AI Character Story Image Generator: Academic Narrative & Social Status Frames
AI Character Story Image Generator
Developed By : Ir. MD Nursyazwi
Utilize this tool to automatically create six compelling, character-integrated story images for social media status updates by analyzing the core narrative of any public web link or pasted text. The output is optimized for vertical platforms.
Character Integration Module (Reference Image)
Upload a clear reference image of your character. The generative model will utilize this visual data for Image-to-Image synthesis, maintaining character fidelity in the generated story scenes.
Step-by-Step Usage Protocol
This generator follows a two-stage process: semantic analysis and image synthesis. Adhering to the following protocol ensures optimal output quality and relevance:
- Character Upload: Provide a high-resolution, front-facing image of your character (JPEG or PNG). This is crucial for the Image-to-Image component to correctly map and maintain the character's visual identity across multiple generated frames.
- URL Input (Automatic): Paste the complete, public URL of the article. Use this if you want the AI to read the link and create the prompt automatically. **Uses Google Search grounding.**
- Manual Text Input (Flexible): Use this box to enter a pre-written prompt **OR** paste a whole source text. The AI will engineer a cinematic prompt from this text. **Does not use Google Search grounding.**
- Generation: Click the "Generate 6 Story Images" button. The system will process the content and execute six sequential calls to the generative model.
- Caption Generation: Click the blue button to create high-quality, SEO-friendly marketing text for the linked content. (Only works with a URL).
Dual-Engine AI Architecture: Generative Synthesis and Engineering
The core functionality is realized through a novel combination of two distinct Large Language Model (LLM) services: Semantic Prompt Engineering and Controllable Image Generation. This approach ensures content relevance and high visual fidelity.
1. Semantic Prompt Engineering (Gemini-2.5-Flash)
The initial stage employs the `gemini-2.5-flash` model to perform real-time data retrieval and semantic interpretation.
- If a URL is provided, the model uses Google Search grounding to read the public content.
- If text is pasted manually, the model performs direct text engineering on the source content.
2. Controllable Image Generation (Image-to-Image Synthesis)
The second stage leverages the `gemini-2.5-flash-image-preview` model in an Image-to-Image configuration. This process involves the simultaneous input of the text prompt (from Stage 1) and the character's reference image. This method allows for granular control over the output, mandating the vector cartoon aesthetic and, critically, ensuring the generated scene features the likeness of the user-provided character, contextualized within the narrative derived from the source material. The generation is executed sequentially across six iterations to produce a diverse, multi-frame story sequence.
Academic References for LLM Architecture
- Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems. [Reference on the foundational LLM architecture].
- Hertzmann, A. (2018). Neural Style Transfer and the Synthesis of Artistic Images. *Foundations and Trends in Computer Graphics and Vision*. [Context for controlled aesthetic generation].
- Schmidhuber, J. (215). Deep Learning in Neural Networks: An Overview. *Neural Networks*. [General academic context for deep learning systems].
Curated STEM Resources (Dynamic Exploration Module)
This dynamic exploration module cycles through cutting-edge educational and commercial simulators and tools, offering a seamless viewing experience below. For quick access, use the navigation controls.
Direct Access to Key Educational Resources (Opens in new tab):
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