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Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

Hyungjun Joo, Sehwan Kim, Hyeonggeun Han, Sangwoo Hong, Jungwoo Lee

arXiv:2610.01723v1cs.CV

TL;DR

Diffusion models can reproduce individual training images, while mitigation must preserve useful prompt information for alternative depictions. This paper redistributes cross-attention before reinforcing content and attenuating padding; it reports improved prompt alignment at comparable training-image similarity and favorable empirical trade-offs, including transfer to DeepFloyd IF.

  • Problem

    Diffusion models can closely reproduce individual training images, creating a need for mitigation that still preserves prompt information for useful alternative depictions.

    Source: §1 · p. 1

  • Method

    A training-free intervention redistributes cross-attention with Gaussian smoothing before reinforcing content-token contributions and attenuating padding, without extra denoiser evaluations.

    Source: §1 · p. 1, §3.1 · p. 3

  • Results

    All evaluated smoothing widths lie on empirical Pareto frontiers for training-image similarity versus prompt alignment and image preference on two Stable Diffusion benchmarks; an SD-selected configuration reduces template reproduction in DeepFloyd IF without further tuning.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Jointly controlling conditioning allocation and strength supports generating prompt-consistent alternatives while mitigating reproduction.

    Source: §5 · p. 9

    The local analysis applies under value-coherence conditions, and wider smoothing increases transport distance and the associated coherence requirement.

    Source: §3.2 · pp. 4–5

Abstract

from arXiv · show

Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual training examples. Effective mitigation must preserve useful prompt information to guide alternative depictions. We introduce a training-free method that redistributes cross-attention with Gaussian smoothing before reinforcing content-token contributions and attenuating padding contributions, without additional denoiser evaluations. With this intervention, stronger content conditioning can improve prompt alignment at comparable training-image similarity. A local analysis identifies when reinforcement preserves shared value information while redistribution reduces localized attention mass. On Stable Diffusion v1.4 and v2.0, all evaluated smoothing widths lie on the empirical Pareto frontiers for training-image similarity versus both prompt alignment and image preference. A configuration selected on Stable Diffusion reduces template reproduction in DeepFloyd IF without further tuning. These findings support jointly controlling conditioning allocation and strength to generate prompt-consistent alternatives.

1 INTRODUCTION

Diffusion models can reproduce individual training images, so mitigation must reduce copying while retaining enough prompt information to create useful alternatives. The paper reinforces content within redistributed cross-attention and reports improved prompt alignment at comparable training-image similarity, with favorable benchmark trade-offs and transfer to DeepFloyd IF.

1 INTRODUCTION

  • Mitigation must offer a useful prompt-following alternative, not merely move an image away from a training reference, because prompts permit many valid depictions.Memorization can reproduce distinctive composition and visual details beyond semantic resemblance to the same subject.Source: §1 · p. 1
  • Unlike inference-time approaches that weaken, avoid, postpone, or reverse conditional responses, this method makes content reinforcement active during mitigation.Prior approaches include guidance reduction, noise adjustment, delayed or reversed guidance, and reduced attention to prompt words.Source: §1 · p. 1
  • The training-free intervention applies Gaussian redistribution before reinforcing content and attenuating padding, retaining the original prompt and frozen model without extra denoiser evaluations.The sequence changes attention allocation before strengthening the redistributed content contributions.Source: §1 · p. 1, Figure 1 · p. 2
  • Content reinforcement improves prompt alignment at comparable training-image similarity, and redistribution recovers prompt alignment and image preference over reweighting alone while retaining most suppression.The redistribution comparison holds token gains fixed.Source: §1 · pp. 1–2
  • All evaluated smoothing widths lie on the empirical Pareto frontiers for training-image similarity versus prompt alignment and image preference on two Stable Diffusion benchmarks.A Stable Diffusion-selected configuration also reduces template reproduction in DeepFloyd IF without further tuning.Source: §1 · p. 2

2 RELATED WORK

Related work examines what diffusion models reproduce, how memorization may be represented, and how inference-time interventions mitigate it. The paper positions its approach as redistribution across token positions followed by content amplification, joining prompt-semantic fidelity with memorization mitigation.

