Pith. sign in

REVIEW 5 major objections 5 minor 2 cited by

Preference Adaptive and Sequential Text-to-Image Generation

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read PASTA turns text-to-image generation into a five-turn dialogue, using prompt expansions chosen by a learned value function to push images toward what a user actually wants, and reports an 85% relative gain over a baseline in final-turn…

desk verdict PASTA's released data and latent-user model are the real contributions, but its headline human-eval gains are measured on a distilled model, not the trained selector. read the letter →

arxiv 2412.10419 v2 pith:ZHKUQMU7 submitted 2024-12-10 cs.CV cs.AIcs.CLcs.LGcs.SYeess.SY

Ofir Nabati , Guy Tennenholtz , ChihWei Hsu , Moonkyung Ryu , Deepak Ramachandran , Yinlam Chow , Xiang Li , Craig Boutilier This is my paper · ORCID
classification cs.CVcs.AIcs.CLcs.LGcs.SYeess.SY
keywords text-to-imagegenerationmulti-turninteractionreinforcementlearningpreferenceelicitationusermodelingpromptexpansionimplicitQ-learninghumanevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that text-to-image generation can be made collaborative: instead of issuing one prompt, a user picks the best of several image columns at each turn while an agent steadily expands the prompt. The proposed agent, PASTA, learns which prompt expansions to show by training a value-based selector on a mix of human-annotated sequential preferences and simulated user interactions. If the claim holds, multi-turn co-creation becomes a practical extension of off-the-shelf diffusion models, and the released sequential preference dataset lets other researchers build on the same problem setup. The headline result is that PASTA's final-turn images receive an 85% relative improvement over an off-the-shelf Gemini 1.5 Flash baseline in direct human comparison.

What carries the argument

The load-bearing object is the candidate-generator/candidate-selector pair. A fixed multimodal language model proposes $L_C=25$ prompt expansions split into five semantic categories; a learned value function $f_\phi(h,p)$ scores each (history, prompt) pair; and the value of a slate is the average of the individual prompt values, reducing the choice of $L=4$ prompts to sorting. The value function is trained offline with implicit Q-learning, using an $\alpha$-expectile estimate to avoid querying out-of-distribution slates. Supporting this is the user simulator: a CLIP-based score model with a residual encoder per user type, a softmax choice model with a learned temperature, and an EM procedure that alternates posterior updates over user types with score-model updates to produce the simulated trajectories and reward labels used for training.

What would settle it

Run the original non-distilled PASTA candidate-generator/selector pipeline and the distilled model on the same interaction histories and initial prompts, then have raters compare the final images; if the distilled model's choices diverge often or the original pipeline does not reproduce the 85% relative gain, the reported improvement cannot be attributed to PASTA's learned selector.

Watch

Extended reading notes

Core claim

The central claim is that a prompt-expansion policy trained with offline reinforcement learning on human and simulated sequential preferences produces image slates that users prefer over the course of an interaction, with the strongest gain at the final turn: an 85% relative improvement over the untrained baseline in direct final-turn comparison, and a 20% relative improvement when the same policy is paired with a different T2I model without retraining. The authors frame the task as a latent contextual MDP in which the user's hidden type determines both which image column they choose and how much they like the result. Training begins with an EM-based user model with a discrete set of latent user types; that model generates more than 30,000 simulated five-turn trajectories, and the agent's value function is trained with implicit Q-learning using a sparse final-turn reward. Human-rater evaluations then compare the distilled PASTA agent with a Gemini 1.5 Flash baseline, and the paper reports that training on human and simulated data together outperforms either data source alone.

Load-bearing premise

The evaluation assumes the distilled Gemini 1.5 Flash model used in human studies behaves like the trained candidate selector that PASTA's value function defines, because raters only saw the distilled model.

Editorial extensions

If this is right

  • Multi-turn T2I agents can be built without modifying the underlying diffusion model; PASTA changes only the prompt stream, so it can wrap any T2I model.
  • Combining real human-rater trajectories with simulated user trajectories is the training regime that generalizes to human raters; training on simulated data alone degrades performance.
  • PASTA's gains transfer across T2I models: the policy trained on SDXL images improves final-turn outcomes with Flux.1 without any retraining.
  • A relatively small number of latent user types, roughly 16, is enough to capture much of the diversity in image preferences, and distinct types emerge as visually distinct styles.
  • The public release of the sequential rater dataset and simulated rollouts allows other researchers to train and compare multi-turn generation agents on a common foundation.

Reading between the lines

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

  • The decomposition of slate value into an average of individual prompt values could transfer to other generative domains—such as text or video—whenever a candidate generator can enumerate diverse outputs and a user selects among them.
  • The learned posterior over user types could serve as an interpretable preference profile; one testable extension is to show that profile to the user mid-interaction and check whether it speeds convergence to their intent.
  • Because the human evaluation was run on a distilled single-model version of PASTA, a natural next check is to compare the distilled model with the explicit candidate-generator/selector pipeline on paired prompts to confirm that distillation is behavior-preserving.
  • The sparse final-turn reward emphasizes end quality over intermediate progress; a dense-reward variant could change the agent's exploration-exploitation balance and is an untested alternative within the paper's own formulation.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper introduces PASTA, a reinforcement-learning agent for multi-turn text-to-image generation that iteratively proposes prompt expansions to a user. The authors collect a novel human-rater dataset of about 7,000 five-turn interaction sequences, train an EM-based user model with discrete latent user types using this data together with public single-turn preference datasets, and then use the user model to generate over 30,000 simulated rollouts. A value-based candidate selector, trained with implicit Q-learning on a decomposed slate-value function, is used to rank Gemini-generated prompt candidates. Human evaluations are reported as showing improvements over an untrained Gemini 1.5 Flash baseline, including an 85% relative improvement on final-turn image comparison, and the dataset is released.

Significance. If the empirical claims are valid, PASTA is a meaningful step toward interactive, preference-adaptive T2I generation: it provides a reusable sequential-preference dataset, an explicit latent-type user model, and a model-agnostic RL formulation for prompt-expansion selection. The open-sourced data is a concrete community asset, and the framing of multi-turn image generation as a latent-context MDP is well motivated. However, the central evaluation currently has two validity gaps: the human-rated gains are obtained from a distilled model without a fidelity check against the actual candidate-selector pipeline, and several evaluation components reuse the same learned user model that generated the training rewards. These issues need to be resolved before the headline claims can be accepted as evidence for the value-based selector.

