Generative AI Art for Beginners Course

Lesson 1 • What Is Generative AI Art

Defines generative AI art and distinguishes it from traditional digital art. Establishes shared vocabulary used throughout the course.

Lesson 2 • Brief History of AI-Generated Images

Traces AI art from early algorithmic experiments to modern diffusion models. Provides historical context for understanding current tools.

Lesson 3 • How Generative Models Work

Explains the core mechanics of diffusion and transformer models in plain language. Gives students a mental model for predicting tool behaviour.

Lesson 4 • AI Art in Creative Industries

Surveys professional use cases across design, entertainment, marketing, and publishing. Connects course skills to real career opportunities.

Lesson 1 • Iterative Prompt Refinement

Establishes a test-and-refine workflow for improving prompts across multiple generations. Students build a personal prompt library from their experiments.

Lesson 2 • Style Reference Keywords

Introduces artist names, art movements, and medium descriptors as style anchors. Students learn to replicate recognisable aesthetics consistently.

Lesson 3 • Anatomy of an Effective Prompt

Breaks a prompt into subject, style, medium, lighting, and mood components. Students learn to build prompts systematically rather than by guessing.

Lesson 4 • Prompt Weight and Emphasis

Explains syntax for weighting specific words to increase or decrease their influence. Students gain fine-grained control over compositional priorities.

Lesson 1 • Image Resolution and Aspect Ratio

Covers pixel dimensions, aspect ratios, and their impact on composition and platform use. Students match output specs to intended delivery formats.

Lesson 2 • Batch Generation and Efficiency

Teaches batch size settings and grid outputs to accelerate creative exploration. Students reduce time-per-concept by generating multiple options simultaneously.

Lesson 3 • CFG Scale and Prompt Adherence

Defines the guidance scale parameter and its effect on creativity vs. accuracy. Students learn to balance artistic freedom with prompt fidelity.

Lesson 4 • Seeds and Reproducibility

Introduces random seeds as a tool for locking in or exploring visual variations. Students can reproduce exact outputs and create controlled variation sets.

Lesson 1 • Composition Fundamentals for AI Art

Translates rule of thirds, leading lines, and framing into prompt language. Students direct spatial arrangement through descriptive text.

Lesson 2 • Lighting and Color Palette Control

Covers lighting setups and colour theory keywords that shape mood and realism. Students produce images with intentional emotional tone through light and colour.

Lesson 3 • Camera Angle and Perspective Keywords

Maps photography and cinematography terms to AI prompt vocabulary. Students control viewpoint to create dynamic or intimate compositions.

Lesson 4 • Defining and Maintaining a Visual Style

Teaches style consistency across a series of images using anchored prompt elements. Students create cohesive portfolios rather than disconnected one-offs.

Lesson 1 • Image-to-Image Generation Basics

Introduces img2img workflows where an uploaded image guides new generation. Students understand how denoising strength controls fidelity to the source.

Lesson 2 • Using Reference Images Effectively

Covers selecting and preparing reference images for style transfer and composition guidance. Students extract maximum creative value from reference inputs.

Lesson 3 • Inpainting Fundamentals

Teaches masking specific image regions for targeted regeneration. Students repair defects and swap elements without altering the rest of the image.

Lesson 4 • Sketch-to-Image Workflows

Converts rough hand-drawn or digital sketches into polished AI-rendered images. Students use their own drawings as structural guides for generation.

Lesson 1 • Combining Multiple ControlNet Units

Stacks two or more ControlNet conditions to achieve complex structural and stylistic control. Students balance multiple guidance signals without conflicting outputs.

Lesson 2 • Edge and Line Detection Control

Applies Canny and HED edge maps to preserve structural outlines from reference images. Students maintain architectural and object shapes across style changes.

Lesson 3 • Introduction to ControlNet

Explains what ControlNet is and how it adds a conditioning layer to generation. Students understand why ControlNet solves problems that prompts alone cannot.

Lesson 4 • Pose and Skeleton Control

Uses OpenPose maps to define human body positions before generation. Students produce figures in exact poses without relying on prompt description alone.

Lesson 1 • Exporting for Different Delivery Formats

Covers file format selection, colour profiles, and compression settings for varied outputs. Students deliver correctly formatted files for print, web, and social media.

Lesson 2 • Background Removal and Compositing

Isolates subjects from AI-generated backgrounds for use in composite designs. Students integrate AI art into broader design workflows.

Lesson 3 • Colour Correction and Grading

Applies colour correction and cinematic grading to unify tone across an image set. Students achieve professional colour consistency using accessible editing tools.

Lesson 4 • Sharpening, Noise, and Artifact Removal

Addresses common AI artifacts including blur, noise, and compression distortion. Students deliver clean images free of generation defects.

Lesson 1 • Pitching and Client Communication

Develops skills for presenting AI art concepts to clients and responding to briefs. Students convert portfolio interest into paid commissions.

Lesson 2 • Selecting and Curating Portfolio Pieces

Teaches criteria for selecting images that demonstrate range, skill, and consistency. Students reduce large output libraries to a focused, impactful selection.

Lesson 3 • Presenting Work on Digital Platforms

Covers portfolio website setup, social media presentation, and community platforms. Students choose channels that match their target audience and creative goals.

Lesson 4 • Defining Your Creative Niche

Guides students to identify a focused style and subject area that differentiates their work. A clear niche makes portfolio curation and client targeting more effective.