"Prompt (press Ctrl+Enter or Alt+Enter to generate)":"提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
"Negative prompt (press Ctrl+Enter or Alt+Enter to generate)":"反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
"Add a random artist to the prompt.":"随机添加一个艺术家到提示词中",
"Read generation parameters from prompt or last generation if prompt is empty into user interface.":"从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
"Save style":"储存为模版风格",
"Apply selected styles to current prompt":"将所选模板风格，应用于当前提示词",
"Stop processing current image and continue processing.":"停止处理当前图像，并继续处理下一个",
"Stop processing images and return any results accumulated so far.":"停止处理图像，并返回迄今为止累积的任何结果",
"Style to apply; styles have components for both positive and negative prompts and apply to both":"要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
@@ -533,14 +528,47 @@
"Roll three":"抽三位出来",
"Generate forever":"无限生成",
"Cancel generate forever":"停止无限生成",
"how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.":"训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n 率1:步限1, 率2:步限2, ...\n\n如: 0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
"Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.":"训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
"How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results":"迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
"Draw a mask over an image, and the script will regenerate the masked area with content according to prompt":"在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
"Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back":"正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
"Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows":"创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
"Run Python code. Advanced user only. Must run program with --allow-code for this to work":"运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
"Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others":"以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
"Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order":"以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
"Reconstruct prompt from existing image and put it into the prompt field.":"从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
"Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle":"设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
"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",
"Upscaler":"放大算法",
"Start drawing":"开始绘制",
"----无效----":"----以下内容无法被翻译，Bug----",
"Add a random artist to the prompt.":"随机添加一个艺术家到提示词中",
"Read generation parameters from prompt or last generation if prompt is empty into user interface.":"从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
"Save style":"储存为模版风格",
"Apply selected styles to current prompt":"将所选模板风格，应用于当前提示词",
"Upscaler 1":"放大算法 1",
"Upscaler 2":"放大算法 2",
"Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)":"用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
"Select which Real-ESRGAN models to show in the web UI. (Requires restart)":"选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
"Allowed categories for random artists selection when using the Roll button":"使用抽选艺术家按钮时将会随机的艺术家类别",
"Face restoration model":"面部修复模型",
"----已移除----":"----以下内容在webui新版本已移除----",
"▼":"▼",
"History":"历史记录",
"Show Textbox":"显示文本框",
"File with inputs":"含输入内容的文件",
"Prompts":"提示词",
"Disabled when launched with --hide-ui-dir-config.":"启动 --hide-ui-dir-config 时禁用",
"Unload VAE and CLIP from VRAM when training":"训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
"Number of pictures displayed on each page":"每页显示的图像数量",
"Number of grids in each row":"每行显示多少格",
"Start drawing":"开始绘制",
"how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.":"训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n 率1:步限1, 率2:步限2, ...\n\n如: 0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
"Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)":"用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
"Select which Real-ESRGAN models to show in the web UI. (Requires restart)":"选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
"Face restoration model":"面部修复模型",
"Allowed categories for random artists selection when using the Roll button":"使用抽选艺术家按钮时将会随机的艺术家类别",
"favorites":"收藏夹(已保存)",
"others":"其他",
"Collect":"收藏(保存)",
"Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.":"训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
"How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results":"迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
"Draw a mask over an image, and the script will regenerate the masked area with content according to prompt":"在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
"Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back":"正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
"Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows":"创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
"Run Python code. Advanced user only. Must run program with --allow-code for this to work":"运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
"Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others":"以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
"Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order":"以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
"Reconstruct prompt from existing image and put it into the prompt field.":"从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
"Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle":"设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
"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",
