Spontaneous Symmetry Breaking in Generative Diffusion Models

This repository is the official implementation of the NeurIPS 2023 paper Spontaneous Symmetry Breaking in Generative Diffusion Models. For a detailed video explanation, see our video paper presentation.

---

We show that the dynamics of diffusion models exhibit a "spontaneous symmetry breaking" phenomenon, dividing the generative dynamics in two distinct phases: 1) A linear steady-state dynamics around a central fixed-point and 2) an attractor dynamics directed towards the data manifold. These two "phases" are separated by the change in stability of the central fixed-point, with the resulting window of instability being responsible for the diversity of the generated samples. In an intuitive sense, the dynamics of a generated sample passes from a phase of equal potentiality, where any (synthetic) datum could be generated, to a denoising phase where the (randomly) "selected" datum is fully denoised. An overview of spontaneous symmetry breaking in diffusion models is illustrated in the figure below:

This phenomenon is clearly observable in the one-dimensional case. A symmetry-breaking event, marked by a significant change in the potential well's shape, occurs upon reaching a critical value. This triggers a split in the potential well, signifying a shift in the dynamics and effectively illustrating these two generative phases.

Our findings challenge the current dominant conception that the generative process of diffusion models is essentially comprised of a single denoising phase. In particular, we show that an accurate simulation of the early dynamics does not significantly contribute to the final generation, since early fluctuations are reverted to the central fixed point. To leverage this insight, we propose a Gaussian late initialization scheme, which significantly improves model performance, achieving up to 3x FID improvements on fast samplers, while also increasing sample diversity, e.g. racial composition of generated CelebA images (samples below for 5 denoising steps), as illustrated below:

Our work offers a new way to understand the generative dynamics of diffusion models that has the potential to bring about higher performance and less biased fast-samplers.

Demostrations and tutorials

To facilitate your understanding of the phenomenon of spontaneous symmetry breaking in diffusion models, we have provided the following examples.

Link	Description
	Symmetry Breaking in 1D diffusion model
	Load our pretrained checkpoints and play with our Gaussian late initialization

Requirements

Dependencies

To install requirements:

pip install -r requirements.txt

You can directly train and evaluate the model on your particular data following the commands below. However, for merely reproducing the results on the paper using the pretrained models, please refer to the section Pre-trained Models

Training

To train the diffusion model on e.g., CIFAR10, run this command:

python main.py --workdir="./results/ddpm_cifar10" --config="configs/ddpm/cifar10.py" --mode="train"

📋 To train the model on other dataset, just replace the config file and the corresponding workdir For MNIST for example python main.py --workdir="./results/ddpm_mnist" --config="configs/ddpm/mnist.py" --mode="train" For CelebA 64x64 : python main.py --workdir="./results/ddpm_celeba64" --config="configs/ddpm/celeba64.py" --mode="train"

This will train a model using the configuration found in configs/ddpm/XXXXX.py. During training, the validation batch size is the same as the training batch size.

Evaluation

Get Stats Files

To evaluate the trained model we first need to obtain the stats files, you can download the files here or by running the following command:

 python main.py --config="configs/ddpm/cifar10.py" --mode="fid_stats"

FID scores

 python main.py --config="configs/ddpm/cifar10.py" --mode="fid_stats"

Pre-trained Models

You can immediately use our pre-trained models by downloading the corresponding files using the links below:

Checkpoint path	FID
results/checkpoints/ddpm_cifar10/	3.08
results/checkpoints/ddpm_celeba64/	3.2

Make sure to download the following files for evaluation

File	location	Description
Stats files	assets/stats	Files contating the statistics (mean, and covariance) for evaluating FID scores.
Checkpoints	results/checkpoints/ddpm_xxxx/checkpoints	Trained diffusion model checkpoint.

where xxxx is the name of the dataset.

Results

Boosting performance of fast samplers

Dataset	n	gls-DDPM	DDPM	gls-DDIM	DDIM	gls-PNDM	PNDM
MNIST	10	4.21	6.75	2.44	4.46	5.02	14.36
	5	6.95	13.25	6.95	13.25	5.11	21.22
	3	11.92	42.63	11.92	42.63	38.23	154.89
CIFAR10	10	28.77	43.35	15.98	19.79	5.90	8.35
	5	42.46	84.82	26.36	44.61	9.55	13.77
	3	57.03	146.95	42.31	109.37	34.20	103.11
CelebA32	10	11.05	26.79	7.27	11.37	2.88	4.92
	5	14.79	40.92	10.83	23.45	4.2	6.61
	3	18.93	59.75	16.24	45.34	28.60	235.87
Imagenet64	10	57.31	65.68	36.25	38.21	27.9	28.27
	5	75.11	99.99	52.11	68.21	33.35	34.86
	3	91.69	145.71	76.92	126.3	50.92	70.58
CelebA64	10	23.79	36.66	15.82	19.37	6.80	8.03
	5	31.24	48.38	22.06	28.51	9.26	10.26
	3	37.05	62.18	29.96	50.304	51.72	171.75

Table 1: Summary of findings regarding image generation quality, as measured by FID scores. The performance of the stochastic DDPM sampler is compared to the deterministic DDIM and PNDM samplers in the vanilla case, as well as our Gaussian late start initialization scheme denoted as “gls”. Results are presented for 3, 5, and 10 denoising steps (denoted as “n”) across diverse datasets.

Miscellaneous

Acknowledgement - This code implementation is partly based on the DDPM implementation by Song et al. (2021).

Contact - Feel free to reach out by sending an email to [email protected] or opening an issue. Your feedback on what's working well or any issues you encounter is valuable!. We are also curious about how you would use this in your project, so do let us know!

References

Please consider citing our paper if you find the code helpful for your research.

@article{
    raya2023spontaneous,  
    title={Spontaneous symmetry breaking in generative diffusion models},  
    author={Gabriel Raya and Luca Ambrogioni},  
    year={2023},  
    journal={arXiv preprint arxiv:2305.19693}  
}