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: 5,204 Bytes
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import os
from nodes import LoraLoader, CheckpointLoaderSimple
import folder_paths
from server import PromptServer
from folder_paths import get_directory_by_type
from aiohttp import web
import shutil
@PromptServer.instance.routes.get("/pysssss/view/{name}")
async def view(request):
name = request.match_info["name"]
pos = name.index("/")
type = name[0:pos]
name = name[pos+1:]
image_path = folder_paths.get_full_path(
type, name)
if not image_path:
return web.Response(status=404)
filename = os.path.basename(image_path)
return web.FileResponse(image_path, headers={"Content-Disposition": f"filename=\"{filename}\""})
@PromptServer.instance.routes.post("/pysssss/save/{name}")
async def save_preview(request):
name = request.match_info["name"]
pos = name.index("/")
type = name[0:pos]
name = name[pos+1:]
body = await request.json()
dir = get_directory_by_type(body.get("type", "output"))
subfolder = body.get("subfolder", "")
full_output_folder = os.path.join(dir, os.path.normpath(subfolder))
filepath = os.path.join(full_output_folder, body.get("filename", ""))
if os.path.commonpath((dir, os.path.abspath(filepath))) != dir:
return web.Response(status=400)
image_path = folder_paths.get_full_path(type, name)
image_path = os.path.splitext(
image_path)[0] + os.path.splitext(filepath)[1]
shutil.copyfile(filepath, image_path)
return web.json_response({
"image": type + "/" + os.path.basename(image_path)
})
@PromptServer.instance.routes.get("/pysssss/examples/{name}")
async def get_examples(request):
name = request.match_info["name"]
pos = name.index("/")
type = name[0:pos]
name = name[pos+1:]
file_path = folder_paths.get_full_path(
type, name)
if not file_path:
return web.Response(status=404)
file_path_no_ext = os.path.splitext(file_path)[0]
examples = []
if os.path.isdir(file_path_no_ext):
examples += sorted(map(lambda t: os.path.relpath(t, file_path_no_ext),
glob.glob(file_path_no_ext + "/*.txt")))
if os.path.isfile(file_path_no_ext + ".txt"):
examples += ["notes"]
return web.json_response(examples)
@PromptServer.instance.routes.post("/pysssss/examples/{name}")
async def save_example(request):
name = request.match_info["name"]
pos = name.index("/")
type = name[0:pos]
name = name[pos+1:]
body = await request.json()
example_name = body["name"]
example = body["example"]
file_path = folder_paths.get_full_path(
type, name)
if not file_path:
return web.Response(status=404)
if not example_name.endswith(".txt"):
example_name += ".txt"
file_path_no_ext = os.path.splitext(file_path)[0]
example_file = os.path.join(file_path_no_ext, example_name)
if not os.path.exists(file_path_no_ext):
os.mkdir(file_path_no_ext)
with open(example_file, 'w', encoding='utf8') as f:
f.write(example)
return web.Response(status=201)
@PromptServer.instance.routes.get("/pysssss/images/{type}")
async def get_images(request):
type = request.match_info["type"]
names = folder_paths.get_filename_list(type)
images = {}
for item_name in names:
file_name = os.path.splitext(item_name)[0]
file_path = folder_paths.get_full_path(type, item_name)
if file_path is None:
continue
file_path_no_ext = os.path.splitext(file_path)[0]
for ext in ["png", "jpg", "jpeg", "preview.png", "preview.jpeg"]:
if os.path.isfile(file_path_no_ext + "." + ext):
images[item_name] = f"{type}/{file_name}.{ext}"
break
return web.json_response(images)
class LoraLoaderWithImages(LoraLoader):
RETURN_TYPES = (*LoraLoader.RETURN_TYPES, "STRING",)
RETURN_NAMES = (*getattr(LoraLoader, "RETURN_NAMES",
LoraLoader.RETURN_TYPES), "example")
@classmethod
def INPUT_TYPES(s):
types = super().INPUT_TYPES()
types["optional"] = {"prompt": ("STRING", {"hidden": True})}
return types
def load_lora(self, **kwargs):
prompt = kwargs.pop("prompt", "")
return (*super().load_lora(**kwargs), prompt)
class CheckpointLoaderSimpleWithImages(CheckpointLoaderSimple):
RETURN_TYPES = (*CheckpointLoaderSimple.RETURN_TYPES, "STRING",)
RETURN_NAMES = (*getattr(CheckpointLoaderSimple, "RETURN_NAMES",
CheckpointLoaderSimple.RETURN_TYPES), "example")
@classmethod
def INPUT_TYPES(s):
types = super().INPUT_TYPES()
types["optional"] = {"prompt": ("STRING", {"hidden": True})}
return types
def load_checkpoint(self, **kwargs):
prompt = kwargs.pop("prompt", "")
return (*super().load_checkpoint(**kwargs), prompt)
NODE_CLASS_MAPPINGS = {
"LoraLoader|pysssss": LoraLoaderWithImages,
"CheckpointLoader|pysssss": CheckpointLoaderSimpleWithImages,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoraLoader|pysssss": "Lora Loader 🐍",
"CheckpointLoader|pysssss": "Checkpoint Loader 🐍",
}
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