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REVIEW 4 major objections 5 minor 2 cited by

PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A synthesis pipeline and a 20,000-image open dataset are enough to make a fine-tuned multi-layer image model beat its predecessor and approach single-layer generator quality.

desk verdict Useful open dataset for multi-layer transparent generation, with weak matte validation and some circular evaluation, but worth refereeing. read the letter →

arxiv 2505.22523 v1 pith:VGIJVMSB submitted 2025-05-28 cs.CV

Junwen Chen , Heyang Jiang , Yanbin Wang , Keming Wu , Ji Li , Chao Zhang , Keiji Yanai , Dong Chen
show 1 more author
Yuhui Yuan
This is my paper
classification cs.CV
keywords multi-layertransparentimagesalphamattesopendatasettext-to-imagegenerationgenerate-then-mattingdiffusionmodelsimagequalityassessmentlayerediting
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

This paper argues that the real bottleneck in multi-layer transparent image generation is data, not architecture, and that a synthetic open dataset can remove that bottleneck. The authors build PrismLayers (200K images) and its curated subset PrismLayersPro (20K images) by prompting a strong text-to-image diffusion model to draw each object on a solid gray background, extracting soft-edged alpha mattes with an automatic matting model, and compositing the layers according to layouts taken from existing graphic designs. Fine-tuning the ART model on PrismLayersPro yields ART+, which wins roughly 57-60% of head-to-head user-study comparisons against the original ART and is judged close in visual quality to single-layer images from FLUX.1-[dev]. If the claim is right, the field gains a reusable public resource for training and evaluating editable layered images, plus a training-free recipe for generating more such data on demand.

What carries the argument

The mechanism is a two-stage, training-free synthesis pipeline. LayerFLUX appends the suffix prompt "isolated on a solid gray background" to guide FLUX.1-[dev] into generating objects separated from a uniform canvas, then applies RMBG-2.0 to extract soft alpha mattes. MultiLayerFLUX takes a semantic layout extracted from crawled designs or produced by an LLM, generates each layer at its original aspect ratio with LayerFLUX, and composites the layers in the annotated stacking order. Quality control then runs through a BLIP-2 artifact classifier, an aesthetic predictor, a transparent-image preference score, and human selection. Fine-tuning ART on the filtered data is the step that turns the dataset into a stronger model.

What would settle it

Take a random sample of PrismLayersPro layers, have human annotators refine the alpha mattes, and compute boundary error and foreground/background leak between the released mattes and the human mattes; if the error is much larger than inter-annotator disagreement, the dataset's ground-truth status fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that high-quality multi-layer transparent imagery can be produced without training a new transparency-aware generative model. Instead, an off-the-shelf diffusion model generates each layer on a uniform gray canvas, a salient-object matting model extracts the foreground with an alpha matte, and the independent layers are composited according to a user-provided or extracted semantic layout. The authors state that the resulting PrismLayers and PrismLayersPro datasets are the first open, high-quality multi-layer transparent datasets with accurate alpha mattes, and that fine-tuning ART on PrismLayersPro produces ART+, a model that outperforms the original ART in about 60% of head-to-head user-study comparisons and matches the visual quality of modern single-layer text-to-image generation.

Load-bearing premise

The dataset's usefulness rests on the assumption that RMBG-2.0's automatic mattes of FLUX-generated gray-background images are accurate enough to serve as ground truth, and the paper reports no check against human-annotated mattes.

Editorial extensions

If this is right

  • ART+ wins roughly 57-60% of head-to-head user-study comparisons against the original ART across layer quality, global harmonization, and prompt following.
  • ART+ is reported to match the visual quality of FLUX.1-[dev] on merged multi-layer images, not just to beat its immediate predecessor.
  • PrismLayersPro gives the community an open 20,000-sample resource with per-layer captions, RGB layers, and alpha mattes for training and evaluation.
  • The training-free LayerFLUX pipeline can generate additional transparent-layer data on demand without fine-tuning the underlying generator.
  • Fine-tuning the 20K high-quality subset after the 200K set yields the best model, supporting quality-tuning over raw scale.

