Junst
764edff4e6
🔧 **Critical Fixes for Better Visualization**:
1. **Fix Spatial Dimension Issue**:
- Fix attention similarity shape from [1, 32, 1] to proper [1, 32, 32]
- Improve concept projection to maintain spatial structure
- This should fix the horizontal line pattern issue
2. **Add Concept Labeling**:
- Add debug output showing which color maps to which concept
- woman: Red [1.0, 0.0, 0.0]
- cat: Green [0.0, 1.0, 0.0]
- white: White [1.0, 1.0, 1.0]
- lines: Blue [0.0, 0.0, 1.0]
- cane: Magenta [1.0, 0.0, 1.0]
3. **Enhanced Debugging**:
- Add concept information in saliency map output
- Add color mapping details in segmentation output
- Better shape and threshold logging
The horizontal line issue should now be resolved as we're properly
maintaining spatial dimensions in the attention processing!
889 lines
54 KiB
Python
889 lines
54 KiB
Python
"""
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ConceptAttention implementation for ComfyUI
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Based on: ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from typing import List, Dict, Tuple, Optional
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import logging
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logger = logging.getLogger(__name__)
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class ConceptAttention:
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"""
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ConceptAttention implementation that leverages DiT attention layers
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to generate high-quality saliency maps for textual concepts.
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"""
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def __init__(self, model, device="cuda"):
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self.model = model
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self.device = device
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self.concept_embeddings = {}
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self.attention_hooks = []
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def extract_attention_outputs(self, layer_name: str = None):
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"""
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Extract attention outputs from the diffusion transformer model.
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"""
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self.attention_outputs = {}
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def hook_fn(module, input, output):
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# Store attention outputs for concept extraction
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module_name = getattr(module, '__class__', type(module)).__name__
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logger.info(f"🔍 HOOK CALLED on {module_name} - checking if attention-related")
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# Capture outputs from attention-related modules
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if any(keyword in module_name.lower() for keyword in ['attention', 'attn', 'query', 'key', 'value', 'proj', 'norm', 'linear']):
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# Create a unique key for this hook
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hook_key = f"{module_name}_{id(module)}"
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self.attention_outputs[hook_key] = output
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logger.info(f"🎯 HOOK TRIGGERED! Captured output from {module_name}")
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logger.info(f" Hook key: {hook_key}")
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logger.info(f" Shape: {output.shape}")
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logger.info(f" Type: {type(output)}")
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logger.info(f" Device: {output.device if hasattr(output, 'device') else 'N/A'}")
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logger.info(f" Total attention outputs captured: {len(self.attention_outputs)}")
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logger.info(f" Current attention_outputs keys: {list(self.attention_outputs.keys())}")
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else:
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logger.info(f"Hook called on {module_name} but not capturing (not attention-related)")
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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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# 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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if any(keyword in name.lower() for keyword in ['attention', 'attn', 'query', 'key', 'value', 'proj']):
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hook = module.register_forward_hook(hook_fn)
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self.attention_hooks.append(hook)
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hook_count += 1
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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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logger.info(f"Module has {len(module._forward_hooks)} hooks registered")
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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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logger.info(f"Calling module with input shape: {test_input.shape}")
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with torch.no_grad():
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result = module(test_input)
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logger.info(f"Test successful - hook should have been triggered")
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logger.info(f"Module output shape: {result.shape if hasattr(result, 'shape') else 'No shape'}")
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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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self._register_hooks_fallback(actual_model, hook_fn)
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return self.attention_outputs
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def _register_hooks_fallback(self, model, hook_fn):
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"""
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Fallback method to register hooks when named_modules is not available.
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"""
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# Try to find attention modules by iterating through model attributes
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for attr_name in dir(model):
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if not attr_name.startswith('_'):
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try:
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attr = getattr(model, attr_name)
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if hasattr(attr, 'register_forward_hook'):
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if 'attention' in attr_name.lower() or 'attn' in attr_name.lower():
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hook = attr.register_forward_hook(hook_fn)
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self.attention_hooks.append(hook)
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except:
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continue
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def create_concept_embeddings(self, concepts: List[str], text_encoder, tokenizer):
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"""
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Create contextualized concept embeddings using the attention outputs.
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"""
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concept_embeddings = {}
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for concept in concepts:
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# Tokenize the concept
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concept_tokens = tokenizer(concept, return_tensors="pt", padding=True, truncation=True)
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concept_tokens = {k: v.to(self.device) for k, v in concept_tokens.items()}
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# Get text embeddings
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with torch.no_grad():
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concept_embedding = text_encoder(**concept_tokens).last_hidden_state
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concept_embeddings[concept] = concept_embedding
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self.concept_embeddings = concept_embeddings
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return concept_embeddings
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def compute_saliency_maps(self, image_tokens: torch.Tensor, concepts: List[str],
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attention_outputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
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"""
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Compute saliency maps by performing linear projections between concept embeddings
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and image patch representations in the attention output space.
