Innovations & Integrations (Community of Practice)

Thursday 11 January 2024

Stable Diffusion

Stable Diffusion is a type of neural network* model specifically designed for generating images. It works by taking a text description and gradually transforming a random pattern of pixels into a coherent image that matches the description, using its learned understanding of how words relate to visual elements.

This innovative approach allows Stable Diffusion to create a wide range of images, from realistic photographs to artistic renderings, all based on textual descriptions, with processes below:

1. Text-to-Image Conversion: Stable Diffusion takes a text prompt and generates an image that corresponds to the description. This is achieved through a process known as diffusion, which involves gradually refining random noise into a structured image.

2. Training on Datasets: The model is trained on a large dataset of images and their descriptions. During training, it learns how various textual descriptions correlate with visual elements in images.

3. Diffusion Process: The actual image generation process starts with a pattern of random pixels (noise). The model then uses the learned relationships between text and images to gradually adjust this noise, step by step, until it forms an image that matches the input text description.

4. Refinement and Detailing: Throughout this process, the model iteratively refines the image, adding details and adjusting elements to better align with the given text prompt, until a coherent and visually representative image is produced.

You can try generate Images with prompts:

*Neural Network: A neural network is a computer system modeled after the human brain that learns from data by adjusting connections between layers of artificial neurons. It's used in AI to recognize patterns and make decisions, such as identifying objects in images or understanding human speech.

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