Build an iterative image enhancement process using the capabilities of Stable Diffusion and GPT-4 Vision. Allowing the users to generate images based on textual descriptions and iteratively refine these images based on GPT-4Vision's feedback.
- Generate initial images based on user-provided textual descriptions.
- Utilize GPT-4 Vision to analyze generated images and suggest improvements.
- Allow users to refine the image iteratively based on AI suggestions.
- Support multiple rounds of feedback and image regeneration to refine the outcome.
- Clone the Repository: Clone this repository to your local machine.
- Install Dependencies:
pip install streamlit requests python-dotenv
Add your OpenAI API key and Stability AI key to the .env file.
OPENAI_API_KEY = <key_here>
STABILITYAI_API_KEY = <key_here>
To run the application, navigate to the directory containing the app and run:
streamlit run demo.py
- Input Description: Start by entering a description for the image you want to generate.
- Generate Image: Click 'Generate Image' to create the initial image.
- Feedback and Refinement: The AI analyzes the image and provides feedback. Edit this feedback if necessary and use it to regenerate a refined image.
- Iterative Process: Continue refining the image through multiple iterations. Technology
- Stable Diffusion: Used for generating images based on textual descriptions.
- GPT-4 Vision: Provides AI-driven feedback for image refinement. Feel free to explore the code and adapt it for your own projects!
Imagine a young Indian man showcasing his casual fashion sense. He wears a dark pant paired with a subtle, geometric printed full-sleeve shirt. His accessories include a simple wrist watch, and shoes with brown leather. He stands against the backdrop of a an aesthetic grey color.
