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arxiv: 2605.19554 · v1 · pith:WQKOQOA2new · submitted 2026-05-19 · 💻 cs.CV

Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting

Yue Yu , Haibo Chen , Shuo Chen , Jian Yang , Jun Li This is my paper

Pith reviewed 2026-05-20 05:59 UTC · model grok-4.3

classification 💻 cs.CV
keywords text-to-image generationdiffusion modelscreativityspatial weightingKaiser-Bessel windowsemantic alignmentvisual noveltyself-creative generation
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The pith

A diffusion model generates more creative text-to-image objects by weighting central features and balancing semantic match against visual difference.

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

Standard text-to-image models stay inside high-probability regions of their training data and therefore produce safe but unoriginal results. The paper introduces the Self-Creative Diffusion model that adds two targeted changes to escape those regions. A learnable spatial weighting module applies a parametric Kaiser-Bessel window that strengthens the central part of the image, encouraging new compositions. A visual-semantic mixing loss keeps the output faithful to the text prompt while pushing it away from the starting image to increase visual novelty. If the balance holds, the model can deliver images that feel interpretively creative rather than literal averages of seen examples.

Core claim

The Self-Creative Diffusion model consists of a learnable spatial weighting module that uses a parametric Kaiser-Bessel window to reinforce central image features and a visual-semantic mixing loss that combines a similarity term enforcing alignment with the text description and a diversity term that maximizes distinction from the original image, resulting in generations with improved creativity, semantic alignment, and visual coherence.

What carries the argument

Learnable spatial weighting module using a parametric Kaiser-Bessel window together with the visual-semantic mixing loss.

If this is right

  • Generated images display greater visual novelty and surprise while remaining semantically aligned with the input text.
  • The outputs avoid repetitive high-probability patterns typical of standard diffusion sampling.
  • Central features receive amplified emphasis that drives unexpected yet coherent object arrangements.
  • The approach supplies a simple add-on framework that can be applied to existing diffusion pipelines.

Where Pith is reading between the lines

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

  • The same central-weighting idea could be tested in text-to-video or 3D generation to encourage novel motion or structure.
  • Varying the strength of the diversity term would let users control how far the output departs from conventional interpretations.
  • The method points toward lightweight modifications of loss functions as an alternative to retraining entire models for creative control.

Load-bearing premise

Reinforcing central features with the Kaiser-Bessel window and maximizing distinction from the original image will produce genuine creativity and artistic value without creating incoherence or artifacts.

What would settle it

Generate images from the same prompts with both the proposed model and a standard diffusion baseline, then have evaluators rate them for creativity, text alignment, and artifact presence; absence of measurable gains on creativity or coherence would refute the central claim.

Figures

Figures reproduced from arXiv: 2605.19554 by Haibo Chen, Jian Yang, Jun Li, Shuo Chen, Yue Yu.

