Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models (2210.14896v4)

Published 26 Oct 2022 in cs.CV, cs.AI, cs.HC, and cs.LG

Abstract: With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. Apache. 2013. Apache Parquet: Open Source, Column-oriented Data File Format Designed for Efficient Data Storage and Retrieval.
  2. PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations.
  3. Ali Borji. 2022. Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2. arXiv 2210.00586.
  4. Gwern Branwen. 2020. GPT-3 Creative Fiction.
  5. Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains. arXiv 2210.04133.
  6. Alex Clark. 2015. Pillow: Python Imaging Library (Fork).
  7. Datasheets for Datasets. arXiv:1803.09010 [cs].
  8. Google. 2010. Comparative Study of WebP, JPEG and JPEG 2000.
  9. Hierarchical topic models and the nested chinese restaurant process. In Advances in Neural Information Processing Systems, volume 16.
  10. Laura Hanu and Unitary team. 2020. Detoxify: Toxic Comment Classification with Pytorch Lightning and Transformers.
  11. Imagen Video: High Definition Video Generation with Diffusion Models. arXiv 2210.02303.
  12. Oleksii Holub. 2017. DiscordChatExporter: Exports Discord Chat Logs to a File.
  13. David Holz. 2022. Midjourney: Exploring New Mediums of Thought and Expanding the Imaginative Powers of the Human Species.
  14. spaCy: Industrial-strength natural language processing in python.
  15. Harold Hotelling. 1936. Relations Between Two Sets of Variates. Biometrika, 28.
  16. Eero Hyvönen and Eetu Mäkelä. 2006. Semantic Autocompletion. In The Semantic Web – ASWC 2006, volume 4185.
  17. Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers.
  18. A Universally Unique IDentifier (UUID) URN Namespace. Technical report, RFC Editor.
  19. Building and using a semantivisual image hierarchy. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  20. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys.
  21. Vivian Liu and Lydia B Chilton. 2022. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In CHI Conference on Human Factors in Computing Systems.
  22. Explainable Computational Creativity. arXiv 2205.05682.
  23. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
  24. Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17.
  25. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426 [cs, stat].
  26. Yisroel Mirsky and Wenke Lee. 2022. The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys, 54.
  27. Jonas Oppenlaender. 2022. A Taxonomy of Prompt Modifiers for Text-To-Image Generation. arXiv 2204.13988.
  28. Nikita Pavlichenko and Dmitry Ustalov. 2022. Best Prompts for Text-to-Image Models and How to Find Them. arXiv 2209.11711.
  29. Diffusers: State-of-the-art diffusion models.
  30. Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated Art. In Creativity and Cognition.
  31. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research.
  32. Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv 2204.06125.
  33. Laria Reynolds and Kyle McDonell. 2021. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems.
  34. Leonard Richardson. 2007. Beautiful Soup Documentation.
  35. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  36. Kevin Roose. 2022. An A.I.-Generated Picture Won an Art Prize. Artists Aren’t Happy.
  37. Murray Rosenblatt. 1956. Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics, 27.
  38. Learning To Retrieve Prompts for In-Context Learning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
  39. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. arXiv 2205.11487.
  40. LAION-5B: An open large-scale dataset for training next generation image-text models. arXiv 2210.08402.
  41. Sharif Shameem. 2022. Lexica: Building a Creative Tool for the Future.
  42. Bernard W Silverman. 2018. Density Estimation for Statistics and Data Analysis.
  43. StabilityAI. 2022a. Stable Diffusion Discord Server Rules.
  44. StabilityAI. 2022b. Stable Diffusion Dream Studio beta Terms of Service.
  45. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, 9.
  46. Visualization of large hierarchical data by circle packing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
  47. Kyle Wiggers. 2022. Deepfakes for all: Uncensored AI art model prompts ethics questions.
  48. Stable Diffusion Breaks the Internet.
Citations (224)
View on Semantic Scholar

Summary

  • The paper introduces DiffusionDB, a dataset comprising 6.5TB of data with 14M images generated by Stable Diffusion from 1.8M diverse prompts.
  • The paper details an analysis of syntactic and semantic prompt characteristics, uncovering configurations that lead to generation inaccuracies.
  • The paper outlines the dataset's potential to enhance prompt engineering, model fine-tuning, and ethical safeguards in text-to-image generative research.

A Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models

This paper introduces a substantial dataset named "DiffusionDB," designed to support the research community in exploring the complexities of text-to-image generative models. With the rapid advancement of diffusion models, such as those utilized by Stable Diffusion, there has been a surge in the ability to generate high-quality, controlled images from natural language prompts. However, crafting effective prompts remains a challenge, as the influence of prompt variations on output quality is often unclear. This dataset addresses this gap by providing 6.5TB of data, comprising 14 million images generated with Stable Diffusion from 1.8 million unique user-provided prompts, alongside corresponding hyperparameters.

The dataset's scale and diversity render it a unique resource for probing the nuances of prompt-engineering, model error patterns, and potential misuse of generative models. The paper offers a thorough analysis of the syntactic and semantic characteristics of prompts, identifying patterns that can lead to model inaccuracies. Notably, it highlights specific configurations and styles that correlate with failures in image generation, as well as evidences of models producing misinformation or problematic content. This insight is critical for both improving existing models and guiding the design of user interfaces that facilitate more effective human-model interaction.

The introduction of DiffusionDB is timely, providing opportunities for several novel research directions. It enables the development of enhanced prompt autocomplete systems, aids in the fine-tuning of generative models by identifying frequently used prompt styles, and supports the creation of tools to explain and visualize the generative processes. Moreover, the dataset is a valuable resource for tackling the growing issue of deepfake detection.

Researchers are encouraged to leverage this dataset to advance the understanding of the complex interactions between linguistic inputs and generated visual content, thereby contributing to the ongoing discourse on the ethical implications of generative AI technologies. Furthermore, the authors underscore the precautions necessary in dataset utilization, noting the inclusion of NSFW content and the ethical implications surrounding data privacy and intellectual property.

Overall, this dataset serves as an important milestone in the paper of generative models, offering the community a robust platform for exploring both the potential and limitations of current AI systems in synthesizing visual media from textual descriptions. Future research leveraging DiffusionDB may significantly impact the development of more intuitive and reliable AI tools, ultimately enhancing user experience in creative domains and other applicable areas.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. Home | DiffusionDB