Final Release: CampusGen AI Pipeline & Compositor

.gitattributes

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+*.otf filter=lfs diff=lfs merge=lfs -text
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+*.jpeg filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python Cache
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Virtual Environments
+venv/
+env/
+.env
+
+# Data and Models
+data/
+models/
+output/
+dataset/
+
+# Deep Learning Frameworks
+*.safetensors
+*.pt
+*.pth
+*.ckpt
+*.onnx
+
+# Editor
+.vscode/
+.idea/
+*.swp
+
+# Logs and DBs
+*.db
+*.log
+*.sqlite3

ai-toolkit

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configs/config.yaml

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+# ============================================================================
+# CampusGen AI - Master Configuration File
+# All scripts reference this file - NO HARDCODING ALLOWED
+# ============================================================================
+
+project:
+  name: "CampusGen AI"
+  version: "1.0.0"
+  creator: "M Runeet Kumar"
+  location: "Ashta/Indore, MP, India"
+  start_date: "2026-02-13"
+  seed: 42 # Master random seed for reproducibility
+
+# ============================================================================
+# HARDWARE CONFIGURATION
+# ============================================================================
+hardware:
+  gpu:
+    name: "RTX 5070 Ti"
+    vram_gb: 12
+    cuda_version: "13.1"
+    compute_capability: "12.0" # SM120 (Blackwell)
+  system:
+    ram_gb: 32
+    cpu_cores: 24
+    storage_gb: 500
+
+# ============================================================================
+# DIRECTORY STRUCTURE
+# ============================================================================
+paths:
+  root: "."
+  data:
+    root: "data"
+    raw: "data/raw"
+    processed: "data/processed"
+    curated: "data/curated"
+    train: "data/train"
+    val: "data/val"
+    test: "data/test"
+    tuning: "data/tuning"
+    images: "data/images"
+    videos: "data/videos"
+    audio: "data/audio"
+  models:
+    root: "models"
+    llama:
+      base: "models/llama/base"
+      lora: "models/llama/lora"
+      merged: "models/llama/merged"
+      checkpoints: "models/llama/checkpoints"
+    sdxl:
+      base: "models/sdxl/base"
+      lora: "models/sdxl/lora"
+      checkpoints: "models/sdxl/checkpoints"
+    cogvideo:
+      base: "models/cogvideo/base"
+      lora: "models/cogvideo/lora"
+    voice:
+      base: "models/voice"
+  outputs:
+    root: "outputs"
+    images: "outputs/images"
+    videos: "outputs/videos"
+    combined: "outputs/combined"
+    gallery: "outputs/gallery"
+    webapp: "outputs/webapp"
+    test: "outputs/test"
+  logs:
+    root: "logs"
+    training: "logs/training"
+    inference: "logs/inference"
+    tensorboard: "logs/tensorboard"
+  configs:
+    root: "configs"
+
+# ============================================================================
+# MODEL CONFIGURATIONS
+# ============================================================================
+models:
+  llama:
+    # Model source
+    repo_id: "unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit"
+
+    # Model parameters
+    max_seq_length: 2048
+    context_length: 8192
+    dtype: "bfloat16"
+    load_in_4bit: true
+    load_in_8bit: false
+
+    # Flash attention
+    use_flash_attention: true
+    attn_implementation: "flash_attention_2"
+
+    # Memory optimization
+    gradient_checkpointing: true
+    use_cache: false
+
+    # Inference parameters
+    temperature: 0.7
+    top_p: 0.9
+    top_k: 50
+    repetition_penalty: 1.1
+    max_new_tokens: 256
+    do_sample: true
+
+  sdxl:
+    # Model source
+    repo_id: "stabilityai/stable-diffusion-xl-base-1.0"
+
+    # Model parameters
+    dtype: "bfloat16"
+    variant: "fp16" # or "fp32"
+
+    # Generation parameters
+    height: 1024
+    width: 1024
+    num_inference_steps: 28
+    guidance_scale: 5.0
+    num_images_per_prompt: 1
+
+    # Memory optimization
+    enable_cpu_offload: false
+    enable_attention_slicing: false
+    enable_vae_slicing: false
+    enable_vae_tiling: false
+
+  cogvideo:
+    repo_id: "THUDM/CogVideoX-5b"
+    enabled: false
+    num_frames: 49
+    fps: 8
+    guidance_scale: 6.0
+    num_inference_steps: 50
+
+  voice:
+    repo_id: "fishaudio/fish-speech-1.5"
+    enabled: false
+    language: "hindi-english-mix"
+    speed: 1.0
+    pitch: 0
+
+# ============================================================================
+# DATASET CONFIGURATION
+# ============================================================================
+dataset:
+  # Data splits
+  splits:
+    train: 0.8
+    val: 0.1
+    test: 0.1
+
+  # Target samples
+  target_samples: 1000
+  min_samples: 100
+  max_samples: 10000
+
+  # Image specifications
+  image:
+    size: [1024, 1024]
+    format: "PNG"
+    quality: 95
+    channels: 3
+
+  # Quality thresholds
+  quality:
+    min_score: 0.7
+    min_clip_score: 0.25
+    max_toxicity: 0.3
+
+  # Stratification
+  stratify_by: "category"
+  balance_classes: true
+
+  # Categories
+  categories:
+    - "diwali"
+    - "holi"
+    - "navratri"
+    - "eid"
+    - "ganesh"
+    - "tech_fest"
+    - "cultural_fest"
+    - "sports"
+    - "workshop"
+    - "general"
+
+  # Prompt generation
+  prompts:
+    cities:
+      - "Indore"
+      - "Mumbai"
+      - "Delhi"
+      - "Bangalore"
+      - "Hyderabad"
+      - "Pune"
+      - "Chennai"
+      - "Kolkata"
+      - "Ahmedabad"
+      - "Jaipur"
+    colleges:
+      - "IIT"
+      - "NIT"
+      - "IIIT"
+      - "Engineering College"
+      - "University"
+      - "Technical Institute"
+    states:
+      - "Madhya Pradesh"
+      - "Maharashtra"
+      - "Karnataka"
+      - "Tamil Nadu"
+      - "Gujarat"
+
+# ============================================================================
+# API KEYS (used by download_dataset.py)
+# ============================================================================
+api_keys:
+  kaggle:
+    username: "runeetkumarmallarpu"
+    key: "KGAT_2cb1f3383897a1e34688a306c90cab32"
+  unsplash:
+    application_id: "873846"
+    access_key: "JkGKkdqmNPl9DVyTsESIq8FK3PvMP7VaVMelGYXqP5o"
+    secret_key: "-KeP6wUb8OHqT4Uq0uEAuvFEYc_6ol8-fR_E5rUEF6E"
+  pexels:
+    api_key: "WDWkMok7dehZeeigBo34hrpHybhzPuYHvZzbq3NTuPFwVUfc3MPTUz3q"
+
+# ============================================================================
+# SCRAPING CONFIGURATION
+# ============================================================================
+scraping:
+  pinterest:
+    max_images_per_query: 200
+    scroll_pause_seconds: 2.0
+    download_timeout: 15
+    min_resolution: 512
+    headless: true
+
+# ============================================================================
+# DEPLOYMENT CONFIGURATION - HF / GROQ
+# ============================================================================
+deployment_hf:
+  hf_username: "YOUR_HF_USERNAME" # ← CHANGE THIS
+  lora_repo_name: "campus-ai-poster-lora"
+  space_name: "campus-ai-poster-generator"
+  groq:
+    api_key_env: "GROQ_API_KEY" # reads from env variable
+    model: "llama-3.3-70b-versatile"
+
+# ============================================================================
+# TRAINING CONFIGURATION - WITH 10 ANTI-OVERFITTING TECHNIQUES
+# ============================================================================
+training:
+  # Global training settings
+  seed: 42
+  deterministic: true
+  benchmark: false
+
+  # Mixed precision training (Technique 1: Stability)
+  mixed_precision:
+    enabled: true
+    dtype: "bf16" # or "fp16"
+    opt_level: "O2"
+
+  # ==========================================================================
+  # SDXL LORA TRAINING
+  # ==========================================================================
+  sdxl_lora:
+    # LoRA configuration
+    lora:
+      rank: 32
+      alpha: 16
+      dropout: 0.05
+      bias: "none"
+      task_type: "CAUSAL_LM"
+
+    # Optimizer settings
+    optimizer:
+      type: "adamw8bit"
+      learning_rate: 1.0e-4
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+
+    # Learning rate scheduler (Technique 4: LR Scheduling)
+    scheduler:
+      type: "cosine_with_restarts" # Escapes local minima
+      warmup_steps: 100
+      num_cycles: 3 # 3 restarts across 4 epochs
+      min_lr: 1.0e-6
+
+    # Training hyperparameters
+    batch_size: 1
+    gradient_accumulation_steps: 4
+    effective_batch_size: 4 # batch_size * gradient_accumulation_steps
+    max_grad_norm: 1.0 # Technique 5: Gradient Clipping
+    epochs: 4
+    max_steps: 12800 # 20 epochs × 2560 chunks/epoch
+
+    # Min-SNR-γ Loss Weighting (Technique 6: Balanced Noise-Level Learning)
+    # Prevents model from memorizing easy noise levels and ignoring hard ones.
+    # Forces uniform learning across the entire denoising spectrum → generalization.
+    # Paper: "Efficient Diffusion Training via Min-SNR Weighting Strategy"
+    min_snr_gamma:
+      enabled: true
+      gamma: 5.0 # Clamps max loss weight; 5.0 is the paper-recommended default
+
+    # Model checkpointing (Technique 7: Best Model Selection)
+    checkpointing:
+      enabled: true
+      save_strategy: "epoch" # epoch, steps
+      save_steps: 100
+      save_total_limit: 3
+      save_best_only: true
+      monitor: "val_loss"
+      mode: "min"
+
+    # Validation (Technique 8: Cross-Validation Monitoring)
+    validation:
+      enabled: true
+      eval_strategy: "epoch" # epoch, steps
+      eval_steps: 50
+      eval_accumulation_steps: 1
+      per_device_eval_batch_size: 1
+
+    # Data augmentation (Technique 9: Regularization through augmentation)
+    augmentation:
+      enabled: false # For diffusion models, handled differently
+      techniques:
+        - "random_horizontal_flip"
+        - "color_jitter"
+
+    # Logging
+    logging:
+      steps: 10
+      report_to: "tensorboard"
+      log_level: "info"
+
+    # Noise scheduling (Technique 10: Progressive training)
+    noise_schedule:
+      type: "ddpm"
+      beta_start: 0.0001
+      beta_end: 0.02
+      num_train_timesteps: 1000
+
+  # ==========================================================================
+  # LLAMA LORA TRAINING
+  # ==========================================================================
+  llama_lora:
+    # LoRA configuration
+    lora:
+      rank: 32
+      alpha: 32
+      dropout: 0.05 # Technique 2: Dropout
+      target_modules:
+        - "q_proj"
+        - "k_proj"
+        - "v_proj"
+        - "o_proj"
+        - "gate_proj"
+        - "up_proj"
+        - "down_proj"
+      bias: "none"
+      task_type: "CAUSAL_LM"
+      modules_to_save: null
+
+    # Optimizer settings
+    optimizer:
+      type: "adamw8bit"
+      learning_rate: 2.0e-4
+      betas: [0.9, 0.999]
+      eps: 1.0e-8
+      weight_decay: 0.01 # Technique 3: L2 Regularization
+
+    # Learning rate scheduler (Technique 4: LR Scheduling)
+    scheduler:
+      type: "cosine_with_restarts"
+      warmup_steps: 50
+      warmup_ratio: 0.05
+      num_cycles: 2
+      min_lr: 1.0e-6
+
+    # Training hyperparameters
+    batch_size: 2
+    gradient_accumulation_steps: 4
+    effective_batch_size: 8
+    max_grad_norm: 1.0 # Technique 5: Gradient Clipping
+    epochs: 3
+    max_steps: -1
+
+    # Early stopping (Technique 6: Early Stopping)
+    early_stopping:
+      enabled: true
+      patience: 2
+      min_delta: 0.001
+      monitor: "eval_loss"
+      mode: "min"
+
+    # Model checkpointing (Technique 7: Best Model Selection)
+    checkpointing:
+      enabled: true
+      save_strategy: "epoch"
+      save_steps: 50
+      save_total_limit: 2
+      save_best_only: true
+      monitor: "eval_loss"
+      mode: "min"
+
+    # Validation (Technique 8: Cross-Validation Monitoring)
+    validation:
+      enabled: true
+      eval_strategy: "epoch"
+      eval_steps: 25
+      eval_accumulation_steps: 1
+      per_device_eval_batch_size: 2
+
+    # Layer-wise learning rate decay (Technique 9: Progressive unfreezing)
+    layer_wise_lr:
+      enabled: false
+      decay_rate: 0.9
+
+    # Packing and padding (Technique 10: Efficient batching)
+    data:
+      packing: false
+      max_seq_length: 2048
+      padding: "max_length"
+      truncation: true
+
+    # Logging
+    logging:
+      steps: 5
+      report_to: "tensorboard"
+      log_level: "info"
+      log_model: false
+
+# ============================================================================
+# INFERENCE CONFIGURATION
+# ============================================================================
+inference:
+  # Generation parameters
+  num_variants: 4
+  batch_size: 1
+  max_batch_size: 4
+  timeout_seconds: 60
+
+  # Quality control
+  quality:
+    enable_filters: true
+    min_quality_score: 0.6
+    max_toxicity: 0.7
+
+  # Output settings
+  output:
+    format: "PNG"
+    quality: 95
+    save_metadata: true
+    enable_watermark: true
+    watermark_text: "Generated by CampusGen AI"
+
+  # Templates
+  templates:
+    categories:
+      - "Technical Fest Poster"
+      - "Cultural Event Banner"
+      - "Sports Tournament Poster"
+      - "Club Recruitment Flyer"
+      - "Workshop Announcement"
+      - "Assignment Presentation"
+      - "Promotional Reel"
+      - "Academic Seminar"
+    styles:
+      - "Vibrant and Energetic"
+      - "Elegant and Professional"
+      - "Modern Minimalist"
+      - "Traditional Indian"
+      - "Tech-Futuristic"
+      - "Artistic and Creative"
+
+# ============================================================================
+# RESPONSIBLE AI CONFIGURATION
+# ============================================================================
+responsibility:
+  # Content safety
+  toxicity_filter:
+    enabled: true
+    model: "multilingual"
+    threshold: 0.7
+    block_threshold: 0.85
+
+  # Content watermarking
+  watermark:
+    enabled: true
+    type: "C2PA"
+    text: "AI Generated - CampusGen AI"
+    visible: false
+
+  # Logging and monitoring
+  logging:
+    log_all_generations: true
+    log_blocked_content: true
+    log_path: "logs/inference/safety.log"
+
+  # Rate limiting
+  rate_limit:
+    enabled: false
+    max_requests_per_hour: 100
+    max_requests_per_day: 1000
+
+# ============================================================================
+# WEB APPLICATION CONFIGURATION
+# ============================================================================
+webapp:
+  # Server settings
+  server:
+    host: "0.0.0.0"
+    port: 7860
+    ssl: false
+    share: false
+    debug: false
+
+  # UI settings
+  ui:
+    title: "CampusGen AI - Indian College Poster Generator"
+    description: "Generate stunning posters for college events"
+    theme: "soft"
+    max_file_size_mb: 10
+    allow_flagging: false
+
+  # Session settings
+  session:
+    max_concurrent_users: 10
+    timeout_minutes: 30
+
+  # Example prompts
+  examples:
+    - "IIT Indore Techfest 2026 - Innovation and Robotics"
+    - "Cultural Night - Traditional Dance Celebration"
+    - "Inter-College Basketball Championship"
+    - "Photography Club Recruitment Drive"
+    - "Machine Learning Workshop Series"
+
+# ============================================================================
+# EVALUATION CONFIGURATION
+# ============================================================================
+evaluation:
+  # Metrics to compute
+  metrics:
+    - "fid" # Frechet Inception Distance
+    - "clip_score" # Text-image alignment
+    - "inception_score" # Image quality
+    - "lpips" # Perceptual similarity
+
+  # Thresholds
+  thresholds:
+    fid_max: 50.0 # Lower is better
+    clip_score_min: 0.25 # Higher is better
+    inception_score_min: 3.0 # Higher is better
+
+  # Evaluation settings
+  num_samples: 100
+  batch_size: 10
+  save_comparisons: true
+
+# ============================================================================
+# OPTIMIZATION FLAGS
+# ============================================================================
+optimization:
+  # PyTorch optimizations (tuned for SM120 Blackwell / CUDA 13.x)
+  torch:
+    backends:
+      cudnn:
+        enabled: true
+        benchmark: true
+        deterministic: false
+      cuda:
+        matmul:
+          allow_tf32: true # SM120 TF32 tensor cores
+          allow_bf16_reduced_precision_reduction: true # Native bf16 on Blackwell
+          allow_fp16_reduced_precision_reduction: true
+
+  # Memory optimizations
+  memory:
+    empty_cache_steps: 10
+    max_split_size_mb: 1024 # SM120 memory controller handles larger splits
+
+  # Compile settings — SM120 benefits massively from torch.compile
+  compile:
+    enabled: true
+    mode: "max-autotune" # Best for Blackwell's large SM count
+    fullgraph: false
+
+# ============================================================================
+# MONITORING AND DEBUGGING
+# ============================================================================
+monitoring:
+  # TensorBoard
+  tensorboard:
+    enabled: true
+    log_dir: "logs/tensorboard"
+    update_freq: "batch"
+
+  # Weights & Biases
+  wandb:
+    enabled: false
+    project: "campus-ai"
+    entity: null
+    tags: ["indian-college", "poster-generation"]
+
+  # System monitoring
+  system:
+    log_gpu_stats: true
+    log_memory_stats: true
+    log_interval_steps: 10
+
+# ============================================================================
+# DEPLOYMENT CONFIGURATION
+# ============================================================================
+deployment:
+  # Docker settings
+  docker:
+    base_image: "nvidia/cuda:12.1.0-runtime-ubuntu22.04"
+    expose_port: 7860
+
+  # API settings
+  api:
+    enabled: false
+    host: "0.0.0.0"
+    port: 8000
+    workers: 4
+    timeout: 300
+
+  # Resource limits
+  resources:
+    max_memory_gb: 16
+    max_gpu_memory_gb: 16
+    max_cpu_percent: 80

configs/train_sdxl_lora.yaml

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+job: extension
+config:
+  name: campus_ai_poster_sdxl
+  process:
+    - type: sd_trainer
+      training_folder: /mnt/e/campus-ai/models/sdxl/checkpoints
+      device: cuda:0
+      trigger_word: campus_ai_poster
+      network:
+        type: lora
+        linear: 32
+        linear_alpha: 16
+        dropout: 0.05
+        network_kwargs:
+          lora_plus_lr_ratio: 1.0
+      save:
+        dtype: bf16
+        save_every: 500
+        max_step_saves_to_keep: 5
+      datasets:
+        - folder_path: /mnt/e/campus-ai/data/train
+          caption_ext: txt
+          caption_dropout_rate: 0.1
+          shuffle_tokens: true
+          cache_latents_to_disk: true
+          num_workers: 8
+          resolution:
+            - 1024
+            - 1024
+      train:
+        batch_size: 1
+        steps: 12800
+        gradient_accumulation_steps: 4
+        train_unet: true
+        train_text_encoder: false
+        disable_sampling: true
+        gradient_checkpointing: true
+        noise_scheduler: ddpm
+        optimizer: adamw8bit
+        lr: 0.0001
+        lr_warmup_steps: 100
+        min_snr_gamma: 5.0
+        optimizer_params:
+          weight_decay: 0.01
+          betas:
+            - 0.9
+            - 0.999
+        ema_config:
+          use_ema: true
+          ema_decay: 0.999
+        dtype: bf16
+        lr_scheduler: cosine_with_restarts
+        lr_scheduler_params:
+          T_0: 4267
+          T_mult: 1
+          eta_min: 0.00001
+      model:
+        name_or_path: stabilityai/stable-diffusion-xl-base-1.0
+        is_xl: true
+      sample:
+        sampler: euler_a
+        sample_every: 999999
+        width: 512
+        height: 512
+        prompts:
+          - campus_ai_poster a vibrant technology fest poster with neon colors and bold
+            typography
+          - campus_ai_poster a colorful Diwali celebration poster with golden diyas and
+            rangoli
+          - campus_ai_poster a professional workshop seminar poster with modern minimalist
+            design
+          - campus_ai_poster a dynamic sports tournament poster with action silhouettes
+        neg: ""
+        seed: 42
+        walk_seed: true
+        guidance_scale: 4
+        sample_steps: 28
+      logging:
+        log_every: 10
+        use_wandb: false
+        verbose: true
+  meta:
+    name: campus_ai_v1
+    version: "1.0"

configs/train_sdxl_lora_phase2.yaml

@@ -0,0 +1,103 @@
+
+job: extension
+config:
+  name: campus_ai_poster_sdxl_phase2
+  process:
+    - type: sd_trainer
+      training_folder: /mnt/e/campus-ai/models/sdxl/checkpoints
+      device: cuda:0
+      trigger_word: campus_ai_poster
+      network:
+        type: lora
+        linear: 32
+        linear_alpha: 16
+        dropout: 0.1
+        network_kwargs:
+          lora_plus_lr_ratio: 1.0
+        # ==========================================================
+        # PHASE 2: RESUME FROM PHASE 1 WEIGHTS
+        # Load the layout knowledge so we only refine details
+        # ==========================================================
+        pretrained_lora_path: /mnt/e/campus-ai/models/sdxl/checkpoints/campus_ai_poster_sdxl_phase1/campus_ai_poster_sdxl.safetensors
+      save:
+        dtype: bf16
+        save_every: 500
+        max_step_saves_to_keep: 5
+      datasets:
+        - folder_path: /mnt/e/campus-ai/data/train
+          caption_ext: txt
+          caption_dropout_rate: 0.1
+          shuffle_tokens: true
+          cache_latents_to_disk: true
+          num_workers: 8
+          resolution: [1024, 1024]
+        - folder_path: /mnt/e/campus-ai/data/val
+          caption_ext: txt
+          caption_dropout_rate: 0.1
+          shuffle_tokens: true
+          cache_latents_to_disk: true
+          num_workers: 8
+          resolution: [1024, 1024]
+        - folder_path: /mnt/e/campus-ai/data/test
+          caption_ext: txt
+          caption_dropout_rate: 0.1
+          shuffle_tokens: true
+          cache_latents_to_disk: true
+          num_workers: 8
+          resolution: [1024, 1024]
+      train:
+        batch_size: 1
+        # Increased to 20,000 steps since we are now feeding 100% of the dataset
+        steps: 20000
+        gradient_accumulation_steps: 4
+        train_unet: true
+        train_text_encoder: false
+        disable_sampling: true
+        gradient_checkpointing: true
+        noise_scheduler: ddpm
+        optimizer: adamw8bit
+        # ==========================================================
+        # PHASE 2: LOWER LEARNING RATE
+        # Bake in high-frequency detail without blowing out the layout
+        # ==========================================================
+        lr: 2.0e-5
+        lr_warmup_steps: 100
+        min_snr_gamma: 5.0
+        optimizer_params:
+          weight_decay: 0.01
+          betas:
+            - 0.9
+            - 0.999
+        ema_config:
+          use_ema: true
+          ema_decay: 0.999
+        dtype: bf16
+        lr_scheduler: cosine
+      model:
+        name_or_path: stabilityai/stable-diffusion-xl-base-1.0
+        is_xl: true
+      sample:
+        sampler: euler_a
+        sample_every: 999999
+        width: 512
+        height: 512
+        prompts:
+          - campus_ai_poster a vibrant technology fest poster with neon colors and bold
+            typography
+          - campus_ai_poster a colorful Diwali celebration poster with golden diyas and
+            rangoli
+          - campus_ai_poster a professional workshop seminar poster with modern minimalist
+            design
+          - campus_ai_poster a dynamic sports tournament poster with action silhouettes
+        neg: ""
+        seed: 42
+        walk_seed: true
+        guidance_scale: 4
+        sample_steps: 28
+      logging:
+        log_every: 10
+        use_wandb: false
+        verbose: true
+  meta:
+    name: campus_ai_v2_perfection
+    version: "2.0"

configs/train_sdxl_lora_phase3.yaml

@@ -0,0 +1,86 @@
+job: extension
+config:
+  name: campus_ai_poster_sdxl_phase3
+  process:
+    - type: sd_trainer
+      training_folder: /mnt/e/campus-ai/models/sdxl/checkpoints
+      device: cuda:0
+      trigger_word: campus_ai_poster
+      network:
+        type: lora
+        linear: 32
+        linear_alpha: 16
+        dropout: 0.1
+        network_kwargs:
+          lora_plus_lr_ratio: 1.0
+        # ==========================================================
+        # PHASE 3: RESUME FROM PHASE 2 WEIGHTS
+        # Laser-focused tuning on the 5,500 highly curated strict dataset
+        # ==========================================================
+        pretrained_lora_path: /mnt/e/campus-ai/models/sdxl/checkpoints/campus_ai_poster_sdxl_phase2/campus_ai_poster_sdxl_phase2.safetensors
+      save:
+        dtype: bf16
+        save_every: 250
+        max_step_saves_to_keep: 5
+      datasets:
+        - folder_path: /mnt/e/campus-ai/data/tuning
+          shuffle_tokens: true
+          cache_latents_to_disk: true
+          num_workers: 8
+          resolution: [1024, 1024]
+      train:
+        batch_size: 1
+        # Smaller steps since tuning dataset is highly concentrated (6,448 images)
+        steps: 6448
+        gradient_accumulation_steps: 4
+        train_unet: true
+        train_text_encoder: false
+        disable_sampling: true
+        gradient_checkpointing: true
+        noise_scheduler: ddpm
+        optimizer: adamw8bit
+        # ==========================================================
+        # PHASE 3: EXTREMELY LOW LEARNING RATE
+        # Lock in final aesthetic consistency from strictly valid posters
+        # ==========================================================
+        lr: 1.0e-5
+        lr_warmup_steps: 50
+        min_snr_gamma: 5.0
+        optimizer_params:
+          weight_decay: 0.01
+          betas:
+            - 0.9
+            - 0.999
+        ema_config:
+          use_ema: true
+          ema_decay: 0.999
+        dtype: bf16
+        lr_scheduler: cosine
+      model:
+        name_or_path: stabilityai/stable-diffusion-xl-base-1.0
+        is_xl: true
+      sample:
+        sampler: euler_a
+        sample_every: 999999
+        width: 512
+        height: 512
+        prompts:
+          - campus_ai_poster a vibrant technology fest poster with neon colors and bold
+            typography
+          - campus_ai_poster a colorful Diwali celebration poster with golden diyas and
+            rangoli
+          - campus_ai_poster a professional workshop seminar poster with modern minimalist
+            design
+          - campus_ai_poster a dynamic sports tournament poster with action silhouettes
+        neg: ""
+        seed: 42
+        walk_seed: true
+        guidance_scale: 4
+        sample_steps: 28
+      logging:
+        log_every: 10
+        use_wandb: false
+        verbose: true
+  meta:
+    name: campus_ai_v3_tuning
+    version: "3.0"

deployment/README.md

@@ -0,0 +1,84 @@
+---
+title: CampusGen AI - Event Poster Generator
+emoji: 🎨
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 5.12.0
+app_file: app.py
+pinned: true
+license: mit
+hardware: zero-a10g
+---
+
+# 🎨 CampusGen AI – Universal Event Poster Generator
+
+Generate professional event posters for **any occasion** in 10–15 seconds.
+
+## Features
+
+- **5 Generation Modes**: Text→Poster, Reference Image (IP-Adapter), Image Transform, Inpainting, HD Upscale
+- **AI-Powered**: Flux.1-dev fine-tuned on 55,000+ diverse poster images via LoRA
+- **55 Categories**: Tech fests, cultural events, festivals (Diwali, Holi, Navratri), sports, workshops, and more
+- **Smart Prompts**: Groq Llama 3.3 70B understands your event semantics and generates optimal prompts
+- **10 Visual Styles**: Vibrant, Elegant, Minimalist, Traditional Indian, Tech-Futuristic, Neon Glow, and more
+- **HD Upscaling**: Real-ESRGAN 4x for print-ready posters
+- **Batch Generation**: Generate up to 4 variants at once
+- **Zero Cost**: Free deployment via ZeroGPU
+
+## How to Use
+
+### Tab 1: Text → Poster
+
+1. Describe your event (e.g., "IIT Indore Techfest 2026 — Robotics & AI Championships")
+2. Select event type and visual style
+3. Click **Generate Poster**
+
+### Tab 2: Reference Image
+
+1. Upload a poster you like as a reference
+2. Describe your event
+3. Adjust style influence slider
+4. Click **Generate with Reference**
+
+### Tab 3: Image Transform
+
+1. Upload an existing poster
+2. Describe the transformation (e.g., "Make it neon-themed")
+3. Adjust transformation strength
+4. Click **Transform Poster**
+
+### Tab 4: Inpaint / Edit
+
+1. Upload a poster
+2. Draw over the area you want to change
+3. Describe what should fill it
+4. Click **Inpaint Region**
+
+### Tab 5: HD Upscale
+
+1. Upload any image
+2. Select 2x or 4x scale
+3. Click **Upscale**
+
+## Technical Details
+
+| Component | Details |
+|-----------|---------|
+| Base Model | Flux.1-dev (12B params) |
+| Fine-tuning | LoRA (rank 32, bf16) |
+| Dataset | 55,000+ curated event posters, 55 categories |
+| LLM | Llama 3.3 70B via Groq |
+| IP-Adapter | Reference image style extraction |
+| Upscaler | Real-ESRGAN 4x |
+| Hardware | ZeroGPU (shared A100) |
+
+## Pipeline (GPU-Accelerated)
+
+```text
+Scraping (CPU) → Quality Filter (GPU) → Captioning (GPU) → Split → Train LoRA (GPU) → Deploy
+```
+
+## Author
+
+Built with ❤️ by M Runeet Kumar

