VLM-Guided Adaptive Negative Prompting for Creative Generation
Authors: Shelly Golan
Yotam Nitzan
Zongze Wu
Or Patashnik
Venue: ICLR 2026 Poster (2026)
Keywords: Generative models
Computational graphics
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imagination, enabling the discovery of visual concepts that exist in the unexplored spaces between familiar domains.
While text-to-image diffusion models excel at rendering photorealistic scenes that faithfully match user prompts, they still struggle to generate genuinely novel content. 
Existing approaches to enhance generative creativity either rely on interpolation of image features, which restricts exploration to predefined categories, or require time-intensive procedures such as embedding optimization or model fine-tuning.
We propose VLM-Guided Adaptive Negative-Prompting, a training-free, inference-time method that promotes creative image generation while preserving the validity of the generated object.
Our approach utilizes a vision-language model (VLM) that analyzes intermediate outputs of the generation process and adaptively steers it away from conventional visual concepts, encouraging the emergence of novel and surprising outputs.
We evaluate creativity through both novelty and validity, using statistical metrics in the CLIP embedding space. Through extensive experiments, we show consistent gains in creative novelty with negligible computational overhead. 
Moreover, unlike existing methods that primarily generate single objects, our approach extends to complex scenarios, such as generating coherent sets of creative objects and preserving creativity within elaborate compositional prompts. Our method integrates seamlessly into existing diffusion pipelines, offering a practical route to producing creative outputs that venture beyond the constraints of textual descriptions.

Published as a conference paper at ICLR 2026
VLM-GUIDED ADAPTIVE NEGATIVE PROMPTING FOR CREATIVE GENERATION
Shelly Golan
Yotam Nitzan Adobe Research
Zongze Wu Adobe Research
Or Patashnik Tel-Aviv University
Technion,
Tel-Aviv University
pet
chess set
sofa
jackett
buildong
"A photo of a creative... ..."
Figure 1: Our method generates creative concepts such novel pets, uniquely designed jackets, and ventional buildings by steering generation away from conventional patterns using a VL.M-Guided Adaptive Negative Prompting process.
ABSTRACT
Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imagination, enabling the discovery of visual concepts that exist in the un- explored spaces between familiar domains. While text-to-image diffusion models excel at rendering photorealistic scenes that faithfully match user prompts, they still struggle to generate genuinely novel content. Existing approaches to enhance generative creativity either rely on interpolation image features, which restricts exploration to predefined categories, or require time-intensive procedures such as embedding optimization or model fine-tuning. We propose VI.M-Guided Adap- training-free, inference-time method that promotes creative image generation while preserving the validity of the generated object. Our approach utilizes vision-language model (VLM) that analyzes intermediate outputs of the generation process and adaptively steers it away from conventional visual concepts, encouraging the emergence of novel and surprising outputs. We evaluate creativity through both novelty and validity, using statistical metrics show consistent
tive Negative-Prompting
the CLIP embedding space. Through extensive experiments, gains in creative novelty with negligible computational overhead. Moreover, un- like existing methods that primarily generate single objects, our approach extends to complex scenarios, such as generating coherent sets of creative objects and pre- serving creativity within elaborate compositional prompts. Our method integrates seamlessly into existing diffusion pipelines, offering practical route to producing creative outputs that venture beyond the constraints textual descriptions.
INTRODUCTION
growing body of research (Hertzmann, 2018 Yongjun et al., [205 Iveevic & Grandinetti 2024) revolves around somewhat philosophical question: what creativity and originality, and can

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computers create art? One suggestion by Boden (2009) is to categorize computational creativity along a spectrum of increasing novelty. At the lowest level, combinatorial creativity produces un- expected combinations of existing concepts, such hybrid creature that merges features of bee and a giraffe. Exploratory creativity goes further by discovering new possibilities within a known domain while maintaining validity, for instance, inventing an animal species with entirely new but biologically plausible traits. At the highest level, transformational creativity challenges the bound- aries of existing categories altogether, such as conceiving an organism so unlike current life forms that it forces us to reconsider the definition of "animal" itself.
Recent advances in text-to-image (T2I) diffusion models have demonstrated strong capabilities in generating photorealistic images from natural language prompts. These models excel at reproduc- ing and recombining simple visual concepts from their training data, allowing for combinatorial creativity to some extent. However, they still struggle with novelty that falls under the category of exploratory and transformational creativity. This limitation reflects an inherent tension in generative modeling between mode coverage (i.e., capturing the full distribution), and mode seeking (i.e., gen- crating high-quality typical samples). For example, a known technique that attempls to navigate this tradeoff is Classifier-fis free guidance (CFG). Lower guidance scales increase diversity but compromise text alignment, while higher scales improve prompt adherence but generate more typical outputs.
Our experiments show that simple prompt mod- ifications fail to produce creative outputs from current models. As demonstrated in Figure E adding creativity-related terms such as "cre- ative" or "new type of" produces outputs that remain similar to conventional pets like blue cat with wings, kittens, dogs, or a ferret-like an- imal with long ears. On the other hand, our blue pet, presented in Figure [ cannot be described as combination of known pets.
GPT-o3
GPT-4o
SDXI.
FLUX
SD3.5
Figure
Images generated with GPT-o3 OpenAl
2025
GPT-4o (OpenAT] 2024), SDXL. (Podell et al.
2023
FLUX-dev BBlack Forest Labs] 2024), and
Existing frameworks for creative generation fall into two paradigms: combinatorial approaches that blend predefined concept pairs through
SD3.5
(Eser al.
2024) using the prompt "Profes-
sional high-quality photo a new type of pet."
rule-based searches (Li al. (0024) or learnable tokens (Feng et al.
2024), and exploratory methods like ConceptLab (Richardson et al.) 2024) that optimize textual embeddings to discover novel con- cepts. Specifically, Conceptl.an ab formulates creative generation as an iterative optimization problem over learned textual embedding, minimizing loss function that balances two objectives: maintain- ing similarity to a broad target category while maximizing the distance from known subcategories in the CLIP embedding space. While these demonstrate progress, they require either per-concept opti- mization procedures, specialized training on curated datasets, or predefined concept specifications, limiting their practical deployment and scalability.
To address these limitations, we propose VLM-Guided Adaptive Serative-rromptigg. a training- free method that integrates into any diffusion sampler without modifying pretrained weights quiring curated datasets. Unlike previous approaches, our method operates entirely at inference time through a closed-loop feedback mechanism (Figure 3 We leverage lightweight vision-language model (VLM) to adaptively steer the generation process away from its typical predictions and thus towards unexplored regions of possible outputs. Our approach utilizes the VLM to analyze interme- diate denoising predictions at each timestep, identify dominant objects, and adaptively convert these observations into negative prompts that are integrated into the next denoising step.
Through experiments across multiple VLM models, diffussion pipelines, and human evaluation stud- ies, we demonstrate consistent improvements in exploratory creativity while maintaining categorical coherence. Our analysis reveals how adaptive negative prompting guides the denoising trajectories toward unexplored semantic regions and highlights the importance of VL.M feedback during infer- ence. Through extensive ablation studies, we validate our key design choices, including dynamic negative prompt accumulation and per-generation adaptation, showing superiority over alternative approaches. Furthermore, demonstrate capabilities beyond existing methods, including the gen- eration of coherent creative sets and the preservation of creativity within complex compositional prompts, showcasing the versatility of our VLM-guided approach.
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2 RELATED WORK
The pursuit of extending human imagination with machine learning has motivated extensive research in computational creativity, from algorithmic design tools (Cohen-Or & Zhang. 2016; [ims, 1994: 199T, Sun et al., 2025 to theoretical frameworks exam- ining whether computers can create art or merely serve as sophisticated tools for human artists [Hertzmann 2018). Early work, such as Xu et al. (2012), introduced set-evolution framework for creative 3D shape modeling by steering the generation towards user-preferred shapes while main- taining diversity. Other works (Elgammal et al.] 2017: (Sai et al., (019)) proposed modifying losses and training objectives to generate creative art by maximizing deviation from established styles while minimizing deviation from the general art distribution.
Foundations of Creative Generation
Concept Blending and Combinatorial Creativity A significat portion of computational creativ- ity involves combinatorial approaches. Some works (Liew et al., 2022; Zhou et al., 2025) leveraged diffusion models to blend different visual and semantic concepts for the generation of novel outputs. Dorfman et al. (2025) extended this to multiple visual inputs by crafting composite embeddings, stitched from the projections of multiple input images onto concept-specific CLIP-subspaces iden- tified through text. For text- text-based concept pairs, [i et al. (2024) suggested balance swap- sampligg which generates creative combinatorial objects by randomly exchanging intrinsic elements of text embeddings and selecting high-quality combinations based on CLIP distances. [eng et al. (2024) takes a different approach and re-defines "creativity" as a learnable token. They iteratively sam- ple diverse text pairs from their proposed dataset to form adaptive prompts and restrictive prompts, and then optimize the similarity between their respective text embeddings. While these combinato- rial approaches recombine user-specified concepts, we instead discover novel concepts within broad categories without predefined targets.
