Update rest of trainers to not use shell

kohya_gui/class_command_executor.py

@@ -28,7 +28,7 @@ def __init__(self, headless: bool = False):
                 "Stop training", visible=self.process is not None or headless, variant="stop"
             )
 
-    def execute_command(self, run_cmd: str, use_shell: bool = False, **kwargs):
+    def execute_command(self, run_cmd: str, **kwargs):
         """
         Execute a command if no other command is currently running.
 

kohya_gui/finetune_gui.py

@@ -19,7 +19,7 @@
     scriptdir,
     update_my_data,
     validate_paths,
-    validate_args_setting
+    validate_args_setting,
 )
 from .class_accelerate_launch import AccelerateLaunch
 from .class_configuration_file import ConfigurationFile
@@ -530,13 +530,13 @@ def train_model(
     # Get list of function parameters and values
     parameters = list(locals().items())
     global train_state_value
-    
+
     TRAIN_BUTTON_VISIBLE = [
         gr.Button(visible=True),
         gr.Button(visible=False or headless),
         gr.Textbox(value=train_state_value),
     ]
-    
+
     if executor.is_running():
         log.error("Training is already running. Can't start another training session.")
         return TRAIN_BUTTON_VISIBLE
@@ -548,7 +548,7 @@ def train_model(
     log.info(f"Validating lr scheduler arguments...")
     if not validate_args_setting(lr_scheduler_args):
         return
-    
+
     log.info(f"Validating optimizer arguments...")
     if not validate_args_setting(optimizer_args):
         return
@@ -712,7 +712,7 @@ def train_model(
         lr_warmup_steps = 0
     log.info(f"lr_warmup_steps = {lr_warmup_steps}")
 
-    run_cmd = [get_executable_path("accelerate"), "launch"]
+    run_cmd = [rf'{get_executable_path("accelerate")}', "launch"]
 
     run_cmd = AccelerateLaunch.run_cmd(
         run_cmd=run_cmd,
@@ -812,7 +812,9 @@ def train_model(
         "max_bucket_reso": int(max_bucket_reso),
         "max_timestep": max_timestep if max_timestep != 0 else None,
         "max_token_length": int(max_token_length),
-        "max_train_epochs": int(max_train_epochs) if int(max_train_epochs) != 0 else None,
+        "max_train_epochs": (
+            int(max_train_epochs) if int(max_train_epochs) != 0 else None
+        ),
         "max_train_steps": int(max_train_steps) if int(max_train_steps) != 0 else None,
         "mem_eff_attn": mem_eff_attn,
         "metadata_author": metadata_author,
@@ -888,9 +890,9 @@ def train_model(
         for key, value in config_toml_data.items()
         if value not in ["", False, None]
     }
-    
+
     config_toml_data["max_data_loader_n_workers"] = int(max_data_loader_n_workers)
-    
+
     # Sort the dictionary by keys
     config_toml_data = dict(sorted(config_toml_data.items()))
 
@@ -902,7 +904,7 @@ def train_model(
         if not os.path.exists(toml_file.name):
             log.error(f"Failed to write TOML file: {toml_file.name}")
 
-    run_cmd.append(f"--config_file")
+    run_cmd.append("--config_file")
     run_cmd.append(rf"{tmpfilename}")
 
     # Initialize a dictionary with always-included keyword arguments
@@ -939,7 +941,7 @@ def train_model(
         env["TF_ENABLE_ONEDNN_OPTS"] = "0"
 
         # Run the command
-        executor.execute_command(run_cmd=run_cmd, use_shell=use_shell, env=env)
+        executor.execute_command(run_cmd=run_cmd, env=env)
 
         train_state_value = time.time()
 
@@ -1283,7 +1285,7 @@ def list_presets(path):
         )
 
         run_state = gr.Textbox(value=train_state_value, visible=False)
-            
+
         run_state.change(
             fn=executor.wait_for_training_to_end,
             outputs=[executor.button_run, executor.button_stop_training],
@@ -1297,7 +1299,8 @@ def list_presets(path):
         )
 
         executor.button_stop_training.click(
-            executor.kill_command, outputs=[executor.button_run, executor.button_stop_training]
+            executor.kill_command,
+            outputs=[executor.button_run, executor.button_stop_training],
         )
 
         button_print.click(

kohya_gui/lora_gui.py

@@ -869,7 +869,7 @@ def train_model(
     log.info(f"stop_text_encoder_training = {stop_text_encoder_training}")
     log.info(f"lr_warmup_steps = {lr_warmup_steps}")
 
