Image Generation
A technology in which artificial intelligence analyzes text or reference images to generate new visual outcomes. Moving beyond simple image combinations, it implements original images at the pixel level, reflecting composition, art style, lighting, and more based on trained data.
Detailed explanation
Why it matters in tool selection
Image generation tools differ significantly in the aesthetics of their output and prompt adherence depending on the model. In commercial projects, beyond just creating pretty images, 'controllability' to adhere to brand guidelines and the 'copyright and commercial use rights' of the generated images serve as core criteria for tool selection.
What to check
- Is it a license that allows commercial use? (Required to check licenses for each model)
- Is it possible to perform detailed editing and maintain consistency through ControlNet, LoRA, etc.?
- Does it accurately understand complex prompt instructions (such as including text)?
- Can it be automated into existing workflows through API integration?
Examples
Representative examples include a marketer entering a prompt like 'an electric vehicle advertisement image set against a futuristic night view of Seoul' to produce drafts in seconds, or a game designer inputting a character's basic sketch and applying ControlNet to generate character concept art in various styles.
Confusing terms
Image Generation
The process of creating completely new visual data from scratch or text.
Image Editing (Editing/Inpainting)
The technology to modify, delete, or expand (outpaint) specific parts of an already generated image.