Mitigating Social Biases in Text-to-Image Diffusion Models via Linguistic-Aligned Attention Guidance

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-02 14:36:08

Abstract: Recent advancements in text-to-image generative models have showcased remarkable capabilities 
  across various tasks. However, these powerful models have revealed the inherent risks of social biases related to gender, race, and their intersections. Such biases can propagate distorted real-world perspectives and spread unforeseen prejudice and discrimination.  Current debiasing methods are primarily designed for scenarios with a single individual in the image and exhibit homogenous race or gender when multiple individuals are involved, harming the diversity of social groups within the image. To address this problem, we consider the semantic consistency between text prompts and generated images in text-to-image diffusion models to identify how biases are generated. We propose a novel method to locate where the biases are based on different tokens and then mitigate them for each individual. 
  Specifically, we introduce a Linguistic-aligned Attention Guidance module consisting of Block Voting and Linguistic Alignment, to effectively locate the semantic regions related to biases. Additionally, we employ Fair Inference in these regions to generate fair attributes across arbitrary distributions while preserving the original structural and semantic information. 
  Extensive experiments and analyses demonstrate our method outperforms existing methods for debiasing with multiple individuals across various scenarios.

Main paper: 
 related work 2.1 text-to-image generation.: 
 Text-to-image generative methods [13, 34, 54] used to based on Generative Adversarial Networks (GANs) [17] . However, these methods would encounter problems such as unstable training and suboptimal generation quality. With the development of diffusion models [19, 48] , generative results with greater fidelity, higher resolution, and more diverse conditions under stable training have been achieved. Stable Diffusion [39] , trained on large-scale text-image dataset [44] , exhibits impressive performance on text-to-image generation, super-resolution, and unconditional image generation. Furthermore, various text-to-image generation tasks such as imageediting [1, 5, 16, 18, 30, 32] , style-transfer [12, 59] and concepts learning [24, 41] are also achieved and exhibit impressive performance based on diffusion architecture.Pretrained on large image-text dataset [44] selected from the Internet without adequate filtering and labeling, many text-to-image diffusion models [37, 39] are discovered to present potential harmful biases on social dimensions such as gender, race, culture, and so on.For generating prompts describing occupations, personality traits, or simply the word "person", Stable Diffusion [39] and DALLE-v2 [37] present severe biases excluding groups of people [31] . Luccioni et al. [29] and Friedrich et al. [11] also uncover the gender bias for different occupations in diffusion models. Similarly, Bianchi et al. [3] discover that Stable Diffusion exhibits racial, ethnic, and gendered stereotypes and stereotype amplification on neutral prompts. Seshadir et al. [45] present that Stable Diffusion would amplify biases from training data between gender and occupations. Furthermore, Cho et al. [8] discover that Stable Diffusion tends to generate males and skin tone centered on few tones for occupations. Liu et al. [28] uncover the cultural stereotypes with negative impacts on various groups.Existing bias mitigation methods focus on gender, race, and their intersections for occupations. Most of these methods finetuning certain components of the original model [10, 15, 22, 25, 25, 32, 46] . Shen et al. [46] employ biased direct fine-tuning for the sampling process with a distributional alignment loss. Esposito et al. [10] fine-tune text-to-image models on a synthetic fair dataset. Li et al. [25] trained a semantic h-space to achieve fair generation. Kim et al. [22] employ attribute classifiers to prompt-tune the text-toimage models for debiasing, and Li et al. [25] train a mapping network for text embeddings to guide the fair generation. TIME [32] and UCE [15] update the cross-attention layers to achieve semanticaligned text-to-image editing and hence mitigate social biases.Other works [2, 11] utilize fair prompts for debiasing, which are free of re-training or fine-tuning the original model. Bansal et al. [2] incorporate ethical prompt intervention to original prompts. Fairdiffusion [11] employs semantic dimensions [4] to guide debiased generation with a given fair prompt table. However, the above methods focus on scenarios involving only one individual within the images, which harms the diversity of social groups for general contexts with multiple individuals. 
method: 
 To achieve debiasing for each individual, our method consists of two steps, as illustrated in Fig. 3 . We first obtain the semantic regions from text prompt tokens via the Linguistic-aligned Attention Figure 3 : The pipeline of our method. In step one, we employ Linguistic-aligned Attention Guidance to obtain more accurate semantic regions relative to biases. In step two, we employ Fair Inference to generate fair attributes in these regions while persevering original structural and semantic information.Guidance module. After that, we employ the Fair Inference to debias each individual while preserving the original semantic and structural information. Note that our method can achieve arbitrary target distributions without re-training or fine-tuning the original model.Classifier-free Guidance. Text-to-image generative models based on Stable Diffusion [39] employ the classifier-free guidance [20] to generate images conditioned on text prompts without additional pre-trained classifiers. During training, the text prompts 𝑐 𝑝 drops randomly to facilitate unconditional and conditional objectives.During inference, the conditioned text prompts are sent to the noise predictor 𝜖 𝜃 along with the latent code 𝑧 𝑡 to predict noise at different timestep 𝑡 as follows:EQUATIONwhere 𝑠 𝑔 is the guidance scale determining the extent of text-guided instructions. The backbone 𝜖 𝜃 is a conditional UNet [40] to predict the added noises.Attention in Diffusion Models. The attention maps in selfand cross-attention layers of the diffusion UNet are discovered to characterize semantic information such as spatial layout, segmentations, and objects' shapes [16, 33, 38, 49] , which stem from the fully convolutional nature of the UNet [14] . The self-attention maps model the correlations between different patches of the latent code, reflected in the generated image. While the cross-attention maps present the correlations between each text prompt token and generated image patches. Therefore, these attention maps can be employed to obtain the semantic segmentations of the generated images and assess the relevance of image patches to specific words [16, 49] .To understand how biases are generated and locate them, we analyze the text prompt and concentrate on the different tokens. As for the text prompt "A photo of the faces of {number} {occupations}", the nouns "faces" and "{occupations}" are semantically responsible for biases, as the text-to-image model predominately associates these tokens with certain social groups, such as "Male" and "White", which introduces biases for each individual related to them.Semantic Regions. Our goal is to locate the semantic regions associated with these tokens. To achieve this, we first obtain global semantic segmentation maps from self-attention maps which contain rich information about spatial layouts and object shapes [16, 33] . The self-attention maps act as similarity maps, correlating different patches of generated images and generating semantic clusters [16] through spectral clustering [47, 51] as 𝑆𝐶 𝑐 = 𝐹 (𝑆𝐴, 𝑐), where 𝑆𝐴 is the self-attention maps of a certain resolution and 𝐹 is spectral clustering. 𝑆𝐶 𝑐 are the semantic clusters with the number of 𝑐 clusters presenting different semantic segmentations. To obtain the semantic regions 𝑆𝑅 for different tokens 𝑡, crossattention maps are employed as similarity maps denoting correlations between tokens and generated image patches to correlate different tokens and semantic clusters as follows:EQUATIONwhere 𝐶𝐴 𝑡 is the cross-attention map of token 𝑡.(𝑆𝐶 𝑖 •𝐶𝐴 𝑡 ) (𝑆𝐶 𝑖 )is the agreement score [33] . The 𝑆𝐶 𝑖 is labeled as part of the semantic region for token 𝑡 if the agreement score is larger than threshold 𝜏. However, recent work discovers that averaged cross-attention maps can exhibit failures such as undesired attention to the corresponding area, denoted as attention leakage [55] . We observe similar failures in our setting, as shown in Fig. 4 (a), the original cross-attention maps of the token "faces" almost covered the whole image, which may lead to the inaccurate assignment of semantic regions. We further assess the effectiveness of cross-attention maps for semantic regions from different cross-attention blocks and timesteps. The metric denotes facial region accuracy evaluated by IoU scores between semantic regions of different cross-attention maps and facial regions detected by MTCNN face detector [57] . As shown in Fig. 4 (b), there are blocks more effective for semantic regions in early steps while some almost fail to work. This may stem from some attention blocks being more sensitive to objects' shapes while others mainly contain global information like textures, with limited contribution to local semantics. Therefore, averaging all cross-attention maps may result in undesired attention assignments. Besides, obtaining accurate semantic regions may require different threshold 𝜏 following Eq. 2. As shown in Fig. 4 (c), we evaluate the maximum facial region accuracy for the semantic regions of the nouns "faces", "{occupations}", denoted as biased tokens 𝐵 𝑡 and the modifiers "the", "of" and "{number}", denoted as modifier tokens 𝑀 𝑡 , which are linguistically relative to 𝐵 𝑡 . The facial region accuracy corresponding to different tokens varies depending on different thresholds, indicating less robust and stable acquisition of these regions.Based on these observations, we aim to obtain more accurate semantic regions and mitigate the above failures. We propose Block Voting to obtain cross-attention maps aligned with the semantic regions from region-effective blocks. Linguistic Alignment is employed to achieve a more robust and stable acquisition of semantic regions by linguistically correlating different tokens.Block Voting. To mitigate attention leakage, we evaluate whether the cross-attention maps from different blocks and steps effectively represent semantic regions. The cross-attention maps are checked as follows to minimize disruptions of global information.𝐵𝑉 (𝐶𝐴 𝑏,𝑠 𝑡 ) =            𝑟𝑒 𝑗𝑒𝑐𝑡, 𝑖 𝑓 𝑐 ∑︁ 𝑖 (𝑆𝐶 𝑖 )1 𝜏 ( (𝑆𝐶 𝑖 • 𝐶𝐴 𝑏,𝑠 𝑡 ) (𝑆𝐶 𝑖 ) > 𝜏 𝑎 ) ∈ {𝑆𝐶 𝑐 , 0}.𝐶𝐴 𝑏,𝑠 𝑡 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒.