PeoplesWorks_v9_Illusv10_128dim

PeoplesWorks_v9_Illusv10_128dim

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Model description

快速上手 | Quick Start 这是什么？ | What is this?

People's Works是一个实验性的微调模型系列，最初基于一个由Pony V6 XL生成的图片构成的数据集。这个模型的数据集由数千张AI社区用户发布的图片和作者使用AI合成的图片构成，经过人工筛选、编辑、修改和标注后用于训练。除此之外还有一个超过2000张由照片、游戏CG和3d渲染图像构成的辅助数据集，用于额外补充知识。

People’s Works is an experimental fine-tuned model series, originally based on a dataset composed of images generated by Pony V6 XL. The dataset consists of several thousand images published by AI community users, along with images synthesized by the author using AI. These images were manually curated, edited, modified, and annotated before being used for training. In addition, there is an auxiliary dataset of over 2,000 images composed of photographs, game CGs, and 3D renders, used to provide supplementary knowledge.

模型功能 | Model features

本系列模型主要功能是帮助基础模型在不使用画师串、较少的质量提示词的条件下获得相对稳定的风格化图像，为提示词节约token空间。

使用人工挑选和标注的数据集强化训练正面和负面审美提示词。

模型针对flat color、realistic等特定风格肌理进行了强化。

对人物的年龄、族裔、肤质等特征的精细化控制。

使用了较高的训练分辨率，使基础模型在高清修复时表现更好。

通过手工修复图片的方式降低了模型的某些细节瑕疵出现的概率。

The primary function of this series of model is to help the basemodel generate relatively stable, stylized images, without artist keywords or long quality tags, freeing up token space for prompts.

By using manually selected and annotated datasets, the model strengthens both positive and negative aesthetic tags training.

The model includes targeted enhancements for specific visual textures and styles, such as flat color and realistic.

It enables finer control over character attributes, including age, ethnicity, and skin texture.

A higher training resolution is used, improving the basemodel’s performance during high-res upscaling.

By manually editing the images, the likelihood of certain flaws appearing in the model’s outputs has been reduced.

使用方法 | Usage

positive:

masterpiece, best quality, very aesthetic

negative:

low quality, displeasing

更新记录 | Change log v9

更改了系列名称。自这个版本起，训练集中来自Pony v6 XL的图片已经在所有AIGC内容中占比不足1/3。随着越来越多的新模型出现，我有计划在明年将这个系列拓展到其他模型上。为了避免未来用户使用上的混乱和误解，这个系列从这个版本起更改命名。
The series name has been changed. Starting from this version, images sourced from Pony v6 XL make up less than one third of the training data across all AIGC content. As increasing number of new models are emerging, I plan to expand this series to other models next year. To avoid potential confusion and misunderstanding for users in the future, the series name has been changed starting from this version.

这个版本的训练方式是直接训练LoCon，而非训练Checkpoint后再抽取LoRA。模型相较前一个版本效果更强。
This version is trained directly as a LoCon, rather than training a checkpoint first and then extracting a LoRA. Compared to the previous versions, the model delivers stronger effects.

v9全系列使用1536分辨率的训练集。现在使用这个Lora生成图片时支持单边768-1536的分辨率，使用高清修复时也可以尝试更高的denoise参数了。
All v9 models use a 1536-resolution training set. When generating images with this LoRA, single-side resolutions from 768 to 1536 are now supported. When using high-res fix, you can also try higher denoise values.

对训练图片调色。现在模型在没有指定色彩时更倾向于生成暖色调的图片，并且色彩的饱和度略微提高。较暗的场景明暗对比更大了。
Color adjustments were applied to images. When no specific color is specified, the model now tends to produce warmer tones, with slightly increased saturation. Darker scenes also have stronger contrast between light and shadow.

之前版本的数据集中，人物鼻子的画法不统一。出于作者本人的兴趣，手工修改了其中约300幅图片，并暂时排除了约200幅来不及修改的图片。现在人物的鼻子有鼻翼了。
In earlier versions of dataset, nose depiction was inconsistent. Out of personal interest, the author manually modified around 300 images and temporarily excluded about 200 images that could not be edited in time. Characters’ noses now have nose wings.

删除了旧版本中数百张过时的低质量训练数据。
Hundreds of outdated, low-quality images from older versions of dataset have been removed.

添加了一个新的实验性数据集： 使用真人相片作为引导，现在你可以使用以下年龄和族裔标签了：
A new experimental dataset has been added. Using real photographs as guidance, you can now use the following age and ethnicity tags:

child, teenage, adult, mature

Caucasian, Asian, Indian, African

我的标签设计优先选择Danbooru数据集中已经存在的标签，尽管其中很多只有很少量的数据，在原版模型中几乎无法触发。重新启用了已经被删除的danbooru词条Caucasian和teenage，增设了adult和African两个标签。此外，loli和shota因为其文化背景中强烈的性暗示倾向，这两个词条被完全替换，根据具体情况分流入child和teenage。
My tag design prioritizes labels that already exist in the Danbooru dataset, even though many of them had very limited data and are therefore almost impossible to trigger in the base models. The previously removed Danbooru tags Caucasian and teenage have been re-enabled, and two new tags, adult and African, have been added. Additionally, due to the strong sexual connotations of loli and shota in their cultural context, these tags have been completely replaced and redistributed into child and teenage depending on the situation.

v8

Source

https://civitai.com/models/1400090?modelVersionId=2524593

Credit

https://civitai.com/user/Dajiejiekong

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