Instructions to use LiconStudio/LTX-2.3-Multiple-Subject-Reference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LiconStudio/LTX-2.3-Multiple-Subject-Reference with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LiconStudio/LTX-2.3-Multiple-Subject-Reference", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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README.md
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library_name: diffusers
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# Multiple Subject Reference IC-LoRA (Test Version)
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> ⚠️ **This is a test version released for feedback collection to guide future optimization.**
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## 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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- Object identity from one reference, pose/position from another
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- Background elements from scene references
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##
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- **High-motion limb distortion**: Significant degradation in limb quality during fast or complex motion sequences
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- **Slight object consistency loss**: Minor identity drift for objects throughout the video duration
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## Results Showcase
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###
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| <img src="validation/1_1.png" width="120"> <img src="validation/1_2.png" width="120"> | [▶ Play](validation/1_video.mp4) | [▶ Play](validation/1_video_seed.mp4) |
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| <img src="validation/2_1.png" width="80"> <img src="validation/2_2.png" width="80"> <img src="validation/2_3.png" width="80"> <img src="validation/2_4.png" width="80"> | [▶ Play](validation/2_video.mp4) | [▶ Play](validation/2_video_seed.mp4) |
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library_name: diffusers
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## 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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- Object identity from one reference, pose/position from another
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- Background elements from scene references
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## Usage Tips (V1 Version)
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- **Prompt description**: Requires concise but accurate description of reference images. Over-description or under-description both lead to consistency degradation
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- **High-motion scenes**: 50fps recommended to ensure smooth motion coherence
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- **Generation reliability**: Typically requires 2-3 sampling runs to achieve accurate results
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## Results Showcase
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### V1 Version
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Character references + scene background composition:
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| Reference Images | Generated Video |
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| <img src="validition_v1/01/1.jpg" width="80"> <img src="validition_v1/01/2.jpg" width="80"> <img src="validition_v1/01/bg.png" width="80"> | <video src="validition_v1/01/video.mp4" controls width="400"></video> |
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| <img src="validition_v1/07/1.jpg" width="80"> <img src="validition_v1/07/2.jpg" width="80"> <img src="validition_v1/07/bg.png" width="80"> | <video src="validition_v1/07/video.mp4" controls width="400"></video> |
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| <img src="validition_v1/05/1.png" width="70"> <img src="validition_v1/05/2.png" width="70"> <img src="validition_v1/05/5.png" width="70"> <img src="validition_v1/05/bg.png" width="70"> | <video src="validition_v1/05/video.mp4" controls width="400"></video> |
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