major comments (5)
  1. [Section 6.3] The human evaluations rate a distilled Gemini 1.5 Flash LMM that generates image slates directly 'without explicit prompt expansions', not the PASTA pipeline of Section 3.2 consisting of the Gemini candidate generator, Gemma-2B value-based selector, and SDXL image generation. No fidelity check is reported: no comparison of distilled-model prompt slates or final images with the original selector's outputs, no agreement rate, and no analysis of divergence cases. Consequently, the 85% relative improvement over the untrained Gemini Flash baseline cannot be attributed to PASTA's value-based candidate selector; it could reflect generic fine-tuning of the LMM on the rater data. The authors should provide a behavioral-equivalence study (e.g., compare distilled and non-distilled PASTA on simulated users, or on a held-out set of human trajectories) and should report whether the distilled model's candidate selection matches the value-based selector's choices.
  2. [Appendix B.4 and Section 6.1] Rewards for the human-rater trajectories are generated by the learned user utility model rather than taken from observed rater satisfaction. The same user model is used to create simulated training data and to score rollouts in Figure 7. This creates a circularity risk: the IQL objective may be optimizing the user model's internal score rather than actual user satisfaction. The rater study in Appendix B.2 collected turn-over-turn improvement judgments and 5-point satisfaction scores; these should be used as rewards, or at least the authors should report a correlation between model-generated rewards and human-reported satisfaction. In addition, Section 6.1 states that a sparse reward is used with rewards only in the final round, while Appendix B.4 states that rewards are generated for each interaction step; this inconsistency needs to be resolved because it changes the IQL objective.
  3. [Appendix F.1 and Figure 7] The simulated-user experiment evaluates value models against a user simulator that is the same model used to generate the simulated training data. It is therefore unsurprising that the model trained purely on simulated data performs best in that evaluation, and this result cannot serve as independent evidence for PASTA's real-world effectiveness. The authors should either frame this experiment explicitly as a sanity check of the training procedure or provide a distribution-shift analysis comparing simulated-user outcomes with human-rater outcomes on matched trajectories.
  4. [Figure 6 and Appendix B.3] The main human-rater comparison in Figure 6 was not run with concurrent, randomized arms: the authors acknowledge in Appendix B.3 that different experiment arms ran at different times with potentially different rater pools. This introduces a selection-bias risk for the baseline comparison. The authors should report the rater-recruitment timeline, the number of raters per arm, and ideally a concurrent replication of the baseline arm, or a matched-subset analysis controlling for rater identity and prompt distribution.
  5. [Section 3.2] The slate value function is approximated as the average of per-prompt values, q_phi(h,P) = (1/L) sum f_phi(h,p). This decomposition is load-bearing for the O(L_C log L_C) selector, but no evidence is provided that it is a good approximation for this problem (e.g., no comparison against full-slate value estimates on a small candidate set, and no analysis of interaction effects among prompts in a slate). Since the human evaluation does not directly test the selector, this assumption remains unvalidated.
minor comments (5)
  1. [Section 6.2] There is a typo in the sentence 'we assess prediction accuracy of the and Pick-a-Pic testset'; the missing dataset name should be restored.
  2. [Section 6.3] The '85% relative improvement' and the Flux '20% relative improvement' are not defined: the authors should state the exact formula (e.g., win rate difference divided by baseline loss rate) and provide confidence intervals and sample sizes for these direct-comparison studies.
  3. [Appendix E.2] The word-count schedule N^t_w = N^{t-1}_w (N^max_w - N^{t-1}_w) / (H t) appears dimensionally inconsistent and can produce negative values for later turns; please provide the intended formula or pseudocode.
  4. [Equation (4)] The parameterized temperature tau_theta is described as a function of the scores but its range and positivity constraint are not stated; this matters for the Softmax choice probabilities and for reproducibility.
  5. [Algorithm 1] The E-step posterior formula uses a product over M terms that is not clearly tied to the different data types (preference pairs, relevance ratings, sequential choices); please clarify the notation for xi,j and yi,j for each loss.

Circularity Check

2 steps flagged · score 6.0 of 10

Partial circularity: the simulated-user evaluation and the score-model evaluation reuse the same learned user model that generated the training rewards, so those results are forced by construction; the headline human-rater results are independent in data source but are served by a distilled model with no fidelity check.

  1. self definitional [Section 6.3, Figure 7 (PASTA Evaluation with the Score Model)]
    "We also generate reward labels for our human-rater data using the user model, and train our state-action value function in three different settings... We also present the user model score for each step averaged over all rollouts in Figure 7. The user type was chosen as the one that maximizes the posterior over the user’s data. The results show that PASTA monotonically improves the user score along the rollouts and are consistent with the real users’ results in Figure 6."

    The same learned score model sθ supplies both the reward labels used to train PASTA's value function (via Equation (3) and Appendix B.4) and the scores plotted in Figure 7. A policy trained to maximize sθ-based rewards will, if optimization succeeds, appear to monotonically improve under sθ. Thus Figure 7 is a re-measurement of the training objective rather than an independent validation of the user model or of PASTA; it cannot establish correlation with real users because no fresh human labels enter that evaluation.

  2. fitted input called prediction [Appendix F.1, Simulated Users Experiment]
    "The results demonstrate that all value-based methods outperform the random sampling policy. Notably, the value model trained exclusively on simulated data achieves the best performance. This outcome aligns with expectations, as the simulated data matches the dynamics of the simulated user model precisely."

    The simulated trajectories used for training the value models are produced by the same user choice and utility models that generate the 1000 evaluation trajectories in Appendix F.1. Therefore the 'simulated-only' value model trains and is tested on identical dynamics, and its superiority is expected up to optimization and generalization error. The paper's own phrasing concedes the result is expected because the data 'matches... precisely,' so this experiment is a fitted-input-called-prediction: the evaluator is the same fitted simulator that created the training data, providing no independent evidence about real-user satisfaction with PASTA's selector.

full rationale

Two evaluation results in the paper are circular by construction. First, Figure 7 reuses the learned user model both to create the reward labels that train PASTA's value function and to score the resulting rollouts; monotonic improvement there is a check of the training objective, not independent evidence about real users. Second, Appendix F.1 evaluates value models on simulated trajectories drawn from the same user model that generated the training data; the paper explicitly concedes the simulated-data result is expected because the training and evaluation dynamics coincide. These are the 'predictions' that reduce to their own inputs. The central human-rater evidence (Figure 6 and the 85% last-turn comparison in Section 6.3) is not circular in the same way, because it uses independent human judgments. However, its attribution to the value-based candidate selector is weakened by the acknowledged distillation setup: Section 6.3 serves a fine-tuned Gemini 1.5 Flash LMM without explicit prompt expansions and reports no fidelity check against the actual candidate-generator/selector pipeline, and Appendix B.3 acknowledges non-concurrent A/B arms. These are correctness and attribution risks rather than definitional circularity. Self-citations such as Tennenholtz et al. for candidate diversity and Ie et al. for slate-value decomposition are not load-bearing reductions. The score of 6 reflects the partial circularity in the self-evaluation and simulated-user arms, while the headline human comparison retains independent content.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central claim rests on several hand-chosen hyperparameters (K, tau, alpha, LC, loss weights), idealized behavioral assumptions (choice-preference equivalence, utility consistency, best-image selection), and an unvalidated slate-value decomposition. The user types are latent modeling constructs without independent behavioral validation.

