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README.md

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+<div align="center">
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+<h2><a href="https://arxiv.org/abs/2408.10605">MUSES: 3D-Controllable Image Generation via Multi-Modal Agent Collaboration</a></h2>
+
+
+[Yanbo Ding*](https://github.com/DINGYANB),
+[Shaobin Zhuang](https://scholar.google.com/citations?user=PGaDirMAAAAJ&hl=zh-CN&oi=ao), 
+[Kunchang Li](https://scholar.google.com/citations?user=D4tLSbsAAAAJ), 
+[Zhengrong Yue](https://arxiv.org/search/?searchtype=author&query=Zhengrong%20Yue), 
+[Yu Qiao](https://scholar.google.com/citations?user=gFtI-8QAAAAJ&hl), 
+[Yali Wang†](https://scholar.google.com/citations?user=hD948dkAAAAJ)
+
+[![arXiv](https://img.shields.io/badge/arXiv-2408.10605-b31b1b.svg)](https://arxiv.org/abs/2408.10605)
+[![GitHub](https://img.shields.io/badge/GitHub-MUSES-blue?logo=github)](https://github.com/DINGYANB/MUSES)
+[![Hugging Face Space](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/yanboding/MUSES/)
+
+</div>
+
+## 💡 Motivation
+
+Despite recent advancements in text-to-image generation, most existing methods struggle to create images with multiple objects and complex spatial relationships in 3D world. To tackle this limitation, we introduce a generic AI system, namely MUSES, for 3D-controllable image generation from user queries.
+
+<img width="800" alt="image" src="https://github.com/DINGYANB/MUSES/blob/main/assets/demo.png">
+</a>
+
+
+## 🤖 Architecture
+
+Our MUSES realize 3D controllable image generation by developing a progressive workflow with three key components, including:
+1. Layout Manager for 2D-to-3D layout lifting;
+2. Model Engineer for 3D object acquisition and calibration;
+3. Image Artist for 3D-to-2D image rendering.
+
+By mimicking the collaboration of human professionals, this multi-modal agent pipeline facilitates the effective and automatic creation of images with 3D-controllable objects, through an explainable integration of top-down planning and bottom-up generation. 
+
+<img width="800" alt="image" src="https://github.com/DINGYANB/MUSES/blob/main/assets/overview.png">
+</a>
+
+
+## 🔨 Installation
+
+1. Clone this GitHub repository and install the required packages:
+
+    ``` shell
+    git clone https://github.com/DINGYANB/MUSES.git
+    cd MUSES
+
+    conda create -n MUSES python=3.10
+    conda activate MUSES
+
+    pip install -r requirements.txt
+    ```
+
+
+2. Download other required models:
+
+    | Model                |     Storage Path     |    Role    |
+    |----------------------|----------------------|-------------|
+    | [OpenAI ViT-L-14](https://huggingface.co/openai/clip-vit-large-patch14) | `model/CLIP/` | Similarity Comparison |
+    | [Meta Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) | `model/Llama3/` | 3D Layout Planning | 
+    | [stabilityai stable-diffusion-3-medium (SD3)](https://huggingface.co/stabilityai/stable-diffusion-3-medium) | `model/SD3-Base/` | Image Generation |
+    | [InstantX SD3-Canny-ControlNet](https://huggingface.co/InstantX/SD3-Controlnet-Canny) | `model/SD3-ControlNet-Canny/` | Controllable Image Generation |
+    | [examples_features.npy](https://huggingface.co/yanboding/MUSES/upload/main) | `/dataset/` | In-Context Learning |
+    | [finetuned_clip_epoch_20.pth](https://huggingface.co/yanboding/MUSES/upload/main) | `/model/CLIP/` | Orientation Calibration |
+
+    Since our MUSES is a training-free multi-model collaboration system,  feel free to replace the generative models with other competitive ones. For example, we recommend users to replace the Llama-3-8B with more powerful LLMs like [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) and [GPT 4o](https://platform.openai.com/docs/models/gpt-4o).
