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Лицензия
Своя лицензия
Опубликована
24 мая 2024

sotediffusion-wuerstchen3-alpha3

Генерация изображенийТекст → изображение3 профессии

Модель генерирует аниме-иллюстрации по текстовым описаниям и находится в активном обучении.

101скачиваний за 30 дней3лайкавеса обновлены29 мая 2024

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Тесты пользователейпока нет

Никто ещё не опубликовал тест этой модели. Соберите бенчмарк из своих задач — результат появится здесь.

  1. Добавьте модель в бенчмарк

    Вместе с другими моделями категории «Изображения» — сравнение идёт внутри одной категории.

  2. Опишите свои задачи

    Напишите запросы, при необходимости приложите файлы и эталонные ответы.

  3. Запустите и опубликуйте

    Модели сгенерируют результат по каждой задаче. Опубликованный тест появится на этой странице.

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

SoteDiffusion Wuerstchen3 — модель генерации изображений в стиле аниме, созданная дообучением Disty0 на базе Würstchen V3. Она предназначена для создания иллюстраций по текстовым запросам и обучалась на наборе из 2,75 млн изображений. Для работы доступны Diffusers и safetensors; в README приведены настройки генерации, включая разрешения, кратные 128, и примеры параметров для SD.Next.

Модель ориентирована на аниме-иллюстрации; реалистичные изображения отдельно не проверялись. Обучение продолжается, а лицензия указана как other; автор запрещает коммерческое использование.

Сценарии применения

  • создание аниме-иллюстрации по текстовому описанию
  • генерация портретов персонажей в стиле аниме
  • создание сцен с персонажами и сериями по тегам
  • подготовка художественных изображений для творческих проектов

Ограничения

Модель обучается и может переключаться на реалистичный стиль; качество глаз на дальних планах, анатомии и кистей может быть низким. Реалистичные возможности не проверялись, коммерческое использование запрещено автором.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

SoteDiffusion Wuerstchen3

Anime finetune of Würstchen V3.
Still in active training.
No commercial use.

Release Notes

  • Switched to OneTrainer.
  • Trained more.
  • Currently trained on 2,75M images in total
  • Used the FP16 fix script made by KBlueLeaf.

UI Guide

SD.Next

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

Go to Models -> Huggingface and type Disty0/sotediffusion-wuerstchen3-alpha3-decoder into the model name and press download.
Load Disty0/sotediffusion-wuerstchen3-alpha3-decoder after the download process is complete.

Prompt:

extremely aesthetic, best quality, newest,

Negative Prompt:

very displeasing, worst quality, monochrome, sketch, realistic, oldest, visual novel cg,

Parameters:
Sampler: Default

Steps: 30 or 40
Refiner Steps: 10

CFG: 8-6
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-alpha3-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, cat ears, straight hair, pink hair, orange eyes, bare shoulders, looking at viewer, smile, newest, extremely aesthetic, best quality, general, black background, wind, petals, feathers, magic, electricity, bioluminescence, depth of field, fireflies, head tilt, fire, space,"
negative_prompt = "very displeasing, worst quality, monochrome, sketch, realistic, oldest, visual novel cg,"
output = pipe(
    width=1024,
    height=1536,
    prompt=prompt,
    negative_prompt=negative_prompt,
    decoder_guidance_scale=2.0,
    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: 500 (Accumulative starting from alpha1)

dataset nametraining doneremaining
newest100131
recent040132
mid040084
early040030
oldest020done
pixiv042done
visual novel cg028done
anime wallpaper013done
Total333375

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" ./
dataset nametotal imagestotal chunk
newest1.848.331232
recent1.380.630173
mid993.227125
early566.152071
oldest160.397021
pixiv343.614043
visual novel cg231.358029
anime wallpaper104.790014
Total5.628.499708

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:

tagdate
newest2022 to 2024
recent2019 to 2021
mid2015 to 2018
early2011 to 2014
oldest2005 to 2010

Aesthetic Tags:

Model used: shadowlilac/aesthetic-shadow-v2

score greater thantagcount
0.90extremely aesthetic125.451
0.80very aesthetic887.382
0.70aesthetic1.049.857
0.50slightly aesthetic1.643.091
0.40not displeasing569.543
0.30not aesthetic445.188
0.20slightly displeasing341.424
0.10displeasing237.660
rest of themvery displeasing328.712

Quality Tags:

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

score greater thantagcount
0.980best quality1.270.447
0.900high quality498.244
0.750great quality351.006
0.500medium quality366.448
0.250normal quality368.380
0.125bad quality279.050
0.025low quality538.958
rest of themworst quality1.955.966

Rating Tags

tagcount
general1.416.451
sensitive3.447.664
nsfw427.459
explicit nsfw336.925

Custom Tags:

dataset namecustom tag
image boardsdate,
characterscharacter, series
pixivart by Display_Name,
visual novel cgFull_VN_Name (short_3_letter_name), visual novel cg,
anime wallpaperdate, anime wallpaper,

Training Parameters:

Software used: OneTrainer
https://github.com/Nerogar/OneTrainer/

Base model: Disty0/sote-diffusion-cascade-alpha2

Config:

{
    "__version": 3,
    "training_method": "FINE_TUNE",
    "model_type": "STABLE_CASCADE_1",
    "debug_mode": false,
    "debug_dir": "debug",
    "workspace_dir": "/mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/OneTrainer/run",
    "cache_dir": "/mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/OneTrainer/workspace-cache/run",
    "tensorboard": true,
    "tensorboard_expose": false,
    "continue_last_backup": true,
    "include_train_config": "NONE",
    "base_model_name": "Disty0/sotediffusion-wuerstchen3-alpha2",
    "weight_dtype": "BFLOAT_16",
    "output_dtype": "BFLOAT_16",
    "output_model_format": "SAFETENSORS",
    "output_model_destination": "/mnt/DataSSD/AI/SoteDiffusion/Wuerstchen3/OneTrainer/workspace-cache/models",
    "gradient_checkpointing": true,
    "force_circular_padding": false,
    "concept_file_name": "training_concepts/concepts.json",
    "concepts": [
        {
            "__version": 1,
            "image": {
                "__version": 0,
                "enable_crop_jitter": false,
                "enable_random_flip": false,
                "enable_fixed_flip": false,
                "enable_random_rotate": false,
                "enable_fixed_rotate": false,
                "random_rotate_max_angle": 0.0,
                "enable_random_brightness": false,
                "enable_fixed_brightness": false,
                "random_brightness_max_strength": 0.0,
                "enable_random_contrast": false,
                "enable_fixed_contrast": false,
                "random_contrast_max_strength": 0.0,
                "enable_random_saturation": false,
                "enable_fixed_saturation": false,
                "random_saturation_max_strength": 0.0,
                "enable_random_hue": false,
                "enable_fixed_hue": false,
                "random_hue_max_strength": 0.0,
                "enable_resolution_override": false,
                "resolution_override": "1024"
            },
            "text": {
                "__version": 0,
                "prompt_source": "sample",
                "prompt_path": "",
                "enable_tag_shuffling": true,
                "tag_delimiter": ", ",
                "keep_tags_count": 1
            },
            "name": "",
            "path": "/mnt/DataSSD/AI/anime_image_dataset/best/newest_best",
            "seed": -209204630,
            "enabled": true,
            "include_subdirectories": true,
            "image_variations": 1,
            "text_variations": 1,
            "balancing": 1.0,
            "balancing_strategy": "REPEATS",
            "loss_weight": 1.0
        }
    ],
    "circular_mask_generation": false,
    "random_rotate_and_crop": false,
    "aspect_ratio_bucketing": true,
    "latent_caching": true,
    "clear_cache_before_training": false,
    "learning_rate_scheduler": "CONSTANT",
    "learning_rate": 1e-05,
    "learning_rate_warmup_steps": 200,
    "learning_rate_cycles": 1,
    "epochs": 1,
    "batch_size": 16,
    "gradient_accumulation_steps": 1,
    "ema": "OFF",
    "ema_decay": 0.999,
    "ema_update_step_interval": 5,
    "dataloader_threads": 8,
    "train_device": "cuda",
    "temp_device": "cpu",
    "train_dtype": "FLOAT_16",
    "fallback_train_dtype": "BFLOAT_16",
    "enable_autocast_cache": true,
    "only_cache": false,
    "resolution": "1024",
    "attention_mechanism": "SDP",
    "align_prop": false,
    "align_prop_probability": 0.1,
    "align_prop_loss": "AESTHETIC",
    "align_prop_weight": 0.01,
    "align_prop_steps": 20,
    "align_prop_truncate_steps": 0.5,
    "align_prop_cfg_scale": 7.0,
    "mse_strength": 1.0,
    "mae_strength": 0.0,
    "vb_loss_strength": 1.0,
    "loss_weight_fn": "P2",
    "loss_weight_strength": 1.0,
    "dropout_probability": 0.0,
    "loss_scaler": "NONE",
    "learning_rate_scaler": "NONE",
    "offset_noise_weight": 0.0,
    "perturbation_noise_weight": 0.0,
    "rescale_noise_scheduler_to_zero_terminal_snr": false,
    "force_v_prediction": false,
    "force_epsilon_prediction": false,
    "min_noising_strength": 0.0,
    "max_noising_strength": 1.0,
    "noising_weight": 0.0,
    "noising_bias": 0.5,
    "unet": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": 0,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "NONE"
    },
    "prior": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": 0,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "NONE"
    },
    "text_encoder": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": 0,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "NONE"
    },
    "text_encoder_layer_skip": 0,
    "text_encoder_2": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": 30,
        "stop_training_after_unit": "EPOCH",
        "learning_rate": null,
        "weight_dtype": "NONE"
    },
    "text_encoder_2_layer_skip": 0,
    "vae": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": null,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "FLOAT_32"
    },
    "effnet_encoder": {
        "__version": 0,
        "model_name": "/mnt/DataSSD/AI/models/wuerstchen3/effnet_encoder.safetensors",
        "train": true,
        "stop_training_after": null,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "FLOAT_16"
    },
    "decoder": {
        "__version": 0,
        "model_name": "Disty0/sotediffusion-wuerstchen3-alpha2-decoder",
        "train": true,
