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REVIEW 4 major objections 6 minor 49 references

T-Stars-Poster: A Framework for Product-Centric Advertising Image Design

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read T-Stars-Poster generates advertising posters from a product image, taglines, and a target size, and reports better visual appeal and higher click-through rates.

desk verdict A well-engineered industrial framework for product-centric ad design with genuinely new joint layout prediction; the A/B evidence is weaker than reported. read the letter →

arxiv 2501.14316 v3 pith:M6O65Q4P submitted 2025-01-24 cs.CV

Hongyu Chen , Min Zhou , Jing Jiang , Jiale Chen , Yang Lu , Zihang Lin , Bo Xiao , Tiezheng Ge
show 1 more author
Bo Zheng
This is my paper · ORCID
classification cs.CV
keywords advertisingimagegenerationlayouttext-to-imageinpaintingvisuallanguagemodele-commerceA/Btestingproduct-centricdesign
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 claims that advertising images can be generated automatically from three basic inputs—a product foreground image, marketing taglines, and a target size—by decomposing the task into four sequential stages: prompt generation, layout generation, background image generation, and graphics rendering. The central premise is that jointly planning the placement of the product, the graphic elements (taglines, underlays, logos), and the nongraphic elements (objects described in the background prompt) before generating the background resolves the conflicts that arise when these components are designed independently. To support this, the paper introduces two datasets with over 50,000 labeled advertising images and trains separate expert models for prompt and layout generation, plus an SDXL-based inpainting model that accepts layout, prompt, and foreground controls simultaneously. The reported results show that the framework outperforms a stronger baseline pipeline in FID (37.524 vs. 43.221), wins user preference in 74% to 94% of paired comparisons, and increases click-through rate by 3.02% and 3.03% in two online A/B tests. If correct, this means that high-quality advertising creatives can be produced at scale without human designers.

What carries the argument

The load-bearing mechanism is the combination of joint layout prediction (JPGNL) with deep-layer control and LoRA-adapted training in the image generator. JPGNL uses a ratio-keeping bbox representation (RKBR) to preserve the product's aspect ratio and a class-conditioned layout prediction (CCLP) to allow or forbid tagline occlusion depending on product type, so that one model can allocate space for the product, text, decorative underlays, and background elements in a single pass. The generator, built on SDXL, injects layout embeddings through gated self-attention layers placed only in the deep UNet blocks (deep layer control, DLC) and is trained with a LoRA adaptation strategy (LAT) to bridge the new layout path with the pretrained model; this allows prompt, layout, and foreground inpainting to be combined without destabilizing training. What this machinery does is coordinate the three competing signals—foreground identity, textual message, and background scene—so that none is sacrificed for the others.

What would settle it

Run T-Stars-Poster on a random sample of unfiltered product photos from a large e-commerce catalog—including messy backgrounds, close-ups, and poorly composed shots—and measure CTR or user preference against a fixed baseline. If the quality gap and the 3% CTR gain disappear on this unfiltered set, the claimed advantage would be shown to depend on the curated training distribution rather than the framework's design.

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Extended reading notes

Core claim

The central discovery is that making the layout the joint product of foreground, text, and background constraints, rather than an afterthought, is what enables high-quality automatic advertising posters. In the proposed framework, a fine-tuned visual language model first writes a background prompt that matches the product's shape and setting; a second VLM-based module then outputs a single JSON layout that assigns coordinates to the product, the tagline blocks, decorative underlays, and background objects; and an SDXL-based inpainting generator uses that layout as control alongside the prompt and the product foreground. The claim is that this sequence—especially the joint layout prediction and the layout-conditioned generation—produces images that are more harmonious, more readable, and more attractive to users than existing pipelines that generate the background first and place text later.

Load-bearing premise

The framework's training and test data deliberately exclude images with messy backgrounds, plain colors without shadows, unattractive stickers, close-ups, poorly presented taglines, or cluttered elements, so the reported quality and CTR gains may not extend to the full, unfiltered population of real advertising inputs.

Editorial extensions

If this is right

  • Advertising images can be produced at scale from a product photo, taglines, and a target size, which could substantially reduce the manual design effort in e-commerce.
  • The reported 3% CTR lift in two e-commerce scenarios implies that automatically generated posters can improve recommendation engagement, not just offline quality metrics.
  • Because the layout is predicted jointly for graphic and nongraphic elements, the framework can adapt product placement and tagline position to different aspect ratios and product shapes.
  • The two released datasets provide labeled training data for future work on product-centric advertising generation.

