![]() It’s still very hard for these models to reconstruct the key visual features that characterize a specific person. Can’t we just feed the model an extremely detailed description of the person and be done with it? The short answer is no. After all, these new generation image synthesis models have unprecedented expressive power. You may be wondering why we need to follow such a special procedure. To do so, we’ll follow a special procedure to implant ourselves into the output space of an already trained image synthesis model. The first step towards creating images of ourselves using DreamBooth is to teach the model how we look. □įeel free to skip this section if you’re not particularly interested in the theory behind the approach and prefer to dive straight into the implementation. Don’t say we didn’t warn you!Īlso, if you know part of our team, you may recognize some faces in the following images. Be warned, generating images of yourself (or your friends) is highly addictive. To do so, we’ll implant ourselves into a pre-trained Stable Diffusion model’s vocabulary. Now, in this blog post, we will guide you through implementing DreamBooth so that you can generate images like the ones you see below. In our previous post, we discussed text-to-image generation models and the massive impact that models like DALL♾ and Stable Diffusion are having throughout the Machine Learning community. ![]() Have you ever wished you were able to try out a new hairstyle before finally committing to it? How about fulfilling your childhood dream of being a superhero? Maybe having your own digital Funko Pop to use as your profile picture? All of these are possible with DreamBooth, a new tool developed by researchers at Google that takes recent progress in text-conditional image synthesis to the next level.
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