free parameters (5)
  • K: number of discrete user types = 16 (chosen from plateau in Figure 4)
    Model capacity hyperparameter; the paper reports accuracy improving with K and plateauing around 16, so K=16 is a hand-chosen value that the user model and all downstream results depend on.
  • tau_theta: choice temperature = learned (bounded by tau_max=3)
    Parameterized temperature in the Softmax choice model (Eq. 4) that controls choice sharpness; learned from data and central to the user simulator.
  • alpha: IQL expectile = 0.7
    Hand-chosen expectile in the Implicit Q-Learning objective (Appendix E.4); affects how optimistic the value estimate is.
  • Loss balancing coefficients kappa2, kappa3, kappa4 = 0.01, 1, 0.1
    Hand-chosen weights for in-turn preference, cross-turn preference, and CLIP regularization losses in the user model fine-tuning (Table 2).
  • LC and category constraint = LC=25, 5 categories
    Candidate set size and the one-prompt-per-category constraint are hand-chosen (Section 6.1, Appendix E.2) and shape exploration and slate diversity.
assumptions (5)
  • domain assumption Assumption 5.1 (Choice-Preference Equivalence): C is equivalent to P
    Users are assumed to choose the column they prefer, with no position bias or other influences; this grounds the user choice model in the preference model and is used to generate simulated trajectories.
  • domain assumption Assumption 5.2 (Utility Consistency)
    A user prefers one image set to another iff its utility is higher; this ties the utility model to the preference model and justifies using utilities as rewards.
  • ad hoc to paper Slate value decomposes as average of prompt values
    Section 3.2 approximates q_phi(h,P) as (1/L) times the sum of f_phi(h,p) to make slate selection O(LC log LC); this ignores interactions between prompts in a slate and is an unvalidated approximation for the RL objective.
  • domain assumption Independence of initial prompt and user type prior
    Appendix C.1 assumes the joint prior factorizes as nu(p0,u) = nu(p0) times eta0(u) when sampling simulated users; if user types and prompt subjects are correlated, the simulated data distribution is biased.
  • domain assumption Raters select based on the best image in each column
    Used to justify the softmax aggregation in Eq. (3) and reflected in rater instructions (Appendix B.1); if raters instead averaged over the column, the user model's image-level scoring is misspecified.
invented entities (1)
  • Discrete latent user types U = {1,...,K}
    purpose: Latent variable capturing heterogeneous user preferences in the choice and utility models (Section 5.1).
    The user types are unobserved constructs inferred by EM; the paper shows qualitatively different top images per type but provides no external behavioral or demographic validation that the types correspond to real user segments.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Preference Adaptive and Sequential Text-to-Image Generation." pith.science (2026). https://pith.science/paper/ZHKUQMU7

@misc{pith2026241210419,
  author       = {Pith},
  title        = {Pith review of: Preference Adaptive and Sequential Text-to-Image Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZHKUQMU7}},
  note         = {Machine review of arXiv:2412.10419}
}
read the original abstract

We address the problem of interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user through a sequence of prompt expansions. Using human raters, we create a novel dataset of sequential preferences, which we leverage, together with large-scale open-source (non-sequential) datasets. We construct user-preference and user-choice models using an EM strategy and identify varying user preference types. We then leverage a large multimodal language model (LMM) and a value-based RL approach to suggest an adaptive and diverse slate of prompt expansions to the user. Our Preference Adaptive and Sequential Text-to-image Agent (PASTA) extends T2I models with adaptive multi-turn capabilities, fostering collaborative co-creation and addressing uncertainty or underspecification in a user's intent. We evaluate PASTA using human raters, showing significant improvement compared to baseline methods. We also open-source our sequential rater dataset and simulated user-rater interactions to support future research in user-centric multi-turn T2I systems.

Figures

Figures reproduced from arXiv: 2412.10419 by the authors.

Figure 1
Figure 1. An illustration of an agent-user interaction with L = 4 prompt expansions at each step, and M = 1 images per prompt expansion. The user selection is outlined in blue. The agent presents prompt expansions based on the user’s previous responses to maximizes the expected cumulative user satisfaction (i.e., value). See Appendix G for additional examples using M = 4 images for L = 4 prompt expansions. interacts with the … view at source ↗
Figure 2
Figure 2. Top. PASTA policy framework: The LMM is used as a candidate generator of a candidate set, from which the candidate selector policy is used to select a slate. Bottom. Each prompt in the slate is evaluated individually using a prompt-value model, and the overall slate value is calculated as the average of the individual prompt values. 3.2. Value-Based Candidate Selector We use a state-action value function to define o… view at source ↗
Figure 3
Figure 3. Score function architecture. The image and prompt are fed into CLIP encoders followed by user encoders to generate K user embeddings. The score for each user type is the inner product between the corresponding user image embedding and user text embedding. associates to a prompt p using the following model: R k,θ ℓ,t := Rθ  {Im,ℓ,t} M m=1|u = k, p = Agg(sθ(k, p, I1,ℓ,t), . . . , sθ(k, p, IM,ℓ,t)), (3) where Agg is … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The graphs present the performance of a trained user model as a function of the number of user types considered. The top row displays the model’s accuracy on the Pick-a-Pic test set (left) and its Spearman’s rank correlation on the HPS test set (right). The bottom row …
Figure 6
Figure 6. Figure 6: PASTA Evaluation. We ask raters to determine, for each interaction turn, whether the shown images are an improvement over the previous turn. The raters can choose one of three options: Better, Worse, or Same. Results show percentage of raters who chose each option. Exp…
Figure 7
Figure 7. Figure 7: PASTA Evaluation with the Score Model. We use our trained user model to evaluate the rollouts of real users with different baselines. images that are tailored to user preferences. Moreover, a high rank correlation between real user preferences and the predictions from …
Figure 8
Figure 8. Figure 8: An illustration of three different rollouts of PASTA with two different user types for the abstract initial prompt: “An image of love". Rollouts show the final images in the rollout (i.e., at step t = 5). problem, where an RL agent learns to extend and person￾alize a p…
Figure 9
Figure 9. Figure 9: A screenshot of the interface raters used in the interaction phase. Raters were not shown the prompt expansions. [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Illustration shown to raters on how to select a column. Columns should be compared based on the best image of [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Screenshot of the evaluation phase for raters. This second phase (after the interaction phase) included questions [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: A diagram of our user encoder. The same framework is applied to both the text and image encoding process. [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: Template for the multimodal candidate generator prompt at step [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Text-only input templates for the value functions at step [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. T2I-Copilot: A Training-Free Multi-Agent Text-to-Image System for Enhanced Prompt Interpretation and Interactive Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A three-agent pipeline around FLUX.1-dev and PowerPaint clarifies ambiguous prompts, selects the right model, and iteratively regenerates until its evaluator scores the output above 8.0.

  2. RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning

    cs.CV 2025-05 conditional novelty 6.0 of 10

    RePrompt uses RL-trained reasoning traces to enhance text-to-image prompts, boosting spatial composition and counting scores across FLUX, SD3, and PixArt-Σ while keeping image generators fixed.

Reference graph

Works this paper leans on

268 extracted references · 80 canonical work pages · cited by 2 Pith papers

  1. [1]

    Column Preference: Raters were asked to re-confirm their preferred column for each turn, ensuring consistency in their choices (See Figure 11)

  2. [2]

    This score reflected their satisfaction with the best image in the column, relative to their original prompt intent

    Absolute Column Satisfaction: For each of the four columns in each turn, raters provided an absolute satisfaction score on a 5-point scale (1: very dissatisfied, 5: very satisfied). This score reflected their satisfaction with the best image in the column, relative to their original prompt intent. This absolute scoring allowed for a finer-grained assessme...