VLM-Guided Creativity Approaches. Recent research leverages Vision-Language Models (VLMs) to guide creative generation. |Feng et al. (2025) uses VLMMs to supervise distribution- conditional generation, enabling multi-class concept blending through learnable encoder-decoder framework. While the above approaches focus on combinatorial creativity through concept blend- ing, Richardson al.] (2024) introduces ConceptLab, which tackles the more challenging task of exploratory creativity. They formulate the Creative Text-to-Image (CT2I) generation an opti- mization process of learned textual embedding. To prevent convergence to existing concepts, ConceptLab incorporates a question-ansee answering VLM that adaptively adds new constraints to the optimization problem. These VL.M-guided approaches rely on per-concept optimization procedures that require multiple iterations and substantial computational resources. Our approach leverages VLMs as real-time oracles during the denoising process to reduce computational overhead.
Optimization-Free Creative Generation. Han al. (2025) boosts creativity in Stable Diffusion by amplifying features during denoising, primarily affecting color and textures. While we share the goal of optimization-free creativity enhancement, our method operates through dynamic negative prompting to guide the generation away from conventional semantic patterns rather than amplifying existing features. The advantage of such optimization-fres approaches lies in their immediate appli- cability to existing models without requiring additional training or complex optimization procedures.
Theory of Creative Generation Recent work has explored creative generation from a more theo- retical point of view, investigating the relation between memorization and novel sample generation. Lu al. (2024) propose method which improves sample diversity and creativity of ceffusion-bassee image generative models and to prevent training data reproduction. [Shah et al. (2025) investigates whether creative generation requires memorization, proposing ambient diffusion techniques that
duce reliance on reproducing training data while maintaining generation quality. Kamb & Ganguli (025)) provides theoretical foundations by analyzing creativity mechanisms in convolutional dif- fusion models, offering formal frameworks for understanding how diffusion models can generate samples that do not exist in their training distributions.
METHOD
Adaptive Negative-Prompping method enhances creative generation in diffusion models through closed-loop feedback mechanism that dynamically navigates the denoising process away from familiar visual patterns. As illustrated in Figure 3 our method monitors the intermediate denoiser outputs using a Vision-Language Model (VLM), which identifies dominant elements (e.g..
Our
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"Professional high quality photo of new type of pet":
V8
V8
V8
VO
g0+1)
21-11
"cat, dog. bird, hamsteer
CCat"
0
catt dag"
"What pet do you identify in the pkoto?"
V
(T+1) U Pneg
9i))
Figure 3: Overview of our VLM-guided negative prompting method. To generate a creative image (c.g., "new type of pet"), sample Gaussian noise and perform augmented denoising process that maintains an adap- tive list of negative prompts. cach denoising step, we query pre- trained identify visual concepts present in the intermediate output and update the list accordingly, steering the denois- ing process away from them. For example, we add the token "cat" to the accumulating list to shift the denoising trajectory away from generating image resembling cat well the previously detected pets.
Language Model (VLM) to
"cat"" and accumulates them dynamic negative prompts during the generation process. This adaptive accumulation refines the guidance signal at each denoising step.
We begin by establishing the necessary background negative prompting in Section 3.1 and de- tailing our VL.M-guided synthesis strategy in Section 3.2
3.1 BACKGROUND: DIFFUSION MODELS AND NEGATIVE PROMPTING
Diffusion models generate images by gradually denoising a sample from pure noise Iy over series of time steps. Latest diffusion models, including Stable Diffusion 3.5 (Esser al. 2024) used in our experiments, employ flow matching (Lipman et al. 2023) to generate images through iterative denoising. Let 2; denote the noisy image timestep E T ...., 0j. In flow matching. the model learns a velocity field (0 conditioned on text embedding с = E(p) derived from prompt via text encoder E. The denoising process follows the probability flow ODE: de, = oo( f, c). During sampling,
dt
estimate the clean image any timestep using the following equation:
2
(t)
x, - vo(x,, t.
2021) improves conditional generation by combin- ing conditional and unconditional predictions: Uy (x Ø) + w (vu( c) (1 L, Ø)), where Ø denotes the unconditional (null) embedding, and w the guidance scale. When w O. the model generates unconditional samples; w increases, the model increasingly favors features aligned with the conditioning text. The guidance operates by amplifying the difference between con- ditional and unconditional predictions. When w 0, the model generates unconditional samples. As increases, the model increasingly favors features that align with the conditioning text. This mechanism was naturally extended (Saharia et al. (022) to negative prompting, in which the model is explicitly discouraged from generating features associated with negative prompt Pneg- Instead of subtracting the unconditional prediction, we subtract a negatively conditioned prediction:
Classifier-free guidance (CFG) (Ho & Salimans
A 0g
uo( Cneg) + (vo (are, l, Cpns) vol Cneg))
where Cneg E(Pneg) represents the negative prompt embedding derived from the unwanted con- cepts Pneg- This formulation steers generation away from Gueg and toward Cpos by amplifying their differences.
3.2 VLM-GUIDED ADAPTIVE NEGATIVE PROMPTING
To generate creative image from given prompt Ppos, sample initial Gaussian noise Ty \/ I) and initiate an augmented denoising process in which, at each denoising step, we dynami- cally steer the generation away from common visual concepts identified through VLM analysis, as
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Positive prompt Poex: "A photo of creative buildding
VLM questions q: "What design of building?" " "What the shape of the building?" ??, "What the building made of?"
Positive prompt Ppox: "A photo new type pet" VLM questions y: "What you identify the photo!""
Positive prompt Poosi "A photo creative bag" VLM questions "What is the design of the bag "What is the bag made of?", "Whar is the color of the bag?"
Positive prompt Prox: photo new type of fruit"
VLM questions "What fruit do you identify in the photo?"
different object categories. In all categories, method generates creative shapes appearances while preserving object semantics. For instance, buildings with unique forms and textures that retain windows, doors, and balconies, or bags made of varied materials that remain recognizable as bags.
Figure 4: Qualitative results of method
(+) illustrated in Figure Given the intermediate prediction T0 at each timestep t E [o, T), we query the VLM identify The dominant features present in the image. We denote the questioning process as follows:
(0)
(1)0) 9(0))
Where V is the VLM model, (()) is the question, and (1) is the VLM response at timestep Each (t+1) (t) with initialization response (!) is added to growing set of negative prompts: pneg Pneg (I)
Prig Ø. This creates feedback loop where each timestep's 's guidance reflects all previously identified dominant features, progressively steering toward more creative outputs.
Runtime Analysis. Our method adds minimal overhead of 13 seconds when used in the least efficient setting. Querying ViLT (Kim et al. 2021) for 28 steps while using the SD3.5-large decoder for Zo predictions takes total of 35 seconds, compared to 22 seconds for standard SD3.5-large single image generation. In contrast, (Richardson al., 2024 requires approximately 8 minutes to train each concept on a single seed, and C3 requires approximately 30 minutes for amplification factor search using 10 samples per concept. full analysis can be found in Appendix B.4
4 EXPERIMENTS
We comprehensively evaluate our approach through qualitative comparisons with existing creative generation methods, a user study, and quantitative metrics. We validate our design choices with extensive ablations examining the necessity of the VLM feedback A. seed-specific adaptation
A.2 the accumulation strategy A.3] different positive prompts A.5 robustness to different VLM models A.6 the effect of question design the final output A.7 analysis of the VLM response on blurry predictions [A.8, and analysis of the VLM querying frequency Appendices A.4 and B. Finally, present cases and practical applications that approach enables, extending the
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capabilities of previous creativity methods. Additional results and implementation details are in Section and Appendices A to
We display in Figure 4 the diverse creative outputs of our approach across categories ranging from pets to bags. Through seed variation alone, our method explores wide spectrum of novel concepts without requiring retraining additional optimization.
