Updates

README.md

@@ -479,4 +479,6 @@ If you come across a `FileNotFoundError`, it is likely due to an installation is
 * 2023/07/18 (v21.8.4)
   - Relocate LR number of cycles and LR power options
   - Add missing LR number of cycles and LR power to Dreambooth and TI scripts
-  - Fix issue with conv_block_dims and conv_block_alphas
+  - Fix issue with conv_block_dims and conv_block_alphas
+  - Fix 0 noise offset issue
+  - Implement Stop training button on LoRA

library/class_command_executor.py

@@ -0,0 +1,31 @@
+import subprocess
+import psutil
+from library.custom_logging import setup_logging
+
+# Set up logging
+log = setup_logging()
+
+class CommandExecutor:
+    def __init__(self):
+        self.process = None
+
+    def execute_command(self, run_cmd):
+        if self.process and self.process.poll() is None:
+            log.info("The command is already running. Please wait for it to finish.")
+        else:
+            self.process = subprocess.Popen(run_cmd, shell=True)
+
+    def kill_command(self):
+        if self.process and self.process.poll() is None:
+            try:
+                parent = psutil.Process(self.process.pid)
+                for child in parent.children(recursive=True):
+                    child.kill()
+                parent.kill()
+                log.info("The running process has been terminated.")
+            except psutil.NoSuchProcess:
+                log.info("The process does not exist.")
+            except Exception as e:
+                log.info(f"Error when terminating process: {e}")
+        else:
+            log.info("There is no running process to kill.")

library/common_gui.py

@@ -755,8 +755,7 @@ def run_cmd_advanced_training(**kwargs):
     noise_offset_type = kwargs.get('noise_offset_type', 'Original')
     if noise_offset_type == 'Original':
         noise_offset = float(kwargs.get("noise_offset", 0))
-        if noise_offset > 0:
-            run_cmd += f' --noise_offset={noise_offset}'
+        run_cmd += f' --noise_offset={noise_offset}'
 
         adaptive_noise_scale = float(kwargs.get("adaptive_noise_scale", 0))
         if adaptive_noise_scale != 0 and noise_offset > 0:

lora_gui.py

@@ -8,6 +8,7 @@
 import math
 import os
 import subprocess
+import psutil
 import pathlib
 import argparse
 from datetime import datetime
@@ -33,6 +34,7 @@
 from library.class_advanced_training import AdvancedTraining
 from library.class_sdxl_parameters import SDXLParameters
 from library.class_folders import Folders
+from library.class_command_executor import CommandExecutor
 from library.tensorboard_gui import (
     gradio_tensorboard,
     start_tensorboard,
@@ -47,8 +49,14 @@
 # Set up logging
 log = setup_logging()
 
-document_symbol = '\U0001F4C4'   # 📄
+# Setup command executor
+executor = CommandExecutor()
+
+button_run = gr.Button('Start training', variant='primary')
+
+button_stop_training = gr.Button('Stop training')
 
+document_symbol = '\U0001F4C4'   # 📄
 
 def save_configuration(
     save_as,
@@ -480,6 +488,7 @@ def train_model(
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
+    global command_running
 
     print_only_bool = True if print_only.get('label') == 'True' else False
     log.info(f'Start training LoRA {LoRA_type} ...')
@@ -973,10 +982,7 @@ def train_model(
 
         log.info(run_cmd)
         # Run the command
-        if os.name == 'posix':
-            os.system(run_cmd)
-        else:
-            subprocess.run(run_cmd)
+        executor.execute_command(run_cmd=run_cmd)
 
         # check if output_dir/last is a folder... therefore it is a diffuser model
         last_dir = pathlib.Path(f'{output_dir}/{output_name}')
@@ -1402,7 +1408,10 @@ def update_LoRA_settings(LoRA_type):
                 ],
             )
 
-        button_run = gr.Button('Train model', variant='primary')
+        with gr.Row():
+            button_run = gr.Button('Start training', variant='primary')
+
+            button_stop_training = gr.Button('Stop training')
 
         button_print = gr.Button('Print training command')
 