2 RELATED WORK

  • Prior accounts locate memorization in cross-attention neurons or explain it through learned and data manifolds, while studies describe image, template, and partial-image reproduction (Webster, 2023; Chen et al., 2025).Other studies connect memorization to data duplication and image-caption associations (Somepalli et al., 2023b;a; Chen et al., 2024).Source: §2 · p. 2
  • Inference-time mitigation adjusts prompts or noise, changes guidance, or modifies attention; Ren et al. (2024) shift attention toward SOS, whereas this method redistributes across token positions before reinforcing content.The method preserves the original prompt and initial noise without input optimization.Source: §2 · pp. 2–3
  • Prompt-to-Prompt, Structured Diffusion, SynGen, and Attend-and-Excite target semantic fidelity; this method couples that objective with memorization mitigation rather than a subject-specific spatial-attention objective.Those approaches respectively reuse attention maps, incorporate linguistic structure, align entities with modifiers, or strengthen neglected subjects.Source: §2 · p. 3

3 REINFORCING REDISTRIBUTED CROSS-ATTENTION

The method redistributes cross-attention across token positions before reinforcing content and selectively attenuating padding. Its local analysis describes conditions for preserving shared value information while reducing localized attention mass, and the paper adapts the same rule for DeepFloyd IF.

3.1 REDISTRIBUTE, REINFORCE, AND PRESERVE CONTEXT

  • The intervention smooths attention along token positions, then applies recipient-token gains: prompt content is reinforced, the first EOS is preserved as a contextual anchor, and later padding is attenuated.Content positions are prompt-token positions, including subwords; the method uses an early smoothing window and continues weighting throughout sampling.Source: §3 · p. 3, §3.1 · pp. 3–4

3.2 REINFORCEMENT WITH LOCALIZED DILUTION

  • For an interior content token at σ = 1, α = 1, and λw = 2, approximately 0.798 of its original mass returns to that position, although total content mass doubles.This illustrates how reinforcement can increase overall content weight while reducing weight retained at the original location.Source: §3.2 · p. 4
  • Under local value-coherence conditions, an admissible gain preserves or increases shared-feature information while reducing mass assigned to the original token set.When ε + κT < 1, redistribution creates a relative advantage for the shared feature, and reinforcement within the gain window converts it into absolute feature preservation while localized mass remains below its original level.Source: §3.2 · p. 5
  • On Stable Diffusion, smoothing width controls a balance between dilution of localized attention and retention of shared value information, with wider kernels requiring greater value coherence.The local proposition is a one-operation analysis under its stated coherence condition; image-generation consequences are evaluated empirically.Source: §3.2 · p. 5

3.3 TRANSFER TO DEEPFLOYD IF

  • For DeepFloyd IF, the same operator is applied to Stage I’s text-key block, with smoothing across the text block, unchanged image keys, and the SD-selected configuration used without IF-specific strength tuning.Stage II retains its original attention.Source: §3.3 · p. 5

4 EXPERIMENTS

The experiments compare mitigation and generation quality on Stable Diffusion benchmarks, then test reinforcement, redistribution, guidance changes, EOS handling, transfer, and selective application. SSCD measures training-image similarity, while CLIP and ImageReward assess alignment and preference.

4.2 MEMORIZATION–QUALITY TRADE-OFFS

  • All four evaluated settings lie on the empirical Pareto frontiers for SSCD–CLIP and SSCD–ImageReward on both Stable Diffusion models.For example, on SD v1.4 at SSCD 0.2265, σ = 4 has higher CLIP (0.2731 versus 0.2647) and ImageReward (−0.2374 versus −0.3522) than per-sample AIN.Source: Figure 2 · p. 6, §4.2 · p. 6

4.3 REINFORCING REDISTRIBUTED CONTENT

  • With redistribution and padding suppression retained, λw = 2 yields higher CLIP than λw = 1 at comparable SSCD, and the guitar itself depicts the requested blue attribute.SSCD first rises and then returns to a comparable level at gain 2; the wider sweep to 3 extends the decline.Source: §4.3 · pp. 6–7, Figure 4 · p. 7

4.2 MEMORIZATION–QUALITY TRADE-OFFS

  • Figure 3 shows prompt-consistent alternatives: the method retains requested content while depicting the performance prompt more explicitly than GPA and CS.Its output shows the subject performing with a handheld microphone rather than reproducing the training reference’s guitar-playing composition.Source: §4.2 · p. 6, Figure 3 · p. 7

4.4 HOW REDISTRIBUTION SHAPES THE TRADE-OFF

  • At fixed token gains without row normalization, Gaussian redistribution improves alignment and preference over reweighting alone with SSCD increasing only from 0.2052 to 0.2088.With normalized rows, Gaussian redistribution instead lowers SSCD from 0.3831 to 0.3312 at a cost in alignment and preference.Source: §4.4 · pp. 7–8, Table 1 · p. 8

