{"id":896,"date":"2024-11-20T15:33:31","date_gmt":"2024-11-20T15:33:31","guid":{"rendered":"https:\/\/generativeaiassociation.org\/?p=896"},"modified":"2026-10-06T14:39:58","modified_gmt":"2026-10-06T14:39:58","slug":"loras-en-ia-generativa-una-revolucion-en-la-creacion-de-imagenes","status":"publish","type":"post","link":"https:\/\/generativeaiassociation.org\/en\/articles\/loras-en-ia-generativa-una-revolucion-en-la-creacion-de-imagenes\/","title":{"rendered":"LoRAs in generative AI: a revolution in image creation\u00a0"},"content":{"rendered":"<h2 class=\"wp-block-heading\"><strong>Introduction&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Fine-tuning generative AI models is one of the most promising areas of deep learning. Thanks to techniques like fine-tuning and Low-Range Adaptation (LoRA), it&#039;s now possible to customize existing models to suit specific use cases without requiring expensive hardware. This article explores the differences between these techniques, my personal experience applying them, and the many practical applications where they excel.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Fine-tuning vs. LoRA: a technical comparison&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">He <strong>fine-tuning <\/strong>This involves retraining a pre-existing model using a specific dataset, adjusting all its parameters. On the other hand, the <strong>LoRA <\/strong>They allow modifications to be made focused on a reduced dimension space, thus reducing computational complexity.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Aspect<\/td><td>Fine-Tuning<\/td><td>LoRA<\/td><\/tr><tr><td>Flexibility<\/td><td>High<\/td><td>Moderate<\/td><\/tr><tr><td>Requirements<\/td><td>More computing resources<\/td><td>Fewer computing resources<\/td><\/tr><tr><td>Adaptation time<\/td><td>Slow<\/td><td>Fast<\/td><\/tr><tr><td>Precision<\/td><td>Potentially larger<\/td><td>Sufficient for many cases<\/td><\/tr><tr><td>Model size<\/td><td>Elderly<\/td><td>Minor<\/td><\/tr><tr><td>Ideal for<\/td><td>General adaptations<\/td><td>Specific cases<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">With both techniques, we can train different elements such as people, objects, styles, voices, places, textures\/colors\u2026 This is why it has a wide variety of uses, and professionals from different sectors can benefit from this technology. LoRa is especially attractive to those looking for fast and accessible results.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>My experience: Dreambooth vs. LoRA<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In 2022, I discovered Dreambooth, Google&#039;s technology that seemed to offer a revolutionary solution for generating realistic images using AI. However, I soon faced a critical challenge: the models didn&#039;t accurately reproduce the trained features, making the images unsuitable for commercial use.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With the evolution of technology and the emergence of LoRAs, I have regained hope in this technology, and the results obtained this year (2024) are much more promising.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In my case, I wanted to create a new image as if it were a professional, realistic photograph, inspired by a previous photoshoot. Therefore, I trained myself using 25 different images with the following characteristics, which were key to the success of the result; the key is the diversity of the dataset:&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u25cf <strong>Data diversity: <\/strong>backgrounds, lighting, planes and varied expressions.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u25cf <strong>Aspect-ratio relationship: <\/strong>All the images maintained consistent proportions. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u25cf <strong>Quality of references: <\/strong>The images were similar to the desired result.\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here&#039;s a screenshot of the dataset images:&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdFAhrf4xPXICdFbqHWAKxOa6ZF4KIJhaY3JGGdRZBCpSdwEoGscH0h7-caelj-TKCJw6GyQ6cpth_joOE0husx1SrXAMjom11t4_uSQoRLs7WzCW6G3sSynZEGHYBxpGhB8r4Uow?key=zMKEtNN0IcE02MKvLg_rJ7PN\" alt=\"\" style=\"width:700px;height:auto\"\/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The image on the left is the real one, and the one I wanted to base my completely AI-generated variations on (if you look, it&#039;s among the 25 photos in the dataset).<br><\/p>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXe2dys7I8STxtOgjgZcgLnxkKyWqQiPC4T0k0FzSL2VSmII2EKIs70Gd_Yvzjc5MISjO0FY_Z6zelEUvsK_v6os7rK4P0E41PmeJM1R6u2h5vwMWDraFLuPr0aY2onMMeTio2MAGg?key=zMKEtNN0IcE02MKvLg_rJ7PN\" alt=\"\" style=\"width:700px;height:auto\"\/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I am proud of the result of the photographs categorized as \u201cOutput\u201d in that I recognize myself physically, although with a certain embellishment and waxy skin by the model; a tendency or bias that generative AI in general usually presents when creating people, since it tends to polish skin, work on symmetry and mark fractions that we generally identify as beautiful.