@@ -1580,6 +1589,10 @@ def update_LoRA_settings(LoRA_type):
             inputs=[dummy_headless] + [dummy_db_false] + settings_list,
             show_progress=False,
         )
+
+        button_stop_training.click(
+            executor.kill_command
+        )
 
         button_print.click(
             train_model,

presets/lora/SDXL - LoRA adafactor v1.0.json

@@ -0,0 +1,95 @@
+{
+    "LoRA_type": "Standard",
+    "adaptive_noise_scale": 0.00357,
+    "additional_parameters": "--log_prefix=xl-loha",
+    "block_alphas": "",
+    "block_dims": "",
+    "block_lr_zero_threshold": "",
+    "bucket_no_upscale": false,
+    "bucket_reso_steps": 32,
+    "cache_latents": true,
+    "cache_latents_to_disk": true,
+    "caption_dropout_every_n_epochs": 0.0,
+    "caption_dropout_rate": 0,
+    "caption_extension": ".txt2",
+    "clip_skip": "1",
+    "color_aug": false,
+    "conv_alpha": 4,
+    "conv_block_alphas": "",
+    "conv_block_dims": "",
+    "conv_dim": 4,
+    "decompose_both": false,
+    "dim_from_weights": false,
+    "down_lr_weight": "",
+    "enable_bucket": true,
+    "epoch": 30,
+    "factor": -1,
+    "flip_aug": false,
+    "full_bf16": false,
+    "full_fp16": false,
+    "gradient_accumulation_steps": 1.0,
+    "gradient_checkpointing": true,
+    "keep_tokens": 1,
+    "learning_rate": 1.0,
+    "lora_network_weights": "",
+    "lr_scheduler": "adafactor",
+    "lr_scheduler_num_cycles": "1",
+    "lr_scheduler_power": "",
+    "lr_warmup": 0,
+    "max_bucket_reso": 2048,
+    "max_data_loader_n_workers": "0",
+    "max_resolution": "1024,1024",
+    "max_timestep": 1000,
+    "max_token_length": "75",
+    "max_train_epochs": "30",
+    "mem_eff_attn": false,
+    "mid_lr_weight": "",
+    "min_bucket_reso": 64,
+    "min_snr_gamma": 0,
+    "min_timestep": 0,
+    "mixed_precision": "bf16",
+    "module_dropout": 0,
+    "multires_noise_discount": 0,
+    "multires_noise_iterations": 0,
+    "network_alpha": 128,
+    "network_dim": 128,
+    "network_dropout": 0,
+    "no_token_padding": false,
+    "noise_offset": 0.0357,
+    "noise_offset_type": "Original",
+    "num_cpu_threads_per_process": 2,
+    "optimizer": "Adafactor",
+    "optimizer_args": "",
+    "persistent_data_loader_workers": false,
+    "prior_loss_weight": 1.0,
+    "random_crop": false,
+    "rank_dropout": 0,
+    "save_every_n_epochs": 5,
+    "save_every_n_steps": 0,
+    "save_last_n_steps": 0,
+    "save_last_n_steps_state": 0,
+    "save_precision": "bf16",
+    "scale_v_pred_loss_like_noise_pred": false,
+    "scale_weight_norms": 1,
+    "sdxl": true,
+    "sdxl_cache_text_encoder_outputs": false,
+    "sdxl_no_half_vae": true,
+    "seed": "",
+    "shuffle_caption": false,
+    "stop_text_encoder_training_pct": 0,
+    "text_encoder_lr": 1.0,
+    "train_batch_size": 5,
+    "train_on_input": false,
+    "training_comment": "trigger: the white queen",
+    "unet_lr": 1.0,
+    "unit": 1,
+    "up_lr_weight": "",
+    "use_cp": false,
+    "use_wandb": false,
+    "v2": false,
+    "v_parameterization": false,
+    "vae_batch_size": 0,
+    "wandb_api_key": "",
+    "weighted_captions": false,
+    "xformers": true
+}