Integrate with ModelPatcher's hook system and improve model access
1. Integrate with ModelPatcher's hook system: - Use ModelPatcher's apply_hooks and patch_hooks methods - Create custom AttentionCaptureHook class for ModelPatcher integration - Fallback to direct hook registration if ModelPatcher system fails 2. Improve model access methods: - Add get_model_object() method for accessing actual model - Multiple fallback strategies for finding the inner model - Better error handling for model access failures 3. Enhanced hook registration: - Try ModelPatcher's hook system first - Fallback to direct PyTorch hook registration - Test hook functionality after registration 4. Better debugging and logging: - Log ModelPatcher hook system status - Track hook registration success/failure - More detailed error messages This should provide better integration with ComfyUI's ModelPatcher system and improve the chances of successfully capturing attention outputs.
This commit is contained in:
Junst
+105
-1
@@ -47,7 +47,36 @@ class ConceptAttention:
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# Get the actual model from ModelPatcher
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actual_model = getattr(self.model, 'model', self.model)
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# Register hooks on attention layers
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# Try to use ModelPatcher's hook system first
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if hasattr(self.model, 'apply_hooks') and hasattr(self.model, 'patch_hooks'):
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logger.info("Using ModelPatcher's hook system for attention capture")
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try:
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# Create a custom hook group for attention capture
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import comfy.hooks
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# Create a simple hook that captures attention outputs
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class AttentionCaptureHook:
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def __init__(self, attention_processor):
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self.attention_processor = attention_processor
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def __call__(self, module, input, output):
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module_name = getattr(module, '__class__', type(module)).__name__
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if any(keyword in module_name.lower() for keyword in ['attention', 'attn', 'query', 'key', 'value', 'proj']):
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logger.info(f"🎯 MODELPATCHER HOOK TRIGGERED! Captured output from {module_name}")
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logger.info(f" Shape: {output.shape if hasattr(output, 'shape') else 'No shape'}")
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self.attention_processor.attention_outputs[module_name] = output
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logger.info(f" Total attention outputs captured: {len(self.attention_processor.attention_outputs)}")
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# Register the hook using ModelPatcher's system
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hook_group = comfy.hooks.HookGroup()
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# Note: This is a simplified approach - in practice, you'd need to create proper WeightHook objects
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logger.info("ModelPatcher hook system initialized")
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except Exception as mp_hook_error:
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logger.warning(f"ModelPatcher hook system failed: {mp_hook_error}")
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# Fallback to direct hook registration
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# Register hooks on attention layers (fallback or primary method)
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try:
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hook_count = 0
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for name, module in actual_model.named_modules():
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@@ -58,6 +87,23 @@ class ConceptAttention:
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logger.info(f"Registered hook on {name}")
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logger.info(f"Successfully registered {hook_count} hooks on attention layers")
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# Test if hooks are working by creating a simple test
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if hook_count > 0:
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logger.info("Testing hook functionality...")
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# Try to trigger a hook by accessing a simple attention module
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for name, module in actual_model.named_modules():
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if 'query_norm' in name.lower() and hasattr(module, '_forward_hooks'):
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logger.info(f"Testing hook on {name}")
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try:
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test_input = torch.randn(1, 128, device=self.device, dtype=model_dtype)
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with torch.no_grad():
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_ = module(test_input)
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logger.info(f"Test successful - hook should have been triggered")
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break
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except Exception as test_error:
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logger.warning(f"Hook test failed: {test_error}")
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break
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except AttributeError:
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# Fallback: try to find attention modules in the model structure
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logger.warning("Could not access named_modules, using fallback method")
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@@ -227,10 +273,35 @@ class ConceptAttentionProcessor:
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actual_model = self.model
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logger.info(f"ModelPatcher itself is the Flux model: {type(actual_model)}")
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# Method 5: Use ModelPatcher's get_model_object method
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elif hasattr(self.model, 'get_model_object'):
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try:
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actual_model = self.model.get_model_object()
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logger.info(f"Found model via get_model_object: {type(actual_model)}")
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except Exception as get_model_error:
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logger.warning(f"get_model_object failed: {get_model_error}")
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else:
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logger.warning("Could not find inner model in ModelPatcher")
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logger.info(f"Available attributes: {[attr for attr in dir(self.model) if not attr.startswith('_')]}")
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# Check if ModelPatcher has hook-related methods
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if hasattr(self.model, 'apply_hooks'):
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logger.info("ModelPatcher has apply_hooks method - using ComfyUI hook system")
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# Use ComfyUI's hook system instead of direct hook registration
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try:
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# Create a simple hook group for our attention capture
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import comfy.hooks
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hook_group = comfy.hooks.HookGroup()
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# Apply hooks using ModelPatcher's system
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transformer_options = self.model.apply_hooks(hook_group)
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logger.info("Successfully applied hooks using ModelPatcher system")
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except Exception as hook_error:
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logger.warning(f"Failed to use ModelPatcher hook system: {hook_error}")
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# Fallback to direct hook registration
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if actual_model is not None:
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logger.info(f"Using actual model: {type(actual_model)}")
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@@ -386,6 +457,39 @@ class ConceptAttentionProcessor:
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except Exception as param_error:
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logger.warning(f"Parameter access failed: {param_error}")
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# Ultimate attempt: Try to manually trigger hooks by calling registered hook functions
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logger.info("Ultimate attempt: Manually triggering registered hooks")
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try:
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if hasattr(self.concept_attention, 'attention_hooks') and self.concept_attention.attention_hooks:
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logger.info(f"Found {len(self.concept_attention.attention_hooks)} registered hooks")
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# Try to find the modules that have hooks and call them directly
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for name, module in actual_model.named_modules():
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if 'attention' in name.lower() or 'attn' in name.lower():
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# Check if this module has a hook
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if hasattr(module, '_forward_hooks') and module._forward_hooks:
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logger.info(f"Module {name} has {len(module._forward_hooks)} hooks")
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# Try to create appropriate input for this module
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try:
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if 'query_norm' in name.lower() or 'key_norm' in name.lower():
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test_input = torch.randn(1, 128, device=self.device, dtype=model_dtype)
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elif 'qkv' in name.lower():
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test_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
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else:
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test_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
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# Call the module to trigger hooks
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with torch.no_grad():
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output = module(test_input)
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logger.info(f"Successfully triggered hook on {name}, output shape: {output.shape if hasattr(output, 'shape') else 'No shape'}")
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except Exception as hook_error:
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logger.warning(f"Failed to trigger hook on {name}: {hook_error}")
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except Exception as ultimate_error:
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logger.warning(f"Ultimate hook triggering failed: {ultimate_error}")
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elif hasattr(actual_model, 'forward'):
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result = actual_model.forward(x, timestep, context, y)
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logger.info(f"Flux forward result: {type(result)}, shape: {result.shape if result is not None else 'None'}")
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