-    run_cmd = [rf'"{get_executable_path("accelerate")}"', "launch"]
+    run_cmd = [rf'{get_executable_path("accelerate")}', "launch"]
 
     run_cmd = AccelerateLaunch.run_cmd(
         run_cmd=run_cmd,
@@ -888,9 +888,9 @@ def train_model(
     )
 
     if sdxl:
-        run_cmd.append(rf'"{scriptdir}/sd-scripts/sdxl_train_network.py"')
+        run_cmd.append(rf"{scriptdir}/sd-scripts/sdxl_train_network.py")
     else:
-        run_cmd.append(rf'"{scriptdir}/sd-scripts/train_network.py"')
+        run_cmd.append(rf"{scriptdir}/sd-scripts/train_network.py")
 
     network_args = ""
 
@@ -1195,8 +1195,8 @@ def train_model(
         if not os.path.exists(toml_file.name):
             log.error(f"Failed to write TOML file: {toml_file.name}")
 
-    run_cmd.append(f"--config_file")
-    run_cmd.append(rf'"{tmpfilename}"')
+    run_cmd.append("--config_file")
+    run_cmd.append(rf"{tmpfilename}")
 
     # Define a dictionary of parameters
     run_cmd_params = {
@@ -1226,13 +1226,13 @@ def train_model(
         # log.info(run_cmd)
         env = os.environ.copy()
         env["PYTHONPATH"] = (
-            f"{scriptdir}{os.pathsep}{scriptdir}/sd-scripts{os.pathsep}{env.get('PYTHONPATH', '')}"
+            fr"{scriptdir}{os.pathsep}{scriptdir}/sd-scripts{os.pathsep}{env.get('PYTHONPATH', '')}"
         )
         env["TF_ENABLE_ONEDNN_OPTS"] = "0"
 
         # Run the command
 
-        executor.execute_command(run_cmd=run_cmd, use_shell=use_shell, env=env)
+        executor.execute_command(run_cmd=run_cmd, env=env)
 
         train_state_value = time.time()
 

kohya_gui/textual_inversion_gui.py

@@ -668,7 +668,7 @@ def train_model(
     log.info(f"stop_text_encoder_training = {stop_text_encoder_training}")
     log.info(f"lr_warmup_steps = {lr_warmup_steps}")
 
-    run_cmd = [rf'"{get_executable_path("accelerate")}"', "launch"]
+    run_cmd = [rf'{get_executable_path("accelerate")}', "launch"]
 
     run_cmd = AccelerateLaunch.run_cmd(
         run_cmd=run_cmd,
@@ -687,9 +687,9 @@ def train_model(
     )
 
     if sdxl:
-        run_cmd.append(rf'"{scriptdir}/sd-scripts/sdxl_train_textual_inversion.py"')
+        run_cmd.append(rf"{scriptdir}/sd-scripts/sdxl_train_textual_inversion.py")
     else:
-        run_cmd.append(rf'"{scriptdir}/sd-scripts/train_textual_inversion.py"')
+        run_cmd.append(rf"{scriptdir}/sd-scripts/train_textual_inversion.py")
 
     if max_data_loader_n_workers == "" or None:
         max_data_loader_n_workers = 0
@@ -850,8 +850,8 @@ def train_model(
         if not os.path.exists(toml_file.name):
             log.error(f"Failed to write TOML file: {toml_file.name}")
 
-    run_cmd.append(f"--config_file")
-    run_cmd.append(rf'"{tmpfilename}"')
+    run_cmd.append("--config_file")
+    run_cmd.append(rf"{tmpfilename}")
 
     # Initialize a dictionary with always-included keyword arguments
     kwargs_for_training = {
@@ -888,7 +888,7 @@ def train_model(
 
         # Run the command
 
-        executor.execute_command(run_cmd=run_cmd, use_shell=use_shell, env=env)
+        executor.execute_command(run_cmd=run_cmd, env=env)
 
         train_state_value = time.time()