Instructions to use qwecja/ltx-ugc-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use qwecja/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 qwecja/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: 2,091 Bytes
f3739d5 | 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 | import json
import os
import folder_paths
import server
from .utils import find_tags
class easyModelManager:
def __init__(self):
self.img_suffixes = [".png", ".jpg", ".jpeg", ".gif", ".webp", ".bmp", ".tiff", ".svg", ".tif", ".tiff"]
self.default_suffixes = [".ckpt", ".pt", ".bin", ".pth", ".safetensors"]
self.models_config = {
"checkpoints": {"suffix": self.default_suffixes},
"loras": {"suffix": self.default_suffixes},
"unet": {"suffix": self.default_suffixes},
}
self.model_lists = {}
def find_thumbnail(self, model_type, name):
file_no_ext = os.path.splitext(name)[0]
for ext in self.img_suffixes:
full_path = folder_paths.get_full_path(model_type, file_no_ext + ext)
if os.path.isfile(str(full_path)):
return full_path
return None
def get_model_lists(self, model_type):
if model_type not in self.models_config:
return []
filenames = folder_paths.get_filename_list(model_type)
model_lists = []
for name in filenames:
model_suffix = os.path.splitext(name)[-1]
if model_suffix not in self.models_config[model_type]["suffix"]:
continue
else:
cfg = {
"name": os.path.basename(os.path.splitext(name)[0]),
"full_name": name,
"remark": '',
"file_path": folder_paths.get_full_path(model_type, name),
"type": model_type,
"suffix": model_suffix,
"dir_tags": find_tags(name),
"cover": self.find_thumbnail(model_type, name),
"metadata": None,
"sha256": None
}
model_lists.append(cfg)
return model_lists
def get_model_info(self, model_type, model_name):
pass
# if __name__ == "__main__":
# manager = easyModelManager()
# print(manager.get_model_lists("checkpoints")) |