WHAT IS MIDJOURNEY

Midjourney is an example of generative AI that can convert natural language prompts into images. It’s only one of many machine learning-based image generators that have emerged of late. Despite that, it has risen to become one of the biggest names in AI alongside DALL-E and Stable Diffusion.

With Midjourney, you can create high-quality images from simple text-based prompts. You don’t need any specialized hardware or software to use Midjourney either as it works entirely through the Discord chat app. The only downside? You’ll have to pay at least a little bit before you can start generating images. That’s unlike much of the competition, which generally provides at least a few image generations for free.

Still, the barrier to entry with Midjourney is quite low and anyone can use it to generate real-looking images within a matter of minutes. The results can range from uncanny to visually stunning, depending on the prompt.

HOW TO USE MIDJOURNEY

1

Sign up for Discord

2

Sign up for Midjourney

3

Generate your first image

4

Edit your images

5

Save your images

Video : Watch this for easy guide on How to generate image using midjourney

Video : Watch this for easy guide on How to use a proper prompt for best result

HOW DOES MIDJOURNEY WORK

Midjourney relies on two relatively new machine learning technologies, namely large language models and diffusion models. You may already be familiar with the former if you’ve used generative AI chatbots like ChatGPT. A large language model first helps Midjourney understand the meaning of the words you type into your prompts. This is then converted into what is known as a vector, which you can imagine as a numerical version of your prompt. Finally, this vector helps guide another complex process known as diffusion.

Midjourney uses a diffusion model to turn random noise into beautiful art.

Diffusion has only become popular within the past decade or so, which explains the sudden barrage of AI image generators. In a diffusion model, you have a computer gradually add random noise to its training dataset of images. Over time, it learns how to recover the original image by reversing the noise. The idea is that with enough training, such a model can learn how to generate entirely brand-new images.