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REVIEW 4 major objections 5 minor 38 references

Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A training-free guidance term that compares the editing path to a Gaussian-blurred copy of transferred attention, plus a timestep schedule, shifts the faithfulness–editability trade-off of diffusion editing so that unchanged content…

desk verdict A useful training-free plug-in that likely improves faithfulness, but the scheduling formula in Eq. 11 is reversed relative to the text, so the method as written is not reproducible without that fix. read the letter →

arxiv 2506.21045 v1 pith:XWA67Q77 submitted 2025-06-26 cs.CV cs.AIcs.LG

Hansam Cho , Seoung Bum Kim This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords diffusionmodelsimageeditingfaithfulnesseditabilityclassifier-freeguidanceperturbedattentionschedulingPIE-Bench
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Diffusion-based image editing faces a trade-off: strengthening how much the target prompt is realized (editability) tends to erode how well the unchanged parts of the input survive (faithfulness). This paper introduces Faithfulness Guidance and Scheduling (FGS), a training-free plugin that adds a guidance term comparing the editing path against a Gaussian-blurred copy of the attention information transferred from the reconstruction path, plus a timestep schedule that keeps that guidance aligned with the editability guidance. The paper's central claim is that FGS shifts the trade-off outward: on the PIE-Bench dataset with the Prompt-to-Prompt method, structure distance in the unedited region drops from 11.00 to 8.35 while the CLIP score for editability moves only slightly, a better result than simply increasing the information-injection timestep. The same plugin improves content and style preservation when applied to MasaCtrl, StyleAligned, and InfEdit, without retraining. A reader should care because this targets the main practical knob in real-image editing—how much of the original image survives—without asking users to retrain models.

What carries the argument

The central object is the faithfulness guidance term in Eq.(9)–(10), which adds $w_{fg}(\epsilon_\theta(z_t^t,c_t,I)-\epsilon_\theta(z_t^t,c_t,I'))$ to the editing path's noise prediction, where $I$ is the transferred information (attention maps in Prompt-to-Prompt and InfEdit, key/value features in MasaCtrl, shared-attention features in StyleAligned) and $I' = \text{Perturb}(I)$ is a Gaussian-blurred copy meant to approximate an unconditional reference. The second component is the scheduling rule of Eq.(11), $w_d^t = w_d \log(1+k(T-t))/\log(1+k)$ and $w_i^t = w_i \log(1+kt)/\log(1+k)$, which makes one guidance weight decrease and the other increase across timesteps so that early denoising emphasizes one guidance direction and later denoising the other, chosen according to whether the transferred information encodes layout (emphasize faithfulness guidance early) or fine detail (emphasize it late).

What would settle it

For a fixed editing example, compute the cosine similarity between the FG direction $\epsilon_\theta(z_t,c_t,I)-\epsilon_\theta(z_t,c_t,I')$ and the true unconditional direction $\epsilon_\theta(z_t,c_t,I)-\epsilon_\theta(z_t,c_t,\emptyset)$ where the transferred information is omitted entirely; if the similarity is near zero across timesteps, the blurred reference is not acting as an unconditional state and the proposed mechanism fails.

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Extended reading notes

Core claim

The paper's central claim is that faithfulness in diffusion-based real image editing can be improved by treating the information an editing method transfers from the reconstruction path as a condition, creating a second reference by Gaussian-blurring that information, and adding a classifier-free-guidance-style term that pushes the editing path toward the unperturbed transfer. On PIE-Bench with Prompt-to-Prompt at its default injection timestep, FGS lowers the structure distance in the unedited region from 11.00 to 8.35 while the CLIP editability score changes only from 22.76 to 22.41—a better operating point than raising the injection timestep, which reaches 8.91 and 22.28. The same plugin improves content and style preservation when applied to MasaCtrl, StyleAligned, and InfEdit, and the paper attributes the joint benefit to a scheduling rule that keeps the faithfulness guidance and the editability guidance from fighting each other over the denoising trajectory.

Load-bearing premise

The load-bearing premise is that Gaussian-blurring the attention maps or key/value features that a given editing method transfers from the reconstruction path removes the conditioning content, so the blurred output can stand in as an 'unconditional' reference for CFG-style guidance; if blur only degrades the transferred information without truly removing its conditioning, the faithfulness guidance term is an ad hoc image modifier rather than a principled mechanism.