Figure 1
Figure 1. Figure 1: Generating Creative Objects by our SCDiff. (a) shows practical yet innovative designs using the prompt: “A creative S*". (b) and (c) demonstrate its ability to generate distinct conceptual objects within a broad category by varying the random seed. Abstract Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual … view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of SCDiff (Ours) against existing methods (e.g., C3 [12]). Increasing the amplification factor in C3 introduces ghosting ar￾tifacts. One notable approach aimed at mitigating this issue is the Creative Concept Catalyst (C3) method [12]. C3 operates by transforming in￾termediate U-Net features [21] into the Fourier domain using the Fast Fourier Transform (FFT) [22], selectively amplifying low-freq… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our SCDiff. It advances diffusion models through two novel modules: (1) a [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Results using different spatial win￾dow profile: (a) circular binary, (b) Gaussian (η=0.5), (c) Gaussian (η=0.8), and (d) our Kaiser-Bessel window. While compact support eliminates global artifacts, the profile within rij ≤ R determines whether the decoder interprets the modulation as geometric trans￾formation or mere texture variation. We therefore examine three candidate windows: (1) A naive circu￾lar bi… view at source ↗
Figure 5
Figure 5. Figure 5: Visualizing the updated process of our VSML using the prompt [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative Comparisons on CreaCOF demonstrate that our method delivers creative and meaningful outputs. substantial deviation and degraded fidelity. To ensure the rigor and reliability of our evaluation, we applied a filter (P-tile > 0.04) across all methods (including SCDiff and all competitive baselines) to mitigate the impact of low-quality artifacts, which could otherwise distort the FID and Precision… view at source ↗
Figure 12
Figure 12. Figure 12: Ablation analysis. We conducted an ablation study to evaluate the con￾tributions of each key component in SCDiff, as shown in [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative Comparisons on AdjCOF demonstrate that our method consistently produces visually impressive results. right: (i) baseline model without the LSW and VSML modules; (ii) With the LSW module (non adaptive amplification factor α); (iii) With the LSW module (non adaptive Kaiser–Bessel window (β)); (iv) Full model with both LSW and VSML modules. Experimental results confirm that the adaptive design of … view at source ↗
Figure 9
Figure 9. Figure 9: Our method on DiT-based models: (a) SD 3.5, (b) FLUX. Original R = 6 Output Radius = Original 12 Output flower R = 8 R = 20 Original R = 6 R = 8 R = 10 R = 12 R = 14 R = 16 R = 18 best_R = 8.6 “ a creative flower ” Original R = 6 R = 8 R = 10 R = 15 R = 20 [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Visual comparisons of different sampling radii [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 13
Figure 13. Figure 13: The images generated using the adaptively optimized parameters for each block. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative results for intra-class diversity under the prompt “a creative animal”. [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative results for intra-class diversity under the prompt “a creative animal”. [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The user interface used for the human evaluation task. Participants were instructed to [PITH_FULL_IMAGE:figures/full_fig_p018_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: User study comparing different methods on creative prompts (left, CreaCOF) and [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: More visual results. Additional samples generated using the prompt "a creative toy" are [PITH_FULL_IMAGE:figures/full_fig_p020_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: More visual results. Additional samples generated using the prompt "a bioluminescent [PITH_FULL_IMAGE:figures/full_fig_p022_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: More visual results. Additional samples generated using the prompt "a futuristic sofa" [PITH_FULL_IMAGE:figures/full_fig_p023_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: More visual results. Additional samples generated using the prompt "a creative cup" are [PITH_FULL_IMAGE:figures/full_fig_p024_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: More visual results. Visualization of feature amplification across different U-Net layers, [PITH_FULL_IMAGE:figures/full_fig_p025_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: More visual results. Visualization of feature amplification across different U-Net layers, [PITH_FULL_IMAGE:figures/full_fig_p026_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: More visual results. Visualization of feature amplification across different U-Net layers, [PITH_FULL_IMAGE:figures/full_fig_p027_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: More visual results. Visualization of feature amplification across different U-Net layers, [PITH_FULL_IMAGE:figures/full_fig_p028_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: More visual results. Visualization of feature amplification across different U-Net layers, [PITH_FULL_IMAGE:figures/full_fig_p029_26.png] view at source ↗
read the original abstract

Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual novelty and surprise, but also artistic value. Current T2I models, however, are largely optimized for literal text-image alignment with their data distribution, and their noise prediction networks constrain the generation to high-probability regions, consequently generating outputs that lack authentic creativity. To address this, we propose a Self-Creative Diffusion (SCDiff) model for meaningful T2I generations featuring two core modules: a learnable spatial weighting (LSW) module and a visual-semantic mixing loss (VSML). The LSW module designs a parametric Kaiser-Bessel window to reinforce central image features, fostering novel and surprising generation. The VSML module introduces a dual loss function: a similarity loss constrains that the new images align with its textual description, while a diversity loss maximizes its distinction from the original image, enhancing both semantic value and visual novelty. Extensive experiments demonstrate that our model substantially improves creativity, semantic alignment, and visual coherence, offering a simple yet powerful framework for generating creative objects.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 1 minor

Summary. The manuscript proposes a Self-Creative Diffusion (SCDiff) model for text-to-image generation. It introduces a Learnable Spatial Weighting (LSW) module using a parametric Kaiser-Bessel window to reinforce central image features and a Visual-Semantic Mixing Loss (VSML) with a dual objective: a similarity loss to enforce text alignment and a diversity loss to maximize visual distinction from an original image. The authors claim that this combination yields generations with improved creativity, semantic alignment, and visual coherence, supported by extensive experiments.

Significance. If the empirical claims can be substantiated, the approach offers a lightweight way to inject creativity into diffusion-based T2I pipelines by combining spatial feature emphasis with a mixed visual-semantic objective, potentially addressing the tendency of standard models to remain in high-probability regions of the data distribution.

