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CLIP + StyleGAN. Searching in StyleGAN latent space using description embedded with CLIP.
Queries: "A pony that looks like Beyonce", "... like Billie Eilish", ".. like Rihanna"
📐 The basic idea
Generate an image with StyleGAN and pass the image to CLIP for the loss against a CLIP text query representation. You then backprop through both networks and optimize a latent space in StyleGAN.
🤬 Drawbacks 1) it only works on text it knows 2) needs some cherry picking, only about 1/5 are really good.
Source twitt.
Queries: "A pony that looks like Beyonce", "... like Billie Eilish", ".. like Rihanna"
📐 The basic idea
Generate an image with StyleGAN and pass the image to CLIP for the loss against a CLIP text query representation. You then backprop through both networks and optimize a latent space in StyleGAN.
🤬 Drawbacks 1) it only works on text it knows 2) needs some cherry picking, only about 1/5 are really good.
Source twitt.
CLIP + StyleGAN. Searching in StyleGAN latent space using description embedded with CLIP.
Queries: "A pony that looks like Beyonce", "... like Billie Eilish", ".. like Rihanna"
📐 The basic idea
Generate an image with StyleGAN and pass the image to CLIP for the loss against a CLIP text query representation. You then backprop through both networks and optimize a latent space in StyleGAN.
🤬 Drawbacks 1) it only works on text it knows 2) needs some cherry picking, only about 1/5 are really good.
Source twitt.
Queries: "A pony that looks like Beyonce", "... like Billie Eilish", ".. like Rihanna"
📐 The basic idea
Generate an image with StyleGAN and pass the image to CLIP for the loss against a CLIP text query representation. You then backprop through both networks and optimize a latent space in StyleGAN.
🤬 Drawbacks 1) it only works on text it knows 2) needs some cherry picking, only about 1/5 are really good.
Source twitt.
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