ArtLens: User-Driven Customization in AI-Powered Art Generation with Blip2 and Stable Diffusion

AI is changing the interpretation and teaching of art in the digital humanities with models like Blip2 and Stable Diffusion. This paper evaluates these models but particularly looks at how Blip2 can integrate visual and textual data to caption artworks, classify styles, interpret themes, and conduct contextual analysis, while Stable Diffusion can generate high-resolution images from literary prompts. The proposed method involves the use of Blip2 in creating descriptive captions for artworks and then modified with zero-shot prompting based on specific user demands. This updated text combined with the original input is then fed into the Stable Diffusion model to create new visuals reflecting the changed aspects. This method permits greater flexibility and user-driven customisation in creative image production. This work is a demonstration of the use of AI in interpretation with dynamic and interactive art and its ability to provide user-oriented, more subtle and personalized outputs. The embedding of user-specific adjustments into the workflow has enhanced the creative expression and adaptability of the generated content by AI, offering new avenues of applications in digital humanities, art education, and curatorial practices. This study marks the revolutionary potential of AI in most sectors, reflecting how it may inspire new involvement with art and culture.