🚀 5-Session Workshop: Generative AI for Vision
This 5-session hands-on workshop introduces participants to the core concepts and tools needed to build and deploy vision-based generative AI models. From understanding how images are represented in neural networks to creating compelling prompts for diffusion models, you’ll acquire the practical know-how to leverage cutting-edge image synthesis techniques.
🗓 Sessions & Topics
Session 1: Introduction to GEN AI for Vision
What is Generative AI?
Overview of generative models and why they are transformational in computer vision.
Key areas of application: art generation, image-to-image translation, upscaling, etc.
Real-World Examples
How GANs, VAEs, and Diffusion Models are used in the industry.
Live demos of existing state-of-the-art models (e.g., DALL·E, Stable Diffusion).
Hands-on
Setting up your development environment (Python environment, libraries, GPU/Cloud setup).
Introduction to popular frameworks (PyTorch or TensorFlow).
Outcome: Understand the basics of how generative AI works in computer vision and set up the environment for hands-on experimentation.
Session 2: Representation of Images
Image Fundamentals
How images are stored digitally (pixels, channels, color spaces).
Convolutions and feature extraction in deep learning.
Latent Spaces & Embeddings
The concept of embedding high-dimensional images into lower-dimensional latent spaces.
Why latent representations are important for generative tasks.
Hands-on
Exploring image representations with a small CNN or autoencoder.
Visualizing intermediate activations and latent codes.
Outcome: Gain a solid grounding in how models internally “see” and encode images, a critical building block for diffusion models and prompt engineering.
Session 3: Basics of Diffusion Models
How Diffusion Models Work
Step-by-step breakdown of the diffusion and denoising process.
Key variations: DDPM, DDIM, Latent Diffusion.
Training Pipeline
Data requirements: building the right dataset for image generation.
Computational considerations: GPU/TPU usage and training times.
Hands-on
Running a pre-trained diffusion model locally or on a cloud platform.
Generating your first custom images via inference, tweaking basic parameters.
Outcome: Understand the core mechanics of diffusion-based image generation and run your own inference experiments.
Session 4: Prompt Engineering for Images
What is Prompt Engineering?
The role of text prompts in guiding image generation.
Principles of prompt design for coherent output.
Advanced Prompt Techniques
Keyword selection, negative prompts, style references.
Combining textual conditioning with control hints (e.g., ControlNet, pose conditioning).
Hands-on
Experimenting with different prompts using a text-to-image diffusion model.
Building a prompt library for specific styles or use cases (e.g., product design, concept art).
Outcome: Develop the skill to craft prompts that reliably yield high-quality, on-target images using diffusion-based text-to-image models.
Session 5: Project Building & Deployment
Project Assembly
Bringing it all together: from image representation to model inference.
Structuring code for maintainability and scalability (folder organization, Docker, etc.).
Deployment & Serving
Exporting your model for local or cloud deployment (e.g., Flask, FastAPI, Hugging Face Spaces).
Best practices for hosting a generative AI application (security, GPU vs. CPU serving).
Hands-on
Building a mini-project:
Example: A web app that takes a text prompt and outputs AI-generated images.
Final presentation: Each participant showcases their deployed project.
Q&A and Future Directions
Advanced topics: in-painting, out-painting, multi-modal learning.
Next steps: collecting specialized datasets, refining model checkpoints, exploring large-scale training.
Outcome: Learn the end-to-end process of developing, deploying, and showcasing a generative AI for vision project.
🎯 Workshop Outcomes
By the end of this workshop, you will:
Understand the fundamentals of generative AI for computer vision, including image representation and diffusion models.
Be able to configure and run diffusion-based models for image generation.
Master the art of prompt engineering to direct and refine image outputs.
Have practical experience building and deploying a mini-project using generative AI techniques.
