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| 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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