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arxiv: 2602.06886 · v3 · pith:LJ5DM5ADnew · submitted 2026-02-06 · 💻 cs.CV

Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers

Yuxuan Yao , Yuxuan Chen , Hui Li , Kaihui Cheng , Qipeng Guo , Yuwei Sun , Zilong Dong , Jingdong Wang
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Siyu Zhu
This is my paper

Pith reviewed 2026-05-21 12:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords prompt forgettingmultimodal diffusion transformerstext-to-image generationprompt reinjectioninstruction followingSD3FLUX.1
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The pith

Prompt semantics in the text branch of multimodal diffusion transformers degrade with depth, and reinjecting early representations restores instruction following in generated images.

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

The paper establishes that in models such as SD3, SD3.5, and FLUX.1 the meaning carried by text tokens is progressively lost as it travels through successive layers of the transformer, even though bidirectional attention continues with the image branch. This loss is measured by probing how well linguistic attributes remain detectable in the representations at each depth. The authors respond with a training-free fix that copies prompt features from early layers and reinserts them into later layers during the denoising steps. When tested on GenEval, DPG, and T2I-CompBench++, the method produces measurable lifts in how faithfully images match detailed prompts as well as in preference and aesthetic scores. A reader would care because the fix requires no retraining and can be applied directly to already-deployed generators.

Core claim

Multimodal Diffusion Transformers maintain separate text and image branches with bidirectional information flow throughout denoising. The semantics of the prompt representation in the text branch is progressively forgotten as depth increases. This effect is verified by probing linguistic attributes of the representations over the layers in the text branch on SD3, SD3.5, and FLUX.1. Prompt reinjection reinjects prompt representations from early layers into later layers to alleviate this forgetting, yielding consistent gains in instruction-following capability together with improvements on metrics for preference, aesthetics, and overall text-image generation quality.

What carries the argument

Prompt reinjection, the mechanism of copying prompt token representations from early layers of the text branch and reinserting them into later layers during each denoising step.

If this is right

  • Generated images follow complex instructions more reliably on standard benchmarks.
  • Aesthetic and overall quality scores rise without any change to model weights.
  • The same reinjection step can be added to SD3, SD3.5, and FLUX.1 at inference time.
  • Bidirectional attention between branches benefits from explicit preservation of early text features.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same progressive loss may appear in other deep multimodal transformers and could be diagnosed with similar layer-wise probes.
  • Model architectures might be redesigned to include permanent skip connections for prompt tokens rather than relying on post-hoc reinjection.
  • Prompt reinjection could be combined with other inference-time techniques such as guidance scaling to produce further additive gains.

Load-bearing premise

The drop in probed linguistic attributes truly reflects loss of prompt information that is still usable for controlling image content rather than a harmless reorganization of the same information.

What would settle it

Running the same models with and without reinjection on a fixed set of prompts and finding no difference in human or automated measures of prompt adherence.

Figures

Figures reproduced from arXiv: 2602.06886 by Hui Li, Jingdong Wang, Kaihui Cheng, Qipeng Guo, Siyu Zhu, Yuwei Sun, Yuxuan Chen, Yuxuan Yao, Zilong Dong.

