XexFlare/ai-africanheroes-prompts

Prompt strategies for generating realistic, modern, and diverse African characters in AI image and text models by countering common dataset and default-bias limitation # African Character Prompting ## What this repository is This repository is a **practical prompt library** for improving how AI models generate African and African-descended characters. It focuses on **realism**, **modern context**, and **accurate physical representation**, particularly in image generation tools such as: - ChatGPT image generation - Leonardo AI - Z-Image - Stable Diffusion based systems Throughout this repository, you will be introduced to **Adewale** and **Thandiwe**. They are used as recurring examples to demonstrate how well-constructed prompts can produce more believable, modern African characters. You will see how they are generated, what prompt structures are used, and how small changes in wording affect the final result. ### Adewale Adewale is a Nigerian-born Yoruba man represented in a contemporary, urban setting. He is used as an example for generating modern African male characters with realistic structure, presence, and proportion. ### Thandiwe Thandiwe is a Malawian-born Chewa woman represented in a modern African town setting. She is used as an example for generating contemporary African female characters with realistic anatomy, balance, and individuality, without defaulting to rural village imagery or large metropolitan city aesthetics. These examples are not archetypes. They are **practical test cases** for evaluating prompt quality. ## The problem this repository addresses Many current AI models consistently misrepresent African characters in a small number of repeatable ways: - Over-defaulting to rural, tribal, or impoverished imagery - Distorting body proportions and physical structure - Collapsing diverse African populations into a single generic appearance - Applying European fashion-model anatomy as a default - Swinging between underrepresentation and caricature These issues are not usually intentional. They are the result of uneven training data and weak default assumptions. ## What this repository is not To …