| <div align="center"> | |
| <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) | |
| [](https://arxiv.org/abs/2408.10605) [](https://github.com/DINGYANB/MUSES) [](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://huggingface.co/yanboding/MUSES/blob/main/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://huggingface.co/yanboding/MUSES/blob/main/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}, | |
| } | |
| ``` | |