Reading between the lines

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

  • Because the released mattes come from an automatic matting model, downstream users should measure matte error against human annotations before treating them as hard ground truth.
  • The same generate-then-matting recipe should transfer to other diffusion generators, so future layer quality may track the generator's aesthetic ceiling rather than the fixed dataset.
  • A natural next test is whether compositing with shared lighting or shading cues, instead of independently generated layers, removes the inter-layer coherence gap the paper attributes to human selection.
  • If the dataset is as reusable as claimed, it could become a standard benchmark for layer-editing tasks such as text replacement and object swapping, not just full-image generation.
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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 introduces PrismLayers and PrismLayersPro, a synthetic dataset of 200K/20K multi-layer transparent images with alpha mattes, generated by a training-free pipeline (LayerFLUX) that uses FLUX.1-[dev] with a suffix prompt and RMBG-2.0 matting, followed by MultiLayerFLUX composition, artifact filtering, TIPS-based quality selection, and human selection. The authors fine-tune the ART model on PrismLayersPro to obtain ART+, reporting user-study win rates over ART and MultiLayerFLUX, and claim that ART+ matches the visual quality of FLUX.1-[dev]. The paper also proposes a transparent-image preference score (TIPS) for evaluating layer quality.

Significance. If validated, the dataset would be a significant community resource: it is the first open, large-scale, high-aesthetic multi-layer transparent image dataset with per-layer alpha channels, and the training-free generation pipeline is a practical recipe for producing such data at scale. The ART+ baseline demonstrates the utility of the dataset for fine-tuning a state-of-the-art multi-layer generation model. The authors are transparent about the remaining limitations, including cross-layer coherence and the reliance on designer-provided layouts. However, the central claims rest on two pillars that need stronger support: the accuracy of the alpha mattes and the statistical independence of the quantitative evaluation.

