FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation

Authors: christopher t h teo
xinda  ma
ngai-man  cheung
Venue: NeurIPS_cc_2024_Conference
Published: 2024-04-23 04:50:05

Abstract: Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-to-image (T2I) generation. Specifically, this approach leverages readily available reference images to learn inclusive prompts for each target Sensitive Attribute (tSA), allowing for fair image generation. In this work, we first reveal that this prompt learning-based approach results in degraded sample quality. Our analysis shows that the approach's training objective--which aims to align the embedding differences of learned prompts and reference images-- could be sub-optimal, resulting in distortion of the learned prompts and degraded generated images. To further substantiate this claim, **as our major contribution**, we deep dive into the denoising subnetwork of the T2I model to track down the effect of these learned prompts by analyzing the cross-attention maps. In our analysis, we propose a novel prompt switching analysis: I2H and H2I. Furthermore, we propose new quantitative characterization of cross-attention maps. Our analysis reveals abnormalities in the early denoising steps, perpetuating improper global structure that results in degradation in the generated samples. Building on insights from our analysis, we propose two ideas: (i) *Prompt Queuing* and (ii) *Attention Amplification* to address the quality issue. Extensive experimental results on a wide range of tSAs show that our proposed method outperforms SOTA approach's image generation quality, while achieving competitive fairness. More resources at FairQueue Project site: https://sutd-visual-computing-group.github.io/FairQueue