  3. [3]

    Selection Rationale: For their selected column in each turn, raters were asked to provide a free-text explanation of their choice

  4. [4]

    Turn-over-Turn Improvement: For turns 2-5, raters assessed whether the set of images in the current turn was better, worse, or about the same as the set of images in the previous turn. This comparison, again focused on the quality of the selected columns in each turn relative to the original prompt intent, provided a measure of the agent’s progress and it...

  5. [5]

    End Task

    Overall Turn Satisfaction: Raters provided an overall absolute satisfaction score (1-5 scale) for all of the images presented in each turn. This average score across columns gave a holistic impression of the agent’s performance in that turn, complementing the column-specific feedback. 16 Preference Adaptive and Sequential Text-to-Image Generation Raters w...

    2021

  6. [6]

    Mini-Batch Sampling: A mini-batch B is sampled from the dataset, and the posterior is estimated solely for samples within B using a target network to prevent overestimation

  7. [7]

    Prior Update: An exponential moving average of the mini-batch posteriors is used to update the prior

  8. [8]

    The user model is always composed of the score function sθ and may vary for different types of datasets and labels, and need to be adjusted accordingly

    Gradient Descent: Instead of fully optimizing the model parameters, a single gradient descent step is taken with respect to the loss function: L(θ) = −Ei∼B,k∼γi [log σθ(xi, yi, k)]. The user model is always composed of the score function sθ and may vary for different types of datasets and labels, and need to be adjusted accordingly. We provide detailed mo...

Show all 268 references
  1. [9]

    The CLIP model parameters are frozen during this phase

    Main Training: The model is trained on a combination of large-scale datasets: HPS V2 (Wu et al., 2023), Pick-a-Pic (Kirstain et al., 2023), and Simulacra Aesthetic Captions (SAC) (Pressman et al., 2022). The CLIP model parameters are frozen during this phase

    2023

  2. [10]

    Here, the entire model, including the CLIP model, is trained

    Fine-tuning with Sequential Data: A shorter fine-tuning phase follows, utilizing human-rated data. Here, the entire model, including the CLIP model, is trained. 19 Preference Adaptive and Sequential Text-to-Image Generation Algorithm 1 Mini-Batch Expectation-Maximization User ...

    1952

  3. [11]

    The chosen prompt index at each turn: ycm i,t = ci,t ∈ [L]

  4. [12]

    In-turn preferences between the best images for each prompt: yin−pref i,t = {(yi,t ℓ ⪰ yi,t ℓ′ )}ℓ,ℓ′∈Q, where Q represents the set of all unordered pairs of size |Q| = L 2

  5. [13]

    {{ initial_prompt }}

    Cross-turn preferences between chosen images at consecutive turns: ycross−pref i,t = (yi,t ℓ ⪰ yi,t−1 ℓ′ ), resulting in H − 1 cross-turn preferences per sample. Preferences within a sample are modeled by applying the BT model (Equation 5) to both in-turn and cross-turn prefer...

    2021

  6. [17]

    {{ prompt_expansion 4}}

    Prompt: "{{ prompt_expansion 4}}", Samples: << image samples >> USER: User selects prompt number {{ image_number }} corresponding to the prompt: "{{ prompt_expansion }}" AGENT: Agent shows four prompts (with corresponding slates of images) to the user:

  7. [18]

    {{ prompt_expansion 1}}

    Prompt: "{{ prompt_expansion 1}}", Samples: << image samples >>

  8. [19]

    {{ prompt_expansion 2}}

    Prompt: "{{ prompt_expansion 2}}", Samples: << image samples >>

  9. [20]

    {{ prompt_expansion 3}}

    Prompt: "{{ prompt_expansion 3}}", Samples: << image samples >>

  10. [21]

    {{ prompt_expansion 4}}

    Prompt: "{{ prompt_expansion 4}}", Samples: << image samples >> USER: User selects prompt number {{ image_number }} corresponding to the prompt: "{{ prompt_expansion }}" Based on the interaction history above, please provide twenty-five (25) prompt candidates to the final sele...

    2024

  11. [22]

    A train at sunset, on tracks

  12. [23]

    A train in the morning mist

  13. [24]

    A train under a stormy sky

  14. [25]

    A train at night, glowing lights

  15. [26]

    Category: Variations in Train Type and Composition:

    A train in bright sunlight. Category: Variations in Train Type and Composition:

  16. [27]

    A long freight train on tracks

  17. [28]

    A sleek modern passenger train

  18. [29]

    An old steam train on tracks

  19. [30]

    A colorful toy train on tracks

  20. [31]

    Category: Variations in Environment and Setting:

    A double-decker train on tracks. Category: Variations in Environment and Setting:

  21. [32]

    Train on a mountain track

  22. [33]

    Train in a desert landscape

  23. [34]

    Train crossing a bridge

  24. [35]

    Train passing through a forest

  25. [36]

    Category: Completely Different Prompts (Rephrasing):

    Train in a snowy landscape. Category: Completely Different Prompts (Rephrasing):

  26. [37]

    Railroad journey, scenic view

  27. [38]

    Metal beast on steel rails

  28. [39]

    Locomotive speeding onward

  29. [40]

    Tracks vanishing in distance

  30. [41]

    Category: Adding Artistic Styles and Effects:

    Steel wheels on iron rails. Category: Adding Artistic Styles and Effects:

  31. [42]

    Train on tracks, impressionist style

  32. [43]

    Train, hyperrealistic rendering

  33. [44]

    Train on tracks, painted by Monet

  34. [45]

    Train, detailed pencil sketch

  35. [46]

    Railroad journey, scenic view

    Train on tracks, vibrant colors. User User selects prompt number 16: "Railroad journey, scenic view." Agent Category: Expanding the Scene:

  36. [47]

    Railroad tracks winding through a lush green valley, sunlit

  37. [48]

    Train disappearing into a mountain tunnel, dramatic lighting

  38. [49]

    A vintage steam train crossing a high bridge over a river

  39. [50]

    Railroad tracks stretching to the horizon, sunset over fields

  40. [51]

    Category: Focusing on the Train:

    Train passing a quaint countryside village, wildflowers. Category: Focusing on the Train:

  41. [52]

    Majestic steam locomotive pulling luxurious passenger cars

  42. [53]

    Modern high-speed train speeding through a vibrant landscape

  43. [54]

    Freight train carrying colorful containers, scenic backdrop

  44. [55]

    Close-up of a train's wheels turning on the tracks, detailed

  45. [56]

    Category: Artistic Styles:

    A weathered, vintage train car in a beautiful setting. Category: Artistic Styles:

  46. [57]

    Railroad landscape, painted in the style of Van Gogh

  47. [58]

    Impressionist painting of a train journey through fields

  48. [59]

    Photorealistic train passing through a stunning canyon

  49. [60]

    A whimsical, cartoon-style train in a fantasy landscape

  50. [61]