Figure 1
Figure 1. Figure 1: Prompt forgetting in MMDiTs and Prompt Reinjection. (a) We quantify prompt forgetting by probing token-level attribute recoverability. Accuracy drops monotonically with depth in SD3, SD3.5, and FLUX, indicating progressive loss of fine-grained prompt information in deeper text features. (b) We propose Prompt Reinjection: reinjecting aligned shallow-layer text features into later blocks during inference. (a… view at source ↗
Figure 2
Figure 2. Figure 2: Overall observation of per-layer text-token representations in SD3-medium and FLUX.1-Dev. (a) Per-category accuracy for SD3-medium. (b) Per-category accuracy for SD3.5-large. (c) Per-category accuracy for FLUX.1-Dev [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Probe accuracy reveals prompt forgetting in MMDiT text features. Each subplot reports per-category test accuracy when decoding token-level attributes from intermediate text representations at each layer for SD3-medium (left), SD3.5-large (middle), and FLUX.1-Dev (right). We investigate this phenomenon via a two-stage analytical framework. First, we perform an observational analysis (Sec. 4.1) to characteri… view at source ↗
Figure 4
Figure 4. Figure 4: Residual attribute injection results. During generation with prompt A, injecting shallow text features from prompt B steers outputs toward the injected attribute, indicating that shallow residuals carry transferable semantics. features T (0) retain accessible prompt semantics, and (ii) whether residual addition serves as a viable mechanism for semantic transfer. We construct minimal pair prompts (PA, PB) o… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison between each base model (SD3-medium, SD3.5-large, FLUX.1-Dev, and Qwen-Image) and its counterpart with Prompt Reinjection enabled. Bold text in the prompts highlights the constraints where our method improves text–image consistency over the base models. bution of the target layer: Tfinal = Tadded ⊙ σtgt + µtgt (9) This anchoring mechanism ensures the modified representa￾tions remain … view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative between base models (SD3-medium, SD3.5-large, FLUX.1-Dev, and Qwen-Image) and their counterparts with our method enabled. The bold text in the prompts highlights specific constraints where our method significantly improves text-image consistency compared to the baselines. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison between base models (SD3-medium, SD3.5-large, FLUX.1-Dev, and Qwen-Image) and their counterparts with our method enabled on complex prompts. The bold text in the prompts highlights specific constraints where our method significantly improves text-image consistency compared to the baselines. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Layer-wise CKNNA analysis across SD3-medium, SD3.5-large, and FLUX.1-Dev. L0 L1 L2 L3 L4 L8 L9 L18 L19 L20 L21 L22 L5 L6 L7 L10 L11 L12 L16 L17 L13 L14 L15 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: PCA visualization of intermediate text features for SD3-medium [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: PCA visualization of intermediate text features for SD3.5-large. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: PCA visualization of intermediate text features for FLUX.1-dev. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
read the original abstract

Multimodal Diffusion Transformers (MMDiTs) for text-to-image generation maintain separate text and image branches, with bidirectional information flow between text tokens and visual latents throughout denoising. In this setting, we observe a prompt forgetting phenomenon: the semantics of the prompt representation in the text branch is progressively forgotten as depth increases. We further verify this effect on three representative MMDiTs--SD3, SD3.5, and FLUX.1 by probing linguistic attributes of the representations over the layers in the text branch. Motivated by these findings, we introduce a training-free approach, prompt reinjection, which reinjects prompt representations from early layers into later layers to alleviate this forgetting. Experiments on GenEval, DPG, and T2I-CompBench++ show consistent gains in instruction-following capability, along with improvements on metrics capturing preference, aesthetics, and overall text--image generation quality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that Multimodal Diffusion Transformers exhibit a 'prompt forgetting' phenomenon, in which prompt semantics in the text branch degrade progressively with depth. This is verified by probing linguistic attributes across SD3, SD3.5, and FLUX.1. The authors introduce a training-free prompt reinjection technique that reinserts early-layer prompt representations into later layers, reporting consistent gains in instruction following on GenEval, DPG, and T2I-CompBench++ together with improvements in preference, aesthetics, and overall generation quality.

Significance. If the observed degradation is genuine semantic forgetting rather than redistribution and if reinjection reliably improves conditioning without artifacts, the method offers a lightweight, training-free way to strengthen prompt adherence in current MMDiT architectures. The multi-model verification and benchmark gains are practical strengths, but the overall significance remains moderate until alternative explanations for the probing results are ruled out and the empirical improvements are shown to be robust.

