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REVIEW 4 major objections 5 minor 51 references

ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Anchoring reference tokens to their bounding-box centers with a 4D rotary position encoding, plus an attention mask, achieves precise layout-guided multi-instance generation and cuts inference latency by over 80%.

desk verdict The anchored 4D-RoPE trick is a plausible and useful engineering contribution, but the 'native spatial prior' claims outrun the evidence and the numbers need cleanup. read the letter →

arxiv 2608.06878 v1 pith:WSVP6R6B submitted 2026-08-07 cs.CV

Yunkai Yang , Yudong Zhang , Xinying Chen , Haoyuan Liang , Yizhuo Niu , Jinshuai Cheng , Kunquan Zhang , Liziyue Fang
show 2 more authors
Weitao Wan Runmin Dong
This is my paper · ORCID
classification cs.CV
keywords layout-guidedgenerationmulti-instanceimagesynthesisdiffusiontransformer4Drotarypositionembeddingattentionmaskingreferenceconditioningefficientinferenceregionalbinding
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

ControlRef aims to show that layout-guided multi-instance generation can be made both precise and efficient inside a unified multimodal diffusion transformer, without the full-resolution canvas padding and shifted position encodings used by prior approaches. The paper argues that those prior mechanisms inflate computation for sparse layouts and distort low-frequency rotary position features, blurring absolute spatial correspondence. Instead, ControlRef assigns each reference image and each layout token a four-dimensional position anchored at its bounding box's geometric center, with a separate axis isolating instances, and applies one attention mask across all transformer blocks to prevent semantic crosstalk. The reported result is top benchmark performance on instance fidelity and layout accuracy, together with over 80% lower inference latency on sparse layouts and roughly 50% lower memory overhead on dense ones.

What carries the argument

The load-bearing object is Anchored 4D-RoPE, a rotary position embedding over a $(z,y,x,t)$ coordinate tuple, used together with the Unified Instance-Layout Control (UILC) attention mask. In the paper's Equations (4)--(6), reference tokens receive position $(t_{\text{step}} i, H^{i}_{\text{bbox}}, W^{i}_{\text{bbox}}, 0)$, layout tokens receive $(t_{\text{step}} i, \lfloor \bar{y}_i/P\rfloor, \lfloor \bar{x}_i/P\rfloor, \{0,\dots,L^{i}_{\text{region}}-1\})$, and denoising tokens keep their native image grid with $t=0$; RoPE's distance-dependent decay then makes anchored tokens' attention concentrate near their assigned box. The UILC mask defines which query-key pairs are allowed, decoupling instances while preserving global layout awareness. Together they replace the full-resolution canvas and shifted RoPE of prior work.

What would settle it

Measure the attention maps of a trained ControlRef model: for each reference token and layout token, compute the average attention weight received by denoising tokens inside its assigned bounding box versus outside it. If inside-box attention is not distinctly higher, or if removing the anchor centers while keeping the mask and relative positions unchanged produces the same outputs, then the claimed absolute geometric anchoring is not what drives the layout gains.

Watch

Extended reading notes

Core claim

The central discovery is that a multi-axis rotary position encoding can carry layout control by anchoring tokens to absolute geometric coordinates rather than by shifting them relative to a canvas. Anchored 4D-RoPE gives each token a $(z, y, x, t)$ tuple: $(y,x)$ are the spatial coordinates of the bounding box discretized by patch size, $z$ separates reference instances with a fixed stride, and $t$ carries text-sequence position. Reference images are pre-resized to their box resolution and placed at the corresponding box coordinates, while layout tokens are placed at box centers. Because RoPE's attention weight decays with distance, these anchored tokens are claimed to attend preferentially to denoising tokens inside their own box, giving the model native spatial grounding without lossy shifting. The accompanying Unified Instance-Layout Control attention mask blocks cross-instance attention while keeping layout tokens globally accessible, so instances remain semantically separated. On LAMICBench++ and LayoutSAM-Eval, the paper reports that this combination beats prior layout-anchoring methods while running faster and using less memory.

Load-bearing premise

The load-bearing premise is that RoPE's distance-dependent decay makes a token placed at a bounding box's center pay attention mainly to image tokens inside that box, so that absolute geometry survives the relative nature of rotary embeddings.

