Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation

Hoigi Seo; Jae-sun Seo; Se Young Chun; Wongi Jeong

REVIEW 3 major objections 6 minor 1 cited by

Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation

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

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Pith's one-line read Skrr prunes text-to-image text encoders to roughly 42% sparsity and cuts pipeline memory by a third while holding image quality.

desk verdict Solid empirical pruning recipe for T2I text encoders with consistent gains over baselines; the theory is weak and hyperparameters were tuned on the evaluation benchmarks, but the main result likely survives. read the letter →

arxiv 2502.08690 v1 pith:ZL3IYIF6 submitted 2025-02-12 cs.LG cs.AIcs.CV

Hoigi Seo , Wongi Jeong , Jae-sun Seo , Se Young Chun This is my paper · ORCID

classification cs.LGcs.AIcs.CV

keywords text-to-imagediffusiontextencoderpruningblockwiselayerreusememoryefficiencybeamsearchclassifier-freeguidanceT5-XXLcompression

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 claims that the text encoder is the hidden memory hog in text-to-image (T2I) diffusion models: it is invoked once per prompt, yet it holds the bulk of the parameters, while the denoising network that runs many iterations uses comparatively little memory. Skrr ('Skip and Re-use layers') is a training-free pruning recipe for that encoder. A beam search decides which attention and feed-forward sub-blocks to drop, scoring each candidate by the mean-squared change it causes in the projected text embedding that the denoiser actually conditions on, measured for both the prompt and the null condition used in classifier-free guidance. A second stage fills each removed slot with the hidden state of a neighbouring surviving block, and a Lipschitz bound (Theorem 3.2) shows this re-use is strictly safer than leaving the slot empty. On PixArt-Sigma at 41.9% text-encoder sparsity the pipeline memory falls from 10.18 GB to 6.46 GB with FID 19.93 versus 22.89 dense, CLIP 0.312 versus 0.314, and GenEval 0.442 versus 0.539, beating the ShortGPT, LaCo, and FinerCut baselines at the same sparsity.

What carries the argument

The load-bearing object is the discrepancy score $D = D_{f_c} + D_{f_\varnothing}$, where each term is the mean-squared error, in the denoising network's projected conditioning space, between the dense text encoder's output and the pruned encoder's output on a prompt ($f_c$) or on the null condition used for classifier-free guidance ($f_\varnothing$). This score is what makes pruning decisions speak to final image quality instead of to text-embedding statistics. Around it sit two algorithms: Skip, a beam search that keeps the $k$ lowest-discrepancy pruning paths so that interactions between removed sub-blocks are accounted for, and Re-use, which for each skipped sub-block compares feeding it the previous or the next unskipped neighbour's hidden state and keeps whichever lowers $D$. The re-use choice is backed by a Lipschitz argument (Lemma 3.1) bounding the output gap between any two transformer copies, which yields Theorem 3.2: re-using is strictly better than zeroing whenever $\|\theta_i - \theta_i^*\| < \|\theta_i\|$.

What would settle it

Generate two pruning masks with the Skip phase, one from the paper's long-prompt calibration set and one from a short-prompt set in the style of GenEval, then evaluate both pruned models on both prompt distributions; if the long-prompt mask wins on long prompts but clearly loses on short prompts, or if either mask's discrepancy score stays low while its images degrade, the proxy assumption is refuted. A second check is to reproduce the claimed guidance mechanism by perturbing the dense model's null embedding and asking whether FID improves to the same 19.93-class value across several seeds without touching the text encoder.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that transformer sub-blocks inside T2I text encoders are redundant enough that about 40% of the encoder can be dropped or recycled with negligible loss of generation quality, while existing blockwise pruning methods designed for autoregressive LLMs degrade sharply at the same sparsity. Two design choices carry this. First, the pruning metric is computed on the projected condition vector the denoising network consumes, not on raw hidden states, and it includes the null input used for classifier-free guidance; the paper shows that cosine-similarity metrics miss large null-embedding norm shifts that visibly corrupt images. Second, after skipping, the paper re-routes each removed sub-block's input through a neighbouring remaining sub-block, and proves (Theorem 3.2, from a Lipschitz error bound on transformers) that such re-use gives a strictly tighter output error bound than skipping alone whenever the neighbour's parameters are closer to the original than zero is; empirically, re-use restores the prompt adherence that Skip alone loses.

