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Project Phillip is an advanced AI-powered image generation and enhancement suite. It combines cutting-edge machine learning models with a user-friendly interface to democratize access to high-quality AI-generated imagery.
Key Features:
- Text-to-Image Generation: Create images from textual descriptions.
- Image Enhancement: Apply various styles and improve image quality.
- User-Guided Creation: Iterative refinement process for precise results.
- Efficient Model Management: Dynamically switch between AI models.
Our team, comprising PhD students in Machine Learning, undergraduates, and AI enthusiasts, saw a gap between advanced AI capabilities and accessible tools for creatives. We wanted to:
- Bridge the gap between complex AI models and user-friendly applications.
- Empower artists, designers, and content creators with AI-assisted tools.
- Explore the potential of AMD's cloud infrastructure and Instinct MI210 GPUs in AI applications.
- Create a platform for collaborative learning and innovation in AI.
Text-to-Image Generation:
- Users input a text description.
- Our fine-tuned PixArt-alpha models process the text.
- The system speedily generates multiple low-quality guidance image options based on the description (≈2 seconds/image)
Image Enhancement:
- Users can upload existing images or select previously generated ones.
- They choose from various enhancement options (e.g., style transfer, upscaling).
- The selected AI model processes the image to apply the chosen enhancement (Basic enhancement ≈ 25 sec, Larger Enhancement/Style Change ≈ 55 sec).
User-Guided Creation:
- Users can iteratively refine the generated images.
- They can adjust parameters, add more descriptive text, or use reference images.
- The system regenerates images based on the new inputs.
Behind the Scenes:
- Our Flask-based API manages requests and routes them to the appropriate AI models.
- AMD cloud infrastructure and Instinct MI210 GPUs power the computationally intensive processes.
- Efficient model management system switches between different AI models as needed
We started as a diverse group with varying levels of AI experience, united by our fascination with the potential of AI in creative fields. The journey from concept to a working prototype was filled with challenges:
- Initially struggling with cloud configurations and GPU optimizations.
- Overcoming the steep learning curve of advanced AI models.
- Iterating countless times to improve image quality and generation speed.
Our breakthrough moment came when we successfully generated our first coherent image from a text prompt. From there, we rapidly iterated, adding features and refining our models
Today, Project Phillip stands as a testament to collaborative learning and innovation. It represents not just a tool, but a stepping stone towards more accessible and powerful AI-assisted creativity.
Future and GoalsLooking ahead, we aim to:
- Expand into video generation and editing.
- Develop more advanced transfer learning techniques.
- Create educational modules to share our knowledge with the wider community.
Project Phillip is more than just software; it's our contribution to democratizing AI-powered creativity and pushing the boundaries of what's possible at the intersection of technology and art.
Data Flow Diagram
UML Class Diagram
Architecture Diagram
Sequence Diagram
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