Main paper: 
 introduction: 
 There has been significant progress in the quality of text-to-image (T2I) generation [1] [2] [3] resulting in increasing adoption in different applications [4] [5] [6] [7] [8] [9] [10] . With this comes concerns regarding the fairness of these T2I models and their societal impacts [11] [12] [13] [14] [15] . Fair T2I Generation. T2I models may inherit biases present in their training data. Several approaches have been proposed to mitigate these biases [16] [17] [18] [19] (See related work in Supp). Particularly, Inclusive T2I Generation (ITI-GEN) [16] -the existing SOTA-suggests that fair T2I approaches based on hard prompts (HP) (e.g., "A headshot of a person with fair skin tone") are limited by linguistic ambiguity. For example, Skin Tone is often challenging to define and interpret based on HP, resulting in sub-optimal performance. To overcome this linguistic ambiguity, ITI-GEN adopts the notion that "a picture is worth a thousand words" and leverages readily available reference 1e-3 We observe degradation in images generated through ITI-GEN's learned prompts. We note that the direction of reference image embeddings could include unrelated concepts beyond tSA differences (e.g., variations in accessories) resulting in learning of distorted prompts using ITI-GEN. Furthermore, we observe misalignment between the direction of credible hard prompts and that of reference images/learned prompts. (b) As our main contribution and to further understand how these distorted prompts affect the image generation process, we deep dive into the denoising network and analyze the cross-attention maps, revealing their abnormalities e.g., higher activity for maps associated with non-tSA tokens ("of", "a"). We examine the degraded global structures resulting from these distorted prompts in the early denoising steps. Moreover, we propose I2H and H2I (Eq.2) analysis to understand impact of these degraded global structures and abnormalities in later denoising steps. In addition, we propose metrics (Eq.3) on cross-attention maps to quantify these abnormalities.Degraded Samples(c) Building on insights from our analysis, we propose a solution to address distorted prompts while maintaining competitive fairness. Our solution FairQueue includes two ideas: prompt queuing and attention amplification. E T and E I are CLIP text and image encoder resp. [20] . T , F , P are the base prompt, hard prompt with minimal linguistic ambiguity, and ITI-GEN prompt, resp.images to learn an inclusive prompt for each tSA category. This approach translates visual attribute differences present in the reference images into prompt differences, enabling the learned prompts to be used to generate images of all tSA categories, regardless of their linguistic ambiguity. Fairness is achieved by uniformly sampling the learned prompts to condition the T2I generation. Central to this approach is the enforcement of directional alignment between learned prompt embeddings and reference image embeddings corresponding to a pair of tSA categories.In this work, we question the central idea of prompt learning via alignment between the direction of prompt embeddings and the direction of reference image embeddings in the context of fair T2I generative models. Our work starts with examining the generated images and observes that a moderate amount of degraded images are generated based on ITI-GEN. We argue that using the direction of reference image embeddings as guidance could be sub-optimal, as the difference between reference images could include additional unrelated concepts other than the tSA difference (Fig 1 ). For example, reference images of "A headshot of a person smiling" and "A headshot of a person not smiling" could contain differences in poses, accessories, hairstyling, in addition to the difference in smiling. Therefore, the direction of reference image embeddings could be noisy and include additional unrelated concepts other than the tSA difference. We perform an analysis on the direction of embeddings to further understand the issue. We hypothesize that using the direction of reference image embeddings as guidance could lead to distortion in the learned prompts, resulting in artifacts and quality degradation in the images generated by T2I models.To further substantiate this claim, as our major contribution, we deep dive into the denoising subnetwork of the T2I model to analyze ITI-GEN prompts in the generation pipeline. Our analysis include examination of the cross-attention maps of the learned prompts at individual time steps of the denoising process. We propose novel prompt switching analysis: ITI-GEN to HP (I2H), and HP to ITI-GEN (H2I). We further propose new quantitative metrics for cross-attention map characterization. Our analysis reveals cross-attention maps of the learned prompts have abnormalities in the initial time steps of the denoising process. This results in synthesizing improper global structures. Interestingly, we find that the learned prompts have a minimum abnormality in the later steps-the learned prompts perform adequately in generating the desired tSA category provided that proper global image structures could be synthesized in the initial denoising steps. To justify our analysis of cross-attention, we remark that cross-attention contextualizes prompt embeddings with the latent representation of images and has been shown to play a key role in T2I models [21, 22] .Building on the insights of our analysis, we propose a solution to address degraded generated images without compromising fairness and diversity. Particularly, we propose Prompt Queuing to apply base prompts (without tSA tokens) in the initial time steps and ITI-GEN learned prompts in the later time steps of the denoising process. We further propose Attention Amplification to balance the quality and fairness of the T2I generation. Overall, our solution can effectively address the degraded quality issue in ITI-GEN while maintaining competitive fairness. Our contributions are:• We examine the generated images from the prompt learning-based fair T2I generation approach and reveal a moderate amount of generated images with degradation (Sec 3.1).• We argue that the direction of reference image embeddings could be noisy and include unrelated concepts in addition to tSA difference, and prompt learning based on alignment with the direction of reference image embeddings could be sub-optimal (Sec 3.1).• We deep dive into the denoising subnetwork of the T2I model and analyze cross-attention maps with our proposed prompt switching analysis I2H and H2I, and our proposed quantitative metrics for cross-attention maps. Our analysis reveals and characterizes abnormalities in cross-attentions of ITI-GEN prompts in the denoising process (Sec 3.2).• We propose FairQueue, a solution based on prompt queuing and attention amplification to improve generation quality while maintaining competitive fairness (Sec 4).2 Preliminaries T2I Generation. SOTA T2I generation is based on diffusion model (DM) [1] [2] [3] . In the forward diffusion process, Gaussian noise is incrementally added to the training data to train the DM. Then, during reverse diffusion, the DM generates samples by randomly sampling latent noise Z 0 ∼ N (0, I) as an input. For more control, text-conditioning [1, [23] [24] [25] was introduced, where we denote the reverse diffusion (denoising) of a single step t by Z t+1 ← DM (Z t , R, t, s). Here, Z t is the latent of the noisy image, R the input prompt, t ∈ [0, l] the denoising step, and s a random seed. Central to text conditioning is the cross-attention mechanism which contextualizes prompt embeddings with the image latent [21, 26] . Specifically the cross-attention map M ∈ R r×m×n -where r is the number of tokens in the prompt, and m × n shows map size for each token-is computed by:EQUATION) where, Q = ℓ q (ϕ(Z t )) is the linear projection of the latent spatial features ϕ(Z t ), and K = ℓ q (E T (R)) is the linear projection of the textual embedding E T (R) (usually CLIP text encoder [20] ). For ease of notation, we refer to the token-specific attention maps as M [.] e.g., M ["of"] ∈ R m×n refers to the cross-attention map for the token "of" in R. As our work focuses on the reverse diffusion process, we utilize Z 0 as the noisy latent input and Z l as the final latent output. This Z l is then finally passed into the DM decoder to output generated image, D(Z l ).Fairness in Generative Models. In generative models, fairness is defined as equal representation [27, 28] , where for a tSA with K categories, a fair generator will generate an equal number of samples for each category. As an example, for a T2I model G with text prompt "A headshot of a person" as input, we consider G as fair model w.r.t. tSA = Young-with two categories {Young, Old} [29, 27, 28] -if it generates an equal number of samples for each categories of this tSA [30, 31] .Hard Prompts for Fair T2I Generation. A baseline for achieving fairness in T2I models is to append the tSA-related prompt to the base prompt [17, 19] . Considering the same tSA=Young, and the base prompt "A headshot of a person", adding a tSA-related prompt for each category results in the HPs: "A headshot of a person young/old". For a fair generation, we query T2I with each of these HPs uniformly. Note that HP although very effective with certain tSA, in most cases, is ineffective due to the tSAs having linguistic ambiguity [32] -having misleading or deceptive language.Prompt Learning for Fair Text-to-Image Generation. To resolve the issue of ambiguous tSAs, inspired by the recent success of prompt learning [33, 34] , ITI-GEN [16] aims to achieve fairness in a pre-trained T2I model by learning inclusive tokens for each category of the tSA. Assuming the tokenized base prompt as T ∈ R p×d , where p is the number of tokens and d is the dimension of the embedding space, for each category k ∈ {1, ...K} of tSA, it learns q additional tokens.S k = [S k 0 , S k 1 , . . . , S k q-1 ] ∈ R q×d .In [16] , q is set to 3. Then, ITI-GEN prompt is constructed by appending these learned tokens to the original tokens: P k = [T ; S k ] ∈ R (p+q)×d . These tokens are learned using a set of labeled reference images (w.r.t. tSA) D ref = {x i , y i } N i=1 ; y i ∈ {1, ..., K} to provide stronger signals for describing tSA. More specifically, for a pair of categories (i, j) of tSA, a directional loss [35] is used to match the direction of learned prompts and images for this pair in CLIP embedding [20] space i.e., min S i ,S j L dir = 1 -(∆I (i,j) •∆P (i,j) ) (|∆I (i,j) ||∆P (i,j) |), where ∆I (i,j) (∆P (i,j) ) denotes the direction between images (text prompts) of two categories i and j in CLIP's embedding space, and directional loss L dir is minimized to learn tSA tokens S i , S j for these categories. Finally, using P k as input prompt, fairness is achieved by uniformly sampling the K categories of the tSA. We will omit the category index k when it is clear from context, and denote learned prompt and tokens by P and S 0 , . . . , S q-1 resp. 
limitations: 
 Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] There are some limitations inherited from the setup used in the literature regarding the required dataset to learn the prompts and also the possible entanglement between different target Sensitive Attributes (tSAs) Justification: [Yes] Please see Sec. C for the detailed discussion.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
a closer look at prompt learning for fair text-to-image generation: 
 In this section, we take a closer look at ITI-GEN [16] . First, in Sec. 3.1, we analyze ITI-GEN performance where we find quality degradation in moderate number of generated samples. We attribute this to the sub-optimal learning objective in ITI-GEN, which captures unrelated concepts that distort the learned tokens in P . Then, in Sec. 3.2, we analyze ITI-GEN prompts during sample generation by inspecting the cross-attention mechanism. Our analysis reveals that ITI-GEN prompts give rise to abnormality particularly damaging to the early steps of the denoising process.Remark. To conduct the following analysis on ITI-GEN prompts' behavior we require a strong baseline as a pseudo-gold standard to compare against. To address this, we found that when considering certain tSA with minimal linguistic ambiguity (MLA) [32] -a few tSA that can be described without misleading or deceptive language-HPs can serve as this strong baseline. Therefore, in this section, we focus on tSAs with minimum linguistic ambiguity. Later, in experiment section, we will include all tSAs, with or without ambiguity.Although ITI-GEN [16] improves fairness in T2I generation, a closer examination of its outputs reveals a potential trade-off: compromised image quality. In this section, first, we perform a systematic experiment to showcase these quality issues and then explore the potential root causes behind them.Experimental Setup. To evaluate our generated samples, we utilize the metrics: i) Fairness Discrepancy (FD) [27, 31, 11, 36] to measure fairness, ii) Text-Alignment (TA) [37, 22] and FID [38] to measure quality, and iii) DreamSim (DS) [39] to measure semantic preservation. Next, we determine a set of tSA with MLA to compare ITI-GEN with HP (as a pseudo-gold standard). Specifically, we follow [16] and use pre-trained Stable Diffusion (SD) [1] as T2I model. Then as mentioned in Sec. 2, for HP, we append the tSA-related prompts to the base prompt. We empirically found that tSAs {Smiling, High Cheekbones}, are unambiguous by classifying 500 generated sample per HP utilizing CLIP classifier [20] , where on average they both achieve a 98% accuracy (Experiment details in Supp). Then, for ITI-GEN [16] , we strictly follow [16] and use publicly available fair Figure 2 : T2I generation performance of HP, ITI-GEN [16] , and our proposed FairQueue for target Sensitive Attributes (tSAs) with minimal linguistic ambiguities. Samples generated by HP demonstrate outstanding performance with good fairness (FD), high quality (FID and FD), and good semantic preservation (DS). Meanwhile, ITI-GEN moderately degrades sample quality, impacting fairness and semantic preservation. FairQueue demonstrates comparable performance to HP, even surpassing HP in both quality and semantic preservation in many cases. Note that HP only performs well for unambiguous tSAs, and can not be used for general fair T2I generation purposes, as it can not be defined well for ambiguous tSAs (See Supp for detailed discussion). image dataset-sampled from CelebA [29] -as reference images to learn inclusive tokens, S. Finally, we generate and evaluate ITI-GEN samples based on the same latent noise input as HP. See Supp for experiment and metric details. Fig. 2 shows some generated samples together with quantitative results. A moderate number of generated images with ITI-GEN have quality degradation often with unrelated content (e.g., generating dog, multiple degraded faces, vague cartoons, etc.). Quantitative results show that for both tSAs, HP performs better in fairness (lower FD), quality (higher TA, lower FID), and semantic preservation (lower DS). We postulate degraded samples stem from ITI-GEN's sub-optimal training objective.Issue of Directional Loss for Fair T2I Generation. We hypothesize that directional loss is suboptimal in learning tSA-related tokens S. Particularly, the differences in reference images ∆I can include unrelated concepts in addition to variation in tSA categories. For example, considering tSA=Smiling in Fig. 1a (col 2), the reference images used for learning these two categories contain differences in pose, accessories, etc., in addition to the difference in Smiling. We further explore this potential of encoding unrelated concepts in ∆I by taking a closer look into the CLIP embedding space, where ITI-GEN's learning process happens. Recall, that as we utilize tSA with MLA and the HPs only differ in the tSA categories, we can utilize them as references in our analysis. For example, considering tSA=Smiling, the related tokenized prompts of HP in CLIP space can be computed as follows: F i = E T ("A headshot of a person smiling"), and F j = E T ("A headshot of a person not smiling"), with E T denoting CLIP's text encoder. Then ∆F = F i -F j shows the direction of the tokenized prompts in the CLIP embedding space.Our results in Fig. 1a (col3) shows the directional loss between ∆I and ∆F i.e., L dir (I, F ), for different tSAs using the reference images for each tSA. Note that L dir = 0 means perfect alignment. Our comparison reveal considerable misalignment between ∆I and ∆F implying that unrelated concepts are potentially encoded in ∆I. Meanwhile, ∆I and ∆P near perfect alignment implies that ⃝) degrade the global structure in the early denoising steps (e.g., Step 15) , for example, human face in HP vs some unrelated structure in ITI-GEN. The same behavior is observed for some other samples and tSAs (see Supp for more samples, and other tSAs with more denoising steps). these unrelated concepts are potentially transferred to P via ITI-GEN's learning objective, resulting in distorted learned token S.In the previous section, we observed degraded sample quality in ITI-GEN which we attribute to the sub-optimal training objective that results in learning distorted tokens. In this section, we take a step further to answer the question: "Given a pre-trained T2I model and some distorted learned prompts as input, how do these distorted prompts affect the image generation process of the T2I model?"To answer this, we deep dive into the latent denoising network [1] and analyze the cross-attention mechanism [40] -the bridge for text and image modules in T2I models [1, [23] [24] [25] . In this analysis, we visualize the cross-attention maps to investigate potential anomalies caused by distorted tokens in the denoising process. Specifically, we compare cross-attention maps of ITI-GEN prompt against HP with minimal linguistic ambiguity (as reference). To allow fair token-to-token comparison, in this experiment, we lengthen HP by including additional tokens containing synonyms of the tSA. Note that this did not augment HP's behavior, and similar results are seen in the original HP. See Supp for more details.Visualizing Cross-attention Maps. We follow DAAM [41] for visualizing cross-attention maps by tracing attention scores in the cross-attention module to demonstrate how an input token within a prompt influences parts of the generated image. Specifically, to visualize the cross-attention map of a token, DAAM interpolates and accumulates the attention scores over all scales (layer of the U-Net [42] as the denoising network [1] ), and all denoising