You must have heard about AI generating many artworks, especially images; ever wondered how this works? In this blog, We will brief you about AI-generated pictures and how they are generated and processed further.
AI is the abbreviation of Artificial Intelligence. It refers to a computer that is capable of mimicking human intelligence. This can be done by training an AI system on a large dataset using some algorithms. So artificial intelligence is trained on a large dataset to generate the described image by the user. AI images can be realistic or abstract and convey a specific theme or message.
An AI text-to-image generator uses a machine learning technique known as a neural network which can take text as input and generate an image as output. To do this, a neural network requires a lot of training. We can understand this by taking an analogy of a toddler learning for the first time to paint and then making a connection between the painting, objects, and words. To generate images, the system uses two neural networks. The first neural network is used to create the image based on the text input by the user. The second neural network analyzes the generated image with reference images. By comparing the photos, it creates a score to determine the accuracy of the generated image. 
There are a few different types of text-to-image generators. One of them is using diffusion models.Diffusion models are trained on a large dataset of hundreds of millions of images.
A word describes each image so the model can learn the relationship between text and images. It is observed that during this training process, the model also knows other conceptual information, such as what kind of elements would make the image more clear and sharp.
After the model is trained, the models learn to take a text prompt provided by the user, create an LR(low-resolution) image, and then gradually add new details to turn it into a complete image. The same process is repeated until the HR(high-resolution) image is produced.
Green dragon on table
Diffusion models don’t just modify the existing images; they generate everything from scratch without referencing any images available online. It means that if you ask them to generate an image of a “dragon on the table,” they would not find an image of the dragon and table individually on the internet and then process further to put the dragon on the table instead of that they will create the image entirely from scratch based on their understanding of the texts during the training time. 
Sloth in pink water
There are many benefits of using diffusion models over other models. Firstly, these are more efficient to train. The images generated by them are more realistic and connected to the world. Also, it makes it easier to control the generated image, you can just use the color of the dragon(let’s say green dragon) in the text prompt, and the models will generate the image.
There are a few different types of text-to-image generators. One of them is using diffusion models.Diffusion models are trained on a large dataset of hundreds of millions of images.
A word describes each image so the model can learn the relationship between text and images. It is observed that during this training process, the model also knows other conceptual information, such as what kind of elements would make the image more clear and sharp.
After the model is trained, the models learn to take a text prompt provided by the user, create an LR(low-resolution) image, and then gradually add new details to turn it into a complete image. The same process is repeated until the HR(high-resolution) image is produced.
Green dragon on table
Diffusion models don’t just modify the existing images; they generate everything from scratch without referencing any images available online. It means that if you ask them to generate an image of a “dragon on the table,” they would not find an image of the dragon and table individually on the internet and then process further to put the dragon on the table instead of that they will create the image entirely from scratch based on their understanding of the texts during the training time. Sloth in pink water
There are many benefits of using diffusion models over other models. Firstly, these are more efficient to train. The images generated by them are more realistic and connected to the world. Also, it makes it easier to control the generated image, you can just use the color of the dragon(let’s say green dragon) in the text prompt, and the models will generate the image.