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wd-swinv2-tagger-v3

WD SwinV2 Tagger v3 is SmilingWolf's third-generation image tagger using a SwinTransformerV2 backbone, trained on large Danbooru image-tag pairs. It predicts anime illustration tags across character, general, and rating categories. V3 improves tag coverage and accuracy over the previous ViT-based tagger variants, particularly on character attributes and less common tags.

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From the model card

Fields below are copied from the tags and counters on the HuggingFace repository SmilingWolf/wd-swinv2-tagger-v3 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
SmilingWolf
Library
timm
Weight formats
ONNX, safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Downloads (HF counter at last fetch)
414,764
Likes (HF counter at last fetch)
95
Model card
https://huggingface.co/SmilingWolf/wd-swinv2-tagger-v3

Use cases

  • Auto-tagging anime illustration datasets for training diffusion models
  • Filtering image collections by character, style, or content rating
  • Building image search indices for anime art databases
  • Generating descriptive tags for text-to-image prompt engineering
  • Content moderation labelling for anime platforms

Pros

  • SwinV2 backbone provides better spatial hierarchy than ViT for fine-grained tag prediction
  • V3 covers more tags than V2 with improved precision on rare character attributes
  • 86 likes with heavy use in Stable Diffusion dataset pipelines
  • timm-compatible; easy to integrate into existing vision pipelines

Cons

  • Danbooru-trained; generalises poorly to non-anime imagery
  • Rating tags (explicit/questionable) require careful handling in production systems
  • No explicit license; verify terms before commercial dataset use
  • SwinV2 inference is slower than ViT-based taggers for large batches

Tags

timmonnxsafetensorslicense:apache-2.0region:us