steps. However, we tailor DAAM to the requirements of our fine-grained analysis by introducing further controls. First, we isolate the attention maps for each denoising step to allow for both step-wise and multi-step analysis. Second, we introduce a prompt-switching mechanism, allowing for the interchangeable tracing of different prompts at any particular denoising step. "hi gh " "ch eek " "bo nes " S0 S1 S2"of" Amplitude "a" tSA tokensInput "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output Input "A" "he ads ho t" "of " "a" "pe rso n" Output Input "A" "of " "a" "pe rso n" Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "p e rs o n " "he ads ho t" Figure 4 : Analyzing the accumulated cross-attention maps for the denoising process in our proposed prompt switching analysis I2H and H2I. Here, we use two tSAs: Smiling, High Cheekbones. For each tSA, we show the accumulated cross-attention maps for H2I and I2H, with some quantitative results. In the H2I (I2H) experiment, the first row shows the accumulated crossattention maps during the early denoising steps with HP (ITI-GEN) as the input prompt, and the second row shows the maps during later steps, after switching to ITI-GEN (HP). Observation 1: Learned tokens in ITI-GEN affect early denoising steps, degrading global structure synthesis; such degraded global structure disrupts the final output. This is observed in I2H. Observation 2: Learned tokens in ITI-GEN works decently in the later stage of the denoising process if the global structure is synthesized properly. This is observed in H2I. As we show in Supp, similar observations can be made for other samples and other tSAs. Bottom: Histograms of our proposed metrics on cross-attention maps demonstrate the abnormalities in many samples.Abnormalities in the Presence of Distorted Tokens. To investigate potential anomalies arising from distorted tokens learned by ITI-GEN, we comprehensively analyze cross-attention maps of all denoising time steps on 500 generated samples per category of each tSA, for both ITI-GEN and HP. Fig. 3 shows one of these cross-attention maps comparing ITI-GEN and HP for tSA=Smiling (more examples in Supp). Our empirical investigation reveals four points: i) global structure is synthesized in the early steps of the denoising process aligning with previous works [21] that the denoising process progressively synthesizes the image. ii) Learned ITI-GEN tokens have abnormal attention compared to the tSA-related tokens in HP ("Smiling" in col. 7 of HP), e.g., M [S i ] contain unrelated or scatter activation (Issue 1). iii) In the presence of the ITI-GEN tokens, other non-tSA tokens (like M ["a"] and M ["of"]) are abnormally more active (Issue 2). We remark that tokens interact with each other in the denoising steps. iv) Considering Issues 1 & 2, we observe that degraded global structure is synthesized in the early steps of denoising, and eventually a degraded sample is generated at the end of the denoising. Note that similar issues occur with many other samples and tSAs (details in Supp).To further understand issues, we isolate effect of distorted tokens by proposing two analyses focusing on different denoising steps. These two analyses dissect the influence of distorted tokens in key denoising steps (Recall denoising of a single step t is denoted byEQUATIONTo do this, as seen in Fig. 1b (col 2), we first propose Analysis 1: switching prompt from ITI-GEN to HP (I2H) during the denoising process. This allows for a better understanding of how the global structure's degradation in early denoising steps may affect the final generated output. Specifically, as in Eq. 2, I2H first utilizes ITI-GEN prompt P in early denoising steps which potentially leads to degraded global structure. This is then followed by utilizing hard prompt F for the remaining denoising steps. Next, to investigate if ITI-GEN tokens will create the same issues in the later steps of the denoising process, we propose Analysis 2: HP to ITI-GEN (H2I). Converse to the previous experiment, we utilize F in early steps of denoising, and then switch to using P as input prompt.For each experiment, we plot and analyze the cumulative cross-attention maps for early steps (0 to n -1) and later steps (n to l) separately. Fig. 4 shows an example of the cross-attention maps for these two experiments with tSA={Smiling, High Cheekbones}. See Supp for more samples and details. Considering results in Fig. 4 the following observations can be made:Observation 1: Learned tokens in ITI-GEN affect the early steps of the denoising process leading to degradation in synthesizing global structure. More specifically comparing the first row of the cross-attention map between I2H and H2I in Fig. 4 , we can have the following observations: i) ITI-GEN tokens have more scattered attention or attending to unrelated regions compared to tSA-related tokens in HP; ii) non-tSA tokens like "a", and "of" are more active in the presence of the ITI-GEN tokens. These two abnormalities result in degraded global structure in the early steps. In addition, considering the second row of the I2H in Fig. 4 , the degraded global structure in the early steps leads to disrupted final output even though the (non-distorted) HP prompt is used in later steps.Observation 2: Learned tokens in ITI-GEN works decently in the later steps of the denoising process if the global structure is synthesized properly. More specifically, considering H2I in Fig. 4 , when HP prompts synthesize proper global structure in early steps, the ITI-GEN tokens attend to proper regions and contribute to adding the finer details related to tSA, as shown in the second row of H2I.Quantitative Metrics for Cross-attention Maps. In addition to the visual demonstration, we propose two metrics to support further our observed abnormalities of ITI-GEN tokens in the early steps of denoising for a large number of generated samples. Specifically, for each generated sample: i) To quantify abnormally active attention associated with non-tSA tokens, we compute the expectation of attention amplitude: E (x,y) {M [J ]}, where J is a non-tSA token such as "of". ii) We analyze the scatter in attention by measuring the second central moment [43] for each tSA token K:EQUATIONHere,M [K] = (M [K]/ x,y M [K]), and (x, ȳ) is the centroid. The two metrics are computed on the accumulated cross-attention maps from stage 1 of I2H (for ITI-GEN) and H2I (for HP). The histograms of these two metrics for 500 generated samples in Fig. 1b (col 3) and Fig. 4 demonstrate Issues 1 & 2 in many generated samples.Remark. Our thorough analysis in this section shows that distorted tokens learned by ITI-GEN only have destructive performance in the early steps of denoising, and they generally have decent performance in later steps when the global structure is formed properly (H2I). We remark that even though H2I has decent performance in fair and high-quality T2I generation, it is only applicable to tSA with minimal linguistic ambiguity. In the next section, we will discuss our proposed method to address fair and high-quality T2I generation encompassing both ambiguous and unambiguous tSA.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA] We don't have theoretical assumptions and proofs.Justification: [NA] Guidelines:• The answer NA means that the paper does not include theoretical results.• All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced. • All assumptions should be clearly stated or referenced in the statement of any theorems.• The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. • Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced. 
proposed method: 
 In this section, we present our proposed method, FairQueue a new generation framework consisting of two additions: Prompt Queuing and Attention Amplification to improve the sample quality when implementing fair T2I generation. In addition to quality improvements, FairQueue also allows for better semantic preservation of the original sample generated from the base prompt T .Prompt Queuing. Recall that when utilizing ITI-GEN prompt P -which is tuned to generate samples containing the tSA-degraded global structure occurs in early denoising steps for a moderate number of samples. Conversely, utilizing HP with minimal linguistic ambiguity enables high-quality and fair T2I generation. However, as such HPs are not available for all tSAs [16] , we naturally consider the next best available option-the base prompt T (a natural language prompt without the distorted trainable tokens)-and propose prompt queuing. Specifically, as seen in Fig. 1(c ) , prompt queuing first utilizes T in the early n denoising steps, thereby allowing for the global structures to form properly. Next, we transit to ITI-GEN prompt P for the remaining (l -n) steps. This allows the more fine-grained tSA semantics to be developed on top of the already well-defined global structures.Attention Amplification. By implementing prompt queuing, the output samples may experience a reduction in tSA expression due to the reduced exposure to the ITI-GEN prompt P . To address this, we propose Attention Amplification, an intuitive solution that emphasizes the expression of the tSA by scaling the ITI-GEN token's cross-attention maps, i.e., c * M [S i ] where c > 1.Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [Yes] In addition to the basic experimental details provided in the main paper, the detailed setup for enabling reproducibility is discussed in Sec. B. In addition, to facilitate reproducibility we have also provided the anonymous link for the code used in this paper.Justification: [Yes] Please check Supp for the code, and Sec. B for the details of the experimental setup.Guidelines:• The answer NA means that the paper does not include experiments.• If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not. • If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable. • Depending on the contribution, reproducibility can be accomplished in various ways.For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed. • While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. , with an open-source dataset or instructions for how to construct the dataset). (d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 
experiments: 
 In this section, we evaluate our proposed (FairQueue) against the existing SOTA ITI-GEN [16] over various tSA. Then, we conduct an ablation study by first evaluating the contribution brought by each Table 1 : Evaluating Proposed FairQueue against ITI-GEN .We utilize FD: Fairness Discrepancy (↓), TA: Text-Alignment (↑), FID (↓), and DS: DreamSim (↓) to determine the fairness, quality, and semantic preservation, respectively. For FD a combination of CLIP [20] , off-the-shelf classifier [44, 45] and human evaluator were utilized as tSA classifier. For TA we utilize CLIP [20] as the feature extractor. Overall, our proposed method demonstrates the best ability to balance between sample quality and fairness, while preserving the semantics of the original base-prompt T .Single tSA (CelebA)tSA FD (↓) TA (↑) FID (↓) DS (↓)Gender ITI-GEN 6.41e -3 ± 4.2e -3 0.655 ± 1.2e -2 78.9 ± 1.3 0.337 ± 1.4e -2 Ours 6.41e -3 ± 3.8e -3 0.676 ± 5.2e -3 78.3 ± 1.5 0.308 ± 1.2e -2Young ITI-GEN 13.1e -3 ± 8.1e -3 0.653 ± 9.4e -3 82.9 ± 1.4 0.552 ± 3.2e -2 Ours 15.5e -3 ± 3.8e -3 0.678 ± 8.1e -3 75.3 ± 2.1 0.370 ± 2.7e -2Smiling ITI-GEN 124e -3 ± 9.2e -3 0.605 ± 1.2e -2 88.6 ± 0.9 0.557 ± 2.2e -2 Ours 69.0e -3 ± 4.2e -3 0.674 ± 1.7e -2 80.0± 1.3 0.284 ± 1.0e -2 High Cheekbones ITI-GEN 318e -3 ± 12.0e -3 0.595 ± 1.2e -3 86.40 ± 2.1 0.538 ± 1.6e -2 Ours 4.92e -3 ± 3.6e -3 0.685 ± 7.2e -3 79.7 ± 2.4 0.330 ± 2.2e -2 Pale Skin ITI-GEN 1.41e -3 ± 1.2e -3 0.646 ± 1.8e -2 101.3 ± 4.6 0.525 ± 2.8e -2 Ours 1.41e -3 ± 1.2e -3 0.666 ± 1.9e -2 97.0 ± 3.2 0.408 ± 3.0e -2 Eyeglasses ITI-GEN 14.1e -3 ± 2.6e -3 0.654 ± 3.3e -3 83.5 ± 1.4 0.486 ± 1.4e -2 Ours 25.4e -3 ± 1.9e -3 0.670 ± 6.1e -3 79.4 ± 2.3 0.391 ± 1.6e -2 Mustache ITI-GEN 26.2e -3 ± 1.8e -3 0.670 ± 4.2e -3 85.0 ± 3.3 0.452 ± 1.9e -3 Ours 22.6e -3 ± 1.2e -3 0.680 ± 5.3e -3 80.2 ± 3.0 0.345 ± 3.1e -3 Chubby ITI-GEN 112e -3 ± 8.8e -3 0.647 ± 2.2e -3 79.2 ± 1.5 0.551 ± 3.6e -3 Ours 119e -3 ± 7.2e -3 0.675 ± 2.3e -3 78.3 ± 1.4 0.387 ± 3.0e -3 Gray Hair ITI-GEN 286e -3 ± 6.8e -3 0.640 ± 4.3e -3 87.3 ± 2.1 0.533 ± 2.9e -3 Ours 266e -3 ± 7.1e -3 0.669 ± 3.7e -3 82.2 ± 2.3 0.417 ± 3.1e -3 Multi tSA (CelebA) Gender × Young ITI-GEN 39.1e -3 ± 1.2e -3 0.668 ± 7.1e -3 72.6 ± 3.1 0.458 ± 7.8e -3 Ours 12.4e -3 ± 2.3e -3 0.686 ± 5.7e -3 71.7 ± 2.5 0.373 ± 4.4e -3 Gender × Young × Eyeglasses ITI-GEN 257e -3 ± 8.7e -3 0.654 ± 3.3e -3 65.2 ± 1.6 0.475 ± 1.1e -3 Ours 208e -3 ± 7.3e -3 0.671 ± 4.1e -3 61.5 ± 2.7 0.360 ± 6.3e -3 Gender × Young × Eyeglasses × Smiling ITI-GEN 190e -3 ± 1.7e -2 0.643 ± 7.7e -3 65.5 ± 2.7 0.475 ± 9.1e -3 Ours 168e -3 ± 1.0e -2 0.661 ± 2.4e -3 60.8 ± 1.1 0.379 ± 9.7e -3 Multi tSA (Fairface & Fair Benchmark) Gender × Age ITI-GEN 142e -3 ± 4.2e -3 0.659 ± 7.2e -3 58.24 ± 3.4 0.445 ± 1.2e -3 Ours 108e -3 ± 4.3e -3 0.672 ± 1.1e -3 58.81 ± 3.3 0.359 ± 3.5e -3Gender × Skin Tone ITI-GEN 166e -3 ± 3.7e -3 0.670 ± 2.2e -3 59.56 ± 3.6 0.463 ± 7.7e -3 Ours 116e -3 ± 4.4e -3 0.686 ± 2.3e -3 54.66 ± 2.7 0.390 ± 1.8e -3 component for FairQueue i.e., Prompt queuing, and Attention Scaling. Then we revisit the task initially proposed by ITI-GEN : Training Once-for-All Token. Overall, we show that FairQueue achieves new SOTA performance.Experimental Setup. Following [16] , we utilize the publicly available reference dataset from CelebA [29] , FairFace [45] and FAIR benchmark [44] . For CelebA, we perform both single tSA and multi-tSA experiments. Note that in this dataset each tSA has two categories. In FairFace and FAIR datasets, tSAs have more categories, e.g., Age and Skin tones contain 9 and 6 classes, respectively. Therefore, fair generation is more challenging in these datasets. For these experiments we use T =E t ("A headshot of a person") as base prompt, and for a fair comparison, we utilize exactly the same learned P from ITI-GEN's original code [46] for both ITI-GEN and proposed FairQueue. In addition, we randomly sample a set of 500 latent codes and use the same latent codes for both approaches. As earlier discussed in Sec. 3, we utilize fairness discrepancy (FD) to evaluate fairness, Text-Alignment (TA) and FID for quality, and DreamSim (DS) to measure semantic preservation. See Supp for more details. We repeat this process 5 times and report the mean and standard deviation.Our results in Tab. 1 demonstrate that FairQueue is able to match ITI-GEN fairness performance closely, and in some cases even improve upon it. For example, for tSA=Smiling, FairQueue indicates a significantly lower bias (FD=6.9e -3 ) than ITI-GEN (FD=124e -3 ). In addition, considering sample quality, FairQueue achieves an overall better performance than ITI-GEN for all datasets. For example, in CelebA, FairQueue's TA≥ 0.666 while ITI-GEN's TA≤ 0.655, with the worst performance with High Cheekbones (TA=0.595). These results are similarly reflected in FID. We remark that this quality degradation largely contributes to ITI-GEN fairness degradation. Finally, when considering semantic preservation (DS ↓) of the original sample generated with T , FairQueue achieves unparalleled performance by ITI-GEN.Ablation study: evaluating prompt queuing and attention scaling. To evaluate the contribution brought by FairQueue , we consider the same setup as Sec. 5 in the main manuscript focusing on the tSA Smiling. Here, we compare the performance when utilizing different attention amplification scaling factors, c, and different prompt queuing transition points i.e., switching from T to P . Specifically, we consider gradual increments in c ∈ [0, 12] and shifting of the transition points, step ∈ {0, 0.1l, 0.2l, 0.3l} when l = 50.Our results in Fig. 5 illustrate that generally when c increases, fairness improves. However, a saturation point (c = 10) exists where quality and semantic preservation beyond this point degrades. Then when considering different transition points, we find that at step 0.2l FairQueue achieves the best quality and semantic preservation performance while still achieving good fairness measurements. However, increasing beyond this point results in significant fairness degradation. Ablation study: revisiting training once-for-all token. Utilizing FairQueue we follow [16] and re-visit adapting pre-trained ITI-GEN tokens, S i to a new Base Prompt T ′ = E t ("A headshot of a doctor") by pre-pending. Then we generate samples utilizing both FairQueue and ITI-GEN with the same noise input. As seen in Fig. 6 FairQueue demonstrates better performance than ITI-GEN , achieving both better quality and semantic preservation of the sample generated by T ′ while still having good tSA representation-more illustration in Supp. Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] The anonymous link to the code is provided in the Supp. In addition, all datasets used in this paper are publicly available. The link for datasets is also added in the readme file of the uploaded code.Justification: [Yes] Please refer to Supp. Guidelines:• The answer NA means that paper does not include experiments requiring code.• Please see the NeurIPS code and data submission guidelines (https://nips.cc/ public/guides/CodeSubmissionPolicy) for more details. • While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
conclusion: 
 In this paper, we reveal quality degradation in ITI-GEN -the existing SOTA fair T2I prompt learning approach. Our analysis reveals that this quality degradation is due to the distorted learned tokens in ITI-GEN prompt impacting cross-attention in the early steps of the denoising (reverse diffusion) process. To address this, we propose FairQueue a simple but effective solution consisting of: Prompt Queuing and Attention Amplification. Overall, our extensive experimentation demonstrates FairQueue achieves new SOTA performance in balancing quality, fairness, and semantic preservation. Limitation, related work and additional experiments can be found in the Supp. 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] We use the publicly available stable diffusion as our T2I model in our research, and considering that this model already has the safe-checker to prevent generating NSFW content, we do not add any additional safeguards in our research.Justification: [NA] Please read our answer above. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] All dataset, codes and other assets used in this paper are cited properly throughout the whole paper.Justification: [Yes] Please check the citations. Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. 
 
 Appendix: A.1 Identifying FairQueue as the optimal combination In this section, we discuss in more detail how we identified FairQueue -with its two mechanisms: Attention Amplification and Prompt Queuing -as the best-performing solution. Specifically, we summarize our findings, discussed throughout the paper, when exhaustively considering all possible combinations. Our results are as follows:• C1: Base Prompt T Only (no AA no PQ): It lacks tSA-specific knowledge and results in poor fairness. Additionally, without tSA tokens S, AA is not applicable for C3.• C2: ITI-Gen prompt P Only, in Tab. 1 (no AA no PQ). Our analysis in Sec 3.2 shows it has poor quality due to distortion in global structure during sample generation. Without PQ, the issue of distorted global structure persists for some tSAs• C4: Attention Amplification (AA) Only, in Fig. 5 when PQ transition point=0. It results in poor quality since only ITI-Gen is used. We remark that utilizing only AA for ITI-Gen may deceptively improve fairness, but the generated samples have poor quality e.g., Smiling cartoons. The reason is (similar to C2): without PQ, the issue of distorted global structure persists for some tSAs.• C5: Prompt Queuing (PQ) Only, in Fig. 5 when c = 0. By replacing the distorted ITI-Gen prompt with the Base prompt in Stage 1, PQ leads to improved quality, but without AA, the fairness remains poor given reduced exposure to tSA tokens in the denoising process.• C6: FairQueue (PQ+AA), in Tab 1 Our proposed solution with optimal quality and fairness. Specifically, it combines the effects of Prompt Queuing-enabling the global structure to be properly formed resulting in good quality samples, and Attention Amplification-enhancing the tSA-specific expression for better fairness.Overall, our results in Tab. 2 reveal that FairQueue (C6) is the superior combination balancing between fairness and quality. Specifically, Prompt Queuing is necessary whereby utilizing either only ITI-GEN (C2) or only Base Prompt (C1) results in quality and fairness degradation, respectively. Furthermore, our results show that both PQ and AA are necessary to obtain high-quality samples with good fairness performance, as without PQ (C4) sample quality is poor, and without AA (C5) fairness performance is degraded.Sec. 3.2 analyzes the effect of inclusive tokens S k by comparing the accumulated cross-attention maps of individual tokens between HP and ITI-GEN . It is observed that distorted tokens learned by ITI-GEN negatively affect the development of global structure in the early steps of denoising. The destructive effect arises with abnormally high activity of non-tSA tokens (e.g., "of"), and the tSAtokens attend to unrelated regions with scattered attention. A quantitative analysis is performed over 500 sample generations for different tSAs to affirm the observations. The below details how tokenspecific accumulated cross-attention maps are obtained from the SD pipeline, discusses interaction among tokens, and presents additional representative results for tSAs Smiling, High Cheekbones, Gray Hair, and Chubby.Details of visualizing token-specific accumulated cross-attention map. Cross-attention is often used to contextualize prompt embeddings with latent representations per sample generation step.Following DAAM [41] , coordinate-aware attention scores M [S i ] are extracted from the latent diffusion network (i.e., U-Net) for the token S i at the layers where cross-attentions take place. These token-specific attention scores, each with the same spatial dimensions as the latent representation, are upscaled bicubically to the image size (512 × 512 in this case) to reveal where attention is paid per token and accumulated within the assigned step(s). The resulting 2D matrix is visualized in Fig. 3 and Fig. 4 and referred to as an "accumulated cross-attention map".Interaction among tokens. We remark that the cross-attention map of a given token is dependent on the others in the prompt. There are two channels where the effect of tokens may interact: 1) via latent representation, as it is a function of input tokens and serves as the query in the cross-attention (see Sec.2); 2) softmax operation, as a component in the attention pipeline, softmax is taken across all tokens when processing attention scores. These two effects become increasingly apparent as we move through different cross-attention layers of the U-Net and perform more denoising steps.HP vs. ITI-GEN : qualitative analysis. To investigate potential abnormality of ITI-GEN embeddings, images of different tSA are generated conditioning on HP (F ) and ITI-GEN (P ) respectively. The cross-attention map is employed as a tool to explore the cause of degraded generations. In pursuit of a fair token-to-token comparison, for some tSAs the original HPs ("HP1", see Tab. 6) are extended to align with P in the number of tSA tokens ("HP2"). Nonetheless, as one can find in the samples in Fig. 7 to 14, the extension does not change the behavior of HPs significantly.Fig. 7 to 17 give an overview of cross-attention maps during the denoising process. One may find that the tSA tokens in the HP(s) tend to concentrate on the region(s) semantically associated with the tSA, e.g, mouth for tSA Smiling, cheek for tSA High Cheekbones, and hair for tSA Gray Hair.On the other hand, ITI-GEN tSA tokens' activity tends to be less focused and attends broadly. With more steps than Fig. 3 , it is clearer that the global structure of the images is synthesized in the early steps, which motivates the prompt switching experiments.Prompt switching experiments and quantitative analysis. To further investigate HP and ITI-GEN prompts' behaviors in the early steps, the prompt switching experiments (i.e., I2H and H2I) are proposed in Sec. 3.2. Fig. 18 to 21 present representative outcomes of the experiments. One can find that the destructive effect caused by ITI-GEN prompts only occurs at the early steps, i.e., Stage 1 in the figures.In addition, the activation patterns are more clear in the accumulated cross-attention maps. The non-tSA tokens in ITI-GEN prompts are in general more active, and the tSA tokens tend to attend more broadly, which may explain the drastic semantic deviations from HPs in Fig. 7 to 17. The latter observation is particularly evident for tSA Smiling, a highly localized facial expression, which is supported by the histogram of central moments in Fig. 1 . The other tSAs, though may not be directly associated with a specific facial feature, share the same trend, as manifested statistically by the histograms in Fig. 22 and Fig. 23 .Utilizing Base Prompt (T ) in FairQueue . In Prompt Queuing the use of T , in place of the HP, is similarly grounded on the I2H/H2I analysis, as both T and HP are natural language prompts -free of learned tokens. This can be seen in the embedding analysis in Supp B.6 where the HP and T are seen to be close to one another. As a result, the sample generated by T is expected to be of similar quality as the HP. "A" "headshot" "of" "a" "person" "beaming" "grinning"Output "with" "A" "headshot" "of" "a" "person" "smiling" Step In Fig. 24 , we provide further visualizations of T 's effectiveness in generating the global structure in early denoising steps. Specifically, we compare the cross-attention maps of FairQueue with ITI-Gen during sample generation, together with quantitative analysis. Results in col 2 vs 3 illustrate T 's effectiveness in synthesizing the global structure in stage 1, and non-abnormal attention (in Fig. 25 ), resulting in effective global synthesis than ITI-Gen and better sample quality. "of" "a" "person" "beaming" "grinning"Output "with" "A" "headshot" "of" "a" "person" "smiling" Step "of" "a" "person" "beaming" "grinning"Output "with" "A" "headshot" "of" "a" "person" "smiling" Step "A" "headshot" "of" "a" "person" "high" Step "cheek" "bones" "high" "cheek" "bones" "A" "headshot" "of" "a" "person" "high" Step "cheek" "bones" "high" "cheek" "bones" "A" "headshot" "of" "a" "person" "high" Step "cheek" "bones" "high" "cheek" "bones" "of" "a" "person" "fleshy" "obese"Output "chubby" "A" "headshot" "of" "a" "person" Step "of" "a" "person" "fleshy" "obese"Output "chubby" "A" "headshot" "of" "a" "person" Step "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" "s m il in g " "b e a m in g " "g ri n n in g " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "s m ili ng " "b ea m in g" "g ri nn in g" "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "h ig h" "c he ek " "b on es " "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "fl es hy " "o be se " "c hu bb y" "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "p er so n" "w it h " "g ra y " "h a ir " S 0 S 1 S 2Input "A " "h ea ds ho t" "o f" "a " "p er so n" Output Input "A " "h ea ds ho t" "o f" "a " Output "g ra y" "h ai r" "w ith " "p er so n" "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " "w it h " "g ra y " "h a ir " "g ra y" "h ai r" "w ith " Central moment tSA = Gray Hair "of" "a" tSA tokens Amplitude "of" "a" Smiling High CheekbonesIn this section, we carried out a human user study to evaluate the sample quality and fairness of the generated sample from FairQueue against ITI-GEN . Specifically, we utilize the same seed to generate 100 sample pairs with ITI-Gen and FairQueue for { Smiling, High Cheekbones, Gender, and Young }. Then, utilizing Amazon Mechanical Turk we conduct 2 tasks:• Quality comparison by A/B testing: Human labelers select the better quality sample between ITI-Gen and FairQueue (from the same seed). Each sample was given to 3 labelers. • Fairness comparison by human-recognized tSA: labelers identified the tSA class for each sample. The final label was based on the majority of 3 labelers. labelers were also given an "unidentifiable" option if the class could not be determined. Finally, the labels were used to measure FD.Our results in Tab. 3 reveal that FairQueue generates better quality samples than ITI-Gen (>62.0% preference) and Tab. 4 shows that FairQueue achieves competitive fairness with ITI-Gen. Overall, this aligns with our quantitative results in Tab. 1. In this section, we provide more samples generated by FairQueue based on the setup in Sec. 5. Recall that here we utilize the base prompt T = E T "A headshot of a person" and consider the tSA∈ {Male, Young, Smiling, Low Cheekbones, Pale Skin, Eyeglasses, Mustache}. Each sample is then generated based on the same 10 fixed noise inputs. In this section, we provide an experiment to search for tSA with minimal linguistic ambiguity.To do this, we utilize the tSA and their respective HPs in Tab. 6. Then, with these HPs, we generate 500 samples per category of the individual tSA. Finally, we classify the respective samples and evaluate the accuracy of the T2I model in generating samples with the respective categories of the tSA. Our results in Tab. 5 report the accuracy of the T2I generative model in accurately interpreting the HPs to generate the respective tSAs. Based on our results, we can determine that the tSA∈ {Male, Young, Smiling, High Cheekbones} are with minimal linguistic ambiguity, as their HPs can be easily interpreted by the T2I generator as seen by the high accuracy. In this section, we provide more details on the directional loss, L dir utilized by ITI-GEN . Recall that in ITI-GEN the direction loss takes in two components: 1) direction of Image embeddings, ∆I, and 2) direction of ITI-GEN token embedding, ∆P . For ease of discussion, we utilize a binary tSA, but the same concept can be simply scaled to multi-class tSA.Specifically, we first measure ∆I where ITI-GEN first evaluates the mean image embedding for each category of the tSA i.e., α= E k [E I (x k )], where E I is a CLIP Image encoder [20] and x k are the reference image for k samples in a given mini-batch. Then, we calculate the directional Image embeddings: ∆I i,j = α i -α j where i and j are the different categories of the selected tSA. Then when considering direction of ITI-GEN token embedding we simply calculate ∆P = E T (P i ) -E T (P j ), where E T is the CLIP text encoder.Ambiguities arise because of the potentially multiple interpretations of the same utterance. Ambiguity is a well-known concept in Large Language Models (LLMs), and recently there have been some attempts to understand the impact of these ambiguities in the intersection of the LLM and image generation models, a.k.a T2I models. Among different types of ambiguity, three major types can affect the quality of the T2I generation [47] :• Syntax: where there could be different interpretations of the same text. As an example, in input prompt "the mouse looks at the cat standing on rug", it is not clear whether the mouse is standing on the rug or the cat. • Semantics: where the words within a text have multiple meanings. As an example, "a photo of a bat", it is not clear whether it refers to a nocturnal flying mammal or the flat wooden club used in sports like baseball or cricket (with 'cricket' itself having two different meanings ;)). • underspecification where the used text prompt can not completely describe the required attributes in the image. For example, "a woman with light hair" can not specifically determine the category of the hair color.Within the context of this paper, ambiguity is generally used with the Hard Prompts (HP) where we append the description of a specific tSA to the base prompt. For example, considering the base prompt "a headshot of a person", and considering the tSA=Smiling, one can create the HP as "a headshot of a person smiling", and "a headshot of a person not smiling". In our paper, we empirically realize that for some tSA, creating HP like this will result in a strong baseline for fair T2I generation, observed by our quality and fairness metrics. We call these, tSAs with minimum linguistic ambiguity, and as an example, we empirically found that tSAs like Young, Smiling, Gender fall within this category. Due to their superior performance, HP with these tSAs is used as our pseudo-gold standard in the analysis of the quality degradation issue in ITI-GEN is Sec. 3. However, other tSAs suffer ambiguity in either finding proper terms to define them or inferior performance in terms of the quality or fairness of the generation. This enforces using more advanced techniques like prompt learning to overcome these ambiguities.Models and training hyper-parameters. In our experiments, we follow the same setup as ITI-GEN [16] and utilize Stable Diffusion v1.4 [1] as our T2I generator. Then when implementing ITI-GEN we utilize an S i with a token length of 3 per tSA which is optimized based on a learning rate of lr = 0.01. For reference datasets, we utilize [16] readily available datasets with contain 200 reference images per category of each tSA. An Adam [48] optimizer is utilized during prompt learning. For sample generation, we follow the recommended diffusion steps of l = 50 and utilize an Attention scale of c = 10 and an Attention Queuing transitioning step =10.Hard Prompt. Tab. 6 illustrates the list of HPs utilized in our experiments. We remark that not all tSAs have a clear HP. For example, with the tSA=Skin Colour it is difficult to find a reasonable HP to describe an individual with a specific skin tone. Male "A headshot of a person Male" "A headshot of a person Female" Young "A headshot of a person Young" "A headshot of a person Old" Smiling "A headshot of a person with Smiling" "A headshot of a person with no/without Smiling" High Cheekbones "A headshot of a person with high cheekbones" "A headshot of a person with low cheekbones" Pale Skin "A headshot of a person with pale skin" "A headshot of a person with no/without pale skin " Eyeglasses "A headshot of a person with eyeglasses" "A headshot of a person with no/without eyeglasses" Mustache "A headshot of a person with mustache" "A headshot of a person without mustache" Gray Hair "A headshot of a person with gray hair" "A headshot of a person without gray hair" Chubby "A headshot of a person chubby" "A headshot of a person no chubby"Stable Diffusion Version. We verified that the problems of poor performance with Hard Prompts as indicated by Zhang et al. [16] would still persist even in more recent versions of Stable Diffusion e.g., SD 3.0. This issue can be observed by simply inputting the prompt "A headshot of a person without glasses" to SD 3.0 where the generated samples still frequently have the wrong tSA class ("with glasses") indicating this ambiguity still exists. For example, when utilizing the same setup as Tab 1 with HP (from Tab. 6) and SD3.0, our results show poor fairness performance for Eyeglasses (FD=670e -3 ) and Pale Skin (FD=580e -3 ).Increasing the size of the reference dataset (2k reference images per category per tSA). We verified that increasing the size of the reference dataset does not resolve ITI-GEN's quality degradation. Specifically, we repeated the experiment in Tab. 1 with tSA Smiling with 2k reference samples per class. Our results measured an FD=127e -3 , TA=0.591, FID=89.2, and DS=0.532 which is similar to the results in Tab. 1 (based on 200 reference images). This indicates that the core problem may not be the data size, and may need specific data curation (including sample pairs with only semantic differences in tSA, and similar semantics elsewhere), which poses scalability and applicability challenges.Tab. 7 illustrates the amount of the compute including the GPU Hours for different steps of our research and the estimated carbon emission for our experiments. In this section, we provide more detail on the evaluation metrics used in our work.Fairness. We use the fairness discrepancy (FD) metric which compares tSA distribution in generated images with an ideal uniform distribution [27] . In this metric FD=0 would indicate perfect fairness. Following [16, 11, 36] we use a combination of CLIP [20] models, human evaluators, and off-theshelf models 3 for predicting the distribution of the tSA in target images. We remark that to evaluate FD we utilize classifiers that achieve reasonably high accuracy when evaluated on CelebA [29] reference dataset, as seen in Tab. 8. Quality. Text alignment (TA) [37, 22] and Fréchet Inception Distance (FID) [38] are utilized as quality metrics. Specifically, Text-alignment is based on the notation that a sample generated based on ITI-GEN prompt (P ) or HP (F ) for a given tSA, should retain the semantics of the original base prompt T , unless the sample has degraded in quality. For example, samples generated based on F = E T ("A headshot of a person young") should still retain the semantic of T = E T ("A headshot of a person"). Therefore, to evaluate quality, we compare the generated images (based on the P or F ) with base prompt T using text-image cosine similarity in CLIP's feature space [37, 22] . FID [38] then compares the feature statistics of the generated samples against a reference fair FFHQ [49] dataset from the CleanFID [50] library.Semantic Preservation: When a fairness approach enforces fairness w.r.t. a tSA, changes related to that tSA are more favorable. Considering our setup, for the same latent code Z, the generated images with base prompt and fairness scheme are more favorable to be different only in features related to tSA, and similar in other features. To measure this behavior, for each input latent code Z, the generated image by querying the T2I model with base prompt T is considered as the reference image. Then, for the same latent code Z, we measure the similarity of the generated image by querying the T2I with HP and ITI-GEN prompts with that reference image using DreamSim [39] features. DreamSim improves upon existing semantic measurement by considering both low-level features, mid-level, and high-level features for a more holistic semantic measurement.In addition to the direction loss values provided in the main paper to show that ∆P is not aligned well with ∆T as pseudo-gold standard direction-which increases the possibility of encoding unrelated knowledge and leading to distorted learned tokens. Here, we provide an additional visualization of the embedding in Fig. 43 to show the difference between the learned embedding in ITI-GEN and the embeddings of the base prompt and hard prompt. As one can see, even though the embeddings of the base prompt and three different variants of the hard prompt are clustered well, the embeddings of the learned tokens in ITI-GEN are quite far from this cluster increasing the possibility of the encoded unrelated concepts and degrading the generation quality. One may argue that using a regularizer can push these embeddings towards this cluster. However, our experimental results suggested that this will result in a compensated performance in terms of the fair T2I generation. In this section, we discuss some limitations regarding our work as well as some potential societal impacts that it may have.Limitations. Firstly, FairQueue work follows ITI-GEN which utilizes a reference dataset to first optimize a ITI-GEN prompt P . This setup requires dozens of reference images for each category of the tSA which may not always be readily available. Second, although FairQueue provides better quality and semantic preservation of the original base prompt sample, its sample generation is still prone to experiencing entanglement in certain tSA. Specifically, entanglement in tSA could result in some unwanted augmentation of non-tSA during sample generation.Broader impact. Our work FairQueue takes a significant step towards enhancing fairness in textto-image generation. By improving the quality of samples generated through a fair text-to-image algorithm, we facilitate greater adoption of these techniques by the general public. This increased adoption can help prevent the perpetuation of unwanted biases in everyday applications, promoting a more equitable and inclusive use of technology in society.Text-to-Image Generation. There was a surge in text-to-image (T2I) models in the last years, exemplified by models like DALL-E [7, 51] , Stable Diffusion [1] , Midjourney [52] (with over 16 million active users in July 2023), and many others [53, 24, 54] . These models have demonstrated their capability to generate high-quality samples across different domains and new applications are defined around these models in different areas such as art [55] , design, and even medical imaging [56] . In contrast to the earlier generative models which primarily served research purposes within research and scientific settings, current T2I models offer much broader accessibility [57] . However, this increased accessibility also amplifies the potential consequences of bias within these generative models [27] . Our work aims to address this issue by mitigating bias and prompting fair T2I generation.Fairness in Generative Models. In generative modeling, fairness is usually defined as equal representation where all categories of a tSA are supposed to be represented with a similar probability. Different approaches are proposed to improve the fairness of the conventional generative models including weak supervision to achieve fairness with the importance weighting of a fair dataset [28] , transfer learning from a large biased dataset to a small fair dataset [30, 31] , or enforce uniform sampling from the latent feature space [58] . In the context of fair T2I generation, using the pretrained T2I models and adapting prompts for a fair generation has recently attracted a lot of attention. Bansal et al. [17] proposes the use of "ethical interventio" prompts for text-to-image generators to encourage the concept of independence w.r.t. the the sensitive attributes. Specifically, these ethical intervention prompts can be appended to the original prompt e.g., "a photo of a bride from diverse cultures" and "a photo of a person wearing a hat irrespective of their [SA]" Furthermore, the work substantiates this approach by mentioning that these neutral language prompts exist in the training data. Overall, these additional prompts have empirically been shown to produce diverse samples (a different definition of diversity w.r.t. SA) and high-quality samples (human-voted). Chuang et al. [36] proposes to project out of the biased direction of the text embeddings as a form for bias mitigation. Specifically, given a known sensitive attribute, we are able to minimize the equalization loss and find a text embedding that is of equal distance from the biased prompt's embedding. Then utilizing this optimized (debiased) text-embedding, we are able to generate samples with a fairer SA distribution. In addition, recently prompt learning has been proposed to learn the tSA knowledge from a set of reference images in ITI-GEN [16] . In this work, we address the issues that arise due to unrelated concepts being encoded in these prompts, leading to defects in the cross-attention maps and degrading the image generation quality.Prompt Learning. Prompt learning has recently been shown to be a very effective and efficient way of adapting pretrained large vision-language models to different downstream tasks like zero-shot classification [34, 33, 59 ], image editing [21] , personalization of diffusion models [22, [60] [61] [62] , and few-shot image generation [63] . In this work, we analyze the potential issue of prompt learning in the context of fair T2I generation by analyzing the cross-attention module in the presence of the learned prompts and propose a simple yet efficient approach to integrate prompt learning more efficiently for fair T2I generation.1. Claims Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? Answer: [Yes] The main contribution of this paper is the analysis of the shortcomings in prompt learning for fair T2I generation, and then proposing a new approach to address these shortcomings.Justification: [Yes] The claims are justified with both provided extensive analysis in crossattention map (in the main paper and Supp), together with experimental results. Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.https://trust.is.tue.mpg.de/; https://github.com/dchen236/FairFace; https://github. com/SonyResearch/apparent_skincolor 