major comments (4)
  1. [§3.3, Appendix F, Table 7] The claim of 'accurate alpha mattes' (Abstract, Table 1) is not validated at the pixel level. LayerFLUX extracts alpha using RMBG-2.0, chosen empirically in Table 7 based on HPSv2, AE-V2.5, and TIPS scores, none of which measure matte accuracy against ground-truth alpha (e.g., human-annotated mattes). The assumption stated in §3.4 that 'the alpha mask quality of most transparent layers generated with our LayerFLUX and LayerDiffuse methods is satisfactory' is an assumption, not a verification. Since every downstream claim—dataset quality, ART+ fine-tuning, and the 'alpha fidelity' dimension of the user study—inherits the correctness of these mattes, a quantitative comparison of RMBG-2.0, BiRefNet, and SAM2 against human or synthetic ground-truth mattes is needed.
  2. [§3.2, §3.4, Table 2] The quantitative TIPS results in Table 2 are partly circular. TIPS is trained on preference labels derived from a weighted sum of RGB-oriented aesthetic models (§3.4), and the same TIPS score is used in §3.2 to filter and select PrismLayersPro and to discard low-scoring layers. Consequently, ART+ is selected to have high TIPS, so its TIPS improvement over ART in Table 2 is to some degree by construction. The independent evidence is the user study, but the text presents Table 2 as supporting 'significantly outperforms' without acknowledging this selection effect. Please either report user-study numbers as the primary quantitative evidence, or validate TIPS against held-out human preferences and show that the Table 2 conclusion survives when the selection bias is accounted for.
  3. [§4.2, Figure 2] The user study is small and lacks statistical reporting. The study involves 40 samples and over 20 participants, but no confidence intervals, error bars, or significance tests are reported for the win rates in Figure 2. A win rate of 57.9–60% on 40 samples with multiple dimensions and participants could easily be within sampling noise. Please provide per-dimension counts, confidence intervals (e.g., binomial CI), and significance tests, or acknowledge that the preference differences are suggestive rather than established.
  4. [§4.2, 'Comparison to FLUX', Figure 11] The claim that ART+ 'matches the visual quality of images generated by FLUX.1-[dev]' is not directly supported by the experiments. The only quantitative evidence for this claim is FIDmerged in Table 2, which measures distance to FLUX images and is not a perceptual quality metric, and TIPS, which is circular as noted above. No head-to-head user study between ART+ and FLUX.1-[dev] is reported; Figure 11 is purely qualitative. Please add a direct comparison (e.g., a two-alternative forced-choice user study or a validated perceptual metric) to support this headline claim.
minor comments (5)
  1. [§3.4, Eq. (1)] The loss function definition is garbled: 'Lpref = (log 1−logpw)' is not a valid expression and likely should be a cross-entropy or logistic loss. Please correct the equation and specify the exact optimization objective.
  2. [§3.4, §3.2] There is an inconsistency in what data is used to train TIPS. §3.4 says the TIP dataset is collected from LayerFLUX and LayerDiffuse outputs, while §3.2 says TIPS is trained on 'our PrismLayers'. Since PrismLayers is generated by LayerFLUX and MultiLayerFLUX, these statements may be reconcilable, but the precise composition of the training pairs and the filtering step need to be described more carefully to assess circularity.
  3. [Appendix E, Table 5] Table 5 reports TIPS and 'Composed Image Quality' for dataset scales of 10, 100, and 1000 samples, but no error bars or significance information is provided. Given the small scale differences (e.g., TIPS 0.130 vs 0.135), it is unclear whether the trend is meaningful. Please add variance estimates or statistical tests.
  4. [Throughout] There are several typographical errors, including 'prefered' in Eq. (1), 'win-lose' (likely 'win-loss'), and 'recaption' (likely 'recapition' or 'recaptioning'). A careful proofread would improve clarity.
  5. [Section 3.2, 'Human selection'] The human selection step is critical to PrismLayersPro quality, but no details are given on the number of annotators, the selection criteria, inter-annotator agreement, or how many samples were discarded. Please provide this information for reproducibility.

Circularity Check

2 steps flagged · score 4.0 of 10

TIPS-based filtering and evaluation is partially circular; alpha-matte accuracy is asserted, not independently measured.

  1. fitted input called prediction [Section 3.4 (Eqs. 1-2), applied in Section 3.2 and Tables 2/5]
    "Then, we compare each pair of transparent layers based on the weighted sum of the scores predicted by the aforementioned quality scoring models. Here, we assume that the alpha mask quality of most transparent layers generated with our LayerFLUX and LayerDiffuse methods is satisfactory."

    TIPS is a CLIP model fine-tuned on pairwise labels that are themselves a weighted sum of RGB-oriented aesthetic predictors (Aesthetic Predictor V2.5, Image Reward, LAION Aesthetic, HPSV2, VQA Score). The paper then uses this same TIPS both to filter PrismLayersPro ('with reference to the scores by our transparent image preference score (TIPS) predictor') and to report 'TIPS↑' improvements of ART+ in Tables 2 and 5. Consequently, the TIPS-based gains are not independent measurements of transparent-layer quality; they are a distillation of the same preference signal that selected the training data. The human user study in Figure 2 is independent and supports the central claim, which is why this is partial rather than complete circularity.

  2. other [Section 3.3 (LayerFLUX) and Appendix F (Table 7); assumption in Section 3.4]
    "To extract accurate alpha mattes, we explore and evaluate multiple state-of-the-art image matting techniques, including SAM2 [20], BiRefNet [27], and RMBG-2.0 [4]... We empirically find that RMBG-2.0 achieves the best matting quality, and we choose it as our default method. ... We primarily consider the visual aesthetics of the transparent layers after matting and report the quantitative results."