4.1
QUALITATIVE EVALUATION
We begin by comparing our method with the two competing approaches for exploratory creativity within category: ConceptLab (Richardson et al.] 2024) and C3 (Han et al. 2025). As can be seen in Figure ConceptLab generates creative objects but often sacrifices category validity. For example, it may produce a cup that cannot be drunk from or couch with no seat. In contrast, our method produces objects that are both valid and creative. For fair comparison, we use the same base models as ConceptLab and C3, while also demonstrating that our method leverages newer models to produce better results. ConceptLab and C3 have several assumptions preventing them from integrating seamlessly to any base diffusion model.
method with images generated by state-of-the-as art models, including Stable Diffusion 3.5 (Esser al. 2024), FL.UX. (Black Forest Labs] (222)) and GPT-4o [OpenAA[[024).. all prompted with requests for "creative" or "new type of"" vari-
In Figure 7 compare
5015
CFT.0
VUM Cuidco
Cotegay
ations. These comparisons demonstrate that even the most advanced generative models, when used with standard prompting, produce typical category exemplars such as regular cars and fruits rather than creative variations. In contrast, results present novelty while maintaining validity. For example, the vehicle has wheels and space for driver, yet does not correspond to any existing vehicle type.
Concestiab
Nevelty
Figure 5: Trade-off between novelty and category herence our user study. Higher values are better for both axes. Our method (star) uniquely achieves high scores on both dimensions compared to other creative generation methods.
4.2 USER STUDY
Quantitative evaluation remains a fundamental challenge in computational creativity research (Lamb al. 2018). We conduct user study to evaluate the humaa--pecceived creativity and semantic validity of images generated by our VLM-guide approach compared to existing methods. We collected total of 3.200 responses (25 participants x 32 image pairs comparisons), across different categories. The full setup is described in Appendix D For each image pair, participants
Ours Kundinsky Conceptl
C3-SDXL
Owns- SDXL
Ours: SD35
SD3.5
Cup
Plant
Safa
Chair
Car
Building
Tedly bear
Figure 6: Left: Comparison with ConceptLab Richardson et al. (2024) (top row) and our VLMM-Guied method using Kandinsky2 (Razzhigacv et al. 2023) (middle row) and SD3.5 (bottom row). Right: Comparison with C3 (Han al. 2025) using SDXL (Podell al. 2023) (top row) and method using SDXL (middle row) SD3.5 (bottom row). Our method consistently generates more diverse and imaginative variations while maintaining recognizability within each category.

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GPT-40
FLUX I-dev
SD3.5
Ours
Musical instrument
style
set
Figure 7: Creative generation comparison across different categories. Despite prompts explicitly requesting novelty ("A type of [category]" "A creative [categoryl"), GPT-4o, FL.UX and SD3.5 produce typical category exemplars. Our method generates novel variations that navigate unexplored modes of the semantic space. Each column uses identical seeds across all methods fair comparison.
evaluate Creativity/Novelty: How creative novel is the interpretation of the broad category? and validity: How well does the image maintain identification as the specified category? Figure presents the results. "Creative Prompting" methods (SD3.5 and GPT-4o), explicitly requesting novelty via prompts such as "A new photo a [category]". cluster in the upper-left region with high category validity but minimal novelty, confirming our qualitative findings that simple prompt modifications fail to produce creative exemplars. greative-generation methods (Conceptl Lab and C3) achieve moderate creativity results but at a significant cost in validity. In contrast, our method achieves both high novelty and validity, maintaining both high creativity and validity.
4.3 ABLATION STUDIES
A natural question is whether the in lo-ph VLM guidance is necessary does one of two offline alternatives suffices: (i) using an I.LM to derive a negative list from the positive prompt alone, or (ii) using VLM analyze random image
and then statically replaying the resulting list across all seeds. We study four design variants to validate our adaptive negative prompting ap- proach, presented Figure g First, tested whether GPT-4o could generate static negative prompt lists directly from the main object in the positive prompts. Second, applying our accumu- lated negative prompts statically (replaying) from the beginning yields less creative outputs. Third, reusing negative prompts across different seeds (Cross-Seed replay) produces suboptimal results. Finally, removing accumulation allows generations to cycle back the conventional patterns pre- viously identified. Our method achieves the best scores across all reported metrics in Table The full ablation studies are presented in Ap- pendix A They examine computational effi- ciency (1.c., timestep reduction). VLM robust-
ness across different models, question design
impact, and positive prompt variations, all con- firming the robustness of our approach.
12%
4.4 QUANTITATIVE EVALUATION
13%
Existing methods employ different strategies to quantify and evaluate creativity. ConceptLab measures the difference between CLIP similar- ity to the positive concept prompt and the max- imum CLIP similarity to any negative concept prompt. We refer this measure "relative
170
Dreaiie Phomptnng
Comeptiik
Figure 9: Top 5 subcatcgory distribution of 100 gener- ated pets per method classified with GPT-4o.

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Table 1: Quantitative evaluation of creative generation methods different prompting strategies. Refer- ence: SD3.5 with photo of [category]". Creative Prompting: SD3.5 with "A photo creative [category]] VLM-Guided: Our adaptive negative prompting approach. C3 and ConceptLab images are generated as ex- plained in the corresponding papers. The metrics are averaged over 400 samples, cqually generated 100 from 4 categories: pet, plant, garment, vehicle. In bold are best results underline for second best, within each basc- model category. For validity exclude the baselines (Reference & Creative Prompting) from the marking.
Novelty
Diversity
Validity
Relative Typicality
GPT Novelty, Score
Total Variance
CLIP Score
GPT Score
Method
1
t
Vendi f
ConceptLab- 2aniiiissk2
1.922
0.238
0.289
5.119
0.270
0.862
Stable Diffusion 3.5 Large Base Model
Reference SD3.5 Creative Prompting SD3.5
1.640
0.065
0.188
3.174
0.282
1.000
1.645
0.230
0.191
3.139
0.267
0.933
GPT-4o Concepts
0.655
0.093
0.272
4.973
0.262
0.867
GPT-4o 15 Concepts
0.885
0.108
0.277
5,040
0.262
0.805
GPT-4o 28 Concepts
1.043
0.100
0.276
5,067
0,260
0,828
Cross-Seed Replay
1.703
0,065
0.265
4.584
0.261
0.843
No Accumulation
1.610
0.060
0,274
4.355
0.262
0.875
Captions Regeneration
1.317
0.187
0.279
5.020
0.248
0.663
Ours SD3.5 ViLT
1.835
0.157
0,298
5.347
0.264
0,893
Ours SD3.5 BLIP-1
2.005
0.230
0.299
5,414
0.264
0,856
Ours SD3.5 + BLIP-2
2.190
0.370
0.318
5.794
0.261
0.898
Ours SD3.5 Qwen2.5
2.100
0.401
0.308
5,476
0.264
0.917
SDXL-1.0 Base Model
Reference SDXL
1.775
0.015
0.174
2.906
0.283
1.000
Creative Prompting SDXL.
1.540
0.155
0.206
3.640
0.274
0.9125
C3-SDXL
1.075
0.232
0.271
4.726
0.254
0.895
Ours SDXL + Qwen2.5
1.795
0.405
0.296
5.427
0.252
0.895
typicality". C3 evaluates three dimensions of creativity: novelty, diversity, and validity. We eval- uate creativity through complementary metrics that capture novelty, diversity, and validity as well. For novelty, we measure relative typicality (multiplied by 100 for readability) and the GPT Novelty Score. For the diversity we measure Vendi score and total variance. For validity, we employ CLIP alignment and GPT-4 verification. While these metrics have known limitations for creative outputs, as creativity inherently deviates from training distributions, they provide consistent comparative baselines. The formal definitions of the metrics are presented in Appendix
Building
Jacket
Bag
GPT-lo
GPT-4o
Replay
Replay (Cross Seed)
28 concepts
15 concepts
10 concepts
(GPT-4)
(Per Seed)
Accumulation
Ours
Figure 8: Left: Non-Adaptive LLM Approach: GPT-4o (n E [5, 28)) static LLM list n negative
concepts applied at all steps. Ours (GPT-40) dynamic, VLM-guided negatives using GPT-44o as our VLM. Right: Replay (Per-Sced) reuse the accumulated VLM list from the same seed at all steps; Replay (Cross- Seed) reuse list extracted from different seed at all steps; No Accumulation VLM answers (no overy: Ours adaptive accumulation of negative prompts.
only the current step's
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Quantitative Results Table 1 summarizes the quantitative results. We achieve significant im- provements in diversity and novelty metrics with minimal tradcoff in CLIP and GPT scores. All metrics are averaged across four categories: "vehicle", "plant", "pet", and "garment" (100 images each), so improvements reflect cross-category behavior. We divide our results based on the back- bone base model used for the generation. When applied SIXXL (Podell et al. 2023) our method still achieves significant improvements, even though SDXL is an inherently less capable and less diverse base model than SD3.5, our method still produces improvements. This variant also outper- forms C3-SDXL across novelty and diversity metrics while maintaining comparable validity scores. This demonstrates that our method promotes creative exploration regardless of the base model. Our method using SD3.5+Qwen2.5--2
and SD3.5+BLIP-2 achieves the best balance across all three creativity dimensions, leading novelty and diversity, and maintaining competitive validity, while other methods either sacrifice creativity for validity or vise versa. The design variants we evaluate under- under-perform our dynamic, per-stop. per-seed approach, highlighting the importance of both tim- ing and seed-specific guidance. A no-accumulation variant also trails our method, indicating that remembering previously discovered negatives is beneficial, Notably, while Lonceptlaab achieves the highest CLIP score, it shows the lowest GPT verification score. This happens because their optimization process maximizes the CLIP-space distance from negative concepts but can produce adversarial examples that satisfy mathematical constraints without maintaining semantic validity. This manifests as objects that technically align with CLIP embeddings but fail human and GPT-4 verification as functional category members (e.g., cups without cavities and sofas without seating surfaces). In contrast, our method maintains the highest performance across all three evaluation di- mensions: "validity"" "diversity"" and "novelty". The caption regeneration experiment demonstrates limitation of pre-determined prompting: we used Qwen2.5-VE (Bai et al. (025) to generate de- tailed captions of our creative images, then attempted to regenerate those images from the captions alone. Despite having detailed descriptions of creative objects, explicitly prompting for creativity, the regenerated images show substantially lower novelty and validity scores. This demonstrates that even very detailed text prompts cannot replicate the creative exploration achieved by adaptive guidance approach.
GPT Novelty Score In Figure we present the distribution of subcategories classified with GPT- 4o over 100 images of pets generated with ConceptLab, C3, Creative Prompting, and Our VLM- Guided method. While Creative Prompting and C3 generate recognizable dogs and cats, with Con- ceptLab exhibiting intermediate behavior, approach primarily produces unknown or unclassifi- able pets, approximately 87%, demonstrating our method's 's ability to avoid known subcategories.
4.5
USE CASES
Diverse scenarios. Our method generates novel objects within semantic categories and can be used for practical applications by plac- ing these objects diverse contexts and scenes. Recent controllable generation models like 1lde. Kontext (Black Forest Labs) (025) enable users take our creatively generated objects and seamlessly integrate them into vari- ous environments while preserving their unique characteristics. Interestingly, we found that this approach achieves better consistency compared to Conceptl lab's method of integrating opti- mized tokens into different prompts. We show an example of this phenomenon in Figure TO] Each building that is generated by reusing the
Tursteoontest
ConcppLah
"... bird's eye. "... colorful "...a pencil Creative Object view of
Generated
building ..." sketch of ..."
Figure 10: Creative object in different scenes. Left column: Novel objects generated hy our VI.M-guided method and reused with Flux-Kontext [Black Forest
Labs 2025) (top row) and Conceptl.ab (bottom row).
textual token is different than the other. On the other hand, using Flux-Kan Kontext our creative building
looks consistent throughout the scenes.
Complex prompts. Figure [| displays how our VLM-guided approach seamlessly inte- grates with elaborate prompt descriptions," "A photo of an imaginary pet surfing on a board near an island", "A photo of new type of plant
Figure 11: Creative objects presented in complex vironment described by the prompt.

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blooming in an arctic field next penguins", "A photo of woman wearing creative jacket in a french cafe" and "A photo of a new type of fruit sliced on a ceramic plate", enabling creative exploration even within complex requirements.
The adaptive negative prompting mechanism operates orthogonally to these additional constraints, it identifies and steers away from conventional modes of the requested object described "cre- ative". while respecting the stylistic and compositional requirements specified in the prompt. To evaluate our method's controllability, constructed a benchmark of 200 diverse com- plex prompts spanning categories such an- imals, plants, fashion, and food, each embed- ding creative elements within elaborate scene descriptions (e.g., "A photo of a creative insect resting on dew-covered leaf in a quiet morning meadow"). We evaluate prompt adherence and per- ceptual quality using VIEscore (Visual Instruction-guided Explainable) scores (Ku et al. 2024). As shown in Table 2 our method achieves higher VIE-SC scores compared to creative prompting with SD3.5 alone, while maintaining comparable perceptual quality (VIE-PQ). This demonstrates that our adaptive negative prompting generates central objects while respecting complex scene descrip- tions and compositional constraints which aligns better with the prompt requesting for creativity. Full benchmark construction details and automated question generation methodology are provided in Appendix E.6
Method
VIE-SC
VIE-PQ
Total
Creative Prompting
8.992
8.659
8.769
Ours (SD3.5+ViLT)
9.163
8.609
8.848
Table 2: VIE scores on 200 complex prompts.
Beyond single objects. Our method extends naturally from generating individual creative objects to producing coherent sets of related items that share a unified creative vision. By applying approach to prompts that describe collections c.g.. "Creative tea set", as presented in Figure [2] we demonstrate that method maintains validity and consistency across mul- tiple objects while exploring creative varia- tions.
Cutlery set Luggage set Figure 12: Creative generated by our method demonstrating coherent collections of related objects. Each set exhibits individual creativity in its components while maintaining stylistic and functional consistency across the collection.
Tea set
Chess set
5 CONCLUSIONS
We introduced VLM-Guide Adaptive Pegative-Prompting an inference- time method that lever- ages the strength of vision-language models dynamically steer diffusion models toward more creative outcomes. By querying a VLM throughout the denoising process and accumulating seed- specific negative prompts, our approach pushes generation away from conventional patterns while preserving categorical coherence. The fact that VLM is capable of analyzing noisy intermediate states and providing guidance strong enough to redirect the trajectory highlights its potential as a powerful mechanism for creative exploration.
While our -uidduddded approach demonstrates effective creative exploration, several limitations can be addressed in future research. First, method introduces computational overhead through VLM inference at each timestep, though our ablation studies show this can be reduced to the first 10-15 steps without significant quality loss. Second, the quality of creative outputs depends the VLM's ability to identify emerging patterns in noisy intermediate predictions; while we demonstrate robustness across various VLMs, more sophisticated languae models generally yield better results. Third, our approach requires careful question design for optimal performance; different question formulations work better for different semantic categories, and automating this selection remains an open challenge.
Looking ahead, we believe that the integration of feedback-drive driven guidance will open new directions for creativity in generative models, and future work may extend this paradigm to other domains, such as video, 3D, or multimodal content creation.
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Reproducibility Statement We provide the necessary details to reproduce results. Algorith- mic steps and Hyperparameters (feedback window, frequency f. accumulation, replay variants) are specified in the main text and Appendix evaluation protocols, metrics, and prompts are in Ap- pendix C We release per-category negative lists (static and accumulated) and the exact question Random seeds, category splits, and generation counts are stated in the implementation details (Appendix B).
[
templates used by the VIM in Appendix A
We will release the code of our project in the
near future.
Ethics Statement Our study involves image generation within broad, non-sensitive categories. We avoid instructions and outputs that target protected attributes or hazardous content. The user study (Appendix D followed institutional guidelines: no personally identifying information was collected, and data were anonymized and aggregated for analysis. All third-part models and datasets were used under their respective licenses, and we disclose model choices and prompts (Appendices B and C). We report compute and runtime enable the assessment of environmental impact (Appendix B). No conflicts of interest or external sponsorship influenced the findings.
ACKNOWLEDGMENTS
We thank Roy Ganz, Guy Ohayon, Omer Belhasim and Tomer Borreda for their early feedback and helpful suggestions. This work was supported by a research gift from Adobe.
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APPENDIX
This appendix provides comprchensive details supporting main paper. Section pressenss exten- sive ablation studies. Section provides technical implementation specifications. Section details the qualitative evaluation framework and the generation process of the evaluated methods. Section P details evaluation metrics. Section describes our human evaluation protocol.
C
A
ABLATIONS
A.1
NON-ADAPTIVE LLM APPROACH.
