Text-to-Image

Text-to-image is AI technology that generates images from text descriptions. You provide a prompt like "a professional product photo of a ceramic coffee mug on a marble countertop," and the AI creates an image matching that description.

How text-to-image models work

Modern text-to-image models primarily use two approaches:

• Diffusion models (DALL-E, Stable Diffusion, Midjourney): Start with random noise and gradually refine it into an image that matches the text prompt. The model has learned during training how to reverse the process of adding noise to images, guided by text descriptions.
• Autoregressive models (some newer approaches): Generate images pixel by pixel or patch by patch, similar to how LLMs generate text token by token.

Both approaches use a text encoder (often CLIP) to convert the text prompt into a representation the image model can use for guidance.

Key text-to-image platforms

• Midjourney: Known for artistic, high-aesthetic output. Popular with designers and creatives.
• DALL-E (OpenAI): Integrated into ChatGPT and Microsoft products. Strong at following detailed instructions.
• Stable Diffusion: Open-source. Can be run locally with full control. Large community of fine-tuned variants.
• Adobe Firefly: Designed for commercial use with training data licensing considerations addressed.
• Flux: A newer open-source model with strong prompt adherence.

Effective prompting for images

• Be specific: "A golden retriever puppy sitting on a red blanket in a sunlit room, soft focus background" beats "a dog."
• Specify style: "Digital art," "photorealistic," "watercolour painting," "minimalist vector illustration."
• Include technical details: "8K resolution," "studio lighting," "wide angle lens."
• Use negative prompts: Some tools let you specify what to exclude (blurry, distorted, watermark).

Business applications

• Marketing content: Social media images, blog illustrations, ad concepts.
• Product mockups: Visualise product variations before manufacturing.
• Presentations: Custom illustrations instead of stock photos.
• Prototyping: UI mockups and design concepts for rapid iteration.
• E-commerce: Lifestyle product images and backgrounds.

Legal and ethical considerations

• Copyright: The legal status of AI-generated images is evolving. Some jurisdictions do not grant copyright to purely AI-generated works.
• Training data: Some models were trained on copyrighted images, raising ethical and legal questions.
• Deepfakes: The same technology can create deceptive images of real people.
• Disclosure: Growing consensus that AI-generated images should be labelled as such.