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

<!-- 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-maxpmx/output_multi_sd1

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

prompt: denoised image
![images_0)](./images_0.png)
prompt: denoised image
![images_1)](./images_1.png)
prompt: super-resolution ER image
![images_2)](./images_2.png)
prompt: super-resolution F-actin image
![images_3)](./images_3.png)
prompt: super-resolution Microtubules image
![images_4)](./images_4.png)
prompt: Generate the corresponding DAPI protein image
![images_5)](./images_5.png)
prompt: Generate the corresponding CD11B protein image
![images_6)](./images_6.png)
prompt: Generate the corresponding DAPI protein image
![images_7)](./images_7.png)
prompt: Generate the corresponding CD11B protein image
![images_8)](./images_8.png)
prompt: Generate the corresponding DAPI protein image
![images_9)](./images_9.png)
prompt: Generate the corresponding CD11B protein image
![images_10)](./images_10.png)
prompt: CD68 RNA expression
![images_11)](./images_11.png)
prompt: CXCR4 RNA expression
![images_12)](./images_12.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]