    Category: Different Perspectives:

    Train in a vibrant, surreal dreamscape setting. Category: Different Perspectives:

  51. [62]

    Aerial view of a train snaking through a winding valley

  52. [63]

    View from inside a train car, looking out at the scenery

  53. [64]

    A long exposure shot of a train moving at night, light trails

  54. [65]

    Close-up of the train's engine, steam billowing dramatically

  55. [66]

    Category: Rephrasing and Variations:

    View from the train's cab, looking down the tracks. Category: Rephrasing and Variations:

  56. [67]

    Scenic railroad journey through diverse landscapes

  57. [68]

    Picturesque train ride, breathtaking views from the window

  58. [69]

    Railroad adventure, panoramic scenery

  59. [70]

    Journey by rail, spectacular countryside

  60. [71]

    Train in a vibrant, surreal dreamscape setting

    Train on rails, beautiful and idyllic nature. User User selects prompt number 15: "Train in a vibrant, surreal dreamscape setting" Turn 1 Turn 2 30 Preference Adaptive and Sequential Text-to-Image Generation Agent Category: Surreal Landscapes & Trains:

  61. [72]

    A steam train chugging through a field of giant, iridescent flowers under a swirling galaxy

  62. [73]

    A whimsical train journey through a candy-colored forest, lollipop trees, and marshmallow clouds

  63. [74]

    A futuristic train gliding over a rainbow bridge in a city made of crystals and floating islands

  64. [75]

    An antique train winding through a valley of vibrant, bioluminescent plants at twilight

  65. [76]

    Category: Emphasis on Vibrancy & Color:

    A surreal train station nestled within a giant, glowing mushroom forest. Category: Emphasis on Vibrancy & Color:

  66. [77]

    A vibrant, hyperrealistic train speeding through a landscape of impossibly bright colors

  67. [78]

    A psychedelic train ride through a kaleidoscope of swirling patterns and neon lights

  68. [79]

    A train made of stained glass, traversing a landscape of equally colorful stained glass

  69. [80]

    A cartoonish train with exaggerated features, traveling through a bright, playful world

  70. [81]

    Category: Focus on Dreamlike Atmosphere:

    A majestic train, with golden carriages, passing through a field of glowing sunflowers. Category: Focus on Dreamlike Atmosphere:

  71. [82]

    A lone train disappearing into a hazy, dreamlike fog, leaving a trail of shimmering stardust

  72. [83]

    A train floating effortlessly above a sea of clouds, bathed in soft, ethereal light

  73. [84]

    A vintage train gliding silently through a landscape painted in watercolor washes

  74. [85]

    A dreamlike train station, where clocks melt and platforms float, bathed in moonlight

  75. [86]

    Category: Different Interpretations of the Prompt:

    A train composed of shifting, morphing shapes, moving through a constantly changing landscape. Category: Different Interpretations of the Prompt:

  76. [87]

    A train journey through an impossible landscape, defying gravity and perspective

  77. [88]

    The feeling of riding a train in a vibrant dream, expressing freedom and wonder

  78. [89]

    A dream sequence, a train traveling through a bizarre, otherworldly environment

  79. [90]

    A surreal, dreamlike depiction of a train journey, full of vibrant colors and unusual imagery

  80. [91]

    Category: Adding Specific Artistic Styles:

    A train's passage through a vibrant dreamscape, emphasizing the contrast between reality and fantasy. Category: Adding Specific Artistic Styles:

  81. [92]

    A train in a vibrant surreal dreamscape, in the style of a 1950s cartoon

  82. [93]

    A vibrant surreal dreamscape with a train, painted in the style of Salvador Dali

  83. [94]

    A vibrant surreal dreamscape featuring a train, rendered in the style of Alphonse Mucha

  84. [95]

    A train traveling through a vibrant surreal dreamscape, with a focus on Art Nouveau style

  85. [96]

    A surreal train station nestled within a giant, glowing mushroom forest

    A vibrant surreal dreamscape with a train, using bold, expressive brushstrokes in an Impressionistic style. User User selects prompt number 5: "A surreal train station nestled within a giant, glowing mushroom forest" Agent Category: Vibrant Mushroom Forests & Trains:

  86. [97]

    A whimsical train station nestled amongst bioluminescent mushrooms, with a steampunk aesthetic

  87. [98]

    A vibrant, colorful train speeding through a fantastical forest of giant glowing mushrooms

  88. [99]

    A surreal train journey through a mushroom forest, with playful, cartoonish mushrooms

  89. [100]

    A detailed illustration of a train emerging from a glowing mushroom cavern, magical atmosphere

  90. [101]

    Category: Surreal Landscapes & Trains:

    A train puffing steam, passing through a dense forest of giant, otherworldly mushrooms. Category: Surreal Landscapes & Trains:

  91. [102]

    A train gliding across a rainbow bridge in a surreal, dreamlike landscape

  92. [103]

    A train traversing a landscape of floating islands, vibrant colors, and impossible architecture

  93. [104]

    A surreal train journey through a landscape painted in the style of Van Gogh, vibrant colors

  94. [105]

    A hyperrealistic train in a fantastical landscape, filled with impossible flora and fauna

  95. [106]

    Category: Dali-esque Trainscapes:

    A train disappearing into a swirling vortex of colors in a surreal, dreamlike landscape. Category: Dali-esque Trainscapes:

  96. [107]

    A melting clock train in a Dali-esque landscape, with distorted perspective and melting forms

  97. [108]

    A train with elongated limbs and surreal features in a Dali-inspired dreamscape

  98. [109]

    A train composed of melting clocks and surreal objects in a Dali-esque landscape

  99. [110]

    A train traversing a desert landscape with melting rocks and distorted space in Dali's style

  100. [111]

    Category: Ethereal & Dreamlike Trains:

    A surreal train scene with distorted perspective and soft, flowing lines in the style of Dali. Category: Ethereal & Dreamlike Trains:

  101. [112]

    A train floating through a starry night sky, with ethereal lights and glowing constellations

  102. [113]

    A lone train moving through a field of luminous flowers in a soft, ethereal light

  103. [114]

    A dreamy train scene with pastel colors, soft clouds, and a peaceful, tranquil atmosphere

  104. [115]

    A train gently gliding through a soft, misty landscape, with a serene and peaceful atmosphere

  105. [116]

    Category: Completely Different Prompts (Rephrasing):

    A train traveling through a sea of clouds, bathed in soft, golden light, peaceful and serene. Category: Completely Different Prompts (Rephrasing):

  106. [117]

    A detailed photograph of a modern high-speed train passing through a rural area

  107. [118]

    A realistic depiction of a vintage steam train chugging along a mountain pass

  108. [119]

    A classic black and white photo of a train passing through a bustling city

  109. [120]

    An image of a freight train transporting goods along a long railway line

  110. [121]

    A train composed of melting clocks and surreal objects in a Dali-esque landscape

    A simple drawing of a train on tracks in a straightforward, realistic style. User User selects prompt number 13: "A train composed of melting clocks and surreal objects in a Dali-esque landscape" Agent Category: Dali-esque Variations:

  111. [122]

    A surreal train speeding through a landscape of melting clocks and distorted figures, in the style of Salvador Dali

  112. [123]

    A train composed entirely of melting clocks, surreal objects, and impossible architecture, a Dali-esque dreamscape

  113. [124]

    A Dali-esque train journey through a desert landscape, with elongated shadows and distorted perspectives

  114. [125]

    A train with elongated limbs and melting forms, traversing a bizarre, dreamlike landscape, in the style of Salvador Dali

  115. [126]

    Category: Enhanced Surrealism:

    A surreal train station, with distorted architecture and melting clocks, in the style of Salvador Dali, hyperrealistic. Category: Enhanced Surrealism:

  116. [127]

    A train made of liquid metal, flowing through a landscape of shifting colors and impossible geometries

  117. [128]

    A train gliding through a vibrant, surreal dreamscape filled with fantastical creatures and impossible structures

  118. [129]

    A train passing through a swirling vortex of colors and shapes, a surreal and psychedelic journey

  119. [130]

    A train emerging from a giant, melting clock, surrounded by distorted figures and impossible objects

  120. [131]

    Category: Focus on the Train:

    A train journey through a hyperrealistic, surreal landscape, with unexpected elements and juxtapositions. Category: Focus on the Train:

  121. [132]

    Detailed close-up of a surreal train engine, with melting clocks and intricate details

  122. [133]

    A surreal train's interior, filled with melting furniture, distorted mirrors, and bizarre passengers

  123. [134]

    The perspective of someone sitting inside a Dali-esque train, witnessing the surreal landscape

  124. [135]

    A highly detailed rendering of a surreal train, emphasizing textures and materials

  125. [136]

    Category: Different Artistic Styles:

    A surreal train, focusing on its unusual design and mechanics. Category: Different Artistic Styles:

  126. [137]

    A train in a surreal landscape, painted in the style of René Magritte, with a focus on unexpected juxtapositions

  127. [138]

    A train journey through a surreal landscape, rendered in the style of a woodcut print, with bold lines and textures

  128. [139]

    A surreal train scene in the style of Max Ernst, featuring collage-like elements and unexpected combinations

  129. [140]

    A surreal train depicted in a vibrant, pop art style, with bold colors and graphic elements

  130. [141]

    Category: Rephrasing & Simplification:

    A surreal train in a cubist landscape, emphasizing fragmented forms and multiple perspectives. Category: Rephrasing & Simplification:

  131. [142]

    A bizarre, dreamlike train in a surreal landscape

  132. [143]

    A surreal train, simple prompt

  133. [144]

    A melting clock train in a Dali-esque world

  134. [145]

    Surreal train in a distorted landscape

  135. [146]

    An ethereal underwater scene depicting a queen, her crown formed by bioluminescent sea creatures, radiating an enchanting glow

    A dreamlike train journey through a strange land. User User selects prompt number 22: "An ethereal underwater scene depicting a queen, her crown formed by bioluminescent sea creatures, radiating an enchanting glow." Turn 3 Turn 4 Turn 5 31 Preference Adaptive and Sequential Te...

  136. [147]

    Close-up portrait, princess, animal skull mask, cybernetic jellyfish, intricate details, dramatic lighting

  137. [148]

    Princess, skull mask, jellyfish cybernetics, ethereal glow, hyperrealistic, close-up

  138. [149]

    Macro portrait, princess, bone mask, bioluminescent jellyfish, sharp focus, rich colors

  139. [150]

    Detailed portrait, princess, ornate skull mask, glowing cybernetic jellyfish, dark background

  140. [151]

    Category: Alternative Mask & Cybernetics:

    Close-up, princess, animal skull headdress, bio-mechanical jellyfish, cinematic lighting. Category: Alternative Mask & Cybernetics:

  141. [152]

    Princess portrait, elegant bird skull mask, subtle cybernetic enhancements, soft lighting

  142. [153]

    Close-up, princess, futuristic insect mask, integrated cybernetic jellyfish, vibrant colors

  143. [154]

    Macro portrait, princess, ornate dragon skull mask, glowing cybernetic implants, blurred background

  144. [155]

    Princess, skeletal mask, sleek cybernetic arms, ethereal background, detailed texture

  145. [156]

    Category: Variations in Background & Setting:

    Portrait, princess, geometric skull mask, minimalist cybernetic features, sharp focus. Category: Variations in Background & Setting:

  146. [157]

    Princess, skull mask, cybernetic jellyfish, underwater scene, shimmering bubbles

  147. [158]

    Portrait, princess, bone mask, cybernetic enhancements, mystical forest background

  148. [159]

    Close-up, princess, animal skull mask, jellyfish cybernetics, futuristic cityscape backdrop

  149. [160]

    Princess, skull mask, bioluminescent jellyfish, otherworldly nebula background

  150. [161]

    Category: Different Artistic Styles:

    Macro portrait, princess, ornate mask, cybernetic elements, enchanted garden setting. Category: Different Artistic Styles:

  151. [162]

    Princess, skull mask, cybernetic jellyfish, art nouveau style, elegant and flowing lines

  152. [163]

    Portrait, princess, bone mask, cybernetic enhancements, dark fantasy art style

  153. [164]

    Close-up, princess, animal skull mask, jellyfish cybernetics, photorealistic style

  154. [165]

    Princess, skull mask, bioluminescent jellyfish, gothic art style, dramatic lighting

  155. [166]

    Category: Completely Different Concept (Rephrasing):

    Macro portrait, princess, ornate mask, cybernetic elements, digital painting style. Category: Completely Different Concept (Rephrasing):

  156. [167]

    A regal woman, adorned with intricate bone jewelry and glowing bioluminescent accents

  157. [168]

    A mysterious figure, partially obscured by a fantastical mask, with subtle technological elements

  158. [169]

    A powerful queen, with cybernetic enhancements blending seamlessly with organic bone structures

  159. [170]

    A captivating portrait of a woman, combining elegance with futuristic bio-mechanical elements

  160. [171]

    Close-up, princess, animal skull headdress, bio- mechanical jellyfish, cinematic lighting

    An ethereal being, blending organic and synthetic elements in a harmonious composition. User User selects prompt number 5: "Close-up, princess, animal skull headdress, bio- mechanical jellyfish, cinematic lighting" Agent Category: Refined Details & Lighting:

  161. [172]

    Close-up portrait: princess, intricate skull headdress, bioluminescent jellyfish, dramatic lighting, 8k resolution

  162. [173]

    Princess in ornate skull mask, bio-mechanical jellyfish crown, deep shadows, sharp focus, cinematic lighting

  163. [174]

    Close-up, princess, detailed animal skull mask, glowing jellyfish, volumetric lighting, dark background

  164. [175]

    Macro portrait: princess, skull headdress with jellyfish, chiaroscuro lighting, ethereal atmosphere, hyperrealistic

  165. [176]

    Category: Variations on the Jellyfish:

    Close-up, princess, skull mask, bio-mechanical jellyfish, cinematic lighting, rich textures. Category: Variations on the Jellyfish:

  166. [177]

