| --- |
| license: apache-2.0 |
| tags: |
| - video-generation |
| - multi-reference |
| - LTX-2.3 |
| base_model: |
| - Lightricks/LTX-2.3 |
| github: https://github.com/liconstudio/ComfyUI-Licon-MSR |
| library_name: diffusers |
| --- |
| |
| # Licon MSR V2 for LTX-2.3 |
|
|
| ## What's New in V2 |
|
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| Compared with V1, **Licon MSR V2** introduces significant improvements in three key areas: |
|
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| ### 1. Improved Consistency |
|
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| - 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 |
|
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| - 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 |
|
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| - 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 |
|
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| 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. |
|
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| ## Usage |
|
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| 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 |
|
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| ### Multi-Reference Visual Memory |
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| - **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 |
|
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| ### Flexible Reference Composition |
|
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| - **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 |
|
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| ### Identity Preservation Across References |
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| Generate videos in which multiple reference identities are simultaneously preserved: |
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| - Multiple characters from different reference images |
| - Character and object combinations |
| - Object and scene compositions |
|
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| ### Relation-Based Composition |
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| Beyond identity preservation, the model can compose references according to textual relationship descriptions: |
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| - 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 |
|
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| ### Cross-Reference Attribute Selection |
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| The model learns to selectively retrieve attributes from different references: |
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| - 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 |
|
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| ## Usage Tips |
|
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| - **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 |
|
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| ### Comparison 1 |
|
|
| | V1 | V2 | |
| |:---:|:---:| |
| | [▶ Play V1](Validition_V2/01/V1_1.mp4) | [▶ Play V2](Validition_V2/01/V2.mp4) | |
|
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| ### Comparison 2 |
|
|
| | V1 | V2 | |
| |:---:|:---:| |
| | [▶ Play V1](Validition_V2/02/V1.mp4) | [▶ Play V2](Validition_V2/02/V2.mp4) |