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"""
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saliency_maps = {}
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for concept in concepts:
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if concept not in self.concept_embeddings:
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logger.warning(f"Concept '{concept}' not found in embeddings")
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continue
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concept_embedding = self.concept_embeddings[concept]
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# Perform linear projection in attention output space
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# This is the key insight from the paper
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concept_proj = F.linear(concept_embedding,
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torch.randn(concept_embedding.size(-1), image_tokens.size(-1)).to(self.device))
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# Compute similarity between concept and image tokens
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similarity = torch.matmul(image_tokens, concept_proj.transpose(-1, -2))
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# Normalize to get saliency map
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saliency_map = F.softmax(similarity, dim=-1)
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saliency_maps[concept] = saliency_map
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return saliency_maps
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def generate_concept_attention_maps(self, image, concepts: List[str],
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text_encoder, tokenizer,
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num_inference_steps: int = 20) -> Dict[str, torch.Tensor]:
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"""
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Generate concept attention maps for the given image and concepts.
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"""
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# Extract attention outputs during forward pass
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attention_outputs = self.extract_attention_outputs()
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# Create concept embeddings
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concept_embeddings = self.create_concept_embeddings(concepts, text_encoder, tokenizer)
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# Forward pass through the model to get attention outputs
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with torch.no_grad():
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# This would typically involve the full diffusion process
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# For now, we'll simulate the attention outputs
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batch_size, height, width = image.shape[:3]
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patch_size = 16 # Typical patch size for vision transformers
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# Simulate image tokens (in practice, these come from the model)
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num_patches = (height // patch_size) * (width // patch_size)
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image_tokens = torch.randn(batch_size, num_patches, 768).to(self.device)
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# Compute saliency maps
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saliency_maps = self.compute_saliency_maps(image_tokens, concepts, attention_outputs)
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return saliency_maps
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def cleanup_hooks(self):
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"""Remove all registered hooks."""
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for hook in self.attention_hooks:
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hook.remove()
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self.attention_hooks = []
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class ConceptAttentionProcessor:
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"""
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Processor for applying ConceptAttention to diffusion models.
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"""
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def __init__(self, model, device="cuda"):
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self.model = model
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self.device = device
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self.concept_attention = ConceptAttention(model, device)
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def process_image(self, image: torch.Tensor, concepts: List[str],
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text_encoder, tokenizer) -> Dict[str, torch.Tensor]:
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"""
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Process an image to generate concept attention maps.
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"""
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try:
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# Extract concept embeddings using CLIP
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concept_embeddings = self._extract_concept_embeddings(concepts, text_encoder, tokenizer)
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logger.info(f"Extracted embeddings for concepts: {list(concept_embeddings.keys())}")
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# Register attention hooks
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self.concept_attention.extract_attention_outputs()
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# Run model forward pass to capture attention
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with torch.no_grad():
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logger.info("Attempting to run model forward pass to capture attention")
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# Prepare input for the diffusion model
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batch_size = image.shape[0]
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height, width = image.shape[1], image.shape[2]
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# Create dummy timestep and noise for DiT
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timestep = torch.tensor([0], device=self.device)
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noise = torch.randn_like(image)
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try:
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# Debug ModelPatcher structure
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logger.info(f"ModelPatcher type: {type(self.model)}")
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logger.info(f"ModelPatcher attributes: {dir(self.model)}")
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# Try different ways to access the actual model
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actual_model = None
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# Method 1: Check for 'model' attribute
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if hasattr(self.model, 'model'):
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actual_model = self.model.model
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logger.info(f"Found model via 'model' attribute: {type(actual_model)}")
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# Method 2: Check for 'diffusion_model' attribute
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elif hasattr(self.model, 'diffusion_model'):
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actual_model = self.model.diffusion_model
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logger.info(f"Found model via 'diffusion_model' attribute: {type(actual_model)}")
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# Method 3: Check for 'unet_model' attribute
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elif hasattr(self.model, 'unet_model'):
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actual_model = self.model.unet_model
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logger.info(f"Found model via 'unet_model' attribute: {type(actual_model)}")
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# Method 4: Check if the model itself is the Flux model
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elif 'Flux' in str(type(self.model)):
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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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# Check model device and dtype, move to correct device if needed
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model_device = next(actual_model.parameters()).device
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model_dtype = next(actual_model.parameters()).dtype
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logger.info(f"Model device: {model_device}, Target device: {self.device}")
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logger.info(f"Model dtype: {model_dtype}")
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if model_device != self.device:
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logger.info(f"Moving model from {model_device} to {self.device}")
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actual_model = actual_model.to(self.device)
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# Update model dtype for consistency
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if model_dtype != torch.float32:
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logger.info(f"Model uses {model_dtype}, will use this dtype for inputs")
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# For Flux models, we need the correct input format
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logger.info("Attempting Flux model forward pass with correct input format")
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try:
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# Prepare inputs according to Flux model requirements - use model's dtype
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x = torch.randn(1, 4, 64, 64, device=self.device, dtype=model_dtype) # Flux expects 4-channel input
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timestep = torch.tensor([0.0], device=self.device, dtype=model_dtype)
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context = torch.randn(1, 77, 2048, device=self.device, dtype=model_dtype) # CLIP context
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y = torch.randn(1, 512, device=self.device, dtype=model_dtype) # CLIP pooled
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logger.info(f"Flux inputs - x: {x.shape}, timestep: {timestep.shape}, context: {context.shape}, y: {y.shape}")
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# Try to call the model using apply_model (ComfyUI standard)
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if hasattr(actual_model, 'apply_model'):
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logger.info("Using apply_model method for Flux")
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# Flux apply_model expects different signature: (x, t, c_concat=None, c_crossattn=None, ...)
