---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- image-to-image
- diffusers
- controlnet
- jax-diffusers-event
inference: true
library_name: diffusers
---
    
# controlnet- JFoz/dog-cat-pose

Simple controlnet model made as part of the HF JaX/Diffusers community sprint.

These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with pose conditioning generated using the animalpose model of OpenPifPaf. 

Some example images can be found in the following

prompt: a tortoiseshell cat is sitting on a cushion
![images_0)](./images_0.png)
prompt: a yellow dog standing on a lawn
![images_1)](./images_1.png)

Whilst not the dataset used for this model, a smaller dataset with the same
format for conditioning images can be found at https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset

The dataset was generated using the code at https://github.com/jfozard/animalpose/tree/f1be80ed29886a1314054b87f2a8944ea98997ac


# Model Card for dog-cat-pose

This is an ControlNet model which allows users to control the pose of a dog or cat. Poses were extracted from images using the animalpose model of OpenPifPaf https://openpifpaf.github.io/intro.html . Skeleton colouring is as shown in the dataset. See also https://huggingface.co/JFoz/dog-pose



# Model Details

## Model Description

<!-- Provide a longer summary of what this model is/does. -->
This is an ControlNet model which allows users to control the pose of a dog or cat. Poses were extracted from images using the animalpose model of OpenPifPaf https://openpifpaf.github.io/intro.html. Skeleton colouring is as shown in the dataset. See also https://huggingface.co/JFoz/dog-pose

- **Developed by:** John Fozard
- **Model type:** Conditional image generation
- **Language(s) (NLP):** en
- **License:** openrail
- **Parent Model:** https://huggingface.co/runwayml/stable-diffusion-v1-5
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/jfozard/animalpose/tree/f1be80ed29886a1314054b87f2a8944ea98997ac)


# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->

Supply a suitable, potentially incomplete pose along with  a relevant text prompt


## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->

Generating images of non-animals. We advise retaining the stable diffusion safety filter when using this model.


# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model is trained on a relatively small dataset, and may be overfit to those images.


## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->


Maintain careful supervision of model inputs and outputs.


# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Trained on a subset of Laion-5B using clip retrieval with the prompts &#34;a photo of a (dog/cat) (standing/walking)&#34;

## Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

### Preprocessing

Images were rescaled to 512 along their short edge and centrally cropped. The OpenPifPaf pose-detection model was used to extract poses, which were used to generate conditioning images.




## Compute Infrastructure

TPUv4i



### Software

Flax stable diffusion controlnet pipeline



# Model Card Authors [optional]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->

John Fozard