(EQUATIONwhere 𝑡 is token, 𝑏 is attention block and 𝑠 is steps. If all semantic clusters or no clusters are chosen, indicating 𝐶𝐴 𝑏,𝑠 𝑡 hardly obtain effective semantic regions, the 𝐶𝐴 𝑏,𝑠 𝑡 will be rejected. After block voting, accepted 𝐶𝐴 𝑏,𝑠 𝑡 are summarized across different steps and scaled to the same resolution of different blocks, and then averaged.EQUATIONThe final semantic regions 𝑆𝑅 𝑡 can be obtained with more regionaligned cross-attention maps C 𝐴 𝑡 following Eq. 2. As shown in Fig. 4 (a), after Block Voting, the cross-attention maps are more consistent with the semantic clusters and identical to different semantic regions.Linguistic Alignment. To generally obtain effective semantic regions, we aim to find a group of tokens containing semantic information including modifier tokens. The key idea is the crossattention maps of the modifier tokens are supposed to largely overlap those of biased tokens in ideal conditions for semantic consistency [7, 38] . For example, the cross-attention maps of biased tokens 𝐵 𝑡 "faces" and "{occupations}" are semantically aligned with the modifier tokens 𝑀 𝑡 "the", "of", and "{number}". The appropriate threshold can be determined by preserving the linguistic correlation between the semantic regions of these tokens. The ineffective 𝑆𝑅 𝑡 with no semantic information is initialized with an extremely large value. 𝐿 𝑙 = 𝜆 𝑒 ||𝑆𝑅 𝐵 𝑖 ,EQUATIONAs shown in Fig. 4 (d), the obtained semantic regions are more accurate and stable for different thresholds. Furthermore, the obtained semantic region 𝑆𝑅 𝑓 presents where the biases are and is further utilized in the Fair Inference stage for debiasing.To mitigate generated biases, we aim to represent fair attributes of prompts for each individual in the semantic region 𝑆𝑅 𝑓 of tokens introducing biases in alignment with the target distribution. For example, for gender debiasing, the fair prompts 𝑐 𝑓 are "female person" and "male person", which are supposed to be present for individuals with equal probability given the target distribution is equal. Furthermore, the original structure and semantic information of the text prompts 𝑐 𝑝 are supposed to be preserved. During the inference stage, the latent noise conditioned on 𝑐 𝑝 is originally predicted by classifier-free guidance [20] as follows:EQUATIONSimilarly, to generate the fair attributes 𝑐 𝑓 , the predicted noise is:EQUATIONDirectly employ 𝜖 (𝑧 𝑡 , 𝑐 𝑓 ) would distort original structural and semantic information of 𝜖 (𝑧 𝑝 , 𝑐 𝑝 ). To preserve the original information while presenting fair attributes 𝑐 𝑓 , we consider the semantic dimensions of the noises, which correspond to the upper and lower tail of the noise distribution [4, 43] . These subtle semantic dimensions to original noise preserve the structural and semantic information of 𝑐 𝑓 while presenting fair attributes of 𝑐 𝑓 . The semantic dimensions of 𝑐 𝑓 are obtained as follows:EQUATIONEQUATIONwhere𝜂 𝜔 (|𝜙 |) is the 𝜔-th percentile of |𝜙 |.Finally, the classifier-free guidance for generating prompt 𝑐 𝑝 with the semantic dimensions of 𝑐 𝑓 is obtained as follows:𝜖 (𝑧 𝑡 , 𝑐 𝑝 , 𝑐 𝑓 ) = 𝜖 𝜃 (𝑧 𝑡 ) + 𝑠 𝑔 (𝜖 𝜃 (𝑧 𝑡 , 𝑐 𝑝 ) -𝜖 𝜃 (𝑧 𝑡 )) + 𝜙 (𝑧 𝑡 , 𝑐 𝑓 ). (11) For fair generation, we present multiple fair attributes following target distributions 𝑝 𝑖 for each individual 𝑆𝑅 𝑓 𝑛 from the semantic regions 𝑆𝑅 𝑓 of tokens introducing biases. The final noise is predicted as follows:EQUATIONwhere 𝑝 𝑖 = 1, 𝑖 ∈ [1, 2, ..., 𝑑], 𝑑 is the number of different fair attributes. 𝑆𝑅 𝑓 𝑛 are the semantic regions obtained by segmentation algorithm [50] for 𝑛 individuals. 
experiments: 
 In this section, we demonstrate our method outperforms the stateof-the-art debiasing methods to mitigate the social biases with multiple individuals within the image. We conduct both qualitative and quantitative evaluations, and our method achieves superior performance in terms of debiasing as well as preserving the structural and semantic consistency of the original results. Moreover, we apply our method to various scenarios including daily activities, personal descriptors, and style prompts. We further evaluate the debiasing performance with qualitative and quantitative comparisons with the original model.Social Biases. We consider social biases including gender, race, and their intersections. For gender, we adopt binary attributes: {Male, Female}, as non-binary attributes are hard determined by outward appearance and are currently challenging for automatic algorithms to identify. For race, we adopt four categories: {Black, White, Asian, Indian} following [46] , as these can be better distinguished by classifiers trained on fair racial dataset [21] . We consider the uniform distribution of these attributes and the prompt template is "a photo of the faces of {number} {occupation}" for the multi-face scenarios. "{number}" including cardinal numbers such as "two", "three" and quantifiers "few", "some" and "a group of". We employ the occupations from the International Standard Classification of Occupations. Compared Methods. We compare our method with four baselines. (1) Stable Diffusion with Ethical Interventions [2] (SD-EI), which applies ethical interventions via text prompts to the original model.( 2) FairDiffusion [11] , which promotes fairness using a fair prompts table and semantic guidance. (3) Unified Concept Editing (UCE) [15] , debiasing concepts via multiple edit directions simultaneously by updating the cross-attention layers of the original model. (4) Fine-tune [46] , fine-tuning the original model for distribution alignment with a biased gradient. To ensure fair comparisons, all experiments are conducted on Stable Diffusion 1.4 [39] .Evaluation Metrics. We utilize the classifiers [46] trained on CelebA [27] and FairFace [21] datasets for gender and race identification. To access social biases in multi-face scenarios, we evaluate the biases of generated results in two ways: across overall results (Bias-W) and within an image (Bias-P).EQUATION)where 𝑎 represents different attributes, and 𝑛 𝑎 denotes the number of attributes. 