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
| from copy import copy | |
| from .nodes_registry import comfy_node | |
| class DecoderNoise: | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "vae": ("VAE",), | |
| "timestep": ( | |
| "FLOAT", | |
| { | |
| "default": 0.05, | |
| "min": 0.0, | |
| "max": 1.0, | |
| "step": 0.001, | |
| "tooltip": "The timestep used for decoding the noise.", | |
| }, | |
| ), | |
| "scale": ( | |
| "FLOAT", | |
| { | |
| "default": 0.025, | |
| "min": 0.0, | |
| "max": 1.0, | |
| "step": 0.001, | |
| "tooltip": "The scale of the noise added to the decoder.", | |
| }, | |
| ), | |
| "seed": ( | |
| "INT", | |
| { | |
| "default": 42, | |
| "min": 0, | |
| "max": 0xFFFFFFFFFFFFFFFF, | |
| "tooltip": "The random seed used for creating the noise.", | |
| }, | |
| ), | |
| } | |
| } | |
| FUNCTION = "add_noise" | |
| RETURN_TYPES = ("VAE",) | |
| CATEGORY = "lightricks/LTXV" | |
| def add_noise(self, vae, timestep, scale, seed): | |
| result = copy(vae) | |
| if hasattr(result, "first_stage_model"): | |
| result.first_stage_model.decode_timestep = timestep | |
| result.first_stage_model.decode_noise_scale = scale | |
| result._decode_timestep = timestep | |
| result.decode_noise_scale = scale | |
| result.seed = seed | |
| return (result,) | |