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Hur AI-genererade bilder kan effektivisera ditt SEO-spel med DALL-E 2

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How AI-generated images can streamline your SEO game with DALLE-2

30-second summary:

  • SEOs are always on the lookout for innovative technology that can help them amplify content creation effectively
  • One such innovation that is on the cusp of being the next big thing in SEO and content creation is OpenAI’s DALL-E 2
  • What is it, how does it work, and how can SEOs use it (or at least start experimenting with it)?

Have you ever wanted to feel like Salvador Dali? Maybe even create a small cute robot that could look like WALL-E? Your dreams very well might come true with the recent development of the technology behind AI. If that sounds interesting, let’s dive a bit deeper into this topic. Let’s talk about DALL-E 2.

Ok Google, what does AI Do?

Artificial intelligence (AI) aims to create unique algorithms that can behave like people in specific situations – recognize human speech and various objects, write and read texts, and the like. This technology is already far ahead of human capabilities in many spheres involving data processing. Until recently, AI was encroaching mainly on the fields that are linked with technical tasks – predictive analytics, robotization, image, and speech recognition. Today AI surpasses people by 40 percent on trivia

But can AI also take on creative functions? It seems this is the last field to be mastered by neural networks. Art is a complicated combination of skill, creativity, and aesthetic taste, which all are very human elements. However, in April 2022, the OpenAI group proved otherwise by releasing a powerful text-to-image convertor, DALLE – 2, that can transform any text caption into a visual presentation that has never existed before. Its most winning feature is that the tool can precisely and logically convey relationships between objects it displays.

What is DALLE-2?

This neural network was created by OpenAI. Originally, it was GPT-2, a technology that could work with languages – answer questions, complete text, analyze content, and make conclusions. It was improved to GPT-3 – its capabilities expanded beyond textual information and enabled it to work with the images. 

Already in January 2021, this technology was followed by its new mind-blowing version that could build a connection between text and images. This neural network was called DALLE. The most remarkable thing is that it can come up not only with objects known to us but also produce completely new combinations, creating objects that do not exist in nature. In simple words, DALLE is a transformer consisting of the decoder, which processes a sequence of 1280 tokens. These are 256 text tokens and 1024 image part tokens. The algorithm treats image regions in the same way as words in a text and generates new images identically to how GPT-3 generates new text. In 2022, the project was scaled to DALLE-2. The improved version creates an image just from a text prompt.

How does DALLE-2 work?

It is not the first attempt to create a text-to-image generation system. However, the capabilities of DALLE-2 are much broader. This neural network can effectively link textual and visual abstractions and provide a true-to-life image. How does the system know how a particular object is interacting with the environment? The algorithm is quite difficult to be explained in detail. Still, roughly it consists of several stages and uses other OpenAI models – CLIP (Contrastive Language-Image Pre-training) and GLIDE (Guided Language-to-Image Diffusion for Generation and Editing).

  • Mapping the image description to its space presentation via the CLIP text encoder. CLIP is trained on hundreds of millions of images and their associated captions, figuring out how a particular piece of text relates to an image. The model does not predict the caption but learns how it is related to the image. This comparative approach allows establishing the relationship between textual and visual representations of the same abstract object. This stage is critical to the creation of images by the neural network.
  • Encoding the CLIP-learned image. The next task is to create the image, the details of which have been suggested by CLIP. Now, DALLE-2 uses a modified version of another OpenAI model, GLIDE, to create this image. It is based on a diffusion model – data is generated by reversing the process of gradual image noise. The learning process is supplemented with additional textual information, which ultimately leads to the creation of more accurate images. 

Based on the above, DALL-E 2 can generate semantically consistent images that naturally fit any object in the surrounding space.

DALLE-2 for SEO

The vast potential of AI image generation immediately attracted the attention of SEO specialists. They spend a lot of time finding appropriate pictures to support their text content. However, it becomes increasingly difficult to invent something that is not just copied and stitched together from the web. So DALLE-2 can become a great source of a never-ending flow of wholly unique and non-standard images. Interestingly, users will have exclusive rights to use the images they create, including for commercial use.

How it can help SEO

Nowadays, website and content promotion are not possible without attractive visuals. Images add more value to your SEO efforts – your site wins more user engagement and tillgänglighet. But sourcing enough appropriate pictures has always been a headache. DALLE-2 can solve this task with ease. You just need to print a descriptive prompt of your future image, and AI will come up with a result. The text should not exceed 400 characters. But users should be ready to train a little to create explicit requests. It is highly advisable to study Prompt Book and master the basics to avoid weird results. You will learn the most valuable tips on how to get the most out of this fantastic image generator.

If you’d like to further automate your image creation process this tool will allow you to generate a prompt that can be used on DALLE-2.