Editorial extensions

If this is right

  • If the central claim is correct, layout-guided multi-instance generation no longer needs full-resolution canvas padding, so the sequence length scales with bounding-box area rather than canvas area and sparse layouts become much cheaper to run.
  • Anchored absolute spatial correspondence in RoPE means the low-frequency position components that shifting disrupts are preserved, keeping size-aligned reference conditions spatially faithful.
  • A single UILC mask applied across all transformer blocks is enough for instance isolation, indicating that the native multi-reference capabilities of a 4D-RoPE backbone can be used without block-specific masking schemes.
  • The framework is presented as model-agnostic for any backbone with 4D-RoPE, and the paper demonstrates it on two model sizes, so the mechanism could transfer to other 4D-RoPE diffusion transformers.
  • The efficiency gains are reported to grow with instance count in dense scenarios, with memory overhead reduced by about 50% compared with the prior full-canvas approach.

Reading between the lines

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

  • The paper's ablations show that removing either the reference offset or the layout offset degrades spatial precision, suggesting the geometric anchoring itself is doing measurable work; a direct test would be to measure attention maps from anchored tokens to confirm the claimed spatial footprint.
  • The DPO ablation in the appendix shows preference tuning raises aesthetic and text-consistency scores while slightly lowering identity-preservation scores, implying that part of the reported visual fidelity comes from the post-training stage rather than from the positional encoding alone.
  • A natural extension, explicitly flagged in the paper as future work, is carrying the same anchoring mechanism into video generation, where the $t$ axis could encode frame position rather than text position.
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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 proposes ControlRef, a layout-guided multi-instance image generation framework built on the FLUX.2 [klein] 9B backbone. It introduces a Unified Instance-Layout Control (UILC) attention mask that restricts each reference image to attend only to its corresponding bounding-box region, and an 'Anchored 4D-RoPE' positional scheme that assigns absolute geometric center coordinates to layout and reference tokens, stacks reference instances along the z-axis, and pre-aligns reference image resolutions to bounding-box sizes. The authors claim state-of-the-art visual fidelity and localization accuracy on LAMICBench++ and LayoutSAM-Eval, plus over 80% inference-latency reduction in sparse layouts and roughly 50% memory-overhead reduction in dense scenarios relative to ContextGen. The paper includes ablations for the mask, the positional offsets, the backbone choice, and a Diffusion-DPO post-training stage.

Significance. If the central claims hold, ControlRef would provide a practical efficiency improvement for multi-instance generation in unified MM-DiTs by avoiding full-resolution canvas padding and shifted positional encodings. The paper builds on FLUX.2's pre-existing 4D-RoPE rather than deriving results from a self-referential equation, so it is not circular. It also reports several useful ablations, including the DPO strategy and the mask injection schedule, and it clearly documents training hyperparameters. However, the significance is weakened by unresolved numerical inconsistencies between the main table and the appendix, overstated comparison claims, and an unsupported assertion that RoPE's distance behavior 'natively' enforces spatial locality. The core architectural idea is plausible and worth developing, but the evidence as presented does not yet support the strongest claims.

