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With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output. We present techniques…

Computer Vision and Pattern Recognition · Computer Science 2022-11-29 Sam Witteveen , Martin Andrews

We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain…

Computer Vision and Pattern Recognition · Computer Science 2024-05-15 Yifan Wang , Aleksander Holynski , Brian L. Curless , Steven M. Seitz

Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of laborious human engineering, we propose prompt adaptation,…

Computation and Language · Computer Science 2024-01-01 Yaru Hao , Zewen Chi , Li Dong , Furu Wei

Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of…

Human-Computer Interaction · Computer Science 2023-09-29 Vivian Liu , Lydia B. Chilton

Text-to-image generative models have demonstrated remarkable capabilities in generating high-quality images based on textual prompts. However, crafting prompts that accurately capture the user's creative intent remains challenging. It often…

Human-Computer Interaction · Computer Science 2023-04-20 Stephen Brade , Bryan Wang , Mauricio Sousa , Sageev Oore , Tovi Grossman

Taking advantage of the many recent advances in deep learning, text-to-image generative models currently have the merit of attracting the general public attention. Two of these models, DALL-E 2 and Imagen, have demonstrated that highly…

Computer Vision and Pattern Recognition · Computer Science 2022-09-23 Robin Zbinden

Text-to-image AI are capable of generating novel images for inspiration, but their applications for 3D design workflows and how designers can build 3D models using AI-provided inspiration have not yet been explored. To investigate this, we…

Human-Computer Interaction · Computer Science 2023-08-02 Vivian Liu , Jo Vermeulen , George Fitzmaurice , Justin Matejka

Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Of particular note is the field of ``AI-Art'', which has seen unprecedented growth with the emergence of powerful…

Computer Vision and Pattern Recognition · Computer Science 2022-07-27 Robin Rombach , Andreas Blattmann , Björn Ommer

This pictorial presents an ongoing research programme comprising three practice-based Design Research projects conducted through 2024, exploring the affordances of diffusion-based AI image generation systems, specifically Stable Diffusion.…

Human-Computer Interaction · Computer Science 2024-11-21 Joseph Lindley , Roger Whitham

Image generation using generative AI is rapidly becoming a major new source of visual media, with billions of AI generated images created using diffusion models such as Stable Diffusion and Midjourney over the last few years. In this paper…

Human-Computer Interaction · Computer Science 2024-01-29 Jon McCormack , Maria Teresa Llano , Stephen James Krol , Nina Rajcic

Text-to-image diffusion models have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the generative process and how to engineer prompts in order to…

Computer Vision and Pattern Recognition · Computer Science 2024-10-22 Florinel-Alin Croitoru , Vlad Hondru , Radu Tudor Ionescu , Mubarak Shah

The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may get confused when faced with complex texts that contain…

Computer Vision and Pattern Recognition · Computer Science 2024-01-15 Chang Yu , Junran Peng , Xiangyu Zhu , Zhaoxiang Zhang , Qi Tian , Zhen Lei

Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce…

Human-Computer Interaction · Computer Science 2025-06-19 Richa Gupta , Alexander Htet Kyaw

Text-to-image generative models, specifically those based on diffusion models like Imagen and Stable Diffusion, have made substantial advancements. Recently, there has been a surge of interest in the delicate refinement of text prompts.…

Computer Vision and Pattern Recognition · Computer Science 2024-04-08 Wenyi Mo , Tianyu Zhang , Yalong Bai , Bing Su , Ji-Rong Wen , Qing Yang

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be…

Artificial Intelligence · Computer Science 2023-11-02 Yingchaojie Feng , Xingbo Wang , Kam Kwai Wong , Sijia Wang , Yuhong Lu , Minfeng Zhu , Baicheng Wang , Wei Chen

Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the…

Cryptography and Security · Computer Science 2023-01-10 Zeyang Sha , Zheng Li , Ning Yu , Yang Zhang

Text-to-image models have shown remarkable progress in generating high-quality images from user-provided prompts. Despite this, the quality of these images varies due to the models' sensitivity to human language nuances. With advancements…

Artificial Intelligence · Computer Science 2024-06-14 Xinrui Yang , Zhuohan Wang , Anthony Hu

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Siddhartha Datta , Alexander Ku , Deepak Ramachandran , Peter Anderson

Text-to-image generation models~(e.g., Stable Diffusion) have achieved significant advancements, enabling the creation of high-quality and realistic images based on textual descriptions. Prompt inversion, the task of identifying the textual…

Computer Vision and Pattern Recognition · Computer Science 2026-03-06 Mingzhe Li , Kejing Xia , Gehao Zhang , Zhenting Wang , Guanhong Tao , Siqi Pan , Juan Zhai , Shiqing Ma

Text-guided diffusion models such as DALLE-2, Imagen, eDiff-I, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content. In many cases the images…

Computer Vision and Pattern Recognition · Computer Science 2023-09-27 Wan-Duo Kurt Ma , J. P. Lewis , Avisek Lahiri , Thomas Leung , W. Bastiaan Kleijn
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