Reading between the lines

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

  • The framework's success on filtered datasets suggests that its real-world applicability depends on expanding the training distribution to include messy backgrounds, close-ups, and other hard cases; a natural next step is to test on unfiltered product feeds.
  • The joint layout idea is not specific to advertising; it could be applied to other design tasks such as infographics, slides, or UI design where text and content must coexist.
  • The paper's own limitations—single foreground, up to five taglines, and fixed lighting—indicate that the framework is best suited to controlled product shots; extending it to arbitrary angles, lighting, or multiple foregrounds remains open.
  • The reported A/B test gains are measured on one platform and product mix; whether they generalize to other marketplaces or categories is an open question that could be answered by replication.
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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 / 6 minor

Summary. This paper presents T-Stars-Poster, a product-centric framework for automatic advertising image generation. Given a product foreground image, taglines, and a target canvas size, the framework runs four stages: prompt generation with a fine-tuned VLM, joint layout prediction for graphic (tagline, underlay, logo) and nongraphic (background objects) elements with a second VLM, background image generation with an SDXL-based inpainting model that receives prompt, layout, and foreground controls, and a rule-based graphics rendering step. The authors collect and label two datasets, PITA (38,017 images) and PIL (12,247 images), and report offline comparisons against two self-built pipelines, against prompt- and layout-generation baselines, and against layout-controlled inpainting models, plus a user study with 7 users on 200 samples and online A/B tests in two Taobao scenarios. The headline result is a lower FID (37.524 vs. 43.221 for the stronger baseline), favorable layout metrics, user win rates of 74-94%, and CTR increases of 3.02% and 3.03% in the two online scenarios.

Significance. T-Stars-Poster is an industrially motivated and internally coherent system. Its main strengths are the joint prediction of graphic and nongraphic layouts, the use of Deep Layer Control and LoRA adaptation to stabilize the layout-and-inpainting training, the creation and public release of the PITA dataset, and the deployment of the system in live Taobao traffic. If the online gains are real, the paper demonstrates a practical solution to a hard problem. However, the evidence base has three important gaps: the A/B results are reported without confidence intervals, sample sizes, or significance tests (Section 5.5); the training and test distributions are filtered to exclude hard advertising cases (Section 3), and the authors' own Section 6 limitations confirm that angle/lighting, multiple foregrounds, and many-tagline cases remain unsolved; and the full-pipeline comparisons use only author-constructed baselines, with P&R re-implemented by the authors (Section 5.3). These gaps do not invalidate the system, but they materially narrow the scope of the central claim as currently worded.

