Text-to-Image
Category: AI Technology
Definition: A technique in AI image generation that allows users to control the exact pose, position, and posture of subjects in generated images. Uses reference poses or skeletal frameworks to ensure models appear in specific positions while maintaining natural appearance.
Why It Matters: Eliminates need for initial reference photography; allows rapid concept exploration and iteration; enables non-designers to create professional visuals; accelerates creative workflows from weeks to minutes.
Use Cases: Concept development, mood board creation, product visualization before sampling, marketing campaign ideation, social media content generation, creating diverse model representations.
Example of Real Use Case: A fashion designer describes "sustainable linen summer dress, earthy tones, botanical garden setting, natural lighting" and receives multiple image variations to present to stakeholders within minutes instead of organizing a concept photoshoot.
Software/Service: Caimera.ai, Adobe Firefly, Midjourney, Stable Diffusion, Leonardo, Botika, DALL-E 3
Common Issues: Misinterpretation of complex prompts, inconsistent product details, difficulty achieving brand-specific aesthetics, unpredictable color accuracy, challenges with specific garment construction details.
Do's and Don'ts:
✓ Do write detailed, specific prompts with style references
✓ Do iterate and refine prompts based on results
✓ Do use negative prompts to exclude unwanted elements
✓ Do maintain a prompt library for consistency
✗ Don't use vague or ambiguous descriptions
✗ Don't expect photorealistic fabric textures on first try
✗ Don't rely on AI for technical specification images
Related Terms: Generative AI, Diffusion Model, Prompt Engineering, Image-to-Image, AI Fashion Model
Also Known As: Prompt-to-Image, Text-Guided Generation