SD (Stable Diffusion) is a generative artificial intelligence (AI) model that uses deep learning to create photorealistic images, videos, and animations from text and image prompts. It was released in 2022 and is based on a type of deep learning model called a diffusion model.
How Stable Diffusion works:
- Curate Visual Digital Data: images, art, synthetics created by humans and software programs to create a Data Set.
- Process Data Set with standardized naming conventions, formats, etc. to create a Data Model.
- Train Data Model to create a Trained Data Model.
- AI Programs such as Stable Diffusion use Trained Data Models to generate new visual data. That same generated visual data can be used to train brand new data models in the future.
- Stable Diffusion gradually adds noise to the generated images with multiple steps.
- Stable Diffusion learns to reverse the process by removing noise to generate brand new images from scratch based on text prompts created by humans and software programs.
Stable Diffusion data models are trained on large datasets of image-text pairs to learn the relationships between images and the text used to describe them. The models are also designed to generate AI images at a fast pace, sometimes as quickly as real-time typing.
Stable Diffusion is a Latent Diffusion Model: meaning it compresses an image into a latent space before working with it. The latent space is much smaller, making the model faster and more efficient.
REFERENCES
AUTOMATIC1111. (2022, November 19). Stable Diffusion web UI. GitHub. https://github.com/AUTOMATIC1111/stable-diffusion-webui
Python, R. (n.d.). Python Tutorials – Real Python. Realpython.com. https://realpython.com/
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