+
+3. *Optional* Downloads:
+- Download our self-built 3D model shop at this [link](https://huggingface.co/yanboding/MUSES/upload/main), which includes 300 high-quality 3D models, and 1500 images of various objects with different orientations for fine-tuing the [CLIP](https://huggingface.co/openai/clip-vit-base-patch32).
+- Download multiple ControlNets such as [SD3-Tile-ControlNet](https://huggingface.co/InstantX/SD3-Controlnet-Tile), [SDXL-Canny-ControlNet](https://huggingface.co/TheMistoAI/MistoLine), [SDXL-Depth-ControlNet](https://huggingface.co/diffusers/controlnet-zoe-depth-sdxl-1.0), and other image generation models, e.g., [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) with [VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix).
+
+
+## 🌟 Usage
+Use the following command to generate images.
+``` shell
+cd MUSES && bash multi_runs.sh "test_prompt.txt" "test"
+```
+Where the **first argument** is the input txt file containing the prompts in rows, and the **second argument** is the identifier of the current run, which will be appended to the output folder name. For SD3-Canny-ControlNet, each prompt results in 5 images of different control scales.
+
+
+## 📊 Dataset & Benchmark
+### Expanded NSR-1K
+Since the original [NSR-1K](https://github.com/Karine-Huang/T2I-CompBench) dataset lacks layouts in 3D scenes and complex scenes, so we manually add some
+prompts with corresponding layouts.
+Our expanded NSR-1K dataset is in the directory `dataset/NSR-1K-Expand`.
+
+### Benchmark Evaluation
+
+For *T2I-CompBench* evaluation, we follow its official evaluation codes in this [link](https://github.com/Karine-Huang/T2I-CompBench). Note that we choose the best score among the 5 images as the final score. 
+
+Since T2I-CompBench lacks detailed descriptions of complex 3D spatial relationships of multiple objects, we construct our T2I-3DisBench (`dataset/T2I-3DisBench.txt`), which describes diverse 3D image scenes with 50 detailed prompts. 
+For *T2I-3DisBench* evaluation, we employ [Mini-InternVL-2B-1.5](https://huggingface.co/OpenGVLab/Mini-InternVL-Chat-2B-V1-5) to score the generated images from 0.0 to 1.0 across four dimensions, including object count, object orientation, 3D spatial relationship, and camera view. You can download the weights at this [link](https://huggingface.co/OpenGVLab/Mini-InternVL-Chat-2B-V1-5) and put them into the folder `model/InternVL/`.
+
+``` shell
+python inference_code/internvl_vqa.py
+```
+
+After running it, it will output an average score.
+Our MUSES demonstrates state-of-the-art performance on both benchmarks, verifying its effectiveness.
+
+## 💙 Acknowledgement
+MUSES is built upon 
+[Llama](https://github.com/meta-llama/llama3), 
+[NSR-1K](https://github.com/Karine-Huang/T2I-CompBench), 
+[Shap-e](https://github.com/openai/shap-e), 
+[CLIP](https://github.com/openai/CLIP), 
+[SD](https://github.com/Stability-AI/generative-models),
+[ControlNet](https://github.com/lllyasviel/ControlNet).
+We acknowledge these open-source codes and models, and the website [CGTrader](https://www.cgtrader.com) for supporting 3D model free downloads.
+We appreciate as well the valuable insights from the researchers
+at the Shenzhen Institute of Advanced Technology and the
+Shanghai AI Laboratory.
+
+
+## 📝 Citation
+```bib
+@article{ding2024muses,
+      title={MUSES: 3D-Controllable Image Generation via Multi-Modal Agent Collaboration}, 
+      author={Yanbo Ding and Shaobin Zhuang and Kunchang Li and Zhengrong Yue and Yu Qiao and Yali Wang},
+      journal={arXiv preprint arXiv:2408.10605},
+      year={2024},
+}
+```