metadata
base_model:
- Lightricks/LTX-2
datasets:
- Lightricks/Canny-Control-Dataset
language:
- en
license: other
license_name: ltx-2-community-license
license_link: https://www.github.com/Lightricks/LTX-2/LICENSE
pipeline_tag: any-to-any
tags:
- ltx-video
- image-to-video
- text-to-video
pinned: true
LTX-2 19B IC-LoRA Canny Control
This is a Canny control IC-LoRA trained on top of LTX-2-19b, enabling structure-preserving video generation from text and reference frames.
It is based on the LTX-2 foundation model.
- Paper: LTX-2: Efficient Joint Audio-Visual Foundation Model
- Code: GitHub Repository
- Project Page: LTX-2 Playground
What is In-Context LoRA (IC LoRA)?
IC LoRA enables conditioning video generation on reference video frames at inference time, allowing fine-grained video-to-video control on top of a text-to-video, base model. It allows also the usage of an intial image for image-to-video, and generate audio-visual output.
Model Files
ltx-2-19b-ic-lora-canny-control.safetensors
License
See the LTX-2-community-license for full terms.
Model Details
- Base Model: LTX-2-19b Video
- Training Type: IC LoRA
- Control Type: Canny edge conditioning
🔌 Using in ComfyUI
- Copy the LoRA weights into
models/loras. - Use the official IC-LoRA workflow from the LTX-2 ComfyUI repository.
Dataset
The model was trained using the Lightricks/Canny-Control-Dataset.
Citation
@article{hacohen2025ltx2,
title={LTX-2: Efficient Joint Audio-Visual Foundation Model},
author={HaCohen, Yoav and Brazowski, Benny and Chiprut, Nisan and Bitterman, Yaki and Kvochko, Andrew and Berkowitz, Avishai and Shalem, Daniel and Lifschitz, Daphna and Moshe, Dudu and Porat, Eitan and others},
journal={arXiv preprint arXiv:2601.03233},
year={2025}
}
Acknowledgments
- Base model by Lightricks
- Training infrastructure: LTX-2 Community Trainer