mlsanityData to 14 Oct 2026

Beyond Temporal Smoothing: Spatial Energy Budgets Stabilize One-Step Diffusion Editing

arXiv 2609.32841 ↗First posted 26 Sep 2026cs.CVcited by 0

On the map: Diffusion models → Image generation, text-to-image and editing also fits: Diffusion foundations and sampling

Abstract (arXiv)

One-step text-guided diffusion editing is efficient but prone to spatially misallocated updates that distort the edited object and alter the background. Existing methods often improve stability by averaging the editing field across timesteps. We instead identify spatial energy misallocation as a distinct and measurable failure mode: across two independent noise draws, the residual field is essentially unrepeatable, making the background field unreliable for direct transport, while its total energy still sets a usable magnitude for the draw at hand. BudEdit turns that magnitude into an explicit budget and reallocates it to edit-relevant regions selected jointly by residual energy and cross-attention, controlling where editing energy is spent rather than averaging over timesteps. The resulting training-free, inversion-free editor spends the budget on transport and reuses it to scale a correction in a lower-noise gated refinement. The budgeted injection field matches its prescribed budget exactly and vanishes on the identified background support, by construction. On PIE-Bench with SD-Turbo, BudEdit outperforms ChordEdit under each method's reported default settings on all 11 evaluated metrics, including a 2.1,dB gain in background PSNR, 31% lower DINO, and 36% lower LPIPS, while improving all five editing-quality metrics and reporting the lowest runtime in the comparison.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 29 of its 34 references that are in the map. First posted on arXiv 26 Sep 2026.

Builds on

29 of 34 references.

Closest ancestors · 9

Same direction, newest first

  1. 2025SpotEdit: Selective Region Editing in Diffusion Transformers26 Dec
  2. 2025FlowDC: Flow-Based Decoupling-Decay for Complex Image Editing12 Dec
  3. 2025InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow8 Aug
  4. 2024SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion5 Dec
  5. 2024TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models1 Aug
  6. 2023SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation8 Dec
  7. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models31 Jan
  8. 2022InstructPix2Pix: Learning to Follow Image Editing Instructions17 Nov
  9. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Models17 Nov

Other references · 20

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  6. 2022Flow Matching for Generative Modelingcited by 5,702
  7. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
  8. 2023Consistency Modelscited by 1,822
  9. 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,081
  10. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 906
  11. 2023Adversarial Diffusion Distillationcited by 759
  12. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 395
8 more
  1. 2024FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Modelscited by 217
  2. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  3. 2025KV-Edit: Training-Free Image Editing for Precise Background Preservationcited by 49
  4. 2024SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its Teachercited by 45
  5. 2026ChordEdit: One-Step Low-Energy Transport for Image Editingcited by 18
  6. 2026WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editingcited by 2
  7. 2026Rethinking One-Step Image Editing through ChordEdit: Reproduction, Simplification, and New Insightscited by 1
  8. 2026Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editingcited by 1

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
  2. 2026Consistent-Inversion: Reverse Consistency Guidance for Structure-Preserving Visual Editing
  3. 2026ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent
  4. 2026Diffusion Image Editing via Asynchronous Token Decoding
  5. 2026SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking
  6. 2026On the Controllability-Fidelity Frontier in Diffusion Editing
  7. 2025On Exact Editing of Flow-Based Diffusion Models
  8. 2025Edit2Perceive: Image Editing Diffusion Models Are Strong Dense Perceivers