Standard Practices for Image Generation
Part of the Prompt Engineering course
The previous lesson gave you the intuition for how a diffusion model turns a text prompt into an image by denoising from random noise. This lesson turns that intuition into a set of habits for writing image prompts that land closer to what you pictured. The five principles from lesson three still apply here: you give direction, specify what the frame should look like, and iterate deliberately, the same moves you...
Ask an image model for a coffee mug and it will give you something, but the model is filling in every decision you left open: the material, the angle, the light, the mood, whether it looks like a photo or a cartoon. A vague prompt does not fail loudly. It just hands back a generic average of everything it has seen tagged "coffee mug." ...
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Start this lessonStandard Practices for Image GenerationWhat you'll learn
- Prompt structure of subject, medium, style, and lighting
- Art styles and artist or era modifiers
- Composition and camera modifiers
- Negative prompts and aspect ratio
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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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