Junst
45fcc4e1b3
1. Remove Mock Data fallback mechanisms:
- No more automatic fallback to mock data when real extraction fails
- Force real attention extraction to work properly
- Raise RuntimeError instead of silently using mock data
2. Enhanced hook debugging:
- Add clear indicators when hooks are triggered (🎯 HOOK TRIGGERED!)
- Log detailed information about captured attention outputs
- Show total number of captured outputs
- Debug logging for non-attention modules
3. Strict error handling:
- If no attention outputs are captured, raise RuntimeError
- Force debugging of hook registration and execution
- No silent failures that lead to mock data usage
This ensures that either real attention extraction works properly,
or we get clear error messages about what's failing, preventing
silent fallback to mock data.
479 lines
22 KiB
Python
479 lines
22 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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# 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']):
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self.attention_outputs[module_name] = output
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logger.info(f"🎯 HOOK TRIGGERED! Captured output from {module_name}")
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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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else:
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logger.debug(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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# Register hooks on attention layers
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try:
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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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hook = module.register_forward_hook(hook_fn)
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self.attention_hooks.append(hook)
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logger.info(f"Registered hook on {name}")
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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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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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if actual_model is not None:
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logger.info(f"Using actual model: {type(actual_model)}")
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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
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x = torch.randn(1, 4, 64, 64, device=self.device) # Flux expects 4-channel input
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timestep = torch.tensor([0.0], device=self.device)
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context = torch.randn(1, 77, 2048, device=self.device) # CLIP context
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y = torch.randn(1, 512, device=self.device) # 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
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if 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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else:
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logger.warning(f"Model {type(actual_model)} has no forward method")
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except Exception as e:
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logger.warning(f"Flux forward pass failed: {e}")
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logger.info("Will use mock data as fallback")
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else:
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logger.warning("No suitable model found, will use mock data")
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except Exception as e:
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logger.warning(f"ModelPatcher analysis failed: {e}")
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logger.info("Will use mock data as fallback")
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logger.info("Model forward pass completed successfully")
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except Exception as e:
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logger.warning(f"Model forward pass failed: {e}")
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logger.info("Will use mock data as fallback")
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# Process attention outputs to create concept maps
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concept_maps = self._create_concept_maps_from_attention(concept_embeddings, image.shape)
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return concept_maps
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except Exception as e:
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logger.error(f"Error processing image: {e}")
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# No fallback to mock data - force real implementation
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logger.error("Real attention extraction failed. No mock data fallback.")
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raise RuntimeError(f"ConceptAttention extraction failed: {e}")
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finally:
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self.concept_attention.cleanup_hooks()
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def _extract_concept_embeddings(self, concepts: List[str], text_encoder, tokenizer) -> Dict[str, torch.Tensor]:
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"""
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Extract CLIP embeddings for concepts.
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"""
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concept_embeddings = {}
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try:
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for concept in concepts:
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# Tokenize the concept
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if tokenizer:
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tokens = tokenizer(concept, return_tensors="pt", padding=True, truncation=True)
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tokens = {k: v.to(self.device) for k, v in tokens.items()}
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else:
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# Fallback: use simple tokenization
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tokens = {"input_ids": torch.tensor([[1, 2, 3]], device=self.device)} # Dummy tokens
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# Get embeddings from text encoder
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with torch.no_grad():
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if hasattr(text_encoder, 'encode_text'):
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embedding = text_encoder.encode_text(tokens["input_ids"])
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elif hasattr(text_encoder, 'forward'):
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embedding = text_encoder(tokens["input_ids"])
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else:
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# Fallback: create dummy embedding
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embedding = torch.randn(1, 512, device=self.device)
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concept_embeddings[concept] = embedding
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logger.info(f"Extracted embedding for '{concept}': shape {embedding.shape}")
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except Exception as e:
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logger.error(f"Error extracting concept embeddings: {e}")
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# Create dummy embeddings as fallback
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for concept in concepts:
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concept_embeddings[concept] = torch.randn(1, 512, device=self.device)
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return concept_embeddings
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def _create_concept_maps_from_attention(self, concept_embeddings: Dict[str, torch.Tensor],
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image_shape: Tuple[int, ...]) -> Dict[str, torch.Tensor]:
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"""
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Create concept maps from captured attention outputs.
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"""
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concept_maps = {}
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if not hasattr(self.concept_attention, 'attention_outputs') or not self.concept_attention.attention_outputs:
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logger.error("No attention outputs captured! This means hooks are not working properly.")
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logger.error("Available attention_outputs:", getattr(self.concept_attention, 'attention_outputs', 'None'))
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raise RuntimeError("Failed to capture attention outputs from the model. Hooks may not be working correctly.")
