AI Image Generation For Dummies

Apps like Lensa and Picai have exploded in popularity recently, all based around the concept of AI image generation and AI photo manipulation.

Even if AI isn’t necessarily something you have a big interest in, its capabilities make it a potential resource for anyone interested in marketing their business.

In this article I’m going to explore the process behind these AI image apps and explain how you can leverage them in your marketing efforts.

What is AI Image Generation?

In simple terms, AI image generation is the use of apps that rely on AI models on the backend in order to create unique images based on user prompts.

The AI models themselves are trained on actual pieces of art across all genres, including art that is available on the Web.

A common misconception of AI art is that the AI is simply taking other works of art and smooshing them together. This is not true, however.

The art that is generated using these new AI image apps renders through a diffusion process that utilizes data points and connects them to various points and features within the pieces of art the AI is trained on.

In other words the AI apps produce art much in the same way as we do, albeit through a generative rather than manual process.

Much like the copy produced by AI text generators is unique, so is the art produced by AI image generation apps. Though the AI may draw from the styles of real artists or even reference an existent work of art, this process is literally no different than what actual human artists do.

A Close Look at the AI Image Generation Process

So, AI image generation is the process of using artificial intelligence techniques to create new images. This description is very surface-level, however.

Th generation process involves several steps that are aimed at training a model to generate new images based on the input of user data.

Here’s a more detailed look into the process of AI image generation:

• Data collection: The first step in AI image generation is to collect a large dataset of images. This dataset can be created by scraping the web or collecting images from a specific domain.
• Data pre-processing: Once the dataset is collected, the images need to be pre-processed. This includes resizing the images to a common size, converting the images to grayscale, and normalizing the pixel values.
• Training a generative model: The next step is to train a generative model, such as a Generative Adversarial Network (GAN) or a Variational Autoencoder (VAE). These models are trained to generate new images that are similar to the ones in the dataset.
• Testing the AI model: After the model is finished the training phase, it needs to be tested on new data. This is done by providing the model with random noise as input and then generating an image based on that noise.
• Fine-tuning the model: Based on the results of the testing, the model may need to be fine-tuned. This involves adjusting the parameters of the model to improve the quality of the generated images.
• Generating new images: Once the model is trained and fine-tuned, it can be used to generate new images. This is done by providing the model with random noise and then generating an image based on that noise. The generated images may be similar to the ones in the dataset or they may be entirely new and unique.
• Evaluation of generated images: The final step is to evaluate the generated images. This is done by comparing the generated images to the ones in the dataset and checking for quality, diversity, and realism. If the generated images are not of high quality, the model may need to be further fine-tuned or a new dataset may need to be collected.

Clearly, we are not dealing with any kind of “plagiarism” as some AI naysayers would claim.

In fact, most AI image generation models were very crude until recently and served no practical purpose.

Breakthroughs in complex AI algorithms have allowed for larger and more comprehensive data sets.

These recent revolutions have allowed AI to burst into the mainstream with a variety of interesting use cases, one of which happens to be marketing.

Using AI Image Generation in Your Marketing Efforts

One of the greatest benefits of AI is its ability to somewhat level the playing field when it comes to creative output.

No longer are beautiful works of art reserved for people who are gifted with mechanical skill. Now, creativity is the deciding factor in terms of artistic output.

This is a boon for marketers who previously had the vision but lacked the physical talent.

If the AI image generation scene is any indication, this is a rather sizable demographic.

I can relate, as my physical drawing skills always lagged behind what my mind could dream up.

The potential use cases for this kind of technology have not yet been fully realized.

Up until now, stock photography has been heavily relied on throughout the world of marketing simply as a necessity. It is not always possible to source original photography and while UGC and similar forms of content are the current trend, there are certain cases where it does not do the job.

For instance, as effective as UGC (user generated content) is for viral social media marketing, you wouldn’t want your homepage banner image or a product page image to be TikTok-quality, at least most of the time.

There are also certain niches that benefit from more stylized and artistic content such as the image above that I generated. There is no form of UGC that competes with this, it’s impossible to create with standard phone apps. You either have to digitally paint it manually, or generate it with AI, there’s no in between.

Here are some ways anyone can use AI image generation tools and apps in their digital marketing:

• Colorful solid, gradient, and patterned backgrounds for product photography that are don’t need any graphic design skill to create.
• Stylized illustrations of products (turn your current products into anime, Pixar style, bit art, or an impressionist painting with ease).
• Concept art and mockups
• Royalty free blog images
• Simple social media graphics
• Brainstorming and idea generation

If you have the best software and are really great with prompts, you can also:

• Create your own AI influencer / avatar model
• Create your own royalty free images with actual people in them
• Create complex or very specific product images

The use cases are wide ranging and will only increase as the technology evolves.

Graphical elements are one of the pain points for eCommerce businesses. Whether it’s product photography, unique homepage banners, social media graphics, or anything else.

AI image generation gives anyone the ability to create outstanding content that can be utilized in a multitude of creative ways.

How to Start Using AI in Your Marketing

The easiest way to begin using AI image generation in your marketing efforts is to diagnose any weak spots you have when it comes to images and graphics.

Do you need better product photos? Homepage images? Social media graphics?

Determine what your needs are and that will help you figure out how to get started with certain AI tools.

For instance, if you are doing well across the board in terms of images but you just want to leverage AI to add a bit of pop or create some viral buzz, try using Lensa to create AI versions of you or other members of your team. You can post these online to generate some conversation or add a dynamic element to your social media.

If you need more extensive help with graphics, consider using Stable Diffusion or a similar app that has a wide range of capabilities for image generation.

With dedicated AI apps, you can create varied images by inputting different prompts. Well-written and descriptive prompts are the key to getting the best results with AI image generators.

This is where a bit of marketing savvy and creativity still come into play. Dry prompts that lack depth may produce lackluster results, so you may have to master the art of prompts before getting consistent results.

Need a little more background on AI and how it may impact online business? We have an in-depth guide to AI and eCommerce that is worth a look, especially if you are a merchant that is interested in using this technology.