguments); | |
| let stepsWidget = null; | |
| let startAtWidget = null; | |
| let endAtWidget = null; | |
| for (const w of this.widgets || []) { | |
| if (w.name === "steps") { | |
| stepsWidget = w; | |
| } else if (w.name === "start_at_step") { | |
| startAtWidget = w; | |
| } else if (w.name === "end_at_step") { | |
| endAtWidget = w; | |
| } | |
| } | |
| if (stepsWidget && startAtWidget && endAtWidget) { | |
| options.push( | |
| { | |
| content: "Set Denoise", | |
| callback: () => { | |
| const steps = +prompt("How many steps do you want?", 15); | |
| if (isNaN(steps)) { | |
| return; | |
| } | |
| const denoise = +prompt("How much denoise? (0-1)", 0.5); | |
| if (isNaN(denoise)) { | |
| return; | |
| } | |
| stepsWidget.value = Math.floor(steps / Math.max(0, Math.min(1, denoise))); | |
| stepsWidget.callback?.(stepsWidget.value); | |
| startAtWidget.value = stepsWidget.value - steps; | |
| startAtWidget.callback?.(startAtWidget.value); | |
| endAtWidget.value = stepsWidget.value; | |
| endAtWidget.callback?.(endAtWidget.value); | |
| }, | |
| }, | |
| null | |
| ); | |
| } | |
| return r; | |
| }; | |
| }, | |
| }); | |