LTX-2.3-Multiple-Subject-Reference

Licon MSR V2 for LTX-2.3

What's New in V2

Compared with V1, Licon MSR V2 introduces significant improvements in three key areas:

1. Improved Consistency

• Better preservation of character identity, clothing, objects, and scene details
• More consistent appearance across frames
• Improved alignment between multiple reference images and the generated video
• Reduced identity drift and reference attribute loss

2. Improved Stability

• More reliable results across repeated sampling runs
• Reduced visual artifacts, flickering, and temporal inconsistencies
• More stable generation in complex multi-subject compositions
• Improved handling of motion and interactions between subjects

3. Improved Scene Logic

• Better understanding of spatial and action relationships described in prompts
• More natural subject positioning and interaction
• Improved temporal progression from the beginning to the end of a video
• More coherent composition of characters, objects, and backgrounds

Overview

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.

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 the token level instead of compressing them into a single embedding
• Native self-attention retrieval: Target video tokens directly access reference tokens through the model's existing self-attention mechanism, with no additional architectural components required
• In-context conditioning: References serve as visual memory within the main token sequence rather than as external conditioning inputs

Flexible Reference Composition

• 2 to 5 reference images: Supports varying numbers of reference inputs with increasing composition complexity
• Complementary semantic roles: Each reference image can provide different information:
  • Subject identity
  • Object or prop details
  • Scene or background
  • Local textures
  • Multiple viewpoints

What It Can Do

Identity Preservation Across References

Generate videos in which multiple reference identities are simultaneously preserved:

• Multiple characters from different reference images
• Character and object combinations
• Object and scene compositions

Relation-Based Composition

Beyond identity preservation, the model can compose references according to textual relationship descriptions:

• Action interactions, such as handing, picking up, or pushing
• Spatial relationships, such as left and right or foreground and background
• Temporal event structures, such as start → process → result

Cross-Reference Attribute Selection

The model learns to selectively retrieve attributes from different references:

• Face from reference A and clothing from reference B
• Object identity from one reference and pose or position from another
• Background elements from scene references

Usage Tips

• Prompt description: Use concise but accurate descriptions of the reference images. Both excessive and insufficient descriptions may reduce consistency.
• Reference roles: Clearly describe the role of each referenced subject, object, or scene in the target video.
• High-motion scenes: 50 fps is recommended for smoother motion coherence.
• Sampling: Complex multi-subject interactions may still benefit from multiple sampling runs.

V1 vs. V2 Comparison

Comparison 1

Comparison 2