Generative · Guide
Generative AI: Images, Audio, and Multimodal
Beyond text — diffusion models, Stable Diffusion, Whisper, text-to-speech, vision-language models.
Generative AI creates new content — images, audio, and video — not just text. This guide covers diffusion models and how they generate images, Stable Diffusion, image-generation workflows, speech recognition with Whisper, text-to-speech, vision-language models, and responsible generative media.
Generate your own lesson →What you'll learn
- Text-to-Speech and Audio Generation
- Image Generation Workflows
- Multimodal Prompting
- Responsible Generative Media
- Speech Recognition with Whisper
- Video Generation Concepts
- Diffusion Models Explained
- Vision-Language Models
- Hugging Face Diffusers
- Stable Diffusion and Latent Diffusion
Lessons in this guide (10)
Text-to-Speech and Audio Generation
Use generated audio responsibly for narration, accessibility, and media lessons.
Image Generation Workflows
Compare text-to-image, image-to-image, inpainting, and control workflows.
Multimodal Prompting
Write prompts that combine text, images, audio, and structured outputs.
Responsible Generative Media
Label, review, and govern synthetic images, speech, video, and music.
Speech Recognition with Whisper
Transcribe audio into text for lessons, search, and agent workflows.
Video Generation Concepts
Understand temporal coherence, prompts, and product caveats for generated video.
Diffusion Models Explained
Learn how iterative denoising turns noise into images and other media.
Vision-Language Models
Connect images and text for diagrams, screenshots, and multimodal tutoring.
Hugging Face Diffusers
Use pipelines, schedulers, and adapters for diffusion workflows.
Stable Diffusion and Latent Diffusion
Understand why running diffusion in latent space made image generation practical.
Frequently asked questions
What is generative AI?
Generative AI is a class of models that produce new content — text, images, audio, video, or code — by learning the patterns of their training data and sampling new outputs that follow those patterns.
How do diffusion models generate images?
A diffusion model learns to reverse a noising process: it starts from random noise and, guided by your prompt, denoises step by step into a coherent image.
What is a vision-language model?
A vision-language model (VLM) understands images and text together — you can show it a picture and ask questions about it, or have it caption, describe, or reason over visual content.