Update localization for zh-TW

README.md

@@ -513,7 +513,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - `safetensors` is updated. Please see [Upgrade](#upgrade) and update the library.
   - Fixed a bug that the training crashes when `network_multiplier` is specified with multi-GPU training. PR [#1084](https://github.com/kohya-ss/sd-scripts/pull/1084) Thanks to fireicewolf!
   - Fixed a bug that the training crashes when training ControlNet-LLLite.
-  
+
 - Merge sd-scripts v0.8.2 code update
   - [Experimental] The `--fp8_base` option is added to the training scripts for LoRA etc. The base model (U-Net, and Text Encoder when training modules for Text Encoder) can be trained with fp8. PR [#1057](https://github.com/kohya-ss/sd-scripts/pull/1057) Thanks to KohakuBlueleaf!
     - Please specify `--fp8_base` in `train_network.py` or `sdxl_train_network.py`.
@@ -526,15 +526,15 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - For example, if you train with state A as `1.0` and state B as `-1.0`, you may be able to generate by switching between state A and B depending on the LoRA application rate.
     - Also, if you prepare five states and train them as `0.2`, `0.4`, `0.6`, `0.8`, and `1.0`, you may be able to generate by switching the states smoothly depending on the application rate.
     - Please specify `network_multiplier` in `[[datasets]]` in `.toml` file.
-  
+
   - Some options are added to `networks/extract_lora_from_models.py` to reduce the memory usage.
     - `--load_precision` option can be used to specify the precision when loading the model. If the model is saved in fp16, you can reduce the memory usage by specifying `--load_precision fp16` without losing precision.
     - `--load_original_model_to` option can be used to specify the device to load the original model. `--load_tuned_model_to` option can be used to specify the device to load the derived model. The default is `cpu` for both options, but you can specify `cuda` etc. You can reduce the memory usage by loading one of them to GPU. This option is available only for SDXL.
 
   - The gradient synchronization in LoRA training with multi-GPU is improved. PR [#1064](https://github.com/kohya-ss/sd-scripts/pull/1064) Thanks to KohakuBlueleaf!
-  
+
   - The code for Intel IPEX support is improved. PR [#1060](https://github.com/kohya-ss/sd-scripts/pull/1060) Thanks to akx!
-  
+
   - Fixed a bug in multi-GPU Textual Inversion training.
 
   - `.toml` example for network multiplier
@@ -560,7 +560,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
   - Fixed a bug that the VRAM usage without Text Encoder training is larger than before in training scripts for LoRA etc (`train_network.py`, `sdxl_train_network.py`).
     - Text Encoders were not moved to CPU.
-  
+
   - Fixed typos. Thanks to akx! [PR #1053](https://github.com/kohya-ss/sd-scripts/pull/1053)
 
 * 2024/01/15 (v22.5.0)
@@ -578,10 +578,10 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
   - IPEX library is updated. PR [#1030](https://github.com/kohya-ss/sd-scripts/pull/1030) Thanks to Disty0!
   - Fixed a bug that Diffusers format model cannot be saved.
 - Fix LoRA config display after load that would sometime hide some of the feilds
-  
+
 * 2024/01/02 (v22.4.1)
 - Minor bug fixed and enhancements.
-  
+
 * 2023/12/28 (v22.4.0)
 - Fixed to work `tools/convert_diffusers20_original_sd.py`. Thanks to Disty0! PR [#1016](https://github.com/kohya-ss/sd-scripts/pull/1016)
 - The issues in multi-GPU training are fixed. Thanks to Isotr0py! PR [#989](https://github.com/kohya-ss/sd-scripts/pull/989) and [#1000](https://github.com/kohya-ss/sd-scripts/pull/1000)
@@ -596,13 +596,13 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 - The optimizer `PagedAdamW` is added. Thanks to xzuyn! PR [#955](https://github.com/kohya-ss/sd-scripts/pull/955)
 - NaN replacement in SDXL VAE is sped up. Thanks to liubo0902! PR [#1009](https://github.com/kohya-ss/sd-scripts/pull/1009)
 - Fixed the path error in `finetune/make_captions.py`. Thanks to CjangCjengh! PR [#986](https://github.com/kohya-ss/sd-scripts/pull/986)
-  
+
 * 2023/12/20 (v22.3.1)
 - Add goto button to manual caption utility
-- Add missing options for various LyCORIS training algorythms
+- Add missing options for various LyCORIS training algorithms
 - Refactor how feilds are shown or hidden
 - Made max value for network and convolution rank 512 except for LyCORIS/LoKr.
-  
+
 * 2023/12/06 (v22.3.0)
 - Merge sd-scripts updates:
   - `finetune\tag_images_by_wd14_tagger.py` now supports the separator other than `,` with `--caption_separator` option. Thanks to KohakuBlueleaf! PR [#913](https://github.com/kohya-ss/sd-scripts/pull/913)
@@ -616,4 +616,4 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - `--ds_ratio` option denotes the ratio of the Deep Shrink. `0.5` means the half of the original latent size for the Deep Shrink.
     - `--dst1`, `--dst2`, `--dsd1`, `--dsd2` and `--dsr` prompt options are also available.
   - Add GLoRA support
-- 
+-

examples/caption.ps1

@@ -1,6 +1,6 @@
 # This powershell script will create a text file for each files in the folder
 #
-# Usefull to create base caption that will be augmented on a per image basis
+# Useful to create base caption that will be augmented on a per image basis
 
