Skip to main content

Anime Character Segmentation with DINOv2

Project description

AnimeSeg

GitHub release GitHub release Visitor Badge

Anime Character Segmentation using Mask2Former and DINOv2 + U-Net++ with LoRA fine-tuning. Also integrates Background Removal via anime-segmentation.

sample image

sample image

Installation

pip install anime_seg

Usage

from anime_seg import AnimeSegPipeline
pipe = AnimeSegPipeline.from_mask2former().to("cuda")
mask = pipe("path/to/image.jpg")
mask.save("output.png")

# Background Removal (powered by anime-segmentation)
bg_pipe = AnimeSegPipeline.from_bg_remover().to("cuda")
no_bg_img = bg_pipe("path/to/image.jpg")
no_bg_img.save("no_bg_output.png")

AnimeSegPipeline() default constructor is deprecated. Use from_mask2former(), from_dinoV2(), or from_bg_remover().

Optional: output size

# Same as input size (default)
mask_same = pipe("path/to/image.jpg")

# Fixed output size
mask_fixed = pipe("path/to/image.jpg", width=1024, height=1024)

# Width/height can be specified independently
mask_w = pipe("path/to/image.jpg", width=1024)
mask_h = pipe("path/to/image.jpg", height=1024)

Advanced Usage

# Load specific file from HF repo
pipe = AnimeSegPipeline.from_mask2former(
    repo_id="suzukimain/AnimeSeg",
    filename="models/anime_seg_mask2former_v3.safetensors"
).to(device="cuda")

# DINOv2 backend
pipe_dino = AnimeSegPipeline.from_dinoV2(
    filename="models/anime_seg_dinov2_v2.safetensors"
).to("cuda")

# Use PIL Image
from PIL import Image
img = Image.open("image.jpg")
mask = pipe(img)

# Background Removal (powered by anime-segmentation)
bg_pipe = AnimeSegPipeline.from_bg_remover().to("cuda")
no_bg_img = bg_pipe("path/to/image.jpg")
no_bg_img.save("no_bg_output.png")

Model Files

Models should follow the naming convention:

models/anime_seg_{architecture}_v{version}.safetensors

Example:

  • models/anime_seg_dinov2_v2.safetensors
  • models/anime_seg_mask2former_v3.safetensors

Resolution order:

  1. config.json
  2. fallback scan by models/anime_seg_{architecture}_v{max_version}.{ext}

Segmentation Classes and Mask Colors

Default from_mask2former() returns 12 classes:

ID Class Key RGB Color
0 background (0, 0, 0) Black
1 skin (255, 220, 180) Pale Orange
2 face (100, 150, 255) Blue
3 hair_main (255, 0, 0) Red
4 left_eye (0, 255, 255) Cyan
5 right_eye (255, 255, 0) Yellow
6 left_eyebrow (150, 255, 0) Yellow Green
7 right_eyebrow (0, 255, 100) Emerald Green
8 nose (255, 140, 0) Dark Orange
9 mouth (255, 0, 150) Magenta Pink
10 clothes (180, 0, 255) Purple
11 accessory (128, 128, 0) Olive

from_dinoV2() returns 13 classes (includes unknown as ID 12).

DINOv2 Compatibility Note

Earlier versions primarily used DINOv2. Current recommendation is from_mask2former(), while from_dinoV2() remains for compatibility.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

anime_seg-0.3.7.tar.gz (19.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

anime_seg-0.3.7-py3-none-any.whl (24.5 kB view details)

Uploaded Python 3

File details

Details for the file anime_seg-0.3.7.tar.gz.

File metadata

  • Download URL: anime_seg-0.3.7.tar.gz
  • Upload date:
  • Size: 19.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for anime_seg-0.3.7.tar.gz
Algorithm Hash digest
SHA256 c450119ec19f3bd9e1aa12cb56c894695ece1192b0f48b8d2f635b36da2a79b9
MD5 273a5084335ccbbc567370cd98037c06
BLAKE2b-256 9a5bec3b04fe7626be1471b40fe4537fcb9103136de8ee2132eda9c9938e2498

See more details on using hashes here.

File details

Details for the file anime_seg-0.3.7-py3-none-any.whl.

File metadata

  • Download URL: anime_seg-0.3.7-py3-none-any.whl
  • Upload date:
  • Size: 24.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for anime_seg-0.3.7-py3-none-any.whl
Algorithm Hash digest
SHA256 44cab9f102476ebd909a7cfb7cb51ebc1f6c64a3504c46cd70c4fc1e4f545e53
MD5 d519a88f52b1e5947a19a3d357882319
BLAKE2b-256 770b187718f435bab93e27c3e5482284d4fa7b7b9ad0c0097a670e966fe5c88a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page