Load-bearing premise

The method trusts a single proxy: that the mean-squared change in the projected text embedding, measured on a 1,000-prompt calibration set of long CC12M captions, reliably predicts whether final generated images will stay good on prompts that the set never contained.

Editorial extensions

If this is right

At 41.9% text-encoder sparsity on PixArt-Sigma, the pipeline drops from 5.42B to 3.43B parameters and from 10.18 GB to 6.46 GB of memory, with FID 19.93 (dense 22.89), CLIP 0.312 (dense 0.314), and GenEval 0.442 (dense 0.539).
In the high-sparsity regime the LLM-oriented baselines lose most prompt alignment (GenEval overall falls to between 0.087 and 0.367) while Skrr holds 0.442, so the biggest separation happens exactly where blockwise pruning previously failed.
Re-use behaves as a plug-in: applied on top of ShortGPT or FinerCut pruning masks, it restores fidelity and dense-model alignment in the paper's ablations, suggesting the re-routing step can help other pruning methods without retraining.
Compressing all three text encoders of Stable Diffusion 3 (T5-XXL at 41.9%, CLIP-L and CLIP-G at roughly 30%) keeps GenEval at 0.579 versus 0.689 for the dense model, indicating the recipe generalizes to multi-encoder pipelines.
Pruning improves FID from 22.89 to 19.93 while slightly lowering CLIP and GenEval; the paper's null-embedding perturbation experiment (dense FID falls to 20.65 under small noise on the null condition) supports a guidance-like explanation for the fidelity gain.

Reading between the lines

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

If the null-embedding mechanism is right, text-encoder pruning is partly an accidental retuning of classifier-free guidance; a testable extension is to co-optimize the pruning mask and the guidance scale $w$ to harvest the FID gain without the CLIP and GenEval losses.
The calibration set contains only long captions (150-250 tokens), so the proxy is validated on one prompt distribution; short prompts and rare concepts may engage different sub-blocks, and a prompt-length-stratified calibration set would be a cheap stress test before deployment.
The skip-and-reuse pattern should transfer to any memory-bound, single-forward-pass encoder in a generative pipeline, such as CLIP conditioning in video or 3D generators, because the projection-metric trick only requires that the encoder output enter the rest of the network through a fixed linear map.
The paper reports quality deteriorating beyond 50% sparsity; stacking weight quantization on top, a combination it mentions but does not test, could push text-encoder memory below roughly one gigabyte and widen the practical deployment range.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a good empirical paper, not a good theory paper. The empirical recipe — skip text-encoder sub-blocks using a beam search over a T2I-specific discrepancy (projected embedding MSE plus a null-condition term), then re-use adjacent sub-blocks — is new and it works. At 41.9% T5-XXL sparsity on PixArt-Sigma it cuts pipeline memory from 10.18 to 6.46 GB, roughly 36%, while keeping CLIP at 0.312 vs 0.314 dense and FID at 19.93 vs 22.89. It beats ShortGPT, LaCo, and FinerCut on almost every metric at comparable sparsity, and the qualitative results look consistent. The authors also show Re-use plugs into ShortGPT and FinerCut, which is a nice generality check. Credit where due: the ablations are thorough, the null-condition discrepancy is a sensible T2I-specific addition, and the Limitations appendix is honest that performance degrades at high sparsity.

The soft spots are real but not fatal. Lemma 3.1's proof has algebraic errors in the intermediate recurrences (the E3 line uses the wrong index and misplaces a factor of (1+L2)), though the final bound is correct. More importantly, Theorem 3.2 is a tautology: it says if the reused weights are closer to the original than zero, the error bound is tighter. True by definition, but it does not establish that reusing an adjacent block satisfies that condition. The paper overstates this as 'theoretical support.' The empirical support is what carries the paper.

The bigger concern is test-set tuning. The beam size k=3 is selected from Table 3 using the same MS-COCO/GenEval metrics as the final evaluation, and the projection module is justified post hoc via Table A13 on the same metrics. There are no error bars and no code/data release. I checked whether this undermines the headline comparison: even at k=1 (greedy) Skrr beats FinerCut on CLIP, DreamSim, and GenEval, and the no-projection variant still beats FinerCut overall. So the advantage is probably not purely an artifact of the tuned hyperparameters, but the magnitude of the reported margins is likely optimistic. A held-out evaluation or nested hyperparameter selection would settle this.

