These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: A realistic google streetview image, which was assigned a beauty-score of 16.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
images_0)
prompt: A realistic google streetview image, which was assigned a beauty-score of 35.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
images_1)
prompt: A realistic google streetview image, which was assigned a beauty-score of 16.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
images_2)
prompt: A realistic google streetview image, which was assigned a beauty-score of 35.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
images_3)
prompt: A realistic google streetview image, which was assigned a beauty-score of 30.058624, where scores are between 10 and 40 and higher scores indicate more beauty.
images_4)
prompt: A realistic google streetview image, which was assigned a beauty-score of 35.512676, where scores are between 10 and 40 and higher scores indicate more beauty.
images_5)
prompt: A realistic google streetview image, which was assigned a beauty-score of 33.00086, where scores are between 10 and 40 and higher scores indicate more beauty.
images_6)
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