Table Exact GPT-4o used as Pheg
LLM for the Jacket category Figure
N=10
N=
N=28
bomber, biker, trucker, parka, puffer, blazer, varsity, trench, anorak, field
bomber, biker, trucker, parka, pušfer, blazer, varsity, trench, anorak, field, harrington, peaccat, safari, quilted, windbreaker
bonber, biker, trucker, parka, puffer, blazer, varsity, trench, anorak, field, harrington, peacoat, safari, quilted, xindbreaker, denim, leather, fleece, rain, down, coach, double breasted, chore, utility, caqoule, car, duffle,
Table Exact GPT-4o used as Pigg for the Sofa category Figure
N=10
N=15
N=28
sectional, loveseat, chalse, recliner, futon, sleeper, modular, tuxedo, cheaterfield, canelback
sectional, lovescat, chaise, recliner, futon, sleeper, modular, tuxedo, chesterfield, camelback, lawsen, midcentury, slipcovered, daybed, settee
sectional, loveseat, chaise, recliner, futon, aleeper, modular, tuxedo, chesterfield, canelback, Lawson, midcentury, s siipcovered, daybed, settee, track arm, roll arm, arnless, curved, divan, sofa bed, pit, pallet, reclining, convertible, chaise end, bench, ottoman
We used GPT-4o (OpenAl 2024) to generate lists of common sub-categories for each creative prompt at several sizes N jacket", we asked GPT-4o: "List the N most common types of jackets. A single list, separated by commas. Each description a single word". A typical result is: "bomber, biker, trucker ...". We then formatted the list as static negative prompt P.L. and applied it uniformly throughout the (0) (1)
10. 15, 28). For instance, given the prompt photo of creative
entire denoising process Pheg Preg
(T) PLLM, As shown in Figure this approach Pneg
produces less creative results compared to our dynamic method. For example, The second row, generated jacket features smooth, cloud-like spherical ornaments that are atypical for jackets, whereas LLM- lists yield colorful yet conventional wool or fabric designs and do not portray creative ornaments. We attribute this to the lack of alignment between the static, seed seed-independent LLM-generated list and the actual generative trajectory. Such prompts cannot account for the spe- cific visual patterns that emerge during the denoising process, nor for those encoded in the sampled
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initial noise. While the LLM provides semantically reasonable negative concepts, it lacks the visual awareness to recognize which particular modes are being generated from the specific sampled noise at each timestep, resulting in generic rather than targeted steering.
A.2 NON-DYNAMIC REPLAY APPROACHES.
To isolate the importance of the dynamic process, we tested whether the accumulated negative prompts from our full dynamic negatives list could be replayed statically from the beginning of the generation. In this experiment, we first ran our complete dynamic method to generate the final accumulated negative prompt Phey U Prey for given seed, then used this pre-accumulated
prompt uniformly throughout fresh denoising process: pneg Pneg for all timesteps E [0, 7] Despite using the same negative concepts that our dynamic method accumulates, this static applica- tion produces less creative results. For example, the bag in Figure [ in the last row generated with the adaptive method has flower ornaments and a unique shape while the bag under the "Replay (Per- Seed)" column looks like regular plastic bag. This demonstrates that timing and responsiveness to emerging visual patterns are crucial; the same negative prompts, when applied at the wrong times, fail to provide effective steering. The dynamic nature of our approach, which introduces negative concepts precisely when the corresponding visual patterns begin to emerge, is essential for suc- cessful creative exploration. We further investigate whether negative prompts can be reused across different generation seeds to reduce computational overhead. We collected accumulated negative prompts pheg from successful creative generations and applied them to random seeds while main- taining the same positive prompt. This cross-seed reuse consistently produces suboptimal results, emphasizing that each generation seed follows a unique trajectory through the semantic space and requires its own adaptive negative prompting strategy. When the VLM's analysis of intermediate
(t) (T)
predictions =;(") is tailored to the specific seed's denoising path, we achieve superior creative results, as shown in Figure [igureBJunder unnde the column "Replay (Cross-Seed)" For example, the bag in the last row under the "Replay (Cross-Seed)" column looks like a regular paper bag compared to our unique bag design. This finding reinforces the notion that the effectiveness of our method stems from its ability to provide adaptive, crajectory-speciff guidance rather than applying generic steering patterns.
A.3 NON-ACCUMULATING APPROACH.
Next, we explore the importance of our accu- mulation strategy. To test its contribution, we modify our approach use only the current VLM response as the negative prompt at each timestep. Specifically, we replace the negative prompt with pkeg
Building
Bog
(t) ,()) for each E [0, 2T,
discarding all previously accumulated informa- tion. This non-accumulating variant, shown in SigureBunder the column "No Accumulation" fails to maintain memory of previously iden- tified conventional modes, allowing the genera- tion to cycle back toward familiar patterns that were detected and should have been avoided in earlier denoising steps. For example, the build- ing in the first row under the column "No Ac- cumulation" remains similar to the SD3.5 base- line building, whereas our method produces unique, asymmetrically shaped building. For fair comparison, the VLM query is identical across methods: at every timestep, we ask "What type of bag is this?".
Jncket
0-5
D-10
0-15
0-20
0-27
Figure 13: Effect of limiting VLM guidance different ranges of denoising timesteps. Columns correspond to applying our method during only the first 5, 10, 15, 20, or all 27 timesteps, while rows show results for Build- ing, Bag, and Jacket categories.
A.4 TIMESTEPS ANALYSIS.
Our method introduces VLM evaluations at each denoising timestep, which un- avoidably increases computational overhead compared to standard diffusion sam- pling. To improve practical efficiency. we investigate whether the num ber of VLM queries can be reduced without compromising creative quality.
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Table Exact GPT-4o lists used as Pieg
LLM for all categories LLM ablation study presented Table
Category
N=10
N=15
N=28
dog, cat, fish, bf bi rabbit, hanster, guinea pig, turtle, lizard, snake
dog, cat, fish, bird, rabbit, hamster, guinea pig, turtle, lizard, snake, parret, ferret, chinchilla, hedgehog, tarantula
dog, cat, fish, bi bird, rabbit, hamster, guinea pig, turtle, lizard, snake, parrot, ferret, chinchilla, hedgehog, tarantula, gecko, bearded dragon, cockatiel, budgerigar, finch, tortoise, newt, axoloti, hermit crab, dwarf hamster, betta, goldfish, lovebird
Plant
tree, shrub, grass, fern, moss, cactus, succulent, vine, herb, flower
tree, shrub, grass, Sern, moss, cactus, succulent, vine, herb, flower, palm, orchid, bamooo, lily, rose
tree, shrub, grass, fern, moss, cactus, succulent, vine, herb, flower, palm, orehid, bamboo, lily, rose, tulip, daisy, sunflower, maple, oak, pine, conifer, broadleaf, evergreen, deciduous, ivy, sedge, reed
Garment
shirt, dress, parts, skirt, jacket, coat, sweater, hoodie, t-shirt, blouse
shirt, dress, pants, skirt, jacket, coat, sweater, hoodie, t-shirt, blouse, jeans, shorts, suit, cardigan, jumpsuit
shirt, dress, pants, skirt, jacket, coat, sweater, hoodie, t-shirt, blouse, jeans, shorts, suit, cardigan, jumpsuit, blazer, trenchcoat, parka, raincoat, overcoat, waistcoat, sweatshirt, tracksuit, leggings, chinos, dungarees, kimono, sari
Vehicle
car, truck, bus, van, motorcycle, bicycle, scooter, train, tram, subway
car, truck, bus, van, motorcycle, bicycle, scooter, train, tram, subway, boat, ship, ferry, airplane, helicopter
car, truck, bus, van, notorcycle, bicycle, scooter, train, tram, subway, boat, ship, ferry, airplane, helicopter, yacht, canoe, kayak, jet, glider, seaplane, submarine, hovercraft, snownobile, atv, forklift, tractor, bulldozer
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Table 6: Accumulated lists reused for static application in Fig.
Category
Accumulated negative list
brick, regular building, glass, modern, skyscraper, concrete, moderne, modernist, futuristic, curved
Building
tote, satchel, hobo, backpack, clutch, messenger, crossbody, duffel, bucket, wristlet
Bag
Specifically, we analyze the minimum num- ber of timesteps requiring VLM intervention to achieve effective creative steering. As shown in Figure [3] applying VLM guidance during only the first 10 to 15 timesteps sufficiently steers generation toward creative outputs. This effi- ciency results from the momentum effect de- scribed in (Ban et al., 2024) and explained in our Section 2 where early negative prompt ac- cumulation establishes persistent creative tra- jectories that continue throughout the remain- ing denoising process. This finding enables improved computational efficiency, making our approach more practical for real-world deploy- ment. For all methods in this analysis, the VI.M query is identical and fixed at every queried step: "What is the style of the [category]?".
Bag
Jackett
Building
None Figure 14: Effect positive prompt wording on cre- alive generation using our method. Columns corre- spond to alternative prompt formulations ("New type". "Innovative". "Unique", "Creative" and simply "A photo of [category|"), while rows show results for different semantic categorics Across categorics, our ap- proach produces diverse and imaginative outputs.
New Innovative Unique
Creative
A.5 POSITIVE PROMPT SELECTION.