deployment/app.py

@@ -0,0 +1,663 @@
+#!/usr/bin/env python3
+"""
+CampusGen AI – Full-Feature Gradio Application
+Multi-tab poster generation platform for Hugging Face Spaces.
+
+Tabs:
+  1. Text → Poster        (Flux + LoRA + Groq LLM)
+  2. Reference Image       (IP-Adapter + LoRA)
+  3. Image Transform       (Img2Img pipeline)
+  4. Inpainting / Edit     (Mask-based regeneration)
+  5. HD Upscale            (Real-ESRGAN 4x)
+"""
+
+import os
+import time
+import logging
+from typing import Optional
+
+import torch
+import gradio as gr
+
+# HF Spaces ZeroGPU decorator (works even if package isn't installed)
+try:
+    import spaces
+    HAS_SPACES = True
+except ImportError:
+    HAS_SPACES = False
+    class _FakeSpaces:
+        @staticmethod
+        def GPU(duration=60):
+            def decorator(fn):
+                return fn
+            return decorator
+    spaces = _FakeSpaces()
+
+from pipelines import get_pipeline_manager, flush_vram
+from prompt_engine import (
+    build_text2img_prompt,
+    build_img2img_prompt,
+    build_inpaint_prompt,
+    STYLE_MAP,
+    EVENT_TYPE_HINTS,
+)
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Dropdowns
+# ─────────────────────────────────────────────────────────────────────────────
+EVENT_TYPES = list(EVENT_TYPE_HINTS.keys())
+STYLES = list(STYLE_MAP.keys())
+
+RESOLUTION_PRESETS = {
+    "Square (1024×1024)": (1024, 1024),
+    "Portrait (768×1152)": (768, 1152),
+    "Portrait Tall (768×1344)": (768, 1344),
+    "Landscape (1152×768)": (1152, 768),
+    "Landscape Wide (1344×768)": (1344, 768),
+    "Instagram Story (768×1365)": (768, 1365),
+    "A4 Poster (768×1086)": (768, 1086),
+}
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# GENERATION FUNCTIONS
+# ─────────────────────────────────────────────────────────────────────────────
+
+@spaces.GPU(duration=90)
+def generate_text2img(
+    event_description: str,
+    event_type: str,
+    style: str,
+    resolution: str,
+    num_variants: int,
+    num_steps: int,
+    guidance_scale: float,
+    lora_strength: float,
+    enable_upscale: bool,
+    seed: int,
+):
+    """Tab 1: Text-to-Poster generation."""
+    if not event_description.strip():
+        raise gr.Error("Please enter an event description!")
+
+    manager = get_pipeline_manager()
+    pipe = manager.get_text2img()
+
+    # Build prompt via Groq LLM
+    prompt = build_text2img_prompt(event_description, event_type, style)
+    logger.info(f"[Text2Img] Prompt: {prompt[:120]}...")
+
+    # Resolution
+    width, height = RESOLUTION_PRESETS.get(resolution, (1024, 1024))
+
+    # Seed
+    if seed == -1:
+        seed = int(time.time()) % (2**32)
+
+    # LoRA strength
+    if manager.is_lora_loaded:
+        pipe.fuse_lora(lora_scale=lora_strength)
+
+    # Generate variants
+    images = []
+    generator = torch.Generator("cpu").manual_seed(seed)
+    start = time.time()
+
+    for i in range(num_variants):
+        result = pipe(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance_scale,
+            generator=generator,
+        )
+        img = result.images[0]
+
+        if enable_upscale:
+            img = manager.upscale_image(img, scale=2)
+
+        images.append(img)
+
+    elapsed = time.time() - start
+
+    if manager.is_lora_loaded:
+        pipe.unfuse_lora()
+
+    info = (
+        f"**Generated {num_variants} poster(s) in {elapsed:.1f}s** | "
+        f"Seed: {seed} | {width}×{height} | Steps: {num_steps}\n\n"
+        f"**Prompt:**\n{prompt}"
+    )
+
+    return images, info
+
+
+@spaces.GPU(duration=90)
+def generate_with_reference(
+    event_description: str,
+    reference_image,
+    style: str,
+    style_strength: float,
+    resolution: str,
+    num_steps: int,
+    guidance_scale: float,
+    enable_upscale: bool,
+    seed: int,
+):
+    """Tab 2: Reference image + text → poster (IP-Adapter)."""
+    if reference_image is None:
+        raise gr.Error("Please upload a reference image!")
+    if not event_description.strip():
+        raise gr.Error("Please enter an event description!")
+
+    from PIL import Image
+
+    manager = get_pipeline_manager()
+    pipe = manager.get_text2img()
+    pipe = manager.load_ip_adapter(pipe)
+    manager.set_ip_adapter_scale(pipe, scale=style_strength)
+
+    prompt = build_text2img_prompt(event_description, "Other", style)
+    width, height = RESOLUTION_PRESETS.get(resolution, (1024, 1024))
+
+    if seed == -1:
+        seed = int(time.time()) % (2**32)
+
+    generator = torch.Generator("cpu").manual_seed(seed)
+    start = time.time()
+
+    # Prepare reference image
+    ref_img = Image.fromarray(reference_image).convert("RGB").resize((224, 224))
+
+    result = pipe(
+        prompt=prompt,
+        ip_adapter_image=ref_img,
+        height=height,
+        width=width,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+    )
+    img = result.images[0]
+    elapsed = time.time() - start
+
+    if enable_upscale:
+        img = manager.upscale_image(img, scale=2)
+
+    info = (
+        f"**Generated in {elapsed:.1f}s** | Seed: {seed} | "
+        f"Style strength: {style_strength}\n\n"
+        f"**Prompt:**\n{prompt}"
+    )
+
+    return img, info
+
+
+@spaces.GPU(duration=90)
+def generate_img2img(
+    input_image,
+    transform_description: str,
+    style: str,
+    denoising_strength: float,
+    num_steps: int,
+    guidance_scale: float,
+    enable_upscale: bool,
+    seed: int,
+):
+    """Tab 3: Image-to-image transformation."""
+    if input_image is None:
+        raise gr.Error("Please upload an image to transform!")
+
+    from PIL import Image
+
+    manager = get_pipeline_manager()
+    pipe = manager.get_img2img()
+
+    prompt = build_img2img_prompt(transform_description, style)
+
+    if seed == -1:
+        seed = int(time.time()) % (2**32)
+
+    generator = torch.Generator("cpu").manual_seed(seed)
+    init_image = Image.fromarray(input_image).convert("RGB").resize((1024, 1024))
+
+    start = time.time()
+    result = pipe(
+        prompt=prompt,
+        image=init_image,
+        strength=denoising_strength,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+    )
+    img = result.images[0]
+    elapsed = time.time() - start
+
+    if enable_upscale:
+        img = manager.upscale_image(img, scale=2)
+
+    info = (
+        f"**Transformed in {elapsed:.1f}s** | Seed: {seed} | "
+        f"Denoise: {denoising_strength}\n\n"
+        f"**Prompt:**\n{prompt}"
+    )
+
+    return img, info
+
+
+@spaces.GPU(duration=90)
+def generate_inpaint(
+    input_data: dict,
+    fill_description: str,
+    num_steps: int,
+    guidance_scale: float,
+    seed: int,
+):
+    """Tab 4: Inpainting – regenerate masked region."""
+    if input_data is None:
+        raise gr.Error("Please upload an image and draw a mask!")
+
+    from PIL import Image
+    import numpy as np
+
+    manager = get_pipeline_manager()
+    pipe = manager.get_inpaint()
+
+    prompt = build_inpaint_prompt(fill_description)
+
+    if seed == -1:
+        seed = int(time.time()) % (2**32)
+
+    generator = torch.Generator("cpu").manual_seed(seed)
+
+    # Extract image and mask from ImageEditor output
+    source_image = Image.fromarray(input_data["background"]).convert("RGB").resize((1024, 1024))
+
+    # Build mask from composite layers
+    if "layers" in input_data and len(input_data["layers"]) > 0:
+        mask_layer = input_data["layers"][0]
+        mask = Image.fromarray(mask_layer).convert("L").resize((1024, 1024))
+        # Binarize mask
+        mask = mask.point(lambda x: 255 if x > 10 else 0)
+    else:
+        raise gr.Error("Please draw on the image to create a mask!")
+
+    start = time.time()
+    result = pipe(
+        prompt=prompt,
+        image=source_image,
+        mask_image=mask,
+        height=1024,
+        width=1024,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+    )
+    img = result.images[0]
+    elapsed = time.time() - start
+
+    info = (
+        f"**Inpainted in {elapsed:.1f}s** | Seed: {seed}\n\n"
+        f"**Prompt:**\n{prompt}"
+    )
+
+    return img, info
+
+
+def upscale_only(input_image, scale_factor: int):
+    """Tab 5: Standalone HD upscaling."""
+    if input_image is None:
+        raise gr.Error("Please upload an image to upscale!")
+
+    from PIL import Image
+
+    manager = get_pipeline_manager()
+    img = Image.fromarray(input_image).convert("RGB")
+
+    original_size = f"{img.width}×{img.height}"
+
+    start = time.time()
+    result = manager.upscale_image(img, scale=scale_factor)
+    elapsed = time.time() - start
+
+    new_size = f"{result.width}×{result.height}"
+    info = f"**Upscaled in {elapsed:.1f}s** | {original_size} → {new_size}"
+
+    return result, info
+
+
+# ────────────────────────────��────────────────────────────────────────────────
+# GRADIO UI
+# ─────────────────────────────────────────────────────────────────────────────
+
+css = """
+.gradio-container {
+    max-width: 1400px !important;
+    margin: auto;
+}
+.title-text {
+    text-align: center;
+    font-size: 2.5em;
+    font-weight: 800;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin-bottom: 0.2em;
+    letter-spacing: -0.02em;
+}
+.subtitle-text {
+    text-align: center;
+    color: #888;
+    font-size: 1.15em;
+    margin-bottom: 1.5em;
+    font-weight: 300;
+}
+.tab-nav button {
+    font-size: 1.05em !important;
+    font-weight: 600 !important;
+}
+.generate-btn {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+    border: none !important;
+    font-size: 1.1em !important;
+}
+.footer-text {
+    text-align: center;
+    color: #999;
+    font-size: 0.9em;
+    margin-top: 1em;
+    padding: 1em;
+    border-top: 1px solid #333;
+}
+"""
+
+EXAMPLES = [
+    ["IIT Indore Techfest 2026 — Robotics & AI Championships", "Technical Fest", "Tech-Futuristic"],
+    ["Diwali Mela 2026 — Spark of Joy", "Diwali Celebration", "Traditional Indian"],
+    ["Inter-College Basketball Championship", "Sports Tournament", "Vibrant and Energetic"],
+    ["Photography Club Portfolio Night", "Club Recruitment", "Dark Premium"],
+    ["ML/AI Workshop Series — From Zero to GPT", "Workshop / Seminar", "Gradient Modern"],
+    ["Classical Kathak Dance Night", "Cultural Event", "Elegant and Professional"],
+    ["Holi Hai! Campus Color Run", "Holi Festival", "Artistic and Creative"],
+    ["Navratri Garba Night 2026", "Navratri / Garba", "Traditional Indian"],
+    ["End-of-Year Farewell Party", "Freshers / Farewell", "Neon Glow"],
+    ["Blood Donation Camp — Save Lives", "Blood Donation", "Modern Minimalist"],
+]
+
+
+def build_app() -> gr.Blocks:
+    with gr.Blocks(css=css, theme=gr.themes.Soft(), title="CampusGen AI") as demo:
+
+        # ── Header ───────────────────────────────────────────────────
+        gr.HTML(
+            '<div class="title-text">🎨 CampusGen AI</div>'
+            '<div class="subtitle-text">'
+            "Generate stunning event posters in seconds — "
+            "Text · Reference Image · Transform · Inpaint · Upscale"
+            "</div>"
+        )
+
+        with gr.Tabs() as tabs:
+
+            # ═══════════════════════════════════════════════════════════
+            # TAB 1: Text → Poster
+            # ═══════════════════════════════════════════════════════════
+            with gr.Tab("✍️ Text → Poster", id="text2img"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        t2i_event = gr.Textbox(
+                            label="📝 Describe Your Event",
+                            placeholder="e.g., 'Annual tech fest with AI and robotics competitions at IIT Indore, March 2026'",
+                            lines=3,
+                        )
+                        t2i_type = gr.Dropdown(
+                            EVENT_TYPES, value="Technical Fest",
+                            label="🏷️ Event Type",
+                        )
+                        t2i_style = gr.Dropdown(
+                            STYLES, value="Vibrant and Energetic",
+                            label="🎨 Visual Style",
+                        )
+                        t2i_resolution = gr.Dropdown(
+                            list(RESOLUTION_PRESETS.keys()),
+                            value="Portrait (768×1152)",
+                            label="📐 Resolution",
+                        )
+                        t2i_variants = gr.Slider(
+                            1, 4, value=1, step=1,
+                            label="🔢 Number of Variants",
+                        )
+
+                        with gr.Accordion("⚙️ Advanced", open=False):
+                            t2i_steps = gr.Slider(10, 50, value=28, step=1, label="Inference Steps")
+                            t2i_cfg = gr.Slider(1.0, 10.0, value=3.5, step=0.5, label="Guidance Scale")
+                            t2i_lora = gr.Slider(0.0, 1.5, value=0.85, step=0.05, label="LoRA Strength")
+                            t2i_upscale = gr.Checkbox(label="🔍 HD Upscale (2x)", value=False)
+                            t2i_seed = gr.Number(value=-1, label="Seed (-1 = random)")
+
+                        t2i_btn = gr.Button("🚀 Generate Poster", variant="primary", size="lg", elem_classes=["generate-btn"])
+
+                    with gr.Column(scale=1):
+                        t2i_gallery = gr.Gallery(
+                            label="Generated Posters", columns=2,
+                            height=600, object_fit="contain",
+                        )
+                        t2i_info = gr.Markdown(label="Generation Info")
+
+                gr.Examples(
+                    examples=EXAMPLES,
+                    inputs=[t2i_event, t2i_type, t2i_style],
+                    label="💡 Try These Examples",
+                )
+
+                t2i_btn.click(
+                    fn=generate_text2img,
+                    inputs=[
+                        t2i_event, t2i_type, t2i_style, t2i_resolution,
+                        t2i_variants, t2i_steps, t2i_cfg, t2i_lora,
+                        t2i_upscale, t2i_seed,
+                    ],
+                    outputs=[t2i_gallery, t2i_info],
+                )
+
+            # ═══════════════════════════════════════════════════════════
+            # TAB 2: Reference Image
+            # ═══════════════════════════════════════════════════════════
+            with gr.Tab("🖼️ Reference Image", id="reference"):
+                gr.Markdown(
+                    "Upload a poster you like → the AI will extract its **visual style** "
+                    "and blend it with your event description using IP-Adapter."
+                )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        ref_image = gr.Image(
+                            label="📎 Upload Reference Poster",
+                            type="numpy", height=300,
+                        )
+                        ref_event = gr.Textbox(
+                            label="📝 Describe Your Event",
+                            placeholder="e.g., 'Annual cultural night with dance performances'",
+                            lines=2,
+                        )
+                        ref_style = gr.Dropdown(
+                            STYLES, value="Vibrant and Energetic",
+                            label="🎨 Base Style",
+                        )
+                        ref_strength = gr.Slider(
+                            0.0, 1.0, value=0.6, step=0.05,
+                            label="🎚️ Reference Influence (0=ignore, 1=copy)",
+                        )
+                        ref_resolution = gr.Dropdown(
+                            list(RESOLUTION_PRESETS.keys()),
+                            value="Portrait (768×1152)",
+                            label="📐 Resolution",
+                        )
+
+                        with gr.Accordion("⚙️ Advanced", open=False):
+                            ref_steps = gr.Slider(10, 50, value=28, step=1, label="Steps")
+                            ref_cfg = gr.Slider(1.0, 10.0, value=3.5, step=0.5, label="Guidance")
+                            ref_upscale = gr.Checkbox(label="🔍 HD Upscale (2x)", value=False)
+                            ref_seed = gr.Number(value=-1, label="Seed")
+
+                        ref_btn = gr.Button("🚀 Generate with Reference", variant="primary", size="lg", elem_classes=["generate-btn"])
+
+                    with gr.Column(scale=1):
+                        ref_output = gr.Image(label="Generated Poster", type="pil", height=600)
+                        ref_info = gr.Markdown()
+
+                ref_btn.click(
+                    fn=generate_with_reference,
+                    inputs=[
+                        ref_event, ref_image, ref_style, ref_strength,
+                        ref_resolution, ref_steps, ref_cfg, ref_upscale, ref_seed,
+                    ],
+                    outputs=[ref_output, ref_info],
+                )
+
+            # ═══════════════════════════════════════════════════════════
+            # TAB 3: Image Transform
+            # ═══════════════════════════════════════════════════════════
+            with gr.Tab("🔄 Image Transform", id="img2img"):
+                gr.Markdown(
+                    "Upload an existing poster → describe how you want it **transformed**. "
+                    "Lower denoising = subtle changes, higher = dramatic restyle."
+                )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        i2i_image = gr.Image(
+                            label="📎 Upload Poster to Transform",
+                            type="numpy", height=300,
+                        )
+                        i2i_desc = gr.Textbox(
+                            label="🔄 Describe the Transformation",
+                            placeholder="e.g., 'Make it neon-themed with darker background and glow effects'",
+                            lines=2,
+                        )
+                        i2i_style = gr.Dropdown(
+                            STYLES, value="Tech-Futuristic",
+                            label="🎨 Target Style",
+                        )
+                        i2i_denoise = gr.Slider(
+                            0.1, 1.0, value=0.65, step=0.05,
+                            label="🎚️ Transformation Strength (0.1=subtle, 1.0=complete restyle)",
+                        )
+
+                        with gr.Accordion("⚙️ Advanced", open=False):
+                            i2i_steps = gr.Slider(10, 50, value=28, step=1, label="Steps")
+                            i2i_cfg = gr.Slider(1.0, 10.0, value=3.5, step=0.5, label="Guidance")
+                            i2i_upscale = gr.Checkbox(label="🔍 HD Upscale (2x)", value=False)
+                            i2i_seed = gr.Number(value=-1, label="Seed")
+
+                        i2i_btn = gr.Button("🔄 Transform Poster", variant="primary", size="lg", elem_classes=["generate-btn"])
+
+                    with gr.Column(scale=1):
+                        i2i_output = gr.Image(label="Transformed Poster", type="pil", height=600)
+                        i2i_info = gr.Markdown()
+
+                i2i_btn.click(
+                    fn=generate_img2img,
+                    inputs=[
+                        i2i_image, i2i_desc, i2i_style, i2i_denoise,
+                        i2i_steps, i2i_cfg, i2i_upscale, i2i_seed,
+                    ],
+                    outputs=[i2i_output, i2i_info],
+                )
+
+            # ═══════════════════════════════════════════════════════════
+            # TAB 4: Inpainting
+            # ═══════════════════════════════════════════════════════════
+            with gr.Tab("🖌️ Inpaint / Edit", id="inpaint"):
+                gr.Markdown(
+                    "Upload a poster → **draw over the area** you want to change → "
+                    "describe what should replace it. The rest of the poster stays intact."
+                )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        inp_editor = gr.ImageEditor(
+                            label="🖌️ Draw Mask on Poster",
+                            type="numpy",
+                            height=400,
+                            brush=gr.Brush(
+                                default_size=30,
+                                colors=["#FFFFFF"],
+                                color_mode="fixed",
+                            ),
+                            eraser=gr.Eraser(default_size=20),
+                            layers=True,
+                        )
+                        inp_desc = gr.Textbox(
+                            label="📝 What Should Fill the Masked Area?",
+                            placeholder="e.g., 'A golden trophy with confetti'",
+                            lines=2,
+                        )
+
+                        with gr.Accordion("⚙️ Advanced", open=False):
+                            inp_steps = gr.Slider(10, 50, value=28, step=1, label="Steps")
+                            inp_cfg = gr.Slider(1.0, 10.0, value=3.5, step=0.5, label="Guidance")
+                            inp_seed = gr.Number(value=-1, label="Seed")
+
+                        inp_btn = gr.Button("🖌️ Inpaint Region", variant="primary", size="lg", elem_classes=["generate-btn"])
+
+                    with gr.Column(scale=1):
+                        inp_output = gr.Image(label="Inpainted Poster", type="pil", height=600)
+                        inp_info = gr.Markdown()
+
+                inp_btn.click(
+                    fn=generate_inpaint,
+                    inputs=[inp_editor, inp_desc, inp_steps, inp_cfg, inp_seed],
+                    outputs=[inp_output, inp_info],
+                )
+
+            # ═══════════════════════════════════════════════════════════
+            # TAB 5: HD Upscale
+            # ════════════════════════════════════════════════��══════════
+            with gr.Tab("🔍 HD Upscale", id="upscale"):
+                gr.Markdown(
+                    "Upload any image → get a **4x upscaled** HD version using Real-ESRGAN. "
+                    "Great for making generated posters print-ready."
+                )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        up_image = gr.Image(
+                            label="📎 Upload Image",
+                            type="numpy", height=300,
+                        )
+                        up_scale = gr.Radio(
+                            [2, 4], value=4, label="🔍 Scale Factor",
+                        )
+                        up_btn = gr.Button("🔍 Upscale", variant="primary", size="lg", elem_classes=["generate-btn"])
+
+                    with gr.Column(scale=1):
+                        up_output = gr.Image(label="Upscaled Image", type="pil", height=600)
+                        up_info = gr.Markdown()
+
+                up_btn.click(
+                    fn=upscale_only,
+                    inputs=[up_image, up_scale],
+                    outputs=[up_output, up_info],
+                )
+
+        # ── Footer ───────────────────────────────────────────────────
+        gr.HTML(
+            '<div class="footer-text">'
+            "<strong>CampusGen AI</strong> — "
+            "Fine-tuned on 71,000+ event poster images across 57 subcategories | "
+            "Flux.1-dev + LoRA + IP-Adapter + Real-ESRGAN | "
+            "Groq Llama 3.3 70B for smart prompts<br>"
+            "Built with ❤️ for the Indian campus community"
+            "</div>"
+        )
+
+    return demo
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Launch
+# ─────────────────────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    demo = build_app()
+    demo.launch(server_name="0.0.0.0", server_port=7860)

deployment/pipelines.py

@@ -0,0 +1,303 @@
+#!/usr/bin/env python3
+"""
+CampusGen AI – Pipeline Manager
+Centralized lazy-loading of all generation pipelines.
+Shares base model + LoRA across text2img, img2img, inpainting.
+Manages VRAM via CPU offloading for 16GB GPUs / HF ZeroGPU.
+"""
+
+import os
+import gc
+import logging
+from typing import Optional
+from pathlib import Path
+
+import torch
+import numpy as np
+from PIL import Image
+
+# ─── SM120 (Blackwell) CUDA optimizations ───────────────────────────────────
+if torch.cuda.is_available():
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Configuration
+# ─────────────────────────────────────────────────────────────────────────────
+HF_USERNAME = os.environ.get("HF_USERNAME", "YOUR_USERNAME")
+LORA_REPO = f"{HF_USERNAME}/campus-ai-poster-lora"
+LORA_FILENAME = "campus_ai_poster_lora.safetensors"
+BASE_MODEL = "black-forest-labs/FLUX.1-dev"
+
+# IP-Adapter for Flux
+IP_ADAPTER_REPO = "h94/IP-Adapter"
+IP_ADAPTER_SUBFOLDER = "sdxl_models"  # Flux-compatible adapter
+IMAGE_ENCODER_REPO = "openai/clip-vit-large-patch14"
+
+# Real-ESRGAN upscaler
+ESRGAN_MODEL_NAME = "RealESRGAN_x4plus"
+
+
+def flush_vram():
+    """Aggressively free GPU memory."""
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+
+class PipelineManager:
+    """
+    Manages all generation pipelines with shared base model.
+    Only ONE pipeline mode is active at a time to fit in 16GB VRAM.
+    """
+
+    def __init__(self):
+        self._text2img = None
+        self._img2img = None
+        self._inpaint = None
+        self._ip_adapter_loaded = False
+        self._upscaler = None
+        self._active_mode: Optional[str] = None
+        self._lora_loaded = False
+
+    # ── Text-to-Image ────────────────────────────────────────────────────
+
+    def get_text2img(self):
+        """Load or return text-to-image pipeline."""
+        if self._active_mode == "text2img" and self._text2img is not None:
+            return self._text2img
+
+        self._unload_all()
+
+        from diffusers import FluxPipeline
+
+        logger.info("Loading Flux.1-dev text-to-image pipeline...")
+        self._text2img = FluxPipeline.from_pretrained(
+            BASE_MODEL,
+            torch_dtype=torch.bfloat16,
+        )
+        self._text2img.enable_model_cpu_offload()
+        self._load_lora(self._text2img)
+
+        # SM120: compile transformer for faster inference
+        try:
+            self._text2img.transformer = torch.compile(
+                self._text2img.transformer, mode="max-autotune"
+            )
+        except Exception:
+            pass
+
+        self._active_mode = "text2img"
+        logger.info("Text-to-image pipeline ready.")
+        return self._text2img
+
+    # ── Image-to-Image ───────────────────────────────────────────────────
+
+    def get_img2img(self):
+        """Load or return img2img pipeline."""
+        if self._active_mode == "img2img" and self._img2img is not None:
+            return self._img2img
+
+        self._unload_all()
+
+        from diffusers import FluxImg2ImgPipeline
+
+        logger.info("Loading Flux.1-dev img2img pipeline...")
+        self._img2img = FluxImg2ImgPipeline.from_pretrained(
+            BASE_MODEL,
+            torch_dtype=torch.bfloat16,
+        )
+        self._img2img.enable_model_cpu_offload()
+        self._load_lora(self._img2img)
+
+        try:
+            self._img2img.transformer = torch.compile(
+                self._img2img.transformer, mode="max-autotune"
+            )
+        except Exception:
+            pass
+
+        self._active_mode = "img2img"
+        logger.info("Img2img pipeline ready.")
+        return self._img2img
+
+    # ── Inpainting ───────────────────────────────────────────────────────
+
+    def get_inpaint(self):
+        """Load or return inpainting pipeline."""
+        if self._active_mode == "inpaint" and self._inpaint is not None:
+            return self._inpaint
+
+        self._unload_all()
+
+        from diffusers import FluxInpaintPipeline
+
+        logger.info("Loading Flux.1-dev inpainting pipeline...")
+        self._inpaint = FluxInpaintPipeline.from_pretrained(
+            BASE_MODEL,
+            torch_dtype=torch.bfloat16,
+        )
+        self._inpaint.enable_model_cpu_offload()
+        self._load_lora(self._inpaint)
+
+        try:
+            self._inpaint.transformer = torch.compile(
+                self._inpaint.transformer, mode="max-autotune"
+            )
+        except Exception:
+            pass
+
+        self._active_mode = "inpaint"
+        logger.info("Inpainting pipeline ready.")
+        return self._inpaint
+
+    # ── IP-Adapter (style from reference image) ──────────────────────────
+
+    def load_ip_adapter(self, pipe):
+        """
+        Attach IP-Adapter to the current pipeline for reference-image input.
+        Uses CLIP image encoder to extract style features.
+        """
+        if self._ip_adapter_loaded:
+            return pipe
+
+        try:
+            logger.info("Loading IP-Adapter for reference image support...")
+            pipe.load_ip_adapter(
+                IP_ADAPTER_REPO,
+                subfolder=IP_ADAPTER_SUBFOLDER,
+                weight_name="ip-adapter-plus_sdxl_vit-h.safetensors",
+            )
+            self._ip_adapter_loaded = True
+            logger.info("IP-Adapter loaded successfully.")
+        except Exception as e:
+            logger.warning(f"Could not load IP-Adapter: {e}")
+            logger.warning("Reference image feature will be disabled.")
+
+        return pipe
+
+    def set_ip_adapter_scale(self, pipe, scale: float = 0.6):
+        """Set the influence strength of the reference image."""
+        if self._ip_adapter_loaded:
+            pipe.set_ip_adapter_scale(scale)
+
+    # ── Real-ESRGAN Upscaler ─────────────────────────────────────────────
+
+    def get_upscaler(self):
+        """Load and return the Real-ESRGAN upscaler model."""
+        if self._upscaler is not None:
+            return self._upscaler
+
+        try:
+            from realesrgan import RealESRGANer
+            from basicsr.archs.rrdbnet_arch import RRDBNet
+
+            logger.info("Loading Real-ESRGAN x4 upscaler...")
+
+            model = RRDBNet(
+                num_in_ch=3, num_out_ch=3, num_feat=64,
+                num_block=23, num_grow_ch=32, scale=4,
+            )
+
+            self._upscaler = RealESRGANer(
+                scale=4,
+                model_path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+                model=model,
+                tile=512,  # Tile size for memory-efficient upscaling
+                tile_pad=10,
+                pre_pad=0,
+                half=True,  # FP16 for speed
+            )
+            logger.info("Real-ESRGAN upscaler ready.")
+
+        except ImportError:
+            logger.warning(
+                "Real-ESRGAN not installed. Using Pillow LANCZOS fallback."
+            )
+            self._upscaler = "pillow_fallback"
+
+        except Exception as e:
+            logger.warning(f"Could not load Real-ESRGAN: {e}. Using fallback.")
+            self._upscaler = "pillow_fallback"
+
+        return self._upscaler
+
+    def upscale_image(self, image: Image.Image, scale: int = 4) -> Image.Image:
+        """
+        Upscale an image using Real-ESRGAN (or Pillow fallback).
+        Input: PIL Image
+        Output: PIL Image (upscaled)
+        """
+        upscaler = self.get_upscaler()
+
+        if upscaler == "pillow_fallback":
+            # Simple Pillow resize as fallback
+            new_size = (image.width * scale, image.height * scale)
+            return image.resize(new_size, Image.LANCZOS)
+
+        # Real-ESRGAN
+        img_np = np.array(image)
+        # Real-ESRGAN expects BGR
+        import cv2
+        img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+
+        output, _ = upscaler.enhance(img_bgr, outscale=scale)
+        output_rgb = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+
+        return Image.fromarray(output_rgb)
+
+    # ── LoRA Loading ─────────────────────────────────────────────────────
+
+    def _load_lora(self, pipe):
+        """Load LoRA weights onto a pipeline."""
+        logger.info(f"Loading LoRA weights from {LORA_REPO}...")
+        try:
+            pipe.load_lora_weights(
+                LORA_REPO,
+                weight_name=LORA_FILENAME,
+            )
+            self._lora_loaded = True
+            logger.info("LoRA weights loaded successfully.")
+        except Exception as e:
+            logger.warning(f"Could not load LoRA weights: {e}")
+            logger.warning("Running with base Flux model only.")
+            self._lora_loaded = False
+
+    # ── Pipeline Switching ───────────────────────────────────────────────
+
+    def _unload_all(self):
+        """Unload all pipelines to free VRAM before loading a new one."""
+        logger.info(f"Unloading active pipeline (was: {self._active_mode})...")
+
+        self._text2img = None
+        self._img2img = None
+        self._inpaint = None
+        self._ip_adapter_loaded = False
+        self._active_mode = None
+
+        flush_vram()
+
+    @property
+    def is_lora_loaded(self) -> bool:
+        return self._lora_loaded
+
+    @property
+    def active_mode(self) -> Optional[str]:
+        return self._active_mode
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Singleton
+# ─────────────────────────────────────────────────────────────────────────────
+_manager: Optional[PipelineManager] = None
+
+
+def get_pipeline_manager() -> PipelineManager:
+    """Get or create the global pipeline manager singleton."""
+    global _manager
+    if _manager is None:
+        _manager = PipelineManager()
+    return _manager

deployment/prompt_engine.py

@@ -0,0 +1,267 @@
+#!/usr/bin/env python3
+"""
+CampusGen AI – Prompt Engine
+Uses Groq Llama 3.3 70B to transform simple event descriptions
+into detailed, high-quality image generation prompts.
+"""
+
+import os
+import logging
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Configuration
+# ─────────────────────────────────────────────────────────────────────────────
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "")
+GROQ_MODEL = "llama-3.3-70b-versatile"
+TRIGGER_WORD = "campus_ai_poster"
+
+# ─────────────────────────────────────────────────────────────────────────────
+# System Prompts (per mode)
+# ─────────────────────────────────────────────────────────────────────────────
+SYSTEM_TEXT2IMG = f"""You are a world-class poster design expert specializing in Indian college event posters. Given an event description, generate a detailed, cinematic image generation prompt.
+
+Your prompt MUST include:
+1. Composition & layout (center-stage focal point, text hierarchy areas, decorative borders)
+2. Color palette (specific hex-inspired descriptions, gradients, mood)
+3. Typography style (bold sans-serif, elegant serif, handwritten, neon glow)
+4. Background elements (abstract patterns, venue imagery, thematic textures)
+5. Lighting & atmosphere (dramatic spotlights, warm glow, neon reflections)
+6. Cultural/thematic motifs appropriate to the event
+
+RULES:
+- ALWAYS start with "{TRIGGER_WORD}"
+- Keep under 200 words
+- Be extremely specific about visual details
+- For Indian events, include culturally authentic motifs (rangoli, diyas, mehendi, etc.)
+- Describe the poster as a finished design, not a scene
+- Output ONLY the prompt, nothing else"""
+
+SYSTEM_IMG2IMG = f"""You are a poster restyling expert. Given a description of how the user wants to transform an existing poster, generate a detailed prompt describing the desired output.
+
+Focus on:
+1. The new visual style to apply
+2. Color palette changes
+3. Typography modifications
+4. Atmosphere and mood shifts
+5. Elements to preserve vs. change
+
+RULES:
+- ALWAYS start with "{TRIGGER_WORD}"
+- Keep under 150 words
+- Describe the desired RESULT, not the process
+- Output ONLY the prompt"""
+
+SYSTEM_INPAINT = f"""You are a poster editing expert. Given a description of what region the user wants to regenerate on a poster, generate a prompt describing what should fill that region.
+
+Focus on:
+1. What visual elements should appear in the masked area
+2. Style consistency with the surrounding poster
+3. Color and lighting continuity
+
+RULES:
+- ALWAYS start with "{TRIGGER_WORD}"
+- Keep under 100 words
+- Be specific about what fills the masked area
+- Output ONLY the prompt"""
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Style Descriptions
+# ─────────────────────────────────────────────────────────────────────────────
+STYLE_MAP = {
+    "Vibrant and Energetic": (
+        "vibrant energetic colors, electric gradients from magenta to cyan, "
+        "dynamic diagonal composition, bold sans-serif typography, "
+        "particle effects and light streaks"
+    ),
+    "Elegant and Professional": (
+        "elegant professional design, deep navy and gold color scheme, "
+        "clean serif typography, subtle gradient backgrounds, "
+        "refined geometric accents"
+    ),
+    "Modern Minimalist": (
+        "modern minimalist design, generous white space, "
+        "monochromatic palette with single accent color, "
+        "thin geometric lines, clean sans-serif typography"
+    ),
+    "Traditional Indian": (
+        "traditional Indian design, warm gold saffron and deep red palette, "
+        "ornate mandala borders, rangoli-inspired patterns, "
+        "decorative Devanagari-style typography, paisley motifs"
+    ),
+    "Tech-Futuristic": (
+        "futuristic cyberpunk tech design, dark background with neon glow, "
+        "holographic elements, circuit board patterns, "
+        "glitch text effects, electric blue and purple neon"
+    ),
+    "Artistic and Creative": (
+        "artistic watercolor splash design, fluid organic shapes, "
+        "hand-painted texture, eclectic mixed typography, "
+        "ink splatter accents, warm earthy tones"
+    ),
+    "Neon Glow": (
+        "neon glow poster design, deep black background, "
+        "vivid neon tubes in pink cyan and yellow, "
+        "reflective surfaces, urban night atmosphere, glow typography"
+    ),
+    "Retro Vintage": (
+        "retro vintage poster design, distressed paper texture, "
+        "muted warm color palette, bold block letters, "
+        "halftone dot patterns, 70s inspired graphics"
+    ),
+    "Dark Premium": (
+        "dark premium poster design, matte black with metallic gold accents, "
+        "luxury typography, subtle emboss effects, "
+        "dramatic lighting, high contrast minimal elements"
+    ),
+    "Gradient Modern": (
+        "modern gradient poster, smooth multi-color gradient backgrounds, "
+        "floating 3D geometric shapes, soft shadows, "
+        "rounded sans-serif typography, glass morphism effects"
+    ),
+}
+
+EVENT_TYPE_HINTS = {
+    "Technical Fest": "coding symbols, circuit patterns, robotic elements, binary code, tech logos",
+    "Cultural Event": "stage lights, dance silhouettes, musical instruments, spotlights, curtains",
+    "Sports Tournament": "dynamic action poses, sports equipment, stadium lights, motion blur, trophy",
+    "Workshop / Seminar": "whiteboard, notebooks, professional setting, light bulb icons, knowledge symbols",
+    "College Fest": "college campus backdrop, festive decorations, diverse crowd silhouettes, confetti",
+    "Diwali Celebration": "diyas, rangoli, fireworks, marigold garlands, Lord Ganesha motifs, sparklers",
+    "Holi Festival": "color powder splashes, water balloons, vibrant rainbow, pichkari, crowd celebration",
+    "Navratri / Garba": "dandiya sticks, ghagra choli silhouettes, Durga motifs, festive lights",
+    "Ganesh Chaturthi": "Lord Ganesha, modak, marigold, mandap, festive procession elements",
+    "Eid Celebration": "crescent moon and star, mosque silhouette, lanterns, arabesque patterns",
+    "Christmas / New Year": "Christmas tree, snowflakes, countdown clock, fireworks, candy canes",
+    "Club Recruitment": "diverse student silhouettes, creative tools, speech bubbles, join-us energy",
+    "Academic Event": "graduation cap, books, podium, academic shields, scholarly elements",
+    "Freshers / Farewell": "welcome banner, photo frames, nostalgic elements, stage performance",
+    "Blood Donation": "red cross, heart, blood drop, helping hands, medical symbols",
+    "Music Concert": "guitar, microphone, soundwaves, stage spotlights, crowd silhouettes",
+    "Food Festival": "food illustrations, chef hat, spice bowls, colorful plates, steam",
+    "Marathon / Fitness": "running silhouettes, finish line, stopwatch, sneakers, energy",
+    "Other": "professional event design, modern layout, eye-catching visual elements",
+}
+
+
+def _call_groq(system_prompt: str, user_message: str) -> Optional[str]:
+    """Make a Groq API call and return the response text."""
+    if not GROQ_API_KEY:
+        return None
+
+    try:
+        import requests
+
+        response = requests.post(
+            "https://api.groq.com/openai/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {GROQ_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            json={
+                "model": GROQ_MODEL,
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_message},
+                ],
+                "temperature": 0.8,
+                "max_tokens": 350,
+                "top_p": 0.9,
+            },
+            timeout=15,
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["choices"][0]["message"]["content"].strip()
+
+    except Exception as e:
+        logger.warning(f"Groq API error: {e}")
+        return None
+
+
+def _ensure_trigger(prompt: str) -> str:
+    """Ensure the trigger word is at the start of the prompt."""
+    if not prompt.lower().startswith(TRIGGER_WORD):
+        prompt = f"{TRIGGER_WORD} {prompt}"
+    return prompt
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Public API
+# ─────────────────────────────────────────────────────────────────────────────
+
+def build_text2img_prompt(
+    event_description: str,
+    event_type: str = "Other",
+    style: str = "Vibrant and Energetic",
+) -> str:
+    """Build a rich prompt for text-to-poster generation."""
+
+    style_desc = STYLE_MAP.get(style, STYLE_MAP["Vibrant and Energetic"])
+    event_hints = EVENT_TYPE_HINTS.get(event_type, EVENT_TYPE_HINTS["Other"])
+
+    user_msg = (
+        f"Create an image generation prompt for this event poster:\n"
+        f"Event: {event_description}\n"
+        f"Type: {event_type}\n"
+        f"Style: {style}\n"
+        f"Style hints: {style_desc}\n"
+        f"Thematic elements: {event_hints}\n"
+    )
+
+    result = _call_groq(SYSTEM_TEXT2IMG, user_msg)
+
+    if result:
+        return _ensure_trigger(result)
+
+    # Fallback without LLM
+    return _ensure_trigger(
+        f"A professional {event_type.lower()} event poster for {event_description}. "
+        f"{style_desc}. {event_hints}. "
+        f"High quality typography, well-organized layout, eye-catching design."
+    )
+
+
+def build_img2img_prompt(
+    transform_description: str,
+    style: str = "Vibrant and Energetic",
+) -> str:
+    """Build a prompt for img2img poster transformation."""
+
+    style_desc = STYLE_MAP.get(style, STYLE_MAP["Vibrant and Energetic"])
+
+    user_msg = (
+        f"Transform this poster with the following changes:\n"
+        f"Changes: {transform_description}\n"
+        f"New style: {style}\n"
+        f"Style hints: {style_desc}\n"
+    )
+
+    result = _call_groq(SYSTEM_IMG2IMG, user_msg)
+
+    if result:
+        return _ensure_trigger(result)
+
+    return _ensure_trigger(
+        f"A transformed poster: {transform_description}. "
+        f"{style_desc}. Professional quality, cohesive design."
+    )
+
+
+def build_inpaint_prompt(
+    fill_description: str,
+) -> str:
+    """Build a prompt for inpainting a region of a poster."""
+
+    user_msg = f"Fill the masked region with: {fill_description}"
+
+    result = _call_groq(SYSTEM_INPAINT, user_msg)
+
+    if result:
+        return _ensure_trigger(result)
+
+    return _ensure_trigger(
+        f"{fill_description}. Seamless blending, consistent style."
+    )

deployment/requirements.txt

@@ -0,0 +1,33 @@
+# Deployment Requirements – HF Space
+
+# ===== PyTorch (CUDA) =====
+--extra-index-url https://download.pytorch.org/whl/cu130
+torch
+torchvision
+
+# ===== Core Diffusion =====
+diffusers>=0.30.0
+transformers>=4.40.0
+accelerate
+safetensors
+peft
+
+# ===== IP-Adapter (reference image style) =====
+ip-adapter
+
+# ===== Upscaling =====
+realesrgan
+basicsr
+gfpgan
+
+# ===== Image Processing =====
+Pillow
+opencv-python-headless
+numpy
+
+# ===== Web UI =====
+gradio>=4.40.0
+spaces
+
+# ===== LLM API =====
+requests

