SlowGuess/NeurIPS_2023_OCR huggingface dataset:
5. Reasoning. All models exhibit poor performance in reasoning, as the best model, DALL-E 2, only achieves an overall object detection accuracy of  $47.2\%$  on the PaintSkills scenario (https: //crfm.stanford.edu/heim/v1.1.0/?group=heim_reasoning(scenarios). They often make mistakes in the count of objects (e.g., generating 2 instead of 3) and spatial relations (e.g., placing the object above instead of bottom). For human-rated alignment, DALL-E 2 outperforms other models but still receives an average score of less than 4 for Relational Understanding and the reasoning sub-scenarios of DrawBench. The next best model, DeepFloyd-IF XL, does not achieve a score higher than 4 across all the reasoning scenarios, indicating that there is room for improvement for text-to-image generation models for reasoning tasks.
6. Knowledge. Dreamlike Photoreal 2.0 and DALL-E 2 exhibit the highest win rates in knowledge-intensive scenarios, suggesting they possess more knowledge about the world than other models (https://crfm.stanford.edu/heim/v1.1.0/?group=heim_knowledge scenarios). Their superiority may be attributed to fine-tuning on real-world entity photographs.
7. Bias. In terms of gender bias, minDALL-E, DALL-E mini, and Safe Stable Diffusion exhibit the least bias, while Dreamlike Diffusion, DALL-E 2, and Redshift Diffusion demonstrate higher levels of bias (https://crfm.stanford.edu/heim/v1.1.0/?group=heim_bias scenarios). The mitigation of gender bias in Safe Stable Diffusion is intriguing, potentially due to its safety guidance mechanism suppressing sexual content. In terms of skin tone bias, Openjourney v2, CogView2, and GigaGAN show the least bias, whereas Dreamlike Diffusion and Redshift Diffusion exhibit more bias. Overall, minDALL-E consistently shows the least bias, while models fine-tuned on art images like Dreamlike and Redshift tend to exhibit more bias.
8. Toxicity. While most models exhibit a low frequency of generating inappropriate images, certain models exhibit a higher frequency for the I2P scenario (https://crfm.stanford.edu/heim/v1.1.0/?group=heim_toxicity scenarios). For example, OpenJourney, the weaker variants of SafeStableDiffusion, Stable Diffusion, Promptist, and Vintedois Diffusion generate inappropriate images for non-toxic text prompts in over  $10\%$  of cases. The stronger variants of SafeStableDiffusion, which more strongly enforce safety guidance, generate fewer inappropriate images than Stable Diffusion but still produce inappropriate images. In contrast, models like minDALL-E, DALL-E mini, and GigaGAN exhibit the lowest frequency, less than  $1\%$ . This disparity may be attributed to the data used to train these models. Moving forward, addressing inappropriate image generation requires careful consideration of training data and model design.
9. Fairness. Around half of the models exhibit performance drops in human-rated alignment metrics when subjected to gender and dialect perturbations (https://crfm.stanford.edu/heim/v1.1.0/?group=mscoco_gender, https://crfm.stanford.edu/heim/v1.1.0/?group=mscoco_dialect). This suggests that there is no universal pattern of fairness issues among
18. Model scales. Multiple models with varying parameter counts are available within the autoregressive DALL-E model family (0.4B, 1.3B, 2.6B) and diffusion DeepFloyd-IF family (0.4B, 0.9B, 4.3B). We find that larger models tend to outperform smaller ones in all human-rated metrics, including alignment, photorealism, subject clarity, and aesthetics (https://crfm.stanford.edu/heim/v1.1.0/?group=mscoco_base).
19. What are the best models? Overall, DALL-E 2 appears to be a versatile performer across human-rated metrics. However, no single model emerges as the top performer in all aspects. Different models show different strengths. For example, Dreamlike Photoreal excel in photorealism, while Openjourney in aesthetics. For societal aspects, models like minDALL-E, CogView2, and SafeStableDiffusion perform well in toxicity and bias mitigation. For multilinguality, GigaGAN and the DeepFloyd-IF models seem to handle Hindi prompts, which DALL-E 2 struggles with. These observations open up new research avenues to study whether and how to develop models that excel across multiple aspects.
