How Diffusion Models Generate Images
Part of the Prompt Engineering course
Until now every prompt you wrote produced text. This lesson opens Part 3, where the same prompt produces a picture. The medium changes but the mindset from Part 1 does not: you are still steering a model with words, and vague words still get you vague results. Before you can prompt an image model well, it helps to know what it is actually doing when it turns your sentence into a picture.
A common guess is that an image model searches a huge library and stitches together pieces of existing pictures. That is not what happens. There is no collage and no lookup. The model builds every image from scratch, and the raw material it starts from is pure random noise. ...
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Start this lessonHow Diffusion Models Generate ImagesWhat you'll learn
- Diffusion as iterative denoising
- Text conditioning of the image
- Seeds and reproducibility
- Sampling steps and guidance scale
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All lessons in this course
11 lessons- 1What a Prompt Is20 min
- 2How Large Language Models Generate Text30 min
- 3The Five Principles of Prompting30 min
- 4Standard Practices for Text Generation35 min
- 5Prompt Templates and Function Calling35 min
- 6Vector Databases and Retrieval-Augmented Generation40 min
- 7Autonomous Agents: Memory and Tools40 min
- 8How Diffusion Models Generate Images30 min
- 9Standard Practices for Image Generation35 min
- 10Advanced Image Techniques: Inpainting, Outpainting, and ControlNet35 min
- 11Building AI-Powered Applications40 min
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