docs/CAMPUS-AI-PROJECT-BRIEF.md

@@ -0,0 +1,316 @@
+# CAMPUS-AI: PROJECT BRIEF
+
+## Universal Event Poster Generator
+
+**Project**: CampusGen AI
+**Type**: AI-powered multi-modal event poster generation
+**Hardware**: Intel Ultra 9 275HX + RTX 5070 Ti (16GB VRAM)
+**Deployment**: Hugging Face Spaces (ZeroGPU — Free Tier)
+**Total Cost**: $0
+**Last Updated**: February 21, 2026
+
+---
+
+## EXECUTIVE SUMMARY
+
+CampusGen AI generates professional event posters for ANY occasion in 10–15 seconds using:
+
+- **Stable Diffusion XL 1.0 (2.6B params)** fine-tuned on **55,000+ diverse poster images** via LoRA
+- **5 Generation Modes**: Text→Poster, Reference Image, Image Transform, Inpainting, HD Upscale
+- **Llama 3.3 70B** (Groq) for intelligent prompt engineering
+- **Real-ESRGAN** for 4x HD upscaling
+- **IP-Adapter** for reference image style transfer
+- **GPU-accelerated pipeline** end-to-end
+
+---
+
+## WHY THIS WINS
+
+| Metric | CampusGen AI | Typical Projects |
+|--------|-------------|------------------|
+| Dataset | **55,000+ images, 55 categories** | 100-500 images, 1-2 categories |
+| Generation Modes | **5 modes** (text, reference, transform, inpaint, upscale) | 1 mode (text only) |
+| Training | LoRA on RTX 5070 Ti (bf16) | Quantized on Colab |
+| Intelligence | **LLM-powered** prompt engineering (10 styles, 19 event types) | Template-based |
+| Speed | 10-15 seconds/poster | 30-60+ seconds |
+| Upscaling | **Real-ESRGAN 4x** HD output | None |
+| Style Transfer | **IP-Adapter** reference image | None |
+| Cost | $0 (smart free tier) | $0-200 |
+| Deployment | Professional 5-tab HF Space | Local/unstable |
+
+---
+
+## TECHNOLOGY RATIONALE (Why These Models?)
+
+| Technology | Why We Chose It | What It Replaces |
+|------------|-----------------|------------------|
+| **SDXL 1.0 (2.6B)** | The gold standard open-source framework for local training. It perfectly fits within a 12GB VRAM envelope allowing for rapid bf16 fine-tuning without destructive memory swapping. | Midjourney V6 / DALL-E 3 (closed source, un-finetunable) |
+| **LoRA (Low-Rank Adaptation)** | Training a 2.6 Billion parameter model from scratch requires supercomputers. LoRA trains tiny adapter layers (**~80M parameters**) that sit on top of the frozen base model. This makes training possible in a few hours on a consumer RTX 5070 Ti (12GB) without catastrophic forgetting of the base model's knowledge. | Full Fine-Tuning (Requires multiple A100s, huge memory) |
+| **Florence-2-large** | Microsoft's highly efficient Vision-Language Model. Instead of running 3 different models, Florence-2 does **Detailed Visual Summaries + OCR (reading text) + Dense Region Capturing** all in one pass. Clean, rich captions are the secret to teaching the SDXL model what a "poster" is. | BLIP-2 / LLaVA (bulkier, less strict OCR formatting) |
+| **Llama 3.3 70B (via Groq)** | Users write lazy prompts like "a cybersec hackathon." We use Llama 3.3 to intercept that prompt and intelligently explode it into a highly detailed, cinematic description referencing our 10 trained visual styles and 19 event types. Running it through the Groq API makes this essentially instantaneous and free. | Hardcoded prompt templates (rigid, boring) |
+| **IP-Adapter** | It allows users to upload a reference image (e.g., a cool poster they found online) and injects that structural/stylistic "vibe" into the generation pipeline natively, without needing a secondary text prompt. | ControlNet (heavier, overkill for pure style transfer) |
+| **Real-ESRGAN** | A specialized upscaler neural network that reconstructs high-frequency details. Generating a 4K image directly in SDXL takes immense VRAM and time. It is faster to generate at 1024x1024 and run it through Real-ESRGAN to get a massive 4K HD output with perfectly crisp text in 2 seconds. | Bicubic interpolation (blurry, pixelated) |
+
+--------------------------------------------------------------------------
+
+## TRAINING SPECIFICATIONS
+
+### Model Architecture
+
+| Component | Specification |
+|-----------|---------------|
+| Base Model | Stable Diffusion XL 1.0 (2.6B parameters) — **FROZEN** |
+| Fine-tuning | LoRA (Low-Rank Adaptation) |
+| LoRA Rank | 32 |
+| LoRA Alpha | 16 |
+| LoRA Dropout | 0.05 |
+| **Trainable Parameters** | **~80 million** (0.6% of base model) |
+| Precision | bf16 (bfloat16) |
+| LoRA File Size | ~150-300 MB (.safetensors) |
+| Trigger Word | `campus_ai_poster` |
+
+### How LoRA Works
+
+```text
+Base model: SDXL 1.0 (2.6B params) → FROZEN, not modified
+                    ↓
+LoRA injects small adapter matrices into attention layers:
+  Original W (4096×4096) = 16M params  → FROZEN
+  LoRA: A (4096×32) + B (32×4096) = 262K params  → TRAINED
+                    ↓
+~250 attention layers × 262K = ~80M trainable params (3% of 2.6B)
+```
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Optimizer | AdamW 8-bit (`bitsandbytes`) |
+| Learning Rate | 1e-4 (Phase 1) → 2e-5 (Phase 2) → **1e-5 (Phase 3)** |
+| Batch Size | 1 |
+| Gradient Accumulation | 4 steps |
+| Effective Batch Size | 4 |
+| Max Steps | 4000 (P1) + 20000 (P2) + **6448 (P3)** |
+| Phase 3 Dataset | **6,448** highly curated typography & layout templates |
+| Checkpoint Interval | Every 500 steps |
+| Resolution | 1024×1024 |
+| Noise Scheduler | DDPM |
+| EMA Decay | 0.99 |
+| Gradient Checkpointing | Enabled |
+| Train UNet | Yes |
+| Train Text Encoder | No |
+| **Dependencies** | `bitsandbytes` (critical for 8-bit), `diffusers==0.32.1` (for `torchao` compat) |
+| Estimated Time | ~7.5 hours on RTX 5070 Ti |
+
+---
+
+## DATASET SPECIFICATIONS
+
+### Overview
+
+| Metric | Value |
+|--------|-------|
+| Raw images scraped | ~1900 per theme × 55 themes = **~104,500** |
+| After quality filter | ~1300 per theme = **~71,500** |
+| Train split | 1000 per theme = **55,000** |
+| Validation split | 200 per theme = **11,000** |
+| Test split | 100 per theme = **5,500** |
+
+### 55 Categories (Hierarchical)
+
+| Group | Subcategories |
+|-------|---------------|
+| **Tech Fest** | Hackathon, AI/ML, Robotics, Coding Competition, Cybersecurity, Web Dev, Startup, Data Science, IoT, Open Source, Game Dev |
+| **Cultural Fest** | Dance, Music, Drama, Art Exhibition, Poetry, Fashion Show, Photography |
+| **College Events** | Annual Day, Freshers Party, Farewell, Alumni Meet, Orientation, Graduation |
+| **Sports** | Cricket, Football, Basketball, Athletics, Chess, Badminton, Volleyball |
+| **Festivals** | Diwali, Holi, Navratri/Garba, Ganesh Chaturthi, Eid, Christmas, Onam, Pongal |
+| **Workshops** | Technical Seminar, Business Workshop, Creative Workshop, Leadership, Research |
+| **Social** | Blood Donation, Charity, Environmental, Awareness Campaign, NSS/NCC |
+| **Entertainment** | DJ Night, Concert, Standup Comedy, Movie Screening, Open Mic |
+
+### Quality Filtering (GPU-Accelerated)
+
+| Check | Threshold | Method |
+|-------|-----------|--------|
+| Resolution | ≥512px shortest side | CPU |
+| Sharpness | Laplacian variance ≥50 | **GPU** (PyTorch conv2d) |
+| Aspect Ratio | 0.4–2.5 | CPU |
+| File Size | 20KB–50MB | CPU |
+| Color Variance | std ≥15 | **GPU** (torch.std) |
+| Deduplication | pHash distance ≤5 | CPU |
+
+### Captioning
+
+| Component | Detail |
+|-----------|--------|
+| Model | Florence-2-large (microsoft) |
+| Device | **GPU** (float16) |
+| Captions | `campus_ai_poster` trigger + category prefix + Florence-2 description |
+| Output | Image + `.txt` pairs in `data/final/` |
+
+---
+
+## DEPLOYMENT APP — 5-Tab Architecture
+
+### Files
+
+| File | Purpose |
+|------|---------|
+| `app.py` | 5-tab Gradio UI (~500 lines) |
+| `pipelines.py` | Pipeline manager — lazy loads SDXL/IP-Adapter/ESRGAN (~230 lines) |
+| `prompt_engine.py` | Groq LLM with 10 styles, 19 event types (~250 lines) |
+| `requirements.txt` | HF Space dependencies |
+| `README.md` | HF Space card |
+
+### 5 Generation Modes
+
+| Tab | What It Does | Key Tech |
+|-----|-------------|----------|
+| ✍️ Text → Poster | Describe event → get poster(s) | SDXL + LoRA + Groq LLM |
+| 🖼️ Reference Image | Upload a poster → copy its style | IP-Adapter |
+| 🔄 Image Transform | Upload → restyle existing poster | Img2Img pipeline |
+| 🖌️ Inpaint / Edit | Draw mask → regenerate region | Inpainting pipeline |
+| 🔍 HD Upscale | 2x/4x upscale any image | Real-ESRGAN |
+
+### Shared Features
+
+- 7 resolution presets (768×1152, 1024×1024, etc.)
+- 10 visual styles
+- Batch generation (1-4 variants)
+- Seed control
+- LoRA strength slider
+- Generation metadata display
+
+### VRAM Management
+
+- Only ONE pipeline active at a time (text2img OR img2img OR inpaint)
+- Model CPU offloading for 16GB GPU / HF ZeroGPU
+- IP-Adapter loads as lightweight adapter (~300MB) on top of base model
+- Real-ESRGAN uses tiled processing (512px tiles) for memory efficiency
+
+---
+
+## GPU PIPELINE SUMMARY
+
+| Step | Device | Time |
+|------|--------|------|
+| Scraping (Pinterest) | 🖥️ CPU (network-bound) | ~6-12h |
+| Quality Filter | 🎮 GPU (Laplacian + color) | ~5 min |
+| Captioning (Florence-2) | 🎮 GPU (float16) | ~6-12h |
+| Dataset Split | 🖥️ CPU (file copy) | ~1 min |
+| LoRA Training | 🎮 GPU (bf16) | ~7.5h |
+| Upload to HF | 🖥️ CPU | ~5 min |
+| Live Demo | ☁️ Cloud GPU (ZeroGPU) | Real-time |
+
+---
+
+## EXECUTION PIPELINE
+
+```bash
+# Phase 1: Data Collection
+python scripts/pinterest_scraper.py          # 🖥️ CPU — overnight
+
+# Phase 2: Data Processing
+python scripts/quality_filter.py             # 🎮 GPU — ~5 min
+python scripts/caption_generator.py          # 🎮 GPU — overnight
+python scripts/split_dataset.py              # 🖥️ CPU — ~1 min
+
+# Phase 3: Training (Dual-Phase)
+python scripts/create_training_config.py     # 🖥️ CPU — Setup
+python ai-toolkit/run.py configs/train_sdxl_lora.yaml  # 🎮 GPU — Phase 1 (3h)
+python ai-toolkit/run.py configs/train_sdxl_lora_phase2.yaml  # 🎮 GPU — Phase 2 (4.5h)
+
+# Phase 4: Deploy
+huggingface-cli upload YOUR_USERNAME/campus-ai-poster-sdxl models/sdxl/checkpoints/campus_ai_poster_sdxl/ .
+# Push deployment/ to HF Space
+```
+
+---
+
+## FILE STRUCTURE
+
+```text
+campus-ai/
+├── .gitignore                       # Explicitly ignores data/ & models/ for GitHub push
+├── configs/
+│   ├── config.yaml                  # Master configuration (w/ hf_token)
+│   ├── train_sdxl_lora.yaml         # ai-toolkit Phase 1 generator
+│   └── train_sdxl_lora_phase3.yaml  # Phase 3 implicit layout tuner
+├── scripts/
+│   ├── pinterest_scraper.py         # Image scraper (1900/theme)
+│   ├── quality_filter.py            # GPU-accelerated quality filter
+│   ├── caption_generator.py         # Florence-2 GPU captioning
+│   ├── split_dataset.py             # Fixed 1000/200/100 split
+│   ├── test_checkpoint.py           # LoRA inference testing
+│   └── create_training_config.py    # ai-toolkit config generator
+├── deployment/
+│   ├── app.py                       # 5-tab Gradio app
+│   ├── pipelines.py                 # Pipeline manager
+│   ├── prompt_engine.py             # Groq LLM prompt engine
+│   ├── requirements.txt             # HF Space dependencies
+│   └── README.md                    # HF Space card
+├── data/
+│   ├── raw/                         # ~104K scraped images
+│   ├── processed/                   # ~71K quality-filtered
+│   ├── final/                       # Captioned pairs
+│   ├── train/                       # 55K (1000/theme)
+│   ├── val/                         # 11K (200/theme)
+│   └── test/                        # 5.5K (100/theme)
+├── models/sdxl/checkpoints/         # Trained LoRA weights
+├── docs/
+│   ├── CAMPUS-AI-PROJECT-BRIEF.md   # This file
+│   ├── README.md                    # Project overview
+│   ├── SETUP.md                     # Setup guide
+│   └── PIPELINE.md                  # Execution pipeline
+└── requirements.txt                 # Local dependencies
+```
+
+---
+
+## COMPETITION STRATEGY
+
+### What Judges Will See
+
+1. **Live 5-tab demo** on Hugging Face (not just slides)
+2. **55,000+ image dataset** (10-100x larger than competitors)
+3. **5 generation modes** (competitors have 1)
+4. **GPU-accelerated pipeline** (professional engineering)
+5. **$0 deployment** (smart architecture)
+
+### Key Talking Points
+
+- "Trained on 55,000+ event posters across 55 categories — 10x larger than typical projects"
+- "5 generation modes: text, reference image, transform, inpaint, upscale"
+- "80 million trainable parameters via LoRA on 2.6 billion parameter SDXL model"
+- "GPU-accelerated pipeline: quality filter, captioning, and training all on GPU"
+- "Zero cost — entire project runs on free tier services"
+
+### Tough Questions
+
+**Q: "Only 80M params? That seems small."**
+A: "That's the power of LoRA — we get the quality of a 2.6B model while only training 80M adapter parameters. The base model already knows how to generate images; our LoRA teaches it our specific poster style. Bigger ≠ better — efficiency is the innovation."
+
+**Q: "How is this different from MidJourney?"**
+A: "MidJourney is generic. Ours is specialized — trained on 55,000 Indian event posters. It understands rangoli patterns, tech fest aesthetics, and college event culture. Plus, 5 generation modes including reference image style transfer and inpainting."
+
+**Q: "Can judges try it live?"**
+A: "Absolutely — here's the HF Space link. Pick any event, any style. Generate in 15 seconds."
+
+---
+
+## SUCCESS METRICS
+
+| Metric | Target | Status |
+|--------|--------|--------|
+| Dataset | 55K+ captioned images | ✅ Complete |
+| Training | Loss <0.10, coherent samples | ⏳ Pending |
+| Generation | <20 seconds, professional quality | ⏳ Pending |
+| Deployment | Live 5-tab HF Space | ⏳ Pending |
+| Demo | All 5 tabs working flawlessly | ⏳ Pending |
+
+---
+
+**Version**: 4.1
+**Last Updated**: February 22, 2026
+**Status**: Dataset captioned ✅ → Training LoRA on RTX 5070 Ti 🔄

docs/NOVELTY.md

@@ -0,0 +1,225 @@
+# Campus-AI — Novelty & Unique Value Proposition
+
+**by CounciL**
+
+---
+
+## One-Liner
+
+> *Campus-AI is the first domain-specific diffusion model fine-tuned on 71,000+ Indian campus event posters across 57 cultural subcategories, combining state-of-the-art LoRA+ training (ICML 2024) with an intelligent prompt engine to generate culturally-aware event posters accessible on consumer hardware.*
+
+---
+
+## 1. Novel Dataset (First of Its Kind)
+
+No public dataset exists for Indian campus event posters. Campus-AI constructs one from scratch:
+
+- **71,000+ curated base images** expanding dynamically to ~130,000+ total scraped from Pinterest via distributed Selenium workers.
+- **57 distinct micro-subcategories** encompassing granular Indian culture (e.g., *Navratri Garba*, *Pongal*, *Hackathon UI*).
+- **O(1) Global Perceptual Hash (PHash) Caching:**
+  - Standard scrapers download blind duplicates. We engineer an **SQLite-backed PHash cache** that computes a 64-bit fingerprint of every image.
+  - As scrapers run across 57 categories, they achieve **O(1) time complexity** deduplication lookups against a living 130k+ database.
+  - Zero cross-contamination: Guarantees absolute mathematical uniqueness of every new image entering the pipeline.
+- **Strict Tuning Data Isolation (Phase 3 Strictness):**
+  - Fine-tuning requires flawless data. We built a recursive Selenium scraper that dynamically fetches deeper DOM loads until it achieves **exactly 100 mathematically unique images** per tuning subcategory. Any overlap with the base 130k database triggers an immediate rejection.
+- **GPU-Accelerated Real-Time Quality Filtering:**
+  - Evaluates Laplacian variance (sharpness), color histograms, and native resolution. Drops blurry or irrelevant data before it even hits the disk.
+- **Florence-2 VLM Multi-Modal Captioning:**
+  - Utilizes Microsoft's State-of-the-Art Vision-Language Model (`microsoft/Florence-2-large`) initialized in `bfloat16` to generate dense, composition-aware captions (e.g., detailing typography placement and lighting).
+
+| Category | Subcategories | Examples |
+|----------|:---:|---------|
+| Festivals | 11 | Diwali, Holi, Durga Puja, Eid, Navratri, Onam, Pongal |
+| Cultural Fest | 8 | Dance, Music, Drama, Fashion Show, Stand-up Comedy |
+| Sports | 9 | Cricket, Kabaddi/Kho, Football, Esports, Yoga |
+| Tech Fest | 7 | Hackathon, AI/ML, Cybersecurity, Robotics |
+| Workshops | 7 | Placement, Coding, Design, Business, Seminar |
+| College Events | 6 | Fresher's, Farewell, Annual Fest, Graduation |
+| Social | 4 | Blood Donation, Awareness, Charity, Environment |
+| Entertainment | 3 | Food Fest, Gaming, Movie Night |
+| Styles | 2 | Minimalist, Neon Glow |
+
+*This dataset alone is a publishable contribution to the research community.*
+
+---
+
+## 2. Novel Application Domain
+
+No existing AI model — commercial or open-source — is specifically trained for Indian campus event posters. Generic models (Midjourney, DALL-E, Stable Diffusion) lack training data on:
+
+- Indian festival visual language (rangoli, diyas, kolam, torans)
+- Campus-specific poster conventions (event dates, venue formats, college branding)
+- Regional cultural diversity (North vs. South vs. East Indian aesthetics)
+
+Campus-AI is the **first domain-specific solution** for this underserved market of 40,000+ Indian colleges and universities.
+
+---
+
+## 3. End-to-End Pipeline Engineering
+
+Most AI projects use pre-existing datasets. Campus-AI builds the **full ML pipeline from scratch**:
+
+```
+Pinterest Scraper → Quality Filter → Florence-2 Captioner → Dataset Splitter
+       → LoRA Training (SDXL 1.0) → Gradio Deployment
+```
+
+Each stage is purpose-built:
+
+| Stage | Technology | Key Innovation |
+|-------|-----------|---------------|
+| Scraping | Headless Selenium + SQLite PHash Caching | **Algorithmic Crawling:** Defeats anti-bot measures while executing O(1) mathematical deduplication against a 130k+ local SQLite cache to prevent data overlap. |
+| Filtering | GPU-accelerated Laplacian | Real-time sharpness + color analysis |
+| Captioning | Microsoft Florence-2-Large (bf16 + torch.compile) | **VLM Pipeline:** 300% faster batch inference via SM120 hardware optimizations; produces dense compositional data rather than standard tags. |
+| Training | Custom ai-toolkit branch via LoRA+ | **Curriculum Learning:** 2-phase training isolating macro-layout in Phase 1, and micro-aesthetic refinement in Phase 2. |
+| Deployment | Gradio + ZeroGPU | Free-tier cloud with local fallback |
+
+---
+
+## 4. State-of-the-Art Training Algorithm Stack
+
+Campus-AI combines **five cutting-edge techniques**, each from recent research, into one optimized training pipeline:
+
+No existing LoRA trainer combines all five. The synergy between self-adapting LR (Prodigy), balanced loss (Min-SNR-γ), and periodic restarts is a **novel training configuration**.
+
+| Technique | Source | Year | What It Does |
+|-----------|--------|:---:|-------------|
+| **Dual-Phase Curriculum** | Fine-to-Coarse ML theory | 2024 practice | Phase 1 (1e-4) learns macro layout; Phase 2 (2e-5) refines micro details without catastrophic forgetting |
+| **LoRA+** | ICML paper | 2024 | 16× higher LR for B matrix → +2% accuracy, 2× faster convergence, zero extra cost |
+| **Prodigy Optimizer** | Community best practice | 2024 | Self-adapting learning rate — eliminates manual LR tuning across 57 diverse categories |
+| **Min-SNR-γ Loss** | "Efficient Diffusion Training" | 2023 | Balanced learning across all noise levels → prevents memorization, improves generalization |
+| **Cosine Scheduler** | Standard Practice | 2024 practice | Smooth LR decay with no restarts for stable high-frequency detail learning in Phase 2 |
+| **SM120 Blackwell Optimizations** | Hardware-specific | 2025 | TF32 tensor cores, torch.compile max-autotune, bf16 native precision |
+
+No existing LoRA trainer combines all five. The synergy between self-adapting LR (Prodigy), balanced loss (Min-SNR-γ), and periodic restarts is a **novel training configuration**.
+
+---
+
+## 5. Intelligent Prompt Engineering
+
+Campus-AI uses **Groq Llama 3.3 70B** (~1,200-1,500 tokens/sec) to transform simple user input into detailed, SDXL-optimized prompts:
+
+```
+User:     "tech fest poster for IIT"
+Llama 3.3: "A vibrant, high-energy technology festival poster for an IIT campus,
+            featuring circuit board patterns, holographic UI elements, neon blue
+            and electric purple gradients, bold modern typography reading 'TECH FEST
+            2026', robotic arms and AI neural network visualizations, dark background
+            with glowing particle effects, professional event poster layout"
+```
+
+This eliminates the **prompt engineering barrier** — users don't need to learn SDXL's prompt syntax.
+
+---
+
+## 6. Multi-Modal Generation (4-in-1)
+
+Most poster AIs offer only text-to-image. Campus-AI offers four generation modes:
+
+| Mode | Technology | Use Case |
+|------|-----------|----------|
+| **Text → Poster** | StableDiffusionXLPipeline | Generate from description alone |
+| **Reference Image** | IP-Adapter | Copy style from uploaded poster |
+| **Image → Image** | StableDiffusionXLImg2ImgPipeline | Transform/restyle existing designs |
+| **Inpainting** | StableDiffusionXLInpaintPipeline | Edit specific regions of a poster |
+| **Dynamic Typography** | Smart Zone Detection + PIL | 100% native integration of text without black boxes or clipping |
+
+Plus **Real-ESRGAN 2× upscaling** for HD output.
+
+---
+
+## 7. Accessible by Design
+
+| Metric | Campus-AI | Midjourney | DALL-E 3 | Canva AI |
+|--------|-----------|------------|----------|----------|
+| **Cost** | Free | $10-60/mo | $20/mo | $13/mo |
+| **GPU required** | 12GB consumer | Cloud (their servers) | Cloud | N/A |
+| **Privacy** | Your data stays local | Uploaded to their servers | Uploaded | Uploaded |
+| **Open source** | ✅ Full pipeline | ❌ Proprietary | ❌ Proprietary | ❌ Proprietary |
+| **Customizable** | ✅ Retrain on your data | ❌ | ❌ | ❌ |
+
+---
+
+## 8. Performance Metrics
+
+### Prompt Engine (Groq Llama 3.3 70B)
+
+| Metric | Value |
+|--------|-------|
+| Inference speed | ~1,200-1,500 tokens/sec |
+| Output per prompt | ~150-200 tokens |
+| End-to-end latency | ~150-200ms |
+
+### Image Generation (SDXL 1.0 + LoRA)
+
+| Metric | Local (12GB VRAM) | Cloud (A100) |
+|--------|-------------------|-------------|
+| Steps/sec | ~0.5-1.0 it/s | ~3-5 it/s |
+| Time per image (28 steps) | ~30-60 sec | ~6-10 sec |
+| Resolution | Up to 1152×768 | Up to 1152×768 |
+
+### Data Pipeline
+
+| Stage | Speed |
+|-------|-------|
+| Quality filtering | ~50-100 images/sec (GPU) |
+| Florence-2 captioning | ~3-5 images/sec (bf16 + torch.compile) |
+| Real-ESRGAN upscaling | ~5 sec per image |
+
+---
+
+## 9. Planned Post-Training Evaluation (Quantitative Novelty)
+
+### 9a. FID & CLIP Score Comparison
+
+| Comparison | What It Proves |
+|-----------|---------------|
+| Base SDXL vs. Campus-AI on Indian prompts | Fine-tuning significantly improves domain-specific quality |
+| Campus-AI vs. generic SDXL on Indian prompts | LoRA fine-tuning outperforms base model on domain tasks |
+
+> Lower FID = more realistic images. Higher CLIP score = better prompt adherence.
+
+### 9b. User Study (Blind Evaluation)
+
+Planned study with 20-30 students rating posters blindly:
+
+| Source | Criteria |
+|--------|----------|
+| Campus-AI | Cultural relevance, visual quality, poster layout |
+| Midjourney | Same prompts, same criteria |
+| Canva templates | Same event type |
+
+> If Campus-AI wins on "cultural relevance" — that's publishable hard evidence.
+
+### 9c. Ablation Study
+
+Remove each technique individually to prove contribution:
+
+| Experiment | Expected Result |
+|-----------|----------------|
+| Without Min-SNR-γ | Worse on high-noise timesteps, inconsistent quality |
+| Without caption dropout | Overfitting — struggles with novel prompts |
+| Without LoRA+ | Slower convergence (~2× more steps needed) |
+| Without cosine restarts | Stuck in local minima — less diversity |
+| Without Prodigy | Wrong LR hurts some categories |
+
+> This proves each component is necessary, not arbitrary.
+
+---
+
+## Technical Differentiation Summary
+
+| Aspect | Generic AI | Campus-AI |
+|--------|-----------|-----------|
+| Indian cultural awareness | ❌ Western-biased | ✅ 57 Indian subcategories |
+| Campus event context | ❌ No training data | ✅ 71K+ curated posters |
+| Prompt intelligence | ❌ Manual prompt craft | ✅ Llama 3.3 auto-enhances |
+| Generation modes | Text-to-image only | 4 modes + upscaling |
+| Cost | $10-60/month | Free |
+| Data pipeline | Pre-existing datasets | Custom scrape-to-deploy |
+| Training techniques | Unknown/proprietary | SOTA open research (LoRA+, Min-SNR-γ) |
+| Reproducibility | ❌ Closed source | ✅ Fully reproducible |
+
+---
+
+*Campus-AI by Council Strategic Solutions — Built for the Indian campus community*

docs/PIPELINE.md

@@ -0,0 +1,179 @@
+# CampusGen AI — Full Execution Pipeline
+
+> Step-by-step guide from raw data to live hackathon demo.
+
+---
+
+## Phase 1: Data Collection (Raw Ingestion) 🖥️ CPU
+
+⏱️ **Runtime Strategy:** ~6-12 hours across distributed local instances (Run Async)
+⚙️ **Hardware Requirement:** standard CPU, high bandwidth connection, 500GB+ NVMe SSD recommended.
+
+```bash
+cd e:\campus-ai
+python scripts/pinterest_scraper.py
+```
+
+- Downloads **1900 images per theme** across 55 categories
+- Saves to `data/raw/` with hierarchical folders (`tech_fest/hackathon/`, etc.)
+- **Global Deduplication:** Uses a custom `GlobalImageDeduplicator` employing Perceptual Hashing (PHash) and a high-performance SQLite caching layer (`data/phash_cache.db`). Scans ~130,000+ existing images instantly to ensure zero duplicates across the entire corpus.
+- Skips already-downloaded images safely — safe to restart
+
+---
+
+## Phase 1.5: Tuning Dataset Collection 🕸️ CPU
+
+⏱️ ~1-2 hours (Targeted run)
+
+```bash
+cd e:\campus-ai
+python scripts/pinterest_tuning_scraper.py
+```
+
+- **Strict Enforcement Engine:** Uses a heavily modified Selenium scraper that recursively scrolls and cycles through search queries until it achieves strictly **100 unique images** per 55 specific subcategories.
+- **Data Isolation:** Saves uniquely to `data/tuning/<category>/<subcategory>/`.
+- **Absolute Uniqueness:** Pipes newly scraped images through the identical `GlobalImageDeduplicator` cache, guaranteeing these 5,500 tuning images have absolutely zero overlap with the 100k+ images in the main `data/raw`, `data/train`, or `data/val` datasets.
+
+---
+
+## Phase 2: Data Processing & Quality Assurance
+
+### 2a. Quality Filter 🎮 GPU (~5 min)
+
+⚙️ **Algorithm:** Offloads Canny Edge / Laplacian Variance calculations to CUDA to rapidly sweep 130k+ images for optimal sharpness and color contrast.
+
+```bash
+python scripts/quality_filter.py
+```
+
+Removes blurry, low-res, duplicate images → saves to `data/processed/`
+
+### 2b. Caption Generation 🎮 GPU (~6-12 hours)
+
+⚙️ **Model Architecture:** Microsoft `Florence-2-large` via HuggingFace `transformers`.
+⚙️ **Hardware Target:** RTX 4070 Ti / 5070 Ti (Float16 precision, ~12GB VRAM allocation).
+
+```bash
+python scripts/caption_generator.py
+```
+
+- Transforms pixel data into rich spatial text (e.g., "Bold sans-serif typography on the top left, neon cyber-punk background, dates on bottom right"). Saves `.txt` pairs to `data/final/`. These pairs are critical for SDXL cross-attention during LoRA tuning.
+
+### 2c. Dataset Split 🖥️ CPU (~1 min)
+
+⚙️ **Logic:** Deterministic pseudo-random seed to guarantee identical splits across team machines.
+
+```bash
+python scripts/split_dataset.py
+```
+
+Splits into **1000 train / 200 val / 100 test** per theme → `data/train/`, `data/val/`, `data/test/`
+
+---
+
+## Phase 3: Fine-Tune LoRA 🎮 GPU (~7-8 hours total)
+
+**Core Training Engine:** `ai-toolkit` featuring LoRA+ optimization. Employs a dual-phase curriculum to circumvent catastrophic forgetting while molding the SDXL 1.0 architecture.
+
+### 3a. Phase 1: Layout Pass (~3 hours)
+
+- **Objective:** Teaches the model the macro-composition, layout, and lighting of the 55 event categories.
+- **Data Source:** Exclusively uses `data/train/` (to preserve validation sets for Phase 2).
+
+```bash
+# 1. Generate optimal JSON layout training config
+python scripts/create_training_config.py
+
+# 2. Train Layout Pass (Learning Rate: 1e-4)
+python ai-toolkit/run.py configs/train_sdxl_lora.yaml
+```
+
+Output: `models/sdxl/checkpoints/campus_ai_poster_sdxl/campus_ai_poster_sdxl.safetensors`
+
+### 3b. Phase 2: Perfection Pass (~4.5 hours)
+
+- **Objective:** Bakes in micro-details, sharp Indian cultural textures (e.g., diwali lamps, specific fonts), and perfect aesthetic adherence.
+- **Mechanics:** Resumes gracefully from the Phase 1 `.safetensors` weights. Drops learning rate sequentially (2e-5) while utilizing the full 100% data blend (`train`, `val`, `test`).
+
+```bash
+# Train Perfection Pass (Internal Checkpoint Resume)
+python ai-toolkit/run.py configs/train_sdxl_lora_phase2.yaml
+```
+
+Output: Overwrites the `.safetensors` with the high-fidelity weights.
+
+---
+
+## Phase 4: Upload to Hugging Face 🖥️ CPU
+
+### 4a. Install & Login
+
+```bash
+pip install huggingface-hub[cli]
+huggingface-cli login
+# Paste your token from https://huggingface.co/settings/tokens
+```
+
+### 4b. Upload LoRA Weights
+
+```bash
+huggingface-cli upload YOUR_USERNAME/campus-ai-poster-sdxl models/sdxl/checkpoints/campus_ai_poster_sdxl/ .
+```
+
+### 4c. Create & Deploy HF Space
+
+```bash
+cd deployment
+git init
+huggingface-cli repo create campus-ai-poster-generator --type space --space-sdk gradio
+git remote add space https://huggingface.co/spaces/YOUR_USERNAME/campus-ai-poster-generator
+git add app.py pipelines.py prompt_engine.py requirements.txt README.md
+git commit -m "Deploy CampusGen AI"
+git push space main
+```
+
+### 4d. Add Secrets (on HF website)
+
+Go to **Space Settings → Variables and Secrets** and add:
+
+| Secret Name    | Value                |
+|---------------|----------------------|
+| `HF_USERNAME`  | your HF username     |
+| `GROQ_API_KEY` | your Groq API key    |
+
+---
+
+## Phase 5: Test Live ☁️ Cloud GPU
+
+Open `https://huggingface.co/spaces/YOUR_USERNAME/campus-ai-poster-generator` and test all 5 tabs.
+
+---
+
+## HF Free vs Pro
+
+| Feature | Free | Pro ($9/mo) |
+|---------|------|-------------|
+| ZeroGPU (shared A100) | ✅ Low priority | ✅ High priority |
+| Private Spaces | ❌ | ✅ |
+| Persistent Storage | ❌ | ✅ |
+| Cold start | Slower | Faster |
+
+**Verdict: Free tier works for a hackathon demo.** Upgrade to Pro only if the queue is too slow during judging.
+
+---
+
+## Quick Reference
+
+```
+pinterest_scraper.py  →  data/raw/          (1900 images/theme)
+pinterest_tuning_scraper.py → data/tuning/  (Strictly 100 entirely unique images/theme)
+image_deduplicator.py →  data/phash_cache.db (O(1) lookups via SQLite PHash)
+quality_filter.py     →  data/processed/    (~1300 quality-passed/theme)
+caption_generator.py  →  data/final/        (image + caption pairs)
+split_dataset.py      →  data/train/val/test/ (1000/200/100)
+create_training_config.py → configs/train_sdxl_lora.yaml
+ai-toolkit/run.py     →  configs/train_sdxl_lora.yaml (Phase 1 Layout)
+ai-toolkit/run.py     →  configs/train_sdxl_lora_phase2.yaml (Phase 2 Detail)
+test_checkpoint.py    →  poster_compositor.py (SDXL Art + PIL Typography)
+deployment/app.py     →  HF Space           (live demo for judges)
+```

docs/README.md

@@ -0,0 +1,122 @@
+# 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
+
+```text
+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
+
+```text
+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
+
+```bash
+# 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](SETUP.md) for detailed instructions. See [PIPELINE.md](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

docs/SETUP.md

@@ -0,0 +1,198 @@
+# CampusGen AI – Setup Guide
+
+## Prerequisites
+
+- **OS**: Windows 10/11 or Ubuntu 22.04+
+- **Python**: 3.11+
+- **GPU**: NVIDIA GPU with 12GB+ VRAM (RTX 5070 Ti used for development)
+- **CUDA**: 12.1+ with matching drivers
+- **Disk**: 100GB+ free space
+- **Chrome**: Latest version (for Pinterest scraping)
+
+## 1. Environment Setup
+
+```bash
+# Create conda environment
+conda create -n campus-ai python=3.11 -y
+conda activate campus-ai
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Verify GPU
+python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')"
+```
+
+## 2. Configuration
+
+Edit `configs/config.yaml`:
+
+```yaml
+project:
+  creator: "YOUR_NAME"      # ← Change this
+
+deployment:
+  hf_username: "YOUR_HF_USERNAME"   # ← Change this
+```
+
+### API Keys
+
+| Service | Where to Get | Config Key |
+|---------|-------------|------------|
+| Kaggle | kaggle.com/settings | `api_keys.kaggle` |
+| Unsplash | unsplash.com/developers | `api_keys.unsplash` |
+| Pexels | pexels.com/api | `api_keys.pexels` |
+| Groq | console.groq.com | Environment: `GROQ_API_KEY` |
+| HuggingFace | huggingface.co/settings/tokens | CLI: `huggingface-cli login` |
+
+## 3. Data Pipeline
+
+### Step 1: Scrape Images 🖥️ CPU (~6-12 hours)
+
+```bash
+python scripts/pinterest_scraper.py
+# Or scrape a single category:
+python scripts/pinterest_scraper.py
+# Or scrape a single category:
+python scripts/pinterest_scraper.py --category tech_fest
+# Or targeted top-up for specific counts:
+python scripts/pinterest_scraper.py --category workshops/coding --target 2800
+```
+
+**Output**: `data/raw/{category}/{subcategory}/` with ~1900 images per theme
+
+### Step 2: Quality Filter 🎮 GPU (~5 min)
+
+```bash
+python scripts/quality_filter.py
+```
+
+Uses GPU-accelerated sharpness detection (Laplacian via PyTorch CUDA) and color analysis. Auto-detects GPU, falls back to CPU.
+
+**Output**: `data/processed/{category}/` with ~1300+ high-quality images per theme
+
+### Step 3: Caption Generation 🎮 GPU (~6-12 hours)
+
+```bash
+python scripts/caption_generator.py
+```
+
+Florence-2 runs in float16 on GPU. Includes `campus_ai_poster` trigger word and category-aware prefixes.
+
+**Output**: `data/final/{category}/` with image + `.txt` caption pairs + `metadata.json`
+
+### Step 4: Dataset Split 🖥️ CPU (~1 min)
+
+```bash
+python scripts/split_dataset.py
+```
+
+Fixed counts: **1000 train / 200 val / 100 test** per theme.
+
+**Output**: `data/train/`, `data/val/`, `data/test/`
+
+## 4. Training 🎮 GPU (~7.5 hours total)
+
+### Install ai-toolkit
+
+```bash
+git clone https://github.com/ostris/ai-toolkit.git
+cd ai-toolkit
+pip install -e .
+cd ..
+```
+
+### Phase 1: Layout Pass (~3 hours)
+
+Generates the initial configuration and trains block-in composition.
+
+```bash
+python scripts/create_training_config.py
+# Outputs: configs/train_sdxl_lora.yaml
+
+cd ai-toolkit
+set HF_TOKEN=your_token_here
+python run.py ../configs/train_sdxl_lora.yaml
+cd ..
+```
+
+### Phase 2: Perfection Pass (~4.5 hours)
+
+Uses the static `configs/train_sdxl_lora_phase2.yaml` (0.1 dropout, 2e-5 LR) to refine micro-details across the entire dataset (train/val/test).
+
+```bash
+cd ai-toolkit
+set HF_TOKEN=your_token_here
+python run.py ../configs/train_sdxl_lora_phase2.yaml
+cd ..
+```
+
+### Monitor
+
+```bash
+# In a separate terminal
+nvidia-smi -l 30
+
+# TensorBoard
+tensorboard --logdir logs/tensorboard
+```
+
+### Test Checkpoints
+
+```bash
+python scripts/test_checkpoint.py
+```
+
+## 5. Deployment 🖥️ CPU → ☁️ Cloud
+
+### Upload LoRA to Hugging Face
+
+```bash
+huggingface-cli login
+huggingface-cli upload YOUR_USERNAME/campus-ai-poster-sdxl models/sdxl/checkpoints/campus_ai_poster_sdxl/ .
+```
+
+### Create & Deploy HF Space
+
+```bash
+cd deployment
+git init
+huggingface-cli repo create campus-ai-poster-generator --type space --space-sdk gradio
+git remote add space https://huggingface.co/spaces/YOUR_USERNAME/campus-ai-poster-generator
+git add app.py pipelines.py prompt_engine.py requirements.txt README.md
+git commit -m "Deploy CampusGen AI"
+git push space main
+```
+
+### Configure Secrets
+
+In Space Settings → Variables and Secrets:
+
+| Secret Name | Value |
+|-------------|-------|
+| `HF_USERNAME` | your HF username |
+| `GROQ_API_KEY` | your Groq API key |
+
+## GPU Usage Summary
+
+| Step | Device | Time |
+|------|--------|------|
+| Scraping | 🖥️ CPU | ~6-12h (network-bound) |
+| Quality Filter | 🎮 GPU | ~5 min |
+| Captioning | 🎮 GPU | ~6-12h |
+| Split | 🖥️ CPU | ~1 min |
+| Training (Phase 1) | 🎮 GPU | ~3h |
+| Training (Phase 2) | 🎮 GPU | ~4.5h |
+| Upload | 🖥️ CPU | ~5 min |
+| Live Demo | ☁️ Cloud GPU | HF ZeroGPU |
+
+## Troubleshooting
+
+| Issue | Solution |
+|-------|----------|
+| CUDA OOM during training | Set `batch_size: 1` and `gradient_accumulation_steps: 4` in config |
+| Pinterest blocking | Increase sleep time, use VPN, or try alt sources |
+| Blurry outputs | Increase `num_inference_steps` to 40 |
+| Slow cold start on HF | Send Space link 24h before demo to warm it up |
+| Groq rate limit | Create multiple accounts, rotate API keys |
+| GPU not detected | Verify CUDA install: `python -c "import torch; print(torch.cuda.is_available())"` |

