we're adding this method to the KREA Canvas, can't wait to see what y'all create with it! ⚡️https://t.co/NhIB6n9hnp
— KREA AI (@krea_ai) 17 février 2023
we're adding this method to the KREA Canvas, can't wait to see what y'all create with it!
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we're adding this method to the KREA Canvas, can't wait to see what y'all create with it! ⚡️https://t.co/NhIB6n9hnp
— KREA AI (@krea_ai) 17 février 2023
we're adding this method to the KREA Canvas, can't wait to see what y'all create with it!

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https://
reddit.com/r/StableDiffus
ion/comments/113bgke/controlnet_is_such_a_game_changer_for_stable/
…

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https://
reddit.com/r/StableDiffus
ion/comments/114b0ii/comment/j8vpogx/?utm_source=share&utm_medium=web2x&context=3
…

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https://
reddit.com/r/StableDiffus
ion/comments/113bl3c/controlnet_in_automatic1111_for_character_design/
…

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https://
reddit.com/r/StableDiffus
ion/comments/112h3p9/ymca_controlnet_openpose_can_track_at_least_four/
…
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we encourage you to check the appendix from the original paper for more examples: https://
arxiv.org/pdf/2302.05543
.pdf
…
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in this case, the cloned networks are the U-Net encoder/middle layers, and the results of the each trainable copy are fed in each middle/decoder block.

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the use of zero convolutions while training results in a funny behavior that the authors name “sudden convergence phenomenon”, where the model is suddenly able to follow the input conditions, as depicted in the following image:
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the way how this work enables us to control the structure from the images we generate is truly interesting. here are some of our favorite results so far:
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it is worth mentioning that the authors used a convolutional architecture to encode the condition image before feeding it within the cloned U-Net.