Decision: This work focuses on improving fairness and quality in T2I generation via prompt learning and proposed the FairQueue that is demonstrated to be superior over the current ITI-Gen method. Reviewers are generally positive (one accept and three borderline accept) by acknowledging the contribution and its effectiveness, without obvious objections. Though it mainly targets on improving one specific existing work, extensive experiments verify the advantage of proposed methods. It points out a promising direction of how prompt learning affect the generation and the analysis on attention maps help better understand the black box of big diffusion models, which is of great value to the community. Therefore, after checking all comments, the area chair made a decision of acceptance. Some concerns including adding ablation study and necessary explanations presented in the rebuttal should be addressed in the revised version.

Reviews: review: paper_summary: paper_summary: In the field of fair text-to-image generation, the ITI-GEN paper demonstrated good performance by learning and embedding tSA. However, this paper argues that the embeddings learned in ITI-GEN can include unrelated attributes, resulting in noisy embeddings and significantly degrading the quality of the generated samples. To address this, the paper proposes using prompt queuing to apply tSA after a certain timestep and employs attention amplification to prevent the weakening of tSA.
strength_weakness: strength_weakness: First, the degradation seems severe compared to the ITI-GEN paper. If I understand correctly, ITI-GEN appears to aggregate all learned tSA embeddings by summing them, while this paper seems to concatenate a large number of tokens, as indicated by (p+q)*d. This difference in implementation may be causing a decline in baseline performance and requires an explanation.
Additionally, the proposed method to address this issue seems naive, and there is a lack of ablation experiments. Also, even with the proposed method, there appears to be a degradation in sample performance compared to HP. For example, in Figure 2, an originally female image appears to have been changed to a male image using the proposed method.
questions: questions: - Is there a difference between the original ITI-GEN and your re-implementation as I mentioned or did I just misunderstand? (I am not an expert of ITI-GEN)
- Wouldn't this problem be naturally resolved if a large number of reference images were used to learn the embeddings?
limitations: limitations: The authors describe the limitations and potential societal impact in their supplemental material.
paper_summary: paper_summary: This paper introduces an approach to improving fairness and quality in text-to-image generation models by rethinking prompt learning. The current Inclusive Text-to-Image Generation (ITI-GEN) methods often degrade image quality due to suboptimal training objectives and embedding misalignment between learned prompts and reference images.
Key innovations include Prompt Queuing, ensuring regular and fair prompt usage, and Attention Amplification, enhancing the impact of prompts on image generation. Experiments show that FairQueue improves both image quality and fairness compared to state-of-the-art methods.
strength_weakness: strength_weakness: - The readability of the paper is poor.
- The evaluation of the model is insufficient, as there are only evaluations on the datasets of human faces, which lacks in evaluating the generalizability of the model. More experiments on other datasets are needed. 
- The paper emphasizes quantitative metrics but does not give enough attention to qualitative assessments. Including user studies or expert reviews could provide valuable insights into the perceived quality and fairness of the generated images, complementing the quantitative data.
questions: questions: - I'm curious why the experiments were conducted on only one dataset. 
- Some of the metrics used in the paper are unfamiliar. Are these metrics validated, and are they commonly used in other research papers as well?
- Additionally, I would like to know how you plan to address the limitations mentioned in the limitations section.
limitations: limitations: They mentioned limitations and broader impact in the paper.
paper_summary: paper_summary: The authors propose FairQueue, a simple and effective solution to solve the quality degradation problem in ITI_GEN through prompt queuing and attention amplification.
strength_weakness: strength_weakness: Some of the contribution points of the article can be optimized.
questions: questions: 1.	In Chapter 4, the author proposed a method for attention amplification. Is the amplified area Si related to tSA? If so, please explain the specific impact relationship.
2.	Are there specific metrics to measure the relationship between generation quality and fairness?
limitations: limitations: The author can further improve the description of the method part of the paper and describe the innovation more clearly.
paper_summary: paper_summary: This paper examines the prompt learning method used in fair text-to-image (T2I) generation, highlighting its impact on image quality. The authors identify that aligning prompt embeddings with reference image embeddings introduces noise due to unrelated concepts, leading to degraded image quality. They conduct an in-depth analysis of the T2I model's denoising subnetwork, introducing novel prompt switching analyses (I2H and H2I) and new quantitative metrics for cross-attention map characterization. Their findings reveal abnormalities in the early denoising steps, which affect global structure synthesis.

