
Latent Style Representation Learning and Knowledge-Driven Inference for Consistent Prompt-Conditioned Image Generation | IJET â Volume 12 Issue 2 | IJET-V12I2P132

Table of Contents
ToggleInternational Journal of Engineering and Techniques (IJET)
Open Access ⢠Peer Reviewed ⢠High Citation & Impact Factor ⢠ISSN: 2395-1303
Volume 12, Issue 2 | Published: April 2026
Author: Dinesh S, Kalai Kumar K, Jaya Murugan V, Harishwaran P, Naveen K
DOI: https://doi.org/{{doi}} ⢠PDF: Download
Abstract
Palette AI addresses the challenge of maintaining consistent artistic identity in AI-generated images by capturing and reusing the visual âDNAâ of reference artwork. Existing systems often demand repetitive style descriptions in prompts, leading to inconsistency. PaletteAI allows users to upload reference images, which are analysed using multimodal AI to extract visual attributes like color palettes and stylistic techniques. These attributes are converted into structured style representations and vector embeddings, forming a reusable style profile. During image generation, the system uses reasoning-based prompt fusion to integrate the learned style profile with the user’s text prompt, ensuring stylistic consistency. The framework supports both text-to-image and image-to-image style transformation through a graph- based pipeline. Users can control the style’s influence via adjustable weights. The platform also includes a voice-driven assistant for brainstorming. Experiments confirm that PaletteAI improves consistency, reduces prompt engineering, and enhances human-AI creative collaboration.
Keywords
Generative AI, Artistic Style Learning, Visual Feature Extraction, Prompt Fusion, Image Generation, Multimodal AI, Style Transfer, HumanâAI Creative Systems.
Conclusion
PaletteAI provides an effective AI-powered framework for maintaining stylistic consistency in prompt-conditioned image generation. By integrating multimodal style extraction, vector- based style representations, and reasoning-based prompt fusion, the system successfully captures artistic characteristics from reference images and applies them during image generation. The proposed approach enables users to generate visually coherent images without repeatedly describing stylistic attributes within prompts. Evaluation results demonstrate that the system achieves strong style consistency, accurate prompt alignment, and efficient generation performance. The ability to store reusable style profiles and control the influence of style during generation enhances both usability and creative flexibility. These capabilities make PaletteAI suitable for a wide range of applications including digital art creation, graphic design, and AI-assisted media production. Overall, PaletteAI offers a practical and scalable solution for improving the reliability and consistency of AI-generated visual content while supporting humanâAI collaborative creativity.
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APA
{{author}} (April 2026). {{title}}. International Journal of Engineering and Techniques (IJET), 12(2). https://doi.org/{{doi}}
{{author}}, â{{title}},â International Journal of Engineering and Techniques (IJET), vol. 12, no. 2, April 2026, doi: {{doi}}.
