more organization

steal_sdscripts_metadata → obsolete/steal_sdscripts_metadata

@@ -1,103 +1,103 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-"""
-This script automates the process of updating a Stable Diffusion training
-script with settings extracted from a LoRA model's JSON metadata.
-
-It performs the following main tasks:
-1. Reads a JSON file containing LoRA model metadata
-2. Parses an existing Stable Diffusion training script
-3. Maps metadata keys to corresponding script arguments
-4. Updates the script with values from the metadata
-5. Handles special cases and complex arguments (e.g., network_args)
-6. Writes the updated script to a new file
-
-Usage:
-    python steal_sdscripts_metadata <metadata_file> <script_file> <output_file>
-
-This tool is particularly useful for replicating training conditions or
-fine-tuning existing models based on successful previous runs.
-"""
-
-import json
-import re
-import argparse
-
-# Parse command-line arguments
-parser = argparse.ArgumentParser(
-    description='Update training script based on metadata.'
-)
-parser.add_argument(
-    'metadata_file', type=str, help='Path to the metadata JSON file'
-)
-parser.add_argument(
-    'script_file', type=str, help='Path to the training script file'
-)
-parser.add_argument(
-    'output_file', type=str, help='Path to save the updated training script'
-)
-args = parser.parse_args()
-
-# Read the metadata JSON file
-with open(args.metadata_file, 'r', encoding='utf-8') as f:
-    metadata = json.load(f)
-
-# Read the training script
-with open(args.script_file, 'r', encoding='utf-8') as f:
-    script_content = f.read()
-
-# Define mappings between JSON keys and script arguments
-mappings = {
-    'ss_network_dim': '--network_dim',
-    'ss_network_alpha': '--network_alpha',
-    'ss_learning_rate': '--learning_rate',
-    'ss_unet_lr': '--unet_lr',
-    'ss_text_encoder_lr': '--text_encoder_lr',
-    'ss_max_train_steps': '--max_train_steps',
-    'ss_train_batch_size': '--train_batch_size',
-    'ss_gradient_accumulation_steps': '--gradient_accumulation_steps',
-    'ss_mixed_precision': '--mixed_precision',
-    'ss_seed': '--seed',
-    'ss_resolution': '--resolution',
-    'ss_clip_skip': '--clip_skip',
-    'ss_lr_scheduler': '--lr_scheduler',
-    'ss_network_module': '--network_module',
-}
-
-# Update script content based on metadata
-for json_key, script_arg in mappings.items():
-    if json_key in metadata:
-        value = metadata[json_key]
-
-        # Handle special cases
-        if json_key == 'ss_resolution':
-            value = f'"{value[1:-1]}"'  # Remove parentheses and add quotes
-        elif isinstance(value, str):
-            value = f'"{value}"'
-
-        # Replace or add the argument in the script
-        pattern = f'{script_arg}=\\S+'
-        replacement = f'{script_arg}={value}'
-        if re.search(pattern, script_content):
-            script_content = re.sub(pattern, replacement, script_content)
-        else:
-            script_content = script_content.replace(
-                'args=(', f'args=(\n    {replacement}'
-            )
-
-# Handle network_args separately as it's more complex
-if 'ss_network_args' in metadata:
-    network_args = metadata['ss_network_args']
-    NETWORK_ARGS_STR = ' '.join(
-        [f'"{k}={v}"' for k, v in network_args.items()]
-    )
-    PATTERN = r'--network_args(\s+".+")+'
-    replacement = f'--network_args\n               {NETWORK_ARGS_STR}'
-    script_content = re.sub(PATTERN, replacement, script_content)
-
-# Write the updated script
-with open(args.output_file, 'w', encoding='utf-8') as f:
-    f.write(script_content)
-
-print(f"Updated training script has been saved as '{args.output_file}'")
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+This script automates the process of updating a Stable Diffusion training
+script with settings extracted from a LoRA model's JSON metadata.
+
+It performs the following main tasks:
+1. Reads a JSON file containing LoRA model metadata
+2. Parses an existing Stable Diffusion training script
+3. Maps metadata keys to corresponding script arguments
+4. Updates the script with values from the metadata
+5. Handles special cases and complex arguments (e.g., network_args)
+6. Writes the updated script to a new file
+
+Usage:
+    python steal_sdscripts_metadata <metadata_file> <script_file> <output_file>
+
+This tool is particularly useful for replicating training conditions or
+fine-tuning existing models based on successful previous runs.
+"""
+
+import json
+import re
+import argparse
+
+# Parse command-line arguments
+parser = argparse.ArgumentParser(
+    description='Update training script based on metadata.'
+)
+parser.add_argument(
+    'metadata_file', type=str, help='Path to the metadata JSON file'
+)
+parser.add_argument(
+    'script_file', type=str, help='Path to the training script file'
+)
+parser.add_argument(
+    'output_file', type=str, help='Path to save the updated training script'
+)
+args = parser.parse_args()
+
+# Read the metadata JSON file
+with open(args.metadata_file, 'r', encoding='utf-8') as f:
+    metadata = json.load(f)
+
+# Read the training script
+with open(args.script_file, 'r', encoding='utf-8') as f:
+    script_content = f.read()
+
+# Define mappings between JSON keys and script arguments
+mappings = {
+    'ss_network_dim': '--network_dim',
+    'ss_network_alpha': '--network_alpha',
+    'ss_learning_rate': '--learning_rate',
+    'ss_unet_lr': '--unet_lr',
+    'ss_text_encoder_lr': '--text_encoder_lr',
+    'ss_max_train_steps': '--max_train_steps',
+    'ss_train_batch_size': '--train_batch_size',
+    'ss_gradient_accumulation_steps': '--gradient_accumulation_steps',
+    'ss_mixed_precision': '--mixed_precision',
+    'ss_seed': '--seed',
+    'ss_resolution': '--resolution',
+    'ss_clip_skip': '--clip_skip',
+    'ss_lr_scheduler': '--lr_scheduler',
+    'ss_network_module': '--network_module',
+}
+
+# Update script content based on metadata
+for json_key, script_arg in mappings.items():
+    if json_key in metadata:
+        value = metadata[json_key]
+
+        # Handle special cases
+        if json_key == 'ss_resolution':
+            value = f'"{value[1:-1]}"'  # Remove parentheses and add quotes
+        elif isinstance(value, str):
+            value = f'"{value}"'
+
+        # Replace or add the argument in the script
+        pattern = f'{script_arg}=\\S+'
+        replacement = f'{script_arg}={value}'
+        if re.search(pattern, script_content):
+            script_content = re.sub(pattern, replacement, script_content)
+        else:
+            script_content = script_content.replace(
+                'args=(', f'args=(\n    {replacement}'
+            )
+
+# Handle network_args separately as it's more complex
+if 'ss_network_args' in metadata:
+    network_args = metadata['ss_network_args']
+    NETWORK_ARGS_STR = ' '.join(
+        [f'"{k}={v}"' for k, v in network_args.items()]
+    )
+    PATTERN = r'--network_args(\s+".+")+'
+    replacement = f'--network_args\n               {NETWORK_ARGS_STR}'
+    script_content = re.sub(PATTERN, replacement, script_content)
+
+# Write the updated script
+with open(args.output_file, 'w', encoding='utf-8') as f:
+    f.write(script_content)
+
+print(f"Updated training script has been saved as '{args.output_file}'")