 $folder = "D:\some\folder\location\"
 $file_pattern="*.*"

examples/caption_subfolders.ps1

@@ -1,19 +1,19 @@
 # This powershell script will create a text file for each files in the folder
 #
-# Usefull to create base caption that will be augmented on a per image basis
+# Useful to create base caption that will be augmented on a per image basis
 
 $folder = "D:\test\t2\"
 $file_pattern="*.*"
 $text_fir_file="bigeyes style"
 
-foreach ($file in Get-ChildItem $folder\$file_pattern -File) 
+foreach ($file in Get-ChildItem $folder\$file_pattern -File)
 {
     New-Item -ItemType file -Path $folder -Name "$($file.BaseName).txt" -Value $text_fir_file
 }
 
 foreach($directory in Get-ChildItem -path $folder -Directory)
 {
-    foreach ($file in Get-ChildItem $folder\$directory\$file_pattern) 
+    foreach ($file in Get-ChildItem $folder\$directory\$file_pattern)
     {
         New-Item -ItemType file -Path $folder\$directory -Name "$($file.BaseName).txt" -Value $text_fir_file
     }

examples/kohya_train_db_fixed_with-reg_SDv2 512 base.ps1

@@ -61,4 +61,4 @@ accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process tra
     --seed=494481440 `
     --lr_scheduler=$lr_scheduler
 
-# Add the inference yaml file along with the model for proper loading. Need to have the same name as model... Most likelly "last.yaml" in our case.
+# Add the inference yaml file along with the model for proper loading. Need to have the same name as model... Most likely "last.yaml" in our case.

library/wd14_caption_gui.py

@@ -123,7 +123,7 @@ def gradio_wd14_caption_gui_tab(headless=False):
                 value='.txt',
                 interactive=True,
             )
-            
+
             caption_separator = gr.Textbox(
                 label='Caption Separator',
                 value=',',
@@ -199,11 +199,11 @@ def gradio_wd14_caption_gui_tab(headless=False):
                 ],
                 value='SmilingWolf/wd-v1-4-convnextv2-tagger-v2',
             )
-            
+
             force_download = gr.Checkbox(
                 label='Force model re-download',
                 value=False,
-                info='Usefull to force model re download when switching to onnx',
+                info='Useful to force model re download when switching to onnx',
             )
 
