微信小程序中使用自定义图标(阿里icon)的方法

weui提供的图标比较少,有时我们需要更多的图标,可以使用以下方法自定义图标库:

1,到阿里巴巴矢量图标库(http://iconfont.cn/)生成自己的字体图标,下载代码,解压,打开iconfont.css

2,在wxss文件中引用字体

<style type="less">

@font-face {
    font-family: 'iconfont';
    src: url(data:font/truetype;charset=utf-8;base64,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) format('truetype'),
}

.iconfont {
font-family:"iconfont" !important;
font-size:16px;
font-style:normal;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}

.icon-fanhui:before { content: "\f0343"; }

.icon-gengduo:before { content: "\f0344"; }

</style>

3,使用字体

<view class="iconfont icon-fanhui">
           返回
</view>
<view class="iconfont icon-gengduo">
           更多
</view>

4,效果图
这里写图片描述

### 部署 Stable Diffusion 的准备工作 为了成功部署 Stable Diffusion,在本地环境中需完成几个关键准备事项。确保安装了 Python 和 Git 工具,因为这些对于获取源码和管理依赖项至关重要。 #### 安装必要的软件包和支持库 建议创建一个新的虚拟环境来隔离项目的依赖关系。这可以通过 Anaconda 或者 venv 实现: ```bash conda create -n sd python=3.9 conda activate sd ``` 或者使用 `venv`: ```bash python -m venv sd-env source sd-env/bin/activate # Unix or macOS sd-env\Scripts\activate # Windows ``` ### 下载预训练模型 Stable Diffusion 要求有预先训练好的模型权重文件以便能够正常工作。可以从官方资源或者其他可信赖的地方获得这些权重文件[^2]。 ### 获取并配置项目代码 接着要做的就是把最新的 Stable Diffusion WebUI 版本拉取下来。在命令行工具里执行如下指令可以实现这一点;这里假设目标路径为桌面下的特定位置[^3]: ```bash git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git ~/Desktop/stable-diffusion-webui cd ~/Desktop/stable-diffusion-webui ``` ### 设置 GPU 支持 (如果适用) 当打算利用 NVIDIA 显卡加速推理速度时,则需要确认 PyTorch 及 CUDA 是否已经正确设置好。下面这段简单的测试脚本可以帮助验证这一情况[^4]: ```python import torch print(f"Torch version: {torch.__version__}") if torch.cuda.is_available(): print("CUDA is available!") else: print("No CUDA detected.") ``` 一旦上述步骤都顺利完成之后,就可以按照具体文档中的指导进一步操作,比如调整参数、启动服务端口等等。整个过程中遇到任何疑问都可以查阅相关资料或社区支持寻求帮助。
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