major comments (4)
  1. [Quantitative Comparison: Table 1 vs. Appendix Table 1] The same baseline and method report inconsistent numbers in the main table and the appendix. In main Table 1, Klein 9B has Fewer-Subject AVG 65.70 and More-Subject AVG 62.64, with component scores (90.58, 54.12, 40.16, 77.94) and (89.87, 56.53, 26.60, 69.79). In Appendix Table 1, Klein 9B is listed with AVG 62.64 but different component scores (90.21, 55.16, 32.33, 72.86). Similarly, Ours (9B) has More-Subject AVG 63.19 in Table 1 but Appendix AVG 64.72. These differences are not explained by subset averaging, since the Appendix values do not match weighted averages of the two subset rows. Please clarify whether the Appendix table uses a different evaluation subset, a different seed, or a different protocol, and correct the tables accordingly.
  2. [Comparison Results narrative versus Table 1] The text in §5.2 states that ControlRef 'achieves the highest ITC and IPS scores in the more-subject scenario' and 'maintains the best overall performance (highest AVG score) in fewer-subject.' Table 1 contradicts this: in the more-subject columns Seedream, GPT-4o, and Nano Banana all have higher ITC than Ours (91.14), and Nano Banana has higher IPS (74.11 vs. 73.98); in the fewer-subject columns Nano Banana has AVG 67.57 vs. Ours 66.98. Please restrict the superiority claim to open-source baselines, or revise the wording to match the reported numbers.
  3. [Anchored 4D-RoPE: 'native' spatial prior claim] The central novelty claim in §4.2 is that Anchored 4D-RoPE 'natively preserves spatial priors' via RoPE's distance-dependent decay. However, the attention score in Eq. (1) is a sum of cosine terms Re[Σ q_m k_n^* e^{i(m-n)θ_d}], which is oscillatory in the relative distance m−n; no monotone distance decay is guaranteed. The ablations in Table 5 are all run with the full UILC mask, which already forbids cross-instance attention, so they cannot isolate whether the spatial gains come from the RoPE anchoring or from the mask. To support the 'native' claim, please provide either a theoretical analysis of the effective attention weight as a function of distance, an ablation that removes the UILC mask while keeping the Anchored 4D-RoPE, or attention-map visualizations showing that the reference tokens preferentially attend inside their bounding boxes without the mask.
  4. [Abstract and Table 2 efficiency claim] The abstract claims 'slashing inference latency by over 80% in sparse layouts.' Table 2 reports ContextGen at 1.98 s for the Fewer-Subject case and Ours at 0.40 s, which is a 79.8% reduction, not over 80%; the 'over 80%' figure only holds for the More-Subject case (82.3%). Additionally, the headline comparison is against ContextGen, a 12B model, while Ours is a 9B model; the comparison to Klein 9B is reported separately (0.76 s to 0.40 s, 47.4% reduction). Please report the exact percentages with the baseline clearly identified, and either rephrase the abstract or quote the precise numbers.
minor comments (5)
  1. [Table 1] The row 'Ours (9B)' appears twice, once in the open-source section and once under 'Closed-Source Commercial Models'; the duplicate row should be removed for clarity.
  2. [Related Work] The text refers to 'OminiControl (Tan et al. 2025a)' but the citation is OmniControl; please fix the typo.
  3. [Equation (3)] The first condition of the UILC mask is written as 'True, if q ∈ R_img ∪ R_txt ∪ R_lay, k ∈ R_img ∪ R_txt ∪ R_lay' which appears to allow all non-reference tokens to attend to all other non-reference tokens; please clarify whether layout tokens are intended to attend to all layout tokens and whether this is consistent with the instance-isolation description.
  4. [Appendix Table 1] The caption 'Ablation Study on different generative backbone' should state whether the AVG is computed over the combined LAMICBench++ set or over a specific subset, since this is currently ambiguous and is the source of the discrepancy noted in Major Comment 1.
  5. [Tables 3-5] The differences between the best and second-best scores in Tables 3 and 5 are small (e.g., Spatial 94.52 vs. 93.96, Pick 22.45 vs. 22.44), and no standard deviations or repeated-seed evaluations are reported; adding error bars or significance information would strengthen the conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation chain is an architectural construction evaluated on external benchmarks, not a self-referential reduction.

full rationale

The claimed derivation chain is: (i) adopt FLUX.2 [klein]'s 4D-RoPE, cited as external prior work and identified as the only open-source 4D-RoPE backbone; (ii) extend it by assigning layout tokens to bounding-box centers (Eq. 5) and pre-aligning reference images to bounding-box resolutions (Eq. 6); (iii) enforce instance isolation with the UILC mask (Eq. 3); (iv) LoRA-fine-tune on IMIG-100K and evaluate on LayoutSAM-Eval, LAMICBench++, and COCO-MIG. None of these steps defines its target metric in terms of the mechanism being tested, and no fitted parameter is relabeled as a prediction. The 'inherent distance-dependent decay' premise that anchors tokens to their centers is an architectural assumption about how RoPE behaves; even if the premise is contestable because Eq. (1) shows oscillatory relative-position terms, that is an evidence/correctness critique, not a circularity. Citations to ContextGen supply the dataset, benchmark, and DPO recipe, but those are external tools and baselines and do not justify the central claim by self-citation; the key architectural components (FLUX.2 4D-RoPE, GLIGEN Fourier layout tokens, RoPE) are cited to independent prior work. Efficiency figures are measured consequences of reduced token sequence length, not consequences of the evaluation metric. Accordingly, no circular step can be exhibited by quotation and reduction, so the appropriate finding is no significant circularity.

