Update model card with paper, code links and correct pipeline tag

README.md

@@ -1,26 +1,31 @@
 ---
+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
-language:
-- en
-pipeline_tag: text-to-video
-datasets:
-- Lightricks/Canny-Control-Dataset
-base_model:
-- Lightricks/LTX-2
 ---
 
 # 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](https://huggingface.co/papers/2601.03233) foundation model.
+
+- **Paper:** [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://huggingface.co/papers/2601.03233)
+- **Code:** [GitHub Repository](https://github.com/Lightricks/LTX-2)
+- **Project Page:** [LTX-2 Playground](https://app.ltx.studio/ltx-2-playground/i2v)
+
 ## 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.
@@ -42,11 +47,22 @@ See the **LTX-2-community-license** for full terms.
 
 ### 🔌 Using in ComfyUI
 1. Copy the LoRA weights into `models/loras`.
-2. Use the official IC-LoRA workflow from the LTX-2 ComfyUI repository.
+2. Use the official IC-LoRA workflow from the [LTX-2 ComfyUI repository](https://github.com/Lightricks/ComfyUI-LTXVideo/).
 
 ## Dataset
 
-https://huggingface.co/datasets/Lightricks/Canny-Control-Dataset/
+The model was trained using the [Lightricks/Canny-Control-Dataset](https://huggingface.co/datasets/Lightricks/Canny-Control-Dataset/).
+
+## Citation
+
+```bibtex
+@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