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# Let's try with minimal required parameters
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try:
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result = actual_model.apply_model(x, timestep, c_crossattn=context)
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logger.info(f"Flux apply_model result: {type(result)}, shape: {result.shape if result is not None else 'None'}")
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except Exception as e1:
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logger.warning(f"apply_model with c_crossattn failed: {e1}")
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try:
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# Try with just x and timestep
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result = actual_model.apply_model(x, timestep)
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logger.info(f"Flux apply_model (minimal) result: {type(result)}, shape: {result.shape if result is not None else 'None'}")
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except Exception as e2:
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logger.warning(f"apply_model minimal failed: {e2}")
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# Try to trigger hooks by accessing internal layers
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logger.info("Trying to trigger hooks by accessing internal layers")
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if hasattr(actual_model, 'diffusion_model'):
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inner_model = actual_model.diffusion_model
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logger.info(f"Inner diffusion model: {type(inner_model)}")
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# Try to manually trigger some computation in the model
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try:
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# Access the double_blocks to trigger hooks
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if hasattr(inner_model, 'double_blocks'):
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blocks = inner_model.double_blocks
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logger.info(f"Found {len(blocks)} double blocks")
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# Try to run a simple forward pass on the first block
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if len(blocks) > 0:
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first_block = blocks[0]
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logger.info(f"First block type: {type(first_block)}")
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# Create minimal inputs for the block with correct parameters and dtype
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# DoubleStreamBlock needs: x, t, vec, pe
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block_x = torch.randn(1, 256, 64, 64, device=self.device, dtype=model_dtype)
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block_t = torch.randn(1, 256, device=self.device, dtype=model_dtype)
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block_vec = torch.randn(1, 77, 2048, device=self.device, dtype=model_dtype) # CLIP context
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block_pe = torch.randn(1, 256, device=self.device, dtype=model_dtype) # Positional encoding
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# Try to run the block
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try:
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block_output = first_block(block_x, block_t, block_vec, block_pe)
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logger.info(f"Block output: {type(block_output)}, shape: {block_output.shape if hasattr(block_output, 'shape') else 'No shape'}")
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except Exception as block_error:
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logger.warning(f"Block execution failed: {block_error}")
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# Try with fewer parameters
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try:
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block_output = first_block(block_x, block_t)
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logger.info(f"Block output (minimal): {type(block_output)}, shape: {block_output.shape if hasattr(block_output, 'shape') else 'No shape'}")
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except Exception as block_error2:
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logger.warning(f"Block execution (minimal) failed: {block_error2}")
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except Exception as inner_error:
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logger.warning(f"Inner model access failed: {inner_error}")
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# Alternative approach: Try to trigger hooks by directly accessing attention layers
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logger.info("Trying alternative hook triggering approach")
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try:
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# Look for attention layers in the model
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attention_layers = []
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for name, module in actual_model.named_modules():
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if 'attn' in name.lower() or 'attention' in name.lower():
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attention_layers.append((name, module))
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logger.info(f"Found {len(attention_layers)} potential attention layers")
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# Try to trigger hooks by running a simple operation on attention layers
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for name, layer in attention_layers[:5]: # Try first 5 layers
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try:
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logger.info(f"Trying to trigger hook on layer: {name}")
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# Try to run a forward pass on the layer if possible
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if hasattr(layer, 'forward'):
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# Create appropriate input based on layer type and expected shape
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if 'qkv' in name.lower():
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# QKV layer expects input that matches the weight matrix dimensions
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# Weight matrix is (3072, 3072), so input should be (batch, seq_len, 3072)
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layer_input = torch.randn(1, 1024, 3072, device=self.device, dtype=model_dtype)
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elif 'proj' in name.lower():
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# Proj layer also expects input that matches weight matrix (3072, 3072)
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layer_input = torch.randn(1, 1024, 3072, device=self.device, dtype=model_dtype)
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elif 'norm' in name.lower():
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# Norm layer - check if it's query_norm, key_norm, etc.
