AI Image Generation training

Lesson 1 • Ethical and Legal Fundamentals

Introduces copyright, consent, and bias considerations specific to AI-generated imagery. Sets responsible practice expectations carried throughout the course.

Lesson 2 • Core Technology Concepts

Explains diffusion models, GANs, and transformer-based architectures at a conceptual level. Gives learners the vocabulary to understand tool behaviour without requiring coding knowledge.

Lesson 3 • What AI Image Generation Is

Defines AI image generation and distinguishes it from traditional digital art tools. Establishes the baseline understanding needed for all subsequent chapters.

Lesson 4 • Major Platforms and Tools Overview

Surveys the leading AI image generation platforms and their distinct strengths. Helps learners choose the right tool for specific creative goals.

Lesson 1 • Anatomy of an Effective Prompt

Breaks down the components of a well-structured prompt: subject, style, medium, and mood. Provides a reusable framework applied in every subsequent prompting exercise.

Lesson 2 • Descriptive Language Techniques

Trains precise, evocative word choice to guide model output toward intended visuals. Directly improves prompt quality before learners encounter advanced parameters.

Lesson 3 • Negative Prompting

Teaches how to exclude unwanted elements using negative prompt fields. Reduces common artefacts and off-target outputs in generated images.

Lesson 4 • Style and Artist Reference Prompting

Explores how referencing artistic styles, movements, and techniques shapes model output. Expands the learner's creative vocabulary for directing visual aesthetics.

Lesson 1 • Model Selection and Checkpoints

Introduces base models, fine-tuned checkpoints, and their effect on output style. Equips learners to select the right model for each creative brief.

Lesson 2 • Resolution, Aspect Ratio, and Tiling

Covers output dimensions, aspect ratio selection, and seamless tiling settings. Prepares learners to produce images sized correctly for specific professional deliverables.

Lesson 3 • Seed Values and Reproducibility

Explains how seed numbers control randomness and enable exact result reproduction. Enables learners to lock in successful outputs and iterate from a stable baseline.

Lesson 4 • Sampling Methods and Steps

Explains how different samplers affect image quality, speed, and style coherence. Connects sampler choice to practical trade-offs learners will manage in production.

Lesson 1 • Inpainting Fundamentals

Teaches mask-based inpainting to replace or repair specific image regions. Gives learners surgical control over targeted areas without affecting the rest of the image.

Lesson 2 • Image-to-Image Fundamentals

Introduces the img2img pipeline and how input images guide generation. Builds on prompt skills from Chapter 2 by adding visual reference as a second input layer.

Lesson 3 • Sketch and Line Art to Image

Converts rough sketches and line art into fully rendered images using img2img. Bridges traditional ideation with AI-assisted rendering for concept development.

Lesson 4 • Style Transfer with Img2Img

Uses img2img to apply artistic styles to photographs and existing artwork. Extends prompt-based style techniques into image-driven transformation workflows.

Lesson 1 • Pose and Body Control

Uses OpenPose and skeleton maps to control human figure positioning in generated images. Enables accurate character placement for product, fashion, and narrative imagery.

Lesson 2 • Edge and Line Detection Control

Uses Canny, HED, and MLSD edge detectors to preserve structural outlines from reference images. Maintains compositional fidelity while allowing full stylistic freedom.

Lesson 3 • Combining Multiple ControlNet Units

Stacks multiple ControlNet conditions simultaneously for complex compositional control. Prepares learners for advanced production scenarios requiring layered structural guidance.

Lesson 4 • Introduction to ControlNet

Explains ControlNet's role in adding structural conditioning beyond text prompts. Positions it as the primary tool for compositional precision in professional workflows.

Lesson 1 • Training Configuration and Execution

Walks through key training hyperparameters including learning rate, steps, and batch size. Gives learners the controls needed to run stable, efficient training jobs.

Lesson 2 • Evaluating and Iterating on Trained Models

Establishes a structured evaluation process for assessing fine-tuned model output quality. Teaches learners to diagnose overfitting, underfitting, and style drift.

Lesson 3 • Deploying Custom Models in Workflows

Integrates trained models into production generation pipelines alongside ControlNet and prompts. Completes the fine-tuning chapter by connecting training output to real creative use.

Lesson 4 • Dataset Preparation and Curation

Covers image selection, captioning, and preprocessing for effective model training. High-quality datasets are the single largest determinant of fine-tuned model quality.

Lesson 1 • Colour Grading and Tone Adjustment

Applies colour correction and grading techniques to unify AI outputs with brand or project palettes. Bridges AI generation with professional colour workflows used in design and photography.

Lesson 2 • Preparing Files for Delivery

Covers export formats, colour profiles, and resolution standards for print and digital delivery. Ensures learners can hand off professional-grade files that meet client and platform specifications.

Lesson 3 • AI Upscaling Methods

Compares tile-based, latent, and dedicated upscaler models for resolution enhancement. Equips learners to select the upscaling approach that best preserves detail for each use case.

Lesson 4 • Face and Detail Restoration

Uses face restoration tools and detail enhancement passes to fix common AI output defects. Addresses the most frequent quality issues encountered in character and portrait generation.

Lesson 1 • Batch Generation and Automation

Implements batch processing, scripting, and API-driven automation for high-volume output. Scales individual generation skills into production-level throughput.

Lesson 2 • Workflow Design Principles

Establishes the principles of modular, documented, and repeatable AI production workflows. Translates all prior technical skills into a structured professional production system.

Lesson 3 • Estimating and Scoping AI Projects

Provides frameworks for estimating time, compute cost, and iteration cycles for AI image projects. Enables learners to scope and price professional engagements accurately.

Lesson 4 • Prompt and Asset Libraries

Builds reusable prompt libraries, style presets, and asset repositories for efficient production. Reduces per-project setup time and enforces visual consistency across deliverables.