AI image generation glossary
Every term, explained in plain English
The vocabulary of AI image generation - negative prompts, seeds, CFG scale, img2img, samplers and more - defined clearly and accurately, with examples you can actually use.
Core concepts
5 terms- DenoisingDenoising is the core operation of a diffusion model: at each step it predicts and removes a little noise, gradually turning a random field into a clear image.
- Image-to-imageImage-to-image (img2img) is the AI workflow that transforms an existing picture according to your prompt, keeping some of the original structure instead of generating from scratch.
- img2imgimg2img is shorthand for "image-to-image" - the AI workflow that transforms an existing picture using a prompt, keeping some of the original structure instead of generating from scratch.
- Latent spaceLatent space is the compressed, abstract representation a diffusion model works in. Instead of manipulating millions of pixels, the model generates in this smaller space and then decodes it into an image.
- Text-to-imageText-to-image is the AI workflow where you type a written prompt and the model generates a brand-new image from it - no source image required.
Prompting
2 terms- Negative promptA negative prompt is a list of words describing what you do NOT want in the image. The model steers away from those concepts while it generates, which removes common flaws and unwanted elements.
- Prompt weightingPrompt weighting lets you boost or reduce the influence of specific words in your prompt, so the model pays more attention to the parts that matter most to you.
Generation controls
5 terms- Aspect ratioAspect ratio is the proportional relationship between an image's width and height (for example 16:9 or 1:1). It defines the shape of the canvas and strongly influences composition.
- CFG scaleCFG scale (classifier-free guidance scale) controls how strongly the image follows your prompt. Low values are loose and creative; high values stick closely to the prompt but can look over-processed.
- SamplerA sampler is the algorithm that decides how noise is removed at each step of generation. Different samplers reach the final image by different paths, trading off speed, detail and consistency.
- SeedA seed is the number used to initialize the random noise an image is built from. The same seed plus the same prompt and settings produces the same image every time, which makes results reproducible.
- StepsSteps (sampling steps) are the number of denoising passes the model runs to turn noise into an image. More steps can mean more detail, but past a point they only add time, not quality.
Editing & enhancement
4 terms- InpaintingInpainting regenerates only a selected (masked) region of an image while leaving the rest untouched - useful for removing objects, fixing details or replacing part of a scene.
- OutpaintingOutpainting extends an image beyond its original edges, generating new content that continues the scene - used to widen a crop, change the aspect ratio, or build out a larger composition.
- Style transferStyle transfer applies the visual style of one source - an image, artist or aesthetic - onto your own content, keeping your subject while changing its look.
- UpscalingUpscaling increases an image's resolution. AI upscalers do more than stretch pixels - they intelligently add believable detail so the larger image stays sharp.
Models & training
5 terms- CheckpointA checkpoint is a saved AI model file containing the full set of trained weights. It is the complete "brain" that generates images - swapping checkpoints changes the entire look and capability.
- ControlNetControlNet is an add-on that conditions a diffusion model on a reference structure - such as a pose skeleton, edge map or depth map - so you control composition precisely, not just with words.
- Diffusion modelA diffusion model is the type of AI that powers most modern image generators. It learns to turn random noise into a coherent image by reversing a step-by-step noising process.
- LoRAA LoRA (Low-Rank Adaptation) is a small add-on file that teaches a base model a specific style, character, object or concept - without retraining the entire model.
- VAEA VAE (Variational Autoencoder) is the component that converts images between full-resolution pixels and the compressed latent space a diffusion model works in - encoding on the way in and decoding on the way out.
Licensing
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