Home
WSQ - AI Vibe Code of Image Generation

Sessions 2 days
Duration 16 hrs
Level Beginner
Assessment 2 hrs

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

TSC Title: Computational Modelling-5
TSC Code: ICT-DIT-5001-1.1

Funding for this course is valid from 16 Jul 2021 to 15 Jul 2027. Register and complete the course within this period to qualify for funding support.

By the end of the course, learners will be able to:

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: Spearhead the application of algorithms to new domains.
LO6: Establish guidelines on algorithm selection.

Download Course Brochure

Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.
OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ - AI Vibe Code of Image Generation

Course Code: TGS-2021006714

WSQ
SFC
PSEA
UTAP
SFEC
Absentee Payroll
MCES

3 Review(s) | Add Your Review

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.

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more AI vibe coding courses.

Full Fee $800.00 Before GST

GST $72.00 9% of fee

Baseline Nett $472.00 SG/PR age 21+ · 50% funded

MCES / SME Nett $312.00 SG age 40+ · 70% funded

SkillsFuture Enterprise Credit (SFEC)

Eligible companies can use their $10,000 SFEC to cover up to 90% of out-of-pocket course fees for classes ending on or before 30 Nov 2026. A redesigned SFEC starts on 1 Dec 2026.

View on SkillsFuture for Business

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used.

Direct Application on SkillsFuture Portal

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year.

Submit UTAP Claim

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding.

Check PSEA Eligibility

Scroll down to “Keyword Tags” to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form, then submit the completed form to us:

PSEA Withdrawal Form Submit PSEA Form

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

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

Written Exam
Practical Exam

Course Info

Minimum Entry Requirement

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.

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

Python 3 – free
Visual Studio Code – free
Google Colab – free, runs in the browser with a Google account
An AI coding assistant with a free tier, e.g. GitHub Copilot Free in Visual Studio Code

Hardware: Windows PC or Mac laptop

Funding Eligibility Criteria

Self-sponsored learners (individuals)

Singapore Citizens and Singapore Permanent Residents aged 21 and above.
Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.

Company-sponsored learners

Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
Employers can also claim Absentee Payroll funding for employees who attend during working hours.
Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).

Conditions for all funded learners

Attendance is taken digitally with Singpass for every session, including the assessment.
You must complete the course and pass all assessments (assessed as Competent).
If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
9% GST is charged on the full course fee before funding.
Promo and discount codes cannot be used on funded courses.
Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.

How to Apply SkillsFuture Credit Claim for Individuals

Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.

Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
We email you an invoice showing the course fee after funding.
From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
Click Submit a Claim and search for this course by its course code, TGS-2021006714.
Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.

Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.

How to Apply SkillsFuture Enterprise Credit (SFEC)

SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.

Current SFEC: classes ending on or before 30 November 2026

Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.

Check your company's SFEC eligibility and balance on EnterpriseSG.
Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.

Any unused balance of the current SFEC expires on 30 November 2026.

Redesigned SFEC from 1 December 2026

Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.

How to Apply UTAP (NTUC Members)

NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.

Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.

Make sure your NTUC membership is paid up before the course starts.
Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
Attend at least 75% of the course and sit all assessments.
Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
NTUC usually pays the claim within 4 weeks of submission.

How to Apply PSEA

Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.

Check that this course shows the PSEA tag on its MySkillsFuture page.
Check your PSEA balance on MOE's Student Finance System with Singpass.
Download and complete the PSEA withdrawal form, then submit it to us.
We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.

More details: MOE PSEA overview.

Appeal Process

If you disagree with an assessment decision, you have the right to appeal.

When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
Discuss the decision with the assessor or the assessment manager first.
If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

Data Scientist
Machine Learning Engineer
AI Researcher
Deep Learning Specialist
Computer Vision Engineer
AI Product Developer
Graphics Software Developer
Multimedia Artist (using AI)
Creative Technologist
Digital Content Designer
Marketing Creative (producing AI visuals)
R&D Specialist in AI
Game Developer (generating visual assets)
Innovation Manager (in tech firms)
Computational Scientist

Trainers

Dr. Alfred Ang builds creative AI tools as well as teaching them. His public GitHub portfolio includes logomaker, which turns a text description into three logo options using Gemini or OpenAI image models; lipsyncdemo, which turns a portrait and a script into a talking avatar using an in-browser preview, MuseTalk running locally on Apple MPS or CUDA, and HeyGen for cloud renders; and videocreation, which turns a topic into a narrated video with AI-generated scripts, voiceover and animated scenes. Other projects explore generative and visual computing from different angles: fractalapp grows Mandelbrot, Julia and other fractals from a seed using GPU acceleration with Metal, arvr-playground generates and reviews AR and VR scenes from vibe prompts, and passportphotoapp performs on-device background removal and face-aware cropping. As founder of Tertiary Courses and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, he works as a full-stack AI developer, Agentic AI solution architect, courseware developer and corporate trainer. He holds Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102), AWS Certified AI Practitioner and a Certificate in AI Ethics and Governance (Senior Professional) from NTU, and he is an IBM certified instructor for the AI Practitioners course. His academic background is a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, and he has published more than 20 peer-reviewed papers.

