06 · AI Image & Media Generation Basics¶
Image and media generation tools let you produce visual or audio content from a text description or a reference input. This module covers the core concepts, prompting basics that transfer across tools, and — importantly — the practical and ethical considerations that apply regardless of which specific tool you use.
1. What these tools generally do¶
| Category | Typical capability |
|---|---|
| Text-to-image | Generate an image from a written description |
| Image-to-image | Modify or restyle an existing image based on a prompt |
| Text-to-video / image-to-video | Generate short video clips from a description or a starting image |
| Text-to-speech / voice generation | Generate spoken audio from text, sometimes in a chosen voice style |
| Music/audio generation | Generate background music or sound from a description |
| Editing assistance | AI-assisted background removal, upscaling, object removal within existing media |
2. Prompting basics for image/media generation¶
Unlike chat prompting (Module 8), visual generation prompting benefits from being descriptive and specific about visual attributes rather than conversational.
| Element | Why it helps | Example addition |
|---|---|---|
| Subject | The core content, stated plainly | "a small wooden cabin" |
| Style | Sets the overall visual treatment | "in the style of a watercolor illustration" |
| Composition | Framing and perspective | "wide shot, low angle" |
| Lighting/mood | Affects tone significantly | "warm evening light, soft shadows" |
| Level of detail | Guides how much the tool "fills in" | "simple, minimal" vs. "highly detailed" |
| Negative constraints (where supported) | Rules things out explicitly | "no text, no people" |
A practical loop: generate, look at what's wrong or missing, adjust one or two elements at a time, regenerate. Changing everything at once makes it hard to tell which change caused which effect.
3. Common limitations to expect¶
| Limitation | What it means in practice |
|---|---|
| Inconsistency across multiple generations | The same prompt run twice can produce very different results — expect to generate several options and pick |
| Difficulty with precise text in images | Generated text within images is often garbled — treat as unreliable, verify or add text manually afterward |
| Anatomical/structural errors | Hands, small objects, and fine structural details are common failure points — inspect closely |
| Style drift across a series | Getting a consistent look across multiple images/frames often requires deliberate technique or tool features built for it |
4. Rights, attribution, and appropriate use¶
This is the section most beginners skip and shouldn't.
| Consideration | What to check before using generated media |
|---|---|
| Commercial use rights | Whether the specific tool's terms allow commercial use of what you generate, and under what license |
| Training data concerns | Some tools disclose more than others about training sources; if this matters for your use case (e.g., a client project), check the tool's documentation directly |
| Depicting real people | Generating realistic images of real, identifiable people carries distinct ethical and often legal risk — avoid unless you have clear rights and a legitimate purpose |
| Disclosure norms | Many contexts (journalism, some platforms, some client relationships) expect or require disclosure that media is AI-generated — check applicable norms or policy before publishing |
Worked example¶
A hobbyist podcaster wants cover art and a short intro music clip. For the cover art, she writes a prompt with subject, style, composition, and mood (per section 2), generates four variations, and picks the one where the text she'll add separately won't clash with the composition — she doesn't try to get the tool to generate the podcast title as in-image text, since generated text is unreliable (section 3). Before publishing, she checks the image tool's terms of service to confirm commercial use is permitted for her paid tier, since a podcast cover counts as commercial use even for a hobby show with ads. For the intro music, she generates a short clip, listens for anything that sounds too close to an existing recognizable tune (a real risk with music generation), and picks a different generation when in doubt rather than risking an inadvertent resemblance.
How It Actually Works¶
Image generation tools work fundamentally differently from the text-prediction models covered in earlier modules, even though a text-understanding transformer is doing the prompt interpretation. The dominant mechanism is diffusion: during training, the model is shown images with progressively more random noise added to them, and learns to predict and remove that noise step by step to recover the original. At generation time, the process runs in reverse from pure noise: starting from a canvas of random static, the model repeatedly asks "given this noisy image and this text prompt, what would slightly-less-noisy version look like," and after dozens of these denoising steps, a coherent image emerges. Your text prompt doesn't get "drawn" directly — it steers each denoising step toward regions of image-space that a joint text-image model has learned correspond to that description.
This explains several well-known quirks. Prompt wording matters disproportionately because the model is matching your description against statistical associations learned from captioned training images, not parsing your prompt as a literal specification — a word order or synonym swap can shift which learned associations get activated most strongly. Recurring anatomical errors (hands, text within images, reflections) happen because those elements require modeling the same object consistently across many spatially separate patches during the denoising process — the model has no built-in symbolic understanding that "a hand has five fingers," only statistical patterns about what hand-shaped regions usually look like, learned less reliably than large, simple, frequently-photographed subjects. And because training data is scraped from vast collections of existing images, generated output can reproduce stylistic patterns — or, at times, recognizable elements — from that data, which is the technical root of the ethical and copyright considerations any responsible use of these tools has to weigh.
Exercise¶
Using any image generation tool available to you, write a prompt following the six-element structure in section 2 for a real visual you could use (a social media graphic, a presentation image, anything). Generate at least two variations, note one limitation from section 3 that showed up in your result, and check the tool's terms of service for commercial-use rights before you write down whether you could actually use the output for your stated purpose.