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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github: https://github.com/liconstudio/ComfyUI-Licon-MSR
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# Multi-Reference
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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 **
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## Usage
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github: https://github.com/liconstudio/ComfyUI-Licon-MSR
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# Multi-Reference 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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## Usage
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