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

The paper's results rest on the pre-existing FLUX.2 [klein] backbone, its 4D-RoPE and multi-reference KV-cache behavior, a set of hand-selected hyperparameters, and an assumption that RoPE's distance decay produces spatial binding. No new mathematical derivation is given. The trained LoRA weights are the fitted component that actually produces the claimed behavior.

free parameters (4)
  • Trained LoRA weights = Not released (rank r=256)
    All layout-binding behavior is learned by LoRA inserted into FLUX.2 attention and MLP layers; without these weights the method is undefined and the reported scores cannot be reconstructed.
  • Temporal stride t_step for instance separation on z-axis = 10 (FLUX.2 [klein] default)
    Controls spacing between reference instances in the 4D position tuple; the paper does not tune or ablate it.
  • DPO KL regularization beta = 1000
    Chosen following ContextGen; affects post-training stability and final ITC and AES gains.
  • LoRA rank = 256
    Capacity hyperparameter for fine-tuning; not justified by experiments.
assumptions (5)
  • domain assumption RoPE's distance-dependent decay induces spatial binding, so tokens near each other in 4D coordinate space attend more strongly.
    Anchored 4D-RoPE relies on this to bind reference and layout tokens to target regions; stated in the Anchored 4D-RoPE section.
  • domain assumption FLUX.2 [klein]'s 4D-RoPE and multi-reference KV-cache attention behave as described and remain correct under the added UILC mask.
    ControlRef is built directly on FLUX.2 [klein] and depends on its native multi-reference support (Preliminary section and appendix details on the UILC attention mask).
  • domain assumption The evaluation subsets are representative of general layout-guided generation performance.
    LayoutSAM-Eval uses a 1,000-sample subset selected for large boxes; LAMICBench++ is a 160-case aggregated benchmark from ContextGen; no full-benchmark results are provided.
  • domain assumption MLLM- and VQA-based metrics (ITC, IDS, IPS, AES) and CLIP and Pick scores faithfully measure the claimed visual fidelity and localization.
    All headline conclusions are drawn from these proxy metrics with no human study or error bars.
  • standard math Rotary position embedding algebra in Eq. (1) is standard and correct.
    The paper uses the standard RoPE formulation from Su et al. 2024 as a building block; no proof is needed beyond the cited reference.

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Cite this review

Pith. "Pith review of ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE." pith.science (2026). https://pith.science/paper/WSVP6R6B

@misc{pith2026260806878,
  author       = {Pith},
  title        = {Pith review of: ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WSVP6R6B}},
  note         = {Machine review of arXiv:2608.06878}
}
read the original abstract

Layout-guided multi-instance generation is essential for controllable image synthesis in Multi-Modal Diffusion Transformers (MM-DiTs). However, integrating this capability into unified architectures remains challenging. Prior frameworks rely on redundant full-resolution canvas padding and Shifted-RoPE to manage multiple reference images. This mechanism drastically inflates computational overhead for sparse layouts and disrupts critical low-frequency RoPE features, creating a severe spatial-frequency compromise that blurs absolute spatial correspondence. To overcome these limitations, we propose ControlRef, a highly efficient and precise multi-instance synthesis framework. ControlRef utilizes a Unified Instance-Layout Control (UILC) attention mask to strictly decouple inter-instance semantic interactions and enforce precise regional binding. To further promote region-level spatial alignment, we introduce Anchored 4D-RoPE, a novel positional encoding mechanism that directly anchors tokens to their absolute geometric centers. By pre-aligning reference images to their corresponding bounding box resolutions, physically anchoring both layout and reference tokens to their absolute geometric centers, and stacking the references along the z-axis, Anchored 4D-RoPE natively preserves spatial priors and mitigates the spatial-frequency compromise without lossy shifting. Extensive experiments demonstrate that ControlRef achieves state-of-the-art visual fidelity and localization accuracy, while concurrently slashing inference latency by over 80% in sparse layouts and reducing memory overhead by 50% in dense scenarios.

Figures

Figures reproduced from arXiv: 2608.06878 by the authors.