major comments (4)
  1. [Section 5.5] The online A/B test is the only evidence that the method generalizes beyond the filtered PITA/PIL distributions, and it is reported without any uncertainty quantification. The text states that 5,000 products were selected, 5% of main traffic was used, data were collected for one month, and CTR increased by 3.02% and 3.03% in two scenarios, but it gives no impression counts, click counts, confidence intervals, or p-values, and it does not account for intra-product correlation. Under typical e-commerce CTRs, a roughly 3% relative lift with 5,000 products can easily be within sampling noise, especially if the effective number of independent experimental units is smaller. Please report per-scenario sample sizes, CTRs with confidence intervals, and a significance test (or a permutation test over products), and clarify whether the 5,000 products pass the Section 3 filtering criteria or are a random sample of unfiltered advertiser inputs.
  2. [Section 3] The PITA and PIL datasets explicitly exclude images with messy backgrounds, plain colors without shadows, unattractive stickers, close-ups, poorly presented taglines, and cluttered elements. Because every offline result in Tables 1-5 is computed on this filtered distribution, the reported FID, layout metrics, and user preferences are conditional on a curated subset of advertising images. The authors should quantify how representative this subset is of the deployed scenarios: for example, report the fraction of real advertiser submissions that satisfy the filtering criteria, or hold out an unfiltered set and report per-category performance. Without this, the abstract's claim of automatically generating advertising images from basic inputs overstates the demonstrated scope, and the limitation statement in Section 6 suggests that several common cases (lighting/angle mismatch, multiple foregrounds, more than five taglines) are not handled.
  3. [Section 5.3, Table 1] The headline full-pipeline comparison is against two author-constructed baselines, Pipeline 1 and Pipeline 2, and the foreground-based baseline P&R is re-implemented by the authors because the original code is not public. While the authors are transparent about this, the risk of unintended bias is real: the baselines' prompts are generated by GPT-4o with no fine-tuning, the layout method PosterLlama operates on a fixed image, and the same rendering module is applied to all. Please provide a more detailed specification of the P&R re-implementation, state which version or checkpoint of PosterLlama is used, and make the comparison pipeline code available so the baselines can be checked. At a minimum, report the number of human-evaluation samples and the inter-rater agreement for the aesthetic rankings used in Table 1.
  4. [Section 5.4, Table 5] The user study is very small: 7 users, 200 randomly selected test samples, pairwise comparisons. The reported win rates of 74-94% have no confidence intervals, no per-user breakdown, and no measure of inter-rater agreement. Given that this study is one of the two pieces of evidence for the "more visually appealing" claim, please report the full preference matrix, a binomial or mixed-effects confidence interval for each win rate, and the number of comparisons per pair. This is a fixable presentation issue, but without it the reader cannot tell whether the preference differences are statistically reliable.
minor comments (6)
  1. [Section 2.2 and Table 4] SceneBooth [4] is cited as the first method to combine inpainting and layout control in training, but it is not included in the comparisons of Table 4. If SceneBooth is not publicly available or is not applicable to advertising images, please say so explicitly; otherwise add it as a baseline.
  2. [Section 4.3] The RKBR representation is described in prose as [x, y, h*r1/r2, h] but the example JSON in Figure 3 shows a raw arithmetic expression in the bbox field; please make the notation consistent and define r1 and r2 unambiguously with respect to the foreground and canvas aspect ratios.
  3. [Section 5.2] The ED Score is defined as the FID between CLIP features of prompts and ground-truth prompts; please specify the CLIP model variant, the feature extraction layer, and the number of prompt samples used for the FID computation.
  4. [Section 3] The paper states the PITA dataset is available at GitHub but does not state the same for PIL. Please clarify whether PIL will also be released, and specify the exact train/test splits for both datasets so that future comparisons use identical splits.
  5. [Figure 3] In the example tagline translation, "Made in good materialsand not break within 5 yeas" contains a typo ("yeas" should be "years", and there is a missing space before "and"); please correct the figure or its caption.
  6. [Section 6] The limitations paragraph is candid and useful. Please consider adding quantitative evidence for the stated failure modes (for example, the percentage of test cases with lighting mismatch or more than five taglines) so that readers can gauge the operational scope of the method.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central claim rests on held-out evaluations, a user study, and online A/B tests, not on any definitional or self-citational reduction.

full rationale

T-Stars-Poster is a systems paper with no first-principles derivation; its claim is an empirical superiority claim supported by offline metrics on held-out splits of PITA/PIL, a 7-user pairwise preference study, and Taobao A/B tests. No equation defines an output in terms of the quantity it purports to predict, and no fitted parameter is renamed as a prediction. The prompt and layout models are trained on PITA/PIL and evaluated on disjoint test splits using human annotation, Grounding-DINO-based layout metrics, and FID; the ED Score measures closeness to ground-truth prompt annotations, which is a fit metric but not a constructed prediction. The in-house components reused by the paper, namely the CGL-derived portion of PITA [45] and the pretrained inpainting ControlNet [7], are engineering inputs and model initializations, not load-bearing justifications for the superiority claim. The author-built comparison pipelines may raise fairness concerns, and the Section 5.5 A/B CTR lift is reported without confidence intervals or significance tests, which is a statistical support gap rather than circularity. No uniqueness theorem or ansatz is imported from the authors' prior work to force the design. Therefore no circular step can be exhibited by quoting a specific reduction, and the honest finding is no significant circularity.

