mlsanityData to 14 Oct 2026

Exploring the Potential of Generative AI for the World Wide Web

arXiv 2310.17370 ↗First posted 26 Oct 2023cs.AI cs.HC cs.IRcited by 0

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Abstract (arXiv)

Generative Artificial Intelligence (AI) is a cutting-edge technology capable of producing text, images, and various media content leveraging generative models and user prompts. Between 2022 and 2023, generative AI surged in popularity with a plethora of applications spanning from AI-powered movies to chatbots. In this paper, we delve into the potential of generative AI within the realm of the World Wide Web, specifically focusing on image generation. Web developers already harness generative AI to help crafting text and images, while Web browsers might use it in the future to locally generate images for tasks like repairing broken webpages, conserving bandwidth, and enhancing privacy. To explore this research area, we have developed WebDiffusion, a tool that allows to simulate a Web powered by stable diffusion, a popular text-to-image model, from both a client and server perspective. WebDiffusion further supports crowdsourcing of user opinions, which we use to evaluate the quality and accuracy of 409 AI-generated images sourced from 60 webpages. Our findings suggest that generative AI is already capable of producing pertinent and high-quality Web images, even without requiring Web designers to manually input prompts, just by leveraging contextual information available within the webpages. However, we acknowledge that direct in-browser image generation remains a challenge, as only highly powerful GPUs, such as the A40 and A100, can (partially) compete with classic image downloads. Nevertheless, this approach could be valuable for a subset of the images, for example when fixing broken webpages or handling highly private content.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 39 references that are in the map. First posted on arXiv 26 Oct 2023.

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References · 10

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  4. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  5. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  6. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  7. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
  8. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 349
  9. 2023Perception, performance, and detectability of conversational artificial intelligence across 32 university coursescited by 12
  10. 2021Browselite: A Private Data Saving Solution for the Webcited by 2

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  1. 2023Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
  2. 2024Generative AI in Vision: A Survey on Models, Metrics and Applications
  3. 2023Text-to-image Diffusion Models in Generative AI: A Survey
  4. 2026MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation
  5. 2026DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation
  6. 2025Generative AI for Vision: A Comprehensive Study of Frameworks and Applications
  7. 2025Information Retrieval in the Age of Generative AI: The RGB Model
  8. 2024DiffusionAgent: Navigating Expert Models for Agentic Image Generation