Editorial extensions

If this is right

  • FGS can be dropped into existing editing pipelines without training; the reported overhead is about 3 GB extra memory and roughly 4 seconds more per image on a single RTX 4090, while improving faithfulness.
  • On P2P with PIE-Bench, FGS at $\tau=0.5$ achieves lower structure distance than raising $\tau$ to 0.6 (unedited SD 8.35 vs 8.91) while keeping the CLIP score higher (22.41 vs 22.28), so it supplies a better faithfulness–editability operating point than the hyperparameter it would otherwise have to tune.
  • The method extends beyond P2P: content and style preservation improve for MasaCtrl (non-rigid editing), StyleAligned (style transfer), and InfEdit (few-step distilled models).
  • The scheduling principle applies whenever the two guidance directions are misaligned; the paper shows that CFG and FG directions have consistently low cosine similarity across timesteps, and the log schedule is what recovers the joint benefit.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Not in the paper: the same 'perturb the transferred condition' trick could be applied to other conditional inputs in editing, such as depth, sketch, or identity embeddings, using domain-appropriate perturbations instead of Gaussian blur.
  • Not in the paper: the claim that blurring produces an unconditional reference is testable directly by measuring the cosine similarity between the FG direction and a direction computed with the condition fully removed; if they diverge, the mechanism would be reinterpreted as a sharpening operator rather than a true CFG analogue.
  • Not in the paper: the scheduling rule has a single parameter $k$ controlling the transition sharpness, so a natural extension is to learn or adapt $k$ per image based on a faithfulness/editing surrogate during inference.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Faithfulness Guidance and Scheduling (FGS), a training-free plugin for diffusion-based image editing that aims to improve faithfulness (preservation of unedited content) while minimizing the loss of editability. FGS adds a CFG-like guidance term that compares the editing-path prediction conditioned on original transferred information I with a version conditioned on a perturbed I' (Gaussian blur), and combines this with a schedule that varies the FG and CFG weights across denoising steps. The method is evaluated on PIE-Bench with P2P as the main baseline, showing lower structure distance (SD) at a small CLIP score cost, and qualitative results are shown for MasaCtrl, StyleAligned, and InfEdit. The paper includes ablations on the perturbation type and hyperparameters.

Significance. If the central claim holds, FGS offers a simple, training-free way to shift the faithfulness–editability trade-off in real-image editing, applicable to multiple editing frameworks. The idea of treating perturbed transferred information as an unconditional reference is an interesting extension of recent attention-perturbation guidance methods (SEG, PAG). The paper includes quantitative and qualitative results, plus ablations of the perturbation choice and scheduling parameters. However, the current evidence is weakened by the lack of statistical rigor and by an internal inconsistency in the scheduling formulation.

major comments (4)
  1. [Sec. 3.3, Eq. (11)] The temporal behavior of w_d and w_i is reversed relative to the text. Eq. (11) defines w_d^t = w_d log(1+k(T-t))/log(1+k) and w_i^t = w_i log(1+kt)/log(1+k). With the denoising loop t = T, T-1, ..., 1 in Algorithm 1, w_d^t is 0 at t=T and increases to w_d at t=1, so it emphasizes late timesteps; w_i^t is large at t=T and decays, so it emphasizes early timesteps. This directly contradicts the statements that "w_d decreases monotonically over the timesteps, emphasizing the early timesteps" and "w_i increases monotonically, emphasizing the later timesteps." Since the scheduling is a core component of the proposed method and the assignment of w_fg and w_cfg to w_d and w_i is method-dependent, this inconsistency makes the described method ambiguous and not uniquely reproducible. The authors must correct either the equations or the text, and clarify the actual schedule used to produce Table 1.
  2. [Table 1, Sec. 4.1] The central quantitative claim that FGS improves the faithfulness–editability Pareto trade-off is supported by no error bars, confidence intervals, or significance tests. The differences in CLIP score between P2P(0.5)+FGS and P2P(0.6) are 25.34 vs 25.33 (whole image) and 22.41 vs 22.28 (edited region), which are likely within run-to-run variation. Consequently, the claim that FGS maintains editability while improving faithfulness is not statistically established. The authors should provide repeated-run statistics (means and standard deviations over at least several seeds) or another form of significance analysis for both SD and CLIP.
  3. [Sec. 4.4 (Fig. 6) and Table 1] The hyperparameters k, sigma, and w_fg are selected via ablations on the same PIE-Bench benchmark used to report the final numbers. This tuning-on-the-test-set procedure risks overfitting; the reported gains may not generalize to other images or editing methods. The authors should use a held-out validation split (or cross-validation) for hyperparameter selection and then evaluate on a separate test set, or at least explicitly acknowledge and justify the absence of such a split.
  4. [Sec. 3.2, Eq. (9)] The method assumes that applying Gaussian blur to the transferred information I produces an "unconditional" reference for CFG-style guidance. The paper does not provide evidence that blurring attention maps (P2P) or key/value features (MasaCtrl) removes the conditioning content of the input image. If the perturbation merely degrades the information without eliminating its content, the FG term (epsilon_theta(z_t,c_t,I) - epsilon_theta(z_t,c_t,I')) may act as an unprincipled image modifier rather than a faithfulness amplifier. The authors should verify this assumption, e.g., by visualizing the perturbed attention maps or measuring the effect of the perturbation on the network's predictions, and discuss whether the mechanism is indeed one of approximating an unconditional state.
minor comments (5)
  1. [Sec. 4.4] Typo: "several several hyperparameters" should be "several hyperparameters."
  2. [Sec. 4.2] Typo: "IIn the third row" should be "In the third row."
  3. [Supplementary Table 4] The reference for NMG appears to be labeled [21] in the table, but NMG is reference [6] (Noise Map Guidance); please correct this citation.
  4. [Sec. 3.3 and Supplementary Sec. C] The paper states that FGS "does not require additional time-consuming steps" but the supplementary reports an increase of about 4 seconds per image and 3GB memory; please reconcile these statements.
  5. [Secs. 4.2, 4.3] For MasaCtrl and StyleAligned, only qualitative results are provided; consider adding quantitative evaluation on PIE-Bench (or another benchmark) to support the claim of compatibility across editing methods.

Circularity Check

1 steps flagged · score 4.0 of 10

FGS's headline Pareto gain is partly an in-sample tuned result; no equation-level circularity, but hyperparameters were selected on the same PIE-Bench metrics used for the claim.

  1. fitted input called prediction [Section 4.4 (Hyperparameter selection), applied to Table 1 in Section 4.1]
    "To systematically analyze the impact of these hyperparameters, we conducted ablation studies by varying each parameter while keeping the others fixed at their default values: k=100, σ=5, and w_fg=10."

    The FGS row in Table 1 (P2P(0.5)+FGS: SD 19.55 / CLIP 25.34) is produced with k=100, σ=5, w_fg=10, exactly the values selected by the ablation in Sec. 4.4 on PIE-Bench. The same PIE-Bench SD and CLIP metrics used for that selection are then reported as evidence for the load-bearing claim that FGS 'achieves greater faithfulness with a minor reduction in editability than simply increasing τ from 0.5 to 0.6.' Because the hyperparameters were chosen to optimize these very metrics on this benchmark, the reported gain is a tuned, in-sample result rather than a prediction from a fixed a priori method; the improvement is not forced by the equations but is partly an artifact of the selection procedure.

full rationale

The FGS construction (Eqs. 8-12) is a heuristic guidance perturbation plus a logarithmic schedule; no equation makes the reported SD/CLIP gain true by construction, so there is no definitional circularity at the equation level. The load-bearing justification that I' = PERTURB(I) is an unconditional reference is imported from PAG/SEG by analogy and not re-derived for editing-path transferred information; this is an unsupported assumption, but it is not a self-referential reduction of the paper's own output to its input. The main circularity-adjacent issue is empirical: the headline Pareto claim in Sec. 4.1 uses FGS hyperparameters (k, σ, w_fg) chosen by ablating on PIE-Bench in Sec. 4.4 with the same SD/CLIP metrics that Table 1 reports, so the comparison to P2P(0.6) is an in-sample tuned result rather than an independent prediction. The self-citation to NMG (Ref. [6], same first author) appears in the main P2P/MasaCtrl pipeline, but the with/without-FGS comparison holds NMG constant and the supplementary SPDInv/InfEdit results do not rely on it, so the self-citation is not load-bearing. The paper's own Limitations section (Supplementary C) concedes that 'enhancing the transferred information can degrade the editing results if the transferred information is not well disentangled,' which confirms that FG's benefit is conditional and empirical rather than a first-principles guarantee; this does not add circularity but is weighed as evidence that the central claim rests on benchmark results, not derivation. Separately, there is a reproducibility concern that is not circularity: under Algorithm 1's loop t=T,...,1, Eq. 11 gives w_d^T=0 and w_d^1≈w_d, so w_d is large at the end of denoising while the text says it 'emphasizes the early timesteps'; this apparent reversal does not affect the circularity score. Overall, the central method still has independent content and is validated on multiple editing backbones, but the headline quantitative superiority is partly a selected result, warranting score 4.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

