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Enhance model card with metadata, introduction, usage, and citation

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This PR significantly enhances the model card for InstanceAssemble by adding crucial metadata and expanding the descriptive content.

Key improvements include:
- Adding the `pipeline_tag: text-to-image` to improve discoverability and enable the automated Hugging Face inference widget.
- Specifying `library_name: diffusers` to provide a ready-to-use code snippet for users, based on compatibility evidence with SD3/Flux models.
- Incorporating the model's introduction, a visual teaser, and practical usage examples with code snippets directly from the GitHub README, making it easier for users to understand and get started with the model.
- Adding the paper citation from the GitHub repository.
- Retaining the existing license and GitHub repository link.

Please review these updates.

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  1. README.md +42 -1
README.md CHANGED
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  ---
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  license: apache-2.0
 
 
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  ---
 
 
 
 
 
 
 
 
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  This repository contains the model used in [InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention](https://arxiv.org/abs/2509.16691).
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- Code: https://github.com/FireRedTeam/InstanceAssemble.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ pipeline_tag: text-to-image
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+ library_name: diffusers
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  ---
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+
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+ # InstanceAssemble
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+ > Official implementation of "InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention" (NeurIPS 2025).
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+
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+ <p align="center">
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+ <img src="https://github.com/FireRedTeam/InstanceAssemble/raw/main/fig/teaser.jpg" alt="Teaser of InstanceAssemble" width="800">
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+ </p>
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+
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  This repository contains the model used in [InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention](https://arxiv.org/abs/2509.16691).
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+ ## Introduction
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+
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+ InstanceAssemble is a lightweight framework for Layout-to-Image generation that enables precise spatial control. We also introduce DenseLayout and Layout Grounding Score (LGS) for rigorous evaluation, where InstanceAssemble achieves state-of-the-art performance on both sparse and dense layouts.
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+
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+ For the official code and more details, please refer to the GitHub repository: [https://github.com/FireRedTeam/InstanceAssemble](https://github.com/FireRedTeam/InstanceAssemble).
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+
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+ ## Usage
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+
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+ ### Inference
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+ ```bash
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+ # sd3 based
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+ python inference.py --model_type sd3 --input_json ./demo/bigchair.json
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+ # flux based
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+ python inference.py --model_type fluxdev --input_json ./demo/bigchair.json
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+ python inference.py --model_type fluxschnell --input_json ./demo/bigchair.json
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+ ```
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+
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+ ### Streamlit demo
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+ ```bash
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+ streamlit run demo.py
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+ ```
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+
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+ ## Citation
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+
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+ ```
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+ @article{xiang2025instanceassemble,
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+ title={InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention},
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+ author={Qiang Xiang and Shuang Sun and Binglei Li and Dejia Song and Huaxia Li and Nemo Chen and Xu Tang and Yao Hu and Junping Zhang},
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+ journal={arXiv preprint arXiv:2509.16691},
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+ year={2025},
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+ }
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+ ```