major comments (3)
  1. [Abstract] Abstract: the assertion that 'extensive experiments demonstrate that our model substantially improves creativity, semantic alignment, and visual coherence' supplies no quantitative metrics, baselines, datasets, or ablation tables, rendering the central empirical claim unverifiable from the provided description.
  2. [Method (VSML)] VSML module: the diversity term is defined relative to an 'original image' whose precise role during diffusion sampling is not specified (e.g., whether it is a fixed reference, a baseline generation, or an intermediate noisy latent); without this, it is impossible to confirm that the similarity and diversity losses remain compatible across the sampling trajectory and do not induce semantic drift or artifacts.
  3. [Method (LSW)] LSW module: the parametric Kaiser-Bessel window is introduced to reinforce central features for novelty, yet no analysis of parameter sensitivity, interaction with the VSML diversity weight, or failure modes (e.g., boundary artifacts or loss of global coherence) is supplied, leaving the load-bearing assumption about balanced creativity untested.
minor comments (1)
  1. [Title and Abstract] The title refers to 'Text-to-Object Generation' while the abstract and method consistently describe text-to-image synthesis; a brief clarification of scope would improve precision.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify key aspects of our Self-Creative Diffusion (SCDiff) approach. We address each major comment below with specific revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that 'extensive experiments demonstrate that our model substantially improves creativity, semantic alignment, and visual coherence' supplies no quantitative metrics, baselines, datasets, or ablation tables, rendering the central empirical claim unverifiable from the provided description.

    Authors: We agree that the abstract, being a high-level summary, does not include specific quantitative details. The full manuscript reports these in the Experiments section, including metrics on creativity (e.g., via perceptual diversity scores), semantic alignment (CLIP similarity), visual coherence, baselines such as standard Stable Diffusion, and datasets like MS-COCO with ablations. To improve verifiability, we will revise the abstract to concisely reference key results, such as relative improvements in novelty while preserving alignment. revision: yes

  2. Referee: [Method (VSML)] VSML module: the diversity term is defined relative to an 'original image' whose precise role during diffusion sampling is not specified (e.g., whether it is a fixed reference, a baseline generation, or an intermediate noisy latent); without this, it is impossible to confirm that the similarity and diversity losses remain compatible across the sampling trajectory and do not induce semantic drift or artifacts.

    Authors: The original image serves as a fixed reference generated by the unmodified base diffusion model for the input text prompt; the diversity loss is computed in feature space against this reference during training and is incorporated into the sampling objective via a weighted combination with the similarity term. We will add explicit clarification in the revised Method section on its role as a static baseline (not an intermediate latent) and include analysis showing loss compatibility without inducing drift, supported by additional qualitative examples. revision: yes

  3. Referee: [Method (LSW)] LSW module: the parametric Kaiser-Bessel window is introduced to reinforce central features for novelty, yet no analysis of parameter sensitivity, interaction with the VSML diversity weight, or failure modes (e.g., boundary artifacts or loss of global coherence) is supplied, leaving the load-bearing assumption about balanced creativity untested.

    Authors: We acknowledge that the current manuscript lacks dedicated sensitivity analysis for the Kaiser-Bessel parameters and their interplay with the VSML diversity weight. In the revision, we will incorporate an ablation study varying the window parameters (e.g., shape factor and size) and diversity weighting coefficient, along with discussion of failure modes such as boundary effects, demonstrating through experiments that global coherence is preserved via the parametric design and central emphasis. revision: yes

Circularity Check

1 steps flagged

VSML diversity loss reduces to distinction from 'original image' by construction

specific steps
  1. self definitional [Abstract]
    "The VSML module introduces a dual loss function: a similarity loss constrains that the new images align with its textual description, while a diversity loss maximizes its distinction from the original image, enhancing both semantic value and visual novelty."

    The diversity loss is defined to maximize distinction from the original image, and the paper immediately presents this construction as 'enhancing ... visual novelty.' Novelty is thereby achieved by definition of the loss rather than derived or validated independently of the optimization target.

full rationale

The paper proposes SCDiff with LSW and VSML to instill creativity. The VSML diversity term is explicitly constructed to maximize distinction from an original image during generation, and the abstract directly equates this to enhanced novelty and creativity. This makes the core claim of improved visual novelty a direct consequence of the loss definition rather than an independent outcome. However, the LSW module and experimental claims retain some independent content, and no self-citation chain or mathematical derivation reduces the entire result to inputs. The paper remains largely self-contained via its empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The approach appears to rest on standard diffusion assumptions plus two newly introduced components whose independent grounding is not shown.

invented entities (2)
  • Learnable Spatial Weighting (LSW) module no independent evidence
    purpose: Parametric Kaiser-Bessel window to reinforce central features and foster novel generation
    Presented as a core new module without reference to prior independent validation
  • Visual-Semantic Mixing Loss (VSML) no independent evidence
    purpose: Dual loss combining text similarity and distinction from original image
    Introduced as a new loss function to balance alignment and novelty

pith-pipeline@v0.9.0 · 5735 in / 1194 out tokens · 44925 ms · 2026-05-20T05:59:10.814031+00:00 · methodology

discussion (0)

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