SoteDiffusion Wuerstchen3

Anime finetune of Würstchen V3.
Currently is in early state in training.
No commercial use thanks to StabilityAI.

Release Notes

• Ran LLaVa on the images that has "english text" tag in it.
  This adds The text says "text" tag.
  If LLaVa has no idea what the text is, it describes the image instead.

UI Guide

SD.Next

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

Go to Models -> Huggingface and type Disty0/sotediffusion-wuerstchen3-alpha2-decoder into the model name and press download.
Load Disty0/sotediffusion-wuerstchen3-alpha2-decoder after the download process is complete.

Prompt:

very aesthetic, best quality, newest,

Negative Prompt:

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

Parameters:
Sampler: Default

Steps: 30 or 40
Refiner Steps: 10

CFG: 8
Secondary CFG: 1 or 1.2

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
model = "Disty0/sotediffusion-wuerstchen3-alpha2-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 = "1girl, solo, cowboy shot, straight hair, looking at viewer, hoodie, indoors, slight smile, casual, furniture, doorway, very aesthetic, best quality, newest,"
negative_prompt = "very displeasing, worst quality, oldest, monochrome, sketch, realistic,"

output = pipe(
    width=1024,
    height=1536,
    prompt=prompt,
    negative_prompt=negative_prompt,
    decoder_guidance_scale=1.0,
    prior_guidance_scale=8.0,
    prior_num_inference_steps=40,
    output_type="pil",
    num_inference_steps=10
).images[0]

## do something with the output image

Training Status:

GPU used for training: 1x AMD RX 7900 XTX 24GB
GPU Hours: 250 (Accumulative starting from alpha1)

Note: chunks starts from 0 and there are 8000 images per chunk

Dataset:

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

Model used for captioning: SmilingWolf/wd-swinv2-tagger-v3
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" ./

Note:

• Smallest size is 1280x600 | 768.000 pixels
• Deduped based on image similarity using czkawka-cli

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:

Aesthetic Tags:

Model used: shadowlilac/aesthetic-shadow-v2

Quality Tags:

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

Rating Tags

Custom Tags:

Training Parameters:

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

Base model: Disty0/sote-diffusion-cascade-alpha0

Command:

#!/bin/sh

CURRENT=$1
CURRENT_SUB=$2

PAST=$3
PAST_SUB=$4

LD_PRELOAD=/usr/lib/libtcmalloc.so.4 accelerate launch --mixed_precision fp16 --num_cpu_threads_per_process 1 stable_cascade_train_stage_c.py \
--mixed_precision fp16 \
--save_precision fp16 \
--full_fp16 \
--sdpa \
--gradient_checkpointing \
--train_text_encoder \
--resolution "1024,1024" \
--train_batch_size 2 \
--gradient_accumulation_steps 8 \
--learning_rate 1e-5 \
--learning_rate_te1 1e-5 \
--lr_scheduler constant_with_warmup \
--lr_warmup_steps 100 \
--optimizer_type adafactor \
--optimizer_args "scale_parameter=False" "relative_step=False" "warmup_init=False" \
--max_grad_norm 0 \
--token_warmup_min 1 \
--token_warmup_step 0 \
--shuffle_caption \
--caption_separator ", " \
--caption_dropout_rate 0 \
--caption_tag_dropout_rate 0 \
--caption_dropout_every_n_epochs 0 \
--dataset_repeats 1 \
--save_state \
--save_every_n_steps 256 \
--sample_every_n_steps 64 \
--max_token_length 225 \
--max_train_epochs 1 \
--caption_extension ".txt" \
--max_data_loader_n_workers 2 \
--persistent_data_loader_workers \
--enable_bucket \
--min_bucket_reso 256 \
--max_bucket_reso 4096 \
--bucket_reso_steps 64 \
--bucket_no_upscale \
--log_with tensorboard \
--output_name sotediffusion-wr3_3b \
--train_data_dir /mnt/DataSSD/AI/anime_image_dataset/combined/combined-$CURRENT/$CURRENT_SUB \
--in_json /mnt/DataSSD/AI/anime_image_dataset/combined/combined-$CURRENT/$CURRENT_SUB.json \
--output_dir /mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/sotediffusion-wr3_3b-$CURRENT/$CURRENT_SUB \
--logging_dir /mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/sotediffusion-wr3_3b-$CURRENT/$CURRENT_SUB/logs \
--resume /mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/sotediffusion-wr3_3b-$PAST/$PAST_SUB/sotediffusion-wr3_3b-state \
--stage_c_checkpoint_path /mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/sotediffusion-wr3_3b-$PAST/$PAST_SUB/sotediffusion-wr3_3b.safetensors \
--text_model_checkpoint_path /mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/sotediffusion-wr3_3b-$PAST/$PAST_SUB/sotediffusion-wr3_3b_text_model.safetensors \
--effnet_checkpoint_path /mnt/DataSSD/AI/models/wuerstchen3/effnet_encoder.safetensors \
--previewer_checkpoint_path /mnt/DataSSD/AI/models/wuerstchen3/previewer.safetensors \
--sample_prompts /mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/config/sotediffusion-prompt.txt

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 can be bad.
• Anatomy and hands can be bad.
• Still in active training.

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. Meaning, still no commercial use of any kind.