        "stop_training_after": null,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "FLOAT_16"
    },
    "decoder_text_encoder": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": null,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "NONE"
    },
    "decoder_vqgan": {
        "__version": 0,
        "model_name": "",
        "train": true,
        "stop_training_after": null,
        "stop_training_after_unit": "NEVER",
        "learning_rate": null,
        "weight_dtype": "FLOAT_16"
    },
    "masked_training": false,
    "unmasked_probability": 0.1,
    "unmasked_weight": 0.1,
    "normalize_masked_area_loss": false,
    "embedding_learning_rate": null,
    "preserve_embedding_norm": false,
    "embedding": {
        "__version": 0,
        "uuid": "bf3a36b4-bd01-4b46-b818-3c6414887497",
        "model_name": "",
        "placeholder": "<embedding>",
        "train": true,
        "stop_training_after": null,
        "stop_training_after_unit": "NEVER",
        "token_count": 1,
        "initial_embedding_text": "*"
    },
    "additional_embeddings": [],
    "embedding_weight_dtype": "FLOAT_32",
    "lora_model_name": "",
    "lora_rank": 16,
    "lora_alpha": 1.0,
    "lora_weight_dtype": "FLOAT_32",
    "optimizer": {
        "__version": 0,
        "optimizer": "ADAFACTOR",
        "adam_w_mode": false,
        "alpha": null,
        "amsgrad": false,
        "beta1": null,
        "beta2": null,
        "beta3": null,
        "bias_correction": false,
        "block_wise": false,
        "capturable": false,
        "centered": false,
        "clip_threshold": 1.0,
        "d0": null,
        "d_coef": null,
        "dampening": null,
        "decay_rate": -0.8,
        "decouple": false,
        "differentiable": false,
        "eps": 1e-30,
        "eps2": 0.001,
        "foreach": false,
        "fsdp_in_use": false,
        "fused": false,
        "fused_back_pass": true,
        "growth_rate": null,
        "initial_accumulator_value": null,
        "is_paged": false,
        "log_every": null,
        "lr_decay": null,
        "max_unorm": null,
        "maximize": false,
        "min_8bit_size": null,
        "momentum": null,
        "nesterov": false,
        "no_prox": false,
        "optim_bits": null,
        "percentile_clipping": null,
        "r": null,
        "relative_step": false,
        "safeguard_warmup": false,
        "scale_parameter": false,
        "stochastic_rounding": true,
        "use_bias_correction": false,
        "use_triton": false,
        "warmup_init": false,
        "weight_decay": 0.0,
        "weight_lr_power": null
    },
    "optimizer_defaults": {
        "ADAFACTOR": {
            "__version": 0,
            "optimizer": "ADAFACTOR",
            "adam_w_mode": false,
            "alpha": null,
            "amsgrad": false,
            "beta1": null,
            "beta2": null,
            "beta3": null,
            "bias_correction": false,
            "block_wise": false,
            "capturable": false,
            "centered": false,
            "clip_threshold": 1.0,
            "d0": null,
            "d_coef": null,
            "dampening": null,
            "decay_rate": -0.8,
            "decouple": false,
            "differentiable": false,
            "eps": 1e-30,
            "eps2": 0.001,
            "foreach": false,
            "fsdp_in_use": false,
            "fused": false,
            "fused_back_pass": true,
            "growth_rate": null,
            "initial_accumulator_value": null,
            "is_paged": false,
            "log_every": null,
            "lr_decay": null,
            "max_unorm": null,
            "maximize": false,
            "min_8bit_size": null,
            "momentum": null,
            "nesterov": false,
            "no_prox": false,
            "optim_bits": null,
            "percentile_clipping": null,
            "r": null,
            "relative_step": false,
            "safeguard_warmup": false,
            "scale_parameter": false,
            "stochastic_rounding": true,
            "use_bias_correction": false,
            "use_triton": false,
            "warmup_init": false,
            "weight_decay": 0.0,
            "weight_lr_power": null
        }
    },
    "sample_definition_file_name": "training_samples/samples.json",
    "samples": [
        {
            "__version": 0,
            "enabled": true,
            "prompt": "very aesthetic, best quality, newest, sensitive, 1girl, solo, upper body,",
            "negative_prompt": "monochrome, sketch, fat,",
            "height": 1024,
            "width": 1024,
            "seed": 42,
            "random_seed": true,
            "diffusion_steps": 30,
            "cfg_scale": 7.0,
            "noise_scheduler": "EULER_A"
        }
    ],
    "sample_after": 30,
    "sample_after_unit": "MINUTE",
    "sample_image_format": "JPG",
    "samples_to_tensorboard": true,
    "non_ema_sampling": true,
    "backup_after": 30,
    "backup_after_unit": "MINUTE",
    "rolling_backup": true,
    "rolling_backup_count": 10,
    "backup_before_save": true,
    "save_after": 30,
    "save_after_unit": "MINUTE",
    "save_filename_prefix": ""
}

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.

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм

Можно запустить

Подбираем GPU…

Ревизия весов
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