In WSQ - AI Vibe Code of Image Generation, Dr. Ang guides learners through building and customising image generation applications in Python with the help of AI coding assistants. The course opens with the fundamentals of AI image generation and the computational modelling behind it, then works through three families of generative models: Generative Adversarial Networks, where a generator and a discriminator learn against each other; Variational Autoencoders, which learn a compact latent space that can be sampled and interpolated; and Stable Diffusion, including controlled generation with prompts and conditioning. Learners then apply image-to-image transformation and enhancement to practical tasks, and the final topic covers evaluating, selecting, optimising and deploying image generation algorithms. His physics training helps him explain intuitively what a latent space or a noise schedule is doing, and his own projects give him concrete examples of the gap between a model demo and a usable tool, such as handling different output sizes, runtime and hardware constraints, and user-facing controls. He also addresses responsible use, including consent for likenesses, copyright and the labelling of AI-generated images, drawing on his AI ethics and governance training. Sessions are hands-on: learners describe the pipeline they want, review and run the generated code, compare outputs across models and refine both prompts and parameters. As an ACTA-certified trainer and DACE-certified curriculum developer, he keeps the material accessible to creative professionals while giving developers enough depth to deploy their work. To explore his generative AI projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Quah Chee Yong is an ACLP-certified trainer and data science professional with deep expertise in machine learning, computer vision and natural language processing. He has served as AI Solutions Lead at AiDeal Scan, where he developed recommender systems and NLP-driven search engines, and as Data Science Training Lead at MSITEK, delivering AI and machine learning training for SAP, IMDA and Temasek Polytechnic. His commercial experience includes building deep learning systems for chatbots, recommendation engines and image analysis.

In WSQ AI Vibe Code of Image Generation, his image analysis and deep learning experience gives learners a solid grounding in generative modelling. Using natural-language instructions and AI-assisted coding, participants generate, explain, test and refine Python code for image generation workflows. They cover the fundamentals of AI image generation, build models with generative adversarial networks and variational autoencoders, and work with Stable Diffusion for controlled image generation. They then explore image-to-image transformation and enhancement, and learn to evaluate, select, optimise and deploy algorithms. He combines technical depth with practical case studies, so learners can create advanced AI-generated images.

Tan Woei Ming is an ACLP-certified trainer, data scientist and AI engineer with experience across AI, IoT and semiconductor analytics. At Micron Semiconductor, he developed AI-based defect detection systems and wafer map recognition models, applying computer vision and deep learning to optimise manufacturing processes. He has led AI initiatives in predictive analytics and automation across the semiconductor and manufacturing industries, and focuses on translating complex AI technologies into practical business applications. He holds a Master's in Intelligent Systems from NUS and a First-Class Honours degree in Electrical and Electronic Engineering from NTU.

In WSQ - AI Vibe Code of Image Generation, Woei Ming introduces learners to the practical use of generative models for creating and transforming images. Participants cover the fundamentals of AI image generation and computational modelling, generative adversarial networks (GANs), variational autoencoders (VAEs), Stable Diffusion and controlled generation, image-to-image transformation and enhancement, and finally algorithm evaluation, selection, optimisation and deployment, using AI-assisted coding throughout. His sessions highlight applications in pattern recognition and creative design so learners see the business value of these techniques. Participants gain both a sound understanding of how the models work and the practical skills to apply them.

Solomon Soh is a data scientist and AI trainer with significant experience in optimisation, reinforcement learning and generative modelling. At IBM Singapore he supervised numerous machine learning and deep learning projects, coaching teams on feature engineering, model evaluation and deployment. At Workforce Optimizer he led research and development in reinforcement learning to optimise workforce allocation, achieving measurable improvements in efficiency and cost. He brings this mix of research and applied industry work to his teaching.

In WSQ - AI Vibe Code of Image Generation, Solomon focuses on the practical implementation of GANs, VAEs and diffusion models using Python and deep learning frameworks, with AI-assisted coding to generate, explain, test and refine the code. Learners start with the fundamentals of AI image generation and computational modelling, then generate images with generative adversarial networks and variational autoencoders. They go on to Stable Diffusion and controlled image generation, image-to-image transformation and enhancement, and finally algorithm evaluation, selection, optimisation and deployment. His sessions cover data preprocessing, model building and evaluation, equipping participants to experiment confidently with current image generation techniques.

Review

Customer Reviews (3)

will recmmend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

. (Posted on 3/8/2024)

will recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

All is good. (Posted on 8/18/2023)

Dr Alfred is able to explain this tough topic in a simple and easy to understand manner. Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

Dr Alfred is able to explain this tough topic in a simple and easy to understand manner. The course also covered a number of hands on activities which is good for practical work. (Posted on 11/22/2021)