## π€ 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.
## π¨ 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).
## π 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.
## π 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.
## π 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.
## π 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},
}
```