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

The framework's central claim rests on trained models rather than on a derivation, so the ledger contains no hand-fitted constants. The load-bearing assumptions are the quality and representativeness of the annotated datasets, the validity of the aesthetic and CTR proxies, and the transferability of the pretrained SDXL and ControlNet checkpoints. No new entities are introduced.

assumptions (4)
  • domain assumption Automatic annotations in PITA and PIL (captions, masks, bboxes) are sufficiently accurate after manual review to train the VLM and diffusion modules.
    Section 3 states annotations are initially automatic (matting API, GPT-4o captioning, detection, OCR) then manually reviewed. If labels are systematically wrong, the learned experts would underperform.
  • domain assumption The evaluation metrics (FID, CLIP scores, expert ranking, user preference, CTR) capture visual appeal as claimed.
    Sections 5.2, 5.4, and 5.5 use these proxies; FID and CLIP are known to be imperfect aesthetic measures and CTR can be influenced by factors beyond image appeal.
  • domain assumption The image filtering criteria in Section 3 do not remove the cases where the framework would fail, so the datasets are representative.
    The paper excludes messy backgrounds, plain colors without shadows, close-ups, poorly presented taglines, and cluttered elements. This directly bears on generalization of the central claim.
  • domain assumption The pretrained EcomXL inpainting ControlNet [7] and the two-stage training (layout-to-image then inpainting fine-tuning) preserve both layout control and foreground preservation.
    Section 4.4 and Section 5.1 assume the checkpoints transfer and that the GSA plus LoRA strategy resolves conflicts between control signals; the authors acknowledge convergence difficulty but do not ablate the pretrained checkpoint dependency.

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Cite this review

Pith. "Pith review of T-Stars-Poster: A Framework for Product-Centric Advertising Image Design." pith.science (2026). https://pith.science/paper/M6O65Q4P

@misc{pith2026250114316,
  author       = {Pith},
  title        = {Pith review of: T-Stars-Poster: A Framework for Product-Centric Advertising Image Design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M6O65Q4P}},
  note         = {Machine review of arXiv:2501.14316}
}
read the original abstract

Creating advertising images is often a labor-intensive and time-consuming process. Can we automatically generate such images using basic product information like a product foreground image, taglines, and a target size? Existing methods mainly focus on parts of the problem and lack a comprehensive solution. To bridge this gap, we propose a novel product-centric framework for advertising image design called T-Stars-Poster. It consists of four sequential stages to highlight product foregrounds and taglines while achieving overall image aesthetics: prompt generation, layout generation, background image generation, and graphics rendering. Different expert models are designed and trained for the first three stages: First, a visual language model (VLM) generates background prompts that match the products. Next, a VLM-based layout generation model arranges the placement of product foregrounds, graphic elements (taglines and decorative underlays), and various nongraphic elements (objects from the background prompt). Following this, an SDXL-based model can simultaneously accept prompts, layouts, and foreground controls to generate images. To support T-Stars-Poster, we create two corresponding datasets with over 50,000 labeled images. Extensive experiments and online A/B tests demonstrate that T-Stars-Poster can produce more visually appealing advertising images.

Figures

Figures reproduced from arXiv: 2501.14316 by the authors.

Figure 1
Figure 1. Generated advertising images by our methods with a product foreground image, taglines, and a target size as input. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Pipeline of T-Stars-Poster. It consists of four stages and generates advertising images centered around product [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The framework of prompt and layout generation. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Layout control in the image generation model. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Visualization of advertising images designed by different methods. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison of layout generation methods. Orange: tagline, Yellow: underlay, Red: logo, Black: Invalid. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Examples from the PITA dataset, with tagline, un [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Examples in PIL dataset. B Visualization of Prompt Distribution We use t-SNE for dimensionality reduction to visualize the feature distribution of prompts generated by our model before and after training (see [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: T-sne visualization of generated prompts. [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

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Reference graph

Works this paper leans on

49 extracted references · 20 canonical work pages

  1. [1]

    DAMO Academy. 2024. https://vision.aliyun.com/experience/detail?tagName= imageseg&children=SegmentHDCommonImage

    work page 2024

  2. [2]

    Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. 2023. Multidiffusion: Fusing diffusion paths for controlled image generation. (2023)

    work page 2023

  3. [3]

    Tingfeng Cao, Junsheng Kong, Xue Zhao, Wenqing Yao, Junwei Ding, Jinhui Zhu, and Jiandong Zhang. 2024. Product2IMG: Prompt-Free E-commerce Product Back- ground Generation with Diffusion Model and Self-Improved LMM. InProceedings of the 32nd ACM International Conference on Multimedia . 10774–10783

    work page 2024

  4. [4]

