The AI Story Illustration Generator is a generative AI project that combines Prompt conditioning and Batch generation, built with Stable Diffusion / ComfyUI. The project follows a clean, modular pipeline where input handling, generation, and presentation stay separated, making it easy to test, extend, and present. It showcases modern generative AI techniques while producing a working, demo-ready application.
Traditional solutions to this problem are slow, static, and unable to generate new, context-aware content on demand. Without a generative AI approach built on Batch generation and Stable Diffusion / ComfyUI, users cannot get personalized, high-quality outputs quickly, and there is no straightforward way to refine or evaluate the results.
This project applies generative AI through Prompt conditioning, orchestrated with Stable Diffusion / ComfyUI and Batch generation. The pipeline is designed for quality and control, with validation, evaluation, and a clean interface. It generates consistent, context-aware results and can be adapted to related tasks with minimal changes.
Stable Diffusion / ComfyUI
PyTorch
Python 3.11+
Structured prompts and configuration
Error handling and retries
Evaluation and logging
Diffusers library
Modular pipeline around Prompt conditioning and Batch generation
Configurable generation and evaluation settings
Clear logging, retries, and cost tracking
Clean interface for results
Reusable components for related tasks
Evaluation of output quality
The project is layered: the input layer prepares and validates inputs through Prompt conditioning; the generation layer invokes the model with Stable Diffusion / ComfyUI and Batch generation; and the output layer formats and presents results via Inpainting / img2img module. Shared configuration, logging, and evaluation modules support all layers, keeping the system robust and easy to extend.
Set up the Python environment, project structure, and configuration files.
Build the input layer with Prompt conditioning and validate incoming data.
Implement the generation pipeline using Stable Diffusion / ComfyUI and Batch generation.
Add the output and presentation layer via Inpainting / img2img module.
Wire up end-to-end flows and add error handling and retries.
Evaluate output quality, tune prompts, and refine settings.
Package the project, document it, and prepare the demo and viva report.
Build production-style generative AI applications
Apply Image post-processing with OpenCV and img2img and inpainting
Design prompts and evaluation for generated content
Work with LLM and diffusion model APIs
Present and defend a complete GenAI project in viva
Expose the pipeline as a REST API for other apps
Add fine-tuning for higher-quality domain outputs
Add guardrails and content safety checks
Deploy with caching for lower latency and cost
The AI Story Illustration Generator delivers a complete generative AI workflow — from input and generation to evaluation and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI skills.
