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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 4 Pith papers

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

  1. Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior

    cs.LG 2025-09 conditional novelty 6.0 of 10

    Feynman-Kac particle steering, previously diffusion-only, is derived for conditional flow matching and used to generate chirality-correct chemical transition states.

  2. Test-Time Scaling of Diffusion Models via Noise Trajectory Search

    cs.LG 2025-05 conditional novelty 6.0 of 10

    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

    cs.CV 2026-07 conditional novelty 5.0 of 10

    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. 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.

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