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license: apache-2.0
tags:
- video-generation
- image-to-video
- multi-reference
- ltx-2
base_model: Lightricks/LTX-2.3-22B-Dev
github: https://github.com/liconstudio/ComfyUI-Licon-MSR
---
# Multi-Reference Pseudo-Video Context IC-LoRA (Test Version)
> ⚠️ **This is a test version released for feedback collection to guide future optimization.**
## Overview
This model implements a novel approach to multi-reference video generation using **Pseudo-Video Context (PVC)**. 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.
## Usage
This LoRA requires the **[ComfyUI-Licon-MSR](https://github.com/liconstudio/ComfyUI-Licon-MSR)** plugin for ComfyUI. A sample workflow is included in the model files for easy testing and experimentation.
## Key Features
### Multi-Reference Visual Memory
- **Token-level reference preservation**: Multiple reference images are encoded as video latents, preserving fine-grained visual information at token level rather than compressing into a single embedding
- **Native self-attention retrieval**: The target video tokens directly access reference tokens through the model's existing self-attention mechanism—no new architectural components needed
- **In-context conditioning**: References serve as "visual memory" within the main token sequence, not as external conditioning inputs
### Flexible Reference Composition
- **2 to 5 reference images**: Supports varying numbers of reference inputs with increasing complexity
- **Complementary semantic roles**: Each reference image can carry different information:
- Subject identity
- Object/prop details
- Scene/background
- Local textures
- Multiple viewpoints
## What It Can Do
### Identity Preservation Across References
Generate videos where multiple reference identities are simultaneously preserved:
- Multiple characters from different reference images
- Character + object combinations
- Object + scene compositions
### Relation-Based Composition
Beyond mere identity preservation, the model can compose references based on textual relation descriptions:
- Action interactions (handing, picking up, pushing)
- Spatial relationships (left-right, foreground-background)
- Temporal event structures (start → process → result)
### Cross-Reference Attribute Selection
The model learns to selectively retrieve attributes from different references:
- Face from reference A, clothing from reference B
- Object identity from one reference, pose/position from another
- Background elements from scene references
## Current Issues (Test Version)
- **High-motion limb distortion**: Significant degradation in limb quality during fast or complex motion sequences
- **Slight object consistency loss**: Minor identity drift for objects throughout the video duration
## Results Showcase
### 2-Reference Comparison
| Reference Images | Our Model | Seedance |
|:---:|:---:|:---:|
| ![ref1]() ![ref2]() | ![our_output1]() | ![seedance_output1]() |
### 4-Reference Comparison
| Reference Images | Our Model | Seedance |
|:---:|:---:|:---:|
| ![ref3]() ![ref4]() ![ref5]() ![ref6]() | ![our_output2]() | ![seedance_output2]() |
## Citation (Draft)
```bibtex
@misc{multi-ref-pvc-2025,
title={Multi-Reference Pseudo-Video Context for Video Generation},
author={[Author names]},
year={2025},
note={Work in progress - test version}
}
```
--- |