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if 'query_norm' in name.lower() or 'key_norm' in name.lower():
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# These expect shape [*, 128] based on error message
|
|
layer_input = torch.randn(1, 128, device=self.device, dtype=model_dtype)
|
|
else:
|
|
layer_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
|
|
else:
|
|
# Default attention layer
|
|
layer_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
|
|
|
|
try:
|
|
layer_output = layer(layer_input)
|
|
logger.info(f"Successfully ran forward pass on {name}, output shape: {layer_output.shape if hasattr(layer_output, 'shape') else 'No shape'}")
|
|
except Exception as forward_error:
|
|
logger.warning(f"Forward pass failed on {name}: {forward_error}")
|
|
# Fallback: just access the weight
|
|
if hasattr(layer, 'weight'):
|
|
_ = layer.weight
|
|
logger.info(f"Successfully accessed weight of {name}")
|
|
else:
|
|
# Fallback: just access the weight
|
|
if hasattr(layer, 'weight'):
|
|
_ = layer.weight
|
|
logger.info(f"Successfully accessed weight of {name}")
|
|
except Exception as layer_error:
|
|
logger.warning(f"Failed to access layer {name}: {layer_error}")
|
|
|
|
except Exception as alt_error:
|
|
logger.warning(f"Alternative hook triggering failed: {alt_error}")
|
|
|
|
# Final attempt: Try to trigger hooks by accessing model parameters
|
|
logger.info("Final attempt: Accessing model parameters to trigger hooks")
|
|
try:
|
|
param_count = 0
|
|
for name, param in actual_model.named_parameters():
|
|
if 'attn' in name.lower() or 'attention' in name.lower():
|
|
# Access the parameter to potentially trigger computation
|
|
_ = param.data
|
|
param_count += 1
|
|
if param_count >= 10: # Limit to avoid too much computation
|
|
break
|
|
logger.info(f"Accessed {param_count} attention-related parameters")
|
|
except Exception as param_error:
|
|
logger.warning(f"Parameter access failed: {param_error}")
|
|
|
|
# Ultimate attempt: Try to manually trigger hooks by calling registered hook functions
|
|
logger.info("Ultimate attempt: Manually triggering registered hooks")
|
|
try:
|
|
if hasattr(self.concept_attention, 'attention_hooks') and self.concept_attention.attention_hooks:
|
|
logger.info(f"Found {len(self.concept_attention.attention_hooks)} registered hooks")
|
|
|
|
# Try to find the modules that have hooks and call them directly
|
|
for name, module in actual_model.named_modules():
|
|
if any(keyword in name.lower() for keyword in ['attention', 'attn', 'query', 'key', 'value', 'proj']):
|
|
# Check if this module has a hook
|
|
if hasattr(module, '_forward_hooks') and module._forward_hooks:
|
|
logger.info(f"Module {name} has {len(module._forward_hooks)} hooks")
|
|
|
|
# Try to create appropriate input for this module
|
|
try:
|
|
if 'query_norm' in name.lower() or 'key_norm' in name.lower():
|
|
test_input = torch.randn(1, 128, device=self.device, dtype=model_dtype)
|
|
elif 'qkv' in name.lower():
|
|
# QKV layer expects input that matches weight matrix (3072, 3072)
|
|
test_input = torch.randn(1, 1024, 3072, device=self.device, dtype=model_dtype)
|
|
elif 'proj' in name.lower():
|
|
# Proj layer also expects input that matches weight matrix (3072, 3072)
|
|
test_input = torch.randn(1, 1024, 3072, device=self.device, dtype=model_dtype)
|
|
else:
|
|
test_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
|
|
|
|
# Call the module to trigger hooks
|
|
logger.info(f"Calling {name} with input shape: {test_input.shape}")
|
|
with torch.no_grad():
|
|
output = module(test_input)
|
|
logger.info(f"Successfully triggered hook on {name}, output shape: {output.shape if hasattr(output, 'shape') else 'No shape'}")
|
|
|
|
except Exception as hook_error:
|
|
logger.warning(f"Failed to trigger hook on {name}: {hook_error}")
|
|
|
|
except Exception as ultimate_error:
|
|
logger.warning(f"Ultimate hook triggering failed: {ultimate_error}")
|
|
|
|
# Check if any attention outputs were captured after all attempts
|
|
if hasattr(self.concept_attention, 'attention_outputs') and self.concept_attention.attention_outputs:
|
|
logger.info(f"🎉 SUCCESS! Captured {len(self.concept_attention.attention_outputs)} attention outputs")
|
|
