---
title: Muddit Interface
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: apache-2.0
---

# 🎨 Muddit Interface

A unified model interface for **Text-to-Image generation** and **Visual Question Answering (VQA)** powered by advanced transformer architectures.

## ✨ Features

### 🖼️ Text-to-Image Generation
- Generate high-quality images from detailed text descriptions
- Customizable parameters (resolution, inference steps, CFG scale, seed)
- Support for negative prompts to avoid unwanted elements
- Real-time generation with progress tracking

### ❓ Visual Question Answering
- Upload images and ask natural language questions
- Get detailed descriptions and answers about image content
- Support for various question types (counting, description, identification)
- Advanced visual understanding capabilities

## 🚀 How to Use

### Text-to-Image
1. Go to the **"🖼️ Text-to-Image"** tab
2. Enter your text description in the **Prompt** field
3. Optionally add a **Negative Prompt** to exclude unwanted elements
4. Adjust parameters as needed:
   - **Width/Height**: Image resolution (256-1024px)
   - **Inference Steps**: Quality vs speed (1-100)
   - **CFG Scale**: Prompt adherence (1.0-20.0)
   - **Seed**: For reproducible results
5. Click **"🎨 Generate Image"**

### Visual Question Answering
1. Go to the **"❓ Visual Question Answering"** tab
2. **Upload an image** using the image input
3. **Ask a question** about the image
4. Adjust processing parameters if needed
5. Click **"🤔 Ask Question"** to get an answer

## 📝 Example Prompts

### Text-to-Image Examples:
- "A majestic night sky awash with billowing clouds, sparkling with a million twinkling stars"
- "A hyper realistic image of a chimpanzee with a glass-enclosed brain on his head, standing amidst lush, bioluminescent foliage"
- "A samurai in a stylized cyberpunk outfit adorned with intricate steampunk gear and floral accents"

### VQA Examples:
- "What objects do you see in this image?"
- "How many people are in the picture?"
- "What is the main subject of this image?"
- "Describe the scene in detail"
- "What colors dominate this image?"

## 🛠️ Technical Details

- **Architecture**: Unified transformer-based model
- **Text Encoder**: CLIP for text understanding
- **Vision Encoder**: VQ-VAE for image processing
- **Generation**: Advanced diffusion-based synthesis
- **VQA**: Multimodal understanding with attention mechanisms

## ⚙️ Parameters Guide

| Parameter | Description | Recommended Range |
|-----------|-------------|-------------------|
| **Inference Steps** | More steps = higher quality, slower generation | 20-64 |
| **CFG Scale** | How closely to follow the prompt | 7.0-12.0 |
| **Resolution** | Output image size | 512x512 to 1024x1024 |
| **Seed** | For reproducible results | Any integer or -1 for random |

## 🎯 Use Cases

- **Creative Content**: Generate artwork, illustrations, concepts
- **Visual Analysis**: Analyze and understand image content
- **Education**: Learn about visual AI and multimodal models
- **Research**: Explore capabilities of unified vision-language models
- **Accessibility**: Describe images for visually impaired users

## 📄 License

This project is licensed under the Apache 2.0 License.

## 🤝 Contributing

Feedback and contributions are welcome! Please feel free to submit issues or pull requests.

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