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EpiDiff

[CVPR 2024] EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion

🔥 We have released the training code and model checkpoint of our baseline. We will gradually release the complete code and model.

🏠 Project Page | Paper

img:teaser

Abstract: We propose EpiDiff, a localized interactive multiview diffusion model. At the core of the proposed approach is to insert a lightweight epipolar attention block into the frozen diffusion model, leveraging epipolar constraints to enable cross-view interaction among feature maps of neighboring views. The newly initialized 3D modeling module preserves the original feature distribution of the diffusion model, exhibiting compatibility with a variety of base diffusion models.

🔨 Method Overview

img:pipeline

🔥 Updates

  • 2024-04-02: Training code, inference code and model checkpoint of our baseline EpiDiff are released. The model is trained on 16 fixed views and aggregates F=16 views to target view.

🔧 Installation

Just one command to prepare training and test environments:

pip install -r requirements.txt

Preparation for training

Check our dataset examples here. We uniformly render 96 views with a size of 256×256. The layout comprises 6 concentric circles, each as a layer, with elevation angles set at {−10, 0, 10, 20, 30, 40}. Each circle contains 16 cameras spaced evenly from 0 to 360 in azimuth angle.

|-- 00
|   `-- 000a3d9fa4ff4c888e71e698694eb0b0 # object id
|       |-- render_0000.webp
|       |-- ...
|       `-- meta.json # meta info
|-- caption_test.txt
|-- caption_train.txt
`-- caption_val.txt

You can use huanngzh/render-toolbox to render 3D scenes and may get the same format like our dataset examples.

Training

After preparing the data, you need to modify the config file configs/baseline.yaml to meet your needs. Pay special attention to modifying the path in the data set configuration, like root_dir and caption_path.

To train on one node, please use:

torchrun --nnodes=1 --nproc_per_node=8 train.py --config /path/to/your-config.yaml

To train on multiple machines and multiple cards in a distributed environment, you may use the command like:

torchrun \
--nnodes=$WORLD_SIZE \
--node_rank=$RANK \
--master_addr=$HOSTNAME \
--nproc_per_node=8 \
--master_port=$MASTER_PORT \
train.py --config /path/to/your-config.yaml

During training, you will see the validation results in both output_dir and logger (if you desire wandb like the baseline config).

Inference

Please download our pretrained model checkpoint from Huggingface. Then run the following command to inference:

python inference.py \
--config configs/baseline.yaml \
--ckpt /path/to/checkpoint \
--input_img testset/3D_Dollhouse_Lamp.webp \
--output_dir outputs \
--elevation 30 \
--seed 0 \
--device cuda

🤝 Acknowledgement

We appreciate the open source of the following projects:

Zero123   One-2-3-45   SyncDreamer   threestudio   instant-nsr-pl   Stable Diffusion   diffusers   MVDiffusion   GPNR

We also appreciate the strong tools pytorch lightning and hydra, which helps build a good training framework.

📎 Citation

If you find this repository useful, please consider citing:

@article{huang2023epidiff,
  title={EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion},
  author={Huang, Zehuan and Wen, Hao and Dong, Junting and Wang, Yaohui and Li, Yangguang and Chen, Xinyuan and Cao, Yan-Pei and Liang, Ding and Qiao, Yu and Dai, Bo and others},
  journal={arXiv preprint arXiv:2312.06725},
  year={2023}
}