The abstract's 'without compromising performance' is a bit strong — GenEval drops from 0.539 to 0.442 (about 18% relative) at the highest sparsity, and the authors acknowledge this in the Limitations.

Bottom line: this deserves a serious referee. Anyone deploying T2I models on memory-limited hardware will get value from the method, and the main claim should survive closer scrutiny. Recommend peer review with requested revisions: fix or remove the theory, add error bars or multiple seeds, release code, and report hyperparameter selection honestly.

Referee Report

3 major / 6 minor

Summary. This paper presents Skrr, a two-stage blockwise pruning method for the T5-XXL text encoder in text-to-image diffusion models. In the Skip stage, sub-blocks (MHA and FFN) are pruned in an order found by beam search that minimizes an MSE discrepancy between the dense and pruned projected text embeddings, plus a null-condition discrepancy term. In the Re-use stage, each skipped sub-block is replaced by an adjacent kept sub-block if that reduces the discrepancy. The authors report quantitative results on PixArt-Σ at sparsity levels around 24-42%, showing that Skrr maintains or improves FID, keeps CLIP and DreamSim close to dense, and degrades GenEval less than ShortGPT, LaCo, and FinerCut. Qualitative results on Stable Diffusion 3 and FLUX.1-dev are also shown. The paper includes ablations for the beam size, the projection module, and the Re-use component, as well as a theoretical error-bound analysis.

Significance. The problem is well-motivated: text encoders account for a large fraction of parameters and memory in T2I pipelines while contributing little FLOPs, and dedicated text-encoder pruning is under-explored. The proposed method is simple and the empirical results at high sparsity, if taken at face value, would be a practical improvement over existing blockwise pruning methods. The paper ships extensive ablations and qualitative comparisons, and the design choices (projection-aware discrepancy, null-condition term) are task-appropriate. However, the credibility of the central claim is currently limited by the use of the evaluation benchmarks for hyperparameter selection and by the narrow quantitative scope (one base model).

major comments (3)

[Section 4.3, Table 3; Appendix C.5, Table A13] The hyperparameters of Skrr (beam size k and the inclusion of the projection module) are selected using the same evaluation benchmarks that are reported as final results in Table 1. Specifically, Table 3 selects k = 3 by comparing CLIP, DreamSim, and GenEval on the MS-COCO validation subset and the full GenEval set, and Table A13 justifies the projection module on the same metrics. This makes the final comparison subject to selection on the test set. The concern is not hypothetical: the projection module ablation changes GenEval from 0.381 to 0.442 at 41.9% sparsity, which is most of the reported margin over FinerCut (0.442 vs. 0.367). The authors should provide a held-out validation set for hyperparameter tuning, or at least report the full comparison for all k values and with projection disabled, so that the central claim is not affected by this selection.
[Section 4.1, Table 1; Section 4.2, Figure 5] The quantitative comparison against baselines is performed only on PixArt-Σ. The results for SD3 and FLUX.1-dev are qualitative (Figure 5) and the computational-cost comparison in Appendix C.2 does not include image-quality metrics. Since the abstract claims 'state-of-the-art' performance and the method is presented as generally applicable to T2I text encoders, at least one quantitative benchmark (e.g., GenEval or CLIP score) on a second model is needed to support the generality of the central claim.
[Appendix B.3] The calibration set consists exclusively of prompts with 150–250 tokens sampled from CC12M, while the GenEval evaluation prompts are short (typically one or two objects). The discrepancy metric in Eq. (3) is computed on these long calibrated prompts, and the paper does not verify that the resulting block ranking transfers to short prompts. Although the GenEval numbers in Table 1 are positive, a robustness experiment with a short-prompt calibration set (or a mixed-length set) would substantially strengthen the claim that the pruning mask is tailored to T2I rather than to the calibration distribution.

minor comments (6)

