From 7ddc19c1b48ff9092778e4891eb3d7ea7d2d672e Mon Sep 17 00:00:00 2001
From: PXL8D <pxl8d@PXL8Ds-Mac-Studio.local>
Date: Wed, 13 Aug 2025 11:44:36 -0700
Subject: [PATCH] Session 8 Priority 1: Simplify LoRA output format to
 real_name and weight only

- Modified MetaManExtractComponents to return simplified LoRA list
- Enhanced LoRAs: Only real_name and weight fields
- Regular LoRAs: name mapped to real_name, weight preserved
- Removes unnecessary hash, label, and other metadata fields
- Cleaner output for workflow integration
- Updated logging to show simplified LoRA counts
---
 metaman.py | 33 ++++++++++++++++++++++++++++-----
 1 file changed, 28 insertions(+), 5 deletions(-)

diff --git a/metaman.py b/metaman.py
index 963bc20..bd4fe18 100644
--- a/metaman.py
+++ b/metaman.py
@@ -444,13 +444,36 @@ class MetaManExtractComponents:
             
             denoising_strength = float(metadata.get('denoising_strength', 1.0))
             
-            # Enhanced LoRAs - prefer de-obfuscated version if available
+            # Enhanced LoRAs - prefer de-obfuscated version if available (simplified output)
             loras_list = []
             if 'loras_enhanced' in metadata and isinstance(metadata['loras_enhanced'], list):
-                loras_list = metadata['loras_enhanced']  # Use enhanced LoRAs with real names
-                print(f"MetaMan Extract Components: Using enhanced LoRAs with real names")
+                # Simplify enhanced LoRAs to only include real_name and weight
+                raw_loras = metadata['loras_enhanced']
+                for lora in raw_loras:
+                    if isinstance(lora, dict):
+                        simplified_lora = {
+                            'real_name': lora.get('real_name', lora.get('name', '')),
+                            'weight': lora.get('weight', 1.0)
+                        }
+                        loras_list.append(simplified_lora)
+                    else:
+                        # Handle non-dict LoRAs (fallback)
+                        loras_list.append({'real_name': str(lora), 'weight': 1.0})
+                print(f"MetaMan Extract Components: Using simplified enhanced LoRAs ({len(loras_list)} LoRAs)")
             elif 'loras' in metadata and isinstance(metadata['loras'], list):
-                loras_list = metadata['loras']  # Fall back to regular LoRAs
+                # Simplify regular LoRAs to only include name (as real_name) and weight
+                raw_loras = metadata['loras']
+                for lora in raw_loras:
+                    if isinstance(lora, dict):
+                        simplified_lora = {
+                            'real_name': lora.get('name', ''),
+                            'weight': lora.get('weight', 1.0)
+                        }
+                        loras_list.append(simplified_lora)
+                    else:
+                        # Handle non-dict LoRAs (fallback)
+                        loras_list.append({'real_name': str(lora), 'weight': 1.0})
+                print(f"MetaMan Extract Components: Using simplified regular LoRAs ({len(loras_list)} LoRAs)")
             
             # Format embeddings as a list
             embeddings_list = []
@@ -458,7 +481,7 @@ class MetaManExtractComponents:
                 embeddings_list = metadata['embeddings']  # Return the actual list
             
             print(f"MetaMan Extract Components: Successfully extracted {len([x for x in [positive_prompt, negative_prompt, model_name, vae_name] if x])} text components")
-            print(f"MetaMan Extract Components: Extracted {len(loras_list)} LoRAs and {len(embeddings_list)} embeddings")
+            print(f"MetaMan Extract Components: Extracted {len(loras_list)} simplified LoRAs and {len(embeddings_list)} embeddings")
             
             return (positive_prompt, negative_prompt, steps, cfg_scale, sampler, scheduler, seed, width, height, model_name, vae_name, loras_list, embeddings_list, denoising_strength)