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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Z-Sampling claims a pretrained text-to-image diffusion model can improve its own outputs, without retraining, by alternately denoising and inverting each latent so that the guidance gap between the two operations accumulates…

desk verdict A training-free zigzag sampling trick that plausibly improves text-to-image alignment, with an empirical case strong enough to survive a muddled theory section. read the letter →

arxiv 2412.10891 v2 pith:SL6TFGIB submitted 2024-12-14 cs.CV cs.LG

Lichen Bai , Shitong Shao , Zikai Zhou , Zipeng Qi , Zhiqiang Xu , Haoyi Xiong , Zeke Xie This is my paper · ORCID
classification cs.CVcs.LG
keywords diffusionmodelstext-to-imagegenerationsamplingmethodsclassifier-freeguidanceDDIMinversiontraining-freeenhancementpromptalignmentself-reflection
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

The paper's central claim is that a pretrained text-to-image diffusion model can improve its own generation quality and prompt alignment without any retraining, purely by changing the sampling procedure. The proposed Z-Sampling alternately performs one denoising step and one inversion step at each timestep, and the paper argues that the guidance gap between the two operations injects prompt-related semantic information into the latent. The paper derives this semantic accumulation as a sum of squared latent differences, shows that it is larger than the end-to-end analogue by Jensen's inequality, and reports consistent wins over standard sampling across SD-2.1, SDXL, Hunyuan-DiT, and DreamShaper, with HPS v2 winning rates up to 94% on DrawBench. A sympathetic reader would care because the improvement is plug-and-play: it adds a few lines of code, works with accelerated and transformer-based models, and compounds with orthogonal methods such as Diffusion-DPO.

What carries the argument

The named mechanism is diffusion self-reflection, executed as the zigzag cycle of one denoising step and one inversion step at the same timestep. The load-bearing identity is $\delta_{Z\text{-Sampling}}=\sum_t (x_t-\tilde{x}_t)^2 = \sum_t \alpha_t h_t^2(\delta_\gamma(u_\theta(x_t,c,t)-u_\theta(x_t,\varnothing,t)))^2$, with guidance gap $\delta_\gamma=\gamma_1-\gamma_2$ and weights $h_t=\sqrt{1/\alpha_t-1}-\sqrt{1/\alpha_{t-1}-1}$. This identity carries the argument because it turns the semantic gain into a per-step accumulation controlled by a knob the user can set, and it shows why end-to-end inversion, whose gain is a squared sum of the same terms, loses to the step-by-step version by Jensen's inequality. The approximation error $\tau_2$ from imperfect inversion is the opposing term that the paper argues zigzag truncation keeps small.

What would settle it

Run Z-Sampling with $\gamma_2=\gamma_1$ so the guidance gap is zero while keeping the zigzag updates; the paper predicts near-identical output to standard sampling, whereas a significant improvement would show the gain comes from the inversion path rather than the gap. A second check is to replace the inversion map with noise of the same magnitude: if the quality gain persists, the guidance-gap explanation is not needed.

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Extended reading notes

Core claim

Z-Sampling decomposes the DDIM sampler into one-step maps and, at each zigzag step, computes $x_{t-1}=\Phi_t(x_t|c,\gamma_1)$, $\tilde{x}_t=\Psi_t(x_{t-1}|c,\gamma_2)$, then $x_{t-1}=\Phi_t(\tilde{x}_t|c,\gamma_1)$. The paper's central discovery is that the accumulated latent difference $\delta_{Z\text{-Sampling}}=\sum_t(x_t-\tilde{x}_t)^2$ decomposes into a semantic information gain term $\tau_1$ and an inversion approximation error term $\tau_2$, and that, neglecting $\tau_2$, the gain equals $\sum_t \alpha_t h_t^2(\delta_\gamma(u_\theta(x_t,c,t)-u_\theta(x_t,\varnothing,t)))^2$, where $\delta_\gamma=\gamma_1-\gamma_2$. Because this is a sum of squares rather than the square of a sum, the paper argues it accumulates instead of cancelling, and it visualizes the effect via cross-attention maps in which entity tokens sharpen after zigzag steps. Stated on the paper's own terms, diffusion self-reflection is a training-free way for a frozen diffusion model to improve its own alignment and visual quality.

