From 044feebdb0d2b2b3d7aad74ad8a099e1813f1d44 Mon Sep 17 00:00:00 2001 From: dtlnor Date: Tue, 22 Nov 2022 17:08:40 +0900 Subject: [PATCH] Update zh_CN.json --- localizations/zh_CN.json | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json index b0c5966..0b345fe 100644 --- a/localizations/zh_CN.json +++ b/localizations/zh_CN.json @@ -221,7 +221,7 @@ "Include Separate Images": "生成图表时，保留每一张图像", "Keep -1 for seeds": "保持随机种子为-1", "Z type": "Z轴类型", - "Z values": "Z轴数值", + "Z values": "Z轴值", "Primary detection model (A)": "首要检测模型 (A)", "Detection confidence threshold % (A)": "检测置信阈值 % (A)", "Dilation factor (A)": "扩张(Dilation)因子 (A)", @@ -505,7 +505,13 @@ "Dataset Directory": "数据集目录", "Class Prompt": "类(Class)提示词", "Classification Dataset Directory": "分类(Classification)数据集目录", + "Existing Prompt Contents": "现存的提示词内容", "Description": "描述", + "Instance Token + Description": "实例的词元(Token) + 描述", + "Class Token + Description": "类的词元(Token) + 描述", + "Instance Token + Class Token + Description": "实例的词元 + 类的词元 + 描述", + "Instance token to swap": "要互换的实例的词元(Token)", + "Class token(s) to swap, can be comma-separated": "要互换的类的词元(Token)，可以是以英文逗号分割的", "Total Number of Class/Reg Images": "用于分类/规范化的图像总数", "Classification Image Negative Prompt": "分类(classification)图像反向提示词", "Classification CFG Scale": "分类提示词相关性(Classification CFG scale)", @@ -812,6 +818,7 @@ "stable-diffusion-webui-prompt-travel": "提示词变迁", "stable-diffusion-webui-randomize": "随机化", "stable-diffusion-webui-tokenizer": "词元分析器(tokenizer)", + "stable-diffusion-webui-wd14-tagger": "Waifu Diffusion 1.4 标签器", "stable-diffusion-webui-wildcards": "通配符", "training-picker": "训练图挑选器", "unprompted": "非文本（代码化）提示词", @@ -867,6 +874,7 @@ "Comma separated list": "以逗号分割的列表", "Range of stepped values (min, max, step)": "含步幅的随机范围 (最小, 最大, 步幅)", "Float value from 0 to 1": "从 0 到 1 的浮点数数值", + "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换", "Comma separated list. Specify ckpt OR ckpt:word": "以逗号分割的列表。用以指定 ckpt 或 ckpt:字词", "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值", "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值", @@ -953,7 +961,7 @@ "Max Gradient norms.": "最大梯度范数(Max Gradient norms)", "Pad the input images token lenght to this amount. You probably want to do this.": "将输入图像的词元长度垫齐到此数量。你可能会想要这样做", "Subject class to crop (leave blank to auto-detect)": "要裁剪的主体类别(Subject class)（留空以自动检测）", - "Subject Name to replace class with in captions": "描述文本中要替换类(class)的主体名(subject)", + "Subject Name to replace class with in cations": "描述文本中要替换类(class)的主体名(subject)", "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部", "symbol:color-hex, symbol:color-hex, ...": "文字:颜色代码, 文字:颜色代码, ...", "e.g. A portrait photo of embedding_name": "示例： A portrait photo of embedding_name", @@ -971,6 +979,15 @@ "Leave empty for auto": "留空时自动生成", "NAIConvert": "NAI转换", "History": "历史记录", + "Generate all possible prompts up to a maximum of Batch count * Batch size)": "生成不超过（生成批次 * 每批数量）的所有可能的提示词", + "Automatically update your prompt with interesting modifiers. (Runs slowly the first time)": "使用有趣的修饰符自动更新你的提示词。（第一次运行会比较慢）", + "Generate random prompts from lexica.art (your prompt is used as a search query).": "从 lexica.art 生成随机提示词（你的提示词会被用作搜索查询）", + "Use the same seed for all prompts in this batch": "对这批次中的所有提示词使用相同的种子", + "Write all generated prompts to a file": "将所有生成的提示词写入文件", + "If this is set, then random prompts are generated, even if the seed is the same.": "如果设置了此项，则会生成随机提示词，即使种子相同", + "Useful for I'm feeling lucky and Magic Prompt. If this is set, then negative prompts are not generated.": "对手气不错和魔法提示词很有用。如果设置了此项，则不会生成否定提示", + "Disable image generation. Useful if you only want to generate text prompts.": "禁用图像生成。如果你只想生成文本提示词的话", + "Add emphasis to a randomly selected keyword in the prompt.": "在提示词中随机选择一个关键字加上强调符", "Action": "行动", "Aesthetic Gradients": "美术风格梯度", "Create an embedding from one or few pictures and use it to apply their style to generated images.": "用一张或多张图像创建一个 Embedding，并用它将图集的风格转移到要生成的图像上", @@ -1018,6 +1035,8 @@ "Auto TLS-HTTPS": "自动 TLS-HTTPS", "Allows you to easily, or even completely automatically start using HTTPS.": "让你可以很简单地自动配置HTTPS", "Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning.": "通过对比调整提示词，实现可控的单发文本到图像生成", + "WD 1.4 Tagger": "WD 1.4 标签器", + "Uses a trained model file, produces WD 1.4 Tags. Model link - https://mega.nz/file/ptA2jSSB#G4INKHQG2x2pGAVQBn-yd_U5dMgevGF8YYM9CR_R1SY": "使用经过训练的模型文件，生成 Waifu Diffusion 1.4 标签。模型链接 - https://mega.nz/file/ptA2jSSB#G4INKHQG2x2pGAVQBn-yd_U5dMgevGF8YYM9CR_R1SY", "zh_CN Localization": "简体中文语言包", "Simplified Chinese localization": "简体中文本地化", "zh_TW Localization": "正體中文語言包", @@ -1047,7 +1066,6 @@ "Process an image, use it as an input, repeat.": "处理一张图像，将其作为输入，并重复", "Insert selected styles into prompt fields": "在提示词中插入选定的模版风格", "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示词保存为模版风格。如果你在文本中添加{prompt}标记，那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}", - "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换", "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active： 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved： Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM： 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）", "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间", "Start drawing": "开始绘制",