README.md

metadata

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 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

4-Reference Comparison

Reference Images	Our Model	Seedance

Citation (Draft)

@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}
}

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