Merge branch 'pr/3915' into add-updated-content-3

localizations/zh_CN.json

@@ -502,7 +502,7 @@
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
     "fill it with latent space noise": "用潜空间的噪声填充它",
     "fill it with latent space zeroes": "用潜空间的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。请注意，填补像素 仅对 全分辨率局部重绘 生效。",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。\n请注意，填补像素 仅对 全分辨率局部重绘 生效。",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
@@ -560,6 +560,7 @@
     "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）",
@@ -569,5 +570,22 @@
     "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)"    
+    "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": "正常提升图像，将结果分割成瓦片，用img2img改进每个瓦片，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。仅限高级用户。必须用 --allow-code 来运行程序，这样才能工作。",
+    "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": "用逗号隔开一个单词列表，第一个单词将被用作关键词：脚本将在提示中搜索这个单词，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": "用逗号分开一个单词列表，脚本将用这些单词的每一个可能的顺序制作一个变体的提示。",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构提示，并将其放入提示字段。",
+    "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": "在[prompt_words]选项中设置要使用的最大字数；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.": "处理一张图片，将其作为输入，重复。",
+    "Insert selected styles into prompt fields": "在提示字段中插入选定的样式",
+    "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.": "将当前的提示语保存为样式。如果你在文本中添加标记{prompt}，当你将来使用该样式时，该样式会将其作为你的提示的占位符。",
+    "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.": "在制作图像之前从检查点加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为检查点名称。建议与Y轴一起使用以减少切换。",
+    "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: 所有应用程序的显存(VRAM)分配的峰值量 / 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.": "缩放潜在空间中的图像。另一种方法是，从潜在表示中产生完整的图像，提高其比例，然后再将其移回潜在空间。",
+
+    "----": "----"
 }