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WhoJoshi Stable Diffusion

Year

2023

Tech & Technique

React.js, Python, Hugging Face, Civit AI LoRA, Tailwind CSS, Vercel

Description

A generative art platform built for WhoJoshi using Stable Diffusion models to create stylized AI artwork based on user prompts. Integrated with Hugging Face-hosted models and LoRA fine-tunings for high customization.

Key Features:
  • 🎨 Prompt-to-Image Generator: Users can generate AI art using custom text prompts
  • 🔧 LoRA Switching: Dynamic application of different LoRA fine-tuned models for varied styles
  • 🧠 Hugging Face Integration: Hosted and called inference endpoints securely
  • 📱 Responsive Design: Optimized interface for mobile and desktop users
  • ⚡ Fast Generation Feedback: Asynchronous task queue to handle image generation efficiently

Technical Highlights:
  • Integrated Hugging Face Inference API with token-based authorization
  • Built Python backend to manage prompt processing, LoRA selection, and image output
  • Designed frontend with React and Tailwind for a smooth UX
  • Implemented asynchronous queue system (using Celery + Redis or similar) to manage inference load
  • Added local image gallery with download and share options

My Role

Full-Stack Developer
Owned end-to-end development and model integration:
  • ✅ Backend: Created a Python-based server to handle Stable Diffusion prompt processing and LoRA selection
  • 🎨 Frontend: Built the UI in React with Tailwind CSS, optimized for art previews
  • 🧠 Model Ops: Integrated multiple LoRA models via Hugging Face endpoints
  • 🔁 Async Workflow: Set up task queue system for smooth async generation (Celery/Redis)
  • 🚀 Deployment: Deployed backend on AWS EC2 and frontend on Vercel
  • 📂 File Handling: Managed image output storage and delivery via secure download links

DARSHIT

joshidarshit2002@gmail.com