Use cases (blog posts, product images, designs, digital art, thumbnails)

AI algorithms were already used in SEO before for naming objects on the images and creating descriptions for them based on data. With DALLE-2, this process is flipped around, and now you can generate images based on text prompts. No matter whether you are running an online blog or a store – you need lots of visuals to attract new customers and followers. And DALLE-2 can successfully be integrated into any project where you need image supplements –  create illustrations for your blog posts, product descriptions, design sketches, and much more. Moreover, you can further modify already created images. 

You can already see some successful use cases of DALLE-2. 

  • Blog thumbnail optimization. The Deephaven blog thumbnails have been replaced by images fully generated by DALLE-2. It took a couple of minutes and several prompts per image to get the desired result. However, it is a significant time saving compared to what would have been spent on the search for stock images. A nice bonus is that DALLE-2-generated images are fully unique and memorable.
  • Design development. DALLE-2 can become an efficient tool in the design field. And it looks like its capabilities are endless. For example, a picture of the existing garden was taken, and a rectangular swimming pool was applied to it via DALLE-2. It helps the client envision how it might look in reality.

For more use cases and live community discussions join r/dalle.

Currently, users are just experimenting with DALLE-2, but there is no doubt it will be soon actively applied in business, architecture, fashion, and other spheres.

Examples of DALL-E 2

DALL-E 2 is launched in beta version with a credit-based model open to 100,000 users. Another million applicants are waiting for approval to test this AI product. Some users have already shared their first experience with the converter, and the results are impressive. DALL-E 2 processes the craziest requests and offers its interpretation. Here are a few examples:

Prompt #1

A sad beaver in the sweater sitting in front of the screen and thinking about apples.

Examples of AI-generated images can streamline your SEO game with DALLE-2 - Sad beaver

Källa: Twitter

Prompt #2

A charcuterie board floating in a pool on the Amalfi coast.

Examples of AI-generated images can streamline your SEO game with DALLE-2 - Amalfi coast
Källa: Twitter

Prompt #3

Källa: Twitter

Prompt #4

A person in the space suit walking on Mars near the creator with dried-out grass and remnants of the Voyager.

Examples of AI-generated images can streamline your SEO game with DALLE-2 - Space man
Prompt:A person in the space suit walking on Mars near the creator with dried-out grass and remnants of the Voyager

Källa: LinkedIn

Prompt #5

A Ukrainian on the field harvesting crops.

Källa: Twitter

Slutsats

DALL-E 2 is a revolutionary text-to-image converter today. It will help you instantly generate a variety of unique images with only a short text prompt in failry shorter time spans than you would spend on photo stock sites. This technology is an absolute game changer and can rearrange a lot of things in SEO in the coming years. Yet, more live testing is still needed to benefit from DALL-E 2 to the fullest.


Dima Makei is Head of SEO at Omnicom Media Group. He is also passionate about teaching and has previously served as a Marketing Professor at Seneca College. Find him on Twitter @dima_makei.

Prenumerera på Sökmotor Titta på nyhetsbrev för insikter om SEO, söklandskapet, sökmarknadsföring, digital marknadsföring, ledarskap, poddar och mer.

Gå med i samtalet med oss på LinkedIn och Twitter.

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SEO

Hur man optimerar den största innehållsrika färgen och rankas högre

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How To Optimize The Largest Contentful Paint & Rank Higher

How To Measure The Largest Contentful Paint Of Your Website

Run a free website speed test to find out. Your LCP speed will be displayed immediately.

The results of your speed test will tell you if:

  • The LCP threshold is met.
  • You need to optimize any other Core Web Vital.

How Is The Largest Contentful Paint Calculated?

Google looks at the 75th percentile of experiences – that means 25% of real website visitors experience LCP load times of 3.09 seconds or higher, while for 75% of users the LCP is below 3.09 seconds.

In this example, the real-user LCP is shown as 3.09 seconds.

Screenshot of a Core Web Vitals data of DebugBear.com, November 2022

What Are The Lab Test Results On My Core Web Vitals Data?

Med this specific web speed test, you’ll also see lab metrics that were collected in a controlled test environment. While these metrics don’t directly impact Google rankings, there are two advantages of this data:

  1. The metrics update as soon as you improve your website, while Google’s real-time data will take 28 days to fully update.
  2. You get detailed reports in addition to the metrics, which can help you optimize your website.

Dessutom, PageSpeed Insights also provides lab data, but keep in mind that the data it reports can sometimes be misleading due to the simulated throttling it uses to emulate a slower network connection.

How Do You Find Your Largest Contentful Paint Element?

When you run a page speed test with DebugBear, the LCP element is highlighted in the test result.

Sometimes, the LCP element may be a large image, and other times, it could be a large portion of text.

Regardless of whether your LCP element is an image or a piece of text, the LCP content won’t appear until your page starts rendering.

For example, on the page below, a background image is responsible for the largest paint.

How To Optimize The Largest Contentful Paint & Rank Higher In GoogleScreenshot of DebugBear.com, November 2022

In contrast, this page’s LCP is a paragraph of text.

How To Optimize The Largest Contentful Paint & Rank Higher In GoogleScreenshot of DebugBear.com, November 2022

To improve the Largest Contentful Paint (LCP) of your website you need to ensure that the HTML element responsible for the LCP appears quickly.

