# Model Card for Model ID 

 

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

 

Trained on an A6000 graphics card, based on Pytorch code using the FFHQ dataset with image size of 256x256. 

## Model description 

This repo contains the model for the notebook [Neural style transfer](https://keras.io/examples/generative/neural_style_transfer/). 

 

Full credits go to [fchollet](https://twitter.com/fchollet) 

 

# Model info 

 

Project [fbanimegan](https://github.com/SkyTNT/fbanimegan) 

 

### fbanime.pkl 

Fashion MNIST unconditional Unet Model trained using DDPM 

 

Model Hyperparams: 

 

- Model size: 51,834,625 params 

# Model Card for Model ID 

 

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

 

This modelcard aims to be a base template for new models. It has been generated using (https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). 

# Model Card for Model ID 

 

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

 

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

## Model description 

 

This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules [WGAN-GP with R-GCN for the generation of small molecular graphs](https://keras.io/examples/generative/wgan-graphs/). 

 

Full credits go to [Alexander Kensert](https://github.com/akensert) 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 

 

**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel 

# Score-Based Generative Modeling through Stochastic Differential Equations (SDE) 

 

**Paper**: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456) 

 

**Authors**: Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole