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metadata
pipeline_tag: text-to-image
license: other
license_name: faipl-1.0-sd
license_link: LICENSE
base_model: stabilityai/stable-cascade
tags:
  - text-to-image
  - anime
library_name: diffusers
language: en
inference: false
decoder: Disty0/sotediffusion-wuerstchen3-decoder
new_version: Disty0/sotediffusion-v2

New verison is available: https://huggingface.co/Disty0/sotediffusion-v2

SoteDiffusion Wuerstchen3

Anime finetune of Würstchen V3.

Release Notes

  • This release is sponsored by fal.ai/grants
  • Trained on 6M images for 3 epochs using 8x A100 80G GPUs.

API Usage

This model can be used via API with Fal.AI
For more details: https://fal.ai/models/fal-ai/stable-cascade/sote-diffusion

UI Guide

SD.Next

URL: https://github.com/vladmandic/automatic/

Go to Models -> Huggingface and type Disty0/sotediffusion-wuerstchen3-decoder into the model name and press download.
Load Disty0/sotediffusion-wuerstchen3-decoder after the download process is complete.

Prompt:

newest, extremely aesthetic, best quality,

Negative Prompt:

very displeasing, worst quality, monochrome, realistic, oldest, loli,

Parameters:
Sampler: Default

Steps: 30 or 40
Refiner Steps: 10

CFG: 7
Secondary CFG: 2 or 1

Resolution: 1024x1536, 2048x1152
Anything works as long as it's a multiply of 128.

ComfyUI

Please refer to CivitAI: https://civitai.com/models/353284

Code Example

pip install diffusers
import torch
from diffusers import StableCascadeCombinedPipeline

device = "cuda"
dtype = torch.bfloat16 # or torch.float16
model = "Disty0/sotediffusion-wuerstchen3-decoder"

pipe = StableCascadeCombinedPipeline.from_pretrained(model, torch_dtype=dtype)

# send everything to the gpu:
pipe = pipe.to(device, dtype=dtype)
pipe.prior_pipe = pipe.prior_pipe.to(device, dtype=dtype)

# or enable model offload to save vram:
# pipe.enable_model_cpu_offload()



prompt = "newest, extremely aesthetic, best quality, 1girl, solo, cat ears, pink hair, orange eyes, long hair, bare shoulders, looking at viewer, smile, indoors, casual, living room, playing guitar,"
negative_prompt = "very displeasing, worst quality, monochrome, realistic, oldest, loli,"
output = pipe(
    width=1024,
    height=1536,
    prompt=prompt,
    negative_prompt=negative_prompt,
    decoder_guidance_scale=2.0,
    prior_guidance_scale=7.0,
    prior_num_inference_steps=30,
    output_type="pil",
    num_inference_steps=10
).images[0]

## do something with the output image

Training:

Software used: Kohya SD-Scripts with Stable Cascade branch.
https://github.com/kohya-ss/sd-scripts/tree/stable-cascade

GPU used: 8x Nvidia A100 80GB
GPU Hours: 220

Base

parameter value
amp bf16
weights fp32
save weights fp16
resolution 1024x1024
effective batch size 128
unet learning rate 1e-5
te learning rate 4e-6
optimizer Adafactor
images 6M
epochs 3

Final

parameter value
amp bf16
weights fp32
save weights fp16
resolution 1024x1024
effective batch size 128
unet learning rate 4e-6
te learning rate none
optimizer Adafactor
images 120K
epochs 16

Dataset:

GPU used for captioning: 1x Intel ARC A770 16GB
GPU Hours: 350

Model used for captioning: SmilingWolf/wd-swinv2-tagger-v3
Model used for text: llava-hf/llava-1.5-7b-hf

Command:

python /mnt/DataSSD/AI/Apps/kohya_ss/sd-scripts/finetune/tag_images_by_wd14_tagger.py --model_dir "/mnt/DataSSD/AI/models/wd14_tagger_model" --repo_id "SmilingWolf/wd-swinv2-tagger-v3" --recursive --remove_underscore --use_rating_tags --character_tags_first --character_tag_expand --append_tags --onnx --caption_separator ", " --general_threshold 0.35 --character_threshold 0.50 --batch_size 4 --caption_extension ".txt" ./
dataset name total images
newest 1.848.331
recent 1.380.630
mid 993.227
early 566.152
oldest 160.397
pixiv 343.614
visual novel cg 231.358
anime wallpaper 104.790
Total 5.628.499

Note:

  • Smallest size is 1280x600 | 768.000 pixels
  • Deduped based on image similarity using czkawka-cli
  • Around 120K very high quality images got intentionally duplicated 5 times, making the total image count 6.2M

Tags:

Model is trained with random tag order but this is the order in the dataset if you are interested:

aesthetic tags, quality tags, date tags, custom tags, rating tags, character, series, rest of the tags

Date:

tag date
newest 2022 to 2024
recent 2019 to 2021
mid 2015 to 2018
early 2011 to 2014
oldest 2005 to 2010

Aesthetic Tags:

Model used: shadowlilac/aesthetic-shadow-v2

score greater than tag count
0.90 extremely aesthetic 125.451
0.80 very aesthetic 887.382
0.70 aesthetic 1.049.857
0.50 slightly aesthetic 1.643.091
0.40 not displeasing 569.543
0.30 not aesthetic 445.188
0.20 slightly displeasing 341.424
0.10 displeasing 237.660
rest of them very displeasing 328.712

Quality Tags:

Model used: https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/models/aes-B32-v0.pth

score greater than tag count
0.980 best quality 1.270.447
0.900 high quality 498.244
0.750 great quality 351.006
0.500 medium quality 366.448
0.250 normal quality 368.380
0.125 bad quality 279.050
0.025 low quality 538.958
rest of them worst quality 1.955.966

Rating Tags:

tag count
general 1.416.451
sensitive 3.447.664
nsfw 427.459
explicit nsfw 336.925

Custom Tags:

dataset name custom tag
image boards date,
text The text says "text",
characters character, series
pixiv art by Display_Name,
visual novel cg Full_VN_Name (short_3_letter_name), visual novel cg,
anime wallpaper date, anime wallpaper,

Limitations and Bias

Bias

  • This model is intended for anime illustrations.
    Realistic capabilites are not tested at all.

Limitations

  • Can fall back to realistic.
    Add "realistic" tag to the negatives when this happens.
  • Far shot eyes and hands can be bad.

License

SoteDiffusion models falls under Fair AI Public License 1.0-SD license, which is compatible with Stable Diffusion models’ license. Key points:

  1. Modification Sharing: If you modify SoteDiffusion models, you must share both your changes and the original license.
  2. Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too.
  3. Distribution Terms: Any distribution must be under this license or another with similar rules.
  4. Compliance: Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values.

Notes: Anything not covered by Fair AI license is inherited from Stability AI Non-Commercial license which is named as LICENSE_INHERIT.