𝑓 𝑟𝑒𝑞 𝑤 𝑎 , 𝑓 𝑟𝑒𝑞 𝑝 𝑎 indicate the frequency of attribute 𝑎 across all results and within one image. Specifically, the number of 𝑛 𝑎 is 2 for gender, 4 for race, and 8 for their intersections.Furthermore, to evaluate the performance of different methods in preserving structural and semantic information of the original results, we employ conventional metrics including SSIM [52] , PSNR, and LPIPS [58] to measure the structural consistency, and CLIP [36] and DINO [6] embeddings to assess the semantic consistency via cosine similarity. Female","White Male","Black Female","Black Male","Indian Female","Indian Male","Asian Female","Asian Male". Implementation Details. Our framework is based on the Stable Diffusion 1.4 [39] with default hyperparameters over 50 inference steps. The number of semantic clusters is 4, threshold values 𝜏 𝑎 ∈ (0.1, 0.2), and the hyperparameters 𝜆 𝑒 =1. During Fair Inference, we set 𝑠 𝑓 =10. All the experiments are conducted on a single V100 GPU.We present the qualitative results of the comparing methods in Fig. 5 , and the generated occupation is "doctors". Note that the images in the same column are generated with the same random seeds. For convenience, we highlight the demographic attributes in the figure with colored boxes. More varied colored boxes within an image and across the overall results indicate superior debiasing performance. More qualitative results are provided in the Appendix. As depicted in the figure, Stable Diffusion predominantly generates "White Male" individuals, potentially introducing social biases regarding gender and race. SD-EI exhibits biases towards Asians ("Asian Male" and "Asian Female") and the generated categories are almost the same within each image. FairDiffusion maintains good structural and semantic consistency. However, it still exhibits identical gender or race within an image, particularly evident in the third, fifth, and sixth columns in Fig. 5 . Similar patterns are observed in UCE, which predominantly generates "White Female" instead of equitable representation for other groups. Fine-tune exhibits an inclination toward Indians ("Indian Male" and "Indian Female") and the facial characteristics are similar across individuals, appearing as the same person. As designed for single-face scenarios, current debiasing methods all exhibit homogenized attributes within images containing multiple individuals. Besides, SD-EI, UCE, and Fine-tune preserve less structural and semantic information of the original results. This discrepancy may arise from the disruptions of the original generation process caused by updated components or uncontrolled prompt instructions. In contrast, our method exhibits more diverse and fairer results on different attributes within the images and across all results, as well as the best performance at preserving structural and semantic consistency. We generate five hundred images for five occupations using the prompt template involving multiple individuals within the images. We assessed gender, racial, and intersectional biases in terms of overall biases (Bias-W) and biases within images (Bias-P) following Eq. 13. The results are presented in Table 1 . As for gender debiasing, all methods exhibit debiased results in terms of gender, However, UCE and Fine-tune present greater racial bias than the original results, possibly due to fine-tuning the original model with debiasing for one group may inadvertently introduce bias to another. Additionally, they exhibit greater biases within images, indicating a tendency to generate images featuring homogeneous attributes. SD-EI and FairDiffusion demonstrate consistent debiasing performance across three scenarios. However, similar to UCE and Fine-tune, they also focus on the attributes across the entire image instead of individuals, resulting in suboptimal debiasing performance for multiple individuals within the image. In contrast, by identifying the semantic regions from the attention guidance of tokens introducing biases, we effectively locate the biased individuals and debias them while preserving structural and semantic information.Our method achieves consistent lowest biases across all the scenarios, showcasing the best performance at debiasing overall and within images. Furthermore, considering the structural and semantic consistency of the original results, SD-EI, UCE, and Fine-tune exhibit suboptimal quantitative performance. Fair Diffusion better preserves consistency for gender debiasing, while our method outperforms across all scenarios in terms of debiasing, structural integrity, and semantic consistency.Ablation Study. The quantitative and qualitative results of the ablation study are shown in Fig. 6 (a) and Tab. 2. As shown in the results, the generated results without Fair Inference are less similar to the original results in terms of structural and semantic consistency, and without the Linguistic-aligned Attention Guidance, the general debiasing performance declined. The full model performs best at mitigating various biases as well as maintaining structural and semantic consistency. Age Distributions. In addition to biases related to gender, race, and their intersections, we further explore age as another aspect of social biases to facilitate a more comprehensive discussion. However, unlike gender and race, age features continuous discrete values, Other Scenarios. To further evaluate the effectiveness of our method, we conduct qualitative (Fig. 7 ) and quantitative (Tab. 3) experiments on more scenarios with prompt templates involving personal descriptors for different occupations: such as "smiling" and "reading", daily activities: "persons standing next to flowers", and style prompts gathered from LAION-Aesthetics [44] dataset. The 1 https://github.com/serengil/deepface experimental details and more results are reported in the Appendix. For all these scenarios, our method debiases the original Stable Diffusion considering gender, racial, and intersectional biases. We present more diverse social groups within the images and across all results. 
