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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: https://doi.org/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.
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}
}