4.5 GUIDANCE CHANGES BEYOND MAGNITUDE ATTENUATION

  • Restoring the base guidance norm retains an SSCD reduction of 0.2032, close to 0.2007 for the intervention direction and norm together; scalar attenuation alone yields 0.0477.The intervention direction with its own norm gives higher CLIP and ImageReward than restoring the base norm at similar SSCD.Source: §4.5 · p. 8, Table 2 · p. 8

4.6 PRESERVING THE FIRST EOS TOKEN

  • Extending attenuation to EOS reaches a lower SSCD endpoint at greater preference cost, and lowers both SSCD and ImageReward at each fixed width.In the token-removal example, removing EOS changes the requested baby depiction, whereas removing padding retains it.Source: §4.6 · pp. 8–9, Figure 5 · p. 9
  • On 48 DeepFloyd IF template-verbatim prompts, the SD-selected configuration reduces template matches from 38.89% to 20.14% without IF-specific tuning.CLIP and ImageReward also decrease relative to the base model.Source: §4.6 · p. 9, Table 3 · p. 9

4.8 SELECTIVE MITIGATION WITH AN EXISTING DETECTOR

  • At a calibrated 5% false-positive rate, selective intervention lowers mean SSCD on 500 memorized prompts from 0.5486 to 0.3475, with weaker suppression than treating every prompt.Average ImageReward changes by −0.0065 over normal prompts and −0.1291 within the flagged normal subset.Source: §4.8 · p. 9

5 CONCLUSION

The paper concludes that reinforcing prompt content within redistributed attention can improve alignment at comparable training-image similarity, while supporting useful alternatives during mitigation. Its analysis identifies conditions for preserving shared value information while diluting localized attention.

5 CONCLUSION

  • Content reinforcement within redistributed attention improves prompt alignment at comparable training-image similarity and supports useful alternatives while mitigating reproduction.The authors report empirical Pareto-frontier performance on two Stable Diffusion benchmarks and transfer to DeepFloyd IF without further tuning.Source: §5 · p. 9

A.1 OPERATOR AND SCOPE

  • The reflected blur operator need not preserve total attention mass because its column sums can differ from one.The local proposition applies only where reflection is inactive and the kernel has unit column sums.Source: §A.1 · p. 13
  • The operator identity expresses the intervention as a change in value-vector mixtures under the original attention weights, with token gains acting on redistribution recipients.The identity holds for the full reflected operator; amplifying the redistributed pattern need not recover the original conditioning.Source: §A.1 · p. 13

A.2 PROOF OF PROPOSITION 1

  • Under local coherence, the analysis bounds preservation of a shared feature projection and gives conditions for reducing attention mass on a localized token set.The proof uses a nonempty admissible gain interval to ensure both outcomes.Source: §A.2 · p. 13
  • A token-localized shared residual contributes in proportion to attention mass on its token set, linking the mass bound to localized value information.Generation-level effects are evaluated separately using training-image similarity and quality.Source: §A.2 · p. 13

A.3 A TRANSPORT BOUND FOR THE COMPLETE ATTENTION ROW

The complete-row analysis gives conditions for preserving projected features while reducing attention on a localized token set, including cases with background attention and reflection. Numerical checks and examples illustrate the allocation–strength trade-off and its boundaries.

A.3 A TRANSPORT BOUND FOR THE COMPLETE ATTENTION ROW

  • The stated conditions are necessary and sufficient for uniform feature preservation over the specified class and exact for reducing attention on the localized set.These conditions concern the complete reflected operator.Source: §A.3 · p. 14
  • The mass surplus must cover feature variation induced by redistribution; with common budget η, the cost is ητλ.The transport bound makes this preservation requirement explicit.Source: §A.3 · p. 14
  • For a fixed row and blur, feasible gains form an interval after intersecting preservation, λ ≥1, and dilution conditions, and that interval may be empty.The preservation bound is concave and piecewise affine in the gain.Source: §A.3 · p. 14
  • 1.0321 minimum response ratio for ηk=0.07 features accompanies 0.37849 retention of the original token’s attention in the constructed short-content example.The example uses a four-token content span where the local proposition does not apply because the kernel extends beyond content.Source: §A.3 · p. 14

