V2 介绍：

此 LoRA 基于 NoobAIXL_V-Pred1.0

这个版本根据我的喜好精选数据集进行训练，调整了训练方法，并缩小了文件体积。其艺术风格现已体现我的个人偏好，因此失去了通过“year 2024”等年份标签精确控制画风的能力。不过 v2-mini 在某些方面的表现优于 v1，因此请根据您的需求自行选择 v1 或 v2。另外，v2-mini 版本的使用可能存在一些问题，您可以参考我嵌入在示例图像元数据中的工作流进行优化。

This version features a dataset specifically chosen for my preferences, along with changes to the training methodology, resulting in a smaller file size. Its artistic style now caters to my personal preferences. As a result, it has lost the ability to precisely control the style via year tags such as 'year 2024'. v2-mini exhibits superior performance compared to v1 in certain aspects. Therefore, the choice between v1 and v2 should be made according to your individual requirements. Note that v2 might present some challenges; for optimization, consult my workflow, which is included in the metadata of the example images.

V1 介绍：

此 LoRA 基于 NoobAIXL_V-Pred0.65s

您可以通过使用 'year 2015-year 2024' 范围来调整视觉风格，以获得一些变化。

与 Noob0.65V 模型中的 "wlop" 风格相比，此版本更符合我的个人品味。

此模型纯粹出于兴趣训练，主要目的是生成我偏好的图像。请勿用于其他用途。

另一个有趣之处是，本次 LoRA 训练尝试了非常奇特的参数和数据集，但由于过于激进，我就不详细展开了。

最后要说明的是，关于为什么 noob 模型中已包含 "wlop" 画师标签却仍训练了这个 LoRA 模型：在基础模型中直接使用 "wlop" 标签难以精确控制为我所喜爱的画风，且容易导致水印出现。因此，我选择使用 LoRA 模型来精准实现我想要的风格，训练数据集是我精选的不含任何水印的高质量图片。

As a final note, regarding why I trained this LoRA model even though the 'wlop' artist tag is already included in the base model: using the 'wlop' tag directly in the base model doesn't allow for precise control over the style to match my preferences, and it can also lead to watermarks appearing. Therefore, I chose to use a LoRA model to achieve the exact style I wanted, and the training dataset consists of high-quality, hand-picked images without any watermarks.