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
| from server import PromptServer | |
| from aiohttp import web | |
| import os | |
| import folder_paths | |
| dir = os.path.abspath(os.path.join(__file__, "../../user")) | |
| if not os.path.exists(dir): | |
| os.mkdir(dir) | |
| file = os.path.join(dir, "autocomplete.txt") | |
| async def get_autocomplete(request): | |
| if os.path.isfile(file): | |
| return web.FileResponse(file) | |
| return web.Response(status=404) | |
| async def update_autocomplete(request): | |
| with open(file, "w", encoding="utf-8") as f: | |
| f.write(await request.text()) | |
| return web.Response(status=200) | |
| async def get_loras(request): | |
| loras = folder_paths.get_filename_list("loras") | |
| return web.json_response(list(map(lambda a: os.path.splitext(a)[0], loras))) | |