AI Vibe Code of Image Generation
About This Course
This course equips learners with practical skills to use AI vibe coding techniques to build and customise image generation applications. Through natural-language instructions and AI-assisted coding, participants will generate, explain, test, and refine Python code for developing creative AI workflows without needing to write every component manually.
Learners will explore the foundations of generative image models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Stable Diffusion. They will understand how these models learn visual representations, generate new images, and support applications such as text-to-image generation, image-to-image transformation, style variation, image enhancement, and creative asset production.
The course covers essential processes such as preparing image datasets, configuring model parameters, writing and improving prompts, controlling image properties, training or fine-tuning models, and evaluating output quality. Participants will use AI vibe coding to troubleshoot errors, compare different approaches, create user interfaces, and integrate image generation models into practical Python applications.
Through hands-on projects, learners will develop end-to-end image generation workflows for marketing, design, media, and business applications. By the end of the course, participants will be able to use AI-assisted coding to build image generation solutions with Stable Diffusion, GANs, and VAEs while applying responsible practices relating to data quality, copyright, bias, and appropriate content generation.
What You'll Learn
LO1: direct modeling efforts across the organization
LO2: apply computational methodologies to the problem.
LO3: design advanced computational model
LO4: evaluate a broad range of algorithms
LO5: spear the application of algorithms to new domains
LO6: establish guidelines on algorithm selection
Course Outline:
Topic 1: Fundamentals of AI Image Generation and Computational Modelling
Topic 2: Image Generation with Generative Adversarial Networks (GANs)
Topic 3: Image Generation with Variational Autoencoders (VAEs)
Topic 4: Stable Diffusion and Controlled Image Generation
Topic 5: Image-to-Image Transformation, Enhancement and New Applications
Topic 6: Algorithm Evaluation, Selection, Optimisation and Deployment
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum Polytechnic Diploma
• Basic programming skill, preferably Python.
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.