Stable Diffusion Explained

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Recently my Twitter feed got flooded with this technology that generates images for text prompts and I was amazed by the images that are generated. This is surely a great leap in Artificial intelligence.

Stable diffusion is machine learning text-to-image model developed by Stability AI, it generates images according to the prompt entered by the user. It is released as an open source project by the company.

Stable Diffusion is powered by Latent Diffusion, a cutting-edge text-to-image synthesis technique. This method was described in a paper published by AI researchers at the Ludwig Maximilian University of Munich titled “High-Resolution Image Synthesis with Latent Diffusion Models.”

Read more about Latent Diffusion

Stable Diffusion, the follow up to the same teams previous work on Latent Diffusion Models, improved significantly on predecessors both in image quality and scope of capability. It achieved this through a more robust training dataset and meaningful changes to the design structure.

It is trained with dataset LAION-5B by LAION.

The model architecture derives its roots from the initial diffusion models from 2015 and introduces variance in the form of Latent Diffusion Models.

Rather than denoising the image in question to gain context from the picture, the model works towards breaking down the image into a lower-dimensional latent space.

Once the latent vision has been achieved, the primitive method of noising and denoising is applied to gain the final contextual decoding into the pixel space.

DALL-E is text-to-image generator developed by OpenAI. Although OpenAI does not releases information about the dataset used to train the model.

The obvious application for diffusion models is to be integrated into design tools to empower artists to be even more creative and efficient.

In fact, the first wave of these tools has already been announced, including Microsoft Designer which integrates Dall-E 2 into its tooling.

There are significant opportunities in the Retail and eCommerce space, with generative designs for products, fully generated catalogs, alternate angle generation, and much more.

Product design will be empowered with powerful new design tools, that will enhance their creativity and provide the capability to see what products look like in the context of homes, offices, and other scenes.

With advancements in 3D diffusion, full 3D renders of products can be created with a prompt. Taking this to the extreme, these 3D renders can then be printed as a 3D model and come to life in the real world.

Marketing will be transformed, as ad creative can be dynamically generated, providing massive efficiency gains, and the ability to test different creatives will increase the effectiveness of ads.

The entertainment industry will begin incorporating diffusion models into special effects tooling, which will enable faster and more cost-effective productions.

This will lead to more creative and wild entertainment concepts that are limited today due to the high costs of production.

Similarly, Augmented and Virtual Reality experiences will be improved with the near-real-time content generation capabilities of the models. Users will be able to alter their world at will, with just the sound of their voice.

Stable diffusion is groundbreaking research in Artificial intelligence (AI) that leads to many tools that are changing the way artistic tasks were performed. Now AI is competing with artists with many years of experience.

Further scope of improvement is definitely there until then we can take advantage of these tools to reduce human effort.

If you want to learn more this technology here are some awesome resources

1.How stable diffusion works [link]

2.Two minutes Papers [link]

Hope this article helped in the understanding of stable diffusion

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