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Notes on Diffusion Models

Notes on Diffusion Models
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Calvin Luo , Danni Zhang , Luciano Vinas , Google Veo , Deepmind Alphafold , Google Imagen , Eric Ma , Google Deepdream , Jeff Shaw , Simon Willison , Stable Diffusion , Denoising Diffusion Probabilistic , Jia Bin Huang , Latent Artist , Adobe Firefly , Adobe Stock , Imagen Video , Score Distillation Sampling , Stable Video ,

The Illustrated Stable Diffusion

AI image generation is the most recent AI capability blowing people’s minds (mine included). The ability to create striking visuals from text descriptions has a magical quality to it and points clearly to a shift in how humans create art. The release of Stable Diffusion is a clear milestone in this development because it made a high-performance model available to the masses (performance in terms of image quality, as well as speed and relatively low resource/memory requirements).

After experimenting with AI image generation, you may start to wonder how it works.

This is a gentle introduction to how Stable Diffusion works.

Stable Diffusion is versatile in that it can be used in a number of different ways. Let’s focus at first on image generation from text only (text2img). The image above shows an example text input and the resulting generated image (The actual complete prompt is here). Aside from text to image, another main way of usin ....

Robin Rombach , Yan Sidyakin , Dennis Soemers , Google Imagen , Stable Diffusion , Image Generator , Denoising Diffusion Probabilistic , Data Instead , Text Encoder , Transformer Language , Unet Noise , Cohere For , Clip Interrogator ,