license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
base_model: stabilityai/stable-diffusion-2-1-base
inference: true
controlnet-moritzef/model_old
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
prompt: A realistic google streetview image which was assigned a beautiful-score of 35, where scores are between 10 and 40 and higher scores indicate more beautiful.
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]