Image Generation | InvokeAI Documentation

Image Generation

Using InvokeAI

Prompt Crafting

Prompts are the basis of using InvokeAI, providing the models directions on what to generate. As a general rule of thumb, the more detailed your prompt is, the better your result will be.

Prompt Structuring Template

To get started, here’s an easy template to use for structuring your prompts: Subject, Style, Quality, Aesthetic

There are two prompt boxes: Positive Prompt & Negative Prompt.

Some example prompts you can try on your own:

Advanced Prompting

Learn how to structure prompts, use positive and negative prompts well, and iterate toward better results.

Learn InvokeAI's advanced prompt weighting and composition syntax, including +, -, .blend(), and .and().

Expand one prompt into many prompt variations with curly-brace syntax.

Generation Workflows

Invoke offers a number of different workflows for interacting with models to produce images. Each is extremely powerful on its own, but together provides you an unparalleled way of producing high quality creative outputs that align with your vision.

Improving Image Quality

  1. Fine-tuning your prompt:
    The more specific you are, the closer the image will turn out to what is in your head. Adding more details in the Positive or Negative Prompt can help add or remove parts of the image. You can also use advanced techniques like upweighting and downweighting to control the influence of specific words.

  2. Explore different models:
    Other models can produce different results due to the data they’ve been trained on. Each model has specific language and settings it works best with; a model’s documentation is your friend here.

  3. Increasing Steps:
    The number of steps used controls how much time the model is given to produce an image, and depends on the “Scheduler” used. More steps tends to mean better results, but will take longer. We recommend at least 30 steps for most.

  4. Tweak and Iterate:
    Remember, it’s best to change one thing at a time so you know what is working and what isn’t.

  5. Explore Advanced Settings:
    InvokeAI has a full suite of tools available to allow you complete control over your image creation process.

Terms & Concepts

Stable Diffusion

Stable Diffusion is a deep learning, text-to-image model that is the foundation of the capabilities found in InvokeAI.

Prompts

Prompts provide the models directions on what to generate. As a general rule of thumb, the more detailed your prompt is, the better your result will be.

Models

Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see.

Schedulers & Steps

Schedulers guide the process of removing noise (de-noising) from data. They determine the number of steps to take to remove the noise and the specific method (algorithm) used for de-noising.

Steps represent the number of de-noising iterations each generation goes through.

Additional Concepts

Low-Rank Adaptations (LoRAs):
LoRAs are like a smaller, more focused version of models, intended to focus on training a better understanding of how a specific character, style, or concept looks.

Textual Inversion Embeddings:
Like LoRAs, embeddings assist with more easily prompting for certain characters, styles, or concepts. They are trained to update the relationship between a specific word (known as the “trigger”) and the intended output.

ControlNet:
ControlNets are neural network models that are able to extract key features from an existing image and use these features to guide the output of the image generation model.

VAE:
A Variational Auto-Encoder (VAE) is an encode/decode model that translates the “latents” image produced during the image generation process to the large pixel images that we see.