README.md

---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
- 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-EpsilonGreedy/Clothes2PersonCL1e-050.5

These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.

prompt: C2PDress, a woman, refracted line and sparkles, bold graphics, suki, barcodes, gliter, round-cropped, flat texture, galactic, palladium, lower part of face, stands in front of a white background
![images_0)](./images_0.png)
prompt: C2PUpperBody, a woman wearing black leather skirt, anthro, minimalist photorealist, t-top, pelisse, a new, features between french, inspired by James Baynes, flat icon, oppai, cut out, very ahestetic, stands in front of a white background
![images_1)](./images_1.png)
prompt: C2PLowerBody, a woman in a black top with a black jacket, elegant cape, wearing a cropped top, 4 arms, posh, cloth sim, inspired by Rose ONeill, listing image, proportions off, bubblegum, lower part of face, stands in front of a white background
![images_2)](./images_2.png)
prompt: C2PUpperBody, a man in tan shorts standing in front of a white wall, discord moderator, round-cropped, summer vibrance, simplified, dividing it into nine quarters, sculls, scandinavian style, nbc, lower part of face, stands in front of a white background
![images_3)](./images_3.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]