docs/architecture.html

@@ -0,0 +1,1004 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Campus-AI — Architecture | CounciL</title>
+  <link
+    href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap"
+    rel="stylesheet">
+  <style>
+    :root {
+      --bg: #06070f;
+      --card: rgba(14, 17, 38, 0.85);
+      --border: rgba(120, 140, 255, 0.12);
+      --blue: #4f8ff7;
+      --purple: #8b5cf6;
+      --pink: #ec4899;
+      --green: #22d3ee;
+      --orange: #f59e0b;
+      --text: #c8d6f0;
+      --muted: #5a6488;
+      --glow-blue: rgba(79, 143, 247, 0.35);
+      --glow-purple: rgba(139, 92, 246, 0.35);
+      --glow-pink: rgba(236, 72, 153, 0.35);
+    }
+
+    * {
+      margin: 0;
+      padding: 0;
+      box-sizing: border-box;
+    }
+
+    body {
+      background: var(--bg);
+      font-family: 'Space Grotesk', sans-serif;
+      color: var(--text);
+      overflow-x: hidden;
+    }
+
+    /* Animated background grid */
+    body::before {
+      content: '';
+      position: fixed;
+      inset: 0;
+      background:
+        linear-gradient(rgba(79, 143, 247, 0.03) 1px, transparent 1px),
+        linear-gradient(90deg, rgba(79, 143, 247, 0.03) 1px, transparent 1px);
+      background-size: 60px 60px;
+      animation: gridMove 20s linear infinite;
+      z-index: 0;
+    }
+
+    @keyframes gridMove {
+      0% {
+        transform: translate(0, 0);
+      }
+
+      100% {
+        transform: translate(60px, 60px);
+      }
+    }
+
+    /* Ambient orbs */
+    .orb {
+      position: fixed;
+      border-radius: 50%;
+      filter: blur(100px);
+      opacity: 0.15;
+      z-index: 0;
+      animation: float 15s ease-in-out infinite alternate;
+    }
+
+    .orb-1 {
+      width: 500px;
+      height: 500px;
+      background: var(--blue);
+      top: -100px;
+      left: -100px;
+    }
+
+    .orb-2 {
+      width: 400px;
+      height: 400px;
+      background: var(--purple);
+      top: 40%;
+      right: -100px;
+      animation-delay: -5s;
+    }
+
+    .orb-3 {
+      width: 450px;
+      height: 450px;
+      background: var(--pink);
+      bottom: -100px;
+      left: 30%;
+      animation-delay: -10s;
+    }
+
+    @keyframes float {
+      0% {
+        transform: translate(0, 0) scale(1);
+      }
+
+      100% {
+        transform: translate(40px, 30px) scale(1.1);
+      }
+    }
+
+    .container {
+      max-width: 1300px;
+      margin: 0 auto;
+      padding: 50px 30px;
+      position: relative;
+      z-index: 1;
+    }
+
+    /* ═══ HEADER ═══ */
+    .header {
+      text-align: center;
+      margin-bottom: 60px;
+    }
+
+    .header .badge {
+      display: inline-block;
+      padding: 6px 18px;
+      border-radius: 50px;
+      font-size: 0.7rem;
+      font-weight: 600;
+      letter-spacing: 2px;
+      text-transform: uppercase;
+      border: 1px solid rgba(139, 92, 246, 0.3);
+      color: var(--purple);
+      background: rgba(139, 92, 246, 0.08);
+      margin-bottom: 20px;
+    }
+
+    .header h1 {
+      font-size: 3rem;
+      font-weight: 700;
+      line-height: 1.1;
+      background: linear-gradient(135deg, #fff 0%, #4f8ff7 40%, #8b5cf6 60%, #ec4899 100%);
+      -webkit-background-clip: text;
+      -webkit-text-fill-color: transparent;
+      margin-bottom: 12px;
+    }
+
+    .header p {
+      color: var(--muted);
+      font-size: 1.05rem;
+      max-width: 500px;
+      margin: 0 auto;
+    }
+
+    /* ═══ PHASE SECTIONS ═══ */
+    .phase {
+      margin-bottom: 24px;
+      position: relative;
+    }
+
+    .phase-header {
+      display: flex;
+      align-items: center;
+      gap: 14px;
+      margin-bottom: 24px;
+    }
+
+    .phase-number {
+      width: 36px;
+      height: 36px;
+      border-radius: 10px;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      font-weight: 700;
+      font-size: 0.85rem;
+      flex-shrink: 0;
+    }
+
+    .p1 .phase-number {
+      background: rgba(79, 143, 247, 0.15);
+      color: var(--blue);
+      border: 1px solid rgba(79, 143, 247, 0.3);
+    }
+
+    .p2 .phase-number {
+      background: rgba(139, 92, 246, 0.15);
+      color: var(--purple);
+      border: 1px solid rgba(139, 92, 246, 0.3);
+    }
+
+    .p3 .phase-number {
+      background: rgba(236, 72, 153, 0.15);
+      color: var(--pink);
+      border: 1px solid rgba(236, 72, 153, 0.3);
+    }
+
+    .phase-title {
+      font-size: 1.1rem;
+      font-weight: 600;
+      color: #fff;
+    }
+
+    .phase-desc {
+      font-size: 0.78rem;
+      color: var(--muted);
+    }
+
+    /* ═══ FLOW (horizontal cards with arrows) ═══ */
+    .flow {
+      display: flex;
+      align-items: center;
+      gap: 0;
+      overflow-x: auto;
+      padding-bottom: 8px;
+    }
+
+    .card {
+      background: var(--card);
+      border: 1px solid var(--border);
+      border-radius: 16px;
+      padding: 24px 22px;
+      min-width: 200px;
+      backdrop-filter: blur(20px);
+      transition: all 0.35s cubic-bezier(0.4, 0, 0.2, 1);
+      position: relative;
+      overflow: hidden;
+    }
+
+    .card::before {
+      content: '';
+      position: absolute;
+      top: 0;
+      left: 0;
+      right: 0;
+      height: 2px;
+      border-radius: 16px 16px 0 0;
+      opacity: 0;
+      transition: opacity 0.35s;
+    }
+
+    .card:hover {
+      transform: translateY(-4px);
+      border-color: rgba(120, 140, 255, 0.3);
+    }
+
+    .card:hover::before {
+      opacity: 1;
+    }
+
+    .p1 .card::before {
+      background: linear-gradient(90deg, var(--blue), var(--green));
+    }
+
+    .p1 .card:hover {
+      box-shadow: 0 8px 40px rgba(79, 143, 247, 0.12);
+    }
+
+    .p2 .card::before {
+      background: linear-gradient(90deg, var(--purple), var(--blue));
+    }
+
+    .p2 .card:hover {
+      box-shadow: 0 8px 40px rgba(139, 92, 246, 0.12);
+    }
+
+    .p3 .card::before {
+      background: linear-gradient(90deg, var(--pink), var(--orange));
+    }
+
+    .p3 .card:hover {
+      box-shadow: 0 8px 40px rgba(236, 72, 153, 0.12);
+    }
+
+    .card-icon {
+      font-size: 2rem;
+      margin-bottom: 12px;
+      display: block;
+    }
+
+    .card-name {
+      font-size: 0.92rem;
+      font-weight: 600;
+      color: #fff;
+      margin-bottom: 6px;
+    }
+
+    .card-detail {
+      font-size: 0.73rem;
+      color: var(--muted);
+      line-height: 1.5;
+    }
+
+    .card-tag {
+      display: inline-block;
+      margin-top: 10px;
+      padding: 3px 10px;
+      border-radius: 6px;
+      font-size: 0.62rem;
+      font-weight: 600;
+      font-family: 'JetBrains Mono', monospace;
+    }
+
+    /* Flow arrows */
+    .flow-arrow {
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      padding: 0 6px;
+      flex-shrink: 0;
+    }
+
+    .flow-arrow svg {
+      width: 40px;
+      height: 20px;
+    }
+
+    .flow-arrow line,
+    .flow-arrow polyline {
+      stroke: var(--muted);
+      stroke-width: 1.5;
+      fill: none;
+      stroke-dasharray: 4 3;
+      animation: dashFlow 1.5s linear infinite;
+    }
+
+    @keyframes dashFlow {
+      0% {
+        stroke-dashoffset: 0;
+      }
+
+      100% {
+        stroke-dashoffset: -14;
+      }
+    }
+
+    /* Big down arrow between phases */
+    .phase-connector {
+      display: flex;
+      justify-content: center;
+      padding: 16px 0;
+    }
+
+    .phase-connector svg {
+      width: 24px;
+      height: 50px;
+    }
+
+    .phase-connector line {
+      stroke: rgba(139, 92, 246, 0.3);
+      stroke-width: 1.5;
+      stroke-dasharray: 4 3;
+      animation: dashDown 1.5s linear infinite;
+    }
+
+    .phase-connector polygon {
+      fill: rgba(139, 92, 246, 0.4);
+    }
+
+    @keyframes dashDown {
+      0% {
+        stroke-dashoffset: 0;
+      }
+
+      100% {
+        stroke-dashoffset: -14;
+      }
+    }
+
+    /* ═══ TRAINING - special 3-col layout ═══ */
+    .training-layout {
+      display: grid;
+      grid-template-columns: 1fr 1.8fr 1fr;
+      gap: 20px;
+      align-items: start;
+    }
+
+    .train-core {
+      background: linear-gradient(145deg, rgba(30, 20, 60, 0.9), rgba(14, 10, 35, 0.95));
+      border: 1.5px solid rgba(139, 92, 246, 0.25);
+      border-radius: 20px;
+      padding: 32px 28px;
+      text-align: center;
+      position: relative;
+      overflow: hidden;
+    }
+
+    .train-core::after {
+      content: '';
+      position: absolute;
+      inset: -1px;
+      border-radius: 20px;
+      background: linear-gradient(135deg, rgba(139, 92, 246, 0.15), transparent 50%, rgba(79, 143, 247, 0.1));
+      z-index: 0;
+      pointer-events: none;
+    }
+
+    .train-core>* {
+      position: relative;
+      z-index: 1;
+    }
+
+    .train-core .card-icon {
+      font-size: 3rem;
+    }
+
+    .train-core .card-name {
+      font-size: 1.3rem;
+      color: var(--purple);
+    }
+
+    .lora-badge {
+      display: inline-block;
+      margin-top: 14px;
+      padding: 8px 20px;
+      border-radius: 10px;
+      background: rgba(139, 92, 246, 0.12);
+      border: 1px solid rgba(139, 92, 246, 0.25);
+      font-family: 'JetBrains Mono', monospace;
+      font-size: 0.75rem;
+      color: var(--purple);
+    }
+
+    .train-specs {
+      display: grid;
+      grid-template-columns: 1fr 1fr;
+      gap: 8px;
+      margin-top: 16px;
+    }
+
+    .spec {
+      background: rgba(139, 92, 246, 0.06);
+      border-radius: 8px;
+      padding: 8px 10px;
+      text-align: center;
+    }
+
+    .spec-val {
+      font-family: 'JetBrains Mono', monospace;
+      font-size: 0.8rem;
+      font-weight: 600;
+      color: #fff;
+    }
+
+    .spec-label {
+      font-size: 0.6rem;
+      color: var(--muted);
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+    }
+
+    .side-stack {
+      display: flex;
+      flex-direction: column;
+      gap: 12px;
+    }
+
+    .side-card {
+      background: var(--card);
+      border: 1px solid var(--border);
+      border-radius: 12px;
+      padding: 14px 16px;
+      backdrop-filter: blur(20px);
+      transition: all 0.3s;
+    }
+
+    .side-card:hover {
+      border-color: rgba(139, 92, 246, 0.3);
+      transform: translateX(4px);
+    }
+
+    .side-card-title {
+      display: flex;
+      align-items: center;
+      gap: 8px;
+      font-size: 0.82rem;
+      font-weight: 600;
+      color: #fff;
+      margin-bottom: 4px;
+    }
+
+    .side-card-title .emoji {
+      font-size: 1.1rem;
+    }
+
+    .side-card-detail {
+      font-size: 0.68rem;
+      color: var(--muted);
+      line-height: 1.5;
+      padding-left: 28px;
+    }
+
+    /* ═══ INFERENCE - 3 col ═══ */
+    .inference-layout {
+      display: grid;
+      grid-template-columns: 240px 1fr 200px;
+      gap: 20px;
+      align-items: center;
+    }
+
+    .inf-input {
+      display: flex;
+      flex-direction: column;
+      gap: 14px;
+    }
+
+    .inf-output {
+      display: flex;
+      flex-direction: column;
+      gap: 14px;
+    }
+
+    .inf-engine {
+      background: linear-gradient(145deg, rgba(50, 15, 40, 0.85), rgba(20, 8, 25, 0.9));
+      border: 1.5px solid rgba(236, 72, 153, 0.2);
+      border-radius: 20px;
+      padding: 28px 24px;
+      position: relative;
+      overflow: hidden;
+    }
+
+    .inf-engine::after {
+      content: '';
+      position: absolute;
+      inset: -1px;
+      border-radius: 20px;
+      background: linear-gradient(135deg, rgba(236, 72, 153, 0.1), transparent 50%, rgba(245, 158, 11, 0.08));
+      z-index: 0;
+      pointer-events: none;
+    }
+
+    .inf-engine>* {
+      position: relative;
+      z-index: 1;
+    }
+
+    .engine-label {
+      text-align: center;
+      font-size: 0.72rem;
+      text-transform: uppercase;
+      letter-spacing: 2px;
+      color: var(--pink);
+      font-weight: 600;
+      margin-bottom: 18px;
+    }
+
+    .modes {
+      display: grid;
+      grid-template-columns: 1fr 1fr;
+      gap: 10px;
+    }
+
+    .mode {
+      background: rgba(236, 72, 153, 0.06);
+      border: 1px solid rgba(236, 72, 153, 0.12);
+      border-radius: 12px;
+      padding: 16px 12px;
+      text-align: center;
+      transition: all 0.3s;
+    }
+
+    .mode:hover {
+      background: rgba(236, 72, 153, 0.12);
+      border-color: rgba(236, 72, 153, 0.3);
+      transform: scale(1.03);
+    }
+
+    .mode-icon {
+      font-size: 1.4rem;
+      margin-bottom: 6px;
+    }
+
+    .mode-name {
+      font-size: 0.78rem;
+      font-weight: 600;
+      color: #fff;
+    }
+
+    .mode-sub {
+      font-size: 0.62rem;
+      color: var(--muted);
+      margin-top: 2px;
+    }
+
+    .engine-footer {
+      text-align: center;
+      margin-top: 14px;
+      font-size: 0.65rem;
+      color: var(--muted);
+      font-family: 'JetBrains Mono', monospace;
+    }
+
+    /* Result card glow */
+    .result-card {
+      border-color: rgba(34, 211, 238, 0.25) !important;
+    }
+
+    .result-card:hover {
+      box-shadow: 0 8px 40px rgba(34, 211, 238, 0.12) !important;
+    }
+
+    .result-card .card-name {
+      color: var(--green);
+    }
+
+    /* ═══ DEPLOY BAR ═══ */
+    .deploy {
+      display: flex;
+      gap: 16px;
+      justify-content: center;
+      margin-top: 28px;
+      flex-wrap: wrap;
+    }
+
+    .deploy-chip {
+      display: flex;
+      align-items: center;
+      gap: 8px;
+      padding: 10px 22px;
+      border-radius: 50px;
+      font-size: 0.78rem;
+      font-weight: 500;
+      transition: all 0.3s;
+    }
+
+    .deploy-chip:hover {
+      transform: scale(1.05);
+    }
+
+    .chip-local {
+      background: rgba(34, 211, 238, 0.08);
+      border: 1px solid rgba(34, 211, 238, 0.2);
+      color: var(--green);
+    }
+
+    .chip-cloud {
+      background: rgba(79, 143, 247, 0.08);
+      border: 1px solid rgba(79, 143, 247, 0.2);
+      color: var(--blue);
+    }
+
+    /* ═══ STATS BAR ═══ */
+    .stats-bar {
+      display: flex;
+      justify-content: center;
+      gap: 40px;
+      margin-top: 50px;
+      padding: 30px 0;
+      border-top: 1px solid var(--border);
+      flex-wrap: wrap;
+    }
+
+    .stat {
+      text-align: center;
+    }
+
+    .stat-value {
+      font-size: 1.6rem;
+      font-weight: 700;
+      font-family: 'JetBrains Mono', monospace;
+      background: linear-gradient(135deg, var(--blue), var(--purple));
+      -webkit-background-clip: text;
+      -webkit-text-fill-color: transparent;
+    }
+
+    .stat-label {
+      font-size: 0.65rem;
+      color: var(--muted);
+      text-transform: uppercase;
+      letter-spacing: 1.5px;
+      margin-top: 4px;
+    }
+
+    .footer {
+      text-align: center;
+      margin-top: 40px;
+      font-size: 0.72rem;
+      color: var(--muted);
+    }
+
+    .footer span {
+      color: var(--pink);
+    }
+
+    /* Responsive */
+    @media (max-width: 900px) {
+
+      .training-layout,
+      .inference-layout {
+        grid-template-columns: 1fr;
+      }
+
+      .flow {
+        flex-wrap: wrap;
+        justify-content: center;
+      }
+
+      .flow-arrow {
+        transform: rotate(90deg);
+      }
+    }
+  </style>
+</head>
+
+<body>
+
+  <div class="orb orb-1"></div>
+  <div class="orb orb-2"></div>
+  <div class="orb orb-3"></div>
+
+  <div class="container">
+
+    <!-- HEADER -->
+    <div class="header">
+      <div class="badge">System Architecture</div>
+      <h1>Campus-AI</h1>
+      <p style="color:var(--purple); font-size:0.85rem; font-weight:600; margin-bottom:8px;">by CounciL</p>
+      <p>End-to-end AI pipeline that scrapes, curates, trains, and generates campus event posters</p>
+    </div>
+
+    <!-- ═══════════════════════════════════════════ -->
+    <!-- PHASE 1: DATA PIPELINE                     -->
+    <!-- ═══════════════════════════════════════════ -->
+    <div class="phase p1">
+      <div class="phase-header">
+        <div class="phase-number">01</div>
+        <div>
+          <div class="phase-title">Data Pipeline</div>
+          <div class="phase-desc">Collect → Filter → Caption → Split</div>
+        </div>
+      </div>
+
+      <div class="flow">
+        <div class="card">
+          <span class="card-icon">🕷️</span>
+          <div class="card-name">Pinterest Scraper</div>
+          <div class="card-detail">Selenium headless browser with automatic scrolling &amp; perceptual hash
+            deduplication</div>
+          <div class="card-tag" style="background:rgba(79,143,247,0.1); color:var(--blue);">57 subcategories × 1,900
+          </div>
+        </div>
+
+        <div class="flow-arrow"><svg>
+            <line x1="0" y1="10" x2="32" y2="10" />
+            <polyline points="30,6 36,10 30,14" />
+          </svg></div>
+
+        <div class="card">
+          <span class="card-icon">🔬</span>
+          <div class="card-name">Quality Filter</div>
+          <div class="card-detail">GPU-accelerated Laplacian sharpness, resolution, aspect ratio &amp; color diversity
+            checks</div>
+          <div class="card-tag" style="background:rgba(34,211,238,0.1); color:var(--green);">~68% pass rate</div>
+        </div>
+
+        <div class="flow-arrow"><svg>
+            <line x1="0" y1="10" x2="32" y2="10" />
+            <polyline points="30,6 36,10 30,14" />
+          </svg></div>
+
+        <div class="card">
+          <span class="card-icon">📝</span>
+          <div class="card-name">Florence-2 Captioner</div>
+          <div class="card-detail">Microsoft Florence-2-large generates detailed captions in bf16 with torch.compile
+          </div>
+          <div class="card-tag" style="background:rgba(139,92,246,0.1); color:var(--purple);">SM120 optimized</div>
+        </div>
+
+        <div class="flow-arrow"><svg>
+            <line x1="0" y1="10" x2="32" y2="10" />
+            <polyline points="30,6 36,10 30,14" />
+          </svg></div>
+
+        <div class="card">
+          <span class="card-icon">✂️</span>
+          <div class="card-name">Dataset Splitter</div>
+          <div class="card-detail">Stratified splitting by category into training, validation &amp; test sets</div>
+          <div class="card-tag" style="background:rgba(245,158,11,0.1); color:var(--orange);">~55K train images</div>
+        </div>
+      </div>
+    </div>
+
+    <!-- Connector -->
+    <div class="phase-connector">
+      <svg>
+        <line x1="12" y1="0" x2="12" y2="40" />
+        <polygon points="6,40 12,50 18,40" />
+      </svg>
+    </div>
+
+    <!-- ═══════════════════════════════════════════ -->
+    <!-- PHASE 2: TRAINING                          -->
+    <!-- ═══════════════════════════════════════════ -->
+    <div class="phase p2">
+      <div class="phase-header">
+        <div class="phase-number">02</div>
+        <div>
+          <div class="phase-title">Training Pipeline</div>
+          <div class="phase-desc">Fine-tune Flux.1-dev with LoRA adapters</div>
+        </div>
+      </div>
+
+      <div class="training-layout">
+        <!-- Left: Optimizer & Loss -->
+        <div class="side-stack">
+          <div class="side-card">
+            <div class="side-card-title"><span class="emoji">⚡</span> Prodigy Optimizer</div>
+            <div class="side-card-detail">Self-adapting LR = 1.0<br>No manual LR tuning needed</div>
+          </div>
+          <div class="side-card">
+            <div class="side-card-title"><span class="emoji">📉</span> Min-SNR-γ Loss</div>
+            <div class="side-card-detail">γ = 5.0 — balanced learning<br>across all noise levels</div>
+          </div>
+          <div class="side-card">
+            <div class="side-card-title"><span class="emoji">🔄</span> Cosine Warm Restarts</div>
+            <div class="side-card-detail">3 cycles over 4 epochs<br>escapes local minima</div>
+          </div>
+        </div>
+
+        <!-- Center: Core model -->
+        <div class="train-core">
+          <span class="card-icon">🧠</span>
+          <div class="card-name">Flux.1-dev</div>
+          <div class="card-detail" style="margin-top:8px;">12 billion parameter<br>transformer diffusion model</div>
+          <div class="lora-badge">+ LoRA Adapter (Rank 16, α=16)</div>
+          <div class="train-specs">
+            <div class="spec">
+              <div class="spec-val">40M</div>
+              <div class="spec-label">Trainable Params</div>
+            </div>
+            <div class="spec">
+              <div class="spec-val">bf16</div>
+              <div class="spec-label">Precision</div>
+            </div>
+            <div class="spec">
+              <div class="spec-val">4</div>
+              <div class="spec-label">Eff. Batch Size</div>
+            </div>
+            <div class="spec">
+              <div class="spec-val">~55K</div>
+              <div class="spec-label">Optimizer Steps</div>
+            </div>
+          </div>
+        </div>
+
+        <!-- Right: Anti-overfitting & Hardware -->
+        <div class="side-stack">
+          <div class="side-card">
+            <div class="side-card-title"><span class="emoji">🛡️</span> Anti-Overfitting</div>
+            <div class="side-card-detail">Caption dropout 10%<br>LoRA dropout 8%<br>L2 weight decay 0.01</div>
+          </div>
+          <div class="side-card">
+            <div class="side-card-title"><span class="emoji">⚙️</span> LoRA+ (ICML '24)</div>
+            <div class="side-card-detail">B matrix gets 16× higher LR<br>Free +2% accuracy boost</div>
+          </div>
+          <div class="side-card">
+            <div class="side-card-title"><span class="emoji">🖥️</span> SM120 Blackwell</div>
+            <div class="side-card-detail">TF32 tensor cores<br>torch.compile max-autotune</div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <!-- Connector -->
+    <div class="phase-connector">
+      <svg>
+        <line x1="12" y1="0" x2="12" y2="40" />
+        <polygon points="6,40 12,50 18,40" />
+      </svg>
+    </div>
+
+    <!-- ═══════════════════════════════════════════ -->
+    <!-- PHASE 3: INFERENCE & DEPLOYMENT            -->
+    <!-- ═══════════════════════════════════════════ -->
+    <div class="phase p3">
+      <div class="phase-header">
+        <div class="phase-number">03</div>
+        <div>
+          <div class="phase-title">Inference &amp; Deployment</div>
+          <div class="phase-desc">Prompt → Generate → Upscale → Deliver</div>
+        </div>
+      </div>
+
+      <div class="inference-layout">
+        <!-- Left: input -->
+        <div class="inf-input">
+          <div class="card">
+            <span class="card-icon">👤</span>
+            <div class="card-name">User Input</div>
+            <div class="card-detail">Event description, type, visual style &amp; resolution preset</div>
+          </div>
+
+          <div style="text-align:center;">
+            <svg width="24" height="30">
+              <line x1="12" y1="0" x2="12" y2="22" stroke="var(--muted)" stroke-width="1.5" stroke-dasharray="4 3">
+                <animate attributeName="stroke-dashoffset" from="0" to="-14" dur="1.5s" repeatCount="indefinite" />
+              </line>
+              <polygon points="6,22 12,30 18,22" fill="var(--muted)" opacity="0.5" />
+            </svg>
+          </div>
+
+          <div class="card">
+            <span class="card-icon">🦙</span>
+            <div class="card-name">Groq Llama 3.3 70B</div>
+            <div class="card-detail">Enhances plain text into detailed Flux-optimized prompts</div>
+            <div class="card-tag" style="background:rgba(245,158,11,0.1); color:var(--orange);">~200ms API</div>
+          </div>
+        </div>
+
+        <!-- Center: engine -->
+        <div class="inf-engine">
+          <div class="engine-label">Flux.1-dev + LoRA Inference Engine</div>
+          <div class="modes">
+            <div class="mode">
+              <div class="mode-icon">✍️</div>
+              <div class="mode-name">Text → Poster</div>
+              <div class="mode-sub">From description only</div>
+            </div>
+            <div class="mode">
+              <div class="mode-icon">🖼️</div>
+              <div class="mode-name">Reference Style</div>
+              <div class="mode-sub">IP-Adapter transfer</div>
+            </div>
+            <div class="mode">
+              <div class="mode-icon">🔄</div>
+              <div class="mode-name">Image → Image</div>
+              <div class="mode-sub">Transform existing art</div>
+            </div>
+            <div class="mode">
+              <div class="mode-icon">🎭</div>
+              <div class="mode-name">Inpainting</div>
+              <div class="mode-sub">Edit specific regions</div>
+            </div>
+          </div>
+          <div class="engine-footer">CPU offload • ~10GB peak VRAM • bf16 precision</div>
+        </div>
+
+        <!-- Right: output -->
+        <div class="inf-output">
+          <div class="card">
+            <span class="card-icon">🔎</span>
+            <div class="card-name">Real-ESRGAN 2×</div>
+            <div class="card-detail">AI upscaling for crisp HD output at any size</div>
+          </div>
+
+          <div style="text-align:center;">
+            <svg width="24" height="30">
+              <line x1="12" y1="0" x2="12" y2="22" stroke="var(--muted)" stroke-width="1.5" stroke-dasharray="4 3">
+                <animate attributeName="stroke-dashoffset" from="0" to="-14" dur="1.5s" repeatCount="indefinite" />
+              </line>
+              <polygon points="6,22 12,30 18,22" fill="var(--muted)" opacity="0.5" />
+            </svg>
+          </div>
+
+          <div class="card result-card">
+            <span class="card-icon">🎨</span>
+            <div class="card-name">Generated Poster</div>
+            <div class="card-detail">1024×1024 to 1152×768<br>Multiple variants supported</div>
+          </div>
+        </div>
+      </div>
+
+      <!-- Deploy chips -->
+      <div class="deploy">
+        <div class="deploy-chip chip-local">🖥️ Local — RTX 5070 Ti (12GB VRAM)</div>
+        <div class="deploy-chip chip-cloud">☁️ Cloud — HF Spaces + ZeroGPU</div>
+      </div>
+    </div>
+
+    <!-- ═══ STATS ═══ -->
+    <div class="stats-bar">
+      <div class="stat">
+        <div class="stat-value">71K+</div>
+        <div class="stat-label">Training Images</div>
+      </div>
+      <div class="stat">
+        <div class="stat-value">57</div>
+        <div class="stat-label">Subcategories</div>
+      </div>
+      <div class="stat">
+        <div class="stat-value">12B</div>
+        <div class="stat-label">Base Params</div>
+      </div>
+      <div class="stat">
+        <div class="stat-value">40M</div>
+        <div class="stat-label">LoRA Params</div>
+      </div>
+      <div class="stat">
+        <div class="stat-value">SM120</div>
+        <div class="stat-label">GPU Arch</div>
+      </div>
+      <div class="stat">
+        <div class="stat-value">~46h</div>
+        <div class="stat-label">Training Time</div>
+      </div>
+    </div>
+
+    <div class="footer">Campus-AI · CounciL · Built with <span>❤️</span> for the Indian campus
+      community</div>
+
+  </div>
+</body>
+
+</html>

requirements.txt

@@ -0,0 +1,57 @@
+# CampusGen AI - Requirements
+# Python 3.11.14 | CUDA 13.0 (cu130)
+
+# ===== PyTorch (CUDA 13.0) =====
+--extra-index-url https://download.pytorch.org/whl/cu130
+torch
+torchvision
+torchaudio
+
+# ===== Hugging Face Ecosystem =====
+transformers
+diffusers
+datasets
+peft
+trl
+huggingface_hub
+accelerate
+safetensors
+
+# ===== Unsloth (fast LoRA fine-tuning) =====
+unsloth
+
+# ===== Quantization =====
+bitsandbytes
+
+# ===== Evaluation Metrics =====
+torchmetrics
+
+# ===== Toxicity Detection =====
+detoxify
+
+# ===== Web UI =====
+gradio
+
+# ===== Evaluation Dependencies =====
+scipy
+open-clip-torch
+
+# ===== Dataset Downloading =====
+kaggle
+
+# ===== Scraping & Web =====
+selenium
+webdriver-manager
+beautifulsoup4
+
+# ===== Image Processing =====
+opencv-python
+imagehash
+
+# ===== Data & Utilities =====
+numpy
+Pillow
+tqdm
+requests
+pyyaml
+tensorboard

scripts/augment_specific_v3.py

@@ -0,0 +1,162 @@
+
+import os
+import shutil
+import logging
+from pathlib import Path
+from collections import defaultdict
+import glob
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    datefmt="%H:%M:%S"
+)
+logger = logging.getLogger(__name__)
+
+# Try to import optional dependencies
+try:
+    from PIL import Image
+    PIL_AVAILABLE = True
+except ImportError:
+    PIL_AVAILABLE = False
+    logger.warning("⚠️ PIL (Pillow) not found. Image validation will be skipped (only file extension check).")
+
+try:
+    from tqdm import tqdm
+    TQDM_AVAILABLE = True
+except ImportError:
+    TQDM_AVAILABLE = False
+
+# Configuration
+TARGET_COUNT = 1300  # Safety margin above 1000
+TARGET_CATEGORIES = [
+    "workshops/coding",
+    "workshops/design"
+]
+
+DATA_ROOT = Path("data")
+RAW_ROOT = DATA_ROOT / "raw"
+PROCESSED_ROOT = DATA_ROOT / "processed"
+
+def get_image_files(directory):
+    """Recursively get all image files in a directory."""
+    extensions = {'*.jpg', '*.jpeg', '*.png', '*.webp', '*.bmp'}
+    files = []
+    if not directory.exists():
+        return files
+    
+    for ext in extensions:
+        # Case insensitive search would be better but glob is case sensitive on Linux/WSL usually.
+        # We will try both cases or just standarize. 
+        # Walking is safer for case insensitivity if needed, but glob is faster.
+        files.extend(directory.glob(f"**/{ext}"))
+        files.extend(directory.glob(f"**/{ext.upper()}"))
+    return sorted(list(set(files)))
+
+def check_image_quality(file_path):
+    """
+    Basic quality check using PIL (if available).
+    Returns (Passed: bool, Message: str)
+    """
+    if not PIL_AVAILABLE:
+        # If PIL is missing, we assume file is okay if it exists and has size
+        if file_path.stat().st_size < 5120: # < 5KB is suspect
+            return False, "File too small"
+        return True, "No PIL check"
+
+    try:
+        with Image.open(file_path) as img:
+            width, height = img.size
+            if width < 256 or height < 256:
+                return False, f"Low resolution: {width}x{height}"
+            
+            # Aspect ratio check
+            aspect = width / height
+            if aspect < 0.4 or aspect > 2.5:
+                return False, f"Extreme aspect ratio: {aspect:.2f}"
+                
+            return True, "OK"
+    except Exception as e:
+        return False, f"Corrupt image: {str(e)}"
+
+def process_category(relative_path):
+    """Process a single category."""
+    category_name = str(relative_path).replace("\\", "/")
+    logger.info(f"🔍 Checking category: {category_name}")
+
+    raw_path = RAW_ROOT / relative_path
+    processed_path = PROCESSED_ROOT / relative_path
+
+    # Ensure processed directory exists
+    processed_path.mkdir(parents=True, exist_ok=True)
+
+    # 1. Count current Processed
+    processed_files = get_image_files(processed_path)
+    current_count = len(processed_files)
+    processed_filenames = {f.name for f in processed_files}
+    
+    logger.info(f"   Existing processed images: {current_count}")
+
+    if current_count >= TARGET_COUNT:
+        logger.info(f"   ✅ Already met target of {TARGET_COUNT}. Skipping.")
+        return
+
+    needed = TARGET_COUNT - current_count
+    logger.info(f"   ⚠️ Need {needed} more images.")
+
+    # 2. Get Raw Candidates
+    raw_files = get_image_files(raw_path)
+    logger.info(f"   Found {len(raw_files)} raw images available.")
+
+    # Filter out files that are already in processed (by filename)
+    candidates = [f for f in raw_files if f.name not in processed_filenames]
+    logger.info(f"   {len(candidates)} new unique candidates available to process.")
+
+    if not candidates:
+        logger.warning("   ❌ No new candidates found in raw folder!")
+        return
+
+    # 3. Copy Candidates
+    added_count = 0
+    passed_check = 0
+    failed_check = 0
+
+    # Progress bar setup
+    iterator = tqdm(candidates, unit="img") if TQDM_AVAILABLE else candidates
+
+    for src_file in iterator:
+        if added_count >= needed:
+            break
+
+        # Quality Check
+        is_ok, msg = check_image_quality(src_file)
+        if not is_ok:
+            failed_check += 1
+            continue
+        
+        # Copy
+        dst_file = processed_path / src_file.name
+        try:
+            shutil.copy2(src_file, dst_file)
+            added_count += 1
+            passed_check += 1
+        except Exception as e:
+            logger.error(f"Failed to copy {src_file.name}: {e}")
+
+    logger.info(f"   🎉 Added {added_count} images.")
+    logger.info(f"   Final Count: {current_count + added_count}")
+    logger.info("-" * 40)
+
+def main():
+    logger.info("🚀 Starting targeted dataset augmentation...")
+    logger.info(f"📂 Data Root: {DATA_ROOT.absolute()}")
+    logger.info(f"🎯 Target: {TARGET_COUNT} images per category")
+
+    for cat in TARGET_CATEGORIES:
+        process_category(Path(cat))
+
+    logger.info("✨ Done.")
+
+if __name__ == "__main__":
+    main()

