feat: add sort-by-confidence option to WD14 captioning

.github/workflows/typos.yaml

@@ -18,4 +18,4 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: typos-action
-        uses: crate-ci/typos@v1.32.0
+        uses: crate-ci/typos@v1.34.0

README.md

@@ -27,7 +27,8 @@ Support for Linux and macOS is also available. While Linux support is actively m
     - [Colab](#-colab)
     - [Runpod, Novita, Docker](#runpod-novita-docker)
 - [Custom Path Defaults](#custom-path-defaults)
-    - [LoRA](#lora)
+- [Server Options](#server-options)
+- [LoRA](#lora)
   - [Sample image generation during training](#sample-image-generation-during-training)
   - [Troubleshooting](#troubleshooting)
   - [Page File Limit](#page-file-limit)
@@ -101,9 +102,9 @@ I would like to express my gratitude to camenduru for their valuable contributio
 
 These options are for users running training on hosted GPU infrastructure or containers.
 
-- **[Runpod setup](docs/runpod_setup.md)** – Ready-made GPU background training via templates.
-- **[Novita setup](docs/novita_setup.md)** – Similar to Runpod, but integrated into the Novita UI.
-- **[Docker setup](docs/docker.md)** – For developers/sysadmins using containerized environments.
+- **[Runpod setup](docs/installation_runpod.md)** – Ready-made GPU background training via templates.
+- **[Novita setup](docs/installation_novita.md)** – Similar to Runpod, but integrated into the Novita UI.
+- **[Docker setup](docs/installation_docker.md)** – For developers/sysadmins using containerized environments.
 
 
 ## Custom Path Defaults with `config.toml`
@@ -180,6 +181,21 @@ If you prefer to name your configuration file differently or store it in another
 
 By effectively using `config.toml`, you can significantly speed up your training setup process. Always refer to the `config example.toml` for the most up-to-date list of configurable paths.
 
+## Server Options
+
+The `config.toml` file can also be used to configure the Gradio server.
+
+### `allowed_paths`
+
+The `allowed_paths` option allows you to specify a list of directories that the application can access. This is useful if you want to store your models, datasets, or other files on an external drive or in a location outside of the application's root directory.
+
+**Example:**
+
+```toml
+[server]
+allowed_paths = ["/mnt/external_drive/models", "/home/user/datasets"]
+```
+
 ## LoRA
 
 To train a LoRA, you can currently use the `train_network.py` code. You can create a LoRA network by using the all-in-one GUI.

config example.toml

@@ -4,6 +4,9 @@
 [settings]
 use_shell = false # Use shell during process run of sd-scripts oython code. Most secure is false but some systems may require it to be true to properly run sd-scripts.
 
+[server]
+allowed_paths = [] # A list of paths that the application is allowed to access.
+
 # Default folders location
 [model]
 models_dir = "./models"                    # Pretrained model name or path

gui-uv.sh

@@ -52,4 +52,40 @@ if [[ "$uv_quiet" == "--quiet" ]]; then
 fi
 
 git submodule update --init --recursive
+
+# --- bitsandbytes compilation (optional, similar to Colab setup) ---
+# The pyproject.toml specifies bitsandbytes, which uv will install.
+# However, if you encounter issues with the pre-compiled version,
+# you might need to compile it from source, similar to the Colab notebook.
+# Uncomment and adapt the following lines if needed.
+# Ensure you have the necessary build tools (like CUDA toolkit for cuda11x).
+
+# BUILD_BITSANDBYTES_FROM_SOURCE=false # Set to true to enable
+# if [ "$BUILD_BITSANDBYTES_FROM_SOURCE" = true ]; then
+#   echo "Attempting to build bitsandbytes from source..."
+#   if [ ! -d "bitsandbytes" ]; then
+#     git clone -b 0.41.0 https://github.com/TimDettmers/bitsandbytes
+#   fi
+#   cd bitsandbytes || exit 1
+#   # IMPORTANT: Adjust CUDA_VERSION if necessary for your setup (e.g., 118 for CUDA 11.8, 12x for CUDA 12.x)
+#   # The Colab notebook used CUDA_VERSION=118
+#   # Common options: cuda11x (for 11.0-11.8), cuda12x (for 12.0-12.x)
+#   # Ensure the corresponding CUDA toolkit is installed and in PATH.
+#   # For ROCm, the build process is different.
+#   # Check bitsandbytes documentation for the correct make target for your GPU/driver.
+#   # Example for CUDA 11.8:
+#   # CUDA_VERSION=118 make cuda11x
+#   # Example for CUDA 12.1:
+#   # make cuda12x
+#   # Then, install using uv within the environment (or pip if uv is not active yet for this part)
+#   # Assuming uv environment is active or will be activated by the main command:
+#   uv pip install .
+#   # Or, if building before activating the main uv environment:
+#   # python setup.py install
+#   cd .. || exit 1
+#   echo "bitsandbytes build attempt finished."
+# fi
+# --- end of bitsandbytes compilation ---
+
+echo "Launching Kohya GUI via uv..."
 uv run $uv_quiet kohya_gui.py --noverify "${args[@]}"

kohya_gui.py

@@ -102,6 +102,7 @@ def UI(**kwargs):
         "share": False if kwargs.get("do_not_share", False) else kwargs.get("share", False),
         "root_path": kwargs.get("root_path", None),
         "debug": kwargs.get("debug", False),
+        "allowed_paths": config.allowed_paths,
     }
 
     # This line filters out any key-value pairs from `launch_params` where the value is `None`, ensuring only valid parameters are passed to the `launch` function.

kohya_gui/class_gui_config.py

@@ -16,6 +16,7 @@ def __init__(self, config_file_path: str = "./config.toml"):
         Initialize the KohyaSSGUIConfig class.
         """
         self.config = self.load_config(config_file_path=config_file_path)
+        self.allowed_paths = self.get_allowed_paths()
 
     def load_config(self, config_file_path: str = "./config.toml") -> dict:
         """
@@ -81,6 +82,12 @@ def get(self, key: str, default=None):
         log.debug(f"Returned {data}")
         return data
 
+    def get_allowed_paths(self) -> list:
+        """
+        Retrieves the list of allowed paths from the [server] section of the config file.
+        """
+        return self.get("server.allowed_paths", [])
+
     def is_config_loaded(self) -> bool:
         """
         Checks if the configuration was loaded from a file.

kohya_gui/class_tensorboard.py

@@ -3,12 +3,21 @@
 import subprocess
 import time
 import webbrowser
+import shutil
+import cpuinfo
 
-try:
-    os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
-    import tensorflow  # Attempt to import tensorflow to check if it is installed
+def check_avx_support():
+    try:
+        info = cpuinfo.get_cpu_info()
+        return 'avx' in info['flags']
+    except Exception:
+        return False
 
-    visibility = True
+try:
+    if shutil.which("tensorboard") and check_avx_support():
+        visibility = True
+    else:
+        visibility = False
 except ImportError:
     visibility = False