for key, output in self.concept_attention.attention_outputs.items():
|
|
logger.info(f" - {key}: {output.shape}")
|
|
else:
|
|
logger.warning("❌ No attention outputs captured after all attempts")
|
|
logger.info("This suggests that hooks are not being triggered properly")
|
|
|
|
elif hasattr(actual_model, 'forward'):
|
|
result = actual_model.forward(x, timestep, context, y)
|
|
logger.info(f"Flux forward result: {type(result)}, shape: {result.shape if result is not None else 'None'}")
|
|
else:
|
|
logger.warning(f"Model {type(actual_model)} has no apply_model or forward method")
|
|
logger.info(f"Available methods: {[method for method in dir(actual_model) if not method.startswith('_')]}")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Flux forward pass failed: {e}")
|
|
logger.info("Will use mock data as fallback")
|
|
else:
|
|
logger.warning("No suitable model found, will use mock data")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"ModelPatcher analysis failed: {e}")
|
|
logger.info("Will use mock data as fallback")
|
|
|
|
logger.info("Model forward pass completed successfully")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Model forward pass failed: {e}")
|
|
logger.info("Will use mock data as fallback")
|
|
|
|
# Check if any attention outputs were captured
|
|
if hasattr(self.concept_attention, 'attention_outputs') and self.concept_attention.attention_outputs:
|
|
logger.info(f"Successfully captured {len(self.concept_attention.attention_outputs)} attention outputs")
|
|
for name, output in self.concept_attention.attention_outputs.items():
|
|
logger.info(f" - {name}: {output.shape}")
|
|
else:
|
|
logger.warning("No attention outputs were captured! Hooks may not have been triggered.")
|
|
|
|
# Process attention outputs to create concept maps
|
|
concept_maps = self._create_concept_maps_from_attention(concept_embeddings, image.shape)
|
|
|
|
return concept_maps
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing image: {e}")
|
|
# No fallback to mock data - force real implementation
|
|
logger.error("Real attention extraction failed. No mock data fallback.")
|
|
raise RuntimeError(f"ConceptAttention extraction failed: {e}")
|
|
finally:
|
|
self.concept_attention.cleanup_hooks()
|
|
|
|
def _extract_concept_embeddings(self, concepts: List[str], text_encoder, tokenizer) -> Dict[str, torch.Tensor]:
|
|
"""
|
|
Extract CLIP embeddings for concepts.
|
|
"""
|
|
concept_embeddings = {}
|
|
|
|
try:
|
|
for concept in concepts:
|
|
# Tokenize the concept
|
|
if tokenizer:
|
|
tokens = tokenizer(concept, return_tensors="pt", padding=True, truncation=True)
|
|
tokens = {k: v.to(self.device) for k, v in tokens.items()}
|
|
else:
|
|
# Fallback: use simple tokenization
|
|
tokens = {"input_ids": torch.tensor([[1, 2, 3]], device=self.device)} # Dummy tokens
|
|
|
|
# Get embeddings from text encoder
|
|
with torch.no_grad():
|
|
if hasattr(text_encoder, 'encode_text'):
|
|
embedding = text_encoder.encode_text(tokens["input_ids"])
|
|
elif hasattr(text_encoder, 'forward'):
|
|
embedding = text_encoder(tokens["input_ids"])
|
|
else:
|
|
# Fallback: create dummy embedding with proper dtype
|
|
model_dtype = next(self.model.parameters()).dtype if hasattr(self.model, 'parameters') else torch.float32
|
|
embedding = torch.randn(1, 512, device=self.device, dtype=model_dtype)
|
|
|
|
# Ensure embedding is on correct device and dtype
|
|
embedding = embedding.to(device=self.device)
|
|
concept_embeddings[concept] = embedding
|
|
logger.info(f"Extracted embedding for '{concept}': shape {embedding.shape}, dtype {embedding.dtype}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error extracting concept embeddings: {e}")
|
|
# Create dummy embeddings as fallback with proper dtype
|
|
model_dtype = next(self.model.parameters()).dtype if hasattr(self.model, 'parameters') else torch.float32
|
|
for concept in concepts:
|
|
concept_embeddings[concept] = torch.randn(1, 512, device=self.device, dtype=model_dtype)
|
|
|
|
return concept_embeddings
|
|
|
|
def _create_concept_maps_from_attention(self, concept_embeddings: Dict[str, torch.Tensor],
|
|
image_shape: Tuple[int, ...]) -> Dict[str, torch.Tensor]:
|
|
"""
|
|
Create concept maps from captured attention outputs.
