AI image generation has its own vocabulary, and learning it makes you significantly more effective at getting the images you want. This glossary covers the 50 most important terms — from basic concepts to technical parameters — in plain, practical English.
Core Concepts
Text-to-Image (T2I): Generating an image from a written text description (prompt). The foundational capability of modern AI image generators.
Image-to-Image (I2I): Using an existing image as input, with a prompt, to generate a modified version. Useful for style transfer, variation generation, and controlled editing.
Prompt: The text description you provide to guide image generation. Can be a simple phrase or a detailed multi-sentence description.
Negative Prompt: Text that tells the AI what NOT to include in the image. Used to avoid common artifacts (blurry, low quality) or specific content.
Generation / Inference: The process of producing an image from a prompt. "Running inference" means executing the AI model to produce output.
Iteration: Generating multiple versions of an image, often with slight prompt variations, to find the best result.
Models
Foundation Model: A large AI model trained on massive datasets. The base model that specific-use models are built on (e.g., Stable Diffusion, Flux).
Flux: AI image generation model developed by Black Forest Labs. Currently industry-leading for photorealistic generation. Used in Lensgo.ai.
DALL-E 3: OpenAI's image generation model, integrated with ChatGPT. Known for strong text rendering within images.
Stable Diffusion (SD): Open-source foundation model with a large ecosystem of community fine-tunes, LoRAs, and extensions.
Midjourney: Proprietary image generation service known for artistic, painterly aesthetic. Accessible via its web app and Discord.
Fine-Tuned Model: A foundation model that's been further trained on a specific dataset to specialize in a style or subject (portraits, anime, products, etc.).
LoRA (Low-Rank Adaptation): A technique for efficiently fine-tuning a model on a small dataset. LoRAs add style or subject specialization to a base model without retraining the whole model.
Checkpoint: A saved state of a model's weights during or after training. "Loading a checkpoint" means using a specific version of a model.
Technical Parameters
Seed: A number that initializes the random generation process. The same seed + same prompt + same settings produces the same image. Used to reproduce results.
Steps (Inference Steps): The number of denoising iterations the model performs during generation. More steps = more refined result (to a point) but slower generation.
CFG Scale (Classifier-Free Guidance): How strongly the generation follows the prompt. Higher CFG = closer adherence to prompt but potentially less realistic. Lower CFG = more "creative" but may drift from prompt.
Sampler: The algorithm used to denoise the image during generation. Different samplers (Euler, DPM++, DDIM) produce slightly different aesthetic results.
Resolution: The pixel dimensions of the generated image. Higher resolution requires more computational resources and time.
Aspect Ratio: The ratio of width to height (1:1, 16:9, 9:16, 4:5). Determines the image's shape independent of resolution.
Upscaling: Increasing an image's resolution. AI upscaling adds detail rather than just enlarging pixels.
Latent Space: The mathematical space in which diffusion models process image information. Images are encoded into latent space, processed, then decoded.
Generation Techniques
Diffusion Model: The type of AI model that most image generators use. Works by gradually removing noise from a random starting image, guided by the prompt.
Denoising: The core process of diffusion models — progressively removing noise from a noisy image to produce a clean output.
Inpainting: Editing a specific region of an image while leaving the rest unchanged. Defines a "mask" area that the AI fills with new content.
Outpainting: Extending an image beyond its original boundaries. The AI generates content outside the original frame.
ControlNet: A technique that adds precise compositional control to image generation. Allows using reference images to control pose, depth, edges, and more.
Style Transfer: Applying the visual style of one image to the content of another. AI style transfer reimagines your photo in an artistic medium.
Super Resolution: AI upscaling that adds realistic detail to low-resolution images, not just enlargement.
VAE (Variational Autoencoder): The component that encodes images into latent space and decodes latent representations back into images.
Prompt Writing
Subject: The main element of the image — what the image is about.
Setting / Scene: Where the subject is located, environmental context.
Style: The visual aesthetic — photorealistic, cinematic, oil painting, anime, etc.
Lighting: Descriptors for the light in the scene — golden hour, studio lighting, overcast, etc.
Composition: How elements are arranged in the frame — close-up, aerial, rule of thirds, centered.
Quality Modifier: Terms that influence output quality and detail level — though effective quality modifiers have evolved and some traditional ones ("8K", "hyperrealistic") can backfire for photorealism.
Token / Token Weight: Diffusion models process prompts as sequences of tokens (roughly words or subwords). More tokens = more processing. Some models allow weighting specific tokens to emphasize them.
Prompt Engineering: The practice of crafting effective prompts to achieve desired outputs. A significant skill developed through practice.
Image Editing
Background Removal / Matting: Isolating a subject from its background. AI background removal handles complex edges (hair, fur, transparency).
Object Removal / Inpainting: Removing specific elements from a photo and filling the gap with AI-generated background content.
Face Restoration: Specialized AI enhancement for facial regions — recovering detail, correcting blur, and improving quality in portrait photos.
Magic Eraser: Consumer-friendly name for AI-powered object removal tools.
Video Generation
Text-to-Video (T2V): Generating a video clip from a text description.
Image-to-Video (I2V): Animating a still image into a short video clip.
Temporal Consistency: How stable and coherent objects appear across video frames. Poor temporal consistency produces flickering or morphing objects.
Seedance: AI video model (Seedance 2.0) known for high quality and natural motion. Available in Lensgo.ai.
Kling: AI video generation model known for realistic motion physics. Available in Lensgo.ai.
Wan: AI video generation model. Available in Lensgo.ai.
Platform Concepts
Credits: The unit of usage on most AI image platforms. Each generation consumes credits.
Free Daily Credits: Credits that refresh daily, allowing limited free use of the platform.
Commercial License: Permission to use generated images for commercial purposes (selling, advertising, business use). Policies vary by platform — on Lensgo.ai, all users retain commercial usage rights, with free-tier images watermarked and paid plans watermark-free.
Watermark: Visual marker added to images on free tiers; typically removed on paid plans.
Start generating images — free daily credits, no technical knowledge required.
