Manifold Preserving Guided Diffusion

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 3.8× speed-ups with the same number of diffusion steps while maintaining high sample quality compared to the baselines.

Authors

• Yutong He
• Naoki Murata
• Chieh-Hsin Lai
• Yuhta Takida
• Toshimitsu Uesaka
• Dongjun Kim
• Wei-Hsiang Liao 0001
• Yuki Mitsufuji
• J. Zico Kolter
• Ruslan Salakhutdinov
• Stefano Ermon

Keywords

• generative model
• diffusion model
• controllable generation