Figure 1
Figure 1. ControlRef resolves the spatial-frequency compromise in MM-DiTs by utilizing a novel Anchored 4D-RoPE and UILC [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) Overview of ControlRef. Conditioned jointly on GLIGEN-style layout tokens and corresponding reference images, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visual comparison on LAMICBench++. steps, strictly adhering to the preference alignment strategy established in ContextGen (Xu et al. 2025). Datasets and Evaluation LayoutSAM-Eval. Our model is evaluated on a subset of 1,000 LayoutSAM-Eval (Zhang et al. 2025b) samples con￾taining large bounding boxes for reliable instance-level anal￾ysis. We measure instance-level attributes (spatial layout, color, texture, shape) v… view at source ↗
Figures from the paper (2 more)
Figure 1
Figure 1. Figure 1: The attention mask settings in ablation experiments. [PITH_FULL_IMAGE:figures/full_fig_p010_1.png]
Figure 2
Figure 2. Figure 2: Visual comparison of DPO Strategy. Method ITC AES IDS IPS AVG ContextGen (SFT) 86.84 54.19 32.37 76.78 62.55 ContextGen (SFT+DPO) 91.03 57.10 26.83 75.71 62.67 Ours (SFT) 91.33 55.63 32.79 76.95 64.17 Ours (SFT+DPO) 92.73 57.74 31.86 76.53 64.72 [PITH_FULL_IMAGE:figur…

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Reference graph

Works this paper leans on

51 extracted references · 15 canonical work pages

  1. [1]

    Advances in neural information processing systems , volume=

    Denoising diffusion probabilistic models , author=. Advances in neural information processing systems , volume=

  2. [2]

    arXiv preprint arXiv:2010.02502 , year=

    Denoising diffusion implicit models , author=. arXiv preprint arXiv:2010.02502 , year=

    arXiv 2010

  3. [3]

    arXiv preprint arXiv:2209.03003 , year=

    Flow straight and fast: Learning to generate and transfer data with rectified flow , author=. arXiv preprint arXiv:2209.03003 , year=

    arXiv

  4. [4]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Scalable diffusion models with transformers , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  5. [5]

    arXiv preprint arXiv:2511.22699 , year=

    Z-image: An efficient image generation foundation model with single-stream diffusion transformer , author=. arXiv preprint arXiv:2511.22699 , year=

    arXiv

  6. [6]

    Forty-first international conference on machine learning , year=

    Scaling rectified flow transformers for high-resolution image synthesis , author=. Forty-first international conference on machine learning , year=

  7. [7]

    2024 , howpublished=

    Black Forest Labs , title=. 2024 , howpublished=

    2024

  8. [8]

    Advances in Neural Information Processing Systems , volume=

    Lumina-next: Making lumina-t2x stronger and faster with next-dit , author=. Advances in Neural Information Processing Systems , volume=

    work page

Show all 51 references
  1. [9]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Ominicontrol: Minimal and universal control for diffusion transformer , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  2. [10]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Gligen: Open-set grounded text-to-image generation , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  3. [11]

    Neurocomputing , volume=

    Roformer: Enhanced transformer with rotary position embedding , author=. Neurocomputing , volume=. 2024 , publisher=

    2024

  4. [12]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. Iclr , volume=

  5. [13]

    arXiv preprint arXiv:2510.11000 , year=

    ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation , author=. arXiv preprint arXiv:2510.11000 , year=

  6. [14]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Dreamrenderer: Taming multi-instance attribute control in large-scale text-to-image models , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  7. [15]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Less-to-more generalization: Unlocking more controllability by in-context generation , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  8. [16]

    arXiv preprint arXiv:2509.26641 , year=

    Query-kontext: An unified multimodal model for image generation and editing , author=. arXiv preprint arXiv:2509.26641 , year=

  9. [17]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Creatilayout: Siamese multimodal diffusion transformer for creative layout-to-image generation , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  10. [18]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Migc: Multi-instance generation controller for text-to-image synthesis , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  11. [19]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=

    Migc++: Advanced multi-instance generation controller for image synthesis , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=. 2024 , publisher=

    2024

  12. [20]

    arXiv preprint arXiv:2603.05769 , year=

    Layer-wise instance binding for regional and occlusion control in text-to-image diffusion transformers , author=. arXiv preprint arXiv:2603.05769 , year=

  13. [21]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Layoutdiffusion: Controllable diffusion model for layout-to-image generation , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  14. [22]

    Advances in Neural Information Processing Systems , volume=

    Groundit: Grounding diffusion transformers via noisy patch transplantation , author=. Advances in Neural Information Processing Systems , volume=

