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Manifold Preserving Guided Diffusion

ICLR 2024 · Image generation › Fast diffusion sampling · approach: Guidance for conditional diffusion · 0 citations (OpenAlex)

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Proposes MPGD, a training-free guided diffusion framework that applies guidance gradients on the clean estimate x0|t (a shortcut) with autoencoder-based manifold projection, yielding faster and higher-fidelity conditional generation.

Problem

Training-free guided diffusion methods for conditional image generation (inverse problems, FaceID guidance, style guidance) are slow, unreliable, and can drive samples off the data manifold, requiring step-size tuning or many diffusion steps.

Approach

Leverage the manifold hypothesis: apply guidance gradients to the clean-data estimate x0|t instead of x_t (a shortcut that avoids backpropagating through the diffusion model), and project the gradient onto the data tangent space via a pretrained autoencoder (MPGD-AE, MPGD-Z) so samples stay on the manifold; extend to latent diffusion models (MPGD-LDM).

Key results

• On CelebA-HQ FaceID guidance, MPGD achieves FaceID loss 0.5163 vs 0.5690 for FreeDoM, at 5.82s vs 10.65s inference time.
• For style-guided Stable Diffusion generation, MPGD-LDM runs in 19.83s and 15.53 GB VRAM, versus 26.50s for FreeDoM and 31.65 GB for LGD-MC, where the baselines could not fit in 16GB.
• The abstract claims up to 3.8x speed-ups with the same number of diffusion steps while maintaining sample quality compared to baselines.

Measured effects reported in the paper

Surprising findings

• The paper reports that the simple MPGD shortcut without projection already outperforms several baselines, with manifold projection giving further large fidelity gains in super-resolution.

Limitations

• Perfect-autoencoder theory is only approximated by imperfect VQGAN autoencoders, and the manifold guarantees rely on a linear subspace manifold hypothesis.
• Requires pretrained autoencoders for projection, limiting applicability when none are available.
• Style-guided results show a trade-off: lower style loss than DDIM comes with reduced CLIP score relative to DDIM.

Experimental setup

Datasets: FFHQ ImageNet CelebA-HQ WikiArt PartiPrompts

Benchmarks: FFHQ super-resolution FFHQ Gaussian deblurring ImageNet noisy linear inverse problems CelebA-HQ FaceID guidance WikiArt style guidance with PartiPrompts

Metrics: KID LPIPS FaceID loss Style loss CLIP score inference time VRAM

Baselines: DPS LGD-MC MCG FreeDoM DDIM

Builds on: DDIM DPS FreeDoM LGD Repaint

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