    The dataset's defining property, 'accurate alpha mattes', is produced by selecting RMBG-2.0 based only on HPSv2, AE-V2.5, and TIPS aesthetic scores of matted layers, with no comparison to ground-truth or human-annotated alpha. The paper then explicitly assumes alpha quality is satisfactory when building the preference dataset and excludes transparency-related factors from TIPS. The 'Alpha Quality: good/excellent' row in Table 1 and the 'alpha fidelity' dimension of the user study therefore inherit this unverified assumption. This is not a full by-construction equivalence, but the claimed accuracy of the core data product is asserted rather than measured, and the only quantitative justification is an aesthetic metric that was designed to ignore alpha.

full rationale

The paper's central user-study claims are independent: ART+ vs ART and LayerFLUX vs LayerDiffuse are judged by human participants, and PrismLayersPro curation includes manual selection. Those parts are not circular. The circular component is confined to the TIPS-based quantitative comparisons: TIPS is a fine-tuned distillation of a weighted sum of RGB aesthetic models, and the same model is used to filter the dataset and to evaluate the fine-tuned model, so Tables 2 and 5 'TIPS' columns do not provide independent evidence. Separately, the 'accurate alpha mattes' claim is not validated against any ground-truth mattes; matting-model selection is based on aesthetic scores, and Section 3.4 explicitly assumes alpha quality is satisfactory. This is a validation gap that should be flagged, but it is not a by-construction reduction. Overall, the main contribution (open dataset and fine-tuned ART+ with human-preference wins) stands on independent evidence, so circularity is moderate rather than severe.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claims rest on multiple unverified domain assumptions: matting accuracy, independent-layer coherence, layout validity, and the model-derived TIPS quality signal. The free parameters, including filter thresholds, the style set, and TIPS label weights, are hand-chosen and mostly unpublished, so the exact dataset is not fully reproducible from the paper alone.

free parameters (5)
  • Aesthetic-score selection proportion for 80K reference pool = Not specified
    A fixed proportion of top-scoring images per layer-count group is selected in Section 3.2 (D to E); the exact proportion changes the style and layer distribution of PrismLayersPro.
  • Artifact classifier acceptance threshold = Not specified
    The BLIP-2 artifact classifier is trained on 8K manually annotated images and a threshold is applied to keep 200K images; the threshold is not published.
  • TIPS preference label ensemble weights = Not specified
    Pairwise preferences in Section 3.4 are a weighted sum of Aesthetic Predictor V2.5, Image Reward, LAION Aesthetic, HPSv2, and VQA scores; the weights define TIPS and therefore affect dataset filtering and evaluation.
  • Style keyword set and per-style sampling count = 20 or 21 styles, 2,000 layouts per style
    The style taxonomy, including toy, melting silver, line draw, ink, and doodle art, is hand-defined in Section 3.2 and determines PrismLayersPro's style distribution.
  • Suffix prompt phrase and color = isolated on a solid gray background
    Chosen empirically via attention-map and matting metrics in Appendices I and J; it affects whether foreground objects can be cleanly cut out from the generated background.
assumptions (4)
  • domain assumption RMBG-2.0 produces alpha mattes accurate enough to serve as the ground-truth alpha channel in PrismLayers.
    Invoked in Section 3.3 when matting is used to extract alpha mattes; no validation against human-annotated mattes is reported.
  • domain assumption Generating layers independently with FLUX.1-dev on gray backgrounds yields objects that composite into coherent multi-layer images.
    The authors explicitly acknowledge in Section 3.2 Discussion that MultiLayerFLUX cannot fully guarantee inter-layer consistency; coherence is instead mitigated by human selection.
  • domain assumption The 800K crawled commercial graphic designs provide valid semantic layouts and layer ordering for training data.
    Used in Section 3.2 (A to 1 to B) as the source of all layouts; the layout distribution therefore inherits the crawled design distribution.
  • domain assumption TIPS scores correlate with human aesthetic preference for transparent layers.
    TIPS is trained in Section 3.4 on pairwise labels derived from a weighted sum of RGB-oriented model scores, not directly from human judgments; this assumption is load-bearing for filtering and evaluation.

