https://github.com/anushreenagaraj/storyboard-creator
AI-powered storyboard creator that generates visual frames from text prompts using Stable Diffusion and Streamlit, with optional AI-assisted prompt generation.
https://github.com/anushreenagaraj/storyboard-creator
creative-ai diffusers image-generation pdf-export prompt-engineering stable-diffusion storyboard streamlit text-to-image torch
Last synced: 2 months ago
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AI-powered storyboard creator that generates visual frames from text prompts using Stable Diffusion and Streamlit, with optional AI-assisted prompt generation.
- Host: GitHub
- URL: https://github.com/anushreenagaraj/storyboard-creator
- Owner: anushreenagaraj
- Created: 2025-07-19T14:54:40.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-19T20:21:00.000Z (12 months ago)
- Last Synced: 2025-07-27T04:21:29.516Z (11 months ago)
- Topics: creative-ai, diffusers, image-generation, pdf-export, prompt-engineering, stable-diffusion, storyboard, streamlit, text-to-image, torch
- Language: Python
- Homepage:
- Size: 13.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎬 Storyboard Creator
**Storyboard Creator** is an AI-powered tool that helps you generate visual storyboards from simple text prompts. Whether you're a filmmaker, animator, or content creator, this tool allows you to prototype scenes and create frame-by-frame visuals quickly.
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## 🚀 Features
- 🔮 **Prompt-Based Frame Generation**
Input a single scene description or multiple prompts for each frame.
- 🎨 **AI Image Generation**
Uses Stable Diffusion for generating high-quality storyboard frames.
- 🧠 **Prompt Assistance (Optional)**
Get auto-generated AI prompts to assist with creativity.
- 📄 **PDF Export**
Save your entire storyboard as a downloadable PDF.
- ⚡ **Simple Web Interface**
Built using Streamlit — clean, fast, and user-friendly.
---
## 🛠️ Installation
```bash
# 1. Clone the repository
git clone https://github.com/anushreenagaraj/Storyboard-Creator.git
cd Storyboard-Creator
# 2. Create a virtual environment
python -m venv .venv
.\.venv\Scripts\activate # On Windows
# 3. Install dependencies
pip install -r requirements.txt