This repository provides tools and scripts for training and fine-tuning the LTX-Video (LTXV) model, enabling users to create custom video effects and control adapters. It targets researchers and power users interested in advanced video generation and transformation, offering LoRA and full fine-tuning capabilities.

How It Works

The trainer supports LoRA and IC-LoRA (In-Context LoRA) training, allowing for efficient fine-tuning of specific video styles or behaviors. It leverages control models like Depth, Pose, and Canny for video-to-video transformations, enabling precise control over the output based on input conditioning maps. This approach allows for specialized video effects and adaptable generation pipelines.

Quick Start & Requirements

• Installation: Primarily through provided scripts and configurations. Refer to the docs/training_guide.md for detailed instructions.
• Prerequisites: Requires significant computational resources, likely including powerful GPUs with substantial VRAM, and potentially specific CUDA versions. Detailed requirements are available in the documentation.
• Resources: Training custom models can be resource-intensive and time-consuming.

Highlighted Details

• Supports LoRA and IC-LoRA training for custom video effects.
• Includes pretrained control models for Depth, Pose, and Canny edge conditioning.
• Offers examples of LoRA effects like "Cakeify" and "Squish."
• Provides integration with ComfyUI via dedicated nodes.

Maintenance & Community

• Active development with recent updates (July 2025) adding IC-LoRA support and (May 2025) LTXV 13B support.
• Community engagement encouraged via a Discord server.
• Contributions are welcomed through GitHub issues and pull requests.

Licensing & Compatibility

• The repository's license is not explicitly stated in the provided README snippet. Compatibility for commercial or closed-source use requires verification of the specific license terms.

Limitations & Caveats

• The README does not specify the exact license, which is crucial for commercial use.
• Training requires significant computational resources and technical expertise.
• The project is community-driven, and the pace of development or support may vary.