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: 2,042 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 | from .constants import get_category, get_name
from nodes import LoraLoader
import folder_paths
class RgthreeLoraLoaderStack:
NAME = get_name('Lora Loader Stack')
CATEGORY = get_category()
@classmethod
def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP", ),
"lora_01": (['None'] + folder_paths.get_filename_list("loras"), ),
"strength_01":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_02": (['None'] + folder_paths.get_filename_list("loras"), ),
"strength_02":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_03": (['None'] + folder_paths.get_filename_list("loras"), ),
"strength_03":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_04": (['None'] + folder_paths.get_filename_list("loras"), ),
"strength_04":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}
}
RETURN_TYPES = ("MODEL", "CLIP")
FUNCTION = "load_lora"
def load_lora(self, model, clip, lora_01, strength_01, lora_02, strength_02, lora_03, strength_03, lora_04, strength_04):
if lora_01 != "None" and strength_01 != 0:
model, clip = LoraLoader().load_lora(model, clip, lora_01, strength_01, strength_01)
if lora_02 != "None" and strength_02 != 0:
model, clip = LoraLoader().load_lora(model, clip, lora_02, strength_02, strength_02)
if lora_03 != "None" and strength_03 != 0:
model, clip = LoraLoader().load_lora(model, clip, lora_03, strength_03, strength_03)
if lora_04 != "None" and strength_04 != 0:
model, clip = LoraLoader().load_lora(model, clip, lora_04, strength_04, strength_04)
return (model, clip)
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