In this section, we explain some basic concepts about Generative AI processes. If you have questions about a specific topic, you can get in touch with us and we will try to help!
Generative AI Concepts:
- Temperature or Guidance Scale
- AI Biases
Every time you run a model on a powered with AI app, you’re creating a prediction. A prediction is an object that represents a single result from running the model, from an imput to an ouput. As an input it could include the prompt you provided, an image, or the settings. The output is the text, image, sound, or other kind of result but also other metadata like the model version, the user who created it, and timestamps.
Whenever you run a model, you’re creating a prediction. AI predictions could take a few milliseconds or minutes, even hours, depending on the model and server.
The temperature, or guidance scale (sometimes referred as cfg - classifier free guidance) is a parameter that controls how much the image generation process follows the text prompt. The higher the value, the more image sticks to a given text input but also the less diversity and quality you will get. Give it a try in this website.
A machine learning dataset is a collection of data that is used to train the model. A dataset acts as an example to teach the machine learning algorithm how to make predictions. The common types of data include: Text data. Image data.
Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning (ML) process. Deep learning modules tend to reproduce or exacerbate biases that are already present in the training dataset.
Adapters are added to AI-pre-trained text-to-image models. They let us input visual information to guide large text-to-image models during the generation process. Some examples are ControlNet or T2I-Adapter.