    Shang Chai, Zihang Lin, Min Zhou, Xubin Li, Liansheng Zhuang, and Houqiang Li. 2025. SceneBooth: Diffusion-based Framework for Subject-preserved Text-to- Image Generation. arXiv preprint arXiv:2501.03490 (2025)

    work page Pith review arXiv 2025

  5. [5]

    Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. 2023. Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models. ACM Transactions on Graphics (TOG) 42, 4 (2023), 1–10

    2023

  6. [6]

    Minghao Chen, Iro Laina, and Andrea Vedaldi. 2024. Training-free layout control with cross-attention guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 5343–5353

    2024

  7. [7]

    Alimama Creative. 2024. https://huggingface.co/alimama-creative/EcomXL_ controlnet_inpaint

    work page 2024

  8. [8]

    Xiyang Dai, Yinpeng Chen, Bin Xiao, Dongdong Chen, Mengchen Liu, Lu Yuan, and Lei Zhang. 2021. Dynamic Head: Unifying Object Detection Heads With Attentions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021 . Computer Vision Foundation / IEEE, 7373–7382. https://doi.org/10.1109/CVPR46437.2021.00729

    arXiv 2021

Show all 49 references
  1. [9]

    Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, et al. 2024. Internlm- xcomposer2: Mastering free-form text-image composition and comprehension in vision-language large model. arXiv preprint arXiv:2...

    2024 arXiv

  2. [10]

    Zhenbang Du, Wei Feng, Haohan Wang, Yaoyu Li, Jingsen Wang, Jian Li, Zheng Zhang, Jingjing Lv, Xin Zhu, Junsheng Jin, et al . 2024. Towards Reliable Ad- vertising Image Generation Using Human Feedback. In European Conference on Computer Vision. Springer, 399–415

    2024

  3. [11]

    Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. 2022. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 (2022)

    2022 arXiv

  4. [12]

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 770–778. https://doi.org/10.1...

    2016 doi

  5. [13]

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017)

    2017

  6. [14]

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840–6851

    2020

  7. [15]

    Hsiao Yuan Hsu, Xiangteng He, Yuxin Peng, Hao Kong, and Qing Zhang. 2023. Posterlayout: A new benchmark and approach for content-aware visual-textual presentation layout. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6018–6026

    2023

  8. [16]

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)

    2021 arXiv

  9. [17]

    Yunji Kim, Jiyoung Lee, Jin-Hwa Kim, Jung-Woo Ha, and Jun-Yan Zhu. 2023. Dense text-to-image generation with attention modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 7701–7711

    2023

  10. [18]

    Fengheng Li, An Liu, Wei Feng, Honghe Zhu, Yaoyu Li, Zheng Zhang, Jingjing Lv, Xin Zhu, Junjie Shen, Zhangang Lin, et al. 2023. Relation-aware diffusion model for controllable poster layout generation. In Proceedings of the 32nd ACM International Conference on Information and ...

    2023

  11. [19]

    Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. 2023. Gligen: Open-set grounded text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 22511–22521

    2023

  12. [20]

    Zhaochen Li, Fengheng Li, Wei Feng, Honghe Zhu, An Liu, Yaoyu Li, Zheng Zhang, Jingjing Lv, Xin Zhu, Junjie Shen, et al. 2023. Planning and Rendering: Towards End-to-End Product Poster Generation. arXiv preprint arXiv:2312.08822 (2023)

    2023 arXiv

  13. [21]

    Jinpeng Lin, Min Zhou, Ye Ma, Yifan Gao, Chenxi Fei, Yangjian Chen, Zhang Yu, and Tiezheng Ge. 2023. Autoposter: A highly automatic and content-aware design system for advertising poster generation. In Proceedings of the 31st ACM International Conference on Multimedia . 1250–1260

    2023

  14. [22]

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024. Visual instruc- tion tuning. Advances in neural information processing systems 36 (2024)

    2024

  15. [23]

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. 2024. Grounding dino: Marry- ing dino with grounded pre-training for open-set object detection. In European Conference on Computer Vision . Springer, 38–55

    2024

  16. [24]

    Zeyu Liu, Weicong Liang, Zhanhao Liang, Chong Luo, Ji Li, Gao Huang, and Yuhui Yuan. 2024. Glyph-byt5: A customized text encoder for accurate visual text rendering. In European Conference on Computer Vision . Springer, 361–377

    2024

  17. [25]

    Jacob Menick and Nal Kalchbrenner. 2018. Generating high fidelity images with subscale pixel networks and multidimensional upscaling. arXiv preprint arXiv:1812.01608 (2018)

    2018 arXiv

  18. [26]

    OpenAI. 2024. https://openai.com/index/hello-gpt-4o/

    2024

  19. [27]

    PaddlePaddle. 2023. https://github.com/PaddlePaddle/PaddleOCR

    2023

  20. [28]

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)

    2023 arXiv

  21. [29]

    Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer

    Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer

  22. [30]

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 22500–22510

    2023

  23. [31]

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Lud- wig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. 202...

