Stable Diffusion Versus Imagen

There are numerous text-to-image artificial intelligence (AI) solutions out there that have been specifically created as a means of enabling anyone, no matter what kind of skill level they have in the art creation department, to generate art based on just the text inputs they use within the system.

However, not all AI tools are created equally, as some of them stand out a bit more when compared to others. In this article, we’ll take a look at two unique solutions known as Stable Diffusion and Imagen. They each have their own strengths and weaknesses that differentiate them in a variety of ways.

What Is Imagen?

Imagen is a text-to-image diffusion model created in part by Google’s research team that aims to bring an uncanny level of photorealism to AI image generation. It’s still not publicly available but the results showcased thus far make it seem like it will stand head and shoulders above competing models in the field. 

Imagen is essentially an AI system aiming to create photorealistic images from text input alone. A prompt is fed into an encoder, which then converts the prompt to a numerical representation, which encapsulates the semantic information that is stored within the text.

Then, the image-generation model creates an image by starting with noise, after which it transforms it into an output image slowly. The image generation model receives the text encoding as an input and has the effect of indicating to the model what the caption is so that it can create the image. Initially, this is a small image that then gets passed into a super-resolution model, ensuring that it can grow in terms of size. The result is a medium-sized image, which goes through another super-resolution model, creating a 1024 x 1024 pixel image that visually reflects the semantics within the caption.

What Is Stable Diffusion?

When we look at Stable Diffusion as an AI image generator, it is essentially a solution–or in other words, a specific model that was purpose-built to enable the generation of virtual creations, where it leverages the power of AI in order to achieve its specific goal. What makes it stand out when compared to other models is the fact that it is fully open-source, alongside the fact that it employs a frozen CLIP ViT-L/14 text encoder, through which it can condition the model on text prompts. 

What this means is that it can operate the image generation process by, as its name implies, "diffusion" at runtime. It starts with noise and gradually improves upon an image until when all of it is gone, which makes it as close as possible to the text input. 

What We Have Learned From These Tools

Imagen is Google's project that utilises AI to create images. Being backed by such a large company filled with talent could potentially allow it to exceed in terms of long-term performance. However, Stable Diffusion has showcased a lot of prowess and is open-source, which makes it a lot more accessible.

Both of them can generate images that are around 1024 x 1024 in terms of resolution, and both are easily accessible; however, it is clear that both solutions are still in the development stage and will likely have a long way to go in terms of development.

The Future of AI Image Generation

When we look at the overall quality, it is clear that AI images that are generated from both Stable Diffusion and Imagen have come a long way in terms of what was possible just years ago when AI was first utilised to generate images.

Stable Diffusion is much more cost-efficient, though, and Imagen might suffer due to its image-generation process, during which it has to keep upscaling the image. In any case, both of these solutions empower modern image generation procedures and provide users of any skill level to create digital artworks that would have otherwise taken a lot of time to achieve. These solutions are revolutionising the way we perceive art and online images.

If you are curious about how Stable Diffusion stacks up against other competing solutions, you can check out our recent articles on Stable Diffusion versus DALL-E 2 or Stable Diffusion versus Midjourney.