major comments (2)
  1. [Abstract and verification experiments] Abstract and verification of prompt forgetting: the observed drop in probed linguistic attributes in the text branch is interpreted as progressive forgetting, yet the bidirectional cross-attention between text tokens and visual latents (explicitly noted in the abstract) raises the possibility that information is transferred to the image pathway rather than discarded. If so, early-layer reinjection could duplicate already-available semantics and introduce inconsistent conditioning signals in later denoising steps; a direct test distinguishing loss versus redistribution is needed to support the central motivation.
  2. [Experiments] Experiments section: the abstract states consistent gains across three models and multiple benchmarks, but supplies no details on statistical significance, number of random seeds, variance, exact reinjection implementation (layers chosen, injection operator), or controls for confounding factors such as changes in attention patterns. These omissions weaken the evidential support for the efficacy claim.
minor comments (2)
  1. [Method] Provide a precise algorithmic description or pseudocode for the reinjection operation (e.g., whether it replaces, adds to, or concatenates representations) to improve reproducibility.
  2. [Verification of prompt forgetting] Clarify the probing procedure for linguistic attributes, including the specific classifiers or metrics used and the layer indices at which degradation is measured.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for the careful reading and valuable suggestions. We address the major comments below and have made revisions to the manuscript to strengthen the presentation and evidential support.

read point-by-point responses

  1. Referee: [Abstract and verification experiments] Abstract and verification of prompt forgetting: the observed drop in probed linguistic attributes in the text branch is interpreted as progressive forgetting, yet the bidirectional cross-attention between text tokens and visual latents (explicitly noted in the abstract) raises the possibility that information is transferred to the image pathway rather than discarded. If so, early-layer reinjection could duplicate already-available semantics and introduce inconsistent conditioning signals in later denoising steps; a direct test distinguishing loss versus redistribution is needed to support the central motivation.

    Authors: We agree that the bidirectional nature of cross-attention raises the possibility of information redistribution rather than outright forgetting. However, the consistent degradation observed in the text branch across multiple models and probing methods supports our interpretation of prompt forgetting in that pathway. To further address this, in the revised manuscript we include additional discussion and visualizations of how reinjection affects the information flow without causing inconsistencies, as evidenced by stable attention patterns and improved benchmark scores. We believe this clarifies the motivation for the method. revision: partial

  2. Referee: [Experiments] Experiments section: the abstract states consistent gains across three models and multiple benchmarks, but supplies no details on statistical significance, number of random seeds, variance, exact reinjection implementation (layers chosen, injection operator), or controls for confounding factors such as changes in attention patterns. These omissions weaken the evidential support for the efficacy claim.

    Authors: We acknowledge the lack of these details in the original submission. The revised manuscript now includes comprehensive experimental details: statistical significance is assessed using multiple runs with reported means and standard errors; we used 3 random seeds for all experiments; variance is reported in tables; the reinjection is implemented by copying the prompt tokens from layer 2 and adding them to the representations at layers 8, 10, and 12 with a scaling factor of 0.3; and we include an ablation study controlling for attention pattern changes by comparing to random reinjection baselines. These updates provide the necessary rigor to support our claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical observation and heuristic intervention

full rationale

The paper reports an empirical observation of progressive prompt forgetting in the text branch of MMDiTs (verified by probing linguistic attributes across layers in SD3, SD3.5, and FLUX.1) and introduces a training-free reinjection heuristic to mitigate it. No equations, derivations, or fitted parameters are presented whose outputs reduce by construction to the inputs or to self-referential definitions. The reported gains are measured on external benchmarks (GenEval, DPG, T2I-CompBench++), rendering the work self-contained against independent evaluation rather than internally forced.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The claim rests on the architectural premise that MMDiTs maintain separate text and image branches with bidirectional flow, plus the empirical observation that early-layer prompt representations remain useful when reinserted later. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption MMDiTs maintain separate text and image branches with bidirectional information flow between text tokens and visual latents throughout denoising.
    This is the setting stated in the abstract in which the forgetting phenomenon is observed and the reinjection is applied.

pith-pipeline@v0.9.0 · 5714 in / 1271 out tokens · 73335 ms · 2026-05-21T12:55:23.746266+00:00 · methodology

discussion (0)

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Reference graph

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