AI Campus • Practical resources
Generative AI & Multimodal Synthesis
Understand generation pipelines, controls and reproducibility before claiming reliable creative output.
Before you start
- Python basics for runnable examples; a compatible device and downloaded model only for optional local inference.
The reading and planning exercises need no paid course or API. Optional inference, training, IDE features and hosted services may require hardware, accounts or paid access. Use the local or manual alternative before spending money.
Start with the official learning material
Free-to-read references checked 3 November 2026. Reading documentation does not activate a provider or create a subscription.
Practice with a concrete exercise
1. Map a generation pipeline
Read a Diffusers introduction and identify tokenizer/text encoder, denoiser and decoder roles. List the model, seed, resolution and inference settings needed to reproduce a run. Distinguish conceptual study from actually generating an image.
2. Compare one controlled change
If hardware permits, follow an official local example with a compatible model and license. Keep the seed and settings fixed while changing one prompt detail. Save outputs and metadata. Otherwise compare the documented examples and describe what you cannot test locally.
3. Assess a second modality
Use the lecture transcription project to compare a one-minute reference transcript with local Whisper output. Mark errors in names and numbers, then explain why transcription accuracy and summary quality need separate checks.
Check what you learned
- A comparison changes one variable at a time.
- Artifacts include model/settings and limitations.
- No claim of fidelity, copyright permission or factual accuracy follows merely from successful generation.
Keep your artifact, observed checks and remaining limitations in a short README. Mark any exercise you only designed rather than executed.