  15. [23]

    arXiv preprint arXiv:2308.06721 , year=

    Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models , author=. arXiv preprint arXiv:2308.06721 , year=

    arXiv

  16. [24]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  17. [25]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Adding conditional control to text-to-image diffusion models , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  18. [26]

    arXiv preprint arXiv:2503.08280 , year=

    Ominicontrol2: Efficient conditioning for diffusion transformers , author=. arXiv preprint arXiv:2503.08280 , year=

    arXiv

  19. [27]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Unicombine: Unified multi-conditional combination with diffusion transformer , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  20. [28]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Law-diffusion: Complex scene generation by diffusion with layouts , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  21. [29]

    arXiv preprint arXiv:2511.20635 , year=

    iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation , author=. arXiv preprint arXiv:2511.20635 , year=

    arXiv

  22. [30]

    2025 , howpublished=

    Black Forest Labs , title=. 2025 , howpublished=

    2025

  23. [31]

    Advances in neural information processing systems , volume=

    Direct preference optimization: Your language model is secretly a reward model , author=. Advances in neural information processing systems , volume=

  24. [32]

    arXiv preprint arXiv:2408.01800 , year=

    Minicpm-v: A gpt-4v level mllm on your phone , author=. arXiv preprint arXiv:2408.01800 , year=

    arXiv

  25. [33]

    Advances in neural information processing systems , volume=

    Pick-a-pic: An open dataset of user preferences for text-to-image generation , author=. Advances in neural information processing systems , volume=

  26. [34]

    International Conference on Learning Representations , volume=

    mplug-owl3: Towards long image-sequence understanding in multi-modal large language models , author=. International Conference on Learning Representations , volume=

  27. [35]

    European conference on computer vision , pages=

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection , author=. European conference on computer vision , pages=. 2024 , organization=

    2024

  28. [36]

    arXiv preprint arXiv:2304.07193 , year=

    Dinov2: Learning robust visual features without supervision , author=. arXiv preprint arXiv:2304.07193 , year=

    arXiv

  29. [37]

    arXiv preprint arXiv:2105.04714 , year=

    Sample and computation redistribution for efficient face detection , author=. arXiv preprint arXiv:2105.04714 , year=

    arXiv

  30. [38]

    2023 , url =

    Christoph Schuhmann , title =. 2023 , url =

    2023

  31. [39]

    European conference on computer vision , pages=

    Microsoft coco: Common objects in context , author=. European conference on computer vision , pages=. 2014 , organization=

    2014

  32. [40]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Lamic: Layout-aware multi-image composition via scalability of multimodal diffusion transformer , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  33. [41]

    Advances in Neural Information Processing Systems , volume=

    Xverse: Consistent multi-subject control of identity and semantic attributes via dit modulation , author=. Advances in Neural Information Processing Systems , volume=

  34. [42]

    International Conference on Learning Representations , volume=

    Ms-diffusion: Multi-subject zero-shot image personalization with layout guidance , author=. International Conference on Learning Representations , volume=

  35. [43]

    arXiv preprint arXiv:2508.02324 , year=

    Qwen-image technical report , author=. arXiv preprint arXiv:2508.02324 , year=

    arXiv

  36. [44]

    arXiv preprint arXiv:2506.18871 , year=

    Omnigen2: Exploration to advanced multimodal generation , author=. arXiv preprint arXiv:2506.18871 , year=

    arXiv

  37. [45]

    1 kontext: Flow matching for in-context image generation and editing in latent space , author=

    Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space , author=. arXiv e-prints , pages=

  38. [46]

    arXiv preprint arXiv:2509.20427 , year=

    Seedream 4.0: Toward next-generation multimodal image generation , author=. arXiv preprint arXiv:2509.20427 , year=

    arXiv

  39. [47]

    arXiv preprint arXiv:2410.12669 , year=

    3dis: Depth-driven decoupled instance synthesis for text-to-image generation , author=. arXiv preprint arXiv:2410.12669 , year=

  40. [48]

    Proceedings of the 7th ACM International Conference on Multimedia in Asia , pages=

    Eligen: Entity-level controlled image generation with regional attention , author=. Proceedings of the 7th ACM International Conference on Multimedia in Asia , pages=

  41. [49]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Easycontrol: Adding efficient and flexible control for diffusion transformer , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  42. [50]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Diffusion model alignment using direct preference optimization , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  43. [51]

    arXiv preprint arXiv:1707.06347 , year=

    Proximal policy optimization algorithms , author=. arXiv preprint arXiv:1707.06347 , year=

    arXiv

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