limitations and ethical considerations: 
 In this work, the discussion of gender categories is restricted to binary-valued attributes, as automated gender classification currently remains limited to binary-valued gender. Similarly, the categorization of race is simplified to four groups, which requires more discussion when considering other racial categories. Additionally, our method relies on fair prompts for debiasing. If there is a misalignment of the original text-to-image model between prompts and the visual results. Our method may fail to present fair attributes accordingly. 
conclusion: 
 In this work, we introduce a Linguistic-aligned Attention-guided Fair Generation Model to mitigate social biases in terms of gender, race and their intersections. We initially deal with multiple individuals within images of diverse genders and races. Specifically, we propose a Linguistic-aligned Attention Guidance module to identify the semantic regions of tokens introducing biases. Fair Inference is employed to achieve arbitrary target attribute distributions in these regions for each individual, as well as preserving original structural and semantic information. Extensive experimental results and analyses demonstrate our method achieves superior debiasing performance in multi-face scenarios compared with state-of-the-art debiasing approaches. Our method is intended to pave the way for the advancement of more general and robust social-aligned T2I generative AIs.  
: 
 in Fig. 1 , the classic text-to-image generative model Stable Diffusion [39] demonstrates biases toward "Male" and "White" in most occupations, whereas, for housekeeper, the model skews toward "Female". A similar pattern is observed in DALLE-v2 [8, 31] . These biases may propagate a distorted worldview and spread unforeseen prejudice and discrimination against certain groups.Some progress has been made to mitigate these biases for diffusionbased text-to-image generative models. Most of them [10, 15, 22, 25, 25, 32, 46] re-train or fine-tune different components of the original model, necessitating expensive computational resources. Other works [2, 11] are training-free and only require fair prompts for debiasing. However, all these debiasing methods are designed for scenarios involving only one individual. When applied to more general contexts where multiple individuals appear in one image, they often produce homogeneous social groups. As shown in Fig. 2 , current debiasing methods exhibit almost the same race or gender to all individuals in an image, which harms the diversity of social groups within the image. In contrast, our method enables individualized debiasing and exhibits diverse genders and races.To address the challenge of debiasing for more general scenarios with multiple individuals and representing diverse social groups within the image, we consider how these biases are generated. For text-to-image generation, the generated results are semantically instructed by the conditioned text prompts, benefitting from the architecture of the diffusion model [39] . This property indicates that the tokens of the prompt are accountable for the semantic information within the image [23, 49] , potentially introducing biases. For instance, tokens like "faces" and "doctors" may be associated with certain social groups in the generated results, such as "Male" and "White", which introduce biases for each individual related to them. By identifying the semantic regions associated with these tokens, we can locate where the biases are and subsequently debiasing them for each individual.In this work, we introduce a Linguistic-aligned Attention-Guided Fair Generation Model, which locates the semantic regions related to biases based on different tokens and generates fair attributes in these regions to mitigate biases. To enhance the accuracy and robustness of the extracted semantic regions, we introduce a Linguisticaligned Attention Guidance module consisting of Block Voting and Linguistic Alignment. Block Voting aims to obtain more regioneffective cross-attention maps and avoid disturbance of global information. Linguistic Alignment facilitates robust and stable semantic regions from different thresholds by maintaining the linguistic correlations of different tokens. Once the semantic regions are obtained, we further employ Fair Inference to ensure each individual exhibits fair attributes while preserving the original structural and semantic information. Furthermore, the debiasing for each individual is achieved by confirming the target distributions of social groups. Given intended fair prompts, such as gender and race, our method achieves fair generation in more general scenarios including multiple individuals within the image and is free of training.Our contribution can be summarized as follows:• We introduce a Linguistic-aligned Attention-guided Fair Generation Model to mitigate social biases in general contexts including multi-face scenarios. To the best of our knowledge, this is the first debiasing method capable of dealing with multiple individuals within the image. • We propose a Linguistic-aligned Attention Guidance module to effectively locate the semantic regions containing biases and Fair Inference to achieve fair attributes for each individual while preserving original structural and semantic information. • Comprehensive analysis and experiments are conducted to demonstrate the superiority of our method in mitigating biases with multiple individuals across various scenarios. 
 