    Princess, ornate skull mask, bioluminescent jellyfish tendrils, dark background, cinematic lighting

  167. [178]

    Close-up portrait, princess, skull headdress, iridescent jellyfish, dark moody ambiance, sharp details

  168. [179]

    Princess, skeletal mask, cybernetic jellyfish swarm, deep ocean setting, cinematic lighting

  169. [180]

    Close-up: princess, skull headdress, bio-mechanical jellyfish, neon lighting, underwater effect

  170. [181]

    Category: Emphasis on the Skull:

    Princess, skull mask, pulsating jellyfish, dark background, dramatic lighting, 8k resolution. Category: Emphasis on the Skull:

  171. [182]

    Close-up portrait, princess, intricately carved skull mask, jellyfish accents, cinematic lighting

  172. [183]

    Princess, detailed animal skull mask, bio-mechanical jellyfish, dark fantasy art, dramatic lighting

  173. [184]

    Close-up: princess, ornate skull headdress, bio-mechanical jellyfish, dark fantasy art, sharp focus

  174. [185]

    Princess, realistic skull mask, cybernetic jellyfish, dark background, cinematic lighting, detailed texture

  175. [186]

    Category: Completely Different Style:

    Macro shot: princess, decaying skull mask, bio-mechanical jellyfish, dramatic lighting, ethereal feel. Category: Completely Different Style:

  176. [187]

    Elegant princess, futuristic crown, subtle technological details, soft lighting, pastel colors

  177. [188]

    Portrait of a regal woman, ethereal crown, flowing gown, soft light, serene expression

  178. [189]

    A powerful queen, majestic pose, ornate jewelry, warm lighting, rich colors

  179. [190]

    Young woman, delicate flower crown, flowing dress, soft light, gentle expression

  180. [191]

    Category: Exploring Backgrounds & Settings:

    Beautiful woman, simple headband, natural light, soft focus, dreamy atmosphere. Category: Exploring Backgrounds & Settings:

  181. [192]

    Close-up, princess, skull headdress, bio-mechanical jellyfish, foggy forest background

  182. [193]

    Princess, skull mask, jellyfish crown, cinematic lighting, ancient ruins background

  183. [194]

    Close-up portrait, princess, skull headdress, bio-mechanical jellyfish, underwater scene

  184. [195]

    Princess, skull mask, jellyfish, cinematic lighting, gothic architecture background

  185. [196]

    Close-up: princess, skull headdress, bio-mechanical jellyfish, neon lighting, underwater effect

    Close-up: princess, skull headdress, bio-mechanical jellyfish, starry night sky background. User User selects prompt number 9: "Close-up: princess, skull headdress, bio-mechanical jellyfish, neon lighting, underwater effect" Turn 1 Turn 2 32 Preference Adaptive and Sequential ...

  186. [197]

    Princess, skull jellyfish crown, neon coral reef, vibrant underwater city

  187. [198]

    Underwater princess, glowing jellyfish headdress, neon lights, art deco style

  188. [199]

    Cyberpunk princess, skull mask with neon jellyfish, futuristic underwater city

  189. [200]

    Princess in neon-lit underwater cave, skull headdress, bioluminescent jellyfish

  190. [201]

    Category: Subtle Shifts in Lighting and Background:

    Electric princess, skull and jellyfish crown, neon ocean, 80s synthwave aesthetic. Category: Subtle Shifts in Lighting and Background:

  191. [202]

    Close-up portrait: princess, skull headdress, bioluminescent jellyfish, soft neon glow, dark background

  192. [203]

    Princess, skull jellyfish crown, underwater scene, subtle neon accents, calm atmosphere

  193. [204]

    Underwater princess, skull headdress, bio-mechanical jellyfish, pastel neon, serene background

  194. [205]

    Princess, skull and jellyfish crown, deep ocean, cool neon hues, mysterious ambiance

  195. [206]

    Category: Variations on the Jellyfish:

    Close-up, princess, skull headdress, jellyfish, dim neon lighting, smoky underwater cave. Category: Variations on the Jellyfish:

  196. [207]

    Princess, skull headdress, glowing jellyfish tentacles, neon lights, dark ocean

  197. [208]

    Princess, skull crown, iridescent jellyfish, neon underwater landscape, detailed textures

  198. [209]

    Underwater princess, skull headdress, bioluminescent jellyfish swarm, neon glow

  199. [210]

    Close-up: princess, skull mask, intricate jellyfish headdress, neon coral, deep ocean

  200. [211]

    Category: Completely Different Style:

    Princess, skull headdress, bio-mechanical jellyfish, neon kelp forest, flowing fabric. Category: Completely Different Style:

  201. [212]

    Portrait of a regal woman, ornate mask, intricate jewelry, rich textures, dramatic lighting

  202. [213]

    Elegant woman, mysterious headdress, flowing gown, detailed portrait, studio lighting

  203. [214]

    A woman with a captivating gaze, elaborate headdress, rich colors, painterly style

  204. [215]

    Close-up portrait, strong female figure, enigmatic expression, rich textures, studio setting

  205. [216]

    Category: Emphasizing Texture and Detail:

    Fashion portrait, stylish woman, unique headpiece, luxurious fabrics, sophisticated look. Category: Emphasizing Texture and Detail:

  206. [217]

    Hyperrealistic portrait: princess, detailed skull headdress, bio-mechanical jellyfish, neon lighting, underwater scene

  207. [218]

    Close-up, princess, skull headdress, intricate jellyfish details, neon lights, underwater world

  208. [219]

    Princess, skull jellyfish crown, highly detailed textures, neon lighting, underwater cave

  209. [220]

    Macro portrait: princess, textured skull mask, bio-mechanical jellyfish, neon glow, underwater rocks

  210. [221]

    Princess, skull jellyfish crown, highly detailed textures, neon lighting, underwater cave

    Detailed portrait: princess, skull headdress, realistic jellyfish, neon accents, ocean floor. User User selects prompt number 23: "Princess, skull jellyfish crown, highly detailed textures, neon lighting, underwater cave" Agent Category: Neon Underwater Princesses:

  211. [222]

    A princess adorned with a bioluminescent jellyfish crown, swimming in a vibrant neon coral reef, intricate details, 8k resolution

  212. [223]

    Princess in an underwater palace, wearing a jellyfish headdress that glows with neon light, surrounded by glowing flora and fauna

  213. [224]

    Portrait of a princess, her face partially obscured by a neon jellyfish crown, underwater scene, hyperrealistic details

  214. [225]

    An ethereal princess with flowing hair, a jellyfish headdress emitting neon light, swimming gracefully amidst glowing jellyfish

  215. [226]

    Category: Dark Fantasy Princesses:

    Close-up of a princess wearing a skull-shaped jellyfish crown, vibrant neon lighting, underwater cave setting, detailed textures. Category: Dark Fantasy Princesses:

  216. [227]

    A princess in a dark underwater grotto, wearing a crown of glowing jellyfish and skeletal elements, mysterious and ethereal

  217. [228]

    Dark fantasy portrait: a princess with a jellyfish crown made of bone and neon lights, surrounded by deep sea creatures

  218. [229]

