---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
inference: true
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# 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)](./images_0.png)
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)](./images_1.png)
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)](./images_2.png)
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)](./images_3.png)
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)](./images_4.png)
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)](./images_5.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]