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arXiv · 2311.16424

Manifold Preserving Guided Diffusion

Yutong He·Naoki Murata·Chieh-Hsin Lai·Yuhta Takida·Toshimitsu Uesaka·Dongjun Kim·Wei-Hsiang Liao·Yuki Mitsufuji·J. Zico Kolter·Ruslan Salakhutdinov·Stefano Ermon
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Abstract

Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training. In this paper, we propose Manifold Preserving Guided Diffusion (MPGD), a training-free conditional generation framework that leverages pretrained diffusion models and off-the-shelf neural networks with minimal additional inference cost for a broad range of tasks. Specifically, we leverage the manifold hypothesis to refine the guided diffusion steps and introduce a shortcut algorithm in the process. We then propose two methods for on-manifold training-free guidance using pre-trained autoencoders and demonstrate that our shortcut inherently preserves the manifolds when applied to latent diffusion models. Our experiments show that MPGD is efficient and effective for solving a variety of conditional generation applications in low-compute settings, and can consistently offer up to 3.8x speed-ups with the same number of diffusion steps while maintaining high sample quality compared to the baselines.

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Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon. 2023-11-28. Manifold Preserving Guided Diffusion. https://arxiv.org/abs/2311.16424

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