docs/README1.md

CampusGen AI – Universal Event Poster Generator

AI-powered event poster generation for any occasion in 10–15 seconds.

Overview

CampusGen AI generates professional event posters using:

• Stable Diffusion XL 1.0 fine-tuned on 55,000+ diverse poster images via LoRA
• Llama 3.3 70B (Groq) for natural language event understanding
• 5 Generation Modes: Text→Poster, Reference Image, Img2Img, Inpainting, HD Upscale
• GPU-accelerated pipeline from data processing to training
• Zero cost deployment on Hugging Face Spaces (ZeroGPU)

Architecture

User Input → Groq LLM (prompt engineering) → SDXL 1.0 + LoRA → HD Upscale → Poster
                                                  ↑
                                     IP-Adapter (reference style)
                                     Img2Img (transform)
                                     Inpainting (edit regions)

Component	Details
Base Model	Stable Diffusion XL 1.0 (2.6B params)
Fine-tuning	Dual-Phase LoRA rank 32, bf16, 55K+ images
Curriculum	Phase 1 (Layout/1e-4) → Phase 2 (Perfection/2e-5)
Dataset	55,000+ curated event posters, 55 categories
LLM	Llama 3.3 70B via Groq (free tier)
Upscaler	Real-ESRGAN 4x
Deployment	HF Spaces with ZeroGPU

Categories (55 themes)

Group	Subcategories
Tech Fest	Hackathons, AI/ML, robotics, coding competitions, cyber security
Cultural Event	Dance, music, drama, art exhibitions, poetry
College Events	Annual days, freshers, farewell, alumni meets
Sports	Cricket, football, basketball, athletics, chess
Festivals	Diwali, Holi, Navratri, Ganesh Chaturthi, Eid, Christmas
Workshops	Seminars, webinars, training sessions, conferences
Social	Blood donation, charity, environmental drives
Entertainment	DJ nights, concerts, standup comedy, movie screenings

Project Structure

campus-ai/
├── configs/
│   └── config.yaml                  # Master configuration
├── scripts/
│   ├── pinterest_scraper.py         # Image scraper (CPU, network-bound)
│   ├── quality_filter.py            # GPU-accelerated quality filtering
│   ├── caption_generator.py         # Florence-2 captioning (GPU)
│   ├── split_dataset.py             # Dataset splitting (1000/200/100)
│   ├── test_checkpoint.py           # LoRA inference testing
│   └── create_training_config.py    # ai-toolkit config generator
├── deployment/
│   ├── app.py                       # 5-tab Gradio application
│   ├── pipelines.py                 # Pipeline manager (SDXL/IP-Adapter/ESRGAN)
│   ├── prompt_engine.py             # Groq LLM prompt engineering
│   ├── requirements.txt             # HF Space dependencies
│   └── README.md                    # HF Space card
├── data/
│   ├── raw/                         # Scraped images (~1900/theme)
│   ├── processed/                   # GPU-filtered images (~1300/theme)
│   ├── final/                       # Captioned dataset (GPU)
│   ├── train/                       # 1000 images/theme
│   ├── val/                         # 200 images/theme
│   └── test/                        # 100 images/theme
├── models/                          # Trained LoRA checkpoints
├── outputs/                         # Generated outputs
├── docs/
│   ├── README.md                    # This file
│   ├── SETUP.md                     # Setup guide
│   └── PIPELINE.md                  # Execution pipeline
└── requirements.txt                 # Local dependencies

Quick Start

# 1. Setup
conda create -n campus-ai python=3.11
conda activate campus-ai
pip install -r requirements.txt

# 2. Data Pipeline
python scripts/pinterest_scraper.py      # 🖥️ CPU  — Scrape posters (overnight)
python scripts/quality_filter.py         # 🎮 GPU  — Filter quality (~5 min)
python scripts/caption_generator.py      # 🎮 GPU  — Generate captions (overnight)
python scripts/split_dataset.py          # 🖥️ CPU  — Split 1000/200/100

# 3. Training
python scripts/create_training_config.py # 🖥️ CPU  — Generate ai-toolkit config
cd ai-toolkit && python run.py ../configs/train_sdxl_lora.yaml  # 🎮 GPU  — Phase 1 (Layout)
cd ai-toolkit && python run.py ../configs/train_sdxl_lora_phase2.yaml # 🎮 GPU — Phase 2 (Perfection)

# 4. Deploy
huggingface-cli upload YOUR_USERNAME/campus-ai-poster-sdxl models/sdxl/lora/ .  # Upload LoRA
# Push deployment/ files to HF Space

See SETUP.md for detailed instructions. See PIPELINE.md for step-by-step execution guide.

Hardware

• GPU: NVIDIA RTX 5070 Ti (12GB VRAM) — used for quality filtering, captioning, training
• CPU: Intel Ultra 9 275HX (24 cores) — used for scraping, splitting
• RAM: 32GB
• Training time: ~7.5 hours (Phase 1 Layout + Phase 2 Perfection)

Author

M Runeet Kumar – Ashta/Indore, MP, India

License

MIT