To address these issues, the paper proposes two solutions: Prompt Queuing, which applies base prompts initially and ITI-GEN prompts later in the denoising process, and Attention Amplification, which balances quality and fairness. Extensive experiments demonstrate that these methods improve image quality while maintaining competitive fairness and diversity. The paper's contributions include identifying issues in the current prompt learning approach, providing a detailed analysis of the denoising process, and proposing effective solutions to enhance T2I generation quality.
strength_weakness: strength_weakness: 1. The proposed innovations, Prompt Queuing and Attention Amplification, are relatively incremental and might be seen as a combination of existing techniques rather than a breakthrough innovation. The combination of these two mechanisms does improve performance but lacks a strong theoretical underpinning to justify why this specific combination is superior.
2. The proposed method, FairQueue, while innovative, could benefit from a more detailed explanation of how it addresses the issues identified in the paper. In the current form, the explanation of Prompt Queuing lacks depth regarding why the base prompt T is more effective in the early denoising steps. The authors should delve deeper into the reasons behind this, possibly by providing theoretical analysis and specific experimental results that demonstrate how the base prompt gradually establishes the fundamental elements of the image during the denoising process. 
3. Additionally, the transition to ITI-GEN prompts in the later stages should be better explained, particularly in terms of how it enhances the detailed expression of tSAs. Comparative illustrations of image generation results at different stages using different prompts would effectively show the benefits of this transition.
4. The mechanism of Attention Amplification needs a clearer explanation. The authors should detail why scaling the cross-attention maps enhances tSA expression, possibly through mathematical derivations or experimental data showing the impact of different scaling factors on image quality and tSA expression. 
5. Although the experimental results are comprehensive, the paper lacks detailed descriptions of the experimental settings and specific parameters used, which could hinder reproducibility.
questions: questions: 1. The authors provide a more detailed explanation or evidence on why the base prompt T is more effective in forming global structures during the early denoising steps compared to ITI-GEN prompts. Including theoretical justifications or experimental comparisons that illustrate how the base prompt helps establish the foundational elements of the image more effectively would be beneficial. Specific examples or visualizations from your experiments could clarify this point.
2. How does switching to ITI-GEN prompts in the later stages of denoising improve the detailed expression of tSAs without compromising the overall image quality?
3. The author should elaborate on the mechanism by which attention amplification enhances tSA expression. How is the scaling factor chosen and validated?
4. The paper would be strengthened by more detailed experiments and results analysis, especially comparisons between using only the base prompt, only ITI-GEN prompts, and the FairQueue method. A thorough analysis of these results would help demonstrate how FairQueue effectively addresses the issues identified with the original method. By incorporating these detailed explanations and analyses, the paper would more convincingly show how FairQueue resolves the problems it aims to address, enhancing its credibility and scientific rigor.
limitations: limitations: 1. Although the authors have identified and analyzed the issues with the current methods, the explanation of how their proposed method, FairQueue, effectively addresses these issues lacks detail. Regarding the effectiveness of Prompt Queuing, the authors should provide a deeper explanation of why the base prompt is more effective in the early denoising steps. This could be supported by theoretical analysis and specific experimental results, such as demonstrating how the base prompt gradually establishes the fundamental elements of the image during the denoising process. Further explanation of how using the base prompt in the early steps helps form a robust global structure, allowing the application of ITI-GEN prompts in later steps to better add details without compromising the overall structure, would be beneficial.
2. For the mechanism of Attention Amplification, the authors need to elaborate on why scaling the cross-attention maps effectively enhances tSA expression. This section could include mathematical derivations or experimental data to show how different scaling factors impact image quality and tSA expression. Providing specific examples of how attention amplification compensates for the reduced tSA expression due to prompt queuing in various scenarios would strengthen the argument.
3. The authors should include more experimental results, particularly comparisons between using only the base prompt, only ITI-GEN prompts, and the FairQueue method. Detailed analysis of these results would help demonstrate how FairQueue addresses the issues identified with the original method, such as image quality degradation and insufficient tSA expression. Providing specific quantitative metrics and visual comparisons can more intuitively show the advantages of the proposed method.
4. The paper lacks a thorough analysis of the computational complexity and the necessary resources for implementing FairQueue. Understanding the computational demands is essential for assessing how feasible and scalable the method is in real-world applications. Without this information, it is difficult to determine whether FairQueue can be efficiently deployed in various environments, particularly those with limited computational resources.

