Welcome to Civitai.com's official beginner's guide to AI art. My name is Tyler, and throughout this series, I will be your guide as we go from zero to generating our first AI images.

Throughout these videos, you can expect to learn about the core concepts and the terminology behind AI art and stable diffusion.

We're going to discuss and walk through how to install the various pieces of software and programs you will need to generate AI images on your own local machine, and we're going to learn how to navigate these programs, as well as how to properly download and store resources from the Civitai.

com resource library. Before we get to installing anything, there are a lot of core concepts and terminology used throughout AI art and stable diffusion that, if you're new to all this, really might be overwhelming or not familiar.

So, in this video, we're going to discuss some common terms, abbreviations, and concepts that you will encounter as you're browsing websites like Civitai and interacting with software like Automatic1111, Fooocus, ComfyUI, or Easy Diffusion.

So, let's get started by discussing the various concept types of image generation that you'll be doing throughout your time making AI images, starting with our very first concept and the most common, which is text-to-image.

You're going to see this term a lot, and this refers to taking a text prompt and generating an image out of nothing, using only the text and telling the AI exactly what you would like to see in your image.

Then we have image-to-image and batch image-to-image. This is the process of taking an existing image or a reference photo, for example, a photo of myself or a photo of a friend, and using that photo as the input for the AI to then take your prompt, reference the photo and build the output image on top of the already existing photo.

For this, you'll be using something called a ControlNet, which we will talk about in the extensions part of this video.

Image to image is doing so with only one single image, whereas a batch image to image is taking a folder of images and running them through the diffusion process all at the same time.

Next, we have inpainting, which is the practice of using a painted mask area to add or remove objects from an image.

Think of this as generative fill from Photoshop, except it lives locally in your stable diffusion software and you get to paint right on your image with a brush tool, punching the prompt exactly what you want to happen in the part of the image that you painted.

Next, we have text to video and video to video, or as you'll see them referred to, text to vid or vid to vid.

These are the processes of taking a text prompt and getting a video output with motion, or taking an existing video input and transforming that video utilizing your prompt.

Next, we have the most important part: the prompt and the negative prompt. The prompt is the text input that you give your stable diffusion based software or any AI image generation software in general to tell it exactly what you would like it to output in your image.

This is where you take your text input and tell Stable Diffusion what you do not want in your photo. Next, we have upscaling. Upscaling is the process of taking low resolution media, think an image that is a 512 x 512 small little square, and converting it to high resolution media, think a square that is 1080 x 1080.

This is usually done by enhancing the existing pixels and most of the time we are now doing this through either AI models that are built into our Stable Diffusion software and interfaces or we're using external programs like Topaz Photo AI or Topaz Video AI to upscale our images and videos before we go and we share them on the internet or post them wherever we want.

The upscaling process is usually going to be the last part before you're ready to share your images. These are the core concepts that you will be utilizing anytime you sit down to generate something with Stable Diffusion.

Next, we're going to dive into the models, assets, and resources that you're going to come across on a regular basis.

So to start off, checkpoints. Checkpoints are now more commonly referred to as models, but you will see these terms used interchangeably as you go from site to site and you're looking for different models or checkpoints to use in your generations.

A model is the product of the training millions of images scraped from all over the web, and this file drives our results from text-to-image, image-to-image, and text-to-video generations.

This is the heartbeat of everything you will be doing in Stable Diffusion. Typically, your model will dictate the overall style that you will get out of your image.

Some models are really great all-arounders, some are very strictly trained on anime, and some are very strictly trained on realistic images.

Choosing the right model is vital to getting the image that you would like out of Stable Diffusion.

All right, let's move on to checkpoints and safe tensors. Now, checkpoints are a file format created by PyTorch Lightning. It contains a machine learning model which is used by Stable Diffusion to generate our image outputs.

Now, this checkpoint or the CKPT file is superseded and has mostly been replaced by SafeTensor files.

SafeTensor files are essentially the same thing, except they are less susceptible to having malicious code put in them. So whenever possible, you would want to look for the SafeTensor version of a model rather than a CKPT.

This is also why it is good to read reviews before you download any models and install them into your hard drive on your machine.

You want to make sure that you're not downloading anything malicious.

Now, anytime you hear the term training data, it's referring to a set of many images that are used to train a Stable Diffusion model, LORA, or embedding.

LAION-5B: This is a large-scale dataset for research purposes that has been trained on 5.85 billion CLIP-filtered text-to-image pairs.

This is the dataset that Stable Diffusion was trained on, which brings us to Stable Diffusion 1.5, or also referred all over the internet as SD 1.5.

This is a latent text-to-image model trained on 595,000 steps at a resolution of 512x 512 images from the LAION-5B model.

This has now been superseded by Stability AI's latest release, Stable Diffusion XL 1.0.

However, a lot of the community still uses Stable Diffusion 1.5 because of its flexibility and the sheer amount of resources that are available for SD 1.5.

Next up, we have LORA, L.O.R.A., which stands for Low-Rank Adaption. Now, a LORA is essentially a model but trained on a much, much, much smaller dataset geared towards a very specific thing.

This thing could be a person, a style, or a concept.

So you will find many LORAs trained on specific anime characters, so that when you include the LORA in your image generation process, it is going to push your image output to have that specific character in the final image.

Textual inversions and embeddings, well, these are similar to LORAs, but they're trained on even smaller data sets and really geared towards capturing concepts such as fixing bad hands, fixing bad eyes, objects, and specific faces.

VAEs are optional, detail-oriented files that sometimes come built into your models, or more often, you will have to include a VAE next to your model for your image generation.

You can think of VAEs as the final touch to getting a really crisp, sharp, colorful image. Some models without the use of a VAE, the colors will feel very dull and washed out, or they will have less details.

So you either want to make sure that the model that you are currently running has a VAE built into it, or if not, you want to use your own VAE alongside of it.

That just about covers the model section. Now, let's jump into some of the most important and common extensions that you will encounter while you're using Stable Diffusion.

All right, so our first extension, and quite possibly one of the most important things you will come across while you're using Stable Diffusion, if you want to do anything outside of just basic text-to-image prompting, is ControlNet.

ControlNet consists of a bunch of different models that are trained on specific data sets to read different structures of an image such as straight lines, depth, character position, where it will actually position a dummy inside of the character in your photo so that you can then take that dummy and generate a whole new person on top of the exact pose that that person was in.

ControlNets are essential if you want to do anything involving image-to-image or video-to-video.

Next, we have DeForum. DeForum is a community of AI image synthesis developers, enthusiasts, and artists that build a large set of generative AI tools.

They are most commonly known for their super popular Automatic1111 extension that can take a text prompt and generate a really, really smooth video output that you can also keyframe specific zooming, panning, and turning motions into.

Next, we have ESRGAN, the Enhanced Super-Resolution Generative Adversarial Network. ESRGAN is a technique that is used to generate high-resolution images from low-resolution pixels.

Think upscaling a 720p image up to 1080p. This model is commonly found in a lot of Stable Diffusion interfaces.

Next, we have AnimateDiff. AnimateDiff is a technique used to inject motion into text-to-image and even image-to-image generations. These are all of the core concepts and terminology and terms that you will come across during your time using Stable Diffusion.

If at any point you get lost or you need some extra help figuring out what something means or you need something to refer to, feel free to visit our Stable Diffusion glossary in the Civitai.

com education hub. We'll see you guys in the next video.

Civitai Beginners Guide To AI Art // #1 Core Concepts

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Civitai Beginners Guide To AI Art // #1 Core Concepts

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