Load-bearing premise

The proof that the guidance gap is what injects semantics assumes the inverted latent $\tilde{x}_t$ equals the denoised latent $x_t$ when the network evaluates both branches, whereas the method's mechanism depends on their being different; if inversion cannot be both accurate and different, the stated identity does not follow.

Editorial extensions

If this is right

  • Without any training, a frozen model's outputs can be improved on human-preference and alignment metrics; for instance, DreamShaper-xl-v2-turbo with Z-Sampling reaches a 94% HPS v2 winning rate over its own standard sampling on DrawBench.
  • The method applies to U-Net, DiT, and distilled-turbo architectures and to few-step samplers such as a 4-step setting, so it is not limited to slow or single-family models.
  • Challenging compositional prompts such as counting, position, color binding, and multi-object co-occurrence show the largest alignment gains, which is exactly where standard classifier-free guidance tends to fail.
  • Z-Sampling composes with orthogonal improvements: AYS sampling and Diffusion-DPO both improve further when combined with it.
  • The benefit requires a deterministic sampler; with stochastic samplers such as ancestral Euler, inversion error grows and the gains shrink or reverse.

Reading between the lines

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

  • A natural next experiment is to measure per-prompt gain against the size of $u_\theta(x_t,c,t)-u_\theta(x_t,\varnothing,t)$; if the mechanism is right, prompts with larger conditional-minus-unconditional score gaps should benefit most.
  • The early-steps result, where most gain comes from the first zigzag steps, suggests a cheap variant that applies the zigzag only where it matters, reducing the extra network calls.
  • If the latent-semantics story is correct, Z-Sampling should also improve prompt alignment for other deterministic conditional diffusion outputs, including video and 3D, whenever inversion is accurate enough; stochastic samplers would need a learned or corrected inversion first.
  • The paper's own zero-gap experiment implies a possible diagnostic: with $\delta_\gamma=0$, residual differences measure pure inversion error, so reporting the ratio of $\tau_1$ to $\tau_2$ effects would let future work separate semantic accumulation from artifact reduction.
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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

3 major / 6 minor

Summary. The paper proposes Z-Sampling, a training-free modification of DDIM-style sampling for text-to-image diffusion models. At each timestep in a chosen range, the method alternately performs a denoising step with classifier-free guidance scale gamma_1 and an inversion step with a smaller guidance scale gamma_2, then denoises again from the inverted latent. The authors claim this zigzag 'self-reflection' operation exploits the guidance gap delta_gamma = gamma_1 - gamma_2 to inject prompt-related semantic information into the latent path, improving generation quality and prompt alignment. The paper presents theoretical results (Theorems 1--3) intended to show that the cumulative squared latent displacement is proportional to the accumulated squared guidance-gap-weighted conditional--unconditional score difference, and extensive experiments across SD-2.1, SDXL, Hunyuan-DiT, and DreamShaper-xl-v2-turbo on Pick-a-Pic, DrawBench, GenEval, PartiPrompts, and MS-COCO, plus combinations with AYS and Diffusion-DPO, report consistent improvements over standard sampling.

Significance. If the empirical claims hold, Z-Sampling is practically valuable: it is a plug-and-play, training-free sampler that improves multiple diffusion architectures, including distilled and transformer-based models, and it can be combined with orthogonal methods. The experimental evaluation is broad, the reported winning rates on HPS v2 are high, and the public code is a concrete reproducibility asset. However, the theoretical contribution is not sound as written. The key theorem relies on an assumption that contradicts the algorithm's own definition, and the claimed derivation of the guidance-gap scaling is therefore invalid in its present form. The empirical results remain the main strength of the paper, but the theoretical narrative needs substantial repair before the paper can be accepted.

major comments (3)
  1. [Appendix F.3, Theorem 3 / Eq. (11)] The proof of Theorem 3 contains an internal contradiction. It states that 'the step-by-step approach results in x_t and \tilde{x}_t being the same at each timestep t,' but Proof F.2, Eq. (22), derives \tilde{x}_t = x_t + \sqrt{\alpha_t} h_t (\epsilon^\theta_t(x_t) - \epsilon^\theta_t(\tilde{x}_{t-1})), so the two latents differ by exactly the quantity whose accumulation the method exploits. The substitution of Eq. (5) into Eq. (24) is therefore valid only under the auxiliary assumption \tilde{x}_t = x_t, which would make the zigzag step a no-op in the exact-inversion limit. The note at the end of Proof F.2 redefines \epsilon^\theta_t(\tilde{x}_t) as the score evaluated at x_t with guidance \gamma_2, whereas Eq. (5) defines it at the latent \tilde{x}_t; this notational shift is load-bearing and is not justified. As written, Eq. (11) does not follow from Eq. (10). The theorem should either be proved under a consistent definition of the zigzag update, stated as an approximation with an explicit error bound, or removed and replaced by a heuristic argument.
  2. [Section 3.3, Theorem 2 / Eq. (10)] The paper treats the squared latent displacement \delta_{Z\text{-Sampling}} = \sum_t (x_t - \tilde{x}_t)^2 as a 'semantic information gain' without proving that larger displacement corresponds to better prompt alignment or image quality. Theorems 1--3 establish algebraic relationships among latent displacements and score differences; they do not establish a monotonic link between these displacements and any quality metric. The connection is asserted through the seed-level examples in Sections 3.1 and 3.2 and is supported only indirectly by the downstream experiments. This assumption should be stated explicitly as a heuristic, or replaced with an analysis that connects the displacement to the conditional score and to an image-quality measure, rather than being presented as part of the theoretical derivation.
  3. [Section 4, Figures 7 and 8] The empirical observation that the gains vanish as \gamma_2 approaches \gamma_1 is presented as verification of Eq. (11), but it is also a consistency property of the algorithm's definition: when \gamma_2 = \gamma_1 and inversion is an exact inverse of the denoising step, the zigzag update leaves x_t unchanged, so any method of this form would show a disappearing effect at zero gap. The experiments confirm the expected qualitative trend but do not independently validate the quadratic-in-\delta_\gamma form or the specific score-difference expression in Eq. (11). This is not a criticism of the empirical improvement itself, which is substantial, but it should not be cited as evidence for the flawed theorem.
minor comments (6)
  1. [Algorithm 1 and Section 4.1] Algorithm 1 uses the condition `if t > T - \lambda`; with the default \lambda = T - 1 stated in Section 4.1, the zigzag operation is applied for t = T, ..., 2, not 'throughout the entire path,' because t = 1 is excluded. Please clarify the intended range or set \lambda = T.
  2. [Section 4.1] 'lug (2022)' in the baselines paragraph is a broken citation; the corresponding reference list entry for RePaint lacks author names and is formatted inconsistently with the other references.
  3. [Section 4.2] The main text says 'As Table 10' when referring to the Semantic-CFG comparison, but that table is numbered Table 3 in the main text; the duplicate numbering of Semantic-CFG tables in the appendix should be reconciled.
  4. [Throughout] There are numerous typos and inconsistent spellings, including 'denosing' (Abstract, Section 2), 'procss' (Section 2), 'numeber' (Section 2), 'sclae' (Appendix D.4), 'Pick-a-Pick' versus 'Pick-a-Pic' throughout, and '\epsilon^j_\theta' in Eq. (21) of Proof F.1.
  5. [Table 12] Table 12's header lists 'AES↓', but AES is a higher-is-better metric; the arrow appears to be a typo and should be 'AES↑.'
  6. [Appendix E.1] The sentence 'a deterministic sampler implies that the inversion process is imprecise' appears to contradict the surrounding discussion, which argues that stochastic samplers introduce inversion inaccuracy; the intended statement is likely about stochastic samplers.

Circularity Check

1 steps flagged · score 6.0 of 10

Theorem 3's guidance-gap scaling is obtained by identifying x_t and \tilde{x}_t, making Eq. 11 an algebraic identity of the CFG formula rather than a property of the zigzag path.