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try:
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# Get the first available attention output
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attention_output = next(iter(self.concept_attention.attention_outputs.values()))
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logger.info(f"Processing attention output with shape: {attention_output.shape}")
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# Reshape attention to spatial dimensions
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batch_size, seq_len, dim = attention_output.shape
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# Assume square spatial layout (common in DiT models)
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spatial_size = int(np.sqrt(seq_len))
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if spatial_size * spatial_size != seq_len:
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# Fallback: use rectangular layout
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spatial_size = int(np.sqrt(seq_len))
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logger.warning(f"Non-square sequence length {seq_len}, using {spatial_size}x{spatial_size}")
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# Reshape attention to spatial format
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attention_spatial = attention_output.view(batch_size, spatial_size, spatial_size, dim)
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# Process each concept
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for concept, embedding in concept_embeddings.items():
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# Compute similarity between attention and concept embedding
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embedding_norm = F.normalize(embedding, dim=-1)
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attention_norm = F.normalize(attention_spatial, dim=-1)
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# Compute cosine similarity
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similarity = torch.sum(attention_norm * embedding_norm, dim=-1)
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# Resize to match image dimensions
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h, w = image_shape[1], image_shape[2]
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if similarity.shape[1] != h or similarity.shape[2] != w:
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similarity = F.interpolate(
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similarity.unsqueeze(0).unsqueeze(0),
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size=(h, w),
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mode='bilinear',
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align_corners=False
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).squeeze()
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# Apply softmax to get attention weights
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concept_map = F.softmax(similarity.flatten(), dim=0).view(h, w)
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concept_maps[concept] = concept_map
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logger.info(f"Created concept map for '{concept}': shape {concept_map.shape}")
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except Exception as e:
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logger.error(f"Error creating concept maps from attention: {e}")
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# Fallback to mock data
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return self._create_mock_saliency_maps(torch.randn(image_shape), list(concept_embeddings.keys()))
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return concept_maps
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def _create_mock_saliency_maps(self, image: torch.Tensor, concepts: List[str]) -> Dict[str, torch.Tensor]:
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"""
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Create mock saliency maps for testing purposes.
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In a real implementation, this would extract actual attention maps.
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"""
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print(f"DEBUG: _create_mock_saliency_maps - concepts: {concepts}")
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saliency_maps = {}
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h, w = image.shape[1], image.shape[2]
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print(f"DEBUG: Image dimensions: {h}x{w}")
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for i, concept in enumerate(concepts):
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print(f"DEBUG: Creating saliency map for concept {i}: '{concept}'")
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# Create a mock saliency map with some pattern
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# This simulates where the concept might be located
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saliency_map = torch.zeros((h, w))
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# Create different patterns for different concepts
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if 'woman' in concept.lower():
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# Center region for woman
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center_h, center_w = h // 2, w // 2
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y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
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dist = torch.sqrt((x - center_w)**2 + (y - center_h)**2)
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saliency_map = torch.exp(-dist / (min(h, w) * 0.2))
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elif 'cat' in concept.lower():
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# Shoulder region for cat
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saliency_map[h//4:h//2, w//3:2*w//3] = torch.rand(h//4, w//3) * 0.8
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elif 'white' in concept.lower():
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# Scattered white regions
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saliency_map = torch.rand(h, w) * 0.6
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# Add some bright spots
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for _ in range(5):
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y, x = torch.randint(0, h, (1,)), torch.randint(0, w, (1,))
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saliency_map[max(0, y-10):min(h, y+10), max(0, x-10):min(w, x+10)] = 1.0
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elif 'lines' in concept.lower():
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# Horizontal and vertical lines pattern
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for i in range(0, h, h//10):
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saliency_map[i:i+2, :] = 0.8
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for j in range(0, w, w//10):
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saliency_map[:, j:j+2] = 0.8
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elif 'cane' in concept.lower():
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# Vertical line on the right side for cane
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saliency_map[h//3:2*h//3, 3*w//4:] = torch.rand(h//3, w//4) * 0.7
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else:
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# Random pattern for other concepts
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saliency_map = torch.rand(h, w) * 0.5
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# Normalize
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saliency_map = (saliency_map - saliency_map.min()) / (saliency_map.max() - saliency_map.min() + 1e-8)
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saliency_maps[concept] = saliency_map
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return saliency_maps
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def visualize_saliency_maps(self, saliency_maps: Dict[str, torch.Tensor],
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original_image: torch.Tensor) -> Dict[str, torch.Tensor]:
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"""
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Visualize saliency maps by overlaying them on the original image.
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"""
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visualized_maps = {}
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for concept, saliency_map in saliency_maps.items():
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# Resize saliency map to match image dimensions
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h, w = original_image.shape[-2:]
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saliency_resized = F.interpolate(
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saliency_map.unsqueeze(0).unsqueeze(0),
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size=(h, w),
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mode='bilinear',
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align_corners=False
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).squeeze()
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# 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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