Update README.md

README.md

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----
-license: other
-license_name: ltx-2-community-license
-license_link: https://www.github.com/Lightricks/LTX-2/LICENSE
----
+---
+license: other
+license_name: ltx-2-community-license
+license_link: https://www.github.com/Lightricks/LTX-2/LICENSE
+
+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.
+
+## 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
+1. Copy the LoRA weights into `models/loras`.
+2. Use the official IC-LoRA workflow from the LTX-2 ComfyUI repository.
+
+## Dataset
+
+https://huggingface.co/datasets/Lightricks/Canny-Control-Dataset/
+
+## Acknowledgments
+
+- Base model by **Lightricks**
+- Training infrastructure: **LTX-2 Community Trainer**