scripts/caption_generator.py

@@ -0,0 +1,379 @@
+#!/usr/bin/env python3
+
+"""
+Caption Generator — Florence-2 Native (transformers >= 4.56)
+
+Multi-task captioning: MORE_DETAILED_CAPTION + OCR + DENSE_REGION_CAPTION
+
+SETUP (run ONCE):
+    pip install "transformers==4.57.3" tokenizers --upgrade
+    rm -rf ~/.cache/huggingface/modules/transformers_modules/
+
+Outputs:
+    data/{split}/{category}/image.txt
+    data/{split}/metadata.json
+"""
+
+import os
+import re
+import sys
+import json
+import logging
+import argparse
+import traceback
+import warnings
+from pathlib import Path
+from datetime import datetime
+
+import yaml
+import torch
+from PIL import Image, ImageFile
+from tqdm import tqdm
+
+Image.MAX_IMAGE_PIXELS = None
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+import transformers
+transformers.logging.set_verbosity_error()
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=UserWarning)
+
+if torch.cuda.is_available():
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Logging
+# ─────────────────────────────────────────────────────────────────────────────
+Path("logs").mkdir(exist_ok=True)
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.StreamHandler(),
+        logging.FileHandler("logs/caption_generator.log"),
+    ],
+)
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Config
+# ─────────────────────────────────────────────────────────────────────────────
+def load_config(config_path: str = "configs/config.yaml") -> dict:
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Constants
+# ─────────────────────────────────────────────────────────────────────────────
+MODEL_ID = "ducviet00/Florence-2-large-hf"
+
+TASKS    = ["<MORE_DETAILED_CAPTION>", "<OCR>", "<DENSE_REGION_CAPTION>"]
+TASK_KEY = {
+    "<MORE_DETAILED_CAPTION>": "visual",
+    "<OCR>":                   "ocr",
+    "<DENSE_REGION_CAPTION>":  "regions",
+}
+CATEGORY_LABELS = {
+    "tech_fest":      "A technology fest event poster",
+    "cultural_fest":  "A cultural festival event poster",
+    "college_events": "A college event poster",
+    "sports":         "A sports tournament event poster",
+    "festivals":      "A festival celebration event poster",
+    "workshops":      "A workshop or seminar event poster",
+    "social":         "A social awareness event poster",
+    "entertainment":  "An entertainment event poster",
+    "styles":         "A stylized event poster",
+    "general":        "An event poster",
+    "diwali":         "A Diwali celebration event poster",
+    "holi":           "A Holi festival event poster",
+    "navratri":       "A Navratri festival event poster",
+    "eid":            "An Eid celebration event poster",
+    "ganesh":         "A Ganesh Chaturthi event poster",
+}
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Cache guard
+# ─────────────────────────────────────────────────────────────────────────────
+def _check_stale_cache():
+    stale = Path.home() / ".cache" / "huggingface" / "modules" / "transformers_modules"
+    if stale.exists():
+        logger.warning(
+            f"Stale remote-code cache at {stale} — "
+            "run: rm -rf ~/.cache/huggingface/modules/transformers_modules/"
+        )
+
+# ─────────────────────���───────────────────────────────────────────────────────
+# Florence-2 Captioner
+# Direct-class loading — bypasses auto_map, no Auto* classes used
+# ─────────────────────────────────────────────────────────────────────────────
+class Florence2Captioner:
+    """Multi-task Florence-2 captioner using native transformers classes."""
+
+    def __init__(self, device: str = "auto"):
+        from transformers import Florence2ForConditionalGeneration, Florence2Processor
+        from transformers.models.bart import BartTokenizerFast
+        from transformers.models.clip.image_processing_clip import CLIPImageProcessor
+
+        self.device = "cuda" if torch.cuda.is_available() else "cpu" if device == "auto" else device
+        self.dtype  = torch.bfloat16 if self.device == "cuda" else torch.float32
+
+        logger.info(f"transformers : {transformers.__version__}")
+        logger.info(f"torch        : {torch.__version__}")
+        logger.info(f"device/dtype : {self.device} / {self.dtype}")
+        logger.info(f"Loading {MODEL_ID} ...")
+
+        # Direct tokenizer load — bypasses AutoTokenizer & auto_map
+        tokenizer = BartTokenizerFast.from_pretrained(MODEL_ID)
+
+        # Patch image_token if missing (required by Florence2Processor.__init__)
+        if not hasattr(tokenizer, "image_token") or tokenizer.image_token is None:
+            tok_vocab   = tokenizer.get_vocab()
+            image_token = next(
+                (t for t in ["<image>", "</s>", "<unk>"] if t in tok_vocab), None
+            )
+            if image_token is None:
+                tokenizer.add_tokens(["<image>"], special_tokens=True)
+                image_token = "<image>"
+            tokenizer.image_token    = image_token
+            tokenizer.image_token_id = tokenizer.convert_tokens_to_ids(image_token)
+            logger.info(f"Patched image_token='{image_token}' (id={tokenizer.image_token_id})")
+
+        # Direct image processor load — bypasses AutoImageProcessor & auto_map
+        image_processor = CLIPImageProcessor.from_pretrained(MODEL_ID)
+
+        # Assemble processor from components (bypasses from_pretrained's AutoTokenizer call)
+        self.processor = Florence2Processor(
+            image_processor=image_processor,
+            tokenizer=tokenizer,
+        )
+
+        # Direct model load — bypasses AutoModel & auto_map in config.json
+        self.model = Florence2ForConditionalGeneration.from_pretrained(
+            MODEL_ID,
+            torch_dtype=self.dtype,
+            ignore_mismatched_sizes=False,
+        ).to(self.device)
+        self.model.eval()
+        logger.info("Florence-2 loaded successfully.")
+
+    # ── helpers ───────────────────────────────────────────────────────────────
+
+    def _safe_to_device(self, inputs: dict) -> dict:
+        out = {}
+        for k, v in inputs.items():
+            if not isinstance(v, torch.Tensor):
+                out[k] = v
+            elif v.is_floating_point():
+                out[k] = v.to(device=self.device, dtype=self.dtype)
+            else:
+                out[k] = v.to(device=self.device)
+        return out
+
+    def _run_task(self, image: Image.Image, task: str) -> str:
+        """Run one Florence-2 task; returns clean decoded string."""
+        inputs = self.processor(text=task, images=image, return_tensors="pt")
+        inputs = self._safe_to_device(inputs)
+
+        with torch.no_grad():
+            generated_ids = self.model.generate(
+                input_ids=inputs["input_ids"],
+                pixel_values=inputs["pixel_values"],
+                max_new_tokens=512,
+                num_beams=3,
+                do_sample=False,
+            )
+
+        # Decode directly — post_process_generation raises
+        # "Unsupported parse task: pure_text/description_with_bboxes"
+        # in transformers 4.57.3 due to processor_config task-type mismatch.
+        # Direct decoding gives identical text for all tasks we use.
+        text = self.processor.batch_decode(
+            generated_ids, skip_special_tokens=True
+        )[0].strip()
+
+        # Strip task prompt tokens if they leaked through decode
+        for tok in TASKS:
+            text = text.replace(tok, "").strip()
+
+        # DENSE_REGION_CAPTION contains <loc_NNN> coordinate tokens;
+        # strip them to keep only the human-readable region labels
+        if task == "<DENSE_REGION_CAPTION>":
+            text = re.sub(r"<loc_\d+>", "", text)
+            text = re.sub(r"\s{2,}", " ", text).strip(" ,")
+
+        return text
+
+    # ── public API ───��────────────────────────────────────────────────────────
+
+    def caption(self, image: Image.Image) -> dict:
+        """Run all tasks; returns {visual, ocr, regions}."""
+        if image.width < 16 or image.height < 16:
+            raise ValueError(f"Image too small: {image.size}")
+        results = {}
+        for task in TASKS:
+            key = TASK_KEY[task]
+            try:
+                results[key] = self._run_task(image, task)
+            except Exception as e:
+                logger.warning(f"Task {task} failed: {e}\n{traceback.format_exc()}")
+                results[key] = ""
+        return results
+
+    def build_caption(self, task_results: dict, category: str) -> str:
+        """Merge multi-task results into one Flux fine-tuning caption."""
+        parent = category.split("/")[0] if "/" in category else category
+        prefix = CATEGORY_LABELS.get(category, CATEGORY_LABELS.get(parent, "An event poster"))
+
+        visual  = task_results.get("visual", "").strip()
+        ocr     = task_results.get("ocr", "").strip()
+        regions = task_results.get("regions", "").strip()
+
+        parts = [f"campus_ai_poster {prefix}."]
+        if visual:
+            parts.append(visual)
+        if ocr:
+            ocr_clean = " | ".join(dict.fromkeys(
+                t.strip() for t in ocr.replace("\n", " | ").split(" | ") if t.strip()
+            ))
+            parts.append(f"[Text on poster: {ocr_clean}]")
+        if regions:
+            r = regions[:400].rsplit(".", 1)[0] + "." if len(regions) > 400 else regions
+            parts.append(f"[Design elements: {r}]")
+
+        return " ".join(parts)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Checkpoint helpers
+# ─────────────────────────────────────────────────────────────────────────────
+def load_checkpoint(path: Path) -> set:
+    return set(json.loads(path.read_text())) if path.exists() else set()
+
+def save_checkpoint(path: Path, done: set):
+    path.write_text(json.dumps(sorted(done)))
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Pipeline
+# ─────────────────────────────────────────────────────────────────────────────
+IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp"}
+
+
+def run_captioning(config: dict, splits: list):
+    _check_stale_cache()
+
+    data_paths = config.get("paths", {}).get("data", {})
+    if not data_paths:
+        logger.error("Missing 'paths.data' in config.yaml")
+        sys.exit(1)
+
+    try:
+        captioner = Florence2Captioner()
+    except Exception:
+        logger.error(f"Could not load Florence-2:\n{traceback.format_exc()}")
+        sys.exit(1)
+
+    for split in splits:
+        if split not in data_paths:
+            logger.warning(f"'{split}' not in config paths. Skipping.")
+            continue
+        split_dir = Path(data_paths[split])
+        if not split_dir.exists():
+            logger.warning(f"Dir not found: {split_dir}. Skipping.")
+            continue
+
+        logger.info(f"\n{'='*60}")
+        logger.info(f"  Split: {split.upper()} ({split_dir})")
+        logger.info(f"{'='*60}")
+
+        ckpt_path = split_dir / ".caption_checkpoint.json"
+        done = load_checkpoint(ckpt_path)
+        logger.info(f"Checkpoint: {len(done)} already captioned.")
+
+        all_imgs = []
+        for root, _, files in os.walk(split_dir):
+            rp = Path(root)
+            for fname in sorted(files):
+                fp = rp / fname
+                if fp.suffix.lower() in IMAGE_EXTS:
+                    cat = str(rp.relative_to(split_dir)).replace("\\", "/")
+                    all_imgs.append((cat if cat != "." else "general", fp))
+
+        logger.info(f"Total : {len(all_imgs)}  |  Remaining : {len(all_imgs) - len(done)}")
+        remaining = [(c, p) for c, p in all_imgs if str(p) not in done]
+
+        if not remaining:
+            logger.info("Already complete.")
+            continue
+
+        meta_path = split_dir / "metadata.json"
+        metadata: list = []
+        if meta_path.exists():
+            try:
+                metadata = json.loads(meta_path.read_text(encoding="utf-8"))
+            except Exception:
+                logger.warning("Could not read existing metadata; starting fresh.")
+
+        failed = 0
+        sample_logged = False
+
+        for cat, img_path in tqdm(remaining, desc=split):
+            try:
+                img = Image.open(img_path).convert("RGB")
+                img.load()
+            except Exception as e:
+                logger.warning(f"Bad image [{img_path.name}]: {e}")
+                failed += 1
+                continue
+
+            try:
+                results = captioner.caption(img)
+                caption = captioner.build_caption(results, cat)
+                if not sample_logged:
+                    logger.info(f"Sample caption:\n  {caption[:300]}...")
+                    sample_logged = True
+            except Exception:
+                logger.warning(f"Caption failed [{img_path.name}]:\n{traceback.format_exc()}")
+                failed += 1
+                continue
+
+            img_path.with_suffix(".txt").write_text(caption, encoding="utf-8")
+            metadata.append({
+                "image":        str(img_path),
+                "caption_file": str(img_path.with_suffix(".txt")),
+                "caption":      caption,
+                "visual":       results.get("visual", ""),
+                "ocr":          results.get("ocr", ""),
+                "regions":      results.get("regions", ""),
+                "category":     cat,
+                "width":        img.size[0],
+                "height":       img.size[1],
+                "timestamp":    datetime.now().isoformat(),
+            })
+            done.add(str(img_path))
+            if len(done) % 50 == 0:
+                save_checkpoint(ckpt_path, done)
+
+        save_checkpoint(ckpt_path, done)
+        meta_path.write_text(
+            json.dumps(metadata, indent=2, ensure_ascii=False), encoding="utf-8"
+        )
+        logger.info(f"Done — captioned: {len(metadata)}, failed/skipped: {failed}")
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    logger.info("All splits complete.")
+
+
+def main():
+    p = argparse.ArgumentParser(description="Florence-2 Caption Generator")
+    p.add_argument("--config", default="configs/config.yaml")
+    p.add_argument("--splits", nargs="+", default=["train", "val", "test"])
+    args = p.parse_args()
+    run_captioning(load_config(args.config), args.splits)
+
+
+if __name__ == "__main__":
+    main()

scripts/count_images.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+# Count images in data/processed subdirectories
+# Usage: bash scripts/count_images.sh
+
+TARGET=1300
+DATA_DIR="data/processed"
+
+echo "=================================================="
+echo "  PROCESSED IMAGE COUNT REPORT (Target: $TARGET)"
+echo "=================================================="
+printf "%-40s %6s %10s\n" "CATEGORY" "COUNT" "STATUS"
+echo "--------------------------------------------------------"
+
+total_imgs=0
+pass_count=0
+fail_count=0
+
+# Find all subdirectories that contain images
+# Using find to get directories, then counting files inside
+find "$DATA_DIR" -mindepth 2 -maxdepth 2 -type d | sort | while read -r dir; do
+    # Count image files (case insensitive extensions)
+    count=$(find "$dir" -maxdepth 1 -type f | grep -iE "\.(jpg|jpeg|png|webp|bmp)$" | wc -l)
+    
+    # Get relative path (category/subcategory)
+    rel_path=${dir#$DATA_DIR/}
+    
+    if [ "$count" -ge "$TARGET" ]; then
+        status="✅ PASS"
+        ((pass_count++))
+    else
+        status="❌ FAIL"
+        ((fail_count++))
+    fi
+    
+    if [ "$count" -gt 0 ]; then
+        printf "%-40s %6d %10s\n" "$rel_path" "$count" "$status"
+        total_imgs=$((total_imgs + count))
+    fi
+done
+
+echo "--------------------------------------------------------"
+# Recalculate total because of pipe subshell scope issue in bash
+grand_total=$(find "$DATA_DIR" -type f | grep -iE "\.(jpg|jpeg|png|webp|bmp)$" | wc -l)
+echo "TOTAL: $grand_total images across all processed categories"
+echo "=================================================="
+
+# Check for failures (Need a separate loop or temp file to persist fail_count if strict, 
+# but for visual report this is fine)
+# To actually return bad exit code if failed:
+failures=$(find "$DATA_DIR" -mindepth 2 -maxdepth 2 -type d | while read -r d; do 
+  c=$(find "$d" -maxdepth 1 -type f | grep -iE "\.(jpg|jpeg|png|webp|bmp)$" | wc -l); 
+  if [ "$c" -lt "$TARGET" ] && [ "$c" -gt 0 ]; then echo "fail"; fi; 
+done | wc -l)
+
+if [ "$failures" -gt 0 ]; then
+  echo "⚠️  $failures categories are below target ($TARGET)."
+  echo "    Run 'python scripts/targeted_filter_v2.py' to fix."
+else
+  echo "🎉 All categories meet the target goal!"
+fi

scripts/count_splits.py

@@ -0,0 +1,59 @@
+
+import os
+from pathlib import Path
+
+# Config
+data_root = Path("data")
+train_dir = data_root / "train"
+val_dir = data_root / "val"
+test_dir = data_root / "test"
+IMG_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+
+def count_images_in_dir(d: Path) -> int:
+    if not d.exists():
+        return 0
+    return len([f for f in os.listdir(d) if Path(f).suffix.lower() in IMG_EXTENSIONS])
+
+# Find all categories from processed dir (source of truth)
+processed_dir = data_root / "processed"
+categories = set()
+
+if processed_dir.exists():
+    for root, dirs, files in os.walk(processed_dir):
+        if any(Path(f).suffix.lower() in IMG_EXTENSIONS for f in files):
+            rel = Path(root).relative_to(processed_dir)
+            categories.add(str(rel).replace("\\", "/"))
+else:
+    # Fallback: finding categories from splits directly
+    for d in [train_dir, val_dir, test_dir]:
+        if d.exists():
+            for root, dirs, files in os.walk(d):
+                if any(Path(f).suffix.lower() in IMG_EXTENSIONS for f in files):
+                    rel = Path(root).relative_to(d)
+                    categories.add(str(rel).replace("\\", "/"))
+
+print(f"{'Category':<40} | {'Train':<6} | {'Val':<5} | {'Test':<5} | {'Total':<6} | {'% Train':<8}")
+print("-" * 100)
+
+grand_totals = {"train": 0, "val": 0, "test": 0, "total": 0}
+
+for cat in sorted(list(categories)):
+    c_train = count_images_in_dir(train_dir / cat)
+    c_val = count_images_in_dir(val_dir / cat)
+    c_test = count_images_in_dir(test_dir / cat)
+    total = c_train + c_val + c_test
+    
+    grand_totals["train"] += c_train
+    grand_totals["val"] += c_val
+    grand_totals["test"] += c_test
+    grand_totals["total"] += total
+
+    pct_train = (c_train / total * 100) if total > 0 else 0.0
+    
+    print(f"{cat:<40} | {c_train:<6} | {c_val:<5} | {c_test:<5} | {total:<6} | {pct_train:.1f}%")
+
+print("-" * 100)
+t_train = grand_totals['train']
+t_total = grand_totals['total']
+t_pct = (t_train / t_total * 100) if t_total > 0 else 0
+print(f"{'TOTAL':<40} | {t_train:<6} | {grand_totals['val']:<5} | {grand_totals['test']:<5} | {t_total:<6} | {t_pct:.1f}%")

scripts/create_training_config.py

@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+"""
+Create Training Config
+Reads the master config.yaml and generates an ai-toolkit compatible
+YAML training config at configs/train_sdxl_lora.yaml.
+"""
+
+import os
+import sys
+import argparse
+import logging
+from pathlib import Path
+
+import yaml
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+
+def load_config(config_path: str = "configs/config.yaml") -> dict:
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def generate_ai_toolkit_config(config: dict, output_path: str):
+    """
+    Generate an ai-toolkit compatible training config from master config.
+    ai-toolkit expects a specific YAML schema for training SDXL LoRA.
+    """
+    sdxl_cfg = config.get("models", {}).get("sdxl", {})
+    training_cfg = config.get("training", {})
+    sdxl_lora_cfg = training_cfg.get("sdxl_lora", {})
+    lora_cfg = sdxl_lora_cfg.get("lora", {})
+    optim_cfg = sdxl_lora_cfg.get("optimizer", {})
+    sched_cfg = sdxl_lora_cfg.get("scheduler", {})
+    snr_cfg = sdxl_lora_cfg.get("min_snr_gamma", {})
+    paths_cfg = config.get("paths", {})
+
+    # Base model
+    base_model = sdxl_cfg.get("repo_id", "stabilityai/stable-diffusion-xl-base-1.0")
+
+    # Paths
+    data_dir = os.path.abspath(paths_cfg.get("data", {}).get("train", "data/train"))
+    output_dir = os.path.abspath(
+        paths_cfg.get("models", {}).get("sdxl", {}).get("checkpoints", "models/sdxl/checkpoints")
+    )
+    log_dir = os.path.abspath(
+        paths_cfg.get("logs", {}).get("tensorboard", "logs/tensorboard")
+    )
+
+    # LoRA params
+    rank = lora_cfg.get("rank", 32)
+    alpha = lora_cfg.get("alpha", 16)
+    dropout = lora_cfg.get("dropout", 0.05)
+
+    # Training params
+    batch_size = sdxl_lora_cfg.get("batch_size", 1)
+    grad_accum = sdxl_lora_cfg.get("gradient_accumulation_steps", 4)
+    lr = optim_cfg.get("learning_rate", 1e-4)
+    epochs = sdxl_lora_cfg.get("epochs", 4)
+    max_steps = sdxl_lora_cfg.get("max_steps", 12800)
+    warmup_steps = sched_cfg.get("warmup_steps", 100)
+    weight_decay = optim_cfg.get("weight_decay", 0.01)
+    
+    betas = optim_cfg.get("betas", [0.9, 0.999])
+
+    # Resolution
+    height = sdxl_cfg.get("height", 1024)
+    width = sdxl_cfg.get("width", 1024)
+
+    # Seed
+    seed = config.get("project", {}).get("seed", 42)
+
+    # Mixed precision
+    mixed_prec = training_cfg.get("mixed_precision", {})
+    dtype = mixed_prec.get("dtype", "bf16")
+
+    # Build ai-toolkit config
+    aitk_config = {
+        "job": "extension",
+        "config": {
+            "name": "campus_ai_poster_sdxl",
+            "process": [
+                {
+                    "type": "sd_trainer",
+                    "training_folder": output_dir,
+                    "device": "cuda:0",
+                    "trigger_word": "campus_ai_poster",
+                    "network": {
+                        "type": "lora",
+                        "linear": rank,
+                        "linear_alpha": alpha,
+                        "dropout": dropout,
+                        "network_kwargs": {
+                            "lora_plus_lr_ratio": lora_cfg.get("lora_plus_ratio", 1.0),
+                        },
+                    },
+                    "save": {
+                        "dtype": dtype,
+                        "save_every": sdxl_lora_cfg.get("checkpointing", {}).get("save_steps", 500),
+                        "max_step_saves_to_keep": sdxl_lora_cfg.get("checkpointing", {}).get("save_total_limit", 5),
+                    },
+                    "datasets": [
+                        {
+                            "folder_path": data_dir,
+                            "caption_ext": "txt",
+                            "caption_dropout_rate": 0.1,
+                            "shuffle_tokens": True,
+                            "cache_latents_to_disk": True,
+                            "num_workers": 8,
+                            "resolution": [width, height],
+                        }
+                    ],
+                    "train": {
+                        "batch_size": batch_size,
+                        "steps": max_steps if max_steps > 0 else 12800,
+                        "gradient_accumulation_steps": grad_accum,
+                        "train_unet": True,
+                        "train_text_encoder": False,
+                        "disable_sampling": True,
+                        "gradient_checkpointing": True,
+                        "noise_scheduler": "ddpm",
+                        "optimizer": optim_cfg.get("type", "adamw8bit"),
+                        "lr": lr,
+                        "lr_warmup_steps": warmup_steps,
+                        "min_snr_gamma": snr_cfg.get("gamma", 5.0) if snr_cfg.get("enabled", True) else None,
+                        "optimizer_params": {
+                             "weight_decay": weight_decay,
+                             "betas": betas,
+                        },
+                        "ema_config": {
+                            "use_ema": True,
+                            "ema_decay": 0.999,
+                        },
+                        "dtype": dtype,
+                        "lr_scheduler": sched_cfg.get("type", "cosine_with_restarts"),
+                        "lr_scheduler_params": {
+                            "T_0": max(1, (max_steps if max_steps > 0 else 12800) // sched_cfg.get("num_cycles", 3)),
+                            "T_mult": 1,
+                            "eta_min": lr / 10,
+                        },
+                    },
+                    "model": {
+                        "name_or_path": base_model,
+                        "is_xl": True,
+                    },
+                    "sample": {
+                        "sampler": "euler_a",
+                        "sample_every": 999999,
+                        "width": width,
+                        "height": height,
+                        "prompts": [
+                            "campus_ai_poster a vibrant technology fest poster with neon colors and bold typography",
+                            "campus_ai_poster a colorful Diwali celebration poster with golden diyas and rangoli",
+                            "campus_ai_poster a professional workshop seminar poster with modern minimalist design",
+                            "campus_ai_poster a dynamic sports tournament poster with action silhouettes",
+                        ],
+                        "neg": "",
+                        "seed": seed,
+                        "walk_seed": True,
+                        "guidance_scale": 5,
+                        "sample_steps": 28,
+                    },
+                    "logging": {
+                        "log_every": sdxl_lora_cfg.get("logging", {}).get("steps", 10),
+                        "use_wandb": config.get("monitoring", {}).get("wandb", {}).get("enabled", False),
+                        "verbose": True,
+                    },
+                }
+            ],
+            "meta": {
+                "name": "campus_ai_v1",
+                "version": "1.0",
+            },
+        },
+    }
+
+    # Write output
+    output_file = Path(output_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        yaml.dump(aitk_config, f, default_flow_style=False, sort_keys=False, allow_unicode=True)
+
+    logger.info(f"ai-toolkit training config written to: {output_file}")
+    logger.info(f"  Base model: {base_model}")
+    logger.info(f"  Dataset dir: {data_dir}")
+    logger.info(f"  Output dir: {output_dir}")
+    logger.info(f"  LoRA rank: {rank}, alpha: {alpha}")
+    logger.info(f"  Batch size: {batch_size}, Grad accum: {grad_accum}")
+    logger.info(f"  Learning rate: {lr}")
+    logger.info(f"  Resolution: {width}x{height}")
+    logger.info(f"  Mixed precision: {dtype}")
+
+    return aitk_config
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate ai-toolkit Training Config")
+    parser.add_argument("--config", default="configs/config.yaml", help="Path to master config.yaml")
+    parser.add_argument("--output", default="configs/train_sdxl_lora.yaml", help="Output path for ai-toolkit config")
+    args = parser.parse_args()
+
+    config = load_config(args.config)
+    generate_ai_toolkit_config(config, args.output)
+
+
+if __name__ == "__main__":
+    main()

scripts/deploy_to_hf.py

@@ -0,0 +1,56 @@
+import os
+import sys
+from huggingface_hub import HfApi, login
+
+def deploy_model():
+    print("="*60)
+    print("  CAMPUS AI - HUGGING FACE DEPLOYMENT")
+    print("="*60)
+    
+    # 1. Ask for credentials and repo ID
+    hf_token = input("\nEnter your Hugging Face WRITE Token (paste and press Enter): ").strip()
+    repo_id = input("Enter your Hugging Face Repository ID (e.g. your_username/campus-ai-poster-sdxl): ").strip()
+    
+    if not hf_token or not repo_id:
+        print("\n[!] Error: Token and Repository ID are required.")
+        sys.exit(1)
+        
+    try:
+        # 2. Authenticate
+        print("\n[+] Authenticating with Hugging Face...")
+        login(token=hf_token)
+        api = HfApi()
+        
+        # 3. Verify Phase 3 Model exists
+        model_dir = "models/sdxl/checkpoints/campus_ai_poster_sdxl_phase3"
+        model_file = os.path.join(model_dir, "campus_ai_poster_sdxl_phase3.safetensors")
+        
+        if not os.path.exists(model_file):
+            print(f"\n[!] Error: Phase 3 model not found at {model_file}!")
+            print("Make sure Phase 3 training has finished successfully.")
+            sys.exit(1)
+            
+        print("\n[+] Creating/Verifying repository...")
+        api.create_repo(repo_id=repo_id, exist_ok=True, private=False)
+        
+        # 4. Upload the model
+        print(f"\n[+] Uploading Phase 3 Model to {repo_id}...")
+        api.upload_file(
+            path_or_fileobj=model_file,
+            path_in_repo="campus_ai_poster_sdxl_phase3.safetensors",
+            repo_id=repo_id,
+            repo_type="model",
+            commit_message="Upload final Campus AI Phase 3 LoRA weights"
+        )
+        
+        print("\n" + "="*60)
+        print(f"  ✅ DEPLOYMENT SUCCESSFUL!")
+        print(f"  Model is now live at: https://huggingface.co/{repo_id}")
+        print("="*60)
+        print("You can now connect this model directly to your Hugging Face space.")
+        
+    except Exception as e:
+        print(f"\n[!] Deployment Failed: {str(e)}")
+
+if __name__ == "__main__":
+    deploy_model()

scripts/image_deduplicator.py

@@ -0,0 +1,152 @@
+import os
+import sqlite3
+import imagehash
+from PIL import Image
+from pathlib import Path
+from tqdm import tqdm
+import logging
+from concurrent.futures import ThreadPoolExecutor
+import time
+
+logger = logging.getLogger(__name__)
+
+class GlobalImageDeduplicator:
+    """
+    Globally tracks perceptual hashes of all images in the data directory
+    to prevent downloading duplicates across all subfolders and phases.
+    Uses an SQLite database for persistent caching to speed up initialization.
+    """
+    def __init__(self, data_dir: str, db_path: str = None, hash_size: int = 8, threshold: int = 5):
+        self.data_dir = Path(data_dir)
+        if db_path is None:
+            # Store at root/data/phash_cache.db
+            self.db_path = self.data_dir / "phash_cache.db"
+        else:
+            self.db_path = Path(db_path)
+            
+        self.hash_size = hash_size
+        self.threshold = threshold
+        self.hashes = [] # List of (filepath, imagehash.ImageHash)
+        
+        logger.info(f"Initializing Global Image Deduplicator using DB: {self.db_path}")
+        self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
+        self._init_db()
+        self._load_and_sync()
+
+    def _init_db(self):
+        with self.conn:
+            self.conn.execute('''
+                CREATE TABLE IF NOT EXISTS phashes (
+                    filepath TEXT PRIMARY KEY,
+                    mtime REAL,
+                    hash_str TEXT
+                )
+            ''')
+            
+    def _load_and_sync(self):
+        logger.info(f"Scanning {self.data_dir} for images...")
+        all_files = []
+        for ext in ('*.jpg', '*.jpeg', '*.png', '*.webp'):
+            all_files.extend(self.data_dir.rglob(ext))
+            
+        # Get existing from DB
+        cursor = self.conn.cursor()
+        cursor.execute("SELECT filepath, mtime, hash_str FROM phashes")
+        db_records = {row[0]: (row[1], row[2]) for row in cursor.fetchall()}
+        
+        to_hash = []
+        to_delete = []
+        
+        # Determine what needs hashing
+        current_files = set(str(f) for f in all_files)
+        
+        for f in all_files:
+            f_str = str(f)
+            mtime = os.path.getmtime(f)
+            if f_str in db_records:
+                # If modified time changed, rehash
+                if db_records[f_str][0] < mtime:
+                    to_hash.append((f_str, f, mtime))
+            else:
+                to_hash.append((f_str, f, mtime))
+                
+        for db_file in db_records:
+            if db_file not in current_files:
+                to_delete.append(db_file)
+                
+        # Delete missing files from DB
+        if to_delete:
+            logger.info(f"Removing {len(to_delete)} deleted files from cache.")
+            with self.conn:
+                self.conn.executemany("DELETE FROM phashes WHERE filepath = ?", [(f,) for f in to_delete])
+                
+        # Hash new or modified files
+        if to_hash:
+            logger.info(f"Hashing {len(to_hash)} new/modified images. This might take a while...")
+            
+            def compute_hash(args):
+                f_str, f, mtime = args
+                try:
+                    with Image.open(f) as img:
+                        # Convert to RGB to be safe and avoid issues with alpha channels
+                        conv_img = img.convert("RGB")
+                        h = imagehash.phash(conv_img, hash_size=self.hash_size)
+                        return f_str, mtime, str(h)
+                except Exception as e:
+                    logger.debug(f"Error hashing {f}: {e}")
+                    return None
+            
+            results = []
+            with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
+                for res in tqdm(executor.map(compute_hash, to_hash), total=len(to_hash), desc="Hashing"):
+                    if res is not None:
+                        results.append(res)
+            
+            # Save new hashes to DB
+            with self.conn:
+                self.conn.executemany("INSERT OR REPLACE INTO phashes (filepath, mtime, hash_str) VALUES (?, ?, ?)", results)
+                
+        # Load all hashes into memory for fast comparison
+        cursor.execute("SELECT filepath, hash_str FROM phashes")
+        
+        for filepath, hash_str in cursor.fetchall():
+            self.hashes.append((filepath, imagehash.hex_to_hash(hash_str)))
+            
+        logger.info(f"Loaded {len(self.hashes)} image hashes for deduplication.")
+
+    def is_duplicate(self, img: Image.Image, save_path: str = None) -> bool:
+        """
+        Check if an image is a duplicate of any globally known image.
+        If save_path is provided, and it's NOT a duplicate, it adds the hash to the in-memory 
+        cache immediately so we don't download the same duplicate in the same session.
+        """
+        # Ensure RGB
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+            
+        h = imagehash.phash(img, hash_size=self.hash_size)
+        
+        for existing_path, existing_hash in self.hashes:
+            if abs(h - existing_hash) <= self.threshold:
+                # logger.debug(f"Duplicate found! Matches {existing_path}")
+                return True
+                
+        if save_path:
+            self.hashes.append((str(save_path), h))
+            
+        return False
+        
+    def add_to_disk_cache(self, filepath: str, img: Image.Image):
+        """
+        Manually add an image to the DB cache. Use this after saving an image to disk
+        so next time we run, it's already in the DB.
+        """
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+        h = imagehash.phash(img, hash_size=self.hash_size)
+        # Wait slightly to ensure mtime is written
+        time.sleep(0.01)
+        mtime = os.path.getmtime(filepath)
+        with self.conn:
+            self.conn.execute("INSERT OR REPLACE INTO phashes (filepath, mtime, hash_str) VALUES (?, ?, ?)", 
+                            (str(filepath), mtime, str(h)))

scripts/monitor_downloads.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""Monitor download progress across all subcategories."""
+import os
+import time
+from pathlib import Path
+
+RAW_DIR = Path("data/raw")
+TARGET_PER_SUBFOLDER = 1900
+
+def count_images():
+    """Count images in each subfolder and show progress."""
+    os.system("cls" if os.name == "nt" else "clear")
+
+    total_images = 0
+    total_target = 0
+    rows = []
+
+    for parent in sorted(RAW_DIR.iterdir()):
+        if not parent.is_dir():
+            continue
+        for sub in sorted(parent.iterdir()):
+            if not sub.is_dir():
+                continue
+            count = sum(
+                1 for f in sub.iterdir()
+                if f.suffix.lower() in {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+            )
+            remaining = max(0, TARGET_PER_SUBFOLDER - count)
+            pct = min(100, count / TARGET_PER_SUBFOLDER * 100)
+            bar = "█" * int(pct // 5) + "░" * (20 - int(pct // 5))
+            status = "✅" if count >= TARGET_PER_SUBFOLDER else "⏳"
+
+            category = f"{parent.name}/{sub.name}"
+            rows.append((category, count, remaining, pct, bar, status))
+            total_images += count
+            total_target += TARGET_PER_SUBFOLDER
+
+    # Print header
+    total_remaining = max(0, total_target - total_images)
+    total_pct = total_images / total_target * 100 if total_target > 0 else 0
+    print(f"{'='*80}")
+    print(f"  📊 DOWNLOAD MONITOR  |  {total_images:,} / {total_target:,} images  "
+          f"({total_pct:.1f}%)  |  {total_remaining:,} remaining")
+    print(f"{'='*80}")
+    print(f"  {'Category':<35} {'Count':>6} {'Left':>6} {'Progress':<24} ")
+    print(f"  {'-'*35} {'-'*6} {'-'*6} {'-'*24}")
+
+    for category, count, remaining, pct, bar, status in rows:
+        print(f"  {category:<35} {count:>6} {remaining:>6} {bar} {pct:5.1f}% {status}")
+
+    print(f"  {'-'*35} {'-'*6} {'-'*6} {'-'*24}")
+    total_bar = "█" * int(total_pct // 5) + "░" * (20 - int(total_pct // 5))
+    print(f"  {'TOTAL':<35} {total_images:>6} {total_remaining:>6} {total_bar} {total_pct:5.1f}%")
+    print(f"\n  Last updated: {time.strftime('%H:%M:%S')}  |  Refreshing every 1s  |  Ctrl+C to stop")
+
+if __name__ == "__main__":
+    while True:
+        try:
+            count_images()
+            time.sleep(30)
+        except KeyboardInterrupt:
+            print("\n\n  Monitoring stopped.")
+            break

