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--- |
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base_model: |
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- Lightricks/LTX-2 |
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datasets: |
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- Lightricks/Canny-Control-Dataset |
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language: |
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- en |
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license: other |
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license_name: ltx-2-community-license |
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license_link: https://www.github.com/Lightricks/LTX-2/LICENSE |
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pipeline_tag: any-to-any |
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tags: |
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- ltx-video |
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- image-to-video |
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- text-to-video |
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pinned: true |
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--- |
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# LTX-2 19B IC-LoRA Canny Control |
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This is a Canny control IC-LoRA trained on top of **LTX-2-19b**, enabling structure-preserving video generation from text and reference frames. |
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It is based on the [LTX-2](https://huggingface.co/papers/2601.03233) foundation model. |
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- **Paper:** [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://huggingface.co/papers/2601.03233) |
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- **Code:** [GitHub Repository](https://github.com/Lightricks/LTX-2) |
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- **Project Page:** [LTX-2 Playground](https://app.ltx.studio/ltx-2-playground/i2v) |
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## What is In-Context LoRA (IC LoRA)? |
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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. |
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It allows also the usage of an intial image for image-to-video, and generate audio-visual output. |
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## Model Files |
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`ltx-2-19b-ic-lora-canny-control.safetensors` |
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## License |
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See the **LTX-2-community-license** for full terms. |
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## Model Details |
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- **Base Model:** LTX-2-19b Video |
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- **Training Type:** IC LoRA |
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- **Control Type:** Canny edge conditioning |
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### 🔌 Using in ComfyUI |
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1. Copy the LoRA weights into `models/loras`. |
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2. Use the official IC-LoRA workflow from the [LTX-2 ComfyUI repository](https://github.com/Lightricks/ComfyUI-LTXVideo/). |
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## Dataset |
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The model was trained using the [Lightricks/Canny-Control-Dataset](https://huggingface.co/datasets/Lightricks/Canny-Control-Dataset/). |
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## Citation |
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```bibtex |
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@article{hacohen2025ltx2, |
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title={LTX-2: Efficient Joint Audio-Visual Foundation Model}, |
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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}, |
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journal={arXiv preprint arXiv:2601.03233}, |
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year={2025} |
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} |
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``` |
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## Acknowledgments |
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- Base model by **Lightricks** |
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- Training infrastructure: **LTX-2 Community Trainer** |