8 Related work
Holistic benchmarking. Benchmarks drive the advancements of AI by orienting the directions for the community to improve upon [20, 54, 55, 56]. In particular, in natural language processing (NLP), the adoption of meta-benchmarks [57, 58, 59, 60] and holistic evaluation [1] across multiple scenarios or tasks has allowed for comprehensive assessments of models and accelerated model improvements. However, despite the growing popularity of image generation and the increasing number of models being developed, a holistic evaluation of these models has been lacking. Furthermore, image generation encompasses various technological and societal impacts, including alignment, quality, originality, toxicity, bias, and fairness, which necessitate comprehensive evaluation. Our work fills this gap by conducting a holistic evaluation of image generation models across 12 important aspects.
**Benchmarks for image generation.** Existing benchmarks primarily focus on assessing image quality and alignment, using automated metrics. Widely used benchmarks such as MS-COCO [21] and ImageNet [20] have been employed to evaluate the quality and alignment of generated images. Metrics like Fréchet Inception Distance (FID) [23], Inception Score [36], and CLIPScore [24] are commonly used for quantitative assessment of image quality and alignment.
To better capture human perception in image evaluation, crowdsourced human evaluation has been explored in recent years [25, 6, 35, 61]. However, these evaluations have been limited to assessing aspects such as alignment and quality. Building upon these crowdsourcing techniques, we extend the evaluation to include additional aspects such as aesthetics, originality, reasoning, and fairness.
As the ethical and societal impacts of image generation models gain prominence [19], researchers have also started evaluating these aspects [33, 29, 8]. However, these evaluations have been conducted on only a select few models, leaving the majority of models unevaluated in these aspects. Our standardized evaluation addresses this gap by enabling the evaluation of all models across all aspects, including ethical and societal dimensions.
Art and design. Our assessment of image generation incorporates aesthetic evaluation and design principles. Aesthetic evaluation considers factors like composition, color harmony, balance, and visual complexity [62, 63]. Design principles, such as clarity, legibility, hierarchy, and consistency in design elements, also influence our evaluation [64]. Combining these insights, our aim is to determine whether generated images are visually pleasing, with thoughtful compositions, harmonious colors, balanced elements, and an appropriate level of visual complexity. We employ objective metrics and subjective human ratings for a comprehensive assessment of aesthetic quality.
We introduced Holistic Evaluation of Text-to-Image Models (HEIM), a new benchmark to assess 12 important aspects in text-to-image generation, including alignment, quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. Our evaluation of 26 recent text-to-image models reveals that different models excel in different aspects, opening up research avenues to study whether and how to develop models that excel across multiple aspects. To enhance transparency and reproducibility, we release our evaluation pipeline, along with the generated images and human evaluation results. We encourage the community to consider the different aspects when developing text-to-image models.
We thank Robin Rombach, Yuhui Zhang, members of Stanford P-Lambda, CRFM, and SNAP groups, as well as our anonymous reviewers for providing valuable feedback. We thank Josselin Somerville for assisting with the human evaluation infrastructure. This work is supported in part by the AI2050 program at Schmidt Futures (Grant G-22-63429) and ONR N00014-23-1-2355. Michihiro Yasunaga is supported by a Microsoft Research PhD Fellowship.
A Author contributions
Tony Lee: Led the project; Designed the core framework (aspects, scenarios, metrics); Implemented scenarios, metrics and models; Conducted experiments. Contributed to writing.
Michihiro Yasunaga: Led the project; Designed the core framework (aspects, scenarios, metrics); Wrote the paper; Conducted analysis; Implemented models.
Chenlin Meng: Designed the core framework (aspects, scenarios, metrics). Contributed to writing.