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Cite this review

Pith. "Pith review of PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models." pith.science (2026). https://pith.science/paper/VGIJVMSB

@misc{pith2026250522523,
  author       = {Pith},
  title        = {Pith review of: PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VGIJVMSB}},
  note         = {Machine review of arXiv:2505.22523}
}
read the original abstract

Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing text outputs from LLMs. However, the development of multi-layer generative models lags behind that of conventional text-to-image models due to the absence of a large, high-quality corpus of multi-layer transparent data. In this paper, we address this fundamental challenge by: (i) releasing the first open, ultra-high-fidelity PrismLayers (PrismLayersPro) dataset of 200K (20K) multilayer transparent images with accurate alpha mattes, (ii) introducing a trainingfree synthesis pipeline that generates such data on demand using off-the-shelf diffusion models, and (iii) delivering a strong, open-source multi-layer generation model, ART+, which matches the aesthetics of modern text-to-image generation models. The key technical contributions include: LayerFLUX, which excels at generating high-quality single transparent layers with accurate alpha mattes, and MultiLayerFLUX, which composes multiple LayerFLUX outputs into complete images, guided by human-annotated semantic layout. To ensure higher quality, we apply a rigorous filtering stage to remove artifacts and semantic mismatches, followed by human selection. Fine-tuning the state-of-the-art ART model on our synthetic PrismLayersPro yields ART+, which outperforms the original ART in 60% of head-to-head user study comparisons and even matches the visual quality of images generated by the FLUX.1-[dev] model. We anticipate that our work will establish a solid dataset foundation for the multi-layer transparent image generation task, enabling research and applications that require precise, editable, and visually compelling layered imagery.

Figures

Figures reproduced from arXiv: 2505.22523 by the authors.

Figure 1
Figure 1. Illustration of key statistics from PrismLayers (number of layers) and PrismLayersPro (different of styles), along with representative high-quality synthetic multi-layer transparent images from PrismLayersPro. ∗Research intern at Microsoft. Corresponding author: yuhui.yuan@microsoft.com Preprint. Under review. arXiv:2505.22523v1 [cs.CV] 28 May 2025 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. User study results on the effectiveness of PrismLayersPro. Left: ART+ v.s. ART. Right: ART+ v.s. MultiLayerFLUX. With fine-tuning on PrismLayersPro, ART+ achieves the best performance. Abstract Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing text outputs from LLMs. However, the developme… view at source ↗
Figure 3
Figure 3. Illustrating the key dataset statistics on PrismLayers and PrismLayersPro Dataset # Samples # Layers Open Source Source Data Alpha Quality Aesthetic Multi-layer Dataset [25] ∼ 1 M 2 ✗ commercial, generated good good LAION-L2 I [26] ∼ 57 M 2 ✗ LAION normal normal MLCID [8] ∼ 2 M [2,3,4] ✗ LAION poor poor MLTD [19] ∼ 1 M 2 ∼ 50 ✗ Graphic design website good normal MAGICK [5] ∼ 150 K 1 ✓ Synthetic good good MuLAn [21] … view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Illustrating the aesthetic quality of the crawled data (columns 1 and 4), synthetic data (columns 2 and 5), and high-quality synthetic data generated with a style prompt (columns 3 and 6). Multi-layer prompts and semantic layout from crawled data. A → 1 → B We begin by…
Figure 5
Figure 5. Figure 5: Dataset Curation Pipeline of PrismLayers and PrismLayersPro. We first extract semantic layouts from a database of 800K crawled multi-layer graphic design images. Then, we apply MultiLayerFLUX to generate high-quality multi-layer transparent images. An Artifact Classifi…
Figure 6
Figure 6. Figure 6: LayerFLUX and MultiLayerFLUX Framework. In this section, we present the mathe￾matical formulation of the multi-layer transparent image generation task, fol￾lowed by key insights and implemen￾tation details of our LayerFLUX and MultiLayerFLUX models. Formulation. The tr…
Figure 7
Figure 7. Figure 7: Illustrating the artifact multi-layer transparent images that our classifier can identify and filter out [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison results between ART (top row) and ART+ (bottom row) [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: Qualitative comparison results between MultiLayerFLUX (top row) and ART+ (bottom row) [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 11
Figure 11. Figure 11: Qualitative comparison results between FLUX.1-[dev] (1st row), MultiLayerFLUX (2nd row), ART (3rd row), and ART+ (4th row) across 7 cases (columns). The rightmost columns show composed multi-layer images. Comparison to FLUX [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 12
Figure 12. Figure 12: Qualitative comparison of different salient object matting models. From left to right, we show the matted results with RMBG-2.0, BiRefNet, and SAM2. Method Natural Object Layer Quality Sticker Layer Quality Creative Object Layer Quality HPSv2 ↑ AE-V2.5 ↑ TIPS ↑ HPSv2 …
Figure 13
Figure 13. Figure 13: Qualitative comparison of results with SOTA on Layer-Bench. The first row shows the results generated with LayerDiffuse, while the second row shows the results generated with our LayerFLUX. 0 50 100 Layer Quality Aesthetics Prompt Following 19.9 23.3 13.7 26.8 13.6 25…
Figure 14
Figure 14. Figure 14: Illustrating the win-rate on single-layer transparent image generation benchmark Layer-Bench. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]
Figure 15
Figure 15. Figure 15: Visualizing High-Quality Transparent Image Samples of PrismLayersPro (1/3). 18 [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]
Figure 16
Figure 16. Figure 16: Visualizing High-Quality Transparent Image Samples of PrismLayersPro (2/3). 19 [PITH_FULL_IMAGE:figures/full_fig_p019_16.png]
Figure 17
Figure 17. Figure 17: Visualizing High-Quality Transparent Image Samples of PrismLayersPro (3/3). 20 [PITH_FULL_IMAGE:figures/full_fig_p020_17.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    A two-model framework generates images as transparent layers and decomposes finished designs into ordered, complete semantic layers, outperforming prior decomposition models on per-layer fidelity and editability.

