Phase 1 - Input validation

modules/ui/AdditionalEmbeddingsTab.py

@@ -1,4 +1,3 @@
-from pathlib import Path
 
 from modules.ui.ConfigList import ConfigList
 from modules.util.config.TrainConfig import TrainConfig, TrainEmbeddingConfig
@@ -89,9 +88,9 @@ def __init__(self, master, element, i, open_command, remove_command, clone_comma
         # embedding model names
         components.label(top_frame, 0, 2, "base embedding:",
                          tooltip="The base embedding to train on. Leave empty to create a new embedding")
-        components.file_entry(
+        components.path_entry(
             top_frame, 0, 3, self.ui_state, "model_name",
-            path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+            mode="file", path_modifier=components.json_path_modifier
         )
 
         # placeholder

modules/ui/CloudTab.py

@@ -66,7 +66,7 @@ def __init__(self, master, train_config: TrainConfig, ui_state: UIState, parent)
 
         components.label(self.frame, 7, 0, "SSH keyfile path",
                  tooltip="Absolute path to the private key file used for SSH connections. Leave empty to rely on your system SSH configuration.")
-        components.file_entry(self.frame, 7, 1, self.ui_state, "secrets.cloud.key_file") #TODO Replace with path_entry in a future PR
+        components.path_entry(self.frame, 7, 1, self.ui_state, "secrets.cloud.key_file", mode="file")
 
         components.label(self.frame, 8, 0, "SSH password",
                          tooltip="SSH password for password-based authentication. If you try to use native SCP requires sshpass to be installed. Leave empty to use key-based authentication.")

modules/ui/ConceptTab.py

@@ -10,8 +10,8 @@
 from modules.util.enum.ConceptType import ConceptType
 from modules.util.image_util import load_image
 from modules.util.ui import components
-from modules.util.ui.ui_utils import DebounceTimer
 from modules.util.ui.UIState import UIState
+from modules.util.ui.validation import DebounceTimer
 
 import customtkinter as ctk
 from PIL import Image

modules/ui/ConceptWindow.py

@@ -147,14 +147,14 @@ def __general_tab(self, master, concept: ConceptConfig):
         # path
         components.label(frame, 3, 0, "Path",
                          tooltip="Path where the training data is located")
-        components.dir_entry(frame, 3, 1, self.ui_state, "path")
+        components.path_entry(frame, 3, 1, self.ui_state, "path", mode="dir")
         components.button(frame, 3, 2, text="download now", command=self.__download_dataset_threaded,
                           tooltip="Download dataset from Huggingface now, for the purpose of previewing and statistics. Otherwise, it will be downloaded when you start training. Path must be a Huggingface repository.")
 
         # prompt source
         components.label(frame, 4, 0, "Prompt Source",
                          tooltip="The source for prompts used during training. When selecting \"From single text file\", select a text file that contains a list of prompts")
-        prompt_path_entry = components.file_entry(frame, 4, 2, self.text_ui_state, "prompt_path")
+        prompt_path_entry = components.path_entry(frame, 4, 2, self.text_ui_state, "prompt_path", mode="file")
 
         def set_prompt_path_entry_enabled(option: str):
             if option == 'concept':

modules/ui/ConvertModelUI.py

@@ -1,5 +1,4 @@
 import traceback
-from pathlib import Path
 from uuid import uuid4
 
 from modules.util import create
@@ -8,6 +7,7 @@
 from modules.util.enum.DataType import DataType
 from modules.util.enum.ModelFormat import ModelFormat
 from modules.util.enum.ModelType import ModelType
+from modules.util.enum.PathIOType import PathIOType
 from modules.util.enum.TrainingMethod import TrainingMethod
 from modules.util.ModelNames import EmbeddingName, ModelNames
 from modules.util.torch_util import torch_gc
@@ -85,9 +85,9 @@ def main_frame(self, master):
         # input name
         components.label(master, 2, 0, "Input name",
                          tooltip="Filename, directory or hugging face repository of the base model")
-        components.file_entry(
+        components.path_entry(
             master, 2, 1, self.ui_state, "input_name",
-            path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+            mode="file", path_modifier=components.json_path_modifier
         )
 
         # output data type
@@ -110,7 +110,11 @@ def main_frame(self, master):
         # output model destination
         components.label(master, 5, 0, "Model Output Destination",
                          tooltip="Filename or directory where the output model is saved")
-        components.file_entry(master, 5, 1, self.ui_state, "output_model_destination", is_output=True)
+        components.path_entry(
+            master, 5, 1, self.ui_state, "output_model_destination",
+            mode="file",
+            io_type=PathIOType.MODEL,
+        )
 
         self.button = components.button(master, 6, 1, "Convert", self.convert_model)
 

modules/ui/LoraTab.py

@@ -1,10 +1,10 @@
-from pathlib import Path
 
 from modules.util.config.TrainConfig import TrainConfig
 from modules.util.enum.DataType import DataType
 from modules.util.enum.ModelType import PeftType
 from modules.util.ui import components
 from modules.util.ui.UIState import UIState
+from modules.util.ui.validation_helpers import check_range
 
 import customtkinter as ctk
 
@@ -64,9 +64,9 @@ def setup_lora(self, peft_type: PeftType):
         # lora model name
         components.label(master, 0, 0, f"{name} base model",
                          tooltip=f"The base {name} to train on. Leave empty to create a new {name}")
-        entry = components.file_entry(
+        entry = components.path_entry(
             master, 0, 1, self.ui_state, "lora_model_name",
-            path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+            mode="file", path_modifier=components.json_path_modifier
         )
         entry.grid(row=0, column=1, columnspan=4)
 
@@ -89,12 +89,12 @@ def setup_lora(self, peft_type: PeftType):
             # rank
             components.label(master, 1, 0, f"{name} rank",
                             tooltip=f"The rank parameter used when creating a new {name}")
-            components.entry(master, 1, 1, self.ui_state, "lora_rank")
+            components.entry(master, 1, 1, self.ui_state, "lora_rank", required=True, extra_validate=check_range(lower=1, message="Rank must be at least 1"))
 
             # alpha
             components.label(master, 2, 0, f"{name} alpha",
                             tooltip=f"The alpha parameter used when creating a new {name}")
-            components.entry(master, 2, 1, self.ui_state, "lora_alpha")
+            components.entry(master, 2, 1, self.ui_state, "lora_alpha", required=True)
 
             # Dropout Percentage
             components.label(master, 3, 0, "Dropout Probability",
@@ -119,7 +119,7 @@ def setup_lora(self, peft_type: PeftType):
             # Block Size
             components.label(master, 1, 0, f"{name} Block Size",
                             tooltip=f"The block size parameter used when creating a new {name}")
-            components.entry(master, 1, 1, self.ui_state, "oft_block_size")
+            components.entry(master, 1, 1, self.ui_state, "oft_block_size", required=True)
 
             # COFT
             components.label(master, 1, 3, "Constrained OFT (COFT)",

modules/ui/ModelTab.py

@@ -1,10 +1,10 @@
-from pathlib import Path
 
 from modules.util import create
 from modules.util.config.TrainConfig import TrainConfig
 from modules.util.enum.ConfigPart import ConfigPart
 from modules.util.enum.DataType import DataType
 from modules.util.enum.ModelFormat import ModelFormat
+from modules.util.enum.PathIOType import PathIOType
 from modules.util.enum.TrainingMethod import TrainingMethod
 from modules.util.ui import components
 from modules.util.ui.UIState import UIState
@@ -365,9 +365,9 @@ def __create_base_dtype_components(self, frame, row: int) -> int:
         # base model
         components.label(frame, row, 0, "Base Model",
                          tooltip="Filename, directory or Hugging Face repository of the base model")
-        components.file_entry(
+        components.path_entry(
             frame, row, 1, self.ui_state, "base_model_name",
-            path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+            mode="file", path_modifier=components.json_path_modifier
         )
 
         # compile
@@ -410,9 +410,9 @@ def __create_base_components(
                 # prior model
                 components.label(frame, row, 0, "Prior Model",
                                  tooltip="Filename, directory or Hugging Face repository of the prior model")
-                components.file_entry(
+                components.path_entry(
                     frame, row, 1, self.ui_state, "prior.model_name",
-                    path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+                    mode="file", path_modifier=components.json_path_modifier
                 )
 