 Appendix:  


Decision: The paper proposes a method for debiasing gender and racial biases in diffusion models. The reviewers mention (+) possibility to decrease gender and racial biases which is an important topic, (+) possibility to handle images with multiple humans, (+) competitive results compared to other methods, (+) no need for fine-tuning. The reviewers have some concerns about (-) computational limitations of the method, (-) the number results shown in the paper. Most concerns seem to be cleared up during the rebuttal.

The paper received one positive (weak accept) and two negative (2x borderline reject) initial ratings. After the rebuttal response, the weak accept acknowledged that they think it should be accepted. One of the borderline rejects upgraded their rating to borderline accept, stating that their concerns were solved and that the paper has merit which would make it interesting to discuss at the venue. 

The third review is still negative. Unfortunately, the initial review was very low quality, short, and did not include constructive feedback. The authors posted a detailed confidential comment to AC regarding this review, discussing the low quality of the review. I agree that the review quality is rather low, but unfortunately, the reviewer did not revise their review after multiple emails during the initial review phase.
After the rebuttal, it was lowered to "weak reject" with the justification "After reading the rebuttal and the comments from other reviewers, I lowered my rating.", which is a hard to follow judgement considering that the other reviews were accepts... Due to the very low quality of this review and its revisions, I do not believe it should be included in the final judgement. 