scripts/pinterest_scraper.py

@@ -0,0 +1,862 @@
+#!/usr/bin/env python3
+"""
+Pinterest Poster Image Scraper
+Config-driven scraper using Selenium + BeautifulSoup.
+Reads queries from config.yaml, downloads poster images to data/raw/{category}/
+"""
+
+import os
+import sys
+import time
+import hashlib
+import logging
+import argparse
+from pathlib import Path
+from io import BytesIO
+from urllib.parse import urljoin
+import yaml
+import requests
+import imagehash
+from PIL import Image
+from tqdm import tqdm
+
+from image_deduplicator import GlobalImageDeduplicator
+
+try:
+    from selenium import webdriver
+    from selenium.webdriver.chrome.service import Service
+    from selenium.webdriver.chrome.options import Options
+    from selenium.webdriver.common.by import By
+    from selenium.webdriver.common.keys import Keys
+    from selenium.webdriver.support.ui import WebDriverWait
+    from selenium.webdriver.support import expected_conditions as EC
+    from webdriver_manager.chrome import ChromeDriverManager
+    HAS_SELENIUM = True
+except ImportError:
+    HAS_SELENIUM = False
+    print("WARNING: selenium/webdriver_manager not installed. Install with:")
+    print("  pip install selenium webdriver-manager")
+
+try:
+    from bs4 import BeautifulSoup
+    HAS_BS4 = True
+except ImportError:
+    HAS_BS4 = False
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Logging
+# ─────────────────────────────────────────────────────────────────────────────
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[logging.StreamHandler()]
+)
+logger = logging.getLogger(__name__)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Config
+# ─────────────────────────────────────────────────────────────────────────────
+def load_config(config_path: str = "configs/config.yaml") -> dict:
+    """Load master config file."""
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Default search queries (per category) – can be overridden in config
+# ─────────────────────────────────────────────────────────────────────────────
+DEFAULT_QUERIES = {
+    # ══════════════════════════════════════════════════════════════
+    # TECH FEST  (parent: tech_fest/)
+    # ══════════════════════════════════════════════════════════════
+    "tech_fest/hackathon": [
+        "hackathon poster design",
+        "24 hour hackathon event poster",
+        "code sprint competition poster",
+        "startup hackathon poster design",
+        "programming hackathon poster",
+        "hackathon flyer template",
+        "university hackathon invite",
+        "tech hackathon banner",
+        "coding marathon event poster",
+        "hackathon winner announcement",
+        "virtual hackathon poster",
+        "hackathon timeline graphic",
+        "innovate hackathon poster",
+        "hackathon ideas poster",
+        "hackathon challenge flyer"
+    ],
+    "tech_fest/coding_competition": [
+        "coding competition poster design",
+        "competitive programming poster",
+        "code challenge event poster",
+        "algorithm contest poster",
+        "debug code competition poster",
+        "bug bounty event poster",
+        "coding battle flyer",
+        "programming contest flyer",
+        "code war poster design",
+        "coding tournament bracket"
+    ],
+    "tech_fest/ai_ml": [
+        "artificial intelligence conference poster",
+        "machine learning workshop poster",
+        "deep learning summit poster",
+        "data science event poster",
+        "AI summit poster design",
+    ],
+    "tech_fest/robotics": [
+        "robotics event poster design",
+        "robot competition poster",
+        "robotics workshop poster",
+        "drone racing event poster",
+    ],
+    "tech_fest/cybersecurity": [
+        "cyber security event poster",
+        "ethical hacking workshop poster",
+        "CTF competition poster",
+        "cybersecurity conference poster",
+    ],
+    "tech_fest/web_app_dev": [
+        "web development bootcamp poster",
+        "app development workshop poster",
+        "full stack developer event poster",
+        "software engineering meetup poster",
+    ],
+    "tech_fest/general": [
+        "tech fest poster design",
+        "technology conference poster",
+        "tech expo poster design",
+        "tech summit poster design",
+        "innovation challenge poster",
+        "tech symposium poster",
+        "engineering college fest poster",
+        "tech week event flyer",
+        "future tech event poster",
+        "technology showcase poster",
+        "IT fest poster design"
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # CULTURAL FEST  (parent: cultural_fest/)
+    # ══════════════════════════════════════════════════════════════
+    "cultural_fest/dance": [
+        "dance competition poster design",
+        "classical dance event poster",
+        "hip hop dance poster",
+        "bollywood dance night poster",
+        "dance festival poster design",
+    ],
+    "cultural_fest/music": [
+        "music concert poster design",
+        "live music event poster",
+        "DJ night poster design",
+        "band performance poster",
+        "acoustic night event poster",
+        "indie music festival poster",
+    ],
+    "cultural_fest/drama_theatre": [
+        "theatre play poster design",
+        "drama festival poster",
+        "street play nukkad natak poster",
+        "stage performance poster",
+    ],
+    "cultural_fest/art_exhibition": [
+        "art exhibition poster design",
+        "painting exhibition poster",
+        "modern art show poster",
+        "sculpture exhibition poster",
+        "photography exhibition poster",
+    ],
+    "cultural_fest/fashion_show": [
+        "fashion show poster design",
+        "college fashion event poster",
+        "runway show poster design",
+        "fashion week poster design",
+    ],
+    "cultural_fest/literary": [
+        "literary festival poster",
+        "poetry slam event poster",
+        "book launch poster design",
+        "debate competition poster",
+        "storytelling event poster",
+        "quiz competition poster",
+    ],
+    "cultural_fest/standup_comedy": [
+        "standup comedy show poster",
+        "open mic night poster",
+        "comedy night poster design",
+        "improv comedy poster",
+    ],
+    "cultural_fest/general": [
+        "cultural fest poster design",
+        "college cultural event poster",
+        "cultural night poster India",
+        "talent show poster design",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # SPORTS  (parent: sports/)
+    # ══════════════════════════════════════════════════════════════
+    "sports/cricket": [
+        "cricket tournament poster",
+        "IPL fan event poster",
+        "cricket match poster design",
+        "T20 cricket championship poster",
+    ],
+    "sports/football": [
+        "football tournament poster design",
+        "soccer championship poster",
+        "inter-college football poster",
+        "futsal tournament poster",
+    ],
+    "sports/basketball": [
+        "basketball tournament poster design",
+        "3x3 basketball event poster",
+        "college basketball championship poster",
+    ],
+    "sports/badminton_tennis": [
+        "badminton tournament poster",
+        "tennis championship poster",
+        "table tennis tournament poster",
+        "squash competition poster",
+    ],
+    "sports/athletics": [
+        "athletics meet poster design",
+        "track and field event poster",
+        "marathon poster design",
+        "fun run event poster",
+    ],
+    "sports/esports": [
+        "esports tournament poster",
+        "gaming event poster design",
+        "BGMI tournament poster",
+        "valorant tournament poster",
+        "FIFA tournament poster",
+    ],
+    "sports/kabaddi_kho": [
+        "kabaddi tournament poster India",
+        "kho kho competition poster",
+        "traditional Indian sports poster",
+    ],
+    "sports/yoga_fitness": [
+        "yoga day event poster",
+        "fitness challenge poster",
+        "gym event poster design",
+        "wellness camp poster",
+        "cycling event poster",
+    ],
+    "sports/general": [
+        "sports tournament poster design",
+        "college sports day poster",
+        "inter-college sports poster",
+        "sports carnival poster",
+        "annual sports meet poster",
+        "sports championship flyer",
+        "athletic meet event poster",
+        "intramural sports poster",
+        "sports league banner",
+        "team sports event poster"
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # COLLEGE EVENTS  (parent: college_events/)
+    # ══════════════════════════════════════════════════════════════
+    "college_events/annual_fest": [
+        "college fest poster India",
+        "university festival poster",
+        "college annual day poster",
+        "campus fest poster design",
+    ],
+    "college_events/freshers": [
+        "freshers party poster design",
+        "freshers welcome poster India",
+        "welcome party poster design",
+        "fresher orientation poster",
+    ],
+    "college_events/farewell": [
+        "farewell party poster college",
+        "goodbye seniors poster design",
+        "senior farewell poster",
+        "farewell ceremony poster",
+    ],
+    "college_events/graduation": [
+        "graduation ceremony poster",
+        "convocation poster design",
+        "degree ceremony poster",
+        "graduation day poster",
+    ],
+    "college_events/clubs_recruitment": [
+        "student club poster design",
+        "college society recruitment poster",
+        "club recruitment drive poster",
+        "join our club poster design",
+    ],
+    "college_events/alumni_reunion": [
+        "alumni meet poster design",
+        "class reunion poster",
+        "homecoming event poster",
+        "alumni networking event poster",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # FESTIVALS  (parent: festivals/)
+    # ══════════════════════════════════════════════════════════════
+    "festivals/diwali": [
+        "Diwali celebration poster",
+        "Diwali event poster design",
+        "Diwali festival poster",
+        "Deepavali poster design",
+        "Diwali mela poster",
+        "Diwali night event poster",
+    ],
+    "festivals/holi": [
+        "Holi festival poster design",
+        "Holi event poster colorful",
+        "Holi party poster design",
+        "Holi DJ night poster",
+        "Holi splash event poster",
+    ],
+    "festivals/navratri_garba": [
+        "Navratri celebration poster",
+        "Navratri garba night poster",
+        "dandiya event poster",
+        "Navratri festival poster design",
+        "garba night pass design",
+        "dandiya raas invitation",
+        "navratri dandiya night flyer",
+        "gujarati garba night poster",
+        "navratri utsav poster",
+        "dandiya night ticket design"
+    ],
+    "festivals/durga_puja": [
+        "Durga puja poster design",
+        "Durga puja pandal poster",
+        "Durga puja celebration poster",
+    ],
+    "festivals/ganesh_chaturthi": [
+        "Ganesh Chaturthi poster design",
+        "Ganpati festival poster",
+        "Ganesh utsav poster",
+        "eco friendly Ganpati poster",
+    ],
+    "festivals/eid": [
+        "Eid celebration poster design",
+        "Eid mubarak event poster",
+        "Eid ul fitr poster",
+        "Ramadan event poster",
+        "iftar party poster",
+    ],
+    "festivals/christmas": [
+        "Christmas party poster design",
+        "Christmas celebration event poster",
+        "Christmas carnival poster",
+    ],
+    "festivals/new_year": [
+        "new year celebration poster",
+        "new year eve party poster",
+        "new year countdown poster",
+    ],
+    "festivals/onam": [
+        "Onam festival poster design",
+        "Onam celebration poster",
+        "Kerala Onam poster",
+    ],
+    "festivals/pongal_sankranti": [
+        "Pongal celebration poster",
+        "Makar Sankranti poster design",
+        "Lohri celebration poster",
+        "harvest festival poster India",
+    ],
+    "festivals/independence_republic": [
+        "independence day poster India",
+        "republic day poster design",
+        "15 August celebration poster",
+        "26 January event poster",
+        "patriotic event poster India",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # WORKSHOPS & ACADEMIC  (parent: workshops/)
+    # ══════════════════════════════════════════════════════════════
+    "workshops/coding": [
+        "coding workshop poster",
+        "python workshop poster",
+        "programming workshop poster design",
+        "hackathon coding workshop poster",
+        "web dev bootcamp poster",
+        "learn to code event poster",
+        "java programming workshop poster",
+        "c++ workshop poster design",
+        "react js workshop poster",
+        "machine learning workshop poster design",
+        "app development workshop poster",
+        "coding bootcamp flyer design",
+        "programming contest poster",
+        "software engineering workshop poster",
+        "game development workshop poster",
+        "data structures workshop poster",
+        "coding marathon poster design",
+        "algorithm workshop poster",
+        "backend development workshop poster",
+        "frontend workshop poster design"
+    ],
+    "workshops/design": [
+        "graphic design workshop poster",
+        "UI UX design workshop poster",
+        "video editing workshop poster",
+        "photography workshop poster",
+        "logo design workshop poster",
+        "poster design workshop flyer",
+        "typography workshop poster",
+        "adobe photoshop workshop poster",
+        "adobe illustrator workshop poster",
+        "digital art workshop poster",
+        "creative design workshop poster",
+        "branding workshop poster design",
+        "product design workshop poster",
+        "animation workshop poster design",
+        "3d design workshop poster",
+        "figma workshop poster",
+        "canva design workshop poster",
+        "sketching workshop poster design",
+        "motion graphics workshop poster",
+        "visual design workshop poster"
+    ],
+    "workshops/business": [
+        "entrepreneurship seminar poster",
+        "startup workshop poster",
+        "business plan competition poster",
+        "marketing workshop poster",
+        "business strategy workshop flyer",
+        "startup weekend poster",
+        "business model canvas workshop",
+        "digital marketing seminar poster",
+        "finance workshop poster",
+        "MBA event poster design"
+    ],
+    "workshops/soft_skills": [
+        "public speaking workshop poster",
+        "leadership workshop poster",
+        "communication skills seminar poster",
+        "resume building workshop poster",
+    ],
+    "workshops/seminar": [
+        "seminar poster template professional",
+        "webinar event poster",
+        "guest lecture poster design",
+        "research paper workshop poster",
+    ],
+    "workshops/conference": [
+        "academic conference poster",
+        "research symposium poster",
+        "TEDx event poster design",
+        "panel discussion poster",
+        "keynote speaker event poster",
+    ],
+    "workshops/placement": [
+        "placement drive poster design",
+        "career fair poster",
+        "campus hiring poster design",
+        "internship drive poster",
+        "job recruitment poster",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # SOCIAL & AWARENESS  (parent: social/)
+    # ══════════════════════════════════════════════════════════════
+    "social/blood_donation": [
+        "blood donation camp poster",
+        "blood donation drive poster",
+        "donate blood save life poster",
+    ],
+    "social/environment": [
+        "environment day poster design",
+        "tree planting event poster",
+        "cleanliness drive poster",
+        "earth day poster design",
+    ],
+    "social/charity": [
+        "charity event poster design",
+        "fundraiser poster",
+        "NGO event poster",
+        "donation drive poster design",
+    ],
+    "social/awareness": [
+        "health awareness camp poster",
+        "women empowerment event poster",
+        "mental health awareness poster",
+        "road safety awareness poster",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # FOOD & ENTERTAINMENT  (parent: entertainment/)
+    # ══════════════════════════════════════════════════════════════
+    "entertainment/food_fest": [
+        "food festival poster design",
+        "food carnival poster",
+        "street food event poster",
+        "bake sale poster design",
+        "cooking competition poster",
+    ],
+    "entertainment/movie_night": [
+        "movie night event poster",
+        "film screening poster design",
+        "cinema night poster",
+        "short film festival poster",
+    ],
+    "entertainment/gaming": [
+        "gaming night poster design",
+        "LAN party poster",
+        "board game event poster",
+        "game jam poster design",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # DESIGN STYLES  (parent: styles/)
+    # ══════════════════════════════════════════════════════════════
+    "styles/minimalist": [
+        "minimalist event poster design",
+        "clean modern poster layout",
+        "simple elegant poster design",
+        "white space poster design",
+    ],
+    "styles/neon_glow": [
+        "neon glow party poster design",
+        "glowing neon event poster",
+        "cyberpunk poster design",
+        "neon lights party poster",
+    ],
+    "styles/retro_vintage": [
+        "retro vintage poster design",
+        "80s style event poster",
+        "vintage college event poster",
+        "retro music poster design",
+    ],
+    "styles/3d_futuristic": [
+        "3D event poster design",
+        "futuristic poster design",
+        "sci-fi event poster",
+        "holographic poster design",
+    ],
+    "styles/watercolor": [
+        "watercolor event poster design",
+        "hand painted poster design",
+        "artistic poster illustration",
+        "brush stroke poster design",
+    ],
+    "styles/gradient": [
+        "gradient poster design modern",
+        "colorful gradient event poster",
+        "vibrant gradient poster",
+        "modern abstract poster design",
+    ],
+    "styles/dark_theme": [
+        "dark theme poster design",
+        "black background event poster",
+        "dark mode poster design",
+        "dark elegant poster",
+    ],
+    "styles/typography": [
+        "typography poster design",
+        "bold text poster design",
+        "kinetic typography poster",
+        "lettering poster design",
+    ],
+    "styles/illustration": [
+        "illustrated event poster",
+        "cartoon style poster design",
+        "hand drawn poster design",
+        "vector illustration poster",
+    ],
+
+    # ══════════════════════════════════════════════════════════════
+    # GENERAL  (catch-all)
+    # ══════════════════════════════════════════════════════════════
+    "general": [
+        "event poster design modern",
+        "professional poster layout",
+        "modern event flyer design",
+        "creative poster design 2024",
+        "minimalist event poster",
+    ],
+}
+
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Perceptual Hash Dedup  (Moved to image_deduplicator.py)
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Pinterest Scraper
+# ─────────────────────────────────────────────────────────────────────────────
+class PinterestScraper:
+    """Scrape poster images from Pinterest using Selenium."""
+
+    PINTEREST_SEARCH_URL = "https://www.pinterest.com/search/pins/?q={query}"
+    TARGET_PER_THEME = 1900  # Download extra to ensure 1300+ survive quality filtering
+
+    def __init__(self, config: dict, output_dir: str):
+        self.config = config
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+
+        scraping_cfg = config.get("scraping", {}).get("pinterest", {})
+        self.scroll_pause = scraping_cfg.get("scroll_pause_seconds", 2.0)
+        self.download_timeout = scraping_cfg.get("download_timeout", 15)
+        self.min_resolution = scraping_cfg.get("min_resolution", 512)
+
+        data_root = self.config.get("paths", {}).get("data", {}).get("root", "data")
+        self.dedup = GlobalImageDeduplicator(data_dir=data_root)
+        self.session = requests.Session()
+        self.session.headers.update({
+            "User-Agent": (
+                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                "AppleWebKit/537.36 (KHTML, like Gecko) "
+                "Chrome/120.0.0.0 Safari/537.36"
+            )
+        })
+
+    def _create_driver(self) -> "webdriver.Chrome":
+        """Create a headless Chrome driver."""
+        opts = Options()
+        opts.add_argument("--headless=new")
+        opts.add_argument("--no-sandbox")
+        opts.add_argument("--disable-dev-shm-usage")
+        opts.add_argument("--disable-gpu")
+        opts.add_argument("--window-size=1920,1080")
+        opts.add_argument(
+            "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+            "AppleWebKit/537.36 (KHTML, like Gecko) "
+            "Chrome/120.0.0.0 Safari/537.36"
+        )
+        service = Service(ChromeDriverManager().install())
+        return webdriver.Chrome(service=service, options=opts)
+
+    def _scroll_and_collect_urls(self, driver, query: str, max_images: int) -> list[str]:
+        """Scroll Pinterest search page and collect image URLs."""
+        url = self.PINTEREST_SEARCH_URL.format(query=query.replace(" ", "+"))
+        driver.get(url)
+        time.sleep(3)
+
+        image_urls: set[str] = set()
+        last_height = driver.execute_script("return document.body.scrollHeight")
+        stall_count = 0
+
+        pbar = tqdm(total=max_images, desc=f"  Scrolling: {query[:40]}")
+        while len(image_urls) < max_images and stall_count < 8:
+            # Scroll down
+            driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
+            time.sleep(self.scroll_pause)
+
+            # Parse page for image URLs
+            soup = BeautifulSoup(driver.page_source, "html.parser")
+            for img_tag in soup.find_all("img"):
+                src = img_tag.get("src", "")
+                # Pinterest uses /originals/ for full-res or /736x/ for medium
+                if "pinimg.com" in src:
+                    # Try to get highest resolution
+                    full_url = src.replace("/236x/", "/originals/").replace("/474x/", "/originals/").replace("/736x/", "/originals/")
+                    image_urls.add(full_url)
+
+            pbar.update(len(image_urls) - pbar.n)
+
+            new_height = driver.execute_script("return document.body.scrollHeight")
+            if new_height == last_height:
+                stall_count += 1
+            else:
+                stall_count = 0
+            last_height = new_height
+
+        pbar.close()
+        return list(image_urls)[:max_images]
+
+    def _download_image(self, url: str, save_path: Path) -> bool:
+        """Download a single image, validate, and dedup."""
+        try:
+            resp = self.session.get(url, timeout=self.download_timeout)
+            resp.raise_for_status()
+
+            img = Image.open(BytesIO(resp.content)).convert("RGB")
+
+            # Check minimum resolution
+            if min(img.size) < self.min_resolution:
+                return False
+
+            # Check duplicate against global corpus cache
+            if self.dedup.is_duplicate(img, save_path=str(save_path)):
+                return False
+
+            img.save(save_path, "JPEG", quality=95)
+            self.dedup.add_to_disk_cache(str(save_path), img)
+            return True
+
+        except Exception as e:
+            logger.debug(f"Failed to download {url}: {e}")
+            return False
+
+    def scrape_category(self, category: str, queries: list[str]) -> int:
+        """
+        Scrape images for one category/theme.
+        Keeps going until TARGET_PER_THEME (1000) is reached.
+        Cycles through queries multiple rounds with increasing scroll depth.
+        Skips already-downloaded images.
+        """
+        cat_dir = self.output_dir / category
+        cat_dir.mkdir(parents=True, exist_ok=True)
+
+        # Count existing images (skip already-downloaded)
+        existing_files = set(f.name for f in cat_dir.glob("*.jpg"))
+        existing_count = len(existing_files)
+        logger.info(f"Category '{category}': {existing_count} existing images")
+
+        if existing_count >= self.TARGET_PER_THEME:
+            logger.info(f"  ✓ Already at target ({self.TARGET_PER_THEME}), skipping!")
+            return existing_count
+
+        remaining = self.TARGET_PER_THEME - existing_count
+        logger.info(f"  Need {remaining} more images to reach {self.TARGET_PER_THEME}")
+
+        if not HAS_SELENIUM:
+            logger.error("Selenium not available — cannot scrape Pinterest.")
+            return 0
+
+        driver = self._create_driver()
+        total_downloaded = existing_count
+        all_seen_urls: set[str] = set()  # Track all URLs across rounds
+
+        try:
+            round_num = 0
+            max_rounds = 5  # Try up to 5 rounds of cycling through queries
+
+            while total_downloaded < self.TARGET_PER_THEME and round_num < max_rounds:
+                round_num += 1
+                round_new = 0
+                # Increase scroll depth each round to find deeper content
+                scroll_target = 300 + (round_num * 200)
+
+                logger.info(f"\n  ── Round {round_num}/{max_rounds} (scroll depth: {scroll_target}) ──")
+
+                for query_idx, query in enumerate(queries):
+                    if total_downloaded >= self.TARGET_PER_THEME:
+                        break
+
+                    # Add variation to queries in later rounds
+                    if round_num > 1:
+                        variations = [
+                            f"{query} HD",
+                            f"{query} professional",
+                            f"{query} creative",
+                            f"{query} inspiration",
+                            f"best {query}",
+                        ]
+                        actual_query = variations[(round_num - 2) % len(variations)]
+                    else:
+                        actual_query = query
+
+                    logger.info(f"  Query [{query_idx+1}/{len(queries)}]: '{actual_query}'")
+                    urls = self._scroll_and_collect_urls(driver, actual_query, scroll_target)
+
+                    # Filter out already-seen URLs
+                    new_urls = [u for u in urls if u not in all_seen_urls]
+                    all_seen_urls.update(urls)
+                    logger.info(f"  Found {len(urls)} URLs ({len(new_urls)} new)")
+
+                    for url in tqdm(new_urls, desc=f"  Downloading", leave=False):
+                        if total_downloaded >= self.TARGET_PER_THEME:
+                            break
+
+                        fname = hashlib.md5(url.encode()).hexdigest() + ".jpg"
+                        save_path = cat_dir / fname
+
+                        # Skip if already downloaded
+                        if fname in existing_files or save_path.exists():
+                            continue
+
+                        if self._download_image(url, save_path):
+                            total_downloaded += 1
+                            round_new += 1
+                            existing_files.add(fname)
+
+                    # Rate-limit between queries
+                    time.sleep(3)
+
+                logger.info(f"  Round {round_num} complete: +{round_new} new images, {total_downloaded} total")
+
+                # If no new images found this round, stop early
+                if round_new == 0:
+                    logger.info(f"  No new images found in round {round_num}, moving on.")
+                    break
+
+        finally:
+            driver.quit()
+
+        new_count = len(list(cat_dir.glob("*.jpg")))
+        logger.info(
+            f"\nCategory '{category}': {new_count}/{self.TARGET_PER_THEME} images "
+            f"({new_count - existing_count} new this session)"
+        )
+        return new_count
+
+    def scrape_all(self, queries_map: dict[str, list[str]] | None = None) -> dict[str, int]:
+        """Scrape all categories."""
+        if queries_map is None:
+            queries_map = DEFAULT_QUERIES
+
+        results = {}
+        for category, queries in queries_map.items():
+            logger.info(f"\n{'='*60}")
+            logger.info(f"Scraping category: {category}")
+            logger.info(f"{'='*60}")
+            count = self.scrape_category(category, queries)
+            results[category] = count
+
+        return results
+
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Main
+# ─────────────────────────────────────────────────────────────────────────────
+def main():
+    parser = argparse.ArgumentParser(description="Pinterest Poster Image Scraper")
+    parser.add_argument("--config", default="configs/config.yaml", help="Path to config.yaml")
+    parser.add_argument("--category", default=None, help="Scrape a single category only")
+    parser.add_argument("--target", type=int, default=None, help="Override target image count (default: 1900)")
+    args = parser.parse_args()
+
+    config = load_config(args.config)
+    
+    # Override target if specified
+    if args.target:
+        PinterestScraper.TARGET_PER_THEME = args.target
+        logger.info(f"🎯 Target count overridden to {args.target} images per category")
+
+    raw_dir = config["paths"]["data"]["raw"]
+
+    scraper = PinterestScraper(config, raw_dir)
+
+    if args.category:
+        queries = DEFAULT_QUERIES.get(args.category, [f"{args.category} poster design"])
+        results = {args.category: scraper.scrape_category(args.category, queries)}
+    else:
+        results = scraper.scrape_all()
+
+    # Summary
+    logger.info("\n" + "=" * 60)
+    logger.info("SCRAPING SUMMARY")
+    logger.info("=" * 60)
+    total = 0
+    for cat, count in results.items():
+        logger.info(f"  {cat:20s}: {count:5d} images")
+        total += count
+    logger.info(f"  {'TOTAL':20s}: {total:5d} images")
+    logger.info("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

scripts/pinterest_tuning_scraper.py

@@ -0,0 +1,368 @@
+import os
+import sys
+import re
+import time
+import random
+import hashlib
+import logging
+import argparse
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+from io import BytesIO
+
+import yaml
+import requests
+from requests.adapters import HTTPAdapter
+from PIL import Image
+from tqdm import tqdm
+from image_deduplicator import GlobalImageDeduplicator
+from tuning_dataset import CATEGORIES
+
+try:
+    from selenium import webdriver
+    from selenium.webdriver.chrome.service import Service
+    from selenium.webdriver.chrome.options import Options
+    from selenium.webdriver.common.by import By
+    from webdriver_manager.chrome import ChromeDriverManager
+    HAS_SELENIUM = True
+except ImportError:
+    HAS_SELENIUM = False
+    print("WARNING: selenium/webdriver_manager not installed.")
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+PINIMG_RE = re.compile(r'https://[a-z0-9]+\.pinimg\.com/[^\s"\'<>]+\.jpg')
+
+
+class PinterestTuningScraper:
+    """Scrape specific tuning poster images from Pinterest using Selenium."""
+
+    PINTEREST_SEARCH_URL = "https://www.pinterest.com/search/pins/?q={query}"
+
+    def __init__(self, config: dict, output_dir: str, target_per_theme: int = 20):
+        self.config = config
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        self.target_per_theme = target_per_theme
+
+        scraping_cfg = config.get("scraping", {}).get("pinterest", {})
+        self.scroll_pause = scraping_cfg.get("scroll_pause_seconds", 2.0)
+        self.download_timeout = scraping_cfg.get("download_timeout", 15)
+        self.min_resolution = scraping_cfg.get("min_resolution", 512)
+
+        data_root = self.config.get("paths", {}).get("data", {}).get("root", "data")
+        self.dedup = GlobalImageDeduplicator(data_dir=data_root)
+
+        self.session = requests.Session()
+        self.session.headers.update({
+            "User-Agent": (
+                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                "AppleWebKit/537.36 (KHTML, like Gecko) "
+                "Chrome/120.0.0.0 Safari/537.36"
+            )
+        })
+        adapter = HTTPAdapter(
+            pool_connections=16,
+            pool_maxsize=16,
+            max_retries=1
+        )
+        self.session.mount("https://i.pinimg.com", adapter)
+        self.session.mount("https://v1.pinimg.com", adapter)
+
+    def _create_driver(self):
+        import undetected_chromedriver as uc
+        import random
+
+        opts = uc.ChromeOptions()
+        opts.add_argument("--no-sandbox")
+        opts.add_argument("--disable-dev-shm-usage")
+        opts.add_argument("--disable-gpu")
+        opts.add_argument("--window-size=1920,1080")
+        opts.add_argument("--mute-audio")
+        opts.add_argument("--no-first-run")
+        opts.add_argument("--disable-background-networking")
+
+        driver = uc.Chrome(options=opts, version_main=145, headless=True)
+        driver.set_page_load_timeout(30)
+        driver.set_script_timeout(10)
+        return driver
+
+    def _nuke_modals(self, driver):
+        try:
+            driver.execute_script('''
+                document.querySelectorAll(
+                    '[data-test-id="giftWrap"],[data-test-id="signup"],'
+                    '[data-test-id="unauthModal"],.Modal__overlay'
+                ).forEach(e => e.remove());
+                document.body.style.overflow = "auto";
+                document.documentElement.style.overflow = "auto";
+            ''')
+        except Exception:
+            pass
+
+    def _scroll_and_collect_urls(self, driver, query: str, max_images: int) -> list[str]:
+        url = self.PINTEREST_SEARCH_URL.format(query=query.replace(" ", "+"))
+
+        try:
+            driver.get(url)
+        except Exception:
+            pass
+
+        # Wait up to 15s for React to hydrate
+        for _ in range(15):
+            if "pinimg.com" in driver.page_source:
+                break
+            time.sleep(1)
+
+        self._nuke_modals(driver)
+
+        image_urls = set()
+        last_height = 0
+        scroll_step = 400          # smaller steps — triggers lazy loader reliably
+        current_pos = 0
+        max_scroll_pos = 80000     # ~80 screens worth, Pinterest never goes deeper
+        no_new_count = 0           # stall on CONTENT not page height
+        height_stall_count = 0
+
+        pbar = tqdm(total=max_images, desc=f"  Scrolling: {query[:40]}")
+
+        while len(image_urls) < max_images and no_new_count < 8 and current_pos < max_scroll_pos:
+            try:
+                current_pos += scroll_step
+                driver.execute_script(f"window.scrollTo(0, {current_pos});")
+                time.sleep(self.scroll_pause + random.uniform(0.3, 1.2))
+            except Exception:
+                no_new_count += 1
+                continue
+
+            prev_count = len(image_urls)
+
+            try:
+                page_source = driver.page_source
+                found = PINIMG_RE.findall(page_source)
+                for src in found:
+                    if "profile_images" in src or "75x75_RS" in src:
+                        continue
+                    # 736x resolution keeps download fast but high-quality enough
+                    src = (src.replace("/236x/", "/736x/")
+                              .replace("/474x/", "/736x/")
+                              .replace("/originals/", "/736x/"))
+                    image_urls.add(src)
+            except Exception:
+                pass
+
+            new_found = len(image_urls) - prev_count
+            if new_found == 0:
+                no_new_count += 1   # count scrolls with ZERO new images
+            else:
+                no_new_count = 0    # reset whenever new images found
+
+            pbar.update(max(0, len(image_urls) - pbar.n))
+
+            try:
+                new_height = driver.execute_script("return document.body.scrollHeight")
+                if current_pos >= new_height:
+                    self._nuke_modals(driver)
+                    if new_height == last_height:
+                        height_stall_count += 1
+                        if height_stall_count >= 3:
+                            # Truly at bottom of page, nothing more to load
+                            break
+                    else:
+                        height_stall_count = 0
+                        last_height = new_height
+                    current_pos = new_height
+            except Exception:
+                no_new_count += 1
+
+        pbar.close()
+        return list(image_urls)[:max_images]
+
+    def _is_valid_url(self, url: str) -> bool:
+        skip = ["profile_images", "75x75", "30x30", "user_images", "avatars"]
+        return not any(s in url for s in skip)
+
+    def _download_image(self, url: str, save_path: Path) -> bool:
+        try:
+            resp = self.session.get(url, timeout=(2, 4))
+            resp.raise_for_status()
+            img = Image.open(BytesIO(resp.content)).convert("RGB")
+            if min(img.size) < self.min_resolution:
+                return False
+            if self.dedup.is_duplicate(img, save_path=str(save_path)):
+                return False
+            img.save(save_path, "JPEG", quality=95)
+            self.dedup.add_to_disk_cache(str(save_path), img)
+            return True
+        except Exception as e:
+            logger.debug(f"Failed to download {url}: {e}")
+            return False
+
+    def scrape_category(self, subcategory_path: str, queries: list[str]) -> int:
+        cat_dir = self.output_dir / subcategory_path
+        cat_dir.mkdir(parents=True, exist_ok=True)
+
+        existing_files = set(f.name for f in cat_dir.glob("*.jpg"))
+        existing_count = len(existing_files)
+        logger.info(f"Subcategory '{subcategory_path}': {existing_count} existing images")
+
+        if existing_count >= self.target_per_theme:
+            logger.info(f"  ✓ Already at target ({self.target_per_theme}), skipping!")
+            return existing_count
+
+        if not HAS_SELENIUM:
+            logger.error("Selenium not available.")
+            return 0
+
+        driver = self._create_driver()
+        total_downloaded = existing_count
+        all_seen_urls = set()
+        queries = list(queries)
+
+        try:
+            query_cycle = 0
+            query_fail_counts = {}
+
+            while total_downloaded < self.target_per_theme:
+                for query in list(queries):
+                    if total_downloaded >= self.target_per_theme:
+                        break
+                    # Mutate query to break pagination bounds and prioritize design aesthetics
+                    active_query = query
+                    if query_cycle > 0:
+                        modifiers = [" poster layout", " graphic design", " aesthetic", " template", " typography"]
+                        active_query = f"{query}{modifiers[query_cycle % len(modifiers)]}"
+
+                    logger.info(f"  Query: '{active_query}' (Cycle {query_cycle + 1})")
+                    target_to_fetch = self.target_per_theme * (query_cycle + 2)
+
+                    try:
+                        urls = self._scroll_and_collect_urls(driver, active_query, target_to_fetch)
+                        query_fail_counts[query] = 0
+                    except Exception as scroll_err:
+                        logger.warning(f"  WebDriver failed/timed out on '{query}': {scroll_err}")
+                        query_fail_counts[query] = query_fail_counts.get(query, 0) + 1
+
+                        if query_fail_counts[query] >= 1:
+                            logger.error(f"  Skipping query '{query}' permanently.")
+                            queries = [q for q in queries if q != query]
+                            if not queries:
+                                logger.error("  All queries failed. Breaking out of category.")
+                                break
+
+                        logger.warning("  Rebooting Chrome driver and retrying...")
+                        time.sleep(random.uniform(3, 6))
+                        try:
+                            driver.quit()
+                        except Exception:
+                            pass
+                        driver = self._create_driver()
+                        continue
+
+                    # Reboot driver if session returned near-zero results (blacklisted)
+                    if len(urls) < 10 and total_downloaded < self.target_per_theme:
+                        logger.warning("  Session returned <10 URLs — rebooting driver.")
+                        try:
+                            driver.quit()
+                        except Exception:
+                            pass
+                        time.sleep(random.uniform(3, 6))
+                        driver = self._create_driver()
+                    new_urls = [u for u in urls if u not in all_seen_urls]
+                    all_seen_urls.update(urls)
+
+                    # FIX 7: parallel downloads — 16 workers instead of sequential
+                    needed = self.target_per_theme - total_downloaded
+                    candidates = [
+                        u for u in new_urls
+                        if self._is_valid_url(u)
+                        and f"tuning_{hashlib.md5(u.encode()).hexdigest()[:12]}.jpg"
+                           not in existing_files
+                    ][:needed * 4]
+
+                    def _dl(u, _cat_dir=cat_dir):
+                        fname = f"tuning_{hashlib.md5(u.encode()).hexdigest()[:12]}.jpg"
+                        sp = _cat_dir / fname
+                        if sp.exists():
+                            return None
+                        return (fname, self._download_image(u, sp))
+
+                    with ThreadPoolExecutor(max_workers=16) as pool:
+                        futures = {pool.submit(_dl, u): u for u in candidates}
+                        pbar_dl = tqdm(total=min(needed, len(candidates)),
+                                       desc="  Downloading", leave=False)
+                        for fut in as_completed(futures):
+                            if total_downloaded >= self.target_per_theme:
+                                pool.shutdown(wait=True, cancel_futures=True)
+                                break
+                            result = fut.result()
+                            if result:
+                                fname, ok = result
+                                if ok:
+                                    total_downloaded += 1
+                                    existing_files.add(fname)
+                                    pbar_dl.update(1)
+                        pbar_dl.close()
+
+                if total_downloaded < self.target_per_theme:
+                    if not queries:
+                        break
+                    logger.warning(
+                        f"  Only at {total_downloaded}/{self.target_per_theme}. "
+                        f"Cycling queries again and scrolling deeper."
+                    )
+                    query_cycle += 1
+                    max_cycles = max(5, len(queries))  # exhaust full query pool
+                    if query_cycle >= max_cycles:
+                        logger.error(
+                            f"  Exhausted all {max_cycles} query cycles. "
+                            f"Stuck at {total_downloaded}/{self.target_per_theme}. Breaking."
+                        )
+                        break
+
+        finally:
+            try:
+                driver.quit()
+            except Exception:
+                pass
+
+        logger.info(f"  ✓ Downloaded {total_downloaded} images for {subcategory_path}.")
+        return total_downloaded
+
+
+def load_config(config_path: str = "configs/config.yaml") -> dict:
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Tuning Dataset Pinterest Scraper")
+    parser.add_argument("--target", default="data/tuning", help="Root directory for tuning data")
+    parser.add_argument("--per-category", type=int, default=100, help="Images per subcategory")
+    args = parser.parse_args()
+
+    config = load_config()
+    target_dir = Path(args.target)
+
+    logger.info("🚀 Starting Pinterest Tuning Scraper")
+    logger.info(f"🎯 Target Count: {args.per_category} images per subcategory")
+
+    scraper = PinterestTuningScraper(config, output_dir=str(target_dir), target_per_theme=args.per_category)
+
+    for subcat, queries in CATEGORIES.items():
+        logger.info(f"\n{'='*60}")
+        logger.info(f"Processing: {subcat}")
+        logger.info(f"{'='*60}")
+        try:
+            count = scraper.scrape_category(subcat, queries)
+            logger.info(f"✅ Finished {subcat}: {count} total images")
+        except Exception as e:
+            logger.error(f"❌ Failed processing {subcat}: {e}")
+        time.sleep(2)
+
+    logger.info("\n🎉 All tuning categories processed safely without duplicates!")
+
+
+if __name__ == "__main__":
+    main()

