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Manifold Preserving Guided Diffusion

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arxiv 2311.16424 v1 pith:32GIXJTE submitted 2023-11-28 cs.LG cs.AIcs.CV

Yutong He , Naoki Murata , Chieh-Hsin Lai , Yuhta Takida , Toshimitsu Uesaka , Dongjun Kim , Wei-Hsiang Liao , Yuki Mitsufuji
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J. Zico Kolter Ruslan Salakhutdinov Stefano Ermon
This is my paper · ORCID
classification cs.LGcs.AIcs.CV
keywords diffusionconditionalgenerationguidedmanifoldcostmodelsmpgd
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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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