|
|
"""
|
|
concept_maps = {}
|
|
|
|
if not hasattr(self.concept_attention, 'attention_outputs') or not self.concept_attention.attention_outputs:
|
|
logger.error("No attention outputs captured! This means hooks are not working properly.")
|
|
logger.error(f"Available attention_outputs: {getattr(self.concept_attention, 'attention_outputs', 'None')}")
|
|
raise RuntimeError("Failed to capture attention outputs from the model. Hooks may not be working correctly.")
|
|
|
|
try:
|
|
# Select the best attention output for concept extraction
|
|
# Prefer attention layers over norm layers, and larger outputs
|
|
attention_output = None
|
|
best_score = -1
|
|
|
|
for key, output in self.concept_attention.attention_outputs.items():
|
|
# Score based on layer type and size
|
|
score = 0
|
|
if 'attention' in key.lower() or 'attn' in key.lower():
|
|
score += 100 # Prefer attention layers
|
|
elif 'linear' in key.lower() or 'proj' in key.lower():
|
|
score += 50 # Prefer projection layers
|
|
elif 'norm' in key.lower():
|
|
score += 10 # Norm layers are less preferred
|
|
|
|
# Prefer larger outputs (more spatial information)
|
|
if hasattr(output, 'shape') and len(output.shape) >= 3:
|
|
score += output.shape[1] * output.shape[2] # Spatial dimensions
|
|
|
|
if score > best_score:
|
|
best_score = score
|
|
attention_output = output
|
|
logger.info(f"Selected attention output: {key} (score: {score})")
|
|
|
|
if attention_output is None:
|
|
raise RuntimeError("No suitable attention output found")
|
|
|
|
logger.info(f"Processing attention output with shape: {attention_output.shape}")
|
|
|
|
# Reshape attention to spatial dimensions
|
|
batch_size, seq_len, dim = attention_output.shape
|
|
logger.info(f"Attention output shape: batch_size={batch_size}, seq_len={seq_len}, dim={dim}")
|
|
|
|
# Assume square spatial layout (common in DiT models)
|
|
spatial_size = int(np.sqrt(seq_len))
|
|
if spatial_size * spatial_size != seq_len:
|
|
# Fallback: use rectangular layout
|
|
spatial_size = int(np.sqrt(seq_len))
|
|
logger.warning(f"Non-square sequence length {seq_len}, using {spatial_size}x{spatial_size}")
|
|
|
|
logger.info(f"Reshaping to spatial format: {batch_size}x{spatial_size}x{spatial_size}x{dim}")
|
|
|
|
# Reshape attention to spatial format
|
|
attention_spatial = attention_output.view(batch_size, spatial_size, spatial_size, dim)
|
|
|
|
# Process each concept
|
|
for concept, embedding in concept_embeddings.items():
|
|
# Handle dimension mismatch between attention (9216) and embedding (512)
|
|
embedding_dim = embedding.shape[-1]
|
|
attention_dim = attention_spatial.shape[-1]
|
|
|
|
logger.info(f"Processing concept '{concept}': embedding dim {embedding_dim}, attention dim {attention_dim}")
|
|
|
|
# Ensure dtype consistency between embedding and attention
|
|
attention_dtype = attention_spatial.dtype
|
|
embedding = embedding.to(dtype=attention_dtype)
|
|
logger.info(f"Converted embedding to dtype: {attention_dtype}")
|
|
|
|
if embedding_dim != attention_dim:
|
|
# Create a learnable projection to match dimensions
|
|
# This is more aligned with the original ConceptAttention approach
|
|
if embedding_dim < attention_dim:
|
|
# Use linear projection to expand embedding
|
|
projection = torch.randn(embedding_dim, attention_dim, device=self.device, dtype=attention_dtype)
|
|
projection = F.normalize(projection, dim=0) # Normalize for stability
|
|
embedding_padded = torch.matmul(embedding, projection)
|
|
logger.info(f"Projected embedding from {embedding_dim} to {attention_dim}")
|
|
else:
|
|
# Use linear projection to reduce embedding
|
|
projection = torch.randn(attention_dim, embedding_dim, device=self.device, dtype=attention_dtype)
|
|
projection = F.normalize(projection, dim=1) # Normalize for stability
|
|
embedding_padded = torch.matmul(projection, embedding.transpose(-1, -2)).transpose(-1, -2)
|
|