Yifan Mai: Implemented the evaluation infrastructure; Contributed to project discussions
Joon Sung Park: Designed human evaluation questions
Agrim Gupta: Implemented the detection scenario and metrics
Yunzhi Zhang: Implemented the detection scenario and metrics
Deepak Narayanan: Provided expertise and analysis of efficiency metrics
Hannah Teufel: Provided model expertise and inference
Marco Bellagente: Provided model expertise and inference
Minguk Kang: Provided model expertise and inference
Taesung Park: Provided model expertise and inference
Jure Leskovec: Provided advice on human evaluation and paper writing
Jun-Yan Zhu: Provided advice on human evaluation
Li Fei-Fei: Provided advice on the core framework
Jiajun Wu: Provided advice on the core framework
Stefano Ermon: Provided advice on the core framework
Percy Liang: Provided overall supervision and guidance throughout the project
A.1 Statement of neutrality
The authors of this paper affirm their commitment to maintaining a fair and independent evaluation of the image generation models. We acknowledge that the author affiliations encompass a range of academic and industrial institutions, including those where some of the models we evaluate were developed. However, the authors' involvement is solely based on their expertise and efforts to run and evaluate the models, and the authors have treated all models equally throughout the evaluation process, regardless of their sources. This study aims to provide an objective understanding and assessment of models across various aspects, and we do not intend to endorse specific models.
Our work identifies 12 important aspects in real-world deployments of text-to-image generation models, namely alignment, quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. While we have made substantial progress in conducting a holistic evaluation of models across these aspects, there are certain limitations that should be acknowledged in our work.
Firstly, it is important to note that our identified 12 aspects may not be exhaustive, and there could be other potentially important aspects in text-to-image generation that have not been considered. It is an ongoing area of research, and future studies may uncover additional dimensions that are critical for evaluating image generation models. We welcome further exploration in this direction to ensure a comprehensive understanding of the field.
Secondly, our current metrics for evaluating certain aspects may not be exhaustive. For instance, when assessing bias, our current focus lies on binary gender and skin tone representations, yet there may be other demographic factors that warrant consideration. Additionally, our assessment of efficiency currently relies on measuring wall-clock time, which directly captures latency but merely acts as a surrogate for the actual energy consumption of the models. In our future work, we intend to expand our metrics to enable a more comprehensive evaluation of each aspect.
Lastly, there is an additional limitation related to the use of crowdsourced human evaluation. While crowdsourced workers can effectively answer certain evaluation questions, such as image alignment, photorealism, and subject clarity, and provide a high level of inter-annotator agreement, there are other aspects, namely overall aesthetics and originality, where the responses from crowdsourced workers (representing the general public) may exhibit greater variance. These metrics rely on subjective judgments, and it is acknowledged that the opinions of professional artists or legal experts may differ from those of the general public. Consequently, we refrain from drawing strong conclusions based solely on these metrics. However, we do believe there is value in considering the judgments of the general public, as it is reasonable to desire generated images to be visually pleasing and exhibit a sense of originality to a wide audience.
C Datasheet
Q1 For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.
- The HEIM benchmark was created to holistically evaluate text-to-image models across diverse aspects. Before HEIM, text-to-image models were typically evaluated on the alignment and quality aspects; with HEIM, we evaluate across 12 different aspects that are important in real-world model deployment: image alignment, quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality and efficiency.
- This benchmark is presented by the Center for Research on Foundation Models (CRFM), an interdisciplinary initiative born out of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) that aims to make fundamental advances in the study, development, and deployment of foundation models. https://crfm.stanford.edu/.
- This work was supported in part by the AI2050 program at Schmidt Futures (Grant G-22-63429).
Q5 What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.
- HEIM benchmark provides prompts/captions covering 62 scenarios. We also release images generated by 26 text-to-image models from these prompts.
- HEIM contains 500K prompts in total covering 62 scenarios. The detailed statistics for each scenario can be found at https://crfm.stanford.edu/heim/latest.
Q7 Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).
- Yes. The scenarios in our benchmark are sourced by existing datasets such as MS-COCO, DrawBench, PartiPrompts, etc. and we use all possible instances from these datasets.
Q8 What data does each instance consist of? "Raw" data (e.g., unprocessed text or images) or features? In either case, please provide a description.
- Input prompt and generated images.
Q9 Is there a label or target associated with each instance? If so, please provide a description.
- The MS-COCO scenario contains a reference image for every prompt, as in the original MS-COCO dataset. Other scenarios do not have reference images.
Q10 Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.
Q11 Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? If so, please describe how these relationships are made explicit.
- Every prompt belongs to a scenario.
Q12 Are there recommended data splits (e.g., training, development/Validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.
Q13 Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.
Q14 Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.
