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: 881 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 | 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")
@PromptServer.instance.routes.get("/pysssss/autocomplete")
async def get_autocomplete(request):
if os.path.isfile(file):
return web.FileResponse(file)
return web.Response(status=404)
@PromptServer.instance.routes.post("/pysssss/autocomplete")
async def update_autocomplete(request):
with open(file, "w", encoding="utf-8") as f:
f.write(await request.text())
return web.Response(status=200)
@PromptServer.instance.routes.get("/pysssss/loras")
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)))
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