{"id":30176,"date":"2022-08-24T11:45:54","date_gmt":"2022-08-24T18:45:54","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=30176"},"modified":"2023-06-23T12:39:54","modified_gmt":"2023-06-23T19:39:54","slug":"research-highlights-an-image-is-worth-one-word-personalizing-text-to-image-generation-using-textual-inversion","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/08\/24\/research-highlights-an-image-is-worth-one-word-personalizing-text-to-image-generation-using-textual-inversion\/","title":{"rendered":"Research Highlights: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion"},"content":{"rendered":"\n

Title of Paper: <\/strong>An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion<\/a><\/p>\n\n\n\n

Code:<\/strong> https:\/\/github.com\/rinongal\/textual_inversion<\/a><\/p>\n\n\n\n

Overview: <\/strong>Recently, there has been an increase of text-to-image models that allow the synthesis of novel scenes and rich images using different styles. In terms of the artistic creation process using these generative models, coming up with effective text descriptions to render a desired target remains a challenge. It’s now clear how to generate images of specific unique concepts, incorporate modifications on appearance, and compose them in different roles and novel scenes. The featured research paper proposes a new approach designed to tackle these challenges and allow for more creative freedom with these generative systems.<\/p>\n\n\n\n

This new research takes a few images for a concept and learns to represent it through new “words” in the embedding space of a frozen text-to-image model. Through a process called “textual inversions,” the goal is to find new pseudo-words in the embedding space that can capture high-level semantics and fine visual details. These words are then used to compose new sentences to guide novel personalized creations. Results demonstrate that this approach for personalizing text-to-image generation can provide high visual fidelity and enables robust editing of scenes. <\/p>\n\n\n

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Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it\u2019s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!<\/p>\n","protected":false},"author":37,"featured_media":22835,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,182,180,67,56,84,1303,1],"tags":[437,133,264,277,1176,96],"acf":[],"yoast_head":"\nResearch Highlights: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/08\/24\/research-highlights-an-image-is-worth-one-word-personalizing-text-to-image-generation-using-textual-inversion\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Highlights: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it\u2019s important to keep connected with the research arm of the field in order to understand where the technology is headed. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/06\/Data-Scientist-shutterstock_768047488.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-7QI","jetpack-related-posts":[{"id":24656,"url":"https:\/\/insidebigdata.com\/2020\/07\/16\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-june-2020\/","url_meta":{"origin":30176,"position":0},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 June 2020","date":"July 16, 2020","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":20335,"url":"https:\/\/insidebigdata.com\/2018\/05\/07\/best-arxiv-org-ai-machine-learning-deep-learning-april-2018\/","url_meta":{"origin":30176,"position":1},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 April 2018","date":"May 7, 2018","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":28141,"url":"https:\/\/insidebigdata.com\/2022\/01\/06\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-december-2021\/","url_meta":{"origin":30176,"position":2},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 December 2021","date":"January 6, 2022","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":23280,"url":"https:\/\/insidebigdata.com\/2019\/09\/18\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-august-2019\/","url_meta":{"origin":30176,"position":3},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 August 2019","date":"September 18, 2019","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":26410,"url":"https:\/\/insidebigdata.com\/2021\/06\/07\/applying-gans-to-image-generation-tasks\/","url_meta":{"origin":30176,"position":4},"title":"Applying GANs to Image Generation Tasks","date":"June 7, 2021","format":false,"excerpt":"This contributed article by Maksym Tatariants, PhD, AI Engineer at MobiDev, overviews StyleGAN2 application to image generation task and is based on MobiDev\u2019s logotype synthesis research. 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