Markdown to PowerPoint Converter

This project converts Markdown files to PowerPoint presentations. You can use this GPTs to generate the base of the Markdown file.

Installation

Follow these steps to set up a Python virtual environment and install the required packages:

1. Clone this repository:

   git clone https://github.com/treeleaves30760/Hackmd_PPT_Converter
   cd Hackmd_PPT_Converter

2. Install the required packages:

   pip install -r requirements.txt

Usage

GUI

If you want use GUI, you can run the GUI script:

• Window

  Start_GUI.bat

• Linux/MacOS

  sh Start_GUI.sh

In the GUI, you can input the hackmd code into textarea, then press the convert button to generate PPT.

API

You can import the converter.py

import converter

converter = MarkdownToPptConverter('', 'example.pptx', mode=1)
converter.convert('example.md')

Format

Below is the example of a markdown file

# Introduction to Stable Diffusion

---

## Table of Contents

1. What is Stable Diffusion?
2. Core Features
3. Use Cases
4. Advantages
5. Limitations

---

## What is Stable Diffusion?

Stable Diffusion is a deep learning model used for generating high-quality images. It can create images based on textual descriptions or edit and enhance existing images.

---

## Core Features

- **Text-to-Image Conversion**: Ability to generate images based on natural language descriptions.
- **Image-to-Image Transformation**: Can transform input images into images of a different style.
- **High-Resolution Support**: Capable of generating high-quality, high-resolution images.
- **Wide Range of Applications**: Suitable for various fields such as art creation, game development, entertainment industry, etc.

---

## Use Cases

- **Art Creation**: Artists and designers use Stable Diffusion to create new artworks.
- **Content Generation**: Automatically generate visual content for social media, advertising, and other domains.
- **Game Development**: Generate game scenes, characters, or textures.

---

## Advantages

- **Fast and Efficient**: Stable Diffusion can generate high-quality images faster compared to traditional image generation techniques.
- **Flexibility**: Users can control the style and details of the generated images by adjusting parameters.

---

## Limitations

- **Creative Constraints**: Generated images may be limited by the training data and may not always fully meet the user's creative requirements.
- **Quality Fluctuations**: While it can produce high-quality images most of the time, there may be instances of unstable image quality.

Usage	Sign	Example
Page break	---	---
Title	#	# PPT to AI
Page Title	##	## What is AI
List Number	1.	1. **The usage of AI**
List Points	-	- **AI Development**

# Stable Diffusion 簡介

---

## 目錄

1. 什麼是Stable Diffusion?
2. 核心特點
3. 使用案例
4. 優勢
5. 限制

---

## 什麼是Stable Diffusion?

Stable Diffusion是一種深度學習模型，用於生成高質量的圖像。它可以根據文字描述創建圖像，或對現有圖像進行編輯和增強。

---

## 核心特點

- **文本到圖像的轉換**：能夠根據自然語言描述生成圖像。
- **圖像到圖像的轉換**：可以將輸入圖像轉換成另一風格的圖像。
- **高分辨率支持**：能生成高質量、高分辨率的圖像。
- **廣泛的應用**：適用於藝術創作、遊戲開發、娛樂產業等多個領域。

---

## 使用案例

- **藝術創作**：藝術家和設計師使用Stable Diffusion來創造新的藝術作品。
- **內容生成**：自動生成社交媒體、廣告等領域的視覺內容。
- **遊戲開發**：生成遊戲場景、角色或質感。

---

## 優勢

- **快速且高效**：相比於傳統的圖像生成技術，Stable Diffusion能更快地產生高質量圖像。
- **靈活性**：用戶可以通過調整參數來控制生成圖像的風格和細節。

---

## 限制

- **創意限制**：生成的圖像可能受到訓練數據的限制，有時可能無法完全符合用戶的創意需求。
- **質量波動**：雖然大部分時候能生成高質量圖像，但在某些情況下可能會出現質量不穩定的問題。

用法	符號	示例
分頁	---	---
標題	#	# AI
頁面標題	##	## 什麼是AI
列表編號	1.	1. **AI的使用方式**
列表項目	-	- **AI的發展**