AI image generation jumped to the fore with DALL-E and Midjourney swiftly following generative AI’s public debut. Despite the controversy, AI image generation morphs marketers' and brands' ability to produce images for their banks of collateral from (relatively slowly) iterating new designs to rapidly proliferating graphics for any purpose in seconds.
Custom AI image generation is a consumer market dream, taking the adage' a picture is worth a thousand words' into entirely new territory. AI image tools, where embraced by design teams instead of shunned, can enhance the editing capability and productivity of designers tenfold. So what can these tools achieve, and why should brand leaders embrace this technology? How can one get started “prompting” tools like Stable Diffusion to churn out a new wave of intricately personalized visuals for customers?
Optimum Use-Cases and Benefits of Custom AI Image Generation
The impact of custom AI image models cannot be understated. They are quite simply upending existing design workflows. Some designers perceive AI image tools as a replacement for their creativity, and we’ll cover this in later articles. Other designers are embracing AI’s ability to amplify their skill set and open up new ways to impart creative influence.
Here at Generative Labs we work with a digital marketing agency that wanted to incorporate generative AI into their existing automated outreach efforts. We fine-tuned a custom AI image model for a campaign targeting 40-60-year-old business professionals. The AI model produced visual assets entirely customized to each individual, at scale, that help the recipients perceive themselves in a particular future state in order to build product interest.
This recent work was completed in collaboration with the agency’s existing marketing design team, and the result was incredibly impactful. It’s just one example of the power of generative AI for intricately personalized marketing, a must for today’s campaigns, and a project delivered with a marketing agency that already had extensive expertise of its own. The use cases for custom AI image generation are much much broader, however, and they range from simple applications for small businesses and independent designers to robust and commanding enterprise solutions.
At the most basic level, having an AI model trained to quickly produce brand-aligned images for social media, blog posts, adverts, personalized emails, presentations, staff training, product showcases, and the like can save hours of time and the cost of having in-house or contract design teams.
If your business is visually focused in any way, then not only are the applications endless, but almost exorbitantly time and cost-saving, even facilitating pivots to new markets in a fraction of the time spent previously.
If you are building a website, application, or game, suddenly, you can create hundreds or thousands of images for every page or user action, all to a set theme and each image in seconds.
One card game developer recently shelled out $90,000 for an AI artist to create card art “because no one comes close to the quality.” Champions of Otherworldly Magic says it pays the artist $1,000 per month for just two days of work, explaining that:
"We pay our AI artist 15,000 USD per month for exactly 10 hours of work. Why? In that time, he still makes HUNDREDS of AMAZING bits of artwork—ASTRONOMICALLY FASTER than ANY team of traditional artists.
His art is 100% AI generated, yet it has no extra fingers, no generic designs, no mistakes... It has consistent evolutions, skins, alt art styles—literally no one is on his level. We don't care how he makes it, we only care that the end user enjoys our game."
The game developer told PC Gamer, “For us to get this with a team of traditional artists it would cost us a lot more money, and time.”
The principle is the same in e-commerce and fashion, where campaigns are stronger with products featured in settings that resonate with the consumer. Imagine shopping for clothes when you can see images of the garment on a model of your exact size and height. Consider architects who can turn structural outlines into 3D-rendered building tours with a mere fraction of the effort required using conventional tools.
The Advantages of Fine-Tuning an AI Image Model
We’ve already touched on some of the advantages of fine-tuning an AI image model, including the capacity to churn out relevant image after image to create campaigns and even entire visual products, like games or apps. But where does fine-tuning really begin to accentuate custom image generation?
Fine-tuning an AI model trains the software on a specific set of data to customize it to a use case or a business. Think of it like developing branding, vision, values, and a mission with product and marketing teams. The AI model is supplied with parameters, examples, styles, themes, and colors but also restrictions on what not to do.
Let’s take Stable Diffusion as an example. This text-to-image generation program can generate realistic images. The model can be trained on a set of existing images and with prompts, so when you need a new image for a blog post or a product shot, all you or your designer have to do is request the new image. The more fine-tuning in advance, the fewer revisions to the new image will be needed.
A designer who wants to improve existing artwork and brand images no longer has to spend hours with tools like Photoshop to make small amends. Using AI, designers can present a suite of possible brand assets to a CEO in a few hours, take the feedback, and finalize the results by the next day.
Custom AI Image Models - The Process
So, what’s the process for fine-tuning a custom AI image model? It really will be based on your use case, the purpose of the images, and the accuracy required. An AI image generator can be trained on hundreds of existing images or human models for maximum output, relevance, and accuracy.
DreamBooth and LoRA (Low-Rank Adaptation) are both examples of tools that can be used to fine-tune Stable Diffusion for customized, high-quality image generation. The process involves defining output requirements, the language that will be used to generate images, gathering training data, uploading the data, training the model, and then testing and iterating.
Depending on the size of the project, the fine-tuning process can require a robust GPU infrastructure and AI expertise for effective training and to mitigate the risks associated with poorer outputs, which can include not only those extra fingers that seem to be appearing across the net but also misrepresentation and other dangers posed by AI use.
The advantages of custom AI image generation, however, are truly exponential, even at this stage of AI’s evolution. It’s possible to realize them internally, with sufficient AI expertise or team dynamics, but if you’re seeking strategic AI consulting and implementation to get AI right fast, consider working with us here at Generative Labs.
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