README.md

BELM: High-quality Exact Inversion sampler of Diffusion Models 🏆

This repository is the official implementation of the NeurIPS 2024 paper: "BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models"

Keywords: Diffusion Model, Exact Inversion, ODE Solver

Fangyikang Wang¹, Hubery Yin², Yuejiang Dong³, Huminhao Zhu¹,
Chao Zhang¹, Hanbin Zhao¹, Hui Qian¹, Chen Li²

¹Zhejiang University ²WeChat, Tencent Inc. ³Tsinghua University

     

🆕 What's New?

🔥 We use the thought of bidirectional explicit to enable exact inversion

Schematic description of DDIM (left) and BELM (right). DDIM uses $\mathbf{x}_i$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_i,i)$ to calculate $\mathbf{x}_{i-1}$ based on a linear relation between $\mathbf{x}_i$, $\mathbf{x}_{i-1}$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_i,i)$ (represented by the blue line). However, DDIM inversion uses $\mathbf{x}_{i-1}$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_{i-1},i-1)$ to calculate $\mathbf{x}_{i}$ based on a different linear relation represented by the red line. This mismatch leads to the inexact inversion of DDIM. In contrast, BELM seeks to establish a linear relation between $\mathbf{x}_{i-1}$, $\mathbf{x}_i$, $\mathbf{x}_{i+1}$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_{i}, i)$ (represented by the green line). BELM and its inversion are derived from this unitary relation, which facilitates the exact inversion. Specifically, BELM uses the linear combination of $\mathbf{x}_i$, $\mathbf{x}_{i+1}$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_{i},i)$ to calculate $\mathbf{x}_{i-1}$, and the BELM inversion uses the linear combination of $\mathbf{x}_{i-1}$, $\mathbf{x}_i$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_{i},i)$ to calculate $\mathbf{x}_{i+1}$. The bidirectional explicit constraint means this linear relation does not include the derivatives at the bidirectional endpoint, that is, $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_{i-1},i-1)$ and $\boldsymbol{\varepsilon}_\theta(\mathbf{x}_{i+1},i+1)$.

🔥 We introduce a generic formulation of the exact inversion samplers, BELM.

the general k-step BELM:

$$\bar{\mathbf{x}}_{i-1} = \sum_{j=1}^{k} a_{i,j}\cdot \bar{\mathbf{x}}_{i-1+j} +\sum_{j=1}^{k-1}b_{i,j}\cdot h_{i-1+j}\cdot\bar{\boldsymbol{\varepsilon}}_\theta(\bar{\mathbf{x}}_{i-1+j},\bar{\sigma}_{i-1+j}).$$

2-step BELM:

$$\bar{\mathbf{x}}_{i-1} = a_{i,2}\bar{\mathbf{x}}_{i+1} +a_{i,1}\bar{\mathbf{x}}_{i} + b_{i,1} h_i\bar{\boldsymbol{\varepsilon}}_\theta(\bar{\mathbf{x}}_i,\bar{\sigma}_i).$$

🔥 We derive the optimal coefficients for BELM via LTE minimization.

Proposition The LTE $\tau_i$ of BELM diffusion sampler, which is given by $\tau_i = \bar{\mathbf{x}}(t_{i-1}) - a_{i,2}\bar{\mathbf{x}}(t_{i+1}) -a_{i,1}\bar{\mathbf{x}}(t_{i}) - b_{i,1} h_i\bar{\boldsymbol{\varepsilon}}_\theta(\bar{\mathbf{x}}(t_i),\bar{\sigma}_i)$, can be accurate up to $\mathcal{O}\left({(h_{i}+h_{i+1})}^3\right)$ when formulae are designed as $a_{i,1} = \frac{h_{i+1}^2 - h_i^2}{h_{i+1}^2}$,$a_{i,2}=\frac{h_i^2}{h_{i+1}^2}$,$b_{i,1}=- \frac{h_i+h_{i+1}}{h_{i+1}} $.

where $h_i = \frac{\sigma_i}{\alpha_i}-\frac{\sigma_{i-1}}{\alpha{i-1}}$

the Optimal-BELM (O-BELM) sampler:

$$\mathbf{x}_{i-1} = \frac{h_i^2}{h_{i+1}^2}\frac{\alpha_{i-1}}{\alpha_{i+1}}\mathbf{x}_{i+1} +\frac{h_{i+1}^2 - h_i^2}{h_{i+1}^2}\frac{\alpha_{i-1}}{\alpha_{i}}\mathbf{x}_{i} - \frac{h_i(h_i+h_{i+1})}{h_{i+1}}\alpha_{i-1}\boldsymbol{\varepsilon}_\theta(\mathbf{x}_i,i).$$

The inversion of O-BELM diffusion sampler writes:

$$\mathbf{x}_{i+1}= \frac{h_{i+1}^2}{h_i^2}\frac{\alpha_{i+1}}{\alpha_{i-1}}\mathbf{x}_{i-1} + \frac{h_i^2-h_{i+1}^2}{h_i^2}\frac{\alpha_{i+1}}{\alpha_{i}}\mathbf{x}_{i}+\frac{h_{i+1}(h_i+h_{i+1})}{h_i}\alpha_{i+1} \boldsymbol{\varepsilon}_\theta(\mathbf{x}_i,i).$$

👨🏻‍💻 Run the code

1) Get start

• Python 3.8.12
• CUDA 11.7
• NVIDIA A100 40GB PCIe
• Torch 2.0.0
• Torchvision 0.14.0

Please follow diffusers to install diffusers.

2) Run

first, please switch to the root directory.

CIFAR10 sampling

python3 ./scripts/cifar10.py --test_num 10 --batch_size 32 --num_inference_steps 100 --sampler_type belm --save_dir YOUR/SAVE/DIR --model_id xxx/ddpm_ema_cifar10

CelebA-HQ sampling

python3 ./scripts/celeba.py --test_num 10 --batch_size 32 --num_inference_steps 100 --sampler_type belm --save_dir YOUR/SAVE/DIR --model_id xxx/ddpm_ema_cifar10

FID evaluation

python3 ./scripts/celeba.py --test_num 10 --batch_size 32 --num_inference_steps 100 --sampler_type belm --save_dir YOUR/SAVE/DIR --model_id xxx/ddpm_ema_cifar10

intrpolation

python3 ./scripts/interpolate.py --test_num 10 --batch_size 1 --num_inference_steps 100  --save_dir YOUR/SAVE/DIR --model_id xx

Reconstruction error calculation

python3 ./scripts/reconstruction.py --test_num 10 --num_inference_steps 100  --directory WHERE/YOUR/IMAGES/ARE --sampler_type belm

Image editing

python3 ./scripts/image_editing.py --num_inference_steps 200 --freeze_step 50 --guidance 2.0  --sampler_type belm --save_dir YOUR/SAVE/DIR --model_id xxxxx/stable-diffusion-v1-5 --ori_im_path images/imagenet_dog_1.jpg --ori_prompt 'A dog' --res_prompt 'A Dalmatian'

🪪 License

This project is licensed under the MIT License - see the LICENSE file for details.

📝 Citation

If our work assists your research, feel free to give us a star ⭐ or cite us using:

@inproceedings{
wang2024belm,
title={{BELM}: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models},
author={Fangyikang Wang and Hubery Yin and Yue-Jiang Dong and Huminhao Zhu and Chao Zhang and Hanbin Zhao and Hui Qian and Chen Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ccQ4fmwLDb}
}

📩 Contact me

My e-mail address:

wangfangyikang@zju.edu.cn