Comments: title: Appreciation for the Discussion
Updating Reviewers on All Addition Experiments in the Rebuttal
Clarifying FairQueue’s as the Best Performing Combination
Response for authors
We appreciate the Reviewer Raising our rating and for the kind words
We appreciate the Reviewer Raising our rating and for the kind words
Addressing Question on Research Gap and Reproducibility
comment: As we will conclude the Rebuttal’s discussion session in 5 hours, we sincerely thank the Reviewer 9buQ for their valuable insights and thoughtful discussion. We hope that our efforts to address the Reviewer’s comments have been satisfactory. 


*If the Reviewer finds our responses appropriate, we kindly ask for a re-consideration of our scores based on the new additional understanding of our work.*
Once again, we thank all the Reviewers for their insightful comments and for those who considered raising their scores after further clarifications.

To help keep all Reviewers updated, in the following, we provide a summary list of the experiments conducted by us, as suggested by the Reviewers (which we will include in the final manuscript):

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1. **Assessing the persistence of linguistic ambiguity in a newer version of T2I generative model**: Given that newer versions of the text-to-image models (like SD 3) are using improved text encoders compared to version 1.4, this experiment aims to assess whether these newer versions have a better understanding of prompts with potential linguistic ambiguity e.g., “without eyeglasses”, and therefore whether HP can be used for all/several tSAs in these T2I generative models. Our results show that linguistic ambiguity *still exists in the latest versions* of SD. This aligns with our findings in the main paper (experimented for SD 1.4) showing that HPs are still not viable solutions to address fairness for all tSAs (FD scores for these tSAs still remain large). Therefore, enforcing fairness across all tSAs requires alternative approaches like prompt learning in our proposed FairQueue.

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2. **User study with human evaluation (Amazon-Mechanical Turk) demonstrating FairQueue’s preferred performance**: In addition to extensive qualitative and quantitative experimental results (using four well-known metrics) already provided in our paper, we have included a user study to compare the quality and fairness of ITI-Gen vs FairQueue based on human assessment. The results show that for all tSAs, the majority of human evaluators also prefer FairQueue sample quality, and the fairness of FairQueue remains competitive with ITI-Gen. These results align with our qualitative and quantitative analysis in the main paper.

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3. **Additional qualitative and quantitative analysis** on the attention maps between BasePrompt vs FairQueue vs ITI-Gen. Our analysis further demonstrates the effectiveness of FairQueue’s mechanism to first utilize the natural language, Base prompt $T$ to first form a good global structure followed by (tSA aware) learned prompt to synthesize tSA expressions (via localized attention).

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4. **Qualitative demonstration of the effectiveness of Attention Amplification (AA)**: This is a qualitative ablation study aimed to show how without AA ($c=0$), tSA expression (tSA=’Smiling’) can still be lacking in many samples. Then, with the gradual introduction of AA ($c>0$), the tSA expression is emphasized (samples slowly begin to smile) while maintaining sample quality.

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5. **Evaluation on a larger reference dataset further supports our proposed solution (2K samples per class of tSA)**: This experiment aims to assess whether the problem of encoding unrelated knowledge can be resolved by increasing the reference dataset size. Our experimental results show that the sub-optimality of the learning objective can not simply be addressed by increasing the reference dataset size, and therefore the quality degradation issue still exists using even larger datasets necessitating using our proposed FairQueue as a solution.

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Overall, all the experiments support FairQueue as an effective solution, achieving both high-quality and fair sample generation, when compared against the existing state-of-the-art.
>R4Q8: Explanation of why the specific combination of Prompt Queuing and Attention Amplification is superior to other possible combinations could be more detailed.  Deeper analysis of why this particular combination works best would strengthen your argument

We thank Reviewer 9buQ for insightful comments. Here we provide analysis of all possible combinations of Prompt Queuing (PQ) and Attention Amplification (AA). We note that all these analyses are already in the paper to derive the best combination, but we fully agree with Reviewer that it is better to have them in one place, to clearly support why our PQ+AA is superior.

Recall:

1. PQ enables proper global structure generation, leading to **improved sample quality**. 
2. AA supplements PQ by enhancing exposure to tSA tokens, leading to **state-of-the-art fairness**.

With these, we provide analysis of why PQ+AA works better than all other combinations.

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Tab i: Analysis of **all possible different combinations** for Prompt Queuing (PQ) and tSA Attention Amplification (AA). We summarize our findings from main paper for the tSA “Smiling”. Note $\alpha (S)$ notates AA for tSA tokens and results in **bold** and *italics* are the best and second best. Notice that C6:FairQueue (PQ+AA) provides the best combination: it achieves **both** outstanding sample quality (C6: TA=0.674 & FID=80.02 similar to C1: TA=0.681 & FID=76.9 with the best quality but poor fairness) and fairness (C6: FD=0.069 similar to C4: FD=0.05 with the best fairness but poor quality).



|  | Prompt Queueing (PQ) | Attention Amplification (AA) | Stage 1 Prompt | Stage 2 Prompt | FD$\downarrow$ | TA$\uparrow$ | FID$\downarrow$ | DS$\downarrow$ | Remarks |
|---|---|---|---|---|---|---|---|---|---|
| C1: **no PQ, no AA** for Base Prompt | No | No | $T$ | $T$ | 0.211 | **0.681** | **76.9** | - |  |
| C2: **no PQ, no AA** for ITI-Gen | No | No | $[T;S]$ | $[T;S]$ | 0.124 | 0.605 | 88.63 | 0.557 |  |
| C3: **AA only** for Base Prompt | No | Yes | $T$ | $T$ | N.A | N.A | N.A | N.A | An **unimplementable combination** due to the absence of tSA tokens for AA. |
| C4: **AA only** for ITI-Gen | No | Yes | $[T;S]$ | $[T;\alpha (S)]$ | **0.05** | 0.61 | 89.41 | 0.55 |  |
| C5: **PQ only** | Yes | No | $T$ | $[T;S]$ | 0.145 | *0.674* | 80.15 | **0.24** |  |
| C6: **PQ + AA** (Our specific combination) | Yes | Yes | $T$ | $[T;\alpha (S)]$ | *0.069* | *0.674* | *80.02* | *0.284* | Both PQ and AA are present i.e., our proposed FairQueue |

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With the results summarized from main paper (above), we provide deeper analyses below with additional explanations for improved clarity:

- **C1: Base Prompt $T$ Only** (no AA no PQ): It lacks tSA-specific knowledge and results in poor fairness. Additionally, without tSA tokens $S$, AA is not applicable for **C3**.