  1. self definitional [Section 3.3, Proof F.3 (Eq. 11); cf. Algorithm 1 and Proof F.2 (Eq. 22)]
    "Although the step-by-step approach results in xt and \tilde{x}t being the same at each timestep t, from equation 5, we note that ϵtθ(xt) and ϵtθ(\tilde{x}t) are obtained under guidance scales γ1 and γ2 respectively. Thus, the effect of Z-Sampling is further equivalent as δZ-Sampling = ΣTt=1 αth2t ((γ1 − γ2)(uθ(xt,c,t)−uθ(xt,∅,t)))2."

    The theorem's conclusion is obtained by setting the two network-input latents equal (xt = \tilde{x}t), even though Algorithm 1 defines \tilde{x}t by inversion (\tilde{x}t = Ψt(xt−1|c,γ2)) and Proof F.2 itself derives \tilde{x}t = xt + √αt ht(ϵtθ(xt)−ϵtθ(\tilde{x}t−1)). With inputs identified, Eq. 11 is just the algebraic difference of the two CFG score definitions in Eq. 5: (1+γ1)u(xt,c)−γ1u(xt,∅) minus (1+γ2)u(xt,c)−γ2u(xt,∅) = (γ1−γ2)(u(xt,c)−u(xt,∅)). So the claimed 'theoretical' dependence on the guidance gap is built into the CFG formula by construction, not derived from the zigzag accumulation mechanism, and it contradicts the method's premise that the inverted latent differs from the denoised latent.

full rationale

The empirical comparisons (Tables 1-5, 6-11, Figure 6) are genuine external benchmarks against standard sampling, Resampling, AYS, CFG++, SEG, and Diffusion-DPO; those wins are not forced by the definitions and are not circular. However, the paper's central theoretical evidence for 'semantic information accumulation' is circular/inconsistent. Proof F.3 reduces the Z-Sampling effect to an identity of classifier-free guidance at a single latent (xt = \tilde{x}t), which is exactly the assumption that makes Eq. 11 equivalent to Eq. 5 rather than a prediction about the zigzag path. This conflicts with Algorithm 1 and Eq. 22, where \tilde{x}t is produced by inversion and differs from xt; the additional Jacobian-type term from the latent difference is dropped without justification. Theorem 2's Jensen comparison also labels an algebraic regrouping (τ1, τ2) as 'semantic information gain' by stipulation rather than by derivation. No load-bearing self-citation chain was found: self-citations to Golden Noise, IV-Mixed Sampler, and related lab work are contextual and not used to forbid alternatives. Overall score 6: the empirical results are independent, but the theory's central prediction reduces by construction.

Assumptions & free parameters 4 free parameters · 5 assumptions · 2 invented entities

The central claim rests on three hand-chosen hyperparameters, one empirical premise about latent semantics, and one internal identity (x_tilde_t = x_t) that the method's own definition contradicts. The method is not burdened by fitted physical constants, but its theoretical story is largely definitional.

