# 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. 

# TerDiT 

 

This repository contains the trained model for the paper "TerDiT: Ternary Diffusion Models with Transformers" 

 

256x256 4.2B model: [TerDiT-4.2B](https://huggingface.co/lucky-lance/TerDiT/tree/main/3B_1180000) 

## 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). 

mage generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.* 

 

**Authors** 

 

*Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer* 

onal generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.* 

 

## Inference 

 

*SDE* models can use **continuous** noise schedulers such as: 

onal generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.* 

 

## Inference 

 

*SDE* models can use **continous** noise schedulers such as: 

# 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 

mage generation of aurora borealis 🌌.  

It is trained on a small collection of aurora pictures and trained for 50 epochs, with 🤗 Accelerate. 

 

## Usage 

 

the generation of small molecular graphs](https://keras.io/examples/generative/wgan-graphs/). 

 

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

 

Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) 

# Denoising Diffusion Probabilistic Models (DDPM) 

 

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

 

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