&nbsp;<br><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Success metrics and limitations&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Despite the progress, a persistent challenge in image generation is the low percentage of truly usable outputs. In my experiments, approximately 80% of the generated images failed to meet the desired standards, primarily due to issues such as a lack of fidelity to the trained object or inconsistent poses. While prompt quality plays a role, as is generally the case with generative AI (garbage in = garbage out), I believe that in this case the main challenge lies not in the prompts used, but in the current limitations of this type of technique for capturing and replicating complex details. Here is a sample of the problems encountered:<\/p>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdrFBaxQnEhRh3qeJOCd92uiNeIUoBJ5tUuoCm9umoLvRCCT3NgHb46LPhpL2UGGbLiWH_RmSU1s2kHlArZICf836QWPXxwFGej9QZ1J-eA1-ywuiPd6n2bNgRL5sN7gz7VSx1iMQ?key=zMKEtNN0IcE02MKvLg_rJ7PN\" alt=\"\" style=\"width:698px;height:auto\"\/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The lack of specific metrics for evaluating training complicates iteration. Ideally, future development would include tools that automatically analyze model quality before mass image generation.&nbsp;<br><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Featured use cases&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LoRa and fine-tuning have applications that transform industries; here are some examples:&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">1. <strong>Fashion and retail<\/strong>: generation of customized digital catalogs at a significantly reduced cost.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">2. <strong>Architecture and interior design<\/strong>Visualization of spaces with varied styles and materials before construction.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">3. <strong>Digital marketing<\/strong>Content adapted to different audiences and platforms without losing the brand&#039;s visual identity.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">4. <strong>Product design<\/strong>: rapid prototyping that facilitates visual iterations in different contexts.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">5. <strong>Entertainment industry<\/strong>: efficient creation of characters, objects and settings for video games and digital content.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Ethical aspects&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">While promising, these advances are not without ethical challenges. The inherent bias in generative models can perpetuate stereotypes, while their ability to replicate faces or styles raises concerns related to intellectual property and privacy. Furthermore, these tools can facilitate the creation and spread of fake news, as they allow for the generation of hyperrealistic visual or textual content that could be used to manipulate information or discredit individuals and institutions.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Therefore, more than ever, it is essential to promote critical thinking, not only among developers and users of these technologies, but also in society in general, to identify and question the veracity of information and the ethical impact of its use.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion&nbsp;<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Fine-tuning and LoRa are powerful tools that are redefining creative workflows. Their success depends on:&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u25cf Prepare diverse and well-curated datasets.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u25cf Understand the technical limitations of each approach.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u25cf Apply these techniques in use cases where variation is more important than absolute accuracy.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As technology evolves, we expect improvements in the quality and accessibility of these tools, as well as deeper integration into industrial and creative processes.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Author: <strong>Montse Labiaga<\/strong>, Consultant in Generative AI<\/p>","protected":false},"excerpt":{"rendered":"<p>Introducci\u00f3n&nbsp; El ajuste fino de modelos de inteligencia artificial generativa es una de las \u00e1reas m\u00e1s prometedoras del deep learning. Gracias a t\u00e9cnicas como el fine-tuning y los LoRA (adaptaci\u00f3n de bajo rango), ahora es posible personalizar modelos existentes para adaptarlos a casos de uso espec\u00edficos, sin necesidad de costosos equipos de hardware. Este art\u00edculo [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":902,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[10],"tags":[21,11],"class_list":["post-896","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-opinion","tag-tipo-articulo","tag-women-in-genai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>LoRAs en IA generativa: una revoluci\u00f3n en la creaci\u00f3n de im\u00e1genes\u00a0 - GenAiA<\/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:\/\/generativeaiassociation.org\/en\/articles\/loras-en-ia-generativa-una-revolucion-en-la-creacion-de-imagenes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LoRAs en IA generativa: una revoluci\u00f3n en la creaci\u00f3n de im\u00e1genes\u00a0 - GenAiA\" \/>\n<meta property=\"og:description\" content=\"Introducci\u00f3n&nbsp; El ajuste fino de modelos de inteligencia artificial generativa es una de las \u00e1reas m\u00e1s prometedoras del deep learning. 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