free parameters (4)
  • denoising guidance scale gamma_1 = 5.5 (SDXL/SD2.1), 6.0 (Hunyuan-DiT), 3.5 (DreamShaper turbo)
    Controls CFG strength; chosen from model defaults, not derived; Z-Sampling's effect depends on it (Table 13).
  • inversion guidance scale gamma_2 = 0
    Sets the guidance gap delta_gamma = gamma_1 - gamma_2; performance varies with gamma_2 (Figure 7); chosen by hand, not derived.
  • zigzag steps lambda = T-1 (full path) in main experiments
    Number of steps with the zigzag operation; winning rate rises with lambda (Figure 9); chosen by hand.
  • backtracking stepsize k = 1
    Number of denoising steps per zigzag cycle; Table 12 shows k=1 is best; chosen by hand.
assumptions (5)
  • domain assumption DDIM denoising (Eq. 2) and inversion (Eq. 4) are approximate inverses under epsilon_t_theta(x_tilde_{t-1}) approximately equal to epsilon_t_theta(x_tilde_t).
    Section 2 states: 'If this approximation error is negligible, Phi and Psi can be proven to be inverse functions.' Z-Sampling's zigzag cycle relies on this.
  • standard math The score network u_theta accurately predicts conditional and unconditional noise, and CFG interpolation (Eq. 5) is valid.
    Used in Eq. 5 and Theorem 3 to separate the guidance gap.
  • domain assumption Latents carrying prompt-related semantic information yield better conditional generations (Section 3.1).
    Motivational observation in Figures 2-3; not proven.
  • ad hoc to paper In Theorem 3, the latent for the denoising and inversion score evaluations is the same, x_tilde_t = x_t.
    Appendix F.3 says 'Although the step-by-step approach results in x_t and x_tilde_t being the same', yet Theorem 2 and Algorithm 1 define the effect through (x_t - x_tilde_t)^2. This assumption is load-bearing for Eq. 11.
  • ad hoc to paper Larger squared latent displacement delta reflects more 'semantic information gain' and better generation.
    Section 3.3 equates the accumulated difference with semantic gain; no formal link to quality metrics is proven.
invented entities (2)
  • semantic information in latent space
    purpose: Explains why low-guidance inversion improves conditional generation; the paper claims this quantity is captured by the guidance gap.
    The paper provides only its own cross-attention visualizations and seed experiments; no independent falsifiable handle outside this work. It is an explanatory label, not a measured quantity.
  • diffusion self-reflection operation
    purpose: Names the zigzag denoise-invert-denoise cycle and frames it as analogous to LLM self-reflection.
    This is the algorithm itself; no external evidence independent of the paper's implementation.

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Cite this review

Pith. "Pith review of Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection." pith.science (2026). https://pith.science/paper/SL6TFGIB

@misc{pith2026241210891,
  author       = {Pith},
  title        = {Pith review of: Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SL6TFGIB}},
  note         = {Machine review of arXiv:2412.10891}
}
read the original abstract

Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However, existing text-to-image diffusion models often fail to maintain high image quality and high prompt-image alignment for those challenging prompts. To mitigate this issue and enhance existing pretrained diffusion models, we mainly made three contributions in this paper. First, we propose diffusion self-reflection that alternately performs denoising and inversion and demonstrate that such diffusion self-reflection can leverage the guidance gap between denoising and inversion to capture prompt-related semantic information with theoretical and empirical evidence. Second, motivated by theoretical analysis, we derive Zigzag Diffusion Sampling (Z-Sampling), a novel self-reflection-based diffusion sampling method that leverages the guidance gap between denosing and inversion to accumulate semantic information step by step along the sampling path, leading to improved sampling results. Moreover, as a plug-and-play method, Z-Sampling can be generally applied to various diffusion models (e.g., accelerated ones and Transformer-based ones) with very limited coding and computational costs. Third, our extensive experiments demonstrate that Z-Sampling can generally and significantly enhance generation quality across various benchmark datasets, diffusion models, and performance evaluation metrics. For example, DreamShaper with Z-Sampling can self-improve with the HPSv2 winning rate up to 94% over the original results. Moreover, Z-Sampling can further enhance existing diffusion models combined with other orthogonal methods, including Diffusion-DPO.

Figures

Figures reproduced from arXiv: 2412.10891 by the authors.