scripts/poster_compositor.py

@@ -0,0 +1,507 @@
+#!/usr/bin/env python3
+"""
+poster_compositor.py
+====================
+Smart Poster Compositor — Text Placement Engine
+
+Renders PIL typography on SDXL-generated artwork with three placement modes:
+
+    "auto"    Scans the image for the quietest region (fewest edges) and
+              places text there automatically.
+    manual    Pass text_position="top" | "center" | "bottom" to pin the text
+              block to a fixed zone — useful when you have already reviewed
+              the artwork and know where the clean space is.
+    "none"    Returns the artwork untouched (useful for debugging raw art).
+
+A feathered dark scrim is applied only under the text block when scrim=True.
+Set scrim=False for bright or vivid artworks where a dark overlay would ruin
+the visual — text rendering already includes drop shadows and strokes for
+standalone legibility.
+
+Styles:
+    modern   Centered Montserrat, accent rules, info pill.
+    bold     Left-aligned heavy display, side accent bars, right-aligned organiser.
+    elegant  Centered Playfair Display, fine horizontal rules.
+"""
+
+from __future__ import annotations
+
+import os
+import textwrap
+from typing import Literal
+
+import numpy as np
+import requests
+from PIL import Image, ImageDraw, ImageFilter, ImageFont
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+_FONTS_DIR = os.path.normpath(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "assets", "fonts")
+)
+
+_FONT_URLS: dict[str, str] = {
+    "Montserrat-Regular":
+        "https://github.com/JulietaUla/Montserrat/raw/master/fonts/ttf/Montserrat-Regular.ttf",
+    "Montserrat-Medium":
+        "https://github.com/JulietaUla/Montserrat/raw/master/fonts/ttf/Montserrat-Medium.ttf",
+    "Montserrat-Bold":
+        "https://github.com/JulietaUla/Montserrat/raw/master/fonts/ttf/Montserrat-Bold.ttf",
+    "Montserrat-ExtraBold":
+        "https://github.com/JulietaUla/Montserrat/raw/master/fonts/ttf/Montserrat-ExtraBold.ttf",
+    "PlayfairDisplay-Bold":
+        "https://github.com/google/fonts/raw/main/ofl/playfairdisplay/PlayfairDisplay%5Bwght%5D.ttf",
+    "PlayfairDisplay-Regular":
+        "https://github.com/google/fonts/raw/main/ofl/playfairdisplay/PlayfairDisplay-Italic%5Bwght%5D.ttf",
+}
+
+Style    = Literal["modern", "bold", "elegant"]
+Position = Literal["auto", "top", "center", "bottom", "none"]
+
+# Vertical centre of the text block as a fraction of image height
+_POSITION_RATIOS: dict[str, float] = {
+    "top":    0.14,   # tight to the very top — above most subjects
+    "center": 0.50,
+    "bottom": 0.80,
+}
+
+# Scrim intensity per style — bold needs more coverage to hide busy artwork
+_SCRIM_INTENSITY: dict[str, float] = {
+    "bold":    0.90,
+    "modern":  0.78,
+    "elegant": 0.75,
+}
+
+
+# ---------------------------------------------------------------------------
+# Font helpers
+# ---------------------------------------------------------------------------
+
+def ensure_fonts() -> str:
+    """Download fonts to the assets directory if they are not already cached."""
+    os.makedirs(_FONTS_DIR, exist_ok=True)
+    for name, url in _FONT_URLS.items():
+        dest = os.path.join(_FONTS_DIR, f"{name}.ttf")
+        if os.path.exists(dest):
+            continue
+        print(f"  Downloading font: {name} ...")
+        try:
+            r = requests.get(url, timeout=30)
+            r.raise_for_status()
+            with open(dest, "wb") as fh:
+                fh.write(r.content)
+        except Exception as exc:
+            print(f"  Warning — could not download {name}: {exc}")
+    return _FONTS_DIR
+
+
+def load_font(name: str, size: int) -> ImageFont.FreeTypeFont:
+    """Return a font by logical name and point size, with graceful fallback."""
+    path = os.path.join(_FONTS_DIR, f"{name}.ttf")
+    if os.path.exists(path):
+        return ImageFont.truetype(path, size)
+    for fallback in ("DejaVuSans.ttf", "arial.ttf"):
+        try:
+            return ImageFont.truetype(fallback, size)
+        except OSError:
+            continue
+    return ImageFont.load_default()
+
+
+# ---------------------------------------------------------------------------
+# Text measurement
+# ---------------------------------------------------------------------------
+
+def _text_size(text: str, font: ImageFont.FreeTypeFont) -> tuple[int, int]:
+    bb = font.getbbox(text)
+    return bb[2] - bb[0], bb[3] - bb[1]
+
+
+def _wrap_title(title: str, style: Style) -> tuple[list[str], int]:
+    """Return (wrapped lines, font size) for the title based on length and style."""
+    length = len(title)
+    if style == "bold":
+        size  = 70 if length < 15 else 56 if length < 25 else 44
+        width = 14 if size > 56 else 18
+    elif style == "elegant":
+        size  = 56 if length < 20 else 44 if length < 30 else 36
+        width = 18 if size > 44 else 22
+    else:  # modern
+        size  = 64 if length < 20 else 50 if length < 30 else 40
+        width = 20 if size > 50 else 24
+
+    display = title if style == "elegant" else title.upper()
+    return textwrap.wrap(display, width=width), size
+
+
+# ---------------------------------------------------------------------------
+# Quiet-zone detection  (used only when text_position="auto")
+# ---------------------------------------------------------------------------
+
+def _score_bands(image: Image.Image, n: int = 5) -> list[tuple[int, int, int, float]]:
+    """Score horizontal bands by edge density. Returns list sorted quietest-first.
+
+    The top 15 % of the image is always excluded — that space is reserved
+    for organiser branding and top chrome elements.
+    """
+    w, h       = image.size
+    edges      = np.array(image.convert("L").filter(ImageFilter.FIND_EDGES), dtype=np.float32)
+    band_h     = h // n
+    top_margin = int(h * 0.15)
+    bands: list[tuple[int, int, int, float]] = []
+
+    for i in range(n):
+        y0 = i * band_h
+        y1 = min((i + 1) * band_h, h)
+        if y1 <= top_margin:
+            score = 9999.0
+        elif y0 < top_margin:
+            score = float(np.mean(edges[top_margin:y1, :]))
+        else:
+            score = float(np.mean(edges[y0:y1, :]))
+        bands.append((i, y0, y1, score))
+
+    bands.sort(key=lambda b: b[3])
+    return bands
+
+
+def _find_text_region(
+    image: Image.Image,
+    block_height: int,
+    n_bands: int = 5,
+) -> tuple[int, int, int, str]:
+    """Return (y_center, y_top, y_bottom, hint) for the quietest usable region."""
+    w, h  = image.size
+    bands = _score_bands(image, n_bands)
+    _, y0, y1, _ = bands[0]
+
+    if (y1 - y0) < block_height:
+        expand = (block_height - (y1 - y0)) // 2
+        y0 = max(0, y0 - expand)
+        y1 = min(h, y1 + expand)
+
+    y_center = (y0 + y1) // 2
+    rel      = y_center / h
+    hint     = "top" if rel < 0.33 else "bottom" if rel > 0.66 else "center"
+    return y_center, y0, y1, hint
+
+
+# ---------------------------------------------------------------------------
+# Localized dark scrim  (feathered, only under the text block)
+# ---------------------------------------------------------------------------
+
+def _apply_scrim(
+    image:     Image.Image,
+    y_top:     int,
+    y_bottom:  int,
+    intensity: float = 0.78,
+) -> Image.Image:
+    """Burn a soft dark gradient over *image* between y_top and y_bottom ONLY.
+
+    60-pixel feathered edges ensure the scrim blends invisibly into the
+    surrounding artwork. Nothing outside the text region is darkened.
+    """
+    w, h        = image.size
+    scrim       = Image.new("RGBA", (w, h), (0, 0, 0, 0))
+    draw        = ImageDraw.Draw(scrim)
+    feather     = 60
+    fade_top    = max(0, y_top    - feather)
+    fade_bottom = min(h, y_bottom + feather)
+
+    for y in range(fade_top, fade_bottom):
+        if y < y_top:
+            t = (y - fade_top) / max(1, y_top - fade_top)
+        elif y > y_bottom:
+            t = 1.0 - (y - y_bottom) / max(1, fade_bottom - y_bottom)
+        else:
+            t = 1.0
+        alpha = min(int(200 * t * intensity), 215)
+        draw.line([(0, y), (w, y)], fill=(0, 0, 0, alpha))
+
+    base = image.convert("RGBA")
+    return Image.alpha_composite(base, scrim).convert("RGB")
+
+
+# ---------------------------------------------------------------------------
+# Text-rendering primitives
+# ---------------------------------------------------------------------------
+
+def _shadowed(
+    draw:          ImageDraw.ImageDraw,
+    xy:            tuple[int, int],
+    text:          str,
+    font:          ImageFont.FreeTypeFont,
+    fill:          str = "#FFFFFF",
+    shadow_color:  str = "#000000",
+    shadow_offset: int = 4,
+    anchor:        str = "lt",
+) -> None:
+    """Render text with a layered drop shadow and thin stroke for legibility."""
+    x, y = xy
+    draw.text((x + shadow_offset,     y + shadow_offset),     text, font=font, fill=(0, 0, 0, 220), anchor=anchor)
+    draw.text((x + shadow_offset * 2, y + shadow_offset * 2), text, font=font, fill=(0, 0, 0, 100), anchor=anchor)
+    draw.text(xy, text, font=font, fill=fill, stroke_width=2, stroke_fill=shadow_color, anchor=anchor)
+
+
+def _pill(
+    draw:    ImageDraw.ImageDraw,
+    xy:      tuple[int, int],
+    text:    str,
+    font:    ImageFont.FreeTypeFont,
+    fill:    str             = "#FFFFFF",
+    bg:      tuple[int, ...] = (0, 0, 0, 160),
+    padding: int             = 12,
+    anchor:  str             = "lt",
+) -> None:
+    """Render text on a semi-transparent rounded-rectangle background."""
+    bb = font.getbbox(text, anchor=anchor)
+    x, y = xy
+    draw.rounded_rectangle(
+        [
+            (x + bb[0] - padding, y + bb[1] - padding),
+            (x + bb[2] + padding, y + bb[3] + padding),
+        ],
+        radius=8,
+        fill=bg,
+    )
+    draw.text(xy, text, font=font, fill=fill, anchor=anchor)
+
+
+# ---------------------------------------------------------------------------
+# Block height estimator
+# ---------------------------------------------------------------------------
+
+def _estimate_block_height(
+    title: str, subtitle: str, date: str, venue: str, organizer: str, style: Style,
+) -> int:
+    lines, size = _wrap_title(title, style)
+    font_title  = load_font(
+        "PlayfairDisplay-Bold" if style == "elegant" else "Montserrat-ExtraBold", size
+    )
+    total = sum(_text_size(ln, font_title)[1] + 12 for ln in lines) + 24
+    if subtitle:  total += 34
+    if date:      total += 28
+    if venue:     total += 28
+    if organizer: total += 36
+    return total + 40
+
+
+# ---------------------------------------------------------------------------
+# Layout: MODERN
+# ---------------------------------------------------------------------------
+
+def _layout_modern(
+    draw: ImageDraw.ImageDraw, w: int, h: int,
+    title: str, subtitle: str, date: str, venue: str,
+    organizer: str, accent: str, start_y: int,
+) -> None:
+    """Centered layout with accent bars top and bottom."""
+    cx  = w // 2
+    gap = 16
+
+    draw.rectangle([(0, 0), (w, 5)], fill=accent)
+    if organizer:
+        font_org = load_font("Montserrat-Medium", 22)
+        _shadowed(draw, (cx, 28), organizer.upper(), font_org, anchor="mt")
+
+    cursor = start_y
+    lines, size = _wrap_title(title, "modern")
+    font_title  = load_font("Montserrat-ExtraBold", size)
+    for line in lines:
+        _shadowed(draw, (cx, cursor), line, font_title, anchor="mt")
+        cursor += _text_size(line, font_title)[1] + 12
+
+    cursor += 6
+    draw.rectangle([(cx - 90, cursor), (cx + 90, cursor + 3)], fill=accent)
+    cursor += 3 + gap
+
+    if subtitle:
+        font_sub = load_font("PlayfairDisplay-Regular", 26)
+        _shadowed(draw, (cx, cursor), subtitle, font_sub, fill=accent, anchor="mt")
+        cursor += _text_size(subtitle, font_sub)[1] + gap
+
+    parts: list[str] = []
+    if date:  parts.append(f"📅  {date}")
+    if venue: parts.append(f"📍  {venue}")
+    if parts:
+        font_info = load_font("Montserrat-Regular", 18)
+        _pill(draw, (cx, cursor), "   •   ".join(parts), font_info,
+              bg=(0, 0, 0, 170), anchor="mt")
+
+    draw.rectangle([(0, h - 5), (w, h)], fill=accent)
+
+
+# ---------------------------------------------------------------------------
+# Layout: BOLD
+# ---------------------------------------------------------------------------
+
+def _layout_bold(
+    draw: ImageDraw.ImageDraw, w: int, h: int,
+    title: str, subtitle: str, date: str, venue: str,
+    organizer: str, accent: str, start_y: int,
+) -> None:
+    """Left-aligned heavy display. Organiser pill pinned top-right."""
+    LEFT = 50
+    gap  = 18
+
+    draw.rectangle([(0, 0),     (6, h)], fill=accent)
+    draw.rectangle([(w - 6, 0), (w, h)], fill=accent)
+
+    # Organiser — top-right so it never clashes with left-aligned title
+    if organizer:
+        font_org = load_font("Montserrat-Bold", 18)
+        _pill(draw, (w - LEFT, 28), organizer.upper(), font_org,
+              fill=accent, bg=(0, 0, 0, 200), padding=10, anchor="rt")
+
+    cursor = start_y
+    lines, size = _wrap_title(title, "bold")
+    font_title  = load_font("Montserrat-ExtraBold", size)
+    for line in lines:
+        _shadowed(draw, (LEFT, cursor), line, font_title, shadow_offset=5)
+        cursor += _text_size(line, font_title)[1] + 8
+    cursor += gap
+
+    if subtitle:
+        font_sub = load_font("Montserrat-Bold", 24)
+        _shadowed(draw, (LEFT, cursor), subtitle.upper(), font_sub, fill=accent)
+        cursor += _text_size(subtitle.upper(), font_sub)[1] + gap
+
+    font_info = load_font("Montserrat-Regular", 20)
+    if date:
+        _shadowed(draw, (LEFT, cursor), f"📅  {date}", font_info, fill="#DDDDDD")
+        cursor += _text_size(f"📅  {date}", font_info)[1] + 10
+    if venue:
+        _shadowed(draw, (LEFT, cursor), f"📍  {venue}", font_info, fill="#DDDDDD")
+
+
+# ---------------------------------------------------------------------------
+# Layout: ELEGANT
+# ---------------------------------------------------------------------------
+
+def _layout_elegant(
+    draw: ImageDraw.ImageDraw, w: int, h: int,
+    title: str, subtitle: str, date: str, venue: str,
+    organizer: str, accent: str, start_y: int,
+) -> None:
+    """Centered serif layout with fine horizontal rules."""
+    cx     = w // 2
+    rule_w = 160
+    gap    = 18
+
+    draw.rectangle([(cx - rule_w, 46), (cx + rule_w, 48)], fill=accent)
+    if organizer:
+        font_org = load_font("Montserrat-Medium", 20)
+        _shadowed(draw, (cx, 62), organizer, font_org, anchor="mt")
+    draw.rectangle([(cx - rule_w, 94), (cx + rule_w, 96)], fill=accent)
+
+    cursor = start_y
+    lines, size = _wrap_title(title, "elegant")
+    font_title  = load_font("PlayfairDisplay-Bold", size)
+    for line in lines:
+        _shadowed(draw, (cx, cursor), line, font_title,
+                  shadow_color="#1A1A1A", shadow_offset=3, anchor="mt")
+        cursor += _text_size(line, font_title)[1] + 14
+
+    cursor += 8
+    draw.rectangle([(cx - 60, cursor), (cx + 60, cursor + 1)], fill=accent)
+    cursor += 1 + gap
+
+    if subtitle:
+        font_sub = load_font("PlayfairDisplay-Regular", 26)
+        _shadowed(draw, (cx, cursor), subtitle, font_sub, fill=accent, anchor="mt")
+        cursor += _text_size(subtitle, font_sub)[1] + gap
+
+    font_info = load_font("Montserrat-Regular", 17)
+    if date:
+        _shadowed(draw, (cx, cursor), date.upper(), font_info,
+                  fill="#E8E8E8", anchor="mt")
+        cursor += _text_size(date.upper(), font_info)[1] + 8
+    if venue:
+        _pill(draw, (cx, cursor), venue, font_info,
+              fill="#FFFFFF", bg=(0, 0, 0, 150), padding=10, anchor="mt")
+
+    draw.rectangle([(cx - rule_w, h - 48), (cx + rule_w, h - 46)], fill=accent)
+    draw.rectangle([(cx - rule_w, h - 36), (cx + rule_w, h - 34)], fill=accent)
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+_LAYOUTS = {
+    "modern":  _layout_modern,
+    "bold":    _layout_bold,
+    "elegant": _layout_elegant,
+}
+
+
+def composite_poster(
+    artwork:       Image.Image,
+    title:         str,
+    subtitle:      str      = "",
+    date:          str      = "",
+    venue:         str      = "",
+    organizer:     str      = "",
+    accent_color:  str      = "#FFD700",
+    style:         Style    = "modern",
+    text_position: Position = "auto",
+    scrim:         bool     = True,
+) -> Image.Image:
+    """Composite event text onto an SDXL artwork image.
+
+    Args:
+        artwork:       Raw SDXL-generated PIL Image.
+        title:         Primary event name (required).
+        subtitle:      Short tagline or theme (optional).
+        date:          Human-readable date string (optional).
+        venue:         Location or venue name (optional).
+        organizer:     Host shown at the top of the poster (optional).
+        accent_color:  Hex colour for decorative elements and rules.
+        style:         "modern" | "bold" | "elegant"
+        text_position: "auto"   — detect quietest region automatically.
+                       "top"    — pin text block near the top (y=14%).
+                       "center" — pin to vertical centre (y=50%).
+                       "bottom" — pin to bottom area (y=80%).
+                       "none"   — return artwork unchanged.
+        scrim:         True  — feathered dark gradient under text block only.
+                               Intensity is style-aware: bold=0.90, others lower.
+                       False — no scrim; rely on shadow/stroke for legibility.
+                               Use for bright, vivid, or light-bg artworks.
+
+    Returns:
+        Composited PIL Image (RGB).
+    """
+    if text_position == "none":
+        return artwork.copy().convert("RGB")
+
+    ensure_fonts()
+
+    img = artwork.copy().convert("RGB")
+    w, h = img.size
+
+    block_h = _estimate_block_height(title, subtitle, date, venue, organizer, style)
+
+    if text_position in _POSITION_RATIOS:
+        y_center = int(h * _POSITION_RATIOS[text_position])
+    else:
+        y_center, _, _, _ = _find_text_region(img, block_h)
+
+    pad = 44
+    if scrim:
+        scrim_top       = max(0, y_center - block_h // 2 - pad)
+        scrim_bottom    = min(h, y_center + block_h // 2 + pad)
+        scrim_intensity = _SCRIM_INTENSITY.get(style, 0.78)
+        img = _apply_scrim(img, scrim_top, scrim_bottom, intensity=scrim_intensity)
+
+    overlay = Image.new("RGBA", (w, h), (0, 0, 0, 0))
+    draw    = ImageDraw.Draw(overlay)
+
+    text_start_y = max(pad, y_center - block_h // 2)
+    _LAYOUTS.get(style, _layout_modern)(
+        draw, w, h, title, subtitle, date, venue, organizer, accent_color, text_start_y
+    )
+
+    result = Image.alpha_composite(img.convert("RGBA"), overlay)
+    return result.convert("RGB")

scripts/quality_filter.py

@@ -0,0 +1,562 @@
+#!/usr/bin/env python3
+"""
+Image Quality Filter (GPU-Accelerated)
+Filters raw scraped images based on resolution, sharpness, aspect ratio,
+file size, and deduplication. Uses GPU for batch sharpness and color analysis.
+Outputs high-quality images to data/processed/.
+"""
+
+import os
+import sys
+import json
+import shutil
+import logging
+import argparse
+from pathlib import Path
+from collections import defaultdict
+
+import yaml
+import cv2
+import numpy as np
+import imagehash
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from tqdm import tqdm
+
+# ─── SM120 (Blackwell) CUDA optimizations ───────────────────────────────────
+if torch.cuda.is_available():
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Logging
+# ─────────────────────────────────────────────────────────────────────────────
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Config
+# ─────────────────────────────────────────────────────────────────────────────
+def load_config(config_path: str = "configs/config.yaml") -> dict:
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# GPU-Accelerated Quality Checker
+# ─────────────────────────────────────────────────────────────────────────────
+class ImageQualityChecker:
+    """
+    Evaluate image quality using GPU-accelerated sharpness and color analysis.
+    Falls back to CPU if no CUDA device is available.
+    """
+
+    # Laplacian kernel for GPU sharpness detection
+    LAPLACIAN_KERNEL = torch.tensor(
+        [[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=torch.float32
+    ).unsqueeze(0).unsqueeze(0)
+
+    def __init__(
+        self,
+        min_resolution: int = 512,
+        min_sharpness: float = 50.0,
+        min_aspect_ratio: float = 0.4,
+        max_aspect_ratio: float = 2.5,
+        min_file_size_kb: int = 20,
+        max_file_size_mb: int = 50,
+        device: str = "auto",
+    ):
+        self.min_resolution = min_resolution
+        self.min_sharpness = min_sharpness
+        self.min_aspect_ratio = min_aspect_ratio
+        self.max_aspect_ratio = max_aspect_ratio
+        self.min_file_size_bytes = min_file_size_kb * 1024
+        self.max_file_size_bytes = max_file_size_mb * 1024 * 1024
+
+        # GPU setup
+        if device == "auto":
+            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        else:
+            self.device = torch.device(device)
+
+        self._kernel = self.LAPLACIAN_KERNEL.to(self.device)
+        logger.info(f"Quality checker using device: {self.device}")
+
+    def _gpu_sharpness(self, img_array: np.ndarray) -> float:
+        """Compute sharpness using Laplacian on GPU."""
+        # Convert to grayscale
+        gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+
+        # Move to GPU as torch tensor
+        tensor = torch.from_numpy(gray.astype(np.float32)).unsqueeze(0).unsqueeze(0)
+        tensor = tensor.to(self.device)
+
+        # Apply Laplacian convolution on GPU
+        laplacian = F.conv2d(tensor, self._kernel, padding=1)
+        sharpness = laplacian.var().item()
+
+        return sharpness
+
+    def _gpu_color_std(self, img_array: np.ndarray) -> float:
+        """Compute color standard deviation on GPU."""
+        tensor = torch.from_numpy(img_array.astype(np.float32)).to(self.device)
+        return tensor.std().item()
+
+    def check(self, image_path: Path) -> tuple[bool, dict]:
+        """
+        Check image quality. Returns (passed, metrics_dict).
+        Sharpness and color checks run on GPU.
+        """
+        metrics = {
+            "path": str(image_path),
+            "passed": False,
+            "reason": None,
+        }
+
+        # File size check (CPU — trivial)
+        file_size = image_path.stat().st_size
+        metrics["file_size_bytes"] = file_size
+        if file_size < self.min_file_size_bytes:
+            metrics["reason"] = "file_too_small"
+            return False, metrics
+        if file_size > self.max_file_size_bytes:
+            metrics["reason"] = "file_too_large"
+            return False, metrics
+
+        # Load image
+        try:
+            img = Image.open(image_path).convert("RGB")
+        except Exception:
+            metrics["reason"] = "unreadable"
+            return False, metrics
+
+        w, h = img.size
+        metrics["width"] = w
+        metrics["height"] = h
+
+        # Resolution check (CPU — trivial)
+        if min(w, h) < self.min_resolution:
+            metrics["reason"] = "low_resolution"
+            return False, metrics
+
+        # Aspect ratio check (CPU — trivial)
+        aspect = w / h
+        metrics["aspect_ratio"] = round(aspect, 3)
+        if aspect < self.min_aspect_ratio or aspect > self.max_aspect_ratio:
+            metrics["reason"] = "bad_aspect_ratio"
+            return False, metrics
+
+        img_array = np.array(img)
+
+        # Sharpness check (GPU-accelerated Laplacian)
+        try:
+            sharpness = self._gpu_sharpness(img_array)
+            metrics["sharpness"] = round(sharpness, 2)
+            if sharpness < self.min_sharpness:
+                metrics["reason"] = "too_blurry"
+                return False, metrics
+        except Exception:
+            metrics["reason"] = "sharpness_check_failed"
+            return False, metrics
+
+        # Color variance check (GPU-accelerated)
+        std = self._gpu_color_std(img_array)
+        metrics["color_std"] = round(float(std), 2)
+        if std < 15.0:
+            metrics["reason"] = "too_uniform"
+            return False, metrics
+
+        metrics["passed"] = True
+        return True, metrics
+
+    def check_batch(self, image_paths: list[Path]) -> list[tuple[bool, dict]]:
+        """
+        Batch quality check — processes multiple images with GPU acceleration.
+        Pre-filters by file size and resolution on CPU, then batches
+        GPU operations for remaining images.
+        """
+        results = []
+        for path in image_paths:
+            results.append(self.check(path))
+        return results
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Deduplicator
+# ─────────────────────────────────────────────────────────────────────────────
+class Deduplicator:
+    """Remove near-duplicate images using perceptual hashing."""
+
+    def __init__(self, hash_size: int = 8, threshold: int = 5):
+        self.hash_size = hash_size
+        self.threshold = threshold
+        self.hashes: dict[str, "imagehash.ImageHash"] = {}
+
+    def is_duplicate(self, image_path: Path) -> bool:
+        try:
+            img = Image.open(image_path).convert("RGB")
+            h = imagehash.phash(img, hash_size=self.hash_size)
+            for existing_path, existing_hash in self.hashes.items():
+                if abs(h - existing_hash) <= self.threshold:
+                    return True
+            self.hashes[str(image_path)] = h
+            return False
+        except Exception:
+            return True  # Can't hash → treat as duplicate
+
+
+class GPUHasher:
+    """
+    GPU-accelerated Perceptual Hashing (pHash).
+    Strictly forces GPU usage.
+    """
+    def __init__(self, device="cuda"):
+        if not torch.cuda.is_available():
+            raise RuntimeError("❌ CUDA is not available! GPUHasher requires a GPU.")
+        
+        self.device = device
+        logger.info(f"⚡ GPUHasher initialized on: {str(self.device).upper()}")
+        self.dct_matrix = self._get_dct_matrix(32).to(self.device)
+
+    def _get_dct_matrix(self, N):
+        """Standard DCT-II matrix."""
+        dct_m = np.zeros((N, N))
+        for k in range(N):
+            for n in range(N):
+                dct_m[k, n] = np.cos(np.pi / N * (n + 0.5) * k)
+        return torch.from_numpy(dct_m).float()
+
+    def compute_hashes(self, image_paths: list[Path], batch_size=64) -> dict[str, int]:
+        """
+        Compute pHash for a list of image paths using GPU acceleration.
+        Returns dictionary {path_str: hash_int}
+        """
+        results = {}
+        
+        # Use tqdm for progress bar
+        with tqdm(total=len(image_paths), desc="  Computing hashes (GPU)", unit="img") as pbar:
+            for i in range(0, len(image_paths), batch_size):
+                batch_paths = image_paths[i : i + batch_size]
+                batch_tensors = []
+                valid_paths = []
+
+                for p in batch_paths:
+                    try:
+                        # Open (L = grayscale)
+                        # We avoid PIL.resize here to save CPU
+                        img = Image.open(p).convert("L")
+                        
+                        # Convert to tensor [1, H, W] directly
+                        t = torch.from_numpy(np.array(img)).float().unsqueeze(0) / 255.0
+                        batch_tensors.append(t)
+                        valid_paths.append(str(p))
+                    except Exception:
+                        pass
+                
+                # Update pbar for the batch processed
+                pbar.update(len(batch_paths))
+
+                if not batch_tensors:
+                    continue
+                
+                # GPU Processing
+                try:
+                    gpu_tensors = []
+                    for t in batch_tensors:
+                        # Move to GPU
+                        t_gpu = t.to(self.device, non_blocking=True).unsqueeze(0) # [1, 1, H, W]
+                        # Resize on GPU
+                        t_resized = F.interpolate(t_gpu, size=(32, 32), mode='bilinear', align_corners=False)
+                        gpu_tensors.append(t_resized.squeeze(0)) # [1, 32, 32]
+                    
+                    # Stack: [B, 32, 32]
+                    pixel_batch = torch.stack(gpu_tensors).squeeze(1)
+                    
+                    # Compute DCT: D * I * D^T
+                    # [32, 32] @ [B, 32, 32] @ [32, 32] -> [B, 32, 32]
+                    dct = torch.matmul(self.dct_matrix, pixel_batch)
+                    dct = torch.matmul(dct, self.dct_matrix.T)
+        
+                    # Extract top-left 8x8 (excluding DC term at 0,0)
+                    # Flatten to [B, 64]
+                    dct_low = dct[:, :8, :8].reshape(-1, 64)
+        
+                    # Compute median per image
+                    medians = dct_low.median(dim=1, keepdim=True).values
+        
+                    # Generate hash: 1 if > median, 0 otherwise
+                    bits = (dct_low > medians).long()
+        
+                    # Convert 64 bits to integer
+                    # Powers of 2 vector: [2^0, 2^1, ... 2^63]
+                    powers = (2 ** torch.arange(64, device=self.device)).long()
+                    hashes = (bits * powers).sum(dim=1).cpu().numpy()
+        
+                    for p, h in zip(valid_paths, hashes):
+                        results[p] = int(h)
+                        
+                except Exception as e:
+                    logger.debug(f"GPU Hash batch failed: {e}")
+                    continue
+    
+        return results
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Main Pipeline
+# ─────────────────────────────────────────────────────────────────────────────
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Main Pipeline
+# ─────────────────────────────────────────────────────────────────────────────
+def run_quality_filter(config: dict) -> dict:
+    """Main quality filter pipeline (GPU-accelerated) with Auto-Scrape Top-Up."""
+    from pinterest_scraper import PinterestScraper, DEFAULT_QUERIES  # Lazy import to avoid circular deps
+    
+    raw_dir = Path(config["paths"]["data"]["raw"])
+    processed_dir = Path(config["paths"]["data"]["processed"])
+    
+    TARGET_COUNT = 1300
+
+    if not raw_dir.exists():
+        logger.error(f"Raw data directory does not exist: {raw_dir}")
+        sys.exit(1)
+
+    # Quality settings from config
+    quality_cfg = config.get("dataset", {}).get("quality", {})
+    
+    checker = ImageQualityChecker(
+        min_resolution=quality_cfg.get("min_resolution", 512),
+        min_sharpness=quality_cfg.get("min_sharpness", 50.0),
+        min_aspect_ratio=quality_cfg.get("min_aspect_ratio", 0.4),
+        max_aspect_ratio=quality_cfg.get("max_aspect_ratio", 2.5),
+    )
+    dedup = Deduplicator()
+    
+    # Initialize scraper (but don't start driver yet)
+    scraper = PinterestScraper(config, str(raw_dir))
+
+    # Log GPU status
+    if torch.cuda.is_available():
+        gpu_name = torch.cuda.get_device_name(0)
+        gpu_mem = torch.cuda.get_device_properties(0).total_memory / (1024**3)
+        logger.info(f"🎮 GPU detected: {gpu_name}. Total memory: {gpu_mem:.2f} GB")
+    else:
+        logger.info("🖥️ No GPU detected — running on CPU (slower)")
+
+    # Stats
+    stats = defaultdict(lambda: {"total": 0, "passed": 0, "failed": 0, "duplicates": 0})
+    
+    # 1. LOAD ALL EXISTING PROCESSED IMAGES (Global Deduplication)
+    logger.info("🧠 Learning ALL existing images to prevent duplicates...")
+    all_processed_files = []
+    for root, _, files in os.walk(processed_dir):
+        for file in files:
+            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.webp')):
+                all_processed_files.append(Path(root) / file)
+                
+    existing_hashes = 0
+    if all_processed_files:
+        hasher = GPUHasher()
+        # Compute hashes for everything currently in processed
+        batch_hashes = hasher.compute_hashes(all_processed_files, batch_size=128)
+        dedup.hashes.update(batch_hashes)
+        existing_hashes = len(batch_hashes)
+        
+    logger.info(f"✅ Memorized {existing_hashes} unique images in processed dataset.")
+
+    # Collect all leaf directories (directories that contain images, not just parents)
+    leaf_dirs = []
+    for root, dirs, files in os.walk(raw_dir):
+        root_path = Path(root)
+        # Check if this is a leaf node we want to process
+        # (It might be empty now but was scraped before, or we want to scrape it)
+        # For now, rely on existing folders in raw.
+        rel_path = root_path.relative_to(raw_dir)
+        
+        # Skip the root directory itself (files directly in data/raw)
+        if str(rel_path) == ".":
+            continue
+            
+        leaf_dirs.append((rel_path, root_path))
+
+    if not leaf_dirs:
+        logger.warning("No directories found in raw data.")
+        return {}
+
+    logger.info(f"Found {len(leaf_dirs)} theme directories to process")
+
+    for rel_path, dir_path in sorted(leaf_dirs):
+        category = str(rel_path).replace("\\", "/")
+        out_dir = processed_dir / rel_path
+        out_dir.mkdir(parents=True, exist_ok=True)
+        
+        # We assume leaf dir if it has no subdirs with images? 
+        # Simpler: just process if we found it.
+
+        while True:
+            # Check current status in processed folder
+            processed_images = [f for f in os.listdir(out_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
+            current_count = len(processed_images)
+            
+            # If we met the target, break loop and move to next category
+            if current_count >= TARGET_COUNT:
+                logger.info(f"✅ {category}: Target met ({current_count} images).")
+                break
+                
+            needed = TARGET_COUNT - current_count
+            logger.info(f"\nCategory: {category}")
+            logger.info(f"  Current: {current_count} | Needed: {needed}")
+
+            # Get raw images
+            raw_images = sorted([
+                dir_path / f for f in os.listdir(dir_path) 
+                if f.lower().endswith(('.jpg', '.jpeg', '.png', '.webp', '.bmp'))
+            ])
+            logger.info(f"  Raw images available: {len(raw_images)}")
+            
+            # Identify candidates (raw images NOT yet in processed folder by filename)
+            existing_filenames = set(processed_images)
+            candidates = [p for p in raw_images if p.name not in existing_filenames]
+            
+            added_this_round = 0
+            
+            if candidates:
+                logger.info(f"  Processing {len(candidates)} new candidates...")
+                pbar = tqdm(candidates, desc=f"  {category} (Filter)", unit="img")
+                for img_path in pbar:
+                    if added_this_round >= needed:
+                        break
+                    
+                    stats[category]["total"] += 1
+        
+                    # Quality check (GPU-accelerated sharpness + color)
+                    passed, metrics = checker.check(img_path)
+                    if not passed:
+                        stats[category]["failed"] += 1
+                        # logger.debug(f"  REJECTED {img_path.name}: {metrics['reason']}")
+                        continue
+        
+                    # Dedup check (Hash-based)
+                    if dedup.is_duplicate(img_path):
+                        stats[category]["duplicates"] += 1
+                        # logger.debug(f"  DUPLICATE {img_path.name}")
+                        continue
+        
+                    # Copy to processed
+                    dest = out_dir / img_path.name
+                    shutil.copy2(img_path, dest)
+                    stats[category]["passed"] += 1
+                    added_this_round += 1
+                    
+                pbar.close()
+                current_count += added_this_round
+                
+                if current_count >= TARGET_COUNT:
+                    continue # Re-evaluate loop condition (which will break)
+            
+            # If still short, trigger scraper
+            needed = TARGET_COUNT - current_count
+            if needed > 0:
+                logger.warning(f"  ⚠️ Short by {needed} images! Launching Scraper to fetch more...")
+                
+                # Fetch query list
+                queries = DEFAULT_QUERIES.get(category)
+                if not queries:
+                    # Fallback queries
+                    theme = category.split("/")[-1]
+                    queries = [f"{theme} poster", f"{theme} design", f"{theme} advertisement"]
+                
+                # Scrape 2x what we need
+                scrape_target = len(raw_images) + (needed * 2) 
+                # Ensure we at least target 2800 if we are really low
+                scrape_target = max(scrape_target, 2800)
+                
+                scraper.TARGET_PER_THEME = scrape_target
+                logger.info(f"  🕷️ Scraping target set to {scrape_target} for {category}...")
+                
+                try:
+                    # scraper.scrape_category downloads to raw_dir/{category}
+                    # It returns total downloaded count
+                    new_total = scraper.scrape_category(category, queries)
+                    logger.info(f"  ✅ Scraping finished. Raw total is now {new_total}. Rescanning...")
+                except Exception as e:
+                    logger.error(f"  ❌ Scraper failed: {e}")
+                    break # Stop trying for this category if scraper fails
+            else:
+                break # Should be caught by top check, but safe fallback
+
+    # Clear GPU memory
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+    return dict(stats)
+
+
+def print_summary(stats: dict):
+    """Print a summary table."""
+    # ... existing print_summary code ...
+    print("\n" + "=" * 60)
+    print(f"{'Category':<35} | {'Total':<8} | {'Pass':<6} | {'Fail':<6} | {'Dupes':<6}")
+    print("-" * 60)
+    
+    total_passed = 0
+    for cat, data in sorted(stats.items()):
+        print(f"{cat:<35} | {data['total']:<8} | {data['passed']:<6} | {data['failed']:<6} | {data['duplicates']:<6}")
+        total_passed += data['passed']
+        
+    print("-" * 60)
+    print(f"Total High-Quality Images: {total_passed}")
+    print("=" * 60 + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run Quality Filter with Auto-Scrape")
+    parser.add_argument("--config", default="configs/config.yaml", help="Path to config.yaml")
+    args = parser.parse_args()
+
+    config = load_config(args.config)
+    
+    # Run pipeline
+    stats = run_quality_filter(config)
+    print_summary(stats)
+
+    logger.info("\n" + "=" * 80)
+    logger.info("QUALITY FILTER SUMMARY")
+    logger.info("=" * 80)
+    logger.info(f"  {'Category':35s} {'Total':>7s} {'Passed':>7s} {'Failed':>7s} {'Dupes':>7s} {'Rate':>7s}")
+    logger.info(f"  {'-'*35} {'-'*7} {'-'*7} {'-'*7} {'-'*7} {'-'*7}")
+
+    grand_total = grand_passed = 0
+    for cat, s in sorted(stats.items()):
+        rate = f"{s['passed']/max(s['total'],1)*100:.1f}%"
+        logger.info(
+            f"  {cat:35s} {s['total']:7d} {s['passed']:7d} "
+            f"{s['failed']:7d} {s['duplicates']:7d} {rate:>7s}"
+        )
+        grand_total += s["total"]
+        grand_passed += s["passed"]
+
+    rate = f"{grand_passed/max(grand_total,1)*100:.1f}%"
+    logger.info(f"  {'-'*35} {'-'*7} {'-'*7} {'-'*7} {'-'*7} {'-'*7}")
+    logger.info(f"  {'TOTAL':35s} {grand_total:7d} {grand_passed:7d}{'':>17s} {rate:>7s}")
+    logger.info("=" * 80)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Image Quality Filter (GPU-Accelerated)")
+    parser.add_argument("--config", default="configs/config.yaml", help="Path to config.yaml")
+    args = parser.parse_args()
+
+    config = load_config(args.config)
+    stats = run_quality_filter(config)
+    print_summary(stats)
+
+
+if __name__ == "__main__":
+    main()

scripts/split_dataset.py

@@ -0,0 +1,136 @@
+
+import os
+import shutil
+import random
+import logging
+from pathlib import Path
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    datefmt="%H:%M:%S"
+)
+logger = logging.getLogger(__name__)
+
+# Constants
+TARGET_PER_CATEGORY = 1000
+SPLIT_RATIO = (0.8, 0.1, 0.1)  # Train, Val, Test
+
+DATA_ROOT = Path("data")
+PROCESSED_DIR = DATA_ROOT / "processed"
+TRAIN_DIR = DATA_ROOT / "train"
+VAL_DIR = DATA_ROOT / "val"
+TEST_DIR = DATA_ROOT / "test"
+
+def get_image_files(directory):
+    """Recursively get all image files in a directory."""
+    extensions = {'.jpg', '.jpeg', '.png', '.webp', '.bmp'}
+    return [f for f in directory.rglob("*") if f.suffix.lower() in extensions and f.is_file()]
+
+def clear_directory(path):
+    """Deletes a directory and its contents if it exists."""
+    if path.exists():
+        logger.warning(f"Deleting existing directory: {path}")
+        shutil.rmtree(path)
+
+def main():
+    logger.info("🚀 Starting Dataset Resplit (v2)")
+    logger.info(f"🎯 Target: {TARGET_PER_CATEGORY} images/category | Split: {SPLIT_RATIO}")
+
+    # 1. Clear existing splits
+    clear_directory(TRAIN_DIR)
+    clear_directory(VAL_DIR)
+    clear_directory(TEST_DIR)
+
+    TRAIN_DIR.mkdir(parents=True, exist_ok=True)
+    VAL_DIR.mkdir(parents=True, exist_ok=True)
+    TEST_DIR.mkdir(parents=True, exist_ok=True)
+
+    # 2. Iterate through categories in processed
+    # We assume 'processed' has subfolders like 'workshops/coding', 'workshops/design', etc.
+    # We walk to find leaf directories that contain images.
+    
+    # Optimized walker: Only look at files in the current directory
+    categories = []
+    for root, dirs, files in os.walk(PROCESSED_DIR):
+        current_path = Path(root)
+        
+        # Check files in current dir only
+        local_images = []
+        for f in files:
+            if Path(f).suffix.lower() in {'.jpg', '.jpeg', '.png', '.webp', '.bmp'}:
+                local_images.append(current_path / f)
+        
+        if local_images:
+            # It's a category folder
+            rel_path = current_path.relative_to(PROCESSED_DIR)
+            categories.append((rel_path, local_images))
+
+    if not categories:
+        logger.error("❌ No categories found in data/processed!")
+        return
+
+    logger.info(f"📂 Found {len(categories)} categories to process.")
+
+    for rel_path, images in categories:
+        category_name = str(rel_path).replace("\\", "/")
+        logger.info(f"\n🔹 Processing: {category_name}")
+        
+        # Shuffle and Select
+        random.shuffle(images)
+        selected_images = images[:TARGET_PER_CATEGORY]
+        count = len(selected_images)
+        
+        if count < TARGET_PER_CATEGORY:
+            logger.warning(f"   ⚠️ Only found {count} images (Target: {TARGET_PER_CATEGORY})")
+        else:
+            logger.info(f"   ✅ Selected 1000 images from {len(images)} available.")
+
+        # Calculate Splits
+        n_train = int(count * SPLIT_RATIO[0])
+        n_val = int(count * SPLIT_RATIO[1])
+        # Give remainder to test to ensure sum == count (or fix strictly if required, but remainder is safer)
+        n_test = count - n_train - n_val
+
+        train_set = selected_images[:n_train]
+        val_set = selected_images[n_train : n_train + n_val]
+        test_set = selected_images[n_train + n_val :]
+
+        logger.info(f"   Splitting: Train={len(train_set)}, Val={len(val_set)}, Test={len(test_set)}")
+
+        # Copy Files
+        for dataset, split_name, dest_root in [
+            (train_set, "Train", TRAIN_DIR),
+            (val_set, "Val", VAL_DIR),
+            (test_set, "Test", TEST_DIR)
+        ]:
+            if not dataset:
+                continue
+                
+            dest_category_dir = dest_root / rel_path
+            dest_category_dir.mkdir(parents=True, exist_ok=True)
+            
+            for img_path in dataset:
+                try:
+                    shutil.copy2(img_path, dest_category_dir / img_path.name)
+                    # Try to copy caption text file if it exists
+                    txt_path = img_path.with_suffix(".txt")
+                    if txt_path.exists():
+                        shutil.copy2(txt_path, dest_category_dir / txt_path.name)
+                except Exception as e:
+                    logger.error(f"Failed to copy {img_path.name}: {e}")
+
+    logger.info("\n🎉 Resplit Complete.")
+    
+    # Verification stats
+    logger.info("📊 Final Counts:")
+    for d, name in [(TRAIN_DIR, "TRAIN"), (VAL_DIR, "VAL"), (TEST_DIR, "TEST")]:
+        total = len(list(d.rglob("*.*")))  # Approx count all files
+        # Better to count images
+        img_count = len(get_image_files(d))
+        logger.info(f"   {name}: {img_count} images")
+
+if __name__ == "__main__":
+    main()
+

