---
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

These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with pose conditioning generated using the animalpose model of OpenPifPaf 
You can find some example images 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)



# Model Card for dog-cat-pose

<!-- Provide a quick summary of what the model is/does. [Optional] -->
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




#  Table of Contents

- [Model Card for dog-cat-pose](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
  - [Direct Use](#direct-use)
  - [Downstream Use [Optional]](#downstream-use-optional)
  - [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  - [Recommendations](#recommendations)
- [Training Details](#training-details)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
    - [Preprocessing](#preprocessing)
    - [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
    - [Testing Data](#testing-data)
    - [Factors](#factors)
    - [Metrics](#metrics)
  - [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)


# 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:** More information needed
    - [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. -->




## 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.

### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

More information needed
 
# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

More information needed


### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

More information needed

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

More information needed

## Results 

More information needed

# Model Examination

More information needed

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed

# Technical Specifications [optional]

## Model Architecture and Objective

More information needed

## Compute Infrastructure

TPUv4i

### Hardware

More information needed

### Software

Flax stable diffusion controlnet pipeline

# Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

More information needed

**APA:**

More information needed

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

More information needed

# More Information [optional]

More information needed

# 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

# Model Card Contact

More information needed

# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(&#34;dog-cat-pose&#34;${model.private ? &#34;, use_auth_token=True&#34; : &#34;&#34;})

</details>