Our approach demonstrates flexibility in pos- itive prompt formulation, accepting various canttiity-indicating phrases such "creative", "innovative", "new", "novel", "unique", and other similar terms produce creative out- puts. Our VLM-guided approach works effec- tively even with ambiguous positive prompts, such as "a new type of...". As demonstrated in Figure 14 different formulations of creative prompts yield diverse creative outputs while maintaining the fundamental steering behavior and the effectiveness of method well as validity. When the indicative adjective is re- moved entirely from the positive prompt (e.g., using simply "A photo of [obj]"), the resulting images are diverse and aesthetically pleasing: however, they lack the creative qualities that distinguish our method.
Boilding
Pet
Jacket
GPT-4o Figure 15:
Qwen2.5 BLIP-2
BLIP-1
ViLT
Comparison of outputs when guiding
method with different Vision-Language Models (VLMs). Columns correspond
GPT-4o (OpenAT
2024).
Qwen2.5 (Bai et al. 2025). BLIP-2 (Li et all
2023
. BLIP-1 (Li et al. 2022). and ViLT (Kim et all
2021 while rows show three semantic categories:
Unique Building, New Pet, and Creative Jacket. Across models, our approach consistently produces creative coherent results, with stronger VLMs generally yielding more novelty, demonstrating robustness of the method to the choice of VLM.
A.6 ROBUSTNESS
TO VLM MODEL. SELECTION.
Our method demonstrates robustness across variety of Vaagon-Language Models that differ in architecture, training data, model size, and capabilities. As shown in Figure 15 we successfully achieve creative outputs using models ranging from lightweight options such as ViLT (Kim et al.
2021) and BLIP-1 (Li et al. 2022 to more sophisticated models like BLIP-2 (Li et al.) 2023). Qwen2.5 (Bai et al. 2025). and GPT-4o (OpenAl 2024). While more capable VLMs generally produce higher quality creative results, the consistent creative steering behavior across different model choices validates the generalization capabilities of our approach. This robustness ensures that practitioners can select VLMS based their specific computational constraints and quality
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requirements while maintaining the fundamental creative exploration functionality. For all methods in this analysis, the VLM query is identical and fixed at every queried step: "What type of [category] this?".
A.7 QUESTION DESIGN FOR CREATIVE EXPLORATION.
The choice of question formulation is crit- ical design parameter that determines which visual features are identified and which steered away from, directly influencing the cre- ative output. Based we recommend object-focused questions (e.g.,
Jacket Building
empirical findings,
"What is the main object in this image?") for generating "new types" of variations within fa- miliar categories(animal, furniture, buildings, etc.). Style or attribute focused questions (e.g., in
Bag
"What is the style/designntexturre/nnee this image?") are optimal for aesthetic novelty and creativity while preserving category coher- ence. Figure [6 presents the variations of the question (()) choice and the direct influence on the output. For example, when the VLM is prompted about materials, the bag output trans- forms from regular leather to a knitted, colorful material.
SD3.5
Material Color
Shape Design
Figure 16: Effect of the VLM question design on cre- ative generation. Rows correspond to three semantic categories. The first column shows Stable Diffusion 3.5 baseline. The remaining columns apply our VLM- Guided Adaptive Pegative-Promphe
while asking the VLM about (i) the material, (ii) the dominant colors, (iii) the object's shape, and (iv) its design.
A.8 VLM PREDICTION ANALYSIS.
To understand how our VLM-guided approach effectively steers generation despite operating on noisy intermediate predictions, we analyze the VLM's ability to identify emerging seman- tic patterns throughout the denoising process. We examine the correlation between VLM pre- dictions early, blurry to estimates and the final generated content across timesteps to 27. Figure 17 shows that VLM correlation rapidly increases during the initial denoising steps, reaching approximately 90% within the first 3 to 5 timesteps, despite the highly noisy nature of the early predictions. The high corre- lation between early VIM predictions and final outputs validates our approach of accumulating negative prompts from the beginning of the denoising process, as the predictions of the VLM are meaningful even under noisy conditions.
Accoracy
B6
Donaising
Figure 17:
Correlation between the VLM answers across different timesteps and the final generated image
A.9 CREATIVE CAPTION GENERATION
To investigate whether detailed text descriptions that prompt for creativity can reproduce our results, we conducted the following experiment: we used Qwen2.5-VL. (Bai et al. 2025) to generate captions for each image we used in our main quantitative experiment (Table [}. i.c., 400 images in total. The prompt to Qwen2.5 generate new images from these detailed captions. This experiment evaluates empirically whether a sufficiently detailed human human--ritte (or VLM-written prompt achieve the same creative results as our adaptive negative prompting approach. As shown in Table T the results of the captions regeneration are less diverse, novel and decrease in validity.
used is "Give detailed caption the image". We then used SD3.5 to
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B IMPLEMENTATION DETAILS
Unless noted, experiments use SD3.5 large, 28 steps and classifier-free guidance (CFG) 4.5. The default VLM is Be-n2.5-VL3B-Insttuue also support BLIP2 (Li al. 2023). BLIP1 (Li 2022),
ViLT (Kim et al. 2021), and GPT-4o (OpenAl 2024). We run on single NVIDIA A40, at 1024 x 1024 resolution.
B.I VLM FEEDBACK WINDOW.
We allow the user to query the VLM over a predefined window of steps to minimize overhead. Let start and fstop be the step indices when both are provided: otherwise, they are set by default to O and 28. Within this window we query at a fixed frequency f. The default set to = (every step), but users may increase f to reduce calls (e.g., every 2 or 4 steps). The feedback window and frequency integrate directly into our guidance loop; see for how VLM answers are accumulated and applied.
B.2 ADAPTIVE NEGATIVE PROMPTING CONSTRUCTION.
At each step t E [0, T). we decode in to RGB and ask set of questions (9.)(t) We then apply light normalizer: remove unwanted prefixes, e.g., "it looks like", drop leading articles, and collapse whitespace and punctuation. We maintain single negative prompt string, containing list of A negatives with: (i) case-insensitive deduplication, (ii) re-encoding only when V changes, and (iii) all the negatives are separated by commas. During the VLM feedback window, negative half of the CFG embedding pair from the comma-joined string of N negatives and keep the positive half unchanged. When leaving the VLM feedback window, we clear the negative prompt and replace it with an empty string.
update the
B.3 DECODING Zn: VAE VS. LINEAR APPROXIMATION.
The diffusion model operates in latent space. Therefore, obtaining clean image predictions dn for input to the VLM requires passing them through the VAE decoder, which is costly at every denoising step. Prior works (Vass 2024 Turner 2022) have empirically shown that the decoders of common text-to-image diffusion models can be well-approximated by a linear transformation, enabling sig- nificant acceleration of the decoding process. For example, Vass (202) showed that, in the case of SDXL, this linear transformation can be expressed by the matrix:
-60
25
-707
60
-5
15
-50
660
10
-5
-35
A similar linear transformation can be applied to SD3.5 with different weight matrix. In our method, using this linear approximation yields creative results comparable those obtained with the full decoder, while substantially reducing computational overhead.
B.4 FULL RUNTIME ANALYSIS.
Our method adds only modest overhead in the lightweight-WILM regimes (VILT/BLIP-I/BIIIP and reducing the amount of querying offers simple, effective way trade compute for guidance strength.
B.5
AUTOMATION
To make our method as easy to use as standard text-conditioned diffusion generation, we added the option for automated question generation. The user passes creative prompt (e.g., "A photo of creative animal") as an argument to the model, and an LLM (GPT-4o (OpenAl 2024)) automatically generates VLM queries without manual tuning. The pipeline consists of three steps: first, we extract the main object we aim to focus on from the positive prompt, i.e., we will extract "[animal]" in this example. Then, we provide the LLM with the analysis conducted the question design as context. The LLM is requested to generate similar questions appropriate for the specific main object. We then pass those questions argument to creative-generation pipeline.
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Table 7: Runtime with VLM-in-the-loog guidance. Total seconds for SD3.5-large single-image generation when querying different VLM oracles either every denoising step (28) or only the early steps (15). The baseline performs no VLM queries. All runs use the same prompt and seed.
