Generative Powers of Ten
We present a method that uses a text-to-image model to generate consistent
content across multiple image scales, enabling extreme semantic zooms into a
scene, e.g., ranging from a wide-angle landscape view of a forest to a macro
shot of an insect sitting on one of the tree branches. We achieve this through
a joint multi-scale diffusion sampling approach that encourages consistency
across different scales while preserving the integrity of each individual
sampling process. Since each generated scale is guided by a different text
prompt, our method enables deeper levels of zoom than traditional
super-resolution methods that may struggle to create new contextual structure
at vastly different scales. We compare our method qualitatively with
alternative techniques in image super-resolution and outpainting, and show that
our method is most effective at generating consistent multi-scale content.
656e9c174b5b6953536dbe36
Xiaojuan Wang
admin_assigned
2023-12-05T09:38:28.597Z
643ee9a6022a0350efd745d1
/avatars/c8c17689ed88b1966d2a36361e3f83cc.svg
Xiaojuan Wang
user
xiaojwan
656e9c174b5b6953536dbe37
Janne Kontkanen
656e9c174b5b6953536dbe38
Brian Curless
656e9c174b5b6953536dbe39
Steve Seitz
656e9c174b5b6953536dbe3a
Ira Kemelmacher
656e9c174b5b6953536dbe3b
Ben Mildenhall
656e9c174b5b6953536dbe3c
Pratul Srinivasan
656e9c174b5b6953536dbe3d
Dor Verbin
admin_assigned
2023-12-05T09:39:24.235Z
6315b0fc915d0b806830b424
/avatars/e25a7c8df2364c6e8024ec5c646a2889.svg
Dor Verbin
user
dorverbin
656e9c174b5b6953536dbe3e
Aleksander Holynski
admin_assigned
2023-12-05T09:39:31.241Z
62f6e2792e53c2efd33faa92
/avatars/85d94b4022577747b8d2d10a82c2f3c7.svg
Aleksander Holynski
user
holynski
Generative Powers of Ten
We present a method that uses a text-to-image model to generate consistent
content across multiple image scales, enabling extreme semantic zooms into a
scene, e.g., ranging from a wide-angle landscape view of a forest to a macro
shot of an insect sitting on one of the tree branches. We achieve this through
a joint multi-scale diffusion sampling approach that encourages consistency
across different scales while preserving the integrity of each individual
sampling process. Since each generated scale is guided by a different text
prompt, our method enables deeper levels of zoom than traditional
super-resolution methods that may struggle to create new contextual structure
at vastly different scales. We compare our method qualitatively with
alternative techniques in image super-resolution and outpainting, and show that
our method is most effective at generating consistent multi-scale content.
A text-to-image model generates consistent multi-scale images through a joint diffusion sampling approach, achieving effective extreme semantic zooms compared to traditional methods.
text-to-image model joint multi-scale diffusion sampling extreme semantic zooms super-resolution methods outpainting