[Section 1, Figure 1(a)] The percentages in Figure 1(a) appear inconsistent with the text: the figure seems to attribute about 3.25% of FLOPs to the text encoders, while the text states they contribute less than 0.5%. Please correct the figure or the text.
[Appendix C.6] The paragraph on FinerCut metrics is internally contradictory: it first says MSE outperforms cosine similarity, then says cosine similarity yielded better performance, and then states the MSE-based implementation was used as the baseline. Please clarify which configuration is used and why.
[Conclusion] The conclusion mentions a 'Skrr dot product' as one of three key components, but the paper's metric is MSE (Eq. (3)); please fix this terminology.
[Appendix A.1, Eq. (A15)] There is an algebraic typo in the recurrence for E3: the term M2||θ1 − θ̂1|| should be M2||θ2 − θ̂2||. The final telescoped bound is correct, but the displayed step is not.
[General] No error bars or confidence intervals are reported for the main metrics; given the relatively small differences between methods (e.g., CLIP 0.312 vs. 0.308 in Table 1), error bars or multiple seeds would help assess significance.
[Section 3.2, Theorem 3.2] The condition ||θi − θ∗i || < ||θi|| is not verified on the actual models; the authors should state whether it holds for the reused blocks reported in Tables A4–A6.

Circularity Check

2 steps flagged · score 2.0 of 10

Mild test-set hyperparameter selection and a tautological Re-use bound; the central external-benchmark comparison is not circular.

fitted input called prediction [Sec. 4.3 (Ablation study, beam size) and Table 3; final results in Table 1]
"We performed an ablation study that evaluated the effect of the beam search at different values of k, as shown in Table 3. As k increases, performance initially improves and then decreases. ... Based on this observation, we selected the optimal beam size k = 3."

Table 3 reports CLIP, DreamSim, and GenEval on a subset of the MS-COCO validation set and the full GenEval set; Table 1 reports the same families of metrics on the same benchmarks as the paper's central evidence. Choosing k to maximize these metrics and then presenting those metrics as evidence that Skrr 'outperforms' baselines means the reported margin is partly selected, not independently predicted. This is test-set hyperparameter fitting rather than evaluation of a fixed method, though the greedy k=1 row already beats the baselines, so the main ranking is not manufactured.
self definitional [Theorem 3.2 and Appendix A.2 (Eq. A18-A26)]
"Theorem 3.2 (Tighter error bound of Re-use). Under the assumptions of Lemma 3.1, let θ∗ i be the parameters of the reused Fi. Define USkip as the error bound for the compressed model with Skip alone and USkip, Re-use as the error bound for the compressed model with Skip and Re-use. If ∥θi − θ∗ i ∥ < ∥θi∥, then the following holds: USkip, Re-use < USkip."

The theorem's condition is exactly the statement that the reused block's parameters are closer to the dense parameters than the skipped block's parameters (zero). The proof expands USkip and USkip,Re-use and immediately obtains the inequality term-by-term; no property of T5-XXL, of text-to-image generation, or of Algorithm 2 is used. Thus the 'theoretical support' for Re-use restates its own assumption: it does not prove that such θ∗ exists for the T5 encoder nor that the greedy discrepancy search finds it. The conclusion is thus equivalent to the hypothesis by construction.

full rationale

The central empirical claim is not circular: Skip and Re-use decisions are made on a 1k CC12M calibration set, while FID, CLIP, DreamSim, and GenEval are computed on MS-COCO and GenEval, so the headline comparison is externally anchored. The circular elements are mild and non-load-bearing: (i) hyperparameters such as beam size k=3, and the projection module, were selected after inspecting the evaluation benchmarks, which can optimistically bias the absolute reported numbers (though even the greedy k=1 row already outperforms the baselines, so the central ranking is not forced by this selection); and (ii) Theorem 3.2 is a tautology whose assumption directly implies its conclusion, providing no independent mathematical evidence for Re-use beyond the algorithm's empirical discrepancy measurements. There is no load-bearing self-citation, no uniqueness argument imported from the authors' prior work, and no ansatz smuggled in via citation. The FinerCut baseline variant choice is a fairness concern but not a circularity. Overall, the derivation is self-contained against external benchmarks, so the circularity score is low.