VLM
Steps
Runtime (Seconds)
Baseline No VLM
28
22
28
35
ViLT
15
29
28
36
BLIP-1
15
30
43
BLIP-2
15
33
28
71
Qwen2.5-3B
15
56
B.6 COMPLEX-PROMPTY BENCHMARK
To quantitatively evaluate controllability in complex scenarios, we created benchmark of 200 di- verse prompts that test whether our method can maintain prompt adherence while modifying only central objects be creative. construct such benchmark we prompted GPT-4o to provide with 200 diverse object categories, similar to those present in the paper (for example, animals, hairstyles, accessories, etc.). Each prompt follows the template "A photo of [creative/mmovativeeew type of/novel/unique] [main object] [scene description]". The template was filled by GPT-4o, accord- ing to the query "Write a prompt using the template: A photo of [creative/innovaiieenee type of/novel/unique] [main object] [scene description]. Choose an appropriate creativity indicator from the list (creative/nnoovaiiveeeee type of/novel/unique], and place the object in logically feasible scene. Describe it briefly," For each prompt we generated automatic questions list using the method described in the previous section.
Example Prompts: "A photo of an imaginary pet resting inside terrarium filled with miniature plants.", "A photo of a creative hairstyle showeased on a model standing in a sunlit desert landscape." "A photo of a creative insect resting on a dew-covered leaf in quiet morning meadow."
C QUALITATIVE EVALUATION FRAMEWORK
For fair evaluation, adopt each bascline's evaluation setting including their prompts, models and experimental protocols. Specifically, we use their original prompts: "a creative [obj]" for C3 and "Professional high quality photo of new type of [obj]. photorealistic, HQ, 4k" for Concept- Lab. We also integrate our method into their respective models: SDXL (Podell et al. (023) for C3 and Kandinsky 2.1 (Razzhigaev al. 2023 for ConceptLab. We note that ConceptLab's method leverages Kandinsky's Diffusion Prior model, which their optimization process specifically requires for learning creative concepts in the prior's output space (Richardson al. 2024). To ensure direct comparability, we integrate our method into each bascline's 's model and generate samples using iden- tical seeds. Additionally, we showcase our method's full potential using Stable Diffusion 3.5 (Esser et al.. 2024). demonstrating superior creative generation with state-of-the thet-artt architectures.
D USER STUDY
Participants view pairwise comparisons of images generated from the same broad category (e.g., "pet", "building", "vehicle"). Each comparison shows outputs from method versus of the four baselines. Creative prompts: SD3.5 and GPT-4o using "A photo of creative/new type of [category]" and creative generation methods: Conceptlab and C3.
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Table 8: User study results showing average ratings (1-5 scale) for novelty and category coherence. Our method achieves the highest novelty while maintaining strong categorical identity.
Method
Novelty t validity 1
SD3.5
1.753
4.886
GPT-4o
2.133
4.785
ConceptL.ab
3.502
3,950
C3
2.934
3.945
VLM-Guided (Ours)
4.550
4.503
E METRICS AND EVALUATION
E.I EVALUATION SETUP
The core idea of our evaluation protocol is to represent images in the CLIP embedding space and compute metrics that characterize the resulting distribution. Standard metrics like the CLIP score measure one-to-one image-text similarity, which is problematic for creativity evaluation creative outputs should deviate from typical patterns while maintaining cate- gory membership. A creative pet that scores lower than a typical cat on CLIP aligmment might actually represent more successful cre- ative generation. Specifically, we use the fol- lowing metries: (1) For validity assessment, employ the CLIP score and GPT-4o verification to ensure outputs remain recognizable valid category members despite their creative varia- tions. Our goal is not to maximize CLIP score but to remain relatively close to reference val- while exploring novel variations; (2) For novelty assessment, we compute relative typicality to measure the difference between broad cate- gory similarity (e.g.. "pet") and average subcategory similarity (c.g.. "cat", "dog"), ensuring outputs avoid conventional modes, alongside GPT-4o Novelty Score which counts how often GPT-4o cannot classify the specific type and responds "unknown": (3) For diversity assessment, we use distribution- based metrics (total variance and Vendi score (Friedman & Dieng 2022) that quantify the spread of creative exploration
VLM Guited Creative
Figure 18: Distribution of fruit CLIP embeddings in 2D PCA space and the Kernel Density Estimation (KDE) of the distributions. Reference images (green): "A photo of a fruit". Creative bascline (bluc): "A photo of a new type of fruit". Our VLM VLM-guided method (red): explores diverse regions with minimal overlap with reference.
CLIP embedding space.
To evaluate and compare the methods quantitatively, we generate 100 images from four different categories: "pet", "garment", "plant" and "vehicle" using our method, C3, ConceptLab, and two baselines. "Reference" images generated with SD3.5 from the prompt "A photo of [category]" and "Creative Prompting" uses the prompt "A photo of a creative new type of [category]".
E.2 VISUALIZING THE DISTRIBUTION
We begin by visualizing the resulting distribution in CLIP's space. To do so, project embeddings to two dimensional space via PCA. In Figure 18 we visualize the CLIP embedding distributions for "Reference", "Creative Prompting", and our VLM-guided approach. The background distribu- tion computed on a discrete grid of size 50 x 50. The density at any point E G is estimated using Gaussian KDE. The plot in Figure 18 shows that approach pushes mass away from typ- ical exemplars, while the "Creative Prompting" remains close and overlaps with the "Reference" distribution.
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E.3 NOVELTY AND DIVERSITY
To quantify deviation from conventional patterns, we employ two complementary metrics: Relative Typicality measures creative deviation from familiar subeategories while maintaining broad category coherence. For a generated image we extract a CLIP embedding 2,, using CLIP-ViT-B32, and measure the alignment the broad category text prompt embedding & e.g., "A photo pet", and subcategory text prompts embeddings c.g., "A photo of a cat", "A photo of dog" etc.). Overall, we compute:
(2,) cosine saiiee_smillartt(ee t.)
max (E
cosine catieessmillaity(ee (2,, (())
(4)
where Le is the CLIP text embedding of the broad category prompt and (10) 1m of subcategory prompts. Positive values indicate the image aligns more with the broad category than with any specific known subcategory, suggesting successful creative generation within the category boundaries.
jj=1 the embeddings
GPT Novelty Score quantifies how often GPT-4o cannot identify the specific type of object. We query GPT-4o to classify each generated image into known subcategories. The score represents the fraction of images classified "unknown" unrecognizable variants, directly measuring deviation from familiar modes.
The Vendi score (Friedman & Dieng 2022) quantifies diversity through the Shannon entropy of the eigenvalues of a normalized similarity matrix. Formally, given a collection of samples ..,... X' and a positive semi-definite similarity function x X' - with k(x, x)) 1, K E Raxx denote the kernel matrix with Ki k(F,, xz). The Vendi score is defined as:
Σ >e log = exp (-tr
n2
K
log n2 )
K
Vendi(X') = exp
=exp
(5)
where A An are the eigenvalues of K/n, with the convention that 0 Olog 0 = 0. This metric can be interpreted as the effective number of dissimilar elements in the sample, ranging from (all identical) to (all maximally distinct).
Total Variance, computed as the trace of the covariance matrix Tr(E.) Σd -i=1 As measures over- all variability across all dimensions in the CLIP embedding space. Higher values indicate greater dispersion and exploration spread.
E.4 VALIDITY
While diversity and novelty distinguish a creative concept from an existing one, validity ensures that it is practical, preventing it from being merely eccentric nonsensical. We compute the practicality of the generated concepts with two metrics, CLIP text-image alignment score and GPT score to verify semantic validity.
For the GPT score, we provide GPT-4o with a generated image and ask it, "Is this a [category]?". Then we compute the number of times the answer was yes divided by the overall amount of images.
E.5 SUBCATEGORY SELECTION
For relative typicality computation, we use the following subcategories: cat, dog, hamster, rabbit, bird, fish, turtle, mouse, gerbil, insect. Vehicle:
Pet:
car, truck, motorcycle, bicycle, bus, train, scooter, van, airplane, drone. tree, flower, cactus, fern, grass, bush, wildflower, moss, wild mushroom. jacket, dress, pants, coat, sweater, hoodie, socks, underwear.
Plant:
Garment:
E.6 VIESCORE
VIEScore (Ku et al. 2024) is an explainable automatic metric for evaluating conditional image gen- eration tasks. Instead of relying similarity scores alone, it uses a Multi-Modal Large Language Model (MLLM, like GPT-4o (OenAl 2024h) to produce both a score and a natural language ex- planation of the judgment. On seven conditional image tasks, VIEScore with GPT-4o reaches
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Spearman correlation of about 0.4 with human ratings (a high correlation value close to the 0.45 toman-to-humann agreement). We use VIEScore (Ku et al. 2024) with GPT-4o (OpenAl 2024) as the base model.
For text-to-image tasks, the metric measures the quality according to two main pillars: first, SC (Semantic Consistency) measures how well the generated image matches the given prompt. This is processed by the MLLM into sub-scores with guiding questions and then combined into single SC score. Second, PQ (Perceptual Quality) measures how good the image looks visually. It rates things like naturalness, absence of artifacts, distortions, watermarks, and other visual defects, again via uco-scores that combined into one PQ score. We add an example of the reasoning explanations in Figure 19]
"A photo of an imaginary pet resting inside terrarium filled with miniature plants."