    2022

  24. [32]

    Jaejung Seol, Seojun Kim, and Jaejun Yoo. 2024. PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation. arXiv preprint arXiv:2404.00995 (2024)

    2024 arXiv

  25. [33]

    Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, and Victor Lempitsky. 2022. Resolution-robust large mask inpainting with fourier convolutions. In Proceedings of the IEEE/CVF...

    2022

  26. [34]

    Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang

  27. [35]

    Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng

  28. [36]

    Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel recurrent neural networks. InInternational conference on machine learning. PMLR, 1747–1756

    2016

  29. [37]

    Shanu Vashishtha, Abhinav Prakash, Lalitesh Morishetti, Kaushiki Nag, Yok- ila Arora, Sushant Kumar, and Kannan Achan. 2024. Chaining Text-to-Image and Large Language Model: A Novel Approach for Generating Personalized e-commerce Banners. In Proceedings of the 30th ACM SIGKDD ...

    2024

  30. [38]

    Haohan Wang, Wei Feng, Yang Lu, Yaoyu Li, Zheng Zhang, Jingjing Lv, Xin Zhu, Junjie Shen, Zhangang Lin, Lixing Bo, et al. 2023. Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style. arXiv preprint arXiv:2312.13309 (2023)

    2023 arXiv

  31. [39]

    Shaodong Wang, Yunyang Ge, Liuhan Chen, Haiyang Zhou, Qian Wang, Xinhua Cheng, and Li Yuan. 2024. Prompt2Poster: Automatically Artistic Chinese Poster Creation from Prompt Only. In ACM Multimedia 2024

    2024

  32. [40]

    Shiyao Wang, Qi Liu, Tiezheng Ge, Defu Lian, and Zhiqiang Zhang. 2021. A hybrid bandit model with visual priors for creative ranking in display advertising. In Proceedings of the web conference 2021 . 2324–2334

    2021

  33. [41]

    Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. 2024. Instancediffusion: Instance-level control for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6232–6242

    2024

  34. [42]

    Haohan Weng, Danqing Huang, Yu Qiao, Zheng Hu, Chin-Yew Lin, Tong Zhang, and CL Chen. 2024. Desigen: A Pipeline for Controllable Design Template Generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12721–12732

    2024

  35. [43]

    Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. 2023. Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision . 7452–7461

    2023

  36. [44]

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding conditional con- trol to text-to-image diffusion models. InProceedings of the IEEE/CVF International Conference on Computer Vision . 3836–3847

    2023

  37. [45]

    Min Zhou, Chenchen Xu, Ye Ma, Tiezheng Ge, Yuning Jiang, and Weiwei Xu

  38. [2020]

    Advances in neural information processing systems 33 (2020), 7537–7547

    Fourier features let networks learn high frequency functions in low di- mensional domains. Advances in neural information processing systems 33 (2020), 7537–7547

    2020

  39. [2021]

    2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 10674–10685

    High-Resolution Image Synthesis with Latent Diffusion Models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 10674–10685. https://api.semanticscholar.org/CorpusID:245335280

    2021

  40. [2022]

    In Proceedings of the Thirty-First International Joint Conference on Artifi- cial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022 , Luc De Raedt (Ed.)

    Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs. In Proceedings of the Thirty-First International Joint Conference on Artifi- cial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022 , Luc De Raedt (Ed.). ijcai.org, 4995–5001. https://doi.or...

    2022

  41. [2024]

    CoRR abs/2411.15098 (2024)

    OminiControl: Minimal and Universal Control for Diffusion Trans- former. CoRR abs/2411.15098 (2024). https://doi.org/10.48550/ARXIV.2411.15098 arXiv:2411.15098

    2024 doi arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.