Home / Current Issue / Paper 1716248
PostForge: A Unified, Locally Deployable Multimodal AI Platform for Social Media Content Generation
Subject area: Science,Engineering and Technology · Area of research: Multimodal AI Content Generation Systems
DOI: 10.64388/IREV9I10-1716248
Abstract
The exponential growth of social media platforms has created a demand for high-quality, optimized content that exceeds the capacity of manual creation methods. While Arti¬ficial Intelligence (AI) offers a solution, current tools are often fragmented, requiring users to navigate multiple applications for image generation, captioning, and hashtag optimization. Furthermore, reliance on third-party APIs raises concerns regarding cost, latency, and data privacy. This paper presents PostForge, a unified web application designed to streamline social media content creation by integrating specialized AI models. Unlike monolithic multimodal models, PostForge em-ploys a modular architecture leveraging distinct state-of-the-art models for specific tasks: Latent Diffusion Models for text-to-image generation, BLIP for image captioning, and transformer-based models for hashtag generation. The system is designed for local deployment, ensuring data sovereignty and reducing operational costs. Experimental evaluation demonstrates the system’s efficacy, achieving a ROUGE-L score of 0.457 and BLEU score of 0.041, validating the feasibility of a unified, privacy-centric approach to content automation.
Keywords
Multimodal AI, Content Generation, Natu¬ral Language Processing, Stable Diffusion, Image Captioning, social media.
References
[1] J. Zhang, Z. Fang, H. Sun, and Z. Wang, “Adaptive Semantic-Enhanced Transformer for Image Captioning,” IEEE Transac-tions on Neural Networks and Learning Systems, vol. 35, no. 2, 2024.
[2] P. Zeng, H. Zhang, J. Song, and L. Gao, “S2 Transformer for Image Captioning,” in Proc. 31st Int. Joint Conf. Artificial Intelligence (IJCAI), 2022, pp. 1608–1614.
[3] H. Zhang, J. Y. Koh, J. Baldridge, H. Lee, and Y. Yang, “Cross-Modal Contrastive Learning for Text-to-Image Generation,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recog¬nition (CVPR), 2021, pp. 833– 842.
[4] T. Yu et al., “Generating Hashtags for Short- form Videos with Guided Signals,” in Proc. 61st Annual Meeting Assoc. Computational Linguistics (ACL), 2023, pp. 9482–9495.
[5] A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” in Proc. Int. Conf. Machine Learning (ICML), 2021.
[6] J. Li, D. Li, C. Xiong, and S. Hoi, “BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Un¬derstanding and Synthesis,” in Proc. 39th Int. Conf. Machine Learning (ICML), 2022, pp. 12888– 12900.
[7] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Mod- els,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022, pp. 10684–10695.
[8] C. Raffel et al., “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,” J. Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020.
[9] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” in Proc. 9th Int. Conf. Learning Representations (ICLR), 2021.
[10] A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” in Proc. 38th Int. Conf. Ma¬chine Learning (ICML), 2021, pp. 8748– 8763.
How to cite this paper
@article{1716248,
author = {Harshit Rasam, Ayush Maurya, Shubham Suryawanshi, Prof. Sangita Nikumbh},
title = {PostForge: A Unified, Locally Deployable Multimodal AI Platform for Social Media Content Generation},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1226-0},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716248.pdf},
abstract = {The exponential growth of social media platforms has created a demand for high-quality, optimized content that exceeds the capacity of manual creation methods. While Arti¬ficial Intelligence (AI) offers a solution, current tools are often fragmented, requiring users to navigate multiple applications for image generation, captioning, and hashtag optimization. Furthermore, reliance on third-party APIs raises concerns regarding cost, latency, and data privacy. This paper presents PostForge, a unified web application designed to streamline social media content creation by integrating specialized AI models. Unlike monolithic multimodal models, PostForge em-ploys a modular architecture leveraging distinct state-of-the-art models for specific tasks: Latent Diffusion Models for text-to-image generation, BLIP for image captioning, and transformer-based models for hashtag generation. The system is designed for local deployment, ensuring data sovereignty and reducing operational costs. Experimental evaluation demonstrates the system’s efficacy, achieving a ROUGE-L score of 0.457 and BLEU score of 0.041, validating the feasibility of a unified, privacy-centric approach to content automation.},
keywords = {Multimodal AI, Content Generation, Natu¬ral Language Processing, Stable Diffusion, Image Captioning, social media.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716248}
}