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

The central result depends on several empirical assumptions: layer redundancy in T5 text encoders, the fidelity of the projected-feature discrepancy as a proxy for image quality, and the representativeness of the 1k CC12M calibration set. The theoretical bound is conditional on Lipschitz assumptions and a proximity condition for reused parameters that is not proven to hold. The method itself introduces no new architectural entities, only hyperparameters such as beam size and candidate limit, which are tuned on the evaluation benchmarks.

free parameters (4)

beam_size_k = 3
Chosen by ablation on the evaluation benchmarks (Table 3); larger k initially improves then degrades performance. This is a hyperparameter tuned on the test set.
candidate_subblock_limit = 24
Beam search is restricted to the top-24 sub-blocks by discrepancy to limit computational cost (Appendix B.1); this restriction could affect the optimality of pruning.
calibration_prompt_length_range = 150-250 tokens
The 1k CC12M calibration prompts were filtered to this length range to keep them semantically rich (Appendix B.3), which may bias the pruning toward long prompts.
null_perturbation_lambda = 1e-2
Used in the Discussion experiment (Eq. 8) to perturb the null-condition embedding; not part of the core pruning method.

assumptions (4)

standard math Each transformer block Fi is Li-Lipschitz in its input and Mi-Lipschitz in its parameters (Lemma 3.1, Eq. A2-A3).
Used to derive the error bound; in practice these constants are unknown and could be large, making the bound qualitative rather than quantitative.
domain assumption Hidden states of adjacent T5 blocks are highly similar, so layers can be skipped with little output change (Fig. 3a).
Empirical observation in T5 used to motivate Skip; could fail for other text encoders or prompt types.
domain assumption The projection module output f = proj(E(c), theta_denoise) captures the information relevant for image generation (Eq. 1).
The discrepancy metric is computed on projected features; if the projection discards information that later layers need, the metric could mis-rank layers.
ad hoc to paper For Re-use, the parameters of an adjacent block are closer to the skipped block's parameters than zero (condition of Theorem 3.2).
The theorem is conditional; the paper relies on the greedy discrepancy check in Algorithm 2 to find such blocks empirically, but provides no guarantee for arbitrary models.

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Cite this review

Pith. "Pith review of Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation." pith.science (2026). https://pith.science/paper/ZL3IYIF6

@misc{pith2026250208690,
  author       = {Pith},
  title        = {Pith review of: Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZL3IYIF6}},
  note         = {Machine review of arXiv:2502.08690}
}

read the original abstract

Large-scale text encoders in text-to-image (T2I) diffusion models have demonstrated exceptional performance in generating high-quality images from textual prompts. Unlike denoising modules that rely on multiple iterative steps, text encoders require only a single forward pass to produce text embeddings. However, despite their minimal contribution to total inference time and floating-point operations (FLOPs), text encoders demand significantly higher memory usage, up to eight times more than denoising modules. To address this inefficiency, we propose Skip and Re-use layers (Skrr), a simple yet effective pruning strategy specifically designed for text encoders in T2I diffusion models. Skrr exploits the inherent redundancy in transformer blocks by selectively skipping or reusing certain layers in a manner tailored for T2I tasks, thereby reducing memory consumption without compromising performance. Extensive experiments demonstrate that Skrr maintains image quality comparable to the original model even under high sparsity levels, outperforming existing blockwise pruning methods. Furthermore, Skrr achieves state-of-the-art memory efficiency while preserving performance across multiple evaluation metrics, including the FID, CLIP, DreamSim, and GenEval scores.

Figures

Figures reproduced from arXiv: 2502.08690 by the authors.

**Figure 1.** (a) FLOPs distribution during image generation in Stable Diffusion 3 (SD3) (Esser et al., 2024). (b) Parameter distribution across modules in SD3. The text encoders contributes less than 0.5% to the overall FLOPs but account for over 70% of the total model parameters. For VAE, only the decoder was considered. 2023; Wang et al., 2024c). With modern architecture and large-scale text encoders, they produce high-quality… view at source ↗

**Figure 2.** The visualization of overall framework of Skrr. (a) shows the Skip phase, which repeatedly assesses each sub-block by determining the output discrepancy (Disc.) between the dense and skipped models using a calibration dataset (Calib. data). To account for block interactions, it keeps the top k options with the smallest discrepancies and uses beam search for refined selection. (b) presents the Re-use phase, evaluatin… view at source ↗

