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: 2,163 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 51 52 53 54 55 56 57 58 59 60 61 62 | from server import PromptServer
from aiohttp import web
import os
import inspect
import json
import importlib
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
import pysssss
root_directory = os.path.dirname(inspect.getfile(PromptServer))
workflows_directory = os.path.join(root_directory, "pysssss-workflows")
workflows_directory = pysssss.get_config_value(
"workflows.directory", workflows_directory)
if not os.path.isabs(workflows_directory):
workflows_directory = os.path.abspath(os.path.join(root_directory, workflows_directory))
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
@PromptServer.instance.routes.get("/pysssss/workflows")
async def get_workflows(request):
files = []
for dirpath, directories, file in os.walk(workflows_directory):
for file in file:
if (file.endswith(".json")):
files.append(os.path.relpath(os.path.join(
dirpath, file), workflows_directory))
return web.json_response(list(map(lambda f: os.path.splitext(f)[0].replace("\\", "/"), files)))
@PromptServer.instance.routes.get("/pysssss/workflows/{name:.+}")
async def get_workflow(request):
file = os.path.abspath(os.path.join(
workflows_directory, request.match_info["name"] + ".json"))
if os.path.commonpath([file, workflows_directory]) != workflows_directory:
return web.Response(status=403)
return web.FileResponse(file)
@PromptServer.instance.routes.post("/pysssss/workflows")
async def save_workflow(request):
json_data = await request.json()
file = os.path.abspath(os.path.join(
workflows_directory, json_data["name"] + ".json"))
if os.path.commonpath([file, workflows_directory]) != workflows_directory:
return web.Response(status=403)
if os.path.exists(file) and ("overwrite" not in json_data or json_data["overwrite"] == False):
return web.Response(status=409)
sub_path = os.path.dirname(file)
if not os.path.exists(sub_path):
os.makedirs(sub_path)
with open(file, "w") as f:
f.write(json.dumps(json_data["workflow"]))
return web.Response(status=201)
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