How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline

controlnet = ControlNetModel.from_pretrained("louistichelman/controlnet_streetview_normalmap_res400")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=controlnet
)

controlnet-louistichelman/controlnet_streetview_normalmap_res400

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 23.188663, 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 31.616573, where scores are between 10 and 40 and higher scores indicate more beauty. images_5)

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]

Training details

[TODO: describe the data used to train the model]

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