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README.md
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@@ -70,7 +70,7 @@ While we’ve been at work on different versions of this tool for over a year, w
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The problem is that AI consists of really complex engineering processes. You must handle large amounts of data, compute at scale, and manage challenging governance.
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That’s just the beginning. The reality is that most models are not just one model, but an ensemble of models and tasks. Take Stable Diffusion 2’s typical architecture. It consists of at least four core models (an image encoder like VAE, a CLIP text encoder, the
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So it’s going to take time and a determined community (like the one we have here) to figure it out.
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The problem is that AI consists of really complex engineering processes. You must handle large amounts of data, compute at scale, and manage challenging governance.
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That’s just the beginning. The reality is that most models are not just one model, but an ensemble of models and tasks. Take Stable Diffusion 2’s typical architecture. It consists of at least four core models (an image encoder like VAE, a CLIP text encoder, the U-Net denoising model, and an image decoder) that each have their own intensive training history with multiple data sets —not including Stable Diffusion’s own training data. The cool thing is that a bunch of people figured out how to make this model work, but there are lingering uncertainties about the model’s actual provenance.
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So it’s going to take time and a determined community (like the one we have here) to figure it out.
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