mlsanityData to 14 Oct 2026

Understanding and Improving Training-free Loss-based Diffusion Guidance

arXiv 2403.12404 ↗First posted 19 Mar 2024cs.LG cs.CVcited by 32

On the map: Diffusion models → Diffusion foundations and sampling

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Abstract (arXiv)

Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, and AI for science. Recently, several studies have proposed training-free loss-based guidance by using off-the-shelf networks pretrained on clean images. This approach enables zero-shot conditional generation for universal control formats, which appears to offer a free lunch in diffusion guidance. In this paper, we aim to develop a deeper understanding of training-free guidance, as well as overcome its limitations. We offer a theoretical analysis that supports training-free guidance from the perspective of optimization, distinguishing it from classifier-based (or classifier-free) guidance. To elucidate their drawbacks, we theoretically demonstrate that training-free guidance is more susceptible to adversarial gradients and exhibits slower convergence rates compared to classifier guidance. We then introduce a collection of techniques designed to overcome the limitations, accompanied by theoretical rationale and empirical evidence. Our experiments in image and motion generation confirm the efficacy of these techniques.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 44 of its 54 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

44 of 54 references.

Closest ancestors · 13

Same direction, newest first

  1. 2024Guidance with Spherical Gaussian Constraint for Conditional Diffusion5 Feb
  2. 2023Manifold Preserving Guided Diffusion28 Nov
  3. 2023AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models20 Jul
  4. 2023Restart Sampling for Improving Generative Processes26 Jun
  5. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model17 Mar
  6. 2023Universal Guidance for Diffusion Models14 Feb
  7. 2022Towards Practical Plug-and-Play Diffusion Models12 Dec
  8. 2022Classifier-Free Diffusion Guidance26 Jul
  9. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps2 Jun
  10. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  11. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  12. 2020Denoising Diffusion Implicit Models6 Oct
1 more
  1. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  5. 2013Intriguing properties of neural networkscited by 5,155
  6. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
  7. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  8. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
  9. 2022Human Motion Diffusion Modelcited by 1,080
  10. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  11. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 906
  12. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
19 more
  1. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  2. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 539
  3. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 508
  4. 2022MotionCLIP: Exposing Human Motion Generation to CLIP Spacecited by 408
  5. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 363
  6. 2019Provably Robust Deep Learning via Adversarially Trained Smoothed Classifierscited by 330
  7. 2020Do Adversarially Robust ImageNet Models Transfer Better?cited by 316
  8. 2023Diffusion Self-Guidance for Controllable Image Generationcited by 305
  9. 2018Adversarial Attacks and Defences: A Surveycited by 259
  10. 2021More Control for Free! Image Synthesis with Semantic Diffusion Guidancecited by 212
  11. 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Plannerscited by 149
  12. 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learningcited by 149
  13. 2023FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Conditioncited by 91
  14. 2021FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizationcited by 71
  15. 2019Revisiting the Polyak step sizecited by 55
  16. 2023Mass-Producing Failures of Multimodal Systems with Language Modelscited by 36
  17. 2023Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learningcited by 26
  18. 2021Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesiscited by 24
  19. 2023Toward Open-ended Embodied Tasks Solvingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 14; 2026: 14.

32 papers.

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  1. 2025Inference-time Scaling of Diffusion Models through Classical Searchcited by 49
  2. 2024Dynamic Negative Guidance of Diffusion Modelscited by 28
  3. 2024DyMO: Training-Free Diffusion Model Alignment with Dynamic Multi-Objective Schedulingcited by 27
  4. 2025Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Modelscited by 24
  5. 2025Feedback Guidance of Diffusion Modelscited by 16
  6. 2025SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Scorecited by 14
  7. 2025Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Modelscited by 11
  8. 2025Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generationcited by 10
  9. 2024Training-Free Guidance for Discrete Diffusion Models for Molecular Generationcited by 6
  10. 2025RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanningcited by 6
  11. 2025NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusioncited by 6
  12. 2024Flow Matching Posterior Sampling: A Training-free Conditional Generation for Flow Matchingcited by 4
20 more
  1. 2025Diffusion Models as Dataset Distillation Priorscited by 3
  2. 2026Alignment of Diffusion Model and Flow Matching for Text-to-Image Generationcited by 3
  3. 2026Refining Compositional Diffusion for Reliable Long-Horizon Planningcited by 2
  4. 2025Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptationcited by 1
  5. 2025Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regionscited by 1
  6. 2025CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidancecited by 1
  7. 2026Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solverscited by 1
  8. 2026LLM Flow Processes for Text-Conditioned Regressioncited by 1
  9. 2026Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defectscited by 1
  10. 2026Seeing to Ground: Visual Attention for Hallucination-Resilient MDLLMscited by 1
  11. 2026Physics-Aware Video Generation via Agentic Planning and Graph-Guided Optimizationcited by 1
  12. 2025Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variationcited by 0
  13. 2025Similarity-Guided Diffusion for Long-Gap Music Inpaintingcited by 0
  14. 2026Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Designcited by 0
  15. 2026NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generationcited by 0
  16. 2026Not All Prediction Targets Keep Training-Free Diffusion Guidance on the Manifoldcited by 0
  17. 2026Training-free Controllable Human Motion Generation under Heterogeneous Constraintscited by 0
  18. 2026Integration Matters: Rollout-Based Training for Constrained Diffusion Modelscited by 0
  19. 2026Adversarial Learning of Classifier-Free Guidance Schedulescited by 0
  20. 2026Opening the Black Box of Classifier-Free Guidance via Information Bottleneckcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Bridging the Gap: Addressing Discrepancies in Diffusion Model Training for Classifier-Free Guidance
  2. 2023Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models
  3. 2023Elucidating The Design Space of Classifier-Guided Diffusion Generation
  4. 2025REG: Rectified Gradient Guidance for Conditional Diffusion Models
  5. 2026Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance
  6. 2026C2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
  7. 2024Plug-and-Play Diffusion Distillation
  8. 2024Guiding a Diffusion Model with a Bad Version of Itself