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#177 Björn Ommer: Diffusion Mods Explained By Stable Diffusion’s Creator
Eye On A.I.
SD as Balancing Local Detail CNN compression and Long-Range Context Diffusion
The brain perceives complex shapes by combining distant elements through attentive and pre-attentive processes. Vision researchers aim to computationally replicate this ability efficiently, without exploring all combinations. Rapid feedforward processes in the brain create the impression of shapes like triangles. Attention helps focus on specific details without scaling to every pixel. The challenge lies in representing scenes with intricate local details like texture and colors, while also capturing overarching shapes, like the triangular structure of a bird. Utilizing a combination of architectures is crucial - convolutional neural networks excel at abstracting local details, while diffusion models capture long-range interactions. Integrating these two architectures results in a balanced approach that compresses local detail and diffuses long-range context effectively, providing stable diffusion in representations.
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