Danbooru Tagging System

What's Danbooru?

Danbooru is an image board whose tagging system describes subjects, appearance, style and composition. Models trained on tagged images can learn associations between those tags and visual features.

When people trained AI models on this data, the models learned to connect specific tags with visual stuff. Those tags basically became a programming language for making images.

Why This Matters

Danbooru tags are precise. Instead of writing:

"a young woman with long flowing blonde hair wearing a red dress"

You break it into chunks the model actually recognizes:

1girl, long hair, blonde hair, red dress

Each tag maps to something the model learned. It combines them. Way more control than natural language.

Tag Types

Character count: 1girl, 1boy, 2girls, solo, multiple girls Physical traits: blonde hair, blue eyes, tall, muscular Clothing: dress, suit, armor, school uniform Poses: standing, sitting, arms crossed, looking at viewer Expressions: smile, serious, crying, surprised Backgrounds: outdoors, classroom, forest, simple background Meta/quality: masterpiece, best quality, highres

Tags vs Plain English

Some models (especially photorealistic ones) like natural language better. But anime models and most SDXL checkpoints expect tags.

Mix and match works too:

1girl, detective, woman in her 30s, noir style, standing in rain

Model grabs what it knows and figures out the rest from context.

Order Matters

Tags up front carry more weight.

1. Quality tags first - sets the baseline
2. Character count early - establishes who's in the shot
3. Physical stuff - general (age, skin) to specific (hair, eyes)
4. Clothing, expression, pose come after

Tags right after a character description bind to that character.