--- 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. ![images_0)](./images_0.png) 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. ![images_1)](./images_1.png) 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. ![images_2)](./images_2.png) 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. ![images_3)](./images_3.png) 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. ![images_4)](./images_4.png) 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. ![images_5)](./images_5.png) 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. ![images_6)](./images_6.png) 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. ![images_7)](./images_7.png) ## Intended uses & limitations #### How to use ```python # 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]