In brief
Image generation means that an AI model creates an image. The input can be a text description, a reference image or a combination of both. You can have a new image generated, explore variations or modify part of an existing image. The result is a visual interpretation of the prompt. The model therefore does not provide any evidence that a person, event, location or product depicted actually exists.
What types of image generation are there?
In text-to-image, text forms the starting point. In image-to-image, an existing image guides the new output. Inpainting focuses on a selected area, whilst image expansion can create extra space around the original composition. Which functions are available depends on the model and the software. Some applications combine multiple models or processing steps rather than performing everything in a single command.
What makes a description useful?
Describe the subject, setting, perspective, lighting and desired composition. Also indicate where space must be left for text or which details must be retained. A reference image can help with shape and proportions. A brief with many conflicting requirements makes verification more difficult. It is preferable to work with a clear basis and targeted adjustments. A seed can make variants comparable within the same environment, but is no guarantee of identical output on other systems.
A practical example
An organisation is looking for an illustration for an internal presentation on maintenance. First, they determine which machine needs to be recognisable and which details are essential. The illustrator produces a few concepts, after which a member of staff selects and checks the best composition. For an image that needs to accurately depict a real product, photography or a controlled product render may be more suitable. The intended use determines which approach is both convincing and practical.
Generating is not the same as understanding images
For example, a vision model interprets a photograph or a document image. A generative image model creates or edits an image. These functions can work together, but they are distinct tasks. A system can first analyse a photograph and then prepare an editing task. Understanding an image does not automatically mean that the system can also accurately reconstruct that image. Assess both steps separately when combining them in a workflow.
From concept to usable visual
Check details, perspective, text within the image and consistency with other images. Select the required resolution based on the final format; a larger file does not automatically correct errors. Record the chosen settings and versions if you want a consistent image series. Also take into account the permitted use of the model and reference material. Generation, selection and post-processing together form the production process.
From concept to practice
How AITJE applies this
When asked about image generation, AITJE investigates which input, processing steps and model capabilities are suitable for the desired image. We can design a bespoke workflow for, for example, preparing tasks, saving variants and having results assessed. The output route is selected based on feasibility, quality, capacity and terms of use. Image generation is a potential bespoke application here; it is not an assumed standard function of the chat assistant.
Explore bespoke AI solutionsFrom explanation to application
Would you like to know what this means for your work?