A.4 IMPLICATIONS AND NUMERICAL VERIFICATION

  • Redistribution changes relative retention of localized mass and projected features, while reinforcement scales both; an admissible gain converts the relative advantage into absolute preservation and dilution.Amplification alone offers no such interval.Source: §A.4 · p. 15
  • At σ=1 and σ=4, (g0, μσ) equals (0.39905, 0.72669) and (0.09991, 3.15669); at λw=2, source-token coefficient retention is 0.79810 and 0.19982.The calculations illustrate that gain and localized influence can move in opposite directions.Source: §A.4 · p. 15
  • Wider smoothing can reduce localized-set retention while increasing transport distance and coherence requirements; smoothing is applied early, then the blur becomes identity.The analysis motivates early redistribution with reinforcement, while schedule and gains are empirical choices.Source: §A.4 · p. 15
  • The analysis is bounded by cases where isolated useful information is attenuated, uniform attention is amplified everywhere, reflection changes row mass, or background inflow outweighs local dilution.Source: §A.4 · p. 15

B IMPLEMENTATION AND REPRODUCIBILITY DETAILS

The implementation checks document close agreement between benchmark and reproduction runs, while component comparisons examine Gaussian redistribution, normalization, and content-only positional mixing.

B IMPLEMENTATION AND REPRODUCIBILITY DETAILS

  • Controlled SD studies use DDIM with 50 steps, CFG 7, 512 × 512 outputs, and a stratified 340-prompt subset sampled with two seeds and four images per prompt and seed.Paired intervals resample prompts; a high-similarity batch has at least one of four images above SSCD 0.5.Source: B IMPLEMENTATION AND REPRODUCIBILITY… · p. 16
  • For the benchmark SD v1.4 base, SSCD/CLIP/ImageReward were 0.5439/0.3012/−0.0140, versus 0.5461/0.3021/−0.0164 in the H100 reproduction.For σ = 4, the corresponding method triplets were 0.2265/0.2731/−0.2374 and 0.2348/0.2706/−0.2794.Source: B IMPLEMENTATION AND REPRODUCIBILITY… · p. 16
  • The main operator uses reflected convolution over positions 1 through L − 1, while Figure 6 depicts smoothing before content reinforcement and padding attenuation.Kernel size depends on σ and is reduced to the largest admissible odd size when necessary.Source: B IMPLEMENTATION AND REPRODUCIBILITY… · p. 16, Figure 6 · p. 17

C COMPONENT VARIATIONS AND EXACT SETTINGS

  • Content-only positional redistribution uses a nonnegative row-stochastic donor-to-recipient matrix that preserves each row’s total content mass; uniform redistribution moves content attention toward its within-span mean.The content-only variants differ in token support and boundary behavior from the main operator, even at the same nominal width.Source: C COMPONENT VARIATIONS AND EXACT… · p. 17
  • The narrower Gaussian kernel favors greater quality recovery, while σ = 4 retains stronger suppression; aggregate gains are mainly driven by TM prompts, with SSCD rising on TM and falling on VM.The comparison holds token gains fixed and varies Gaussian redistribution.Source: C COMPONENT VARIATIONS AND EXACT… · p. 17, Table 6 · p. 18

D CONTENT REINFORCEMENT AND THE SUPPRESSION BOUNDARY

The section examines how content gains, EOS handling, and mechanism controls shape the suppression–quality trade-off, alongside supplementary redistribution comparisons.

D CONTENT REINFORCEMENT AND THE SUPPRESSION BOUNDARY

  • With Gaussian redistribution retained, CLIP increases across the recorded content-gain range, while SSCD first rises and then declines; lowering padding gain reduces both SSCD and CLIP.The padding sweep motivates pairing suppression with content reinforcement to control the alignment cost.Source: D CONTENT REINFORCEMENT AND THE… · p. 18, Figure 7 · p. 18
  • At each fixed width, extending attenuation to EOS lowers SSCD, CLIP, and ImageReward; preserving EOS gives more favorable ImageReward–SSCD operating points, with a smaller CLIP separation.The EOS-suppressed variant reaches minimum SSCD 0.1382 versus 0.1453, accompanied by lower alignment and preference.Source: D CONTENT REINFORCEMENT AND THE… · pp. 18–19, Figure 8 · p. 19, Table 7 · p. 19
  • Removing padding retains the squirrel–burger interaction more closely than removing EOS, which changes the depicted interaction.The figure presents a separate qualitative intervention from the gain-scaling comparison.Source: D CONTENT REINFORCEMENT AND THE… · p. 19, Figure 9 · p. 20

E SUPPLEMENTARY COMPONENT COMPARISONS

  • Global uniform smoothing modifies all token positions, unlike content-only redistribution, which redistributes only existing content mass.The global-smoothing comparison and content-only controls therefore use different redistribution domains.Source: E SUPPLEMENTARY COMPONENT COMPARISONS · p. 20, Table 8 · p. 20

