v8
肌理更新：强化了以下tag的学习：
Texture Update: The following tags have been reinforced in training:
realistic, photorealistic, flat color,shiny skin, matte skin, shiny hair,请注意，在danbooru数据集中有很多个用于描述“照片”和“接近照片的风格”的tag。我在训练集中统一标注这些图片为“photorealistic”。但是使用danbooru训练集训练的SDXL模型大多并不能很好地画出写实图像，因此“photorealistic”只建议在较小权重下用于改变画面的肌理。“realistic”可以在高权重下正常运作。
Please note that Danbooru dataset contains multiple tags to describe "photo" or "photo-like styles". I’ve tagged all such images as “photorealistic” in dataset.
However, most SDXL models trained on the Danbooru dataset do not render realistic images well. “photorealistic” is only recommended at low weight, where it can help adjust texture rather than create realism images. The “realistic” tag can work properly at higher weight.
快速上手 | Quick Start
这是什么？ | What is this?
Pony: People's Works （ppw）是一个实验性的微调模型系列，数据集有约85%是收集自CivitAI上用户发表的AI生成图片。早期ppw的数据集最初建立在由pony v6生成图片的基础上，因此本系列模型生成的图片也带有pony diffusion的特征。
本系列模型使用标准Danbooru标签，主要擅长生成中、近景风格化人像。它们的主要功能是使基础模型可以在不使用画师串、较少的质量提示词的条件下获得相对稳定的图像质量，为提示词节约token空间。
本模型并非风格模型，在不同的提示词和生成条件下可能会有微妙的画风差异。
Pony: People's Works (ppw) is a experimental fine-tuned model series, approximately 85% of the dataset comes from AI-generated images published by users on CivitAI. Since the earlier ppw dataset was built on images generated by Pony V6, the outputs of this series also carry some characteristics of Pony Diffusion.
This series uses standard Danbooru tags and is mainly optimized for generating stylized portraits at medium and close range. The primary effect of this model series is to allow the basemodel to achieve relatively stable image quality, without artist keywords or long quality tags, freeing up token space for prompts.
These models are not style LoRAs. There may be subtle stylistic variations depending on different prompts and generating conditions.
版本信息 | Version Info.
本页面发布的是ppw的高维LoCon版本，也是这个项目的主页面。
LoCon版本的ppw可以灵活搭配各类功能LoRA和底模，并且效果强度有更高的可控性。高维度版本的LoCon拥有更强的泛化性和细节表现，但是会占用更多的储存空间和计算资源。
主要用于在线生成服务和供机器性能较强的用户本地生成使用。
This page features the high dim LoCon version models of ppw, which also serves as the main page of this project.
The LoCon versions of ppw can be flexibly combined with various functional LoRAs and checkpoints, offering greater controllability over effect weight. High dimension versions provide stronger generalization and more detailed rendering, but it requires more storage space and computational resources.
They are mainly intended for online generation services and local use by users with high-performance PCs.
精简版LoCon | Lightweight LoCon ver.
基础模型版 | Checkpoint versions (Illustrious)
基础模型版 | Checkpoint versions (NoobAI)
使用方法 | Usage
positive:
masterpiece, best quality, very aestheticnegative:
low quality, displeasing更新记录 | Change log
v7
v7版本对数据集结构做了较大幅度的调整，并且使用了不同的训练参数和训练策略，因此可能v7会不如原来的版本稳定。
The v7 version has undergone significant structural adjustments to the dataset, and utilizes different training parameters and strategies. As a result, v7 may be less stable than the previous versions.
v-pred模型在civitAI在线生成器上的表现和吐司的在线生成表现完全不一样，相同参数完全无法复现。我也不知道是为什么......
The v-pred model's performance on the CivitAI online generator is completely different from online generation on TensorArt. The results are entirely unreproducible with a same parameters. I have no idea why...
TensorArt version CivitAI ver. with same parameter on CivitAI with higher weight
v7版本简介：
这是一个在前作的数据集基础上发展而来的图像质量LoCon，约90%-95%图片数据来自CivitAI上发布的图片。
它使模型可以在不使用画师串、使用较少的质量提示词的条件下获得相对稳定的图像质量，节约出更多的token空间，同时它还可以修复一部分模型固有的生成瑕疵。（但是不包括手部）
因为数据集选取的原因，生成的图片会带有Pony的质感。但因为它并不指向任何特定的画师、风格和绘画技法，所以在不同的提示词、模型条件下可能会有微妙的画风差异。
This is a generation quality LoCon developed based on the dataset from the previous work. About 90%-95% of the image data comes from CivitAI.
It allows models to achieve relatively stable image quality without artist tags or using long quality prompts, freeing up more token space. Additionally, it can fix some inherent generation flaws of the model. (except for hands)
Due to the dataset selection, the generated images exhibit a Pony-like style. However, since it does not reference any specific artist, style, or painting technique, there may be subtle stylistic variations depending on different prompts and checkpoint conditions.
数据集来源及许可证 | Dataset Source & License
数据集中每一张图片都经过作者本人的人工筛选、分类和标注编辑，其中数百张图片经过手工编辑、修正。
此模型为免费、开源模型，用户可以在私人设备上自行部署该模型。作者并不从模型出售中获取任何报酬。作者并不限制本系列模型用于商业生成服务或者生成图像用于商业用途，但是请注意配合使用的Checkpoint和其他LoRA的许可证限制。
此数据集中约90%-95%数据为AI生成，但是请注意有约250+张收集自公共媒体、新闻媒体和出版物的图像用于概念补充。未来的版本会逐渐更替相关素材。请有商用意向的用户自行注意相关风险。
本数据集没有训练任何独立画师的数据，也没有标注任何画师信息（不排除AI错误标注的情况）。
另外，本模型不允许用作闭源商用、模型出售，也禁止用于闭源商用模型的融合。对于开源融合模型用于生成服务的情形不做限制，但是建议标注融合模型的出处。
Every image in the dataset has been manually selected, categorized, and annotated by the author. Additionally, hundreds of the images have been manually edited and corrected.
This model is free and open-source model, allowing users to deploy it on their personal devices. The author does not receive any compensation from selling the model. The author does not impose restrictions on using this model for commercial image generation services or generating images for commercial purposes. However, please be mindful of the license restrictions of the Checkpoint and other LoRAs used alongside this model.
Approximately 90%-95% of the dataset consists of AI-generated images. However, around 250+ images have been collected from public media, news outlets, and publications to supplement concepts. Future versions will gradually replace these materials. Users with commercial intentions should be aware of the potential risks.
This dataset does not include training data from any individual artist, nor does it contain explicit artist attributions (though AI mistagging cannot be entirely ruled out).
Additionally, this model is not permitted for use in closed-source commercial applications, model resales, or merged into closed-source commercial models. There are no restrictions on open-source merged models being used for image generation services, but it is recommended to credit the sources of any merged models.