**Figure 4.** (a) An image is created by the PixArt-Σ dense text encoder using the prompt “A car made out of vegetables.” with ||f∅||2 = 0.03. For image (b), the 7 th and 22th sub-blocks are excluded, resulting in Metric1 = 0.85, Metric2 = 0.002, and ||f∅||2 = 0.19. Image (c) is generated by removing the 3 rd and 5 th sub-blocks, producing Metric1 = 0.89, Metric2 = 0.04, and ||f∅||2 = 3.34. Despite Metric1 being higher in (c), t… view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Comparison of images generated with baseline and Skrr-compressed text encoders across PixArt-Σ, Stable Diffusion 3 (SD3), and FLUX.1-dev. At low sparsity (level 1–24.3% for ShortGPT and Laco, 26.3% for FinerCut, and 27.0% for Skrr), both methods perform comparably to d…

**Figure 6.** Figure 6: Ablation study on Re-use. Without Re-use, Skip alone leads to images that often misalign with the prompt, while Re-use ensures more faithful adherence to the prompt. 4.2. Qualitative results We present qualitative results that demonstrate the performance of T2I synthe…

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Works this paper leans on

19 extracted references · 19 canonical work pages · cited by 1 Pith paper

[1]

Examples of prompts from the calibration set across various lengths are presented

Single Object Generation – Assesses the model’s ability to generate images from prompts containing a single object (e.g., 16 Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation Table A7. Examples of prompts from the calibration set across various lengths are presented. These text prompts are sampled from the CC12M datas...

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A moment at a subway station with a vintage train numbered “7” at the platform. The platform has safety barriers and a yellow line, illuminated by fluorescent lights. Text reads “last stop for the 7 train” and credits the photographer

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[3]

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A collection of photography equipment neatly arranged on a wooden surface. Items include a camera, smartphone, tablet, drone, portable power bank, tripod, cleaning kit, strap, case, and backpack. The warm wooden background contrasts with the modern gear

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[4]

A Day to Remember

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A pendant light provides a warm glow

A modern living room with a minimalist design. A pendant light provides a warm glow. A wooden table holds a glass of water, a book, a smartphone, and a notebook. A white cabinet and a cityscape view complete the cozy atmosphere

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[6]

A white sink under a window, a glass shower enclosure, and a toilet create a rustic yet clean look

A small modern bathroom with brick-patterned walls and tiled flooring. A white sink under a window, a glass shower enclosure, and a toilet create a rustic yet clean look

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[7]

Buildings vary in color and pattern, resembling a patchwork quilt, creating a dense, lively urban environment

A vibrant digital artwork of a stylized cityscape. Buildings vary in color and pattern, resembling a patchwork quilt, creating a dense, lively urban environment

work page
[8]

In this house we are real, we make mistakes, we say I’m sorry, we give hugs, we give second chances, we forgive, we laugh a lot, we love each other, we are a family

A wall with a playful quote: “In this house we are real, we make mistakes, we say I’m sorry, we give hugs, we give second chances, we forgive, we laugh a lot, we love each other, we are a family.” A guitar leaning against the wall adds a cozy, homey touch

work page

Show all 19 references

[9]

Metal stairs lead to the entrance, possibly part of a museum exhibit

A vintage light-colored train car with blue and white stripes is parked on a track under a metal canopy. Metal stairs lead to the entrance, possibly part of a museum exhibit
[10]

the facade is adorned with intricate mosaics and sculptures, including a central figure that appears to be a religious figure, possibly a saint or deity

The memorial church at stanford university, a large, ornate building with a prominent cross at the top, illuminated at night. the facade is adorned with intricate mosaics and sculptures, including a central figure that appears to be a religious figure, possibly a saint or deit...
[11]

the bouquet consists of various types of flowers, including hydrangea, calla lilies, roses, and gerbera daisies, with burgundy berries interspersed among them

A vibrant bouquet of flowers arranged in a clear glass vase. the bouquet consists of various types of flowers, including hydrangea, calla lilies, roses, and gerbera daisies, with burgundy berries interspersed among them. the flowers are in shades of pink and purple, creating a...
[12]

chill” is prominently displayed in bold, white capital letters. the overall style of the image is modern and graphic, with a clear emphasis on the word “chill

