# gitee_classifier_free_diffusion

**Repository Path**: liu-zhiyong-wx/gitee_classifier_free_diffusion

## Basic Information

- **Project Name**: gitee_classifier_free_diffusion
- **Description**: classifier-free-diffusion 的一个小实现。
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2023-07-21
- **Last Updated**: 2023-07-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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# Unofficial Implementation of [Classifier-free Diffusion Guidance](https://arxiv.org/abs/2207.12598)
The Pytorch implementation is adapted from [openai/guided-diffusion](https://github.com/openai/guided-diffusion) with modifications for classifier-free conditioned generation. The dataset used for training is cifar-10. The platform I use is Ubuntu 20.04.

During the training porcess, I adapt the file *Scheduler.py* from [zoubohao/DenoisingDiffusionProbabilityModel-ddpm-](https://github.com/zoubohao/DenoisingDiffusionProbabilityModel-ddpm-), which is different from my implementation mainly on one technical detail : **How to represent the embeddings of null token of class identifier**. According to another academic paper [Video Diffusion Models](https://arxiv.org/abs/2204.03458) by the same authors, I use the same method as theirs in this detail.
## How to run
First, you need to do some preparations:
```bash
mkdir sample
mkdir model
ln -s absolute/path/to/cifar-10 ./cifar-10-batches-py
```
### How to train
```bash
make train
```
**NOTICE** : hyperparameter settings are in the file *train.py*
### How to sample
To get pictures, you need to excute the following command:
```bash
make samplepict
```
To get .npz files(used for FID calculation), you need to excute the following command:
```bash
make samplenpz
```
**NOTICE** : hyperparameter settings are in the file *sample.py*
## Performance
![generated_740_pict](generated_1400_pict.png)
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# gitee_classifier_free_diffusion

#### 介绍
{**以下是 Gitee 平台说明，您可以替换此简介**
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#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

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