Bug fixes. Added some functionality to help with private extensions

extensions_built_in/dataset_tools/DatasetTools.py

@@ -0,0 +1,20 @@
+from collections import OrderedDict
+import gc
+import torch
+from jobs.process import BaseExtensionProcess
+
+
+def flush():
+    torch.cuda.empty_cache()
+    gc.collect()
+
+
+class DatasetTools(BaseExtensionProcess):
+
+    def __init__(self, process_id: int, job, config: OrderedDict):
+        super().__init__(process_id, job, config)
+
+    def run(self):
+        super().run()
+
+        raise NotImplementedError("This extension is not yet implemented")

extensions_built_in/dataset_tools/__init__.py

@@ -0,0 +1,25 @@
+# This is an example extension for custom training. It is great for experimenting with new ideas.
+from toolkit.extension import Extension
+
+
+# This is for generic training (LoRA, Dreambooth, FineTuning)
+class DatasetToolsExtension(Extension):
+    # uid must be unique, it is how the extension is identified
+    uid = "dataset_tools"
+
+    # name is the name of the extension for printing
+    name = "Dataset Tools"
+
+    # This is where your process class is loaded
+    # keep your imports in here so they don't slow down the rest of the program
+    @classmethod
+    def get_process(cls):
+        # import your process class here so it is only loaded when needed and return it
+        from .DatasetTools import DatasetTools
+        return DatasetTools
+
+
+AI_TOOLKIT_EXTENSIONS = [
+    # you can put a list of extensions here
+    DatasetToolsExtension,
+]