[Music] Welcome to cai.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

properly download and store resources from the cai.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 Automatic 1111,  Focus, Comfy UI, 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 control net, 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 in painting, 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. The negative prompt does the reverse. 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 safe tensor files. Safe tensor 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 safe tensor 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. LAION-5B, or LAION 5B, is a large-scale data set for research purposes that has been trained on 5.85 billion CLIP-filtered text-to-image pairs. This is the data set that Stable Diffusion was trained on, which brings us

to Stable Diffusion 1.5, or also referred to 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 512x512 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 Laura L O R A which stands for low-rank adaptation now Aura is essentially a model but trained on a much much much smaller data set geared towards a very specific thing this thing could be a person a style or a concept

so you will find many Lauras trained on specific anime characters so that when you include the Laura 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 Lauras 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 next we have vaes or vay vays are optional detail oriented files that sometimes come built into your models or more often you will have

to include a vay next to your model for your image generation you can think of vays as the final touch to getting a really crisp sharp colorful image some models without the use of a vay 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 vay built into it or if not you want to use your own vay 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 controlling. ControlNets control Nets consist 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 Automatic 1111 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 720 image up to 1080. This model is commonly found in a lot of

Stable Diffusion interfaces. Next, we have Animate Diff. AnimDiff 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 Co:ty.com education hub. We'll see you guys in the next video.

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핵심 개념과 용어를 다루는 AI 아트 초보자 가이드 개요입니다.

cai.com에서 소프트웨어 설치 및 리소스 다운로드.

초보자를 위한 일반적인 용어와 개념에 대한 설명입니다.

다양한 이미지 생성 유형에 대해 논의합니다.

텍스트-이미지 변환: 텍스트 프롬프트에서 이미지 생성하기.

이미지 대 이미지 및 배치 이미지 대 이미지: 기존 이미지를 입력으로 사용합니다.

채색하기: 마스크를 사용하여 이미지에서 개체를 추가하거나 제거합니다.

텍스트-투-비디오 및 비디오-투-비디오: 동영상 생성 및 변환

프롬프트 및 네거티브 프롬프트: 텍스트 입력으로 이미지 생성 제어하기.

업스케일링: 이미지 해상도 향상.

체크포인트/모델: 이미지 생성을 주도하는 훈련된 모델.

체크포인트(.ckpt) 및 세이프 텐서: 모델용 파일 형식.

학습 데이터: 모델 학습에 사용되는 데이터 세트(예: LAION-5B).

스테이블 디퓨전 1.5 및 스테이블 디퓨전 XL 1.0: 모델 버전.

LORA: 특정 스타일이나 컨셉을 위한 낮은 순위의 적응 모델입니다.

텍스트 반전 및 임베딩: 특정 개념에 대한 더 작은 데이터 세트.

VAE: 이미지 디테일과 색상을 개선하기 위한 옵션 파일입니다.

컨트롤넷: 이미지 구조와 문자 위치를 제어하기 위한 모델입니다.

포럼: 키프레임으로 원활한 동영상 생성을 위한 확장 기능입니다.

ESRGAN: 고해상도 이미지 생성 기술.

AnimDiff: 이미지 생성에 모션을 주입하는 기술입니다.