Incomplete anime pony

Version Arknights update:

Arknights 版本使用50K张明日方舟图片训练。

因为发现对画风造成了有趣影响，于是做成了大模型。

基于原版pony训练。

在A100 40G 上训练了 50小时。

The Arknights version was trained using 50,000 images from Arknights.

Due to the interesting effects observed on the art style, it was developed into a large model.

Based on the original Pony model.

Training took 50 hours on an A100 40G.

Prompt

score_9,score_8_up,score_7_up

Negative prompt

logo,score_4,score_5,score_6,lowres,(bad),text,error,fewer,extra,missing,worst quality,jpeg artifacts,low quality,watermark,unfinished,displeasing,oldest,early,chromatic aberration,signature,extra digits,artistic error,username,scan,[abstract],

--

Version Alpha update:

-Further fine-tuning the furry style in the pony base model to a Japanese anime illustration style.

-The current version of the model can be directly used for generating images.

The base model remains chenkin_20w.safetensors trained using a 200,000 training dataset.

Several manually selected high-quality illustrations are used for targeted training (provided by Euge).

Each illustration is of exceptional quality, labeled as "amazing."

The trained model is merged with chenkin_20w.safetensors with a weight of 0.7.

Therefore, the current quality indicators are:

amazing,best，hight,score_9，

or simply:

amazing,best，hight,

Alpha版本更新：

-pony底模中的furry风格进一步微调为日系动漫插画风格。

-现在版本的模型，可以直接用于出图。

底模依然为使用20w训练集训练的chenkin_20w.safetensors

对若干张人工挑选的超高质量插画进行针对训练。（由尤吉提供）

每一张都是千中挑一的水准，打上了`amazing` 的质量标签。

训练后的模型，以0.7的权重与chenkin_20w.safetensors 合并。

因此，现在的质量提示词是：

amazing,best，hight,score_9，

或者，仅仅使用：

amazing,best，hight,

特别鸣谢：算力赞助 / GPU Sponsor: Neta

特别鸣谢：算力赞助 / GPU Sponsor: nieta.art

This model may not be suitable for generating images and it is not recommended for beginners to download.

Intended as a reference (or lesson) for colleagues who are training on Pony Diffusion V6 XL.

Based on Pony Diffusion V6 XL, trained on 200k anime images, using an A40 48G for over 7 days.

The model was trained to generate images in the style of Japanese anime, but did not achieve the expected results.

1. chenkin_pony.safetensors

Pretrained on 13k random anime images.

2. chenkin_20w.safetensors (Current model)

Trained on top of chenkin_pony.safetensors.

Used 190k selected images from yande with no quality labels (provided by Miss Erity).

Used 10k manually selected high-quality illustrations with the "hight" quality label (provided by Euge).

Using 100 manually selected ultra-high-quality illustrations with the "amazing" quality label (provided by Euge).

amazing,hight,score_9 

Trained using the bmaltais/kohya_ss (github.com) project.

This project consumed a significant amount of GPU computing power, thanks to Neta for providing the computing resources.

Special thanks to GPU Sponsor: Neta.

这个模型可能不适合用来生成图片，同时不建议初学者下载。

旨在为更多在Pony Diffusion V6 XL上训练的同仁提供参考（或者说教训）

基于Pony Diffusion V6 XL 训练，使用200k的动漫图片， 在 A40 48G 训练了超过7天。

该模型是为了更好生成日本动漫风格的图片而训练，但是没有取得预期。

1.chenkin_pony.safetensors

使用了13k随机动漫图片进行预训练。

2.chenkin_20w.safetensors （当前模型）

在chenkin_pony.safetensors的基础上进行训练

使用了190k张来自yande的精选图片，无质量标。（由二小姐提供）

使用10k张人工挑选的高质量插画，打上了`hight` 的质量标签。（由尤吉提供）

使用100张人工挑选的超高质量插画，打上了`amazing` 的质量标签。（由尤吉提供）

amazing,hight,score_9 

使用bmaltais/kohya_ss (github.com) 项目进行训练。

此项目耗费了大量的GPU的算力，感谢 Neta 愿意为我提供算力。

特别鸣谢：算力赞助 / GPU Sponsor: Neta

训练参数如下（train set）：

[sdxl_arguments]
cache_text_encoder_outputs = false
no_half_vae = false
min_timestep = 0
max_timestep = 1000

[model_arguments]
pretrained_model_name_or_path = "/root/autodl-tmp/stable-diffusion-webui/models/Stable-diffusion/chenkin_pony/chenkin_pony.safetensors"

[dataset_arguments]
shuffle_caption = true
debug_dataset = false
train_data_dir = "/root/autodl-tmp/20w"
dataset_repeats = 1
keep_tokens_separator = "|||"
resolution = "1024, 1024"
caption_dropout_rate = 0
caption_tag_dropout_rate = 0
caption_dropout_every_n_epochs = 0
token_warmup_min = 1
token_warmup_step = 0
enable_bucket = true
min_bucket_reso=640 
max_bucket_reso=2048
bucket_reso_steps=64


[training_arguments]
output_dir = "/root/autodl-tmp/stable-diffusion-webui/models/Stable-diffusion/chenkin_20w"
output_name = "chenkin_20w"
save_precision = "fp16"
train_batch_size=6         
vae_batch_size=4 
max_train_epochs=1 
save_every_n_steps = 2000
max_token_length = 225
mem_eff_attn = false
xformers = true
sdpa = false

max_data_loader_n_workers = 8
persistent_data_loader_workers = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
mixed_precision = "fp16"

[sample_prompt_arguments]
sample_every_n_steps = 200
sample_sampler = "euler_a"
sample_prompts="/root/example.txt"

[saving_arguments]
save_model_as = "safetensors"

[optimizer_arguments]
optimizer_type = "AdaFactor"
learning_rate = 7.5e-7
train_text_encoder = false
learning_rate_te1 = 0
learning_rate_te2 = 0
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100
max_grad_norm = 0