- **C2: ITI-Gen prompt $P$ Only**, in Tab.1 (no AA no PQ). Our analysis in Sec 3.2. shows it has poor quality due to distortion in global structure during sample generation. Without PQ, the issue of distorted global structure persists for some tSAs.

- **C4: Attention Amplifcation (AA) Only**, in Supp. A.2. Fig.22 when PQ transition point=0. It results in poor quality since only ITI-Gen is used. We remark that utilizing only AA for ITI-Gen may deceptively improve fairness, but the generated samples have poor quality e.g., Smiling cartoons. The reason is (similar to C2): without PQ, the issue of distorted global structure persists for some tSAs.

- **C5: Prompt Queuing (PQ) Only**, in supp. A.2 Fig 22 when $c=0$. By replacing the distorted ITI-Gen prompt with the Base prompt in Stage 1,  PQ leads to improved quality, but without AA, the fairness remains poor given reduced exposure to tSA tokens in the denoising process.

- **C6: FairQueue (PQ+AA)**, in Tab.1. Our proposed solution with optimal quality and fairness. Specifically, it combines the effects of Prompt Queuing– enabling the global structure to be properly formed resulting in good quality samples, and Attention Amplification–enhancing the tSA-specific expression for better fairness. 

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The results in Tab.i present a summary of related quantitative results:
- Comparing C4, C5, and C6 reveals that both PQ and AA are necessary to obtain high-quality samples with good fairness performance. Specifically, C4 has poor quality, while C5 has poor fairness.
- Comparing C1, C2, and C5 reveals the necessity of Prompt Queuing because utilizing either only ITI-Gen or Base Prompt results in quality and fairness degradation, respectively.

Overall, our quantitative analysis demonstrates **C6:FairQueue** is the superior combination, balancing both fairness and quality. It achieves a TA=0.674 and FID=80.02, the closest to the best quality by C1 with TA=0.681 and FID=76.9 (but poor fairness), while achieving fairness of FD=0.069, the closest to the best fairness by C4 with FD=0.05 (but poor quality).

We hope this further discussion addresses Reviewer’s concerns.
Thank you for the rebuttal. I still have one concern. The authors conducted ablation studies to validate the contribution of each component, which is well-handled. However, the explanation of why the specific combination of Prompt Queuing and Attention Amplification is superior to other possible combinations could be more detailed. A deeper analysis of why this particular combination works best would strengthen your argument.
We are very thankful for the Reviewer's increased rating and that we were able to “addressed all the concerns that I  (the reviewer) raised”

We respectfully seek the reviewer to consider adjusting their presentation and/or confidence score to reflect their new understanding, should the reviewer find it appropriate.
Thank you for the rebuttal. I think the author has addressed all the concerns that I raised. I updated my score to 5.
We are very thankful for the Reviewer's increased rating and kind words in considering our work “... valuable improvements for the community”. 

We respectfully seek the reviewer to consider adjusting their soundness/contributions score to reflect their new understanding, should the reviewer find it appropriate.
I see. Now I understand that while the ITI-GEN performance is strong across the entire dataset, there are some attributes where its results suffer from significant degradation, which this paper addresses. Thank you for clarifying my comments. I am raising my score to 5. However, I did not raise it further because I still feel this work is too dependent on the ITI-GEN method, even though it provides valuable improvements for the community.
Firstly, we really appreciate Reviewer 89En’s prompt and insightful feedback. 


>Q7: Thank you for clarifying my questions and comments. I agree with the rebuttal that a simple method could be proposed due to thorough analysis, and I believe this paper is indeed a suitable follow-up to ITI-GEN. while I still think it may be incremental because it heavily depends on ITI-GEN method.


R7: We would like to emphasize that, as one of our main contributions, we had also proposed new qualitative and quantitative analysis of the effects of learned tokens in individual denoising steps of diffusion models. The proposed method thus grounded on our novel analysis.

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>Q8: which experiment in this paper achieved similar scores under the exact same settings


R8: We thank the Reviewer for the question and clarify that our work considers a more fine-grained performance analysis than ITI-Gen which aligns better with existing SOTA literature [27,28,30,31].


**Quality evaluation**:  Reproducibility of ITI-Gen’s quality is indeed a question raised by other researchers in ITI-Gen’s GitHub. To address this, ITI-Gen authors clarified (in GitHub issues #1 “Missing Performance Metric” and #7 “The gap between FID indicators is too large”, currently closed) that the FID results in ITI-Gen’s paper Tab.2 are achievable when evaluation is performed on data of all 40 tSAs, for both real and generated images (with learned ITI-GEN prompt) gathered as one large dataset (200*40), and FID is calculated on this large dataset. We have also verified this. 

(We will include the links to the GitHub page to AC per NeurIPS guidelines.)

However, as remarked in global responses, such high-level analysis is susceptible to missing the quality degradation existing in specific tSAs (e.g., High-Cheekbone). To address this, in more fine-grain analysis, we instead focus on individual tSA and evaluate their respective quality, aligning with existing fairness literature [27,28,30,31].  With this more fine-grain analysis, we were able to see the distortion in individual learned prompts. In addition, we considered an additional Text-Alignment (TA) quality metric which confirms our findings.
Note that, Tab. 1 in the original ITI-GEN paper has only fairness comparison for specific tSAs, while FID or other quality evaluation for these tSAs is missing.


**Fairness evaluation**: As mentioned in our paper, following existing literature, we utilizes Fairness Discrepancy (FD) metric [27,28,30,31]. Specifically, FD is the same as that implemented with ITI-Gen but instead uses L2 distance in place of  KL divergence.

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>Q9: compare the scores with the original model and HP in Table 1 in the proposed paper?

R9: We provide the following results as per reviewer’s request for HP evaluation:


|  | FD | TA | FID  | DS |
|---|---|---|---|---|
| Gender | $1.4e^{−3}$ | 0.699 | 77.1 | 0.318 |
| Young | 0 | 0.674 | 76.4 | 0.392 |
| Smiling | $8.4e^{−3}$ | 0.672 | 79.8 | 0.323 |
| High C. | $3.68e^{−3}$ | 0.672 | 80.3 | 0.332 |
| Pale Skin | $591e^{−3}$ | 0.660 | 96.2 | 0.397 |
| Eyeglasses | $670e^{−3}$ | 0.670 | 79.1 | 0.468 |
| Mustache | $554e^{−3}$ | 0.674 | 81.4 | 0.372 |

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As mentioned in our paper e.g., Tab 2, many of the HPs perform poorly w.r.t. Fariness due to their tSA having linguistic ambiguity. However, their sample quality is competitive. Overall, FairQueue still achieves best performance–with competitive fairness as ITI-Gen and high-quality samples as HP.

Due to space limitations, we can provide side-by-side comparison with ITI-Gen and FairQueue in another reply if Reviewer allows us (per NeurIPS rules). (ITI-Gen and FairQueue comparison is already in main paper)

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>Q10: Wouldn’t HP perform better with more recent models that use improved text encoders, such as stable diffusion XL or version 3?  Is there a possibility to apply this method to these latest models?


R10: We clarify that we have verified that the problems indicated by the ITI-Gen paper (e.g., in Fig.1) would still persist in more recent versions of Stable Diffusion e.g., SD 3.0. This issue can be observed by simply inputting the prompt “A headshot of a person without glasses” to SD 3.0 demo and generating several samples (provided through the AC comments). The generated samples still frequently have wrong tSA class (“with glasses”) indicating this ambiguity still exists. We provide some results showing the poor fairness of HP with SD3.0. Here, we utilize the same setup as Tab.1 (main paper) with 500 samples per tSA class and HP per Tab.3 (Supp.B.3) to report FD:
 
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|  | FD |
|---|---|
| Eyeglasses | $670e^{−3}$ |
| Pale Skin | $580e^{−3}$ |

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Regarding applying ITI-Gen or FairQueue to more recent models: while in principle our ideas are model agnostics, it requires some effort to implement in the new SDXL or SD3 code base, beyond the duration of this rebuttal. 


**Once again, we thank Reviewer 89En’s prompt and insightful feedback.**
Thank you for clarifying my questions and comments. I agree with the rebuttal that a simple method could be proposed due to thorough analysis, and I believe this paper is indeed a suitable follow-up to ITI-GEN. while I still think it may be incremental because it heavily depends on ITI-GEN method.

There is one more thing I’d like to ask. While I also trust that the authors reproduced the exact results since the code is released, I ask for your understanding in confirming the results because the quality of the results in the paper seems to vary significantly. Could you clarify which experiment in this paper achieved similar scores under the exact same settings as ITI-GEN? In the ITI-GEN paper, most results (Table 1 and 2 in their paper) show better or comparable performance in FID compared to the original stable diffusion, but the reproduced results seem to fall short compared to the original model. To be clarifying this, could you also compare the scores with the original model and HP in Table 1 in the proposed paper?

Additionally, I have a concern that could be raised not only with this paper but also with the ITI-GEN paper. The poor performance of the HP method might be due to stable diffusion v1.4 not properly reflecting the prompts. Wouldn’t HP perform better with more recent models that use improved text encoders, such as stable diffusion XL or version 3? Is there a possibility to apply this method to these latest models?