SplatoonXL
Generates colourful squids and octos.
Usage notes
This model will help SDXL-based checkpoints draw:
in a style reminiscent of Splatoon concept and promotional 2D artwork.
concepts from Splatoon, including Inklings and Octolings.
Recommended checkpoints:
v3.0: Use a Pony V6 checkpoint, such as Pony Diffusion V6 XL, or AutismMix.
v2.0/v1.0: Use an anime checkpoint, like kohakuXL, blue_pencilXL, CounterfeitXL, or Yamer's Anime/Unstable Diffusers.
Guidance:
Prompting for keywords relating to place, ambience, or an activity can help with drawing scenery around characters.
Negative prompting is needed to reduce limb deformities and unwanted clutter.
Use your favourite prompting patterns or these suggestions.
v3.0: Prefix positive prompt with "score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up" following Pony guidelines.
v3.0/v2.0/v1.0: I found "photorealistic, 3d model, bad, worse, worst, ugly, bad anatomy, blurry, close-up, disembodied limb" in the negative prompt (taken from kohakuXL page) to consistently give cleaner images.
Have fun!
Release notes
v3.0 (Pony V6)
Retrained against the Pony Diffusion V6 XL checkpoint.
Training dataset is identical to v2.0.
Adjusted training parameters.
v2.0 (SDXL 1.0)
Primary focus of changes were to improve generated Octoling accuracy, and general control over Inkling/Octoling features.
Cleaned up tagging.
Added more images to dataset.
Tuned training parameters.
v1.0 (SDXL 1.0)
Initial release.
Model information
This model was trained against official Nintendo Splatoon artwork. Art was selected by hand to match a particular visual style (highly saturated flat-shaded art).
See the "About this version" card for information about the dataset and training parameters. Images, tags, and training configuration are provided in an optional zip file along the model.
Attribution
This work was inspired by the following linked model by IWannaGoBack, which targets a similar niche for SD 1.5 based networks.
https://civarchive.com/models/85425/splatoon-style
Thanks to IWannaGoBack. I enjoyed playing with your model, and it inspired me to dive into the deep rabbit hole of LoRA training.
Public domain notice and disclaimers
The author of this generative AI model (model) and curator of its training database (database) releases rights and disclaims liabilities that pertain to the model/database themselves or usage thereof, under the mark of the CC0 1.0 legal code.
http://creativecommons.org/publicdomain/zero/1.0
This is a fan work that derives from intellectual property owned by Nintendo Co., Ltd. It is not endorsed by Nintendo.
Description
Training run 28, epoch 000014
Dataset 16
Images
Count: 77 images
Sources:
Danbooru (search terms: splatoon official_artwork)
Inkipedia
Splatoon Base
Splatoon 3 artbook scans
Scanning methodology
Flatbed scans at 600 dpi
Descreening: https://github.com/6o6o/fft-descreen
Despeckling: Unity (1x) upscaling through R-ESRGAN 4x+ Anime6B
Dedusting: manual painting in an image editor.
Treatment:
Most source images were either unmodified, or simply cropped to isolate individuals or mask logos/text.
Special effort was invested in obtaining additional images of individual Octolings.
Some images were obtained from Splatoon 3 artbook scans.
Some images were obtained by lassoing individuals from scenes, and repainting obscured areas in an image editor. Quality and resolution were enhanced by 4x upscaling through R-ESRGAN 4x+ Anime6B.
Flip augmentation: enabled
Tagging
Methodology: Tags were initially scraped alongside images, or tagged by WD14 (wd-v1-4-swinv2-tagger-v2). Tags were edited by model author for correctness and consistency.
Caption shuffling: All tags are randomly shuffled. No trigger words were used.
Training
Dataset replicates/epoch: 10
Epochs: 14
Batch size: 2
Checkpoint: sd_xl_base_1.0.safetensors
Network configuration: LyCORIS/LoCon (dim=16, alpha=8, conv_dim=8, conv_alpha=1)
LR: 0.0001 for both U-Net and text encoder. Algorithm was "cosine_with_restarts" with 5% warmup.
Optimizer: AdamW