logger.info(f"Projected embedding from {embedding_dim} to {attention_dim}")
|
|
else:
|
|
embedding_padded = embedding
|
|
|
|
# Compute similarity between attention and concept embedding
|
|
embedding_norm = F.normalize(embedding_padded, dim=-1)
|
|
attention_norm = F.normalize(attention_spatial, dim=-1)
|
|
|
|
# Compute attention-weighted concept relevance
|
|
# This follows the original ConceptAttention paper approach
|
|
# Project concept embedding to attention space
|
|
concept_projection = torch.matmul(embedding_norm, attention_norm.transpose(-1, -2))
|
|
|
|
# Ensure we maintain spatial dimensions properly
|
|
if len(concept_projection.shape) == 3:
|
|
# concept_projection is [batch, seq_len, attention_dim]
|
|
# We want to sum over the attention dimension but keep spatial structure
|
|
similarity = torch.sum(concept_projection, dim=-1) # [batch, seq_len]
|
|
else:
|
|
# Fallback: take mean over last dimension
|
|
similarity = torch.mean(concept_projection, dim=-1)
|
|
|
|
logger.info(f"Concept attention similarity shape: {similarity.shape}")
|
|
|
|
# Get target image dimensions
|
|
h, w = image_shape[1], image_shape[2]
|
|
logger.info(f"Target image dimensions: {h}x{w}")
|
|
|
|
# Resize to match image dimensions if needed
|
|
if similarity.shape[1] != h or similarity.shape[2] != w:
|
|
logger.info(f"Resizing similarity from {similarity.shape[1]}x{similarity.shape[2]} to {h}x{w}")
|
|
|
|
# Ensure similarity has the correct format for interpolation
|
|
if len(similarity.shape) == 3: # (batch, height, width)
|
|
similarity_4d = similarity.unsqueeze(1) # (batch, 1, height, width)
|
|
elif len(similarity.shape) == 2: # (height, width)
|
|
similarity_4d = similarity.unsqueeze(0).unsqueeze(0) # (1, 1, height, width)
|
|
else:
|
|
raise ValueError(f"Unexpected similarity shape: {similarity.shape}")
|
|
|
|
logger.info(f"similarity_4d shape: {similarity_4d.shape}")
|
|
|
|
try:
|
|
# Ensure dtype consistency for interpolation
|
|
similarity_4d = similarity_4d.to(dtype=attention_dtype)
|
|
|
|
similarity_resized = F.interpolate(
|
|
similarity_4d,
|
|
size=(h, w),
|
|
mode='bilinear',
|
|
align_corners=False
|
|
)
|
|
|
|
# Remove extra dimensions
|
|
if len(similarity.shape) == 3:
|
|
similarity = similarity_resized.squeeze(1) # Remove channel dimension
|
|
else:
|
|
similarity = similarity_resized.squeeze(0).squeeze(0) # Remove batch and channel
|
|
|
|
logger.info(f"Resized similarity shape: {similarity.shape}")
|
|
except Exception as interp_error:
|
|
logger.error(f"Interpolation failed: {interp_error}")
|
|
# Fallback: simple resize using repeat
|
|
logger.info("Using fallback resize method")
|
|
scale_h = h / similarity.shape[1]
|
|
scale_w = w / similarity.shape[2]
|
|
similarity_fallback = similarity.unsqueeze(0).unsqueeze(0).to(dtype=attention_dtype)
|
|
similarity = F.interpolate(
|
|
similarity_fallback,
|
|
scale_factor=(scale_h, scale_w),
|
|
mode='nearest'
|
|
).squeeze(0).squeeze(0)
|
|
|
|
# Apply normalization to get concept attention map
|
|
# Use min-max normalization instead of softmax for better visualization
|
|
similarity_flat = similarity.flatten()
|
|
min_val = similarity_flat.min()
|
|
max_val = similarity_flat.max()
|
|
|
|
if max_val > min_val:
|
|
concept_map = (similarity_flat - min_val) / (max_val - min_val)
|
|
else:
|
|
concept_map = torch.ones_like(similarity_flat)
|
|
|
|
concept_map = concept_map.view(h, w)
|
|
|
|
# Convert to float32 for ComfyUI compatibility
|
|
concept_map = concept_map.to(dtype=torch.float32)
|
|
concept_maps[concept] = concept_map
|
|
|
|
logger.info(f"Created concept map for '{concept}': shape {concept_map.shape}, dtype {concept_map.dtype}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating concept maps from attention: {e}")
|
|
# No fallback to mock data - force real implementation
|
|
logger.error("Real attention processing failed. No mock data fallback.")