Following, as both other reviewers argue that this paper has merit for MM, I believe this paper should be accepted for a poster submission, discussing this idea in more detail at the venue.

Reviews: review: paper_summary: paper_summary: This work addressed gender and racial bias in text-to-image generation. In this work, bias is considered to be in the diversity of gender and racial attributes of the generated faces. The proposed method locates faces by using cross and self-attention, and then, each face is debiased by using explicit attribute prompts like "female person" or "male person". Results are reported on Stable Diffusion 1.4.
strength_weakness: strength_weakness: - The proposed method can generate more diversity within a group of people in a single image, which is a limitation in current models.
- The proposed method does not require training or fine-tuning.
- The proposed method is compared against multiple debiasing methods and obtains competitive quantitative and qualitative results.
limitations: limitations: **Major**

- The method does not require fine-tuning, but the generation process involves multiple generations for a single image. How much more time and resources does the method need?
- The method only works for simple prompts: "A photo of the faces of {number} {occupations}". In a real scenario with free-form prompts, how can the method detect bias in the self-attention maps?
- There is only a single model used for debiasing, Stable Diffusion 1.4. To show generalization capabilities, the proposed method should be evaluated using several text-to-image generation models. Some models may be difficult to try due to their proprietary nature, but at the very least, different versions of Stable Diffusion, like v2 and sdxl, should be evaluated. 

**Minor**

Figures are underexplained:
- In Figure 1, how is frequency computed? For how many images? How are gender and race detected?
- In Figure 2, which method is used in the second row? Please add references
- In Figure 5, why are there some faces without bounding boxes?

Typos
- L269 Priliminary --> Preliminary
paper_summary: paper_summary: Thia paper introduce a Linguistic-aligned Attention Guidance module consisting of Block Voting and Linguistic Alignment, to effectively locate the semantic regions related to biases.
strength_weakness: strength_weakness: This paper is superior than compared baselines.
limitations: limitations: The novelty is limited, this paper restricted to binary-valued attributes, how about multi-value attribute, the categorization of race is simplified to four groups. The authors only consider diffusion model, how about other generalization model.
I am confused about the motivation,  the motivation seems not direct enough，it looks like the author uses an attention model and mixed some existed methods.
The proposed method lacks theoretical support.
The datasets are small and not enough, the results should compared with recent sota methods.
paper_summary: paper_summary: This paper addresses the diversity of social groups in text-to-image diffusion models. Specifically, it aims to identify how biases are generated between text prompts and generated images, locate where the biases are, and mitigate them for each person.
strength_weakness: strength_weakness: + This proposed method is capable of handling multiple individuals in the same image with diverse genders and races.
+ This paper proposes a Linguistic-aligned Attention Guidance module (via block voting and linguistic alignment) to locate semantic regions/tokens related to biases. Then, Fair inference is employed on the bias regions to generate fair attributes across arbitrary distributions. Original structural and semantic information are preserved.
+ The evaluation results show good visual quality and good isolation from other non-bias semantic tokens.
limitations: limitations: - For cross-attention maps, the use cases of this paper are mainly human subjects and some features are highly overlapped (i.e., human face region). I am particularly interested in how the proposed block voting can improve the cross-attention on concepts with less overlapping in the same region.
- line 906 stated the method relies on fair prompts for debiasing. Please show examples of such cases and how they affect the generated images. 

Comments:
Overall, the proposed approach can identify biased regions and perform fair inference to address the bias concerns within an image. I acknowledge that this paper proposed a novel solution with good visual quality to change the sensitive attribute. However, bias is a subjective topic that is highly subjective to the underlying context. I would suggest to position this work as providing tools for the users to smooth the curb related to bias. Sometimes, being fair to all possibilities can be a bias. In this way, users can use these tools to impose the desired distribution of the sensitive bias. In other words, this work provides tools for handling distributional bias from the training dataset. 

Others
- [suggestion] For Fig 1, it would be best to use pie to demonstrate the bias for some attributes (especially for binary ones)