Manifold Preserving Guided Diffusion
- URL: http://arxiv.org/abs/2311.16424v1
- Date: Tue, 28 Nov 2023 02:08:06 GMT
- Title: Manifold Preserving Guided Diffusion
- Authors: 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
- Abstract summary: Conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
We propose Manifold Preserving Guided Diffusion (MPGD), a training-free conditional generation framework.
- Score: 121.97907811212123
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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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