Among all AI technologies, image generation AI is currently attracting the most attention. Capable of instantaneously generating high-quality photo-like images and illustrations just by providing instructions via text, image generation technology can be utilized in many business scenes. In this article, we will explain in detail the mechanisms of image generation AI, the types of foundational technologies, the benefits of utilization, and business use cases. In the latter half of the article, we also introduce the procedures for developing image generation AI and points to consider during implementation, providing content that will help you understand the flow up to the start of using image generation AI and be useful during its introduction.
Image generation AI is an AI that automatically generates new images based on text (prompts) entered by the user or existing images. For example, if you enter "sunset in a futuristic city with flying cars," the AI will generate an image depicting that scene. By utilizing image generation AI, it is possible to create visual content more quickly and creatively than traditional image production methods that rely on humans.
Many image generation AIs utilize deep learning technology to learn from vast amounts of image data and text data associated with them. They generate images using two main components: a "text encoder" and an "image generator."
| Component | Function/Role |
| Text Encoder | Analyzes the text entered by the user and converts meanings and keywords into numerical values (= latent representation) |
| Image Generator | Generates images based on the latent representation obtained from the text encoder |
In the generation process, the global composition of the image (shape and layout) and the fine details (color and texture) are created step-by-step.
Along with the rising demand for image generation AI, a wide variety of image generation AI services are being provided. The main services are summarized below.
| Service Name | Features |
| DALL-E (OpenAI) | Flexible response to prompts, integrates with ChatGPT (switched to GPT-4o as of March 2025) |
| Midjourney (Midjourney) | Excels at realistic image styles |
| Stable Diffusion (Stability AI) | Open-source and allows for free customization |
Each service has different strengths, and it is best to choose one based on your intended use and needs.
Image generation AI is utilized in various industries and fields. Below are typical usage scenes of image generation AI.
| Usage Scene | Specific Example |
| Design Production | Used for visualizing concepts or in actual work production |
| Advertising | Production of creative visual content |
| Marketing | Generating personalized advertising images or visuals for campaigns |
| Gaming | Character design |
| Generating Training Data for AI | Generating large amounts of data required for training AI models |
A usage method that has attracted particular attention recently is the generation of training data (learning data) used for training AI models. Collecting training data requires an enormous amount of time and cost, posing a heavy burden especially for AI learning that requires massive datasets. By utilizing image generation AI, there is no longer a need to search for required data within vast databases or public data, and large amounts of image data can be obtained quickly. By reducing the effort of data collection, development costs are lowered and AI development efficiency is improved. Therefore, the utilization of image generation AI is attracting attention for preparing diseased area images in the medical field or defective product images for visual inspection in manufacturing, where data collection often takes a particularly long time.
There are various types of foundational technologies for image generation AI. Representative foundational technologies are introduced below.
VAE is a model that learns by efficiently compressing (encoding) features of input data (images) and then restoring (decoding) the original data from those features. Since it can perform image compression and generation simultaneously, it is utilized as a foundational technology for image generation AI. It is also effective for adjusting variations in generated images.
GAN consists of two networks: the Generator and the Discriminator. It is a method where the two networks learn by competing with each other as follows:
1. Generator: Outputs fake images that look exactly like the real thing
2. Discriminator: Tries to see through whether the Generator's generated data is real or fake
By repeating the above flow and learning while mutually improving accuracy, the mechanism eventually generates realistic images indistinguishable from the real thing. In particular, StyleGAN, one of the GAN series, is widely utilized in video production and advertising because it can generate high-resolution face images that look real. However, learning can sometimes become unstable.
Diffusion models gradually add noise to an original image until it eventually becomes complete noise. Next, they learn the reverse process of restoring (denoising) the original image from the state of pure noise. By applying this iterative process, highly high-definition and diverse images can be generated from random noise. A key feature is the ability to create high-quality images stably with detail generation that rivals GANs. Therefore, it has been adopted by many major image generation AI services such as Stable Diffusion, DALL-E 3, and Midjourney. However, the generation time is longer compared to GANs.
Transformers are a technology originally used in natural language processing but have also been applied as image generation models. A mechanism called "Attention" is at its core, learning which parts of the input data to focus on. In particular, the "Diffusion Transformer," which combines Diffusion models and Transformers, enables highly efficient image generation through the combination of Diffusion's detailed image generation capability and Transformer's parallel processing capability.
Image generation AI is creating benefits in various business fields.
By utilizing image generation AI, part of the creative work performed by professional designers can be automated, allowing for a reduction in production costs. For example, initial prototyping of ideas and consideration of visual concepts can be done in a short period, reducing manual repetitive tasks. Therefore, significant cost savings can be expected.
