Want your AI-generated images to capture more attention? Use these 4 essential enhancement techniques:

AI-generated images are more than just a novelty; they are a pivotal tool for captivating your audience and conveying your brand’s message.

However, the real magic lies not just in generating these images but in skillfully enhancing them to stand out. As someone who has delved deep into the intricacies of AI and its applications in software development, I understand the immense potential that lies in fine-tuning these visuals.

In this article, we’re going to explore six essential techniques that can transform your AI-generated images from standard automated outputs to eye-catching masterpieces that truly resonate with your audience.

Whether you’re a seasoned marketer, a creative entrepreneur, or someone just beginning to explore the realm of AI in business, these techniques will equip you with the know-how to elevate your visual content to new heights of engagement and effectiveness.

Hey guys it’s Adrian here. If you appreciate my content consider hitting the like button or sharing this article. It’s the only way the algorithm really notices me.

Use high-resolution data

You’re wasting your time with AI generated images if you are not considering the data you are starting with.

Even with apps like Midjourney or DallE, professionals consider the source of the AI. The use of high-resolution data means feature rich image generation and creativity.

Garbage in, garbage out is a common mantra in artificial intelligence. While there is some room for “generalization”, or flexibility, in the model it’s important to keep in mind the specific purpose the AI was built for.

If the data fed to the generative model is only puppies then you should use it to generate puppy pictures and not kitties. Even though everyone knows cats are better ;)

Fine-tune your model

I have a confession to make. I lied earlier.

All the talk about starting with quality data is only a small piece of a much larger puzzle.

Generative AI models use neural networks, a subclass of deep learning, to generate the end result. As part of the architecture a small portion of the hidden layers are set aside for fine-tuning purposes.

The set of layers set aside is often called the “head” of the neural net. These layers can be given entirely new sets of data and parameters to modify the weights. Weights are what the AI considers important.

An example of this is fine-tuning an LLM with only social media posts. The main functionality stays the same but now it can only output social media posts.

Apply post-processing

The reason you are not satisfied with the quality of your AI generated images is because you are missing something vital.

Recent trends in generative AI have been learning heavily on relatively new technology. Specifically, AI post-processing.

Adobe Sensei has the most impressive results in this exciting new area of generative imaging. After you have generated your image Sensei allows you to improve your images with text commands.

It’s as easy as selecting the part of your image you want to improve, typing your prompt, and being impressed with the output.

While traditional approaches to post-processing like Photoshop and colouring are still applicable we are quickly moving into a completely AI world.

Evaluate your results

You’re not an idiot. Are you?

We all know that images generated by AI are not perfect. They require a period of review. Ensuring that quality standards are met.

It is important to evaluate images that are generated for the reasons we have outlined above. The input images that determine the end result as well as the fine-tuning applied that could tweak the image you expect to receive.

While reviewing the final product can be done manually there is always room for the introduction of artificial intelligence. Often the same models that generate images can be used to analyze them.

Since the model already knows the weights or “what to look for” it can effortlessly input an image and output a quality evaluation.