AI-powered event poster generation for any occasion in 10–15 seconds.
Campus-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
- 6 Generation Modes: Text→Poster, Reference Image, Img2Img, Inpainting, HD Upscale, Edit Poster
- Two-Stage Pipeline: SDXL artwork generation + PIL typography compositor
- Prompt Engine v3.0: 20 visual styles, 30 event types, 7 lighting presets, 10 color harmonies
- Poster Compositor v3.0: 8 typography styles, 30+ premium fonts, 1,700+ Google Fonts on-demand
- GPU-accelerated pipeline from data processing to training
- Zero cost deployment on Hugging Face Spaces (ZeroGPU)
User Input → Groq LLM (prompt engine v3.0) → SDXL 1.0 + LoRA → PIL Compositor → HD Upscale → Poster
↑
IP-Adapter (reference style)
Img2Img (transform)
Inpainting (edit regions)
Edit Poster (re-apply typography)
| Component | Details | |-----------|---------E | Base Model | Stable Diffusion XL 1.0 (2.6B params) | | Fine-tuning | Tri-Phase LoRA rank 32, bf16, 55K+ images | | Curriculum | Phase 1 (Layout/1e-4) → Phase 2 (Perfection/2e-5) → Phase 3 (Style/5e-6) | | Dataset | 55,000+ curated event posters, 55 categories | | LLM | Llama 3.3 70B via Groq (free tier) | | Prompt Engine | v3.0 — 20 styles, 30 events, 7 lighting, 10 color harmonies | | Typography | Compositor v3.0 — 8 layout styles, 30+ fonts, 1,700+ on-demand, caption dropout | | Upscaler | Real-ESRGAN 4x | | Deployment | HF Spaces with ZeroGPU |
| Group | Subcategories | |-------|--------------E | 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 |
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
│ └── create_mixed_genre_dataset.py # Phase 4 cross-genre dataset
├── deployment/
│ ├── app.py # 6-tab Gradio application (v3.0)
│ ├── pipelines.py # Pipeline manager (SDXL/IP-Adapter/ESRGAN)
│ ├── prompt_engine.py # Groq LLM prompt engine v3.0
│ ├── poster_compositor.py # PIL typography compositor
│ ├── requirements.txt # HF Space dependencies
│ └── README.md # HF Space card
├── assets/
│ └── fonts/ # 30+ pre-cached fonts (Google Fonts CDN)
├── 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
│ └── tuning-2/ # Phase 4 mixed-genre dataset
├── models/ # Trained LoRA checkpoints
├── outputs/ # Generated outputs
├── docs/
│ ├── README.md # This file
│ ├── SETUP.md # Setup guide
│ ├── PIPELINE.md # Execution pipeline
│ ├── NOVELTY.md # Novelty & unique value proposition
│ ├── CAMPUS-AI-PROJECT-BRIEF.md # Comprehensive project brief
│ └── architecture.html # Visual architecture diagram
└── requirements.txt # Local dependencies
# 1. Setup
python -m venv venv
venv\Scripts\activate # (or `source venv/bin/activate` on Linux/WSL)
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)
cd ai-toolkit && python run.py ../configs/train_sdxl_lora_phase3.yaml # 🎮 GPU — Phase 3 (Style)
cd ai-toolkit && python run.py ../configs/train_sdxl_lora_phase4.yaml # 🎮 GPU — Phase 4 (Mixed-Genre)
# 4. Local Deployment
python deployment/app.py # 🖥️ CPU / 🎮 GPU — Launch 6-tab Gradio UI
# 5. Future Cloud Deployment (Hugging Face Spaces)
huggingface-cli login
huggingface-cli upload YOUR_USERNAME/campus-ai-poster-sdxl models/sdxl/checkpoints/campus_ai_poster_sdxl/ .
# Push deployment/ files to HF SpaceSee SETUP.md for detailed instructions. See PIPELINE.md for step-by-step execution guide.
- GPU: NVIDIA RTX 5070 Ti (16GB VRAM) — used for quality filtering, captioning, training
- CPU: Intel Ultra 9 275HX — used for scraping, splitting
- RAM: 32GB
- Training time: ~10 hours (Phase 1 Layout + Phase 2 Perfection + Phase 3 Style)
CounciL — Campus-AI by CounciL
MIT