             general_threshold = gr.Slider(

localizations/zh-TW.json

@@ -24,6 +24,8 @@
   "network dim for conv layer in fixed mode": "固定模式下卷積層的網路維度",
   "Sparsity for sparse bias": "稀疏偏差的稀疏度",
   "path for the file to save...": "儲存檔案的路徑...",
+  "Verify LoRA": "驗證 LoRA",
+  "Verify": "驗證",
   "Verification output": "驗證輸出",
   "Verification error": "驗證錯誤",
   "New Rank": "新維度 (Network Rank)",
@@ -49,7 +51,7 @@
   "Show frequency of tags for images.": "顯示圖片的標籤頻率。",
   "Show tags frequency": "顯示標籤頻率",
   "Model": "模型",
-  "Usefull to force model re download when switching to onnx": "切換到 onnx 時，強制重新下載模型",
+  "Useful to force model re download when switching to onnx": "切換到 onnx 時，強制重新下載模型",
   "Force model re-download": "強制重新下載模型",
   "General threshold": "一般閾值",
   "Adjust `general_threshold` for pruning tags (less tags, less flexible)": "調整 `general_threshold` 以修剪標籤 (標籤越少，彈性越小)",
@@ -99,7 +101,7 @@
   "folder where the model will be saved": "模型將會被儲存的資料夾路徑",
   "Model type": "模型類型",
   "Extract LCM": "提取 LCM",
-  "Verfiy LoRA": "驗證 LoRA",
+  "Verify LoRA": "驗證 LoRA",
   "Path to an existing LoRA network weights to resume training from": "要從中繼續訓練的現有 LoRA 網路權重的路徑",
   "Seed": "種子",
   "(Optional) eg:1234": " (選填) 例如：1234",
@@ -137,14 +139,17 @@
   "(Optional) eg: 0.5": " (選填) 例如：0.5",
   "(Optional) eg: 0.1": " (選填) 例如：0.1",
   "Specify the learning rate weight of the down blocks of U-Net.": "指定 U-Net 下區塊的學習率權重。",
-  "Specify the learning rate weight of the mid blocks of U-Net.": "指定 U-Net 中區塊的學習率權重。",
+  "Specify the learning rate weight of the mid block of U-Net.": "指定 U-Net 中區塊的學習率權重。",
   "Specify the learning rate weight of the up blocks of U-Net. The same as down_lr_weight.": "指定 U-Net 上區塊的學習率權重。與 down_lr_weight 相同。",
   "If the weight is not more than this value, the LoRA module is not created. The default is 0.": "如果權重不超過此值，則不會創建 LoRA 模組。預設為 0。",
   "Blocks": "區塊",
   "Block dims": "區塊維度",
   "(Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2": " (選填) 例如：2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2",
   "Specify the dim (rank) of each block. Specify 25 numbers.": "指定每個區塊的維度 (Rank)。指定 25 個數字。",
   "Specify the alpha of each block. Specify 25 numbers as with block_dims. If omitted, the value of network_alpha is used.": "指定每個區塊的 Alpha。與區塊維度一樣，指定 25 個數字。如果省略，則使用網路 Alpha 的值。",
+  "Conv": "卷積",
+  "Conv dims": "卷積維度 (dims)",
+  "Conv alphas": "卷積 Alphas",
   "Extend LoRA to Conv2d 3x3 and specify the dim (rank) of each block. Specify 25 numbers.": "將 LoRA 擴展到 Conv2d 3x3，並指定每個區塊的維度 (Rank)。指定 25 個數字。",
   "Specify the alpha of each block when expanding LoRA to Conv2d 3x3. Specify 25 numbers. If omitted, the value of conv_alpha is used.": "將 LoRA 擴展到 Conv2d 3x3 時，指定每個區塊的 Alpha。指定 25 個數字。如果省略，則使用卷積 Alpha 的值。",
   "Weighted captions": "加權標記文字",
@@ -203,8 +208,8 @@
   "Dreambooth/LoRA Folder preparation": "Dreambooth/LoRA 準備資料夾",
   "Dropout caption every n epochs": "在每 N 個週期 (Epoch) 丟棄標記",
   "DyLoRA model": "DyLoRA 模型",
-  "Dynamic method": "動態方法",
-  "Dynamic parameter": "動態參數",
+  "Dynamic method": "壓縮演算法",
+  "Dynamic parameter": "壓縮參數",
   "e.g., \"by some artist\". Leave empty if you only want to add a prefix or postfix.": "例如，\"由某個藝術家創作\"。如果你只想加入前綴或後綴，請留空白。",
   "e.g., \"by some artist\". Leave empty if you want to replace with nothing.": "例如，\"由某個藝術家創作\"。如果你想用空值取代，請留空白。",
   "Enable buckets": "啟用資料桶",
@@ -227,6 +232,8 @@
   "Flip augmentation": "翻轉增強",
   "float16": "float16",
   "Folders": "資料夾",
+  "U-Net and Text Encoder can be trained with fp8 (experimental)": "U-Net 與 Text Encoder 可以使用 fp8 訓練 (實驗性功能)",
+  "fp8 base training (experimental)": "使用 fp8 基礎訓練 (實驗性功能)",
   "Full bf16 training (experimental)": "完整使用 bf16 訓練 (實驗性功能)",
   "Full fp16 training (experimental)": "完整使用 fp16 訓練 (實驗性功能)",
   "Generate caption files for the grouped images based on their folder name": "根據圖片的資料夾名稱生成標記文字檔案",
@@ -498,4 +505,4 @@
   "Training comment": "訓練註解",
   "Train a TI using kohya textual inversion python code…": "使用 kohya textual inversion Python 程式訓練 TI 模型",
   "Train a custom model using kohya finetune python code…": "使用 kohya finetune Python 程式訓練自定義模型"
-}
+}

setup/debug_info.py

@@ -51,6 +51,6 @@
 
 # Print VRAM warning if necessary
 if gpu_vram_warning:
-    print('\033[33mWarning: GPU VRAM is less than 8GB and will likelly result in proper operations.\033[0m')
+    print('\033[33mWarning: GPU VRAM is less than 8GB and will likely result in proper operations.\033[0m')
 
 print(' ')

textual_inversion_gui.py

@@ -718,8 +718,8 @@ def ti_tab(
             with gr.Tab("Basic", elem_id="basic_tab"):
                 with gr.Row():
                     weights = gr.Textbox(
-                        label="Resume TI training",
-                        placeholder="(Optional) Path to existing TI embeding file to keep training",
+                        label='Resume TI training',
+                        placeholder='(Optional) Path to existing TI embedding file to keep training',
                     )
                     weights_file_input = gr.Button(
                         "",