             # prior weight dtype
@@ -428,9 +428,9 @@ def __create_base_components(
                 # transformer model
                 components.label(frame, row, 0, "Override Transformer / GGUF",
                                  tooltip="Can be used to override the transformer in the base model. Safetensors and GGUF files are supported, local and on Huggingface. If a GGUF file is used, the DataType must also be set to GGUF")
-                components.file_entry(
+                components.path_entry(
                     frame, row, 1, self.ui_state, "transformer.model_name",
-                    path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+                    mode="file", path_modifier=components.json_path_modifier
                 )
 
             # transformer weight dtype
@@ -512,9 +512,9 @@ def __create_base_components(
                 # text encoder 4 weight dtype
                 components.label(frame, row, 0, "Text Encoder 4 Override",
                                  tooltip="Filename, directory or Hugging Face repository of the text encoder 4 model")
-                components.file_entry(
+                components.path_entry(
                     frame, row, 1, self.ui_state, "text_encoder_4.model_name",
-                    path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+                    mode="file", path_modifier=components.json_path_modifier
                 )
 
             # text encoder 4 weight dtype
@@ -529,9 +529,9 @@ def __create_base_components(
             # base model
             components.label(frame, row, 0, "VAE Override",
                              tooltip="Directory or Hugging Face repository of a VAE model in diffusers format. Can be used to override the VAE included in the base model. Using a safetensor VAE file will cause an error that the model cannot be loaded.")
-            components.file_entry(
+            components.path_entry(
                 frame, row, 1, self.ui_state, "vae.model_name",
-                path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+                mode="file", path_modifier=components.json_path_modifier
             )
 
             # vae weight dtype
@@ -548,9 +548,9 @@ def __create_effnet_encoder_components(self, frame, row: int):
         # effnet encoder model
         components.label(frame, row, 0, "Effnet Encoder Model",
                          tooltip="Filename, directory or Hugging Face repository of the effnet encoder model")
-        components.file_entry(
+        components.path_entry(
             frame, row, 1, self.ui_state, "effnet_encoder.model_name",
-            path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+            mode="file", path_modifier=components.json_path_modifier
         )
 
         # effnet encoder weight dtype
@@ -572,9 +572,9 @@ def __create_decoder_components(
         # decoder model
         components.label(frame, row, 0, "Decoder Model",
                          tooltip="Filename, directory or Hugging Face repository of the decoder model")
-        components.file_entry(
+        components.path_entry(
             frame, row, 1, self.ui_state, "decoder.model_name",
-            path_modifier=lambda x: Path(x).parent.absolute() if x.endswith(".json") else x
+            mode="file", path_modifier=components.json_path_modifier
         )
 
         # decoder weight dtype
@@ -616,7 +616,11 @@ def __create_output_components(
         # output model destination
         components.label(frame, row, 0, "Model Output Destination",
                          tooltip="Filename or directory where the output model is saved")
-        components.file_entry(frame, row, 1, self.ui_state, "output_model_destination", is_output=True)
+        components.path_entry(
+            frame, row, 1, self.ui_state, "output_model_destination",
+            mode="file",
+            io_type=PathIOType.MODEL,
+        )
 
         # output data type
         components.label(frame, row, 3, "Output Data Type",

modules/ui/SampleFrame.py

@@ -108,15 +108,15 @@ def __init__(
             # base image path
             components.label(bottom_frame, 6, 0, "base image path:",
                              tooltip="The base image used when inpainting.")
-            components.file_entry(bottom_frame, 6, 1, self.ui_state, "base_image_path",
-                                  allow_model_files=False,
+            components.path_entry(bottom_frame, 6, 1, self.ui_state, "base_image_path",
+                                  mode="file", allow_model_files=False,
                                   allow_image_files=True,
                                   )
 
             # mask image path
             components.label(bottom_frame, 6, 2, "mask image path:",
                              tooltip="The mask used when inpainting.")
-            components.file_entry(bottom_frame, 6, 3, self.ui_state, "mask_image_path",
-                                  allow_model_files=False,
+            components.path_entry(bottom_frame, 6, 3, self.ui_state, "mask_image_path",
+                                  mode="file", allow_model_files=False,
                                   allow_image_files=True,
                                   )