Critical bug fixes: resolve NameError in metadata scope, fix model tracing to continue through node references, improve LoRA mapping by index for Tensor.AI de-obfuscation
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PXL8D
+59
-29
@@ -688,6 +688,14 @@ class MetaManLoadImage:
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if key == 'prompt':
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extracted_params = self._extract_params_from_comfyui_prompt(json_data)
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metadata.update(extracted_params)
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# Phase: De-obfuscate Tensor.AI model names if available (after parameter extraction)
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if extracted_params:
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deobfuscated_info = self._deobfuscate_tensor_ai_models(metadata, extracted_params.get('loras', []))
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if deobfuscated_info:
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metadata.update(deobfuscated_info)
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print(f"MetaMan Load Image: De-obfuscated Tensor.AI models: {list(deobfuscated_info.keys())}")
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print(f"MetaMan Load Image: Extracted ComfyUI params: {list(extracted_params.keys())}")
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except Exception as e:
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@@ -814,12 +822,6 @@ class MetaManLoadImage:
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params.update(model_info)
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print(f"MetaMan Universal: Resolved models: {model_info}")
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# Phase 6.5: De-obfuscate Tensor.AI model names if available
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deobfuscated_info = self._deobfuscate_tensor_ai_models(metadata, params.get('loras', []))
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if deobfuscated_info:
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params.update(deobfuscated_info)
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print(f"MetaMan Universal: De-obfuscated Tensor.AI models: {deobfuscated_info}")
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# Phase 6.7: Extract VAE information
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vae_info = self._extract_vae_info(prompt_data)
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if vae_info:
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@@ -1331,7 +1333,13 @@ class MetaManLoadImage:
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print(f"MetaMan Universal: Traced to model: {model_name}")
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# Remove duplicates and set model info
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unique_models = list(set(used_models))
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unique_models = []
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for model in used_models:
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# Skip None values and node references that weren't resolved
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if model and isinstance(model, str) and not model.startswith('['):
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if model not in unique_models:
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unique_models.append(model)
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if unique_models:
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if len(unique_models) == 1:
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model_info['model_name'] = unique_models[0]
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@@ -1340,6 +1348,8 @@ class MetaManLoadImage:
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model_info['additional_models'] = unique_models[1:] # Additional models
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print(f"MetaMan Universal: Final model resolution: {unique_models}")
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else:
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print(f"MetaMan Universal: No resolvable model names found")
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except Exception as e:
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print(f"MetaMan Universal: Error resolving model names: {e}")
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@@ -1369,16 +1379,22 @@ class MetaManLoadImage:
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for field in model_fields:
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if field in inputs and inputs[field]:
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model_name = inputs[field]
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print(f"MetaMan Universal: Found model '{model_name}' in {node_id}.{field}")
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return model_name
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model_value = inputs[field]
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# Check if this is a string (actual model name) or a list (node reference)
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if isinstance(model_value, str):
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print(f"MetaMan Universal: Found model '{model_value}' in {node_id}.{field}")
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return model_value
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elif isinstance(model_value, list) and len(model_value) >= 2:
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# This is a node reference, trace further
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source_node_id = str(model_value[0])
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print(f"MetaMan Universal: Node {node_id}.{field} references node {source_node_id}, tracing further...")
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return self._trace_model_source(prompt_data, source_node_id)
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# If this node doesn't have a direct model name, trace further
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# Look for model input that might be a reference to another node
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# If this node doesn't have a direct model name, look for model input that might be a reference to another node
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model_input = inputs.get('model')
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if model_input and isinstance(model_input, list) and len(model_input) >= 2:
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source_node_id = str(model_input[0])
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print(f"MetaMan Universal: Node {node_id} model input references node {source_node_id}")
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print(f"MetaMan Universal: Node {node_id} model input references node {source_node_id}, tracing further...")
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return self._trace_model_source(prompt_data, source_node_id)
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return None
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@@ -1519,30 +1535,44 @@ class MetaManLoadImage:
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# Apply de-obfuscation to LoRAs
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if loras:
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deobfuscated_loras = []
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for lora in loras:
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lora_mapping_by_index = {}
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# Create a mapping by order (index) since EMS codes don't directly match filenames
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for i, model_data in enumerate(models_data):
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if model_data.get('type') == 'LORA':
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lora_mapping_by_index[i] = {
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'real_name': model_data.get('modelFileName', ''),
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'hash': model_data.get('hash', ''),
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'weight': model_data.get('weight', 1.0),
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'label': model_data.get('label', '')
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}
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print(f"MetaMan De-obfuscation: Created mapping for {len(lora_mapping_by_index)} LoRAs by index")
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# Map LoRAs by order/index since EMS names don't match real names
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for i, lora in enumerate(loras):
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lora_name = lora.get('name', '') if isinstance(lora, dict) else str(lora)
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lora_weight = lora.get('weight', 1.0) if isinstance(lora, dict) else 1.0
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# Find matching real name
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real_lora_info = None
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for real_name, mapping_data in ems_mapping.items():
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if mapping_data['type'] == 'lora':
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# Create enhanced LoRA info
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enhanced_lora = {
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'name': lora_name, # Keep original EMS name
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'real_name': real_name, # Add real name
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'weight': lora.get('weight', mapping_data['weight']) if isinstance(lora, dict) else mapping_data['weight'],
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'hash': mapping_data['hash'],
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'label': mapping_data['label']
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}
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deobfuscated_loras.append(enhanced_lora)
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print(f"MetaMan De-obfuscation: Enhanced LoRA - {lora_name} → {real_name}")
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break
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if i in lora_mapping_by_index:
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mapping_data = lora_mapping_by_index[i]
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enhanced_lora = {
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'name': lora_name, # Keep original EMS name
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'real_name': mapping_data['real_name'], # Add real name
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'weight': lora_weight, # Use weight from extraction
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'hash': mapping_data['hash'],
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'label': mapping_data['label']
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}
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deobfuscated_loras.append(enhanced_lora)
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print(f"MetaMan De-obfuscation: Enhanced LoRA {i} - {lora_name} → {mapping_data['real_name']}")
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else:
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# Keep original if no mapping found
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deobfuscated_loras.append(lora)
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print(f"MetaMan De-obfuscation: No mapping for LoRA {i}, keeping original: {lora_name}")
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if deobfuscated_loras:
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deobfuscated['loras_enhanced'] = deobfuscated_loras
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print(f"MetaMan De-obfuscation: Created {len(deobfuscated_loras)} enhanced LoRAs")
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# Store the EMS mapping for future reference
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if ems_mapping:
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