If you utilize image generation AI at the initial stages of product development or campaign design, you can generate numerous design proposals and visual concepts in a short period. You can choose the optimal design from more options, speeding up the production process. Furthermore, by providing rapid prototyping with image generation AI, consensus building among stakeholders can be accelerated. As a result, the time taken for production is shortened, and the overall operational efficiency of production is improved.
Image generation AI can generate images in a wide variety of styles, such as anime, watercolor, or realism. It can generate not only images like those produced by professional artists but also visual expressions incorporating novel representations, leading to the creation of highly unique content. Therefore, by utilizing image generation AI, novel and attractive designs that differentiate you from other companies can be realized. Variations in content style and expression methods increase, allowing you to approach your target audience more effectively.
Image generation AI is utilized for unique promotions and advertising activities in various industries. Representative cases are introduced below. For other generative AI use cases, please see "A summary of the latest examples of companies using generative AI! A thorough explanation of the benefits and implementation methods."
Asahi Breweries launched an experiential promotion utilizing the image generation AI "Stable Diffusion" as a first-of-its-kind attempt in Japan to provide new customer experiences. They set up an image creation site called "Create Your DRY CRYSTAL ART" powered by Stable Diffusion, where users upload text or their own images. Doing so creates original art images. Users can also specify tastes such as location, mood, watercolor style, or anime style, and Stable Diffusion transforms the images into art. This successfully expanded product recognition and stimulated purchases by making customers feel a new lifestyle proposal matched with the product's worldview and further promoting customer experiences.
To strengthen in-store sales promotion activities, Ito En adopted posters utilizing original AI characters created with image generation AI. To express the scent of yuzu and aroma in new products, fine details of the entire poster's tone were devised, such as adjusting the character's facial expression, hairstyle, and clothing.
There are several options for implementation methods of image generation AI. Here, we introduce the procedures and benefits of three implementation methods.
You can introduce image generation AI by directly utilizing existing image generation AI services like Stable Diffusion. Using existing services has the following benefits:
Many existing services can be used on a pay-as-you-go or monthly subscription basis. However, attention is required regarding the following:
Be sure to check the terms of use of the image generation AI service you intend to use in advance.
If you have already introduced cloud platforms like those listed below, you can utilize libraries and tools related to image generation AI.
Major image generation AI services provide APIs for incorporating internal tools into external systems. The benefit is that you can build, train, and deploy models relatively easily without worrying about infrastructure construction or management.
It is also possible to build AI models internally by utilizing deep learning frameworks like TensorFlow or PyTorch and image generation libraries like Diffusers, which provide pre-trained Diffusion models. Since open-source code can be modified freely, the benefit is the ability to customize flexibly according to your company's requirements. When introducing image generation AI through this method, consider the following points:
In particular, if introduction proceeds without technical skills, it may lead to long development lead times and adversely affect the quality of final products or services, so you should rely on specialized companies as needed.
Several problems exist regarding the introduction of image generation AI, and it is important to address these challenges appropriately.
If images generated by image generation AI are similar to existing copyright-protected images, it may lead to copyright infringement. Under current copyright law, it is stated that copyright law is violated when the following two points are met:
To avoid legal trouble, it is necessary to obtain appropriate permission when using copyrighted materials. Furthermore, it is necessary to sufficiently check images input into AI with the human eye and confirm that copyright is not being infringed. Conditions, examples, and countermeasures for copyright infringement in the utilization of generative AI are explained in detail in "Does generative AI constitute copyright infringement? Explaining the problematic conditions, examples, and countermeasures!"; by viewing this together, you can deepen your knowledge for utilizing generative AI more safely and appropriately.
Reference: Agency for Cultural Affairs, June 2024, "AI and Copyright"
Some image generation AI services prohibit or restrict commercial use. When considering commercial use of generated images, it is important to carefully check the terms of use and choose services or licenses where commercial use is possible. Also, consider developing models internally by utilizing open-source frameworks, etc.
Image data that image generation AI is learning from may contain biases toward specific attributes such as gender, race, or age. Therefore, images with malice or bias may be generated unknowingly. Furthermore, if generative AI expresses effects or utility that are impossible in an actual product, it may lead to social confusion or a loss of credibility.
Image generation AI is a generative AI that leads to more efficient visual production and the creation of new creative expressions in automatic content generation, prototyping, advertising, design, and more. By using advanced image generation models as foundational technology, it enables a wide range of outputs from realistic photo-like images to abstract art and custom designs. Its utilization is progressing in various fields such as marketing, entertainment, and product design. However, when utilizing image generation AI, care is required regarding the copyright of generated images, data bias and noise, and the risk of fake images.