How To Improve The Largest Contentful Paint

To improve the LCP you need to:

  1. Find out what resources are necessary to make the LCP element appear.
  2. See how you can load those resources faster (or not at all).

For example, if the LCP element is a photo, you could reduce the file size of the image.

After running a DebugBear speed test, you can click on each performance metric to view more information on how it could be optimized.

How To Optimize The Largest Contentful Paint & Rank Higher In GoogleScreenshot of a detailed Largest Contentful Paint analysis in DebugBear.com, November 2022

Common resources that affect the LCP are:

  • Render-blocking resources.
  • Images that are not optimized.
  • Outdated image formats.
  • Fonts that are not optimized.

How To Reduce Render-Blocking Resources

Render-blocking resources are files that need to be downloaded before the browser can start drawing page content on the screen. CSS stylesheets are typically render-blocking, as are many script tags.

To reduce the performance impact of render-blocking resources you can:

  1. Identify what resources are render-blocking.
  2. Review if the resource is necessary.
  3. Review if the resource needs to block rendering.
  4. See if the resource can be loaded more quickly up, for example using compression.

The Easy Way: In the DebugBear request waterfall, requests for render-blocking resources are marked with a “Blocking” tag.

How To Optimize The Largest Contentful Paint & Rank Higher In GoogleScreenshot of DebugBear.com, November 2022

How To Prioritize & Speed Up LCP Image Requests

For this section, we’re going to leverage the new “fetchpriority” attribute on images to help your visitor’s browsers quickly identify what image should load first.

Use this attribute on your LCP element.

Varför?

When just looking at the HTML, browsers often can’t immediately tell what images are important. One image might end up being a large background image, while another one might be a small part of the website footer.

Accordingly, all images are initially considered low priority, until the page has been rendered and the browser knows where the image appears.

However, that can mean that the browser only starts downloading the LCP image fairly late.

The new Priority Hints web standard allows website owners to provide more information to help browsers prioritize images and other resources.

In the example below, we can see that the browser spends a lot of time waiting, as indicated by the gray bar.

How To Optimize The Largest Contentful Paint & Rank Higher In GoogleScreenshot of a low-priority LCP image on DebugBear.com, November 2022

We would choose this LCP image to add the “fetchpriority” attribute to.

How To Add The “FetchPriority” Attribute To Images

Om du helt enkelt lägger till attributet fetchpriority=”high” till en HTML-img-tagg kommer webbläsaren att prioritera att ladda ner den bilden så snabbt som möjligt.

<img src="https://www.searchenginejournal.com/optimize-largest-contentful-paint-debugbear-spcs/471883/photo.jpg" fetchpriority="hög" />

Hur man använder moderna bildformat och storlek på bilder på lämpligt sätt

Högupplösta bilder kan ofta ha en stor filstorlek, vilket gör att de tar lång tid att ladda ner.

I hastighetstestresultatet nedan kan du se det genom att titta på de mörkblå skuggade områdena. Varje rad indikerar en bit av bilden som kommer till webbläsaren.

How To Optimize The Largest Contentful Paint &#038; Rank Higher In GoogleSkärmdump av en stor LCP-bild på DebugBear.com, november 2022

Det finns två sätt att minska bildstorlekar:

  1. Se till att bildupplösningen är så låg som möjligt. Överväg att visa bilder i olika upplösningar beroende på storleken på användarens enhet.
  2. Använd en modern bildformat som WebP, som kan lagra bilder av samma kvalitet i en lägre filstorlek.

Hur man optimerar teckensnittsladdningstider

Om LCP-elementet är en HTML-rubrik eller ett stycke är det viktigt att snabbt ladda teckensnittet för denna textbit.

Ett sätt att uppnå detta skulle vara att använda förladda taggar som kan berätta för webbläsaren att ladda teckensnitten tidigt.

De font-display: swap CSS-regeln kan också säkerställa snabbare rendering, eftersom webbläsaren omedelbart renderar texten med ett standardteckensnitt innan den byter till webbteckensnittet senare.

How To Optimize The Largest Contentful Paint &#038; Rank Higher In GoogleSkärmdump av webbteckensnitt som fördröjer LCP på DebugBear.com, november 2022

Övervaka din webbplats för att hålla LCP snabbt

Att kontinuerligt övervaka din webbplats låter dig inte bara verifiera att dina LCP-optimeringar fungerar, utan ser också till att du blir varnad om din LCP blir sämre.

DebugBear kan övervaka Core Web Vitals och andra mätvärden för webbplatshastighet över tid. Förutom att köra djupgående labbbaserade tester, håller produkten även reda på de verkliga användarvärdena från Google.

Prova DebugBear med en gratis 14 dagars provperiod.

How To Optimize The Largest Contentful Paint &#038; Rank Higher In GoogleSkärmdump av övervakningsdata för webbplatshastighet på DebugBear.com, november 2022



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