Believe it or not, one of the major growing branches of technology is AI. So we are back to introduce another AI software to you.

Stable Diffusion

In 2022, Stability.ai created a deep learning model called Stable Diffusion that could convert words into images. In addition to the text-to-image conversion, this AI can also use the image-to-image procedure. Stable Diffusion has the potential to revolutionize the world of AI, enabling people to generate realistic-looking images from text without any prior knowledge of programming or image editing.

As with Google's Imagen, Stable Diffusion uses a static CLIP ViT-L/14 text encoder to condition the model on text prompts.

Stable Diffusion splits the picture generation process at runtime into a "diffusion" process. This method takes a picture from its initial, noisy state and refines it until it closely matches the specified textual description.

System Requirements

• Windows 10/11 OS
• Nvidia GPU RTX with at least 12 GB of VRAM
• 25 GB of local disk space

Note: A GPU with more memory will be able to generate larger images without requiring upscaling. The model can still run even on 8 GB of VRAM, but you will be limited to 256x256 resolution.

You can use Stable Diffusion on your PC as well as the Stability.ai website. On a PC, however, there are numerous choices for customizing the output image.

You may watch a tutorial for the Stable Diffusion installation at the end of this post.

What are the differences between Stable Diffusion and Dall-E2?

There are many other AIs that generate images out of the text. One of the famous AI is Dall-E2.

On January 5, 2021, OpenAI unveiled Dall-E2, an AI system that can generate images based on written descriptions. To decipher the natural language inputs and produce related visuals, it employs a 12-billion-parameter training version of the GPT-3 transformer model.

Each of these two AIs has its own advantages and disadvantages. So, we highlight a few of them here.

Free & Open Source

Stable Diffusion is free, whereas Dall-E2 is not, which is a significant distinction. Open source is another benefit of stable diffusion.

Open source also means that the code behind Stable Diffusion is available for public scrutiny and review, which can help to ensure the accuracy and reliability of the platform.

Generating Power

Both programs are incredibly powerful, but Stable Diffusion tends to create imagery that is more artistic and beautiful, whilst DALL-E2 sometimes seems more straightforward.

The outcomes vary between landscapes, people, works of art, animals, and other text prompts like robots or futuristic vehicles, so a lot relies on the type of graphic you are creating. Paying attention to the instruction words, such as "highly-detailed," "smooth," or an indicator of the texture you'd like your image to have is one of the finest ways to improve your graphic.

Resolution

Although Stable Diffusion triumphs when it comes to higher-resolution pictures, we believe that each of these AI technologies presents an intriguing opportunity to experiment with image design and development. When compared to DALL-E2 512 x 512 resolution, the application can produce images with a resolution of up to 1024 × 1024, making it a clear choice if you need sharp graphics for use in marketing, gaming, or other fields.

Facial Images

Because Dall-E2 has a wider range and enables you to generate visuals of real individuals, it may be better equipped to produce images of real people (such as celebrities or historical figures).

What Are The Real Uses?

Apart from the joy of creating some images with the careful selection of `words, here is the main question of this blog “what are the real uses of Stable Diffusion AI”?

Video Games

• creating portraits for an Age of Empires 3 Definitive edition game mod
• tileable textures
• Tile finalization

Product & Architecture Design

One of the intriguing features of this AI is sketch-to-image and image-to-image generating models. The architects and product designers would benefit greatly from this.

Marketing

In the Stable Diffusion, because you have the full right to the generated image, you may confidently put it to use in your advertising campaigns. With the right system, AI, and prompts in place, this might result in significant project time savings.

Security

For use on social media and other image-based platforms, photos with recognizable individuals or locations can be removed from them and made anonymous. This process is known as anonymization, and its purpose is to protect the privacy of those individuals or places while still allowing people to view or interact with the image.

Sciences

The Diffusion Model can be used for the fake MRI dataset. It is a process in which latent​ diffusion modeling is used for the generation of brain imaging.

In the below articles, you can read more about the use of diffusion models in the field of science and neurology.

Brain Imaging Generation with Latent Diffusion Models

Deep neural networks have brought remarkable breakthroughs in medical imageanalysis. However, due to their data-hungry nature, the modest dataset sizes inmedical imaging projects might be hindering their full potential. Generatingsynthetic data provides a promising alternative, allowing to comple…

arXiv.orgWalter H. L. Pinaya

LDM 100k Dataset

AI-generated high-resolution Brain MRI imaging data comprising of 100k subjects, with associated information such as age, sex, and brain size normalised by head size (surrogate of atrophy). The data was generated using a 3D Latent Diffusion Model. The model was trained on the Cambridge-1 Super Compu…

Academic Torrents

How to install Stable Diffusion

Here is the video for installing and using Stable Diffusion: