🎯 What is Paper2Slides?

Turns your research papers, reports, and documents into professional slides & posters in minutes.

✨ Key Features

• 📄 Universal Document Support
  Seamlessly process PDF, Word, Excel, PowerPoint, Markdown, and multiple file formats simultaneously.

• 🎯 Comprehensive Content Extraction
  RAG-powered mechanism ensures every critical insight, figure, and data point is captured with precision.

• 🔗 Source-Linked Accuracy
  Maintains direct traceability between generated content and original sources, eliminating information drift.

• 🎨 Custom Styling Freedom
  Choose from professional built-in themes or describe your vision in natural language for custom styling.

• ⚡ Lightning-Fast Generation
  Instant preview mode enables rapid experimentation and real-time refinements.

• 💾 Seamless Session Management
  Advanced checkpoint system preserves all progress—pause, resume, or switch themes instantly without loss.

• ✨ Professional-Grade Visuals
  Deliver polished, presentation-ready slides and posters with publication-quality design standards.

⚡ Easy as One Command

# One command to generate slides from a paper
python -m paper2slides --input paper.pdf --output slides --style doraemon --length medium --fast --parallel 2

🔥 News

• [2025.12.09] Added parallel slide generation (--parallel) for faster processing
• [2025.12.08] Paper2Slides is now open source!

🎨 Custom Styling Showcase

doraemon

academic

custom

doraemon

academic

custom

_{✨ Multiple styles available — simply modify the --style parameter
Examples from DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models}

--style "Studio Ghibli anime style with warm whimsical aesthetic. Use soft watercolor Morandi tones with light cream background, muted sage green and dusty pink accents. Totoro character can appear as a friendly guide relating to the content, with nature elements like soft clouds or leaves."

🌐 Paper2Slides Web Interface

📋 Table of Contents

• 🎯 Quick Start
• 🏗️ Paper2Slides Framework
• 🔧 Configuration
• 📁 Code Structure

🏃 Quick Start

1. Environment Setup

# Clone repository
git clone https://github.com/HKUDS/Paper2Slides.git
cd Paper2Slides

# Create and activate conda environment
conda create -n paper2slides python=3.12 -y
conda activate paper2slides

# Install dependencies
pip install -r requirements.txt

2. Command Line Usage

# Basic usage - generate slides from a paper
python -m paper2slides --input paper.pdf --output slides --length medium

# Generate poster with custom style
python -m paper2slides --input paper.pdf --output poster --style "minimalist with blue theme" --density medium

# Fast mode
python -m paper2slides --input paper.pdf --output slides --fast

# Enable parallel generation (2 workers by default)
python -m paper2slides --input paper.pdf --output slides --parallel 2

# List all processed outputs
python -m paper2slides --list

CLI Options:

💾 Checkpoint & Resume:

Paper2Slides intelligently saves your progress at every key stage, allowing you to:

3. Web Interface

Launch both backend and frontend services:

./scripts/start.sh

Or start services independently:

# Terminal 1: Start backend API
./scripts/start_backend.sh

# Terminal 2: Start frontend
./scripts/start_frontend.sh

Access the web interface at http://localhost:5173 (default)

🏗️ Paper2Slides Framework

Paper2Slides transforms documents through a 4-stage pipeline designed for reliability and efficiency:

💾 Smart Recovery System

Each stage automatically saves progress checkpoints, enabling seamless resumption from any point if the process is interrupted—no need to start over.

Fast Mode vs Normal Mode

Use --fast when:

• Document (text + figures) is short enough to fit in LLM context
• Quick preview/iteration needed
• Don't want to wait for RAG indexing

Use normal mode (default) when:

• Document is long or has many figures
• Multiple files to process together
• Need retrieval for better context selection

⚙️ Configuration

Output Directory Structure

outputs/
├── <project_name>/
│   ├── <content_type>/                   # paper or general
│   │   ├── <mode>/                       # fast or normal
│   │   │   ├── checkpoint_rag.json       # RAG query results & parsed file paths
│   │   │   ├── checkpoint_summary.json   # Extracted content, figures, tables
│   │   │   ├── summary.md                # Human-readable summary
│   │   │   └── <config_name>/            # e.g., slides_doraemon_medium
│   │   │       ├── state.json            # Current pipeline state
│   │   │       ├── checkpoint_plan.json  # Content plan for slides/poster
│   │   │       └── <timestamp>/          # Generated outputs
│   │   │           ├── slide_01.png
│   │   │           ├── slide_02.png
│   │   │           ├── ...
│   │   │           └── slides.pdf        # Final PDF output
│   │   └── rag_output/                   # RAG index storage
│   └── ...
└── ...

Checkpoint Files:

Style Configuration

Image Generation Providers

• Set IMAGE_GEN_PROVIDER in paper2slides/.env to choose the backend:
  • openrouter (default): uses IMAGE_GEN_API_KEY, IMAGE_GEN_BASE_URL, and IMAGE_GEN_MODEL (default google/gemini-3-pro-image-preview)
  • google: uses the official Gemini API at GOOGLE_GENAI_BASE_URL (default https://generativelanguage.googleapis.com/v1beta), IMAGE_GEN_API_KEY, IMAGE_GEN_MODEL (default models/gemini-3-pro-image-preview, must be image-capable), and IMAGE_GEN_RESPONSE_MIME_TYPE (default text/plain; use text types if your model does not support image responses)
• Reference figures are sent as inline data when supported (Google) or as image_url attachments (OpenRouter).

Image Generation Notes

📁 Code Structure

Paper2Slides/
├── paper2slides/                 # Core library
│   ├── main.py                   # CLI entry point
│   ├── core/
│   │   ├── pipeline.py           # Main pipeline orchestration
│   │   ├── state.py              # Checkpoint state management
│   │   └── stages/
│   │       ├── rag_stage.py      # Stage 1: Parse & index
│   │       ├── summary_stage.py  # Stage 2: Extract content
│   │       ├── plan_stage.py     # Stage 3: Plan layout
│   │       └── generate_stage.py # Stage 4: Generate images
│   │
│   ├── raganything/
│   │   ├── raganything.py        # RAG processor
│   │   └── parser.py             # Document parser
│   │
│   ├── summary/
│   │   ├── paper.py              # Paper structure extraction
│   │   └── extractors/           # Figure/table extractors
│   │
│   ├── generator/
│   │   ├── content_planner.py    # Slide/poster planning
│   │   └── image_generator.py    # Image generation
│   │
│   ├── prompts/                  # LLM prompt templates
│   └── utils/                    # Utilities
│
├── api/server.py                 # FastAPI backend
├── frontend/src/                 # React frontend
└── scripts/                      # Shell scripts (start/stop)

🙏 Related Open-Sourced Projects

• LightRAG: Graph-Empowered RAG
• RAG-Anything: Multi-Modal RAG
• VideoRAG: RAG with Extremely-Long Videos

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