A cutting-edge web application for creating and managing multimedia content with integrated AI-powered capabilities
π Documentation β’ π Report Bug β’ β¨ Request Feature
- β¨ Features
- π€ ComfyUI Integration
- β‘ Quick Start
- π§ Manual Setup
- π Project Architecture
- π API Reference
- π Environment Configuration
- π οΈ Development Workflow
- π€ Contributing
- π License
- π οΈ Technology Stack
- π Acknowledgments
| Feature | Description |
|---|---|
| π¬ AI Chat Assistant | Interactive conversations powered by Ollama local models |
| π¨ AI Image Generation | Create stunning images using ComfyUI workflows with GGUF models |
| π¬ Video Processing | Advanced video generation and processing with HunyuanVideo integration |
| π£οΈ Text-to-Speech | Natural voice synthesis powered by Chatterbox technology |
| π΅ Voice Cloning | Personalize audio content with advanced voice replication |
| π± Modern UI/UX | Sleek interface built with Next.js, React, and Tailwind CSS |
| π ComfyUI Integration | Node-based AI workflows for professional content creation |
- GGUF Model Support - Optimized quantized models for efficient inference
- Customizable Pipelines - Node-based workflows that can be modified and extended
- High-Quality Output - Support for various formats with configurable quality settings
- Prompt Engineering - Advanced text encoding with positive/negative prompts
- Real-time Processing - Fast generation with optimized model architectures
- Cross-platform Compatibility - Works on Windows, macOS, and Linux
ConCreat leverages ComfyUI, a powerful node-based interface for AI image and video generation, to provide advanced creative tools.
Advanced image creation using GGUF models like z_image_turbo, with support for LoRA models and custom prompts
Video creation using HunyuanVideo15 models for high-quality video generation from images
- β‘ GGUF Model Support: Optimized quantized models for efficient inference
- π Customizable Pipelines: Node-based workflows that can be modified and extended
- π― High-Quality Output: Support for various image and video formats with configurable quality settings
- π¬ Prompt Engineering: Advanced text encoding with positive and negative prompts
To use the included ComfyUI workflows, you'll need to download the following models and place them in your ComfyUI models directory:
| Model | Filename | Download Link | Location |
|---|---|---|---|
| VAE | ae.safetensors |
Hugging Face | ComfyUI/models/vae/ |
| CLIP | Qwen3-4B-UD-Q6_K_XL.gguf |
Hugging Face | ComfyUI/models/clip/ |
| Unet | z_image_turbo-Q8_0.gguf |
Hugging Face | ComfyUI/models/unet/ |
| Model | Filename | Download Link | Location |
|---|---|---|---|
| Checkpoint | HV15-Rapid-AIO-v1.safetensors |
Hugging Face | ComfyUI/models/checkpoints/ |
| CLIP Vision | sigclip_vision_patch14_384.safetensors |
Hugging Face | ComfyUI/models/clip_vision/ |
# Clone ComfyUI repository
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
# Install dependencies
pip install -r requirements.txt
# Download required custom nodes
# ComfyUI-GGUF: https://github.com/city96/ComfyUI-GGUF
# rgthree-comfy: https://github.com/rgthree/rgthree-comfyπ‘ Note: Model file sizes can be large (several GB). Ensure you have sufficient disk space and a stable internet connection for downloads.
- Node.js: Version 18 or higher
- Python: Version 3.11 or above
- Git: For version control
- Ollama: Local AI model server (latest version recommended)
- Storage: At least 10GB free space for models
-
Clone the Repository
git clone https://github.com/kliewerdaniel/concreat.git cd ConCreat -
Execute Setup Script
npm run setup
This command handles all dependency installations and creates the Python virtual environment automatically.
-
Launch Development Server
npm run dev
-
Access Application Open your browser and navigate to http://localhost:3000
For those preferring step-by-step installation:
npm installpython3 -m venv venv
source venv/bin/activate # Use `venv\Scripts\activate` on Windowspip install -r requirements.txtConCreat uses Ollama for local AI chat functionality. Install and set up Ollama:
# Install Ollama (follow instructions for your OS at https://ollama.ai)
# For macOS/Linux:
curl -fsSL https://ollama.ai/install.sh | sh
# For Windows: Download from https://ollama.ai/download
# Pull recommended models
ollama pull gemma # Default chat model
ollama pull llama2 # Alternative model
ollama pull mistral # Additional model option
# Start Ollama service (runs in background)
ollama serveπ‘ Note: Ollama runs on
localhost:11434by default. The application will automatically detect if Ollama is running and fall back to mock responses if not available.
npm run devConCreat/
βββ π src/app/
β βββ π api/
β β βββ π chat/ # π¬ Chat system endpoints
β β βββ π generate/ # π¨ Content creation APIs
β β βββ π images/ # πΌοΈ Image manipulation APIs
β β βββ π tts/ # π£οΈ Text-to-speech conversion
β β βββ π videos/ # π¬ Video processing endpoints
β β βββ π voices/ # π΅ Voice management system
β βββ π¨ globals.css # Global stylesheet
β βββ π± layout.tsx # Application layout component
β βββ π page.tsx # Main page component
βββ π public/ # Static resources
βββ π§ workflows/ # Workflow configuration files
βββ π tts_service.py # Python TTS service implementation
βββ βοΈ setup.sh # Automated setup script
βββ π requirements.txt # Python package requirements
βββ π¦ package.json # Node.js project configuration
βββ π README.md # Project documentation
π Note: The
chatterbox/directory containing TTS models is generated during setup and not part of the repository.
| Endpoint | Method | Description |
|---|---|---|
/api/chat |
GET/POST | π¬ Interactive chat functionality |
/api/generate |
POST | π¨ AI content generation services |
/api/images |
GET/POST | πΌοΈ Image processing and management |
/api/tts |
POST | π£οΈ Text-to-speech conversion endpoint |
/api/videos |
GET/POST | π¬ Video content operations |
/api/voices |
GET/POST | π΅ Voice cloning and management |
Create a .env.local file in the project root:
# Optional: Hugging Face authentication token for model access
HF_TOKEN=your_huggingface_token_here
| Command | Description |
|---|---|
npm run dev |
π Start development server |
npm run build |
π¨ Create production build |
npm run start |
|
npm run lint |
π Run ESLint code quality checks |
npm run setup |
βοΈ Complete environment setup |
The project maintains high code standards with:
- π ESLint: JavaScript/TypeScript code quality enforcement
- π TypeScript: Enhanced type safety and developer experience
- π¨ Tailwind CSS: Consistent and responsive styling
We β€οΈ contributions! Please follow these steps:
- π΄ Fork the repository
- πΏ Create a feature branch:
git checkout -b feature/amazing-feature - π» Make your changes and commit:
git commit -am 'Add amazing feature' - π€ Push your changes:
git push origin feature/amazing-feature - π Open a Pull Request
- π Bug Reports: Create an issue with the bug label
- β¨ Feature Requests: Create an issue with the enhancement label
- Follow the existing code style
- Write clear, concise commit messages
- Update documentation as needed
- Add tests for new features
- Ensure all tests pass
This project is licensed under the MIT License - see the LICENSE file for details.
- Ollama - Local AI model server for chat functionality
- ComfyUI - Powerful node-based AI interface
- Chatterbox - Advanced TTS technology
- HunyuanVideo - High-quality video generation models
- Next.js - The React framework for production
- Tailwind CSS - A utility-first CSS framework
