WSQ - End-to-End Image Generation Automation with Agentic AI & n8n

Topic 1: Image Generation Models

• Overview of Image Generation Paradigms
• Key Concepts: Latent Space, Noise, Sampling, Conditioning

Topic 2: Automating Prompt-to-Image Generation with n8n

• Image Generation as an Automated Process
• Connecting Image Generation APIs in n8n
• Prompt Engineering Basics (Theory → Practice)
• Dynamic Prompt Construction Using n8n Logic
• Trigger-Based Image Generation (Forms, Webhooks, Schedulers)

Topic 3: Conditional and Controlled Image Generation in Workflows

• Theory of Conditional Image Generation
• Automating Conditional Inputs with n8n
• Parameter Routing and Decision Logic in Workflows

Topic 4: Automating Image-to-Image Generation with n8n

• Image-to-Image Models
• Automating Image-to-Image Pipelines in n8n

Topic 5: Multi-Step Image Generation Pipelines

• Using AI Agents to Plan, Generate, and Refine Images
• Iterative Image Improvement Loops
• Human-in-the-Loop vs Fully Autonomous Workflows

Topic 6: Quality Control, Evaluation, and Deployment Automation

• Automated Image Evaluation and Filtering
• Error Handling and Reliability in n8n

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

Promotion Code

Promo or discount cannot be applied to WSQ courses

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:

Download and Install the following software

• Python
• Visual Studio Code
• Anacoda

Sign up free Google Colab account

Hardware: Window or Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• Data Scientist
• Machine Learning Engineer
• AI Researcher
• Deep Learning Specialist
• Computer Vision Engineer
• AI Product Developer
• Graphics Software Developer
• Multimedia Artist (using AI)
• Bioinformatics Researcher (for GAN-based simulations)
• Financial Modeler (using AI for simulations)
• R&D Specialist in AI
• Robotics Engineer (with AI modeling)
• Game Developer (using GANs for content generation)
• Innovation Manager (in tech firms)
• Computational Scientist

Quah Chee Yong – 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 ML training for SAP, IMDA, and Temasek Polytechnic. His commercial experience includes building deep learning systems for chatbots, recommendation engines, and image analysis.

In his GAN, VAE, and diffusion model training, Quah emphasizes hands-on applications of generative AI techniques in real-world projects. His sessions guide learners through the foundations of generative modeling, including adversarial training, variational autoencoders, and diffusion-based architectures. By combining technical depth with practical case studies, he ensures participants acquire both the coding proficiency and applied knowledge to create advanced AI-generated images.

Solomon Soh – Solomon Soh is a data scientist and AI trainer with significant experience in optimization, reinforcement learning, and generative modeling. At IBM Singapore, he supervised 24 machine learning and deep learning projects, coaching teams on feature engineering, model evaluation, and deployment. At Workforce Optimizer, he spearheaded R&D in reinforcement learning to optimize workforce allocation, achieving measurable improvements in efficiency and cost savings.

In his generative AI training, Solomon focuses on practical implementation of GANs, VAEs, and diffusion models using Python and deep learning frameworks. His sessions cover data preprocessing, model building, and evaluation, enabling participants to generate and refine realistic images. By blending his research background with applied industry projects, Solomon equips learners with the skills to confidently experiment with state-of-the-art image generation techniques.

Tan Woei Ming – Tan Woei Ming is an ACLP-certified trainer and data science professional with more than 10 years of experience in AI, IoT, and semiconductor analytics. He holds a Master’s in Intelligent Systems from NUS and a First-Class Honours degree in Electrical and Electronic Engineering from NTU. At Micron Semiconductor, he developed AI-based defect detection systems and wafer map recognition models, applying computer vision and deep learning to optimize manufacturing processes.

In his training, Woei Ming introduces learners to the practical use of GANs, VAEs, and diffusion models for image generation and analysis. His sessions highlight applications in defect detection, pattern recognition, and creative design, ensuring participants see the business value of generative AI. By integrating academic research with industry applications, he ensures learners gain both theoretical knowledge and real-world skills in advanced image generation.