As AI-driven creativity tools continue to evolve, image prompt generators have become indispensable for artists, designers, and content creators. This glossary is your comprehensive guide to understanding key terms and concepts in this exciting field. Whether you’re a seasoned user or just starting, this guide will ensure you stay ahead of the curve.
Free online services for generating images, including AI-powered tools:
| Service Name | Description | Website |
|---|---|---|
| DALL·E | Text-to-image generator with free credits. | dalle.ai |
| Stable Diffusion | AI art creation via platforms like DreamStudio. | dreamstudio.ai |
| Craiyon | Simple text-to-image generator (formerly DALL·E Mini). | craiyon.com |
| Runway ML | AI tools for image generation and editing. | runwayml.com |
| DeepAI | Text-to-image generator for custom images. | deepai.org |
| Artbreeder | Create unique portraits and abstract art. | artbreeder.com |
| Picsart AI Tools | AI image generator and editor. | picsart.com |
| NightCafe Studio | Daily free credits for AI art generation. | nightcafe.studio |
| Canva (Free Plan) | Includes text-to-image AI tools. | canva.com |
| Adobe Firefly | AI-powered image generator (limited free access). | adobe.com |
| Fotor AI Art Generator | Free AI art creation with limited access. | fotor.com |
| ZMO AI | Free and easy-to-use image generator. | zmo.ai |
| Photopea | Online Photoshop alternative for image editing. | photopea.com |
| Remove.bg | Removes backgrounds from images automatically. | remove.bg |
Key Terms in Image Prompt Generators
1. Prompt
A prompt is the textual input you provide to an AI model to generate an image. Effective prompts include descriptive details like colors, styles, objects, and themes.
- Example: “A futuristic cityscape at sunset with flying cars and neon lights.”
2. AI Model
AI models are algorithms trained to understand prompts and generate images. Popular models include:
- DALL·E (OpenAI): Known for its versatility and detailed outputs.
- MidJourney: Recognized for artistic and imaginative images.
- Stable Diffusion: Ideal for high-quality, customizable outputs.
3. Style Transfer
The ability of AI to mimic specific artistic styles, such as impressionism, realism, or manga, in generated images.
4. Seed
A numerical input that influences the randomness in image generation. Using the same seed and prompt can recreate identical images.
5. Resolution
The clarity and detail of the generated image, often measured in pixels (e.g., 1024×1024). Higher resolutions yield more detailed images.
6. Negative Prompting
A technique to specify elements you do not want in the image, ensuring a cleaner and more focused output.
- Example: “Exclude: rain, dark clouds.”
7. Fine-Tuning
Adjusting an AI model to cater to specific themes or styles, often through additional training on targeted datasets.
8. Post-Processing
Editing and enhancing generated images using tools like Photoshop or Lightroom for added refinement.
9. Token Limit
The maximum number of characters or words an AI can process in a single prompt. Breaching this limit might truncate your input.
Best Practices for Crafting Prompts
- Be Specific: Include details about color, texture, composition, and lighting.
- Example: “A tranquil forest during autumn, with golden leaves and soft sunlight filtering through the trees.”
- Use Adjectives: Words like “serene,” “vivid,” or “dramatic” can shape the mood.
- Combine Concepts: Experiment by blending unrelated ideas.
- Example: “A steampunk robot in a medieval castle.”
- Iterate and Experiment: Modify prompts to refine results.
Understanding AI Image Generation Terms
Core Concepts
- AI Image Generation: Creating images from text descriptions or other inputs using artificial intelligence
- Diffusion Models: The dominant technique where noise is gradually removed to form an image
- Stable Diffusion: Popular open-source model
- DALL-E: OpenAI’s model
- Midjourney: Popular commercial service
- Imagen: Google’s model
Model Types
- Text-to-Image: Generating images from text prompts
- Image-to-Image: Transforming/editing existing images based on prompts
- Inpainting: Filling in missing parts of an image
- Outpainting: Extending an image beyond its original borders
- Upscaling: Increasing image resolution while maintaining quality
Prompt Engineering Terms
- Prompt: The text description used to generate an image
- Prompt Engineering: Crafting effective prompts for desired results
- Negative Prompt: Specifying what NOT to include in the image
- Prompt Weighting: Using syntax like
(word:1.5)to emphasize certain elements - Style Modifiers: Words/phrases that affect artistic style (e.g., “photorealistic,” “oil painting”)
Technical Parameters
- Steps/Iterations: Number of denoising steps (higher = more refined)
- CFG Scale: How closely the AI follows your prompt vs. being creative
- Sampler: The algorithm used in the diffusion process (Euler, DPM, DDIM, etc.)
- Seed: A number that determines initial noise pattern; same seed + prompt = same output
- Checkpoint/Model: The trained neural network file
Generation Techniques
- LoRAs: Small adapters that add specific styles or subjects
- Textual Inversion: Teaching the model new concepts with a few example images
- ControlNet: Adding conditions like pose maps, edges, or depth maps for precise control
- Img2Img Strength: How much to change an input image (0-1 scale)
Resolution & Aspect
- Aspect Ratio: Image dimensions (e.g., 16:9, 1:1, 4:3)
- Upscalers: Algorithms like ESRGAN to increase resolution post-generation
- Hires. Fix: Two-pass generation for better detail
Ethical & Legal Terms
- Training Data: The images used to train the model
- Copyright Issues: Legal questions around AI-generated content
- Bias: Unfair representation in generated images
- Deepfake: Realistic but fake images/video of people
- Watermarking: Identifying AI-generated content
Community Terms
- Model Merging: Combining multiple trained models
- Civitai: Popular community site for sharing models/styles
- Workflow: The specific process/parameters used to create an image
- Bloat: Unnecessary elements in generated images
Quality Indicators
- Artifacts: Visual glitches or distortions
- Coherence: How well elements fit together logically
- Hands Problem: AI’s common difficulty with realistic hands
- Text Rendering: AI’s challenge with readable text in images
Want me to elaborate on any specific area or explain how these terms work together in practice?