Fixed Point Diffusion Models

Xingjian Bai, Luke Melas-Kyriazi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 9430-9440


We introduce the Fixed Point Diffusion Model (FPDM) a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model transforming the diffusion process into a sequence of closely-related fixed point problems. Combined with a new stochastic training method this approach significantly reduces model size reduces memory usage and accelerates training. Moreover it enables the development of two new techniques to improve sampling efficiency: reallocating computation across timesteps and reusing fixed point solutions between timesteps. We conduct extensive experiments with state-of-the-art models on ImageNet FFHQ CelebA-HQ and LSUN-Church demonstrating substantial improvements in performance and efficiency. Compared to the state-of-the-art DiT model FPDM contains 87% fewer parameters consumes 60% less memory during training and improves image generation quality in situations where sampling computation or time is limited.

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@InProceedings{Bai_2024_CVPR, author = {Bai, Xingjian and Melas-Kyriazi, Luke}, title = {Fixed Point Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {9430-9440} }