- Implement HuggingFace inputs in all training tabs

README.md

@@ -404,10 +404,12 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG
 
 ### 2024/04/12 (v23.1.6)
 
-- Rewrote a lot of the code to fix issues resulting from the security upgrades to remove shell=True from process calls.
-- Improved the training and tensorboard buttons
-- Upgrade the gradio version to 4.20.0 to fix a bug with runpod.
-- Various other minor fixes.
+- Rewrote significant portions of the code to address security vulnerabilities and remove the `shell=True` parameter from process calls.
+- Enhanced the training and tensorboard buttons to provide a more intuitive and user-friendly experience.
+- Upgraded the gradio version to 4.20.0 to address a bug that was causing issues with the runpod platform.
+- Added a HuggingFace section to all trainers tabs, enabling users to authenticate and utilize HuggingFace's powerful AI models.
+- Converted the Graphical User Interface (GUI) to use the configuration TOML file format to pass arguments to sd-scripts. This change improves security by eliminating the need for sensitive information to be passed through the command-line interface (CLI).
+- Made various other minor improvements and bug fixes to enhance the overall functionality and user experience.
 
 ### 2024/04/10 (v23.1.5)
 

kohya_gui/class_command_executor.py

@@ -32,12 +32,12 @@
         if self.process and self.process.poll() is None:
             log.info("The command is already running. Please wait for it to finish.")
         else:
-            for i, item in enumerate(run_cmd):
-                log.info(f"{i}: {item}")
+            # for i, item in enumerate(run_cmd):
+            #     log.info(f"{i}: {item}")
 
             # Reconstruct the safe command string for display
             command_to_run = ' '.join(run_cmd)
             log.info(f"Executings command: {command_to_run}")
 
             # Execute the command securely
             self.process = subprocess.Popen(run_cmd, **kwargs)

kohya_gui/class_huggingface.py

@@ -0,0 +1,82 @@
+import gradio as gr
+import toml
+from .class_gui_config import KohyaSSGUIConfig
+
+class HuggingFace:
+    def __init__(
+        self,
+        config: KohyaSSGUIConfig = {},
+    ) -> None:
+        self.config = config
+
+        # Initialize the UI components
+        self.initialize_ui_components()
+
+    def initialize_ui_components(self) -> None:
+        # --huggingface_repo_id HUGGINGFACE_REPO_ID
+        #                         huggingface repo name to upload / huggingfaceにアップロードするリポジトリ名
+        # --huggingface_repo_type HUGGINGFACE_REPO_TYPE
+        #                         huggingface repo type to upload / huggingfaceにアップロードするリポジトリの種類
+        # --huggingface_path_in_repo HUGGINGFACE_PATH_IN_REPO
+        #                         huggingface model path to upload files / huggingfaceにアップロードするファイルのパス
+        # --huggingface_token HUGGINGFACE_TOKEN
+        #                         huggingface token / huggingfaceのトークン
+        # --huggingface_repo_visibility HUGGINGFACE_REPO_VISIBILITY
+        #                         huggingface repository visibility ('public' for public, 'private' or None for private) / huggingfaceにアップロードするリポジトリの公開設定（'public'で公開、'private'またはNoneで非公開）
+        # --save_state_to_huggingface
+        #                         save state to huggingface / huggingfaceにstateを保存する
+        # --resume_from_huggingface
+        #                         resume from huggingface (ex: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type}) / huggingfaceから学習を再開する(例: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type})
+        # --async_upload        upload to huggingface asynchronously / huggingfaceに非同期でアップロードする
+        with gr.Row():
+            self.huggingface_repo_id = gr.Textbox(
+                label="Huggingface repo id",
+                placeholder="huggingface repo id",
+                value=self.config.get("huggingface.repo_id", ""),
+            )
+
+            self.huggingface_token = gr.Textbox(
+                label="Huggingface token",
+                placeholder="huggingface token",
+                value=self.config.get("huggingface.token", ""),
+            )
+
+        with gr.Row():
+            # Repository settings
+            self.huggingface_repo_type = gr.Textbox(
+                label="Huggingface repo type",
+                placeholder="huggingface repo type",
+                value=self.config.get("huggingface.repo_type", ""),
+            )
+
+            self.huggingface_repo_visibility = gr.Textbox(
+                label="Huggingface repo visibility",
+                placeholder="huggingface repo visibility",
+                value=self.config.get("huggingface.repo_visibility", ""),
+            )
+
+        with gr.Row():
+            # File location in the repository
+            self.huggingface_path_in_repo = gr.Textbox(
+                label="Huggingface path in repo",
+                placeholder="huggingface path in repo",
+                value=self.config.get("huggingface.path_in_repo", ""),
+            )
+
+        with gr.Row():
+            # Functions
+            self.save_state_to_huggingface = gr.Checkbox(
+                label="Save state to huggingface",
+                value=self.config.get("huggingface.save_state_to_huggingface", False),
+            )
+
+            self.resume_from_huggingface = gr.Textbox(
+                label="Resume from huggingface",
+                placeholder="resume from huggingface",
+                value=self.config.get("huggingface.resume_from_huggingface", ""),
+            )
+
+            self.async_upload = gr.Checkbox(
+                label="Async upload",
+                value=self.config.get("huggingface.async_upload", False),
+            )

kohya_gui/class_sample_images.py

@@ -17,6 +17,23 @@
 ###
 ### Gradio common sampler GUI section
 ###
+def create_prompt_file(sample_prompts, output_dir):
+    """
+    Creates a prompt file for image sampling.
+
+    Args:
+        sample_prompts (str): The prompts to use for image sampling.
+        output_dir (str): The directory where the output images will be saved.
+
+    Returns:
+        str: The path to the prompt file.
+    """
+    sample_prompts_path = os.path.join(output_dir, "prompt.txt")
+
+    with open(sample_prompts_path, "w") as f:
+        f.write(sample_prompts)
+
+    return sample_prompts_path
 
 
 def run_cmd_sample(

kohya_gui/common_gui.py

@@ -1575,7 +1575,8 @@ def run_cmd_advanced_training(run_cmd: list = [], **kwargs):
 
     # Use Weights and Biases logging
     if kwargs.get("use_wandb"):
-        run_cmd.append("--log_with wandb")
+        run_cmd.append("--log_with")
+        run_cmd.append("wandb")
 
     # V parameterization
     if kwargs.get("v_parameterization"):