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Universal Guidance for Diffusion Models

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arxiv 2302.07121 v1 pith:ZXX4YPZX submitted 2023-02-14 cs.CV cs.LG

Arpit Bansal , Hong-Min Chu , Avi Schwarzschild , Soumyadip Sengupta , Micah Goldblum , Jonas Geiping , Tom Goldstein This is my paper · ORCID
classification cs.CVcs.LG
keywords guidancediffusionmodelsalgorithmmodalitiesuniversalwithoutaccept
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Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work, we propose a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components. We show that our algorithm successfully generates quality images with guidance functions including segmentation, face recognition, object detection, and classifier signals. Code is available at https://github.com/arpitbansal297/Universal-Guided-Diffusion.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    An epsilon-greedy search over per-step noise trajectories improves proxy rewards in diffusion image generation without retraining.

  3. Is Energy Guidance All You Need? Training-Free Norm Injection for Driving World Models

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    Sampling-time energy guidance steers a frozen rectified-flow driving world model's ego trajectory to a braking target, but the generated video does not follow under current joint self-attention.

  4. Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency Models

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A cycle-consistency reconstruction loss on the forward consistency model improves 4-step image inversion and editing, closing most of the quality gap to full-step diffusion editing.

  5. CoDe: Blockwise Control for Denoising Diffusion Models

    cs.CV 2025-02 conditional novelty 5.0 of 10

    CoDe applies blockwise best-of-N sampling during diffusion denoising, with Tweedie-based reward estimates, to align generated images to differentiable or non-differentiable rewards.

  6. Test-time Conditional Text-to-Image Synthesis Using Diffusion Models

    cs.CV 2024-11 conditional novelty 5.0 of 10

    TINTIN conditions Stable Diffusion outputs at test time on color palettes and edge maps by backpropagating losses between decoded images and the condition through the denoising steps.

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