Semantic Consistency:
Reasoning: ".. might argued that cat could common pet and does not heavily emphasize the 'imaginary aspect. Thus, it slightly misses the unique imaginary characteristic."
SD3.5
Perceptual Quality:
Reasoning: ... The image looks very natural overall with the cat comfortably nestled among the plants..."
Semantic Consistency:
Reasoning: "The Image perfectly matches the prompt. features Imaginary ...
Ours+ViLT
Perceptual Quality:
Reasoning: "... The Image appears largely natural, the exception of the context (an animal In plant terrarlum which is an
setup)...
Figure 19: VIE scorer reasoning for controllable creative generation. Top: SD3.5 baseline generates
cat, receiving lower semantic consistency missing "imaginary" aspect. Bottom: Our method produces a more creative creature that better aligns with the "imaginary pet" prompt specification, achieving higher semantic consistency while maintaining perceptual quality.
F
LLM USAGE
Large language models were used exclusively for English language editing and grammatical refine- ment of the manuscript text. Specifically, we employed LLMs improve sentence structure, correct grammatical errors, and enhance clarity of technical descriptions. All research idcation, experimen- tal design, implementation, analysis, and scientific conclusions were conducted by the authors. The core technical contributions, methodology, and experimental results represent original work by the authors.
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Reviews:
[{"review_id": "5knZ02ML8M", "reviewer": "Reviewer_d6am", "summary": "This paper focuses on the creative image generation task, an emerging research direction that explores the ability of image generation models to produce novel and previously unseen images beyond the training distribution. The authors propose VLM-Guided Adaptive Negative Prompting, a method that leverages a vision-language model (VLM) during training to adaptively refine negative prompts, guiding the generative model to diverge from known concept spaces and thereby produce more unexpected and creative visual outcomes.", "questions": "1. The proposed method performs VLM queries at every denoising step. I am curious whether a VLM can effectively recognize images that are still heavily corrupted by noise. It seems unnecessary to query the VLM at each step, since it primarily identifies common object categories and may produce unreliable or meaningless predictions in the early denoising stages. Why not predefine a set of common negative classes at the beginning of the process? In ConceptLab, repeated experiments tend to yield similar negative class sets—mostly consisting of frequent categories such as cat, dog, parrot, rat, and lizard—while rarer categories like fish or monkey seldom appear.\n\n2. Could SD3.5 alone, guided only by human-written prompts, generate the same level of creative results shown in the paper? For example, could it produce a plausible image of an unseen fruit purely based on human imagination without relying on the proposed adaptive negative-prompting mechanism?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "1. The proposed method is simple and requires no additional training overhead.\n2. The experimental results are visually appealing and demonstrate interesting creative effects.", "weaknesses": "1. The method lacks substantial novelty. Its core idea—encouraging the diffusion model to deviate from known concept spaces—was originally introduced by ConceptLab. The main contribution here lies in performing additional VLM-guided queries at each denoising step and using classifier-free guidance (CFG) to avoid categories identified by the VLM. Compared to ConceptLab, this constitutes only a minor incremental improvement. Moreover, querying the VLM at every denoising step could considerably increase inference time, even though the authors claim that it adds only about 13 seconds.\n2. The proposed approach is less flexible than ConceptLab, which operates at the token level, allowing its generated creative concepts to be easily integrated with natural language for diverse styles and contexts. In contrast, the current method requires a separate process for each prompt, causing inference time to scale with the number of prompts and limiting adaptability.\n3. Although the visual results are impressive, it is unclear whether the improvements stem from the proposed method itself or from the use of a stronger base model (SD3.5). When applied to other backbones such as Kindinsky or SD-XL, the method’s performance degrades noticeably (Figure 5).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QXuWkIPYOu", "reviewer": "Reviewer_c3MD", "summary": "The paper presents a training-free method for enhancing creative generation in diffusion models. By leveraging a Vision-Language Model (VLM) to analyze intermediate denoising steps and dynamically accumulate negative prompts, the approach steers generation away from conventional patterns while maintaining categorical validity.", "questions": "See weaknesses for details.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper focuses on an interesting topic in computational creativity: generating novel visual concepts beyond conventional patterns.\n\nThe technical approach is elegantly simple and training-free, leveraging VLM feedback for adaptive negative prompting without modifying pretrained models. This clarity enhances reproducibility and practical deployment.\n\nThe writing is exceptionally clear and well-structured, with logical flow from problem formulation to experiments.", "weaknesses": "The technical contribution is somewhat limited, as the method does not adequately address the VLM's inconsistent perception capabilities across different denoising timesteps. Prior research highlights that control word effectiveness varies with timesteps, yet this work overlooks such dynamics, potentially undermining the robustness of adaptive guidance.\n\nWhile the approach yields intriguing outcomes, it heavily relies on the base model's generative power rather than introducing groundbreaking mechanisms. Similar creative effects might be achievable through carefully engineered prompts or LoRA adaptations, questioning the necessity of the proposed complex feedback loop.\n\nControllability remains a significant issue, as the generation process is highly stochastic. Results are unpredictable and quality assurance depends largely on \"luck-based\" sampling, which fails to guarantee consistency or align with specific user preferences, limiting practical utility.\n\nThe method introduces non-negligible computational overhead due to frequent VLM queries, despite optimizations. This could hinder real-time applications, especially with resource-intensive VLMs, affecting scalability.\n\nEffectiveness is sensitive to VLM selection and question design, requiring manual tuning for different categories. This dependency on external components may reduce generalizability and increase implementation complexity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Xxx6IVZSgB", "reviewer": "Reviewer_VWC1", "summary": "This paper introduces VLM-Guided Adaptive Negative Prompting, a training-free, inference-time method that leverages vision-language models to steer diffusion models away from familiar visual concepts during generation. By dynamically adding negative prompts for concepts identified by the VLM at intermediate denoising steps, the method promotes the creation of novel images.", "questions": "1. Have you explored designing heuristics to automatically select VLM queries based on generation prompts? Such an approach could make the system easier to use and closer in workflow to standard text-conditioned diffusion generation.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well written and easy to follow.\n2. The proposed method is conceptually simple and can be easily integrated into existing diffusion inference pipelines without training.\n3. The method achieves strong qualitative results across diverse categories while remaining completely training-free.", "weaknesses": "1. The paper omits citations of several recent works in performing and understanding creative generation, such as [1], [2], and [3].\n2. While Section 4.5 presents qualitative examples using complex prompts, there is no quantitative or systematic evaluation of controllability. This makes the evidence for controllable generation less conclusive.\n\n[1] Procreate, don’t reproduce! propulsive energy diffusion for creative generation, ECCV 2024\n\n[2] Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion, ICML 2025\n\n[3] An analytic theory of creativity in convolutional diffusion models, ICML 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "v9BCf3GEla", "reviewer": "Reviewer_MwEz", "summary": "In this work, the authors presents a new way to make AI image generation more creative without extra training or heavy computation. The method, called VLM-Guided Adaptive Negative-Prompting, uses a vision-language model to gently push the model away from familiar ideas and toward more surprising and novel results. Unlike older methods that need fine-tuning or stay stuck in known categories, here, the model works on the fly during image creation. The authors demonstrate the effectiveness of the method, showing that their method makes images that are both novel and valid, unlike the other methods keeping realism while adding originality.", "questions": "No questions.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The paper has the following strengths:\n\n1) The images shown on the paper are visually stunning, easily the best I have seen in this topic.\n\n2) The paper is extremely well-written, I really enjoyed reading it. In particular, it has one of the best intros I have ever read with perfect merging of the intro with the figures, a really case-study of how pictures complement the writing. All parts of the papers are really well-written, and all the pictures are nice and helpful.\n\n3) The results, be them qualitative, quantitative, or user study are really good, outperforming the other methods they compare with. The ablations studies are also very nice, further improving the confidence in the paper.\n\n4) Th", "weaknesses": "The paper can be improved on this part:\n\n1) Limited novelty - Probably the only technical contribution of the paper is section 3.2, which is extremely thin. And even then, it is effectively a smart way of doing prompting.\n\nSaying that, I would not penalize the paper for it. The results speak for themselves, so I would actually prefer a simple method compared to a complex one, given the same results (Occam's Razor in reviewing).\n\n2) It would have been nice if the authors would have released the code already (to check the effectiveness of this work) but considering that a) that is not mandatory, b) they promised to release in the near future, I will not penalize them for it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)