node.py

import gc
import torch
import torch.nn.functional as F
import numpy as np
from torch import Tensor
from comfy.ldm.modules import attention as comfy_attention
from comfy.ldm.flux import math as flux_math
from comfy.ldm.flux import layers as flux_layers
from comfy.ldm.chroma import layers as chroma_layers
from comfy import model_management
from typing import List, Dict, Optional
from functools import partial
from einops import rearrange
from nodes import CLIPTextEncode
import latent_preview
import copy
import comfy.ldm.qwen_image.model as qwen_image_model
#import comfy.ldm.wan.model as wan_model
orig_attention = comfy_attention.optimized_attention
orig_apply_rope1 = flux_math.apply_rope1
orig_prepare_callback = latent_preview.prepare_callback

def convert_masks_to_patches(masks: torch.Tensor, pad) -> tuple:
    """
    Convert pixel masks to patch-level masks for attention.
    
    Args:
        masks: [N, H, W] tensor - subject regions in images
        
    Returns:
        patch_masks: [N, h*w] tensor where h=H_latent*8/16, w=W_latent*8/16
        h, w: patch dimensions
    """
    N = masks.shape[0]
    H, W = masks.shape[1], masks.shape[2]
    
    # Calculate patch dimensions (assuming 16x16 pixels per patch, but 1x1 in latent space)
    h = H // 16
    w = W // 16
    
    patch_masks = torch.zeros(N + pad, h, w, device=masks.device, dtype=masks.dtype)
    
    for i in range(N):
        # Convert to patch level using nearest-neighbor interpolation
        mask_resized = F.interpolate(
            masks[i:