README.md

metadata

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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