Figure 1
Figure 1. The qualitative results of Z-Sampling demonstrate the effectiveness of our method in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Semantic-rich latents effectively generate [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 4
Figure 4. The illustration of our method: a) weak guidance sampling; b) strong guidance sampling; c) Z-Sampling (with diffusion self-reflection). 2 PRELIMINARIES In this section, we formally introduce prerequisites and background. Diffusion Model. We define the total numeber of denoising steps T and conditional prompt c. Given the denoising procss Φ : N × C → D and guidance scale γ1, starting from xT ∈ N , we can generate x0 … view at source ↗
Figures from the paper (22 more)
Figure 5
Figure 5. Figure 5: The cross-attention map highlights the interaction between the entity token (red color) and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: The winning rates of Z-Sampling over standard sampling. The blue bars represent the side [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Robustness to the inversion guidance scale. When the gap is zero, i.e., the inversion [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: The guidance gap δγ between γ1 and γ2 influences both the magnitude and direction of semantic injection. When δγ is large (δγ=5), the gain of Z-Sampling becomes pronounced. Con￾versely, when δγ is zero or even negative, it approximately degenerates into standard sampli…
Figure 9
Figure 9. Figure 9: Robustness to the zigzag diffusion steps λ. The horizontal axis shows the number of zigzag operations, and the vertical axis rep￾resents the winning rate over HPS v2 on Pick￾a-Pic. As λ increases, generation quality im￾proves, indicating effective semantic informa￾tion…
Figure 11
Figure 11. Figure 11: Latents with relevant semantic information about a specific concept can generate images [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Given two natural images and their corresponding prompts, we perform DDIM inversion [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Generate images related to “cat” and “spider” using two latents respectively, and calculate [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Qualitative comparison in terms of style. [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: Qualitative comparison in terms of position. [PITH_FULL_IMAGE:figures/full_fig_p020_15.png]
Figure 16
Figure 16. Figure 16: Qualitative comparison in terms of color. A photo of three suitcases A photo of four buses A photo of three kites A photo of four fire hydrants A photo of three sinks A photo of two beds S t a n d a r d S a m p l i n g Z - S a m p l i n g ( O u r s ) [PITH_FULL_IMAGE…
Figure 17
Figure 17. Figure 17: qualitative comparison in terms of counting. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_17.png]
Figure 18
Figure 18. Figure 18: Qualitative comparison in terms of object co-occurrence. [PITH_FULL_IMAGE:figures/full_fig_p021_18.png]
Figure 19
Figure 19. Figure 19: Comparison of Winning Rates with 10 Denoising Steps in the SDXL. [PITH_FULL_IMAGE:figures/full_fig_p022_19.png]
Figure 20
Figure 20. Figure 20: Comparison of Winning Rates with 50 Denoising Steps in the SDXL. [PITH_FULL_IMAGE:figures/full_fig_p022_20.png]
Figure 21
Figure 21. Figure 21: Comparison of Winning Rates with 50 Denoising Steps in the SD 2.1. [PITH_FULL_IMAGE:figures/full_fig_p022_21.png]
Figure 22
Figure 22. Figure 22: Comparison of Winning Rates with 10 Denoising Steps in the Hunyuan-DiT. [PITH_FULL_IMAGE:figures/full_fig_p023_22.png]
Figure 23
Figure 23. Figure 23: Comparison of Winning Rates under different guidance scale γ1. Model: DreamShaper￾xl-turbo-v2. Horizontal axis: guidance scales γ1. Vertical axis: Z-Sampling vs Standard Sampling winning rates on Pick-a-Pic. 1 = 3.5 1 = 7.5 1 = 9.5 Z - S a m plin g ( O u r s ) S t a n…
Figure 24
Figure 24. Figure 24: Qualitative comparison under high guidance scale. When γ1 = 3.5 (the official recom￾mended guidance scale), both Z-Sampling and Standard exhibit no artifacts or degradation in image quality. As γ1 increases, standard sampling exhibits artifacts and oversaturation, whi…
Figure 25
Figure 25. Figure 25: Qualitative comparison under high guidance scales. Standard sampling suffers from supersaturation more significantly than Z-Sampling under the high guidance scales. Model:SDXL. D.5 ADDITIONAL EXPERIMENTS ON VARIOUS GUIDANCE SCALES We report more visual cases in [PITH…
Figure 26
Figure 26. Figure 26: When the semantic term τ1 is removed (e.g., τ1 = 0), the presence of only the error term τ2 degrades the quality of generation results, and this negative gain effect is more pronounced in the end-to-end method. Additionally, we test the performance of end-to-end and s…
Figure 27
Figure 27. Figure 27: The End-to-End injection risks semantic cancellation across stages, leading to suboptimal [PITH_FULL_IMAGE:figures/full_fig_p030_27.png]

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Forward citations

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.