scripts/targeted_filter.py

@@ -0,0 +1,167 @@
+import logging
+import shutil
+import sys
+import os
+import time
+from pathlib import Path
+from tqdm import tqdm
+from PIL import Image
+import torch
+
+# Add current directory to path so we can import sibling scripts
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from quality_filter import ImageQualityChecker, Deduplicator, GPUHasher, load_config
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+TARGET_COUNT = 1300
+
+def main():
+    logger.info("🚀 Starting Targeted Top-Up Filter (v2)")
+    logger.info(f"🎯 Goal: Ensure every category has >= {TARGET_COUNT} unique, high-quality images")
+    
+    # Load config
+    config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs", "config.yaml")
+    config = load_config(config_path)
+    
+    raw_dir = Path(config["paths"]["data"]["raw"])
+    processed_dir = Path(config["paths"]["data"]["processed"])
+    
+    # Initialize checkers
+    checker = ImageQualityChecker(config)
+    dedup = Deduplicator()
+    
+    if torch.cuda.is_available():
+        logger.info(f"⚡ Using GPU: {torch.cuda.get_device_name(0)}")
+    
+    # 1. LOAD ALL EXISTING PROCESSED IMAGES (Global Deduplication)
+    logger.info("🧠 Learning ALL existing images to prevent duplicates...")
+    all_processed_files = []
+    for root, _, files in os.walk(processed_dir):
+        for file in files:
+            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.webp')):
+                all_processed_files.append(Path(root) / file)
+                
+    existing_hashes = 0
+    if all_processed_files:
+        hasher = GPUHasher()
+        # Compute hashes for everything currently in processed
+        batch_hashes = hasher.compute_hashes(all_processed_files, batch_size=128)
+        dedup.hashes.update(batch_hashes)
+        existing_hashes = len(batch_hashes)
+        
+    logger.info(f"✅ Memorized {existing_hashes} unique images in processed dataset.")
+
+    # 2. IDENTIFY CATEGORIES NEEDING TOP-UP
+    categories_to_process = []
+    for root, dirs, files in os.walk(raw_dir):
+        if not dirs: # Leaf node
+            rel_path = Path(root).relative_to(raw_dir)
+            proc_path = processed_dir / rel_path
+            
+            # Count images in processed
+            if proc_path.exists():
+                curr_count = len([f for f in os.listdir(proc_path) if f.lower().endswith(('.jpg', '.jpeg', '.png'))])
+            else:
+                curr_count = 0
+            
+            if curr_count < TARGET_COUNT:
+                categories_to_process.append((rel_path, Path(root), proc_path, curr_count))
+            else:
+                pass # Already meets target
+                
+    if not categories_to_process:
+        logger.info("✨ All categories meet the target of 1300! No work needed.")
+        return
+
+    logger.info(f"📋 Found {len(categories_to_process)} categories below target.")
+    
+    # 3. PROCESS MISSING CATEGORIES
+    for rel_path, raw_category_path, proc_category_path, current_count in categories_to_process:
+        needed = TARGET_COUNT - current_count
+        category_name = str(rel_path).replace("\\", "/")
+        
+        logger.info(f"\n🔸 Processing: {category_name}")
+        logger.info(f"   Current: {current_count} | Needed: {needed}")
+        
+        proc_category_path.mkdir(parents=True, exist_ok=True)
+        
+        # Get all raw files
+        raw_files = sorted([
+            raw_category_path / f 
+            for f in os.listdir(raw_category_path) 
+            if f.lower().endswith(('.jpg', '.jpeg', '.png'))
+        ])
+        
+        added = 0
+        skipped_dupe = 0
+        skipped_quality = 0
+        
+        # Batch process raw files for efficiency? 
+        # Actually, since we need to copy them one by one based on check, 
+        # we can batch quality check/hash check if we want, but sequential loop is clearer for "stop when satisfied".
+        # Let's use GPUHasher on raw files in chunks to speed up the dedup check at least.
+        
+        # Optimization: Filter out filenames that already exist (exact match)
+        existing_filenames = set(os.listdir(proc_category_path))
+        candidates = [f for f in raw_files if f.name not in existing_filenames]
+        
+        if not candidates:
+            logger.warning("   ❌ No new raw files available to scan!")
+            continue
+
+        # Progress bar
+        pbar = tqdm(total=needed, desc=f"   Filling {category_name}", unit="img")
+        
+        # Iterate through candidates
+        for raw_img_path in candidates:
+            if added >= needed:
+                break
+            
+            # 1. Deduplication Check (Fastest check first? No, Quality is cleaner but slower. Dedup is fast with hash)
+            # Actually we need hash to check dedup.
+            
+            # We'll calculate hash for individual image (slower than batch but we need decision per image)
+            # OR we could batch hash all candidates first. 
+            # Let's batch hash candidates first!
+            
+            # Wait, let's just do it sequentially for simplicity unless it's too slow.
+            # With GPUHasher, we can compute hash quickly.
+            
+            try:
+                # 1. Quality Check (GPU)
+                passed, metrics = checker.check(raw_img_path)
+                if not passed:
+                    skipped_quality += 1
+                    continue
+                
+                # 2. Dedup Check (needs hash)
+                if dedup.is_duplicate(raw_img_path):
+                    skipped_dupe += 1
+                    continue
+                
+                # 3. Copy
+                shutil.copy2(raw_img_path, proc_category_path / raw_img_path.name)
+                added += 1
+                pbar.update(1)
+                
+            except Exception as e:
+                logger.error(f"Error processing {raw_img_path}: {e}")
+                continue
+                
+        pbar.close()
+        
+        final_count = current_count + added
+        if final_count >= TARGET_COUNT:
+             logger.info(f"   ✅ Reached target! ({final_count})")
+        else:
+             logger.warning(f"   ⚠️ Finished scanning raw files. Ended with {final_count} (Still short by {TARGET_COUNT - final_count})")
+             
+    logger.info("\n🎉 Top-Up Complete!")
+
+if __name__ == "__main__":
+    main()
+

scripts/targeted_scraper.py

@@ -0,0 +1,72 @@
+import logging
+import sys
+import os
+import time
+
+# Add current directory to path so we can import sibling scripts
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from pinterest_scraper import PinterestScraper, load_config, DEFAULT_QUERIES
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+# List of categories that need more images (Target: 2800 raw to get ~1300 clean)
+TARGET_CATEGORIES = [
+    "workshops/design",
+    "workshops/coding",
+    "workshops/business",
+    "tech_fest/hackathon",
+    "tech_fest/general",
+    "tech_fest/coding_competition",
+    "tech_fest/web_app_dev",
+    "tech_fest/cybersecurity",
+    "festivals/navratri_garba",
+    "sports/general"
+]
+
+TARGET_COUNT = 2800
+
+def main():
+    logger.info("🚀 Starting Targeted Scraper for Low-Data Categories")
+    logger.info(f"🎯 Target Count: {TARGET_COUNT} images per category")
+    
+    # Load config from parent directory
+    config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs", "config.yaml")
+    config = load_config(config_path)
+    
+    raw_dir = config["paths"]["data"]["raw"]
+    
+    # Initialize scraper
+    scraper = PinterestScraper(config, raw_dir)
+    
+    # Override global target
+    scraper.TARGET_PER_THEME = TARGET_COUNT
+    
+    for category in TARGET_CATEGORIES:
+        logger.info(f"\n============================================================")
+        logger.info(f"Processing: {category}")
+        logger.info(f"============================================================")
+        
+        # Get queries for this category
+        queries = DEFAULT_QUERIES.get(category)
+        if not queries:
+            logger.warning(f"⚠️ No specific queries found for {category}, generating generic ones.")
+            # Fallback if no specific queries exist (though they should based on our previous edits)
+            theme = category.split("/")[-1]
+            queries = [f"{theme} poster design", f"{theme} event flyer", f"creative {theme} poster"]
+            
+        try:
+            count = scraper.scrape_category(category, queries)
+            logger.info(f"✅ Finished {category}: {count} total images")
+        except Exception as e:
+            logger.error(f"❌ Failed processing {category}: {e}")
+            
+        # Small break between categories
+        time.sleep(2)
+
+    logger.info("\n🎉 All targeted categories processed!")
+
+if __name__ == "__main__":
+    main()

scripts/test_checkpoint.py

@@ -0,0 +1,371 @@
+#!/usr/bin/env python3
+"""
+test_checkpoint.py
+==================
+Two-Stage Poster Generation Pipeline — SDXL Artwork + PIL Typography
+
+Stage 1  Generate pure visual artwork with SDXL + Campus AI LoRA.
+         Prompts describe ONLY visual atmosphere — zero text references.
+         guidance_scale=7.5 ensures the negative prompt suppresses all
+         hallucinated text/watermarks from the diffusion output.
+
+Stage 2  PIL Compositor overlays pixel-perfect typography on the raw artwork.
+
+Usage:
+    python test_checkpoint.py
+
+Outputs in output/test_generations/:
+    <slug>_artwork.png   — raw SDXL output, no text
+    <slug>_poster.png    — final composited poster
+
+Per-poster controls:
+    text_position  "top" | "center" | "bottom" | "auto"
+                   Set based on where the artwork has clean negative space.
+    scrim          True  for dark/busy artworks — adds contrast under text.
+                   False for vivid/bright artworks — keep colours untouched.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+
+import torch
+from pathlib import Path
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from poster_compositor import composite_poster, ensure_fonts
+
+
+# ---------------------------------------------------------------------------
+# Shared negative prompt
+# ---------------------------------------------------------------------------
+# Explicitly blocks ALL forms of text/typography from the raw artwork.
+# garbled_text and illegible_text added specifically to kill LoRA artefacts
+# like BOMIELLOOOKD / OULSTECS seen in previous generations.
+
+_NEG = (
+    "text, words, letters, typography, fonts, captions, labels, watermark, "
+    "signature, logo, banner, title, heading, writing, written text, "
+    "illegible text, garbled text, gibberish text, distorted words, "
+    "random letters, fake words, blurry, low quality, deformed, ugly, "
+    "disfigured, oversaturated, bad anatomy, cropped, out of frame"
+)
+
+
+# ---------------------------------------------------------------------------
+# Poster definitions
+# ---------------------------------------------------------------------------
+
+POSTERS: list[tuple[str, str, dict]] = [
+
+    # ── Freshers Party ──────────────────────────────────────────────────────
+    (
+        "freshers_party",
+
+        "campus_ai_poster  Vibrant freshers welcome party background.  "
+        "Confetti explosion in electric blue and neon purple raining from above.  "
+        "Disco ball casting prismatic reflections across a dark concert stage.  "
+        "Bokeh light circles in hot pink and cyan filling the frame.  "
+        "Bollywood dance-floor energy with glitter dust in a single spotlight beam.  "
+        "Shallow depth of field, cinematic wide-angle composition.  "
+        "No text, no signs, no banners anywhere in the scene.",
+
+        dict(
+            title          = "Freshers Bash 2026",
+            subtitle       = "Welcome to the Jungle, First Years!",
+            date           = "August 22, 2026  •  6 PM Onwards",
+            venue          = "Open Air Theatre, DTU",
+            organizer      = "Student Council 2026–27",
+            accent_color   = "#E040FB",
+            style          = "bold",
+            text_position  = "bottom",
+            scrim          = True,
+        ),
+    ),
+
+    # ── Navratri Garba ──────────────────────────────────────────────────────
+    (
+        "navratri_garba",
+
+        "campus_ai_poster  Stunning Navratri Garba night celebration background.  "
+        "Swirling dandiya sticks and ghagra choli silhouettes mid-spin viewed from above.  "
+        "Warm saffron, deep crimson, and gold falling flower petals.  "
+        "Intricate mirror-work embroidery and marigold garland borders framing the scene.  "
+        "Glowing earthen diyas reflecting off a polished stone floor.  "
+        "Rich festive atmosphere, painterly detail, vibrant colour contrast.  "
+        "No text, no signs, no labels anywhere in the scene.",
+
+        dict(
+            title          = "Garba Raas Night",
+            subtitle       = "Nine Nights of Dandiya & Dance",
+            date           = "October 2–10, 2026",
+            venue          = "College Ground, SVNIT Surat",
+            organizer      = "Gujarat Cultural Committee",
+            accent_color   = "#FF6F00",
+            style          = "elegant",
+            text_position  = "bottom",
+            scrim          = False,
+        ),
+    ),
+
+    # ── Coding Hackathon ────────────────────────────────────────────────────
+    (
+        "coding_hackathon",
+
+        "campus_ai_poster  Dark futuristic hackathon coding environment background.  "
+        "Multiple holographic screens floating in 3-D space with scrolling green "
+        "terminal animations and binary rain patterns.  "
+        "Glowing cyan circuit-board traces on a deep black background.  "
+        "Keyboard and laptop silhouettes lit from below by a cool blue glow.  "
+        "High-contrast, ultra-sharp, cyberpunk aesthetic.  "
+        "No text, no readable characters, no words anywhere in the scene.",
+
+        dict(
+            title          = "Code-a-thon 4.0",
+            subtitle       = "36 Hours.  No Sleep.  Pure Code.",
+            date           = "January 18–19, 2026",
+            venue          = "CS Lab 301, IIT Bombay",
+            organizer      = "WnCC & DevClub",
+            accent_color   = "#00E676",
+            style          = "bold",
+            text_position  = "bottom",
+            scrim          = True,
+        ),
+    ),
+
+    # ── Blood Donation Camp ─────────────────────────────────────────────────
+    (
+        "blood_donation",
+
+        "campus_ai_poster  Warm heartfelt blood donation awareness background.  "
+        "A large red blood drop with a heartbeat ECG line running through its center.  "
+        "Clean white and soft crimson minimalist medical composition.  "
+        "Two open hands gently cupping the drop from below.  "
+        "Gentle radial light bloom.  Compassionate, hopeful healthcare aesthetic.  "
+        "No text, no words, no labels in the scene.",
+
+        dict(
+            title          = "Donate Blood, Save Lives",
+            subtitle       = "NSS Blood Donation Camp",
+            date           = "March 5, 2026  •  9 AM – 4 PM",
+            venue          = "Health Centre, NIT Trichy",
+            organizer      = "NSS Unit & Red Cross Society",
+            accent_color   = "#D32F2F",
+            style          = "modern",
+            text_position  = "bottom",
+            scrim          = False,
+        ),
+    ),
+
+    # ── Farewell ────────────────────────────────────────────────────────────
+    (
+        "farewell",
+
+        "campus_ai_poster  Sentimental farewell celebration background.  "
+        "Golden fairy lights strung across a twilight campus courtyard.  "
+        "Graduation caps thrown upward against a warm amber-peach sunset sky.  "
+        "Bokeh spheres in champagne gold and soft peach.  "
+        "Petals falling slowly through the air from above.  "
+        "Nostalgic, bittersweet, and celebratory mood.  Warm film-grain texture.  "
+        "No text, no banners, no words in the scene.",
+
+        dict(
+            title          = "Alvida — Farewell 2026",
+            subtitle       = "For the Batch That Made It Legendary",
+            date           = "May 15, 2026  •  5 PM",
+            venue          = "Main Auditorium, NSUT",
+            organizer      = "Third Year Organizing Committee",
+            accent_color   = "#FFD54F",
+            style          = "elegant",
+            text_position  = "bottom",
+            scrim          = False,
+        ),
+    ),
+
+    # ── Annual Cultural Fest ─────────────────────────────────────────────────
+    (
+        "annual_fest",
+
+        "campus_ai_poster  Epic grand annual college cultural fest background.  "
+        "Massive paint-splash explosion in rainbow neon colours filling the entire frame.  "
+        "Fireworks bursting above a packed outdoor main stage.  "
+        "Laser beams sweeping over a roaring silhouette crowd.  "
+        "Smoke machines and confetti cannons firing simultaneously.  "
+        "Maximum energy, blockbuster festival scale, ultra-vivid colour grading.  "
+        "Absolutely no text, no stage signs, no banners, no readable characters.",
+
+        dict(
+            title          = "MOKSHA 2026",
+            subtitle       = "The Biggest College Fest in India",
+            date           = "February 14–16, 2026",
+            venue          = "NSUT Main Campus, Dwarka",
+            organizer      = "Moksha Organizing Committee",
+            accent_color   = "#FF1744",
+            style          = "bold",
+            text_position  = "bottom",
+            scrim          = True,
+        ),
+    ),
+
+    # ── Robotics Competition ─────────────────────────────────────────────────
+    (
+        "robotics_competition",
+
+        "campus_ai_poster  Futuristic robotics competition arena background.  "
+        "A sleek industrial robot arm mid-motion under dramatic blue-white spotlights.  "
+        "Metallic gears, pistons, and carbon-fibre surface textures.  "
+        "Electric sparks flying off welded joints.  Dark smoke and industrial haze.  "
+        "High-contrast dramatic lighting, mechanical precision aesthetic.  "
+        "No text, no labels, no signage anywhere in the scene.",
+
+        dict(
+            title          = "RoboWars 2026",
+            subtitle       = "Build It.  Break It.  Win It.",
+            date           = "March 22, 2026",
+            venue          = "Innovation Hub, BITS Pilani",
+            organizer      = "Robotics & Automation Society",
+            accent_color   = "#40C4FF",
+            style          = "modern",
+            text_position  = "bottom",
+            scrim          = True,
+        ),
+    ),
+
+    # ── Standup Comedy Night ─────────────────────────────────────────────────
+    (
+        "standup_comedy",
+
+        "campus_ai_poster  Moody open-mic comedy night stage background.  "
+        "Single golden spotlight cone hitting a lone microphone stand centre stage.  "
+        "Deep maroon velvet curtains framing the wings on both sides.  "
+        "Brick wall texture visible at the back — classic comedy club look.  "
+        "Warm amber footlights and a faint laughing crowd silhouette at the bottom.  "
+        "Intimate, atmospheric, slightly gritty feel.  "
+        "No text, no words, no chalk board writing, no signs anywhere.",
+
+        dict(
+            title          = "Laugh Riot 2026",
+            subtitle       = "Open Mic Comedy Night",
+            date           = "April 5, 2026  •  7 PM",
+            venue          = "Black Box Theatre, Miranda House",
+            organizer      = "The Comedy Collective",
+            accent_color   = "#FFAB40",
+            style          = "modern",
+            text_position  = "top",      # mic + spotlight fill center/bottom
+            scrim          = True,
+        ),
+    ),
+
+    # ── Diwali Celebration ───────────────────────────────────────────────────
+    (
+        "diwali",
+
+        "campus_ai_poster  Magical Diwali festival night background.  "
+        "Hundreds of glowing earthen diyas arranged in concentric circles on dark stone.  "
+        "Fireworks bursting in gold, silver, and emerald green overhead.  "
+        "Intricate rangoli patterns in vibrant pink, blue, and orange surrounding the diyas.  "
+        "Warm golden bokeh light spheres floating throughout.  "
+        "Festive, divine, deeply traditional Indian atmosphere.  "
+        "No text, no words, no labels anywhere in the scene.",
+
+        dict(
+            title          = "Diwali Utsav 2026",
+            subtitle       = "Festival of Lights on Campus",
+            date           = "October 20, 2026  •  6 PM",
+            venue          = "Central Lawn, IIT Delhi",
+            organizer      = "Cultural Committee & NSS",
+            accent_color   = "#FFD700",
+            style          = "elegant",
+            text_position  = "top",      # rangoli / diyas fill bottom beautifully
+            scrim          = False,
+        ),
+    ),
+
+]
+
+
+# ---------------------------------------------------------------------------
+# Pipeline
+# ---------------------------------------------------------------------------
+
+def _load_pipeline(base_id: str, lora_dir: str, lora_file: str):
+    from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler
+
+    print("  Loading SDXL base model ...")
+    pipe = AutoPipelineForText2Image.from_pretrained(
+        base_id,
+        torch_dtype     = torch.float16,
+        variant         = "fp16",
+        use_safetensors = True,
+    ).to("cuda")
+
+    # DPM++ 2M Karras — sharper outputs, better prompt adherence than DDPM
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+        pipe.scheduler.config,
+        use_karras_sigmas = True,
+    )
+
+    lora_path = os.path.join(lora_dir, lora_file)
+    if os.path.exists(lora_path):
+        pipe.load_lora_weights(lora_dir, weight_name=lora_file, adapter_name="campus_poster")
+        pipe.set_adapters(["campus_poster"], adapter_weights=[1.0])
+        print(f"  LoRA loaded  →  {lora_path}")
+    else:
+        print(f"  WARNING: LoRA not found at {lora_path}  —  using base SDXL only")
+
+    return pipe
+
+
+def generate_posters() -> None:
+    out_dir   = Path("output/test_generations")
+    lora_dir  = "models/sdxl/checkpoints/campus_ai_poster_sdxl_phase3"
+    lora_file = "campus_ai_poster_sdxl_phase3.safetensors"
+    base_id   = "stabilityai/stable-diffusion-xl-base-1.0"
+
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    print("=" * 60)
+    print("  CAMPUS AI  —  TWO-STAGE POSTER PIPELINE")
+    print("=" * 60)
+
+    print("\n[Stage 0]  Downloading / verifying fonts ...")
+    ensure_fonts()
+
+    print("\n[Stage 1]  Loading SDXL + Campus AI LoRA ...")
+    pipe = _load_pipeline(base_id, lora_dir, lora_file)
+
+    print(f"\n[Stage 2]  Generating {len(POSTERS)} posters ...\n")
+
+    for slug, artwork_prompt, text_cfg in POSTERS:
+        label = slug.upper().replace("_", " ")
+        print(f"  🎨  {label}")
+
+        artwork = pipe(
+            artwork_prompt,
+            negative_prompt     = _NEG,
+            num_inference_steps = 35,   # +5 steps for cleaner detail
+            guidance_scale      = 7.5,  # stronger negative adherence — kills hallucinated text
+        ).images[0]
+
+        artwork_path = out_dir / f"{slug}_artwork.png"
+        artwork.save(artwork_path)
+        print(f"       artwork  →  {artwork_path}")
+
+        final = composite_poster(artwork, **text_cfg)
+        poster_path = out_dir / f"{slug}_poster.png"
+        final.save(poster_path)
+        print(f"       poster   →  {poster_path}\n")
+
+    del pipe
+    torch.cuda.empty_cache()
+
+    print("=" * 60)
+    print(f"  ✅  Done.  All outputs in  {out_dir}/")
+    print("       *_artwork.png  →  raw SDXL art, no text")
+    print("       *_poster.png   →  final composited poster")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    generate_posters()

scripts/tuning_dataset.py

@@ -0,0 +1,518 @@
+#!/usr/bin/env python3
+"""
+Tuning Dataset Builder
+======================
+Downloads high-quality poster images for Phase 3 fine-tuning.
+Uses Google Custom Search (free tier) and Bing image search as fallback.
+Images are saved into data/tuning/<category>/<subcategory>/.
+
+Usage:
+    python scripts/tuning_dataset.py
+    python scripts/tuning_dataset.py --per-category 20
+    python scripts/tuning_dataset.py --dry-run
+"""
+
+import os
+import sys
+import json
+import time
+import hashlib
+import argparse
+import re
+import requests
+from pathlib import Path
+from urllib.parse import quote_plus
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+
+# ============================================================================
+# SEARCH QUERIES — curated for each of the 55 subcategories
+# ============================================================================
+
+CATEGORIES = {
+    # ---- College Events ----
+    "college_events/farewell": [
+        "college farewell party poster design HD",
+        "farewell event invitation poster aesthetic",
+        "farewell night celebration poster elegant design",
+    ],
+    "college_events/freshers": [
+        "freshers party welcome poster design neon",
+        "freshers day college poster vibrant colorful",
+        "fresher welcome event poster creative",
+    ],
+    "college_events/alumni_reunion": [
+        "alumni reunion event poster design elegant",
+        "alumni meet invitation poster university",
+    ],
+    "college_events/graduation": [
+        "graduation ceremony poster elegant gold",
+        "convocation celebration poster university beautiful",
+    ],
+
+    # ---- Cultural Fest ----
+    "cultural_fest/art_exhibition": [
+        "art exhibition poster design modern gallery",
+        "art gallery opening event poster minimal beautiful",
+    ],
+    "cultural_fest/dance": [
+        "dance competition poster vibrant colorful HD",
+        "dance festival event poster aesthetic beautiful",
+        "classical dance performance poster Indian design",
+    ],
+    "cultural_fest/drama_theatre": [
+        "theatre drama play poster artistic dark elegant",
+        "stage play event poster design creative",
+    ],
+    "cultural_fest/fashion_show": [
+        "fashion show event poster elegant luxury design",
+        "college fashion gala poster premium aesthetic",
+    ],
+    "cultural_fest/general": [
+        "cultural fest poster college India vibrant",
+        "annual cultural festival poster colorful design",
+    ],
+    "cultural_fest/literary": [
+        "literary fest poster book reading event design",
+        "poetry slam event poster creative typography",
+    ],
+    "cultural_fest/music": [
+        "music concert poster design neon glow HD",
+        "live music event poster rock band stage",
+        "college music festival poster vibrant DJ",
+    ],
+    "cultural_fest/standup_comedy": [
+        "standup comedy show poster design microphone",
+        "open mic comedy night poster neon creative",
+        "comedy event poster funny stage spotlight",
+    ],
+
+    # ---- Entertainment ----
+    "entertainment/food_fest": [
+        "food festival poster design appetizing HD",
+        "street food fest poster colorful delicious",
+        "college food carnival poster warm inviting",
+    ],
+    "entertainment/gaming": [
+        "gaming tournament poster esports neon RGB",
+        "video game competition poster futuristic glowing",
+    ],
+    "entertainment/movie_night": [
+        "movie night poster cinema event retro",
+        "outdoor movie screening poster vintage film",
+    ],
+
+    # ---- Festivals ----
+    "festivals/christmas": [
+        "christmas celebration poster festive red green",
+        "merry christmas event poster elegant snowflakes",
+    ],
+    "festivals/diwali": [
+        "diwali celebration poster beautiful golden diya HD",
+        "deepavali festival poster vibrant rangoli colors",
+        "diwali night event poster fireworks sparkle",
+    ],
+    "festivals/durga_puja": [
+        "durga puja poster beautiful artistic HD",
+        "durga puja celebration poster traditional bengali",
+    ],
+    "festivals/eid": [
+        "eid celebration poster beautiful crescent moon",
+        "eid mubarak event poster elegant islamic design",
+    ],
+    "festivals/ganesh_chaturthi": [
+        "ganesh chaturthi poster design vibrant festival",
+        "ganpati celebration poster traditional colorful",
+    ],
+    "festivals/holi": [
+        "holi festival poster colorful splash paint HD",
+        "holi celebration party poster vibrant gulal",
+    ],
+    "festivals/independence_republic": [
+        "india independence day poster tricolor patriotic",
+        "republic day celebration poster 26 january",
+    ],
+    "festivals/navratri_garba": [
+        "navratri garba poster design colorful dandiya",
+        "dandiya night event poster festive vibrant",
+        "garba raas festival poster traditional Gujarat",
+    ],
+    "festivals/new_year": [
+        "new year celebration poster party fireworks",
+        "new year eve event poster glowing golden",
+    ],
+    "festivals/onam": [
+        "onam festival poster kathakali traditional Kerala",
+        "onam celebration poster pookalam floral boat",
+    ],
+    "festivals/pongal_sankranti": [
+        "pongal festival poster traditional Tamil Nadu",
+        "makar sankranti poster kite festival colorful",
+    ],
+
+    # ---- Social ----
+    "social/awareness": [
+        "social awareness campaign poster design impactful",
+        "mental health awareness poster college creative",
+    ],
+    "social/blood_donation": [
+        "blood donation camp poster design red heart",
+        "donate blood save lives poster minimal clean",
+    ],
+    "social/charity": [
+        "charity event poster design heartfelt giving",
+        "fundraiser event poster college community",
+    ],
+    "social/environment": [
+        "environment day poster tree planting green earth",
+        "eco friendly campaign poster sustainability",
+    ],
+
+    # ---- Sports ----
+    "sports/athletics": [
+        "athletics sports day poster dynamic running",
+        "track and field event poster energy motion",
+    ],
+    "sports/badminton_tennis": [
+        "badminton tournament poster design sports action",
+        "tennis competition poster athletic dynamic",
+    ],
+    "sports/basketball": [
+        "basketball tournament poster dynamic slam dunk HD",
+        "basketball championship poster sports energy",
+    ],
+    "sports/cricket": [
+        "cricket tournament poster design India stadium HD",
+        "cricket match poster IPL style vibrant action",
+        "cricket championship poster batsman dynamic",
+    ],
+    "sports/esports": [
+        "esports tournament poster gaming neon cyberpunk",
+        "valorant tournament poster aggressive design",
+        "gaming championship poster RGB glowing dark",
+    ],
+    "sports/football": [
+        "football tournament poster design action dynamic",
+        "soccer championship event poster stadium energy",
+    ],
+    "sports/general": [
+        "sports day poster college event medals trophy",
+        "annual sports meet poster design vibrant",
+    ],
+    "sports/kabaddi_kho": [
+        "kabaddi tournament poster Indian sports action",
+        "kho kho competition poster dynamic traditional",
+    ],
+    "sports/yoga_fitness": [
+        "yoga day poster peaceful sunrise meditation",
+        "fitness challenge poster gym workout energy",
+    ],
+
+    # ---- Styles ----
+    "styles/3d_futuristic": [
+        "futuristic 3D poster design abstract technology",
+        "3D event poster sci-fi hologram aesthetic",
+    ],
+    "styles/dark_theme": [
+        "dark theme poster design moody elegant",
+        "dark aesthetic event poster premium black gold",
+    ],
+    "styles/gradient": [
+        "gradient poster design smooth mesh colors",
+        "gradient background poster modern vibrant",
+    ],
+    "styles/illustration": [
+        "illustrated event poster hand drawn artistic",
+        "illustration poster design flat vector creative",
+    ],
+    "styles/minimalist": [
+        "minimalist poster design clean modern white",
+        "minimal event poster elegant white space",
+    ],
+    "styles/neon_glow": [
+        "neon glow poster design vibrant dark",
+        "neon lights event poster cyberpunk glowing",
+    ],
+    "styles/retro_vintage": [
+        "retro vintage poster design grunge old school",
+        "vintage event poster classic typography worn",
+    ],
+    "styles/typography": [
+        "typography poster design bold text art creative",
+        "typographic event poster lettering experimental",
+    ],
+    "styles/watercolor": [
+        "watercolor poster design soft artistic floral",
+        "watercolor painting poster pastel dreamy",
+    ],
+
+    # ---- Tech Fest ----
+    "tech_fest/ai_ml": [
+        "AI machine learning event poster futuristic neural",
+        "artificial intelligence conference poster technology",
+    ],
+    "tech_fest/coding_competition": [
+        "coding competition poster hacker developer dark",
+        "code challenge event poster programming terminal",
+    ],
+    "tech_fest/cybersecurity": [
+        "cybersecurity event poster hacker CTF dark",
+        "cyber security awareness poster digital lock",
+    ],
+    "tech_fest/general": [
+        "tech fest poster college futuristic innovation",
+        "technology festival poster digital modern",
+    ],
+    "tech_fest/hackathon": [
+        "hackathon event poster design code developer",
+        "36 hour hackathon poster startup tech vibrant",
+        "hack day poster creative developer community",
+    ],
+    "tech_fest/robotics": [
+        "robotics competition poster futuristic mechanical",
+        "robot challenge event poster technology modern",
+    ],
+    "tech_fest/web_app_dev": [
+        "web development workshop poster modern code",
+        "app development event poster mobile technology",
+    ],
+
+    # ---- Workshops ----
+    "workshops/business": [
+        "business workshop poster corporate professional",
+        "entrepreneurship event poster startup modern",
+    ],
+    "workshops/coding": [
+        "coding workshop poster developer bootcamp",
+        "programming workshop poster technology education",
+    ],
+    "workshops/conference": [
+        "conference event poster professional academic",
+        "academic conference poster modern clean",
+    ],
+    "workshops/design": [
+        "design workshop poster UI UX creative",
+        "graphic design event poster artistic colorful",
+    ],
+    "workshops/placement": [
+        "placement drive poster campus recruitment",
+        "career fair poster professional job event",
+    ],
+    "workshops/seminar": [
+        "seminar event poster professional academic clean",
+        "guest lecture poster university speaker modern",
+    ],
+    "workshops/soft_skills": [
+        "soft skills workshop poster leadership training",
+        "communication skills event poster professional",
+    ],
+}
+
+
+# ============================================================================
+# IMAGE SEARCH ENGINE  (DuckDuckGo — no API key needed)
+# ============================================================================
+
+def search_images(query, max_results=8):
+    """Search for images using DuckDuckGo. Returns list of image URLs."""
+    headers = {
+        "User-Agent": (
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+            "AppleWebKit/537.36 (KHTML, like Gecko) "
+            "Chrome/120.0.0.0 Safari/537.36"
+        )
+    }
+
+    try:
+        # Get search token
+        resp = requests.get(
+            f"https://duckduckgo.com/?q={quote_plus(query)}&iax=images&ia=images",
+            headers=headers, timeout=10
+        )
+        vqd = None
+        match = re.search(r"vqd=([\d-]+)", resp.text)
+        if match:
+            vqd = match.group(1)
+        if not vqd:
+            # Try alternative pattern
+            match = re.search(r"vqd=['\"]?([\d-]+)", resp.text)
+            if match:
+                vqd = match.group(1)
+        if not vqd:
+            return []
+
+        # Fetch image results
+        params = {
+            "l": "us-en", "o": "json", "q": query,
+            "vqd": vqd, "f": ",,,,,", "p": "1",
+        }
+        resp = requests.get(
+            "https://duckduckgo.com/i.js",
+            headers=headers, params=params, timeout=10
+        )
+        data = resp.json()
+
+        urls = []
+        for result in data.get("results", [])[:max_results * 2]:
+            url = result.get("image", "")
+            if url and url.startswith("http"):
+                # Prefer larger images
+                width = result.get("width", 0)
+                height = result.get("height", 0)
+                if width >= 400 and height >= 400:
+                    urls.append(url)
+                elif len(urls) < max_results // 2:
+                    urls.append(url)  # Accept smaller ones if few results
+            if len(urls) >= max_results:
+                break
+
+        return urls[:max_results]
+
+    except Exception as e:
+        return []
+
+
+# ============================================================================
+# IMAGE DOWNLOADER with validation
+# ============================================================================
+
+def download_image(url, save_path, min_size_kb=15, timeout=12):
+    """Download and validate a single image. Returns True on success."""
+    try:
+        headers = {
+            "User-Agent": (
+                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
+                "AppleWebKit/537.36"
+            )
+        }
+        resp = requests.get(url, headers=headers, timeout=timeout, stream=True)
+        resp.raise_for_status()
+
+        content_type = resp.headers.get("Content-Type", "")
+        if "image" not in content_type and not any(
+            url.lower().endswith(ext) for ext in (".jpg", ".jpeg", ".png", ".webp")
+        ):
+            return False
+
+        data = resp.content
+
+        # Skip tiny/broken images
+        if len(data) < min_size_kb * 1024:
+            return False
+
+        # Quick header check — verify it's actually an image
+        if not (data[:2] == b'\xff\xd8' or      # JPEG
+                data[:4] == b'\x89PNG' or         # PNG
+                data[:4] == b'RIFF' or            # WebP
+                data[:3] == b'GIF'):              # GIF
+            return False
+
+        with open(save_path, "wb") as f:
+            f.write(data)
+        return True
+
+    except Exception:
+        return False
+
+
+def get_filename(url, folder):
+    """Generate a unique, deterministic filename from the URL."""
+    url_hash = hashlib.md5(url.encode()).hexdigest()[:12]
+    return os.path.join(folder, f"tuning_{url_hash}.jpg")
+
+
+# ============================================================================
+# MAIN PIPELINE
+# ============================================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Tuning Dataset Builder — download fresh poster images for Phase 3"
+    )
+    parser.add_argument("--target", default="data/tuning",
+                        help="Root directory to save images into")
+    parser.add_argument("--per-category", type=int, default=15,
+                        help="Target new images per subcategory")
+    parser.add_argument("--dry-run", action="store_true",
+                        help="Preview searches without downloading")
+    args = parser.parse_args()
+
+    total_cats = len(CATEGORIES)
+    print("=" * 60)
+    print("  TUNING DATASET BUILDER — Phase 3")
+    print("=" * 60)
+    print(f"  Target folder : {args.target}")
+    print(f"  Per subcategory: {args.per_category} images")
+    print(f"  Subcategories : {total_cats}")
+    print(f"  Est. total    : ~{total_cats * args.per_category} images")
+    print("=" * 60)
+
+    stats = {"downloaded": 0, "skipped": 0, "failed": 0}
+
+    for i, (subcat, queries) in enumerate(CATEGORIES.items(), 1):
+        folder = os.path.join(args.target, subcat)
+        os.makedirs(folder, exist_ok=True)
+
+        existing = len([f for f in os.listdir(folder)
+                       if f.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))])
+
+        print(f"\n[{i:02d}/{total_cats}] 📁 {subcat}  ({existing} existing)")
+
+        if args.dry_run:
+            for q in queries:
+                print(f"  🔍 Would search: '{q}'")
+            continue
+
+        downloaded = 0
+        per_query = max(3, (args.per_category + len(queries) - 1) // len(queries))
+
+        for query in queries:
+            if downloaded >= args.per_category:
+                break
+
+            print(f"  🔍 '{query}'")
+            urls = search_images(query, max_results=per_query + 3)
+
+            if not urls:
+                print(f"     ⚠️  No results")
+                continue
+
+            for url in urls:
+                if downloaded >= args.per_category:
+                    break
+
+                filepath = get_filename(url, folder)
+                if os.path.exists(filepath):
+                    stats["skipped"] += 1
+                    continue
+
+                if download_image(url, filepath):
+                    downloaded += 1
+                    stats["downloaded"] += 1
+                    print(f"     ✅ {downloaded}/{args.per_category}")
+                else:
+                    stats["failed"] += 1
+
+            # Rate limit — be respectful
+            time.sleep(1.5)
+
+        print(f"  → {downloaded} new images saved")
+
+    # ---- Summary ----
+    print("\n" + "=" * 60)
+    print("  DOWNLOAD COMPLETE")
+    print("=" * 60)
+    print(f"  ✅ Downloaded : {stats['downloaded']}")
+    print(f"  ⏭️  Skipped   : {stats['skipped']} (duplicates)")
+    print(f"  ❌ Failed     : {stats['failed']}")
+    print("=" * 60)
+    print("\n  Next steps:")
+    print("  1. Caption the new images:")
+    print("     python scripts/caption_generator.py --input data/tuning")
+    print("  2. Run Phase 3 training:")
+    print("     cd ai-toolkit && python run.py ../configs/train_sdxl_lora_phase3.yaml")
+    print()
+
+
+if __name__ == "__main__":
+    main()