This model implements a novel approach to multi-reference video generation using **Multiple Subject Reference (MSR)**. Instead of introducing additional encoder branches or fusion modules, we transform multiple static reference images into a pseudo-video sequence that shares the same representation space as the target video. This LoRA requires the **ComfyUI-Licon-MSR** plugin for ComfyUI. A sample workflow is included in the model files for easy testing and experimentation. Beyond identity preservation, the model can compose references according to textual relationship descriptions:

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LTX-2.5-Multiple-Subject-Reference

LiconStudio

V2 improves visual consistency, character motion, and camera behavior, and adds audio reference support. Environmental sound generation currently requires **background music in the voice-reference audio**. Without it, environmental sounds may not be generated correctly. **Licon MSR V2** is a multi-reference LoRA trained for **LTX-2.5**, with support for both visual and audio references.

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Ltx2.3-VBVR-lora-I2V

LiconStudio

**To ensure training quality, we preprocessed the full 1,000,000 videos from the official dataset and randomly sample during training to maintain data diversity. We adopt the official parameters with batch_size=16 and rank=32 to prevent catastrophic forgetting caused by excessively large rank.** The VBVR dataset contains 100 reasoning task categories, with ~10,000 variants per task, totaling ~1M videos. Main task types include: This model is trained on the VBVR (Video Benchmark for Video Reasoning) dataset from video-reason.com.

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Minimax-H3-VBVR

LiconStudio

**Licon VBVR** is an image-to-video LoRA for **MiniMax-H3**, trained on VBVR data rebuilt and reprocessed specifically for H3. It improves object consistency and character action logic, reducing unintended object duplication, clipping, and interpenetration during motion and interactions. I rebuilt the VBVR training data around H3's characteristics, adapting data preparation and preprocessing to its training pipeline. The goal is to transfer structured motion and interaction patterns into practical video generation, with a focus on object consistency, spatial relationships, and action logic.

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