Above is the architecture of this project, starting with the Stable diffusion text-to-image generation module, then a pool of images is generated. After that based on the rating of the user, top images are selected and IEA is applied to them and finally the top image of the array is sent to the Stable diffusion image-to-image generation module along with a prompt. Finally some editing can be done and the final evolved image can be obtained.

Development of an Integrated Architecture: The primary aim is to construct a cohesive and adaptable framework that seamlessly incorporates the Stable Diffusion Model with interactive components. This architecture should facilitate smooth communication between the AI system and the user, enabling intuitive feedback mechanisms and enhancing the overall user experience.

1. Text-to-Image Generation: By harnessing the power of the Stable Diffusion Model, the project endeavors to establish a robust text-to-image generation system. The objective is to empower users to effortlessly translate textual descriptions into vivid and contextually relevant visual representations, thereby expanding the accessibility and applicability of AI-generated content.

2. User Feedback Integration: The project is the integration of an Interactive Evolutionary Algorithm (IEA) to gather and assimilate user feedback. This objective seeks to optimize the image selection process by leveraging user preferences, thereby refining the output quality, and ensuring alignment with user expectations. And then mutation is done by adding some noices to the selected images.

3. Image-to-Image Generation: Building upon the foundational text-to-image generation, the project aims to extend the capabilities of the system to accommodate image-to-image transformations. By employing the Stable Diffusion Model in this context, users will have the flexibility to manipulate existing images, fostering creativity, and enabling the creation of personalized visual content.

Post-Processing Options: The project endeavors to provide users with a suite of post-processing tools, including adjustments for brightness, contrast, sharpness, and other parameters. These options are designed to empower users to fine-tune and customize the generated images according to their specific preferences and requirements, thereby enhancing user satisfaction and usability.