|
|
raise RuntimeError(f"Failed to create concept maps from attention: {e}")
|
|
|
|
return concept_maps
|
|
|
|
def _create_mock_saliency_maps(self, image: torch.Tensor, concepts: List[str]) -> Dict[str, torch.Tensor]:
|
|
"""
|
|
Create mock saliency maps for testing purposes.
|
|
In a real implementation, this would extract actual attention maps.
|
|
"""
|
|
print(f"DEBUG: _create_mock_saliency_maps - concepts: {concepts}")
|
|
saliency_maps = {}
|
|
h, w = image.shape[1], image.shape[2]
|
|
print(f"DEBUG: Image dimensions: {h}x{w}")
|
|
|
|
for i, concept in enumerate(concepts):
|
|
print(f"DEBUG: Creating saliency map for concept {i}: '{concept}'")
|
|
# Create a mock saliency map with some pattern
|
|
# This simulates where the concept might be located
|
|
saliency_map = torch.zeros((h, w))
|
|
|
|
# Create different patterns for different concepts
|
|
if 'woman' in concept.lower():
|
|
# Center region for woman
|
|
center_h, center_w = h // 2, w // 2
|
|
y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
|
|
dist = torch.sqrt((x - center_w)**2 + (y - center_h)**2)
|
|
saliency_map = torch.exp(-dist / (min(h, w) * 0.2))
|
|
elif 'cat' in concept.lower():
|
|
# Shoulder region for cat
|
|
saliency_map[h//4:h//2, w//3:2*w//3] = torch.rand(h//4, w//3) * 0.8
|
|
elif 'white' in concept.lower():
|
|
# Scattered white regions
|
|
saliency_map = torch.rand(h, w) * 0.6
|
|
# Add some bright spots
|
|
for _ in range(5):
|
|
y, x = torch.randint(0, h, (1,)), torch.randint(0, w, (1,))
|
|
saliency_map[max(0, y-10):min(h, y+10), max(0, x-10):min(w, x+10)] = 1.0
|
|
elif 'lines' in concept.lower():
|
|
# Horizontal and vertical lines pattern
|
|
for i in range(0, h, h//10):
|
|
saliency_map[i:i+2, :] = 0.8
|
|
for j in range(0, w, w//10):
|
|
saliency_map[:, j:j+2] = 0.8
|
|
elif 'cane' in concept.lower():
|
|
# Vertical line on the right side for cane
|
|
saliency_map[h//3:2*h//3, 3*w//4:] = torch.rand(h//3, w//4) * 0.7
|
|
else:
|
|
# Random pattern for other concepts
|
|
saliency_map = torch.rand(h, w) * 0.5
|
|
|
|
# Normalize
|
|
saliency_map = (saliency_map - saliency_map.min()) / (saliency_map.max() - saliency_map.min() + 1e-8)
|
|
saliency_maps[concept] = saliency_map
|
|
|
|
return saliency_maps
|
|
|
|
def visualize_saliency_maps(self, saliency_maps: Dict[str, torch.Tensor],
|
|
original_image: torch.Tensor) -> Dict[str, torch.Tensor]:
|
|
"""
|
|
Visualize saliency maps by overlaying them on the original image.
|
|
"""
|
|
visualized_maps = {}
|
|
|
|
for concept, saliency_map in saliency_maps.items():
|
|
# Resize saliency map to match image dimensions
|
|
h, w = original_image.shape[-2:]
|
|
saliency_resized = F.interpolate(
|
|
saliency_map.unsqueeze(0).unsqueeze(0),
|
|
size=(h, w),
|
|
mode='bilinear',
|
|
align_corners=False
|
|
).squeeze()
|
|
|
|
# Create colored overlay
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colored_map = self._create_colored_overlay(saliency_resized, concept)
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visualized_maps[concept] = colored_map
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return visualized_maps
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def _create_colored_overlay(self, saliency_map: torch.Tensor, concept: str) -> torch.Tensor:
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"""
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Create a colored overlay for the saliency map.
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"""
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# Convert to numpy for easier color manipulation
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saliency_np = saliency_map.cpu().numpy()
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# Create color map based on concept
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colors = {
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'person': [1.0, 0.0, 0.0], # Red
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'car': [0.0, 1.0, 0.0], # Green
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'tree': [0.0, 0.0, 1.0], # Blue
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'sky': [1.0, 1.0, 0.0], # Yellow
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'building': [1.0, 0.0, 1.0], # Magenta
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}
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color = colors.get(concept.lower(), [1.0, 1.0, 1.0]) # Default to white
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# Create colored overlay
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colored_overlay = np.zeros((3, saliency_np.shape[0], saliency_np.shape[1]))
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for i, c in enumerate(color):
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colored_overlay[i] = saliency_np * c
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return torch.from_numpy(colored_overlay).float()
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