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
prompt: a yellow dog standing on a lawn
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
Table of Contents
- Model Card for dog-cat-pose
- Table of Contents
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications [optional]
- Citation
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- How to Get Started with the Model
Model Details
Model Description
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
Uses
Direct Use
Supply a suitable, potentially incomplete pose along with a relevant text prompt
Out-of-Scope Use
Generating images of non-animals. We advise retaining the stable diffusion safety filter when using this model.
Bias, Risks, and Limitations
Recommendations
Maintain careful supervision of model inputs and outputs.
Training Details
Training Data
Trained on a subset of Laion-5B using clip retrieval with the prompts "a photo of a (dog/cat) (standing/walking)"
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
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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
BibTeX:
More information needed
APA:
More information needed
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
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.
Click to expand
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("dog-cat-pose"${model.private ? ", use_auth_token=True" : ""})