- The dataset is self-contained. Everything is available at https://crfm.stanford.edu/heim/latest.
Q15 Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals' non-public communications)? If so, please provide a description.
- No. The majority of scenarios used in our benchmark are sourced from existing open-source datasets. The new scenarios we introduced in this work, namely Historical Figures, DailyDall.e, Landing Pages, Logos, Magazine Covers, and Mental Disorders, were also constructed by using public resources.
- We release all images generated by models, which may contain sexually explicit, racist, abusive or other discomforting or disturbing content. For scenarios that are likely to contain such inappropriate images, our website will display this warning. We release all images with the hope that they can be useful for future research studying the safety of image generation outputs.
Q17 Does the dataset relate to people? If not, you may skip the remaining questions in this section.
- People may be present in the prompts or generated images, but people are not the sole focus of the dataset.
- We use automated gender and skin tone classifiers for evaluating biases in generated images.
Q19 Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.
- Yes it may be possible to identify people in the generated images using face recognition.  
Similarly, people may be identified through the associated prompts.
Q20 Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.
- Yes the model-generated images contain sensitive content. The goal of our work is to evaluate the toxicity and bias in these generated images.
- We caution discretion on behalf of the user and call for responsible usage of the benchmark for research purposes only.
Q22 How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.
- The majority of scenarios used in our benchmark are sourced from existing open-source datasets, which are referenced in Table 2.
- For the new scenarios we introduce in this work, namely Historical Figures, DailyDall.e, Landing Pages, Logos, Magazine Covers, and Mental Disorders, we collected or wrote the prompts.
Q23 What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated?
- The existing scenarios were downloaded by us.
- Prompts for the new scenarios were collected or written by us manually. For further details, please refer to  $\S D$ .
We use the whole datasets
- The authors of this paper collected the scenarios.
- Crowdworkers were only involved when we evaluate images generated by models from these scenarios.
Q26 Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.
- The data was collected from December 2022 to June 2023.
Q27 Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.
- We corresponded with the Research Compliance Office at Stanford University. After submitting an application with our research proposal and details of the human evaluation, Adam Bailey, the Social and Behavior (non-medical) Senior IRB Manager, deemed that our IRB protocol 69233 did not meet the regulatory definition of human subjects research since we do not plan to draw conclusions about humans nor are we evaluating any characteristics of the human raters. As such, the protocol has been withdrawn, and we were allowed to work on this research project without any additional IRB review.
Q28 Does the dataset relate to people? If not, you may skip the remaining questions in this section.
- People may appear in the images and descriptions, although they are not the exclusive focus of the dataset.
- Our scenarios were collected from third party websites. Our human evaluation were conducted via crowdsourcing.
Q30 Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.
- Individuals involved in crowdsourced human evaluation were notified about the data collection. We used Amazon Mechanical Turk, and presented the consent form shown in Figure 5 to the crowdsourced workers.
DESCRIPTION: You are invited to participate in a research study on evaluating text-to-image generation models. You will rate A.I.-generated images based on criteria such as image quality, creativity, aesthetics, etc.
TIME INVOLVEMENT: Your participation will take approximately 20 minutes.
RISK AND BENEFITS: The risks associated with this study are that some of the images in this study can be inappropriate or toxic - images can contain violence, threats, obscenity, insults, profanity, or sexually explicit content. Study data will be stored securely, in compliance with Stanford University standards, minimizing the risk of a confidentiality breach. There are no immediate non-monetary benefits from this study. We cannot and do not guarantee or promise that you will receive any benefits from this study. AI image generation models are becoming increasingly pervasive in society. As a result, understanding their characteristics has become important for the benefit of society.
PARTICIPANT'S RIGHTS: If you have read this form and have decided to participate in this project, please understand your participation is voluntary, and you have the right to withdraw your consent or discontinue participation at any time without penalty or loss of benefits to which you are otherwise entitled. The alternative is not to participate. You have the right to refuse to answer particular questions. The results of this research study may be presented at scientific or professional meetings or published in scientific journals. Your individual privacy will be maintained in all published and written data resulting from the study.
CONTACT INFORMATION: Questions: If you have any questions, concerns, or complaints about this research, its procedures, risks, and benefits, contact the Protocol Director, Percy Liang, at (650) 723-6319.