Generative AI for image creation
The pre-seen case study follows a creative design company evaluating generative AI approaches for image production. HL Paper 1 Section B is worth 24 marks, so preparation combines technical understanding, independent research, application and balanced evaluation.
What the company is deciding
Image-generation approaches
Text-to-image, conditional/image-to-image and unconditional generation can serve different creative goals.
Candidate architectures
Diffusion models are central. HL also studies GANs and hybrid models in depth, while VAEs and flow-based models need broad understanding for hybrid-model reasoning.
Decision criteria
Output quality, computational efficiency, training stability, flexibility/scalability, character consistency and style adherence.
Ethical/legal factors
Dataset curation, intellectual property, bias/fairness and transparent disclosure of AI-generated content.
Four research questions drive the preparation
1 · Diffusion compute
Explain iterative denoising and evaluate the computational demands of producing high-quality images.
2 · Training data
Evaluate intellectual-property and bias risks in large training datasets and strategies for mitigation.
3 · GAN balance
Explain why generator and discriminator performance must remain balanced enough for useful adversarial training.
4 · Hybrid trade-offs
Evaluate how a hybrid system could combine diffusion, GAN, VAE and flow-based strengths while reducing weaknesses.
From random noise to a coherent image
Competing networks and complementary architectures
GAN training
The generator creates candidates; the discriminator judges real versus generated examples. If one becomes too strong relative to the other, useful learning can stall or become unstable.
Mode collapse is one failure mode: the generator produces too little variety.
Hybrid reasoning
Do not treat “hybrid” as automatically better. Evaluate what each component contributes, what complexity/cost it adds, and whether the combined system actually improves quality, control, stability, transparency or reproducibility.
Case-study vocabulary bank
Diffusion model
Generates images by learning to reverse a gradual noise process through repeated denoising steps.
Denoising
Removing noise step by step so a noisy representation becomes a coherent image.
DDPM
A probabilistic diffusion framework that formalizes the gradual addition and learned removal of noise.
CNN
A neural-network architecture that learns spatial image features; commonly used in image processing and denoising components.
Conditional generation
Generation controlled by input such as text, a class label, a sketch, a segmentation map or another image.
Unconditional generation
Generation without an external condition, relying on patterns learned from the training data.
GAN
A generative system trained through competition between a generator and a discriminator.
Generator
Creates synthetic candidate images from random input.
Discriminator
Tries to distinguish real training examples from generated examples.
Adversarial dynamic
The competitive training relationship that drives generator and discriminator improvement.
Mode collapse
A GAN failure mode where the generator produces too little variety.
VAE
A generative model that learns a structured latent representation and can sample from it to produce new data.
Latent space
A compressed numerical representation in which learned features are encoded.
Flow-based model
A model based on an invertible mapping between data and a simpler probability distribution.
Hybrid model
Combines different generative approaches so one model can compensate for limitations of another.
Dataset curation
Selecting and checking training data for relevance, quality, legality and representational balance.
Bias mitigation
Steps taken to reduce systematic unfairness in data, training or generated outputs.
Training stability
How reliably training converges toward useful behaviour instead of collapse, divergence or severe oscillation.
Character consistency
Keeping a recurring subject recognizably consistent across multiple generated images.
Embedding-based approach
Representing concepts/features as numerical vectors so identity, similarity or style can be controlled.
Text-to-image generation
Creating images from natural-language descriptions.
Image-to-image translation
Transforming one image representation into another, such as sketch-to-render.
Noise injection
Adding random noise to data as part of the diffusion training/generation framework.
Segmentation map
A labelled spatial map of image regions that can be used as a generation condition.
Image generation
Creating new visual outputs using a generative model.
D-dimensional noise vector
A D-value random vector used as input to a generative model such as a GAN.
The case study is a strand, not a final-week topic
Turn research into an exam response
Short parts
Retrieve precise definitions, mechanisms and direct applications quickly. Use the vocabulary of the case study accurately.
AO3 extended response
Build a balanced technical argument: identify criteria, analyse trade-offs, use research evidence, respond to any new stimulus, and reach a justified conclusion tied to the scenario.