    A gothic princess, wearing a headdress crafted from decaying jellyfish and skulls, bathed in neon underwater light, dramatic pose

  219. [230]

    A mysterious underwater princess, her face veiled by a jellyfish headdress with glowing neon accents, dark and moody atmosphere

  220. [231]

    Category: Detailed Jellyfish Headwear:

    Portrait of a princess in a dark, underwater cavern, wearing a crown of bioluminescent jellyfish, surrounded by shadows and glowing accents. Category: Detailed Jellyfish Headwear:

  221. [232]

    Extremely detailed portrait of a princess wearing an intricate jellyfish headdress, neon lighting, underwater setting, realistic textures

  222. [233]

    Close-up, princess with a jellyfish crown, featuring highly detailed tentacles and bioluminescent patterns, underwater scene, 8k resolution

  223. [234]

    Macro shot: princess, ornate jellyfish headdress with intricate skeletal details, neon lighting, underwater environment, sharp focus

  224. [235]

    Princess portrait: elaborate jellyfish crown with glowing neon accents, detailed textures, underwater background, cinematic lighting

  225. [236]

    Category: Alternative Interpretations:

    Highly detailed image of a princess wearing a jellyfish crown, showcasing the intricate textures and neon glow, underwater environment. Category: Alternative Interpretations:

  226. [237]

    A regal figure, partially obscured by a mask of intricately woven light and shadow, with bioluminescent accents, underwater, 8k resolution

  227. [238]

    A captivating woman, her face partially hidden by a fantastical headpiece reminiscent of deep- sea life, bathed in ethereal light

  228. [239]

    A mysterious and elegant woman with a headpiece combining natural and technological elements, emitting a soft, otherworldly glow

  229. [240]

    An enigmatic figure in an underwater setting, adorned with a crown of ethereal light and intricate, bioluminescent patterns

  230. [241]

    Category: Stylistic Variations:

    A vision of elegance and mystery: a woman with a unique headpiece inspired by marine life, emitting a gentle, glowing light. Category: Stylistic Variations:

  231. [242]

    Art Deco style portrait: a princess wearing a geometric jellyfish crown, vibrant neon colors, underwater setting, elegant composition

  232. [243]

    Surrealist portrait: a princess with a melting jellyfish crown, neon lighting, dreamlike underwater scene, unexpected elements

  233. [244]

    Impressionistic underwater portrait: a princess with a jellyfish crown, soft focus, pastel neon colors, flowing lines and shapes

  234. [245]

    A princess with a jellyfish headdress in the style of Alphonse Mucha, elegant lines, soft neon lighting, underwater background

  235. [246]

    Close-up, princess with a jellyfish crown, featuring highly detailed tentacles and bioluminescent patterns, underwater scene, 8k resolution

    A princess wearing a jellyfish crown, painted in the style of Gustav Klimt, with gold and vibrant neon accents, underwater scene. User User selects prompt number 12: "Close-up, princess with a jellyfish crown, featuring highly detailed tentacles and bioluminescent patterns, un...

  236. [247]

    Close-up portrait of a princess wearing a bioluminescent jellyfish crown, intricate details on tentacles, soft underwater lighting, 8k resolution, hyperrealistic

  237. [248]

    Princess with a jellyfish crown, detailed tentacles with iridescent scales, soft blue and pink lighting, underwater scene, serene expression

  238. [249]

    Underwater portrait: princess, jellyfish crown with glowing accents, subtle shimmering particles, cinematic lighting, highly detailed textures

  239. [250]

    Close-up, princess, jellyfish crown, vibrant coral reef background, realistic rendering, sharp focus, detailed textures, soft lighting

  240. [251]

    Category: Stylistic Variations:

    Princess, jellyfish crown, realistic jellyfish anatomy, submerged in crystal clear water, natural lighting, ethereal atmosphere. Category: Stylistic Variations:

  241. [252]

    Art Nouveau princess with a jellyfish crown, flowing lines, pastel colors, underwater setting, elegant composition

  242. [253]

    Princess, jellyfish crown, Art Deco style, geometric patterns, neon lighting, underwater Art Deco architecture

  243. [254]

    Surrealist portrait: princess, melting jellyfish crown, dreamlike underwater environment, vibrantcolors, distorted perspective

  244. [255]

    Impressionist princess, jellyfish crown, soft brushstrokes, underwater scene, blurred background,pastel hues

  245. [256]

    Category: Environmental Changes:

    Princess, jellyfish crown, in the style of Gustav Klimt, gold leaf accents, underwater scene,shimmering effect. Category: Environmental Changes:

  246. [257]

    Princess, jellyfish crown, deep ocean trench, bioluminescent creatures, dark mysterious background, 8k resolution

  247. [258]

    Princess, jellyfish crown, glowing coral reef, diverse marine life, vibrant colors, shallow water, sunlight filtering through

  248. [259]

    Princess, jellyfish crown, ancient sunken city, ruins, mystical underwater atmosphere, detailed textures, cinematic lighting

  249. [260]

    Princess, jellyfish crown, mysterious underwater cave, glowing crystals, ethereal light, sense of wonder, 8k resolution

  250. [261]

    Category: Alternative Headwear Interpretations:

    Underwater princess, jellyfish crown, surrounded by schools of luminous fish, vibrant coral, crystal-clear water, cinematic lighting. Category: Alternative Headwear Interpretations:

  251. [262]

    Close-up portrait of a princess with a headdress made of intricately woven sea anemones and jellyfish, underwater, ethereal lighting

  252. [263]

    Princess, ornate headdress inspired by jellyfish, intricate details, flowing fabric, underwater, cinematic lighting, 8k resolution

  253. [264]

    Portrait of a princess with a crown sculpted from fossilized jellyfish, underwater ruins background, mysterious atmosphere

  254. [265]

    Princess with a headdress resembling a blooming jellyfish, vibrant colors, detailed textures, underwater, soft lighting

  255. [266]

    Category: Completely Different Prompts (Rephrasing):

    Close-up of a princess wearing a delicate headdress crafted from translucent jellyfish, ethereal glow, underwater scene, serene expression. Category: Completely Different Prompts (Rephrasing):

  256. [267]

    A breathtaking underwater portrait of a regal woman, adorned with a luminous, organic headpiece, evoking a sense of otherworldly beauty

  257. [268]

    An ethereal underwater scene depicting a queen, her crown formed by bioluminescent sea creatures, radiating an enchanting glow

  258. [269]

    A captivating image of a majestic figure submerged in an ocean, wearing a radiant headpiece reminiscent of jellyfish, in a hyperrealistic style

  259. [270]

    A mesmerizing underwater portrait, featuring a serene woman with a crown made of glowing jellyfish, showcasing intricate details and vibrant colors

  260. [271]

    An ethereal underwater scene depicting a queen, her crown formed by bioluminescent sea creatures, radiating an enchanting glow

    An elegant woman, her head crowned with a dazzling display of bioluminescent jellyfish, creating a stunning underwater spectacle. User User selects prompt number 22: "An ethereal underwater scene depicting a queen, her crown formed by bioluminescent sea creatures, radiating an...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.