FGS has four practical tuning knobs and one borrowed modeling assumption. The contribution is an empirical plug-in, not a derivation, so the free-parameter count is moderate. The main risk is that the reported gains are partly fitted to PIE-Bench by the ablation-based hyperparameter choices.

free parameters (4)
  • FG scale w_fg = 10 for P2P/MasaCtrl, 3 for StyleAligned, 2.3 for InfEdit
    Controls the strength of faithfulness guidance; chosen via ablation on PIE-Bench (Fig. 6c, Suppl. A) and directly affects reported SD/CLIP.
  • Scheduling curvature k = 100
    Controls how quickly w_d and w_i change over timesteps; selected by ablation on PIE-Bench (Fig. 6a).
  • Gaussian blur sigma = 5
    Perturbation amount applied to transferred information I'; selected via ablation on PIE-Bench (Fig. 6b).
  • Base scheduled scales w_d and w_i = w_d/w_fg base 10 for P2P; w_i base not reported
    Eq. 11 defines the schedules from these base scales; the paper does not state both base values for each editing method, yet they determine the final editability-faithfulness balance.
assumptions (3)
  • domain assumption Gaussian-blurred transferred information I' approximates an unconditional output for CFG-like guidance in the editing path.
    Sec. 3.2, Eq. 9; this is imported from SEG/PAG for internal features and not directly validated for transferred attention maps in the editing setting.
  • domain assumption Diffusion models generate images coarse-to-fine, so emphasizing faithfulness guidance at early timesteps for layout information and later timesteps for style information is beneficial.
    Sec. 3.3 cites [7,8,31] for the coarse-to-fine property; the benefit for the specific scheduling is asserted.
  • domain assumption PIE-Bench masks and SD/CLIP metrics adequately capture faithfulness and editability for real image editing.
    Sec. 4.1 uses structure distance on the whole image and unedited regions and CLIP score on the whole and edited regions; the validity of these metrics as proxies is assumed.

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Cite this review

Pith. "Pith review of Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling." pith.science (2026). https://pith.science/paper/XWA67Q77

@misc{pith2026250621045,
  author       = {Pith},
  title        = {Pith review of: Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XWA67Q77}},
  note         = {Machine review of arXiv:2506.21045}
}
read the original abstract

Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial aspects are editability, which determines the extent of modification, and faithfulness, which reflects how well unaltered elements are preserved. However, achieving optimal results is challenging because of the inherent trade-off between editability and faithfulness. To address this, we propose Faithfulness Guidance and Scheduling (FGS), which enhances faithfulness with minimal impact on editability. FGS incorporates faithfulness guidance to strengthen the preservation of input image information and introduces a scheduling strategy to resolve misalignment between editability and faithfulness. Experimental results demonstrate that FGS achieves superior faithfulness while maintaining editability. Moreover, its compatibility with various editing methods enables precise, high-quality image edits across diverse tasks.

Figures

Figures reproduced from arXiv: 2506.21045 by the authors.

Figure 1
Figure 1. (a) Qualitative performance enhancement when combining FGS with real image editing techniques such as Prompt-to-Prompt [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (a) Real image editing process with diffusion models. The editing process begins by inverting the input image [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Cosine similarity between CFG and FG directions over [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison. Examples of image edits using P2P with different values of [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison of various editing techniques with and without our proposed FGS. (a) MasaCtrl [ [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Ablation study of hyperparameter. (a) The impact of the scheduling parameter [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Limitation examples 12 [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Additional qualitative comparison on P2P and MasaCtrl [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Additional qualitative comparison on StyleAligned and InfEdit [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.