A graphic design with a stylized representation of a face, possibly a deity, with a serene expression. the face is framed by a green border with a white outline and a blue background. above the face, there is a crescent moon and a symbol that resembles a peace sign. below the ...
[13]

the boat is equipped with a dining area featuring a table set for four with blue tableware, and a bar area with a blender, wine glasses, and a bottle of wine

A serene lakeside setting with a houseboat that resembles a private yacht. the boat is equipped with a dining area featuring a table set for four with blue tableware, and a bar area with a blender, wine glasses, and a bottle of wine. the deck is furnished with multiple lounge ...
[15]

a photo of a knife and a stop sign

Two Objects Generation – Evaluates the model’s ability to correctly generate images from prompts with two distinct objects (e.g., “a photo of a knife and a stop sign”)
[16]

a photo of three apples

Counting – Measures whether the model can accurately represent the specified number of objects (e.g. “a photo of three apples”)
[17]

a photo of a pink car

Colors - Verifies whether the generated image correctly reflects the color specified in the prompt (e.g., “a photo of a pink car”)
[18]

Position – Tests the model’s understanding of spatial relationships described in the prompt (e.g., “a photo of a sofa under 17 Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation Table A8. Sparsity ratio of the text encoder, parameter count ...
[19]

a photo of a black car and a green parking meter

Color Attribution – Assesses the correct assignment of specified colors to multiple objects (e.g., “a photo of a black car and a green parking meter”). For evaluation, we generated images using a fixed random seed, producing 553 distinct prompts with four images per prompt, re...
[2020]

(C4), SlimPajama (Soboleva et al., 2023) and WikiText (Merity et al., 2016), primarily focusing on perplexity-based performance metrics, these approaches are not directly applicable to T2I models. To address this gap, we curated a calibration set specifically tailored for the ...

2023

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

Examples of prompts from the calibration set across various lengths are presented

Single Object Generation – Assesses the model’s ability to generate images from prompts containing a single object (e.g., 16 Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation Table A7. Examples of prompts from the calibration set across various lengths are presented. These text prompts are sampled from the CC12M datas...

work page

[2] [2]

7” at the platform. The platform has safety barriers and a yellow line, illuminated by fluorescent lights. Text reads “last stop for the 7 train

A moment at a subway station with a vintage train numbered “7” at the platform. The platform has safety barriers and a yellow line, illuminated by fluorescent lights. Text reads “last stop for the 7 train” and credits the photographer

work page

[3] [3]

Items include a camera, smartphone, tablet, drone, portable power bank, tripod, cleaning kit, strap, case, and backpack

A collection of photography equipment neatly arranged on a wooden surface. Items include a camera, smartphone, tablet, drone, portable power bank, tripod, cleaning kit, strap, case, and backpack. The warm wooden background contrasts with the modern gear

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[4] [4]

A Day to Remember

A person wearing a white t-shirt with the text “A Day to Remember” in pink and black lettering. The shirt features a black collar and short sleeves, displayed plainly for product showcasing

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[5] [5]

A pendant light provides a warm glow

A modern living room with a minimalist design. A pendant light provides a warm glow. A wooden table holds a glass of water, a book, a smartphone, and a notebook. A white cabinet and a cityscape view complete the cozy atmosphere

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[6] [6]

A white sink under a window, a glass shower enclosure, and a toilet create a rustic yet clean look

A small modern bathroom with brick-patterned walls and tiled flooring. A white sink under a window, a glass shower enclosure, and a toilet create a rustic yet clean look

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[7] [7]

Buildings vary in color and pattern, resembling a patchwork quilt, creating a dense, lively urban environment

A vibrant digital artwork of a stylized cityscape. Buildings vary in color and pattern, resembling a patchwork quilt, creating a dense, lively urban environment

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[8] [8]

In this house we are real, we make mistakes, we say I’m sorry, we give hugs, we give second chances, we forgive, we laugh a lot, we love each other, we are a family

A wall with a playful quote: “In this house we are real, we make mistakes, we say I’m sorry, we give hugs, we give second chances, we forgive, we laugh a lot, we love each other, we are a family.” A guitar leaning against the wall adds a cozy, homey touch

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