F MECHANISM CONTROLS AND INTERPRETATION

  • Restoring base guidance norm while retaining the intervention direction yields an SSCD difference of −0.2032 (95% CI [−0.2283, −0.1782]), versus −0.0477 (95% CI [−0.0612, −0.0356]) when only norm changes.These are paired prompt-bootstrap estimates from Table 2.Source: F MECHANISM CONTROLS AND INTERPRETATION · p. 21
  • A separate early-step diagnostic finds that normalized content-entropy and guidance-norm correlations vary by prompt population, including +0.204 for memorized prompts and −0.782 for alignment-failure prompts.After controlling prompt length, the respective correlations are +0.245 and −0.553.Source: F MECHANISM CONTROLS AND INTERPRETATION · p. 21

G ALTERNATIVE SD OPERATING POINT AND DETAILED RESULTS

The moderate-strength comparison evaluates Content-uniform against the Gaussian configuration, then tests consistency across seeds and memorization types.

G ALTERNATIVE SD OPERATING POINT AND DETAILED RESULTS

H DEEPFLOYD IF PROTOCOL AND RESULTS

The DeepFloyd IF experiment transfers Stable Diffusion-selected configurations to a template-memorization evaluation, comparing template matches and image-quality measures.

H DEEPFLOYD IF PROTOCOL AND RESULTS

  • On IF, Content-uniform remains close to the base template-match rate while retaining higher CLIP and ImageReward than Ours, changing the relative suppression–quality behavior of the SD-selected configurations.The complete transfer comparison is reported in Table 12.Source: H DEEPFLOYD IF PROTOCOL AND RESULTS · p. 22, Table 12 · p. 22
  • Template-match difference from base is −18.75 percentage points (95% CI [−34.07, −6.00]) for Ours and −1.39 points (95% CI [−10.32, 5.69]) for Content-uniform.Both configurations retain their Stable Diffusion settings in the IF comparison.Source: Table 12 · p. 22, H DEEPFLOYD IF PROTOCOL AND RESULTS · p. 23
  • The transfer evaluation targets template memorization using selected TV prompts, while 16 historical N prompts provide a supplementary quality check.The IF protocol specifies Stage I and II models, sampling steps, guidance scales, noise level, and shared attention and safety-checking pipelines.Source: H DEEPFLOYD IF PROTOCOL AND RESULTS · p. 22
  • The lower template-match frequency occurs without a clear change in mean continuous masked error, which summarizes discrepancy across outputs including those outside the match region.The primary event searches all 57 reference pairs for outputs meeting the template-match criterion.Source: H DEEPFLOYD IF PROTOCOL AND RESULTS · p. 23
  • On the 16 historical N prompts, no arm matched a template; CLIP/ImageReward means were 0.3150/0.5902 for base, 0.3047/0.3010 for Ours, and 0.3073/0.5275 for Content-uniform.Source: H DEEPFLOYD IF PROTOCOL AND RESULTS · p. 23

I DETECTOR-GATED PIPELINE

The section reports operating characteristics for a detector based on the initial-step norm and the measured effects of applying the full-strength Gaussian configuration at a 5% operating point.

I DETECTOR-GATED PIPELINE

  • At threshold 1.8135, recall and precision are approximately 0.55 and 0.87 when measured baseline similarity (SSCD > 0.5) defines a positive.The detector uses the initial-step norm ∥ϵc −ϵu∥ and thresholds calibrated on 500 normal prompts.Source: I DETECTOR-GATED PIPELINE · p. 23
  • The detector AUROC is 0.902 against normal prompts and 0.949 against the alignment-failure set.Source: I DETECTOR-GATED PIPELINE · p. 23
  • At the 5% operating point, 289 of 500 historically memorized prompts and 25 of 500 normal prompts are flagged; flagged normals show CLIP −0.0039 and reward −0.1291 changes.The pipeline uses the full-strength Gaussian configuration.Source: I DETECTOR-GATED PIPELINE · p. 23

J COMPLETE BASELINE RESULTS

The appendix provides complete aggregate results for Stable Diffusion v1.4 and v2.0, then illustrates how outputs respond to semantic prompt edits and increasing content gain.

J COMPLETE BASELINE RESULTS

K QUALITATIVE BEHAVIOR

  • With Robot or Old edits, this method changes the subject, while the shown SD outputs remain close to a common book-cover template.Source: Figure 10 · p. 25
  • Across six Blue Guitar – Throw Pillow outputs ordered by increasing content gain, higher gain makes the printed guitar blue.Source: Figure 11 · p. 25
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