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
Subject: What your image will be about. E.g. “a futuristic city with trains”, “penguins floating on icebergs”, “friends sharing beers”.
Style: The style or medium in which your image will be in. E.g. “photograph”, “pencil sketch”, “oil paints”, or “pop art”, “cubism”, “abstract”.
Quality: A particular aspect or trait that you would like to see emphasized in your image. E.g. “award-winning”, “featured in relevant set of high quality works”, “professionally acclaimed”. Many people often use “masterpiece”.
Aesthetics: The visual impact and design of the artwork. This can be colors, mood, lighting, setting, etc.
There are two prompt boxes: Positive Prompt & Negative Prompt.
A Positive Prompt includes words you want the model to reference when creating an image.
A Negative Prompt is for anything you want the model to eliminate when creating an image. It doesn’t always interpret things exactly the way you would, but helps control the generation process. Always try to include a few terms - you can typically use lower quality image terms like “blurry” or “distorted” with good success.
Some example prompts you can try on your own:
A detailed oil painting of a tranquil forest at sunset with vibrant colors and soft, golden light filtering through the trees
friends sharing beers in a busy city, realistic colored pencil sketch, twilight, masterpiece, bright, lively
Invoke offers a number of different workflows for interacting with models to produce images. Each is extremely powerful on its own, but together provide you an unparalleled way of producing high quality creative outputs that align with your vision.
Text to Image
Focuses on the key workflow of using a prompt to generate a new image. It includes other features that help control the generation process as well.
Image to Image
Provide an image as a reference (called the “initial image”), which provides more guidance around color and structure to the AI as it generates a new image.
Unified Canvas
An advanced AI-first image editing tool. Drag an image onto the canvas to regenerate elements, edit content or colors (inpainting), or extend the image with consistency and clarity (outpainting).
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. Learn more in the Prompting Guide and Prompting Syntax.
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. Play around with some and see what works best for you!
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.
Tweak and Iterate:
Remember, it’s best to change one thing at a time so you know what is working and what isn’t. Sometimes you just need to try a new image, and other times using a new prompt might be the ticket.
For testing, consider turning off the “random” Seed. Using the same seed with the same settings will produce the same image, which makes it the perfect way to learn exactly what your changes are doing.
Explore Advanced Settings:
InvokeAI has a full suite of tools available to allow you complete control over your image creation process. Check out our features docs if you want to learn more.
Stable Diffusion is a deep learning, text-to-image model that is the foundation of the capabilities found in InvokeAI. Since the release of Stable Diffusion, there have been many subsequent models created based on Stable Diffusion that are designed to generate specific types of images.
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 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.
Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at civitai.com. Each model can produce a unique style of output, based on the images it was trained on.
Schedulers guide the process of removing noise (de-noising) from data. They determine:
The number of steps to take to remove the noise.
Whether the steps are random (stochastic) or predictable (deterministic).
The specific method (algorithm) used for de-noising.
Steps represent the number of de-noising iterations each generation goes through. Schedulers can be intricate and there’s often a balance to strike between how quickly they can de-noise data and how well they can do it. It’s typically advised to experiment with different schedulers to see which one gives the best results.
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.
