Generating images is a task AI solved very solidly. You can generate what you want with great specificity, there are vibrant communities with copious amounts of resources to help you piece together your prompt, and there are multiple different models out there, each offering a set of strengths it is particularly good at. Here are some strategies and practices to get you started on this journey.
Leading image generators
Methods for image prompt engineering:
#1 Snag a prompt for a style you like and edit it:
1. Go to one of these community storehouses of different image prompts.
Important: an image prompt for one model (like Midjourney) may not perform well in another model (like Stable Diffusion). Edit and reuse the prompts with the same model in which the original user ran with.
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2. Look up image styles that you are interested in.
Example: you can look up cartoon style, or photo-realistic style, or in style of an artist like Van Gogh.
3. Copy the prompt of an image whose style you like.
4. Modify the key words in the prompt to generate the content you are looking for while sticking to the same style.
#2 Use a prompting assistant
There are tools which either suggest prompts to an image you already have .
For Stable Diffusion:
#3 Ask ChatGPT for advice
Asking ChatGPT to generate an image prompt is a fool's errand, because much of the data on which ChatGPT was trained has little information contained about image generators. But it is still helpful for brainstorming different descriptors you can use for image prompting. To do so:
- Go to GPT3.5 or GPT4
- Give GPT the following instructions "Please create a table that breaks down an image scene composition into the following elements, where each of these elements is a column.”
- Type in the elements of an image which you are interested in modifying. For example: "Composition, camera angle, style, focal point, textures, details, color palette, lighting, location, time of day, mood, medium, etc."
- Ask it to make a table of all the options: "Fill the table with 10 rows of data, where: composition="<whatever you want the theme of the composition to be>"
In response, you will receive a table like this, from which you can choose the specifiers you are most interested in.