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    A dual-branch CLIP training pipeline with regional prompts and hierarchical feature alignment reaches state-of-the-art on long- and short-text retrieval.

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    2024

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    The text sticker describes/contains/

    The caption begins with "The text sticker describes/contains/" and ends with "isolated on a solid transparent background."

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    Clearly describe the text in the sticker, including the font color, font style, and any visual effects (e.g., shadows, gradients) observed in the image

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    Be explicit about these in your caption

    Keywords usually refer to the text in the sticker, and you may include other relevant descriptive elements. Be explicit about these in your caption

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    penguin",

    Refer to the examples provided for clarity on how to construct your caption. Aim for creativity while adhering to the required structure. Here are some examples for reference: - "The text sticker presents the word ’Focus’ in a sharp, modern font, filled with a gradient of char...

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    Focus solely on the foreground content

    **Ignore the Gray Background:** - Do not mention or describe the gray background in any way. Focus solely on the foreground content

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    - Transcribe every word, symbol, punctuation mark, and character **without omission or modification**

    **Handling Text in the Image:** - If the image contains any textual elements, the description **must** begin with **"Text:"** followed by a precise transcription of all visible text. - Transcribe every word, symbol, punctuation mark, and character **without omission or modific...

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    - Ensure that the provided style, STYLEPROMPT, is seamlessly **integrated into the description**, maintaining coherence and natural flow

    **Handling Non-Text Elements:** - If the image contains **non-text elements**, generate an **detailed** description, capturing all visible aspects. - Ensure that the provided style, STYLEPROMPT, is seamlessly **integrated into the description**, maintaining coherence and natural flow

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    This is a STYLEPROMPT style image

    **Output Format:** - Provide only the description of the image. Do **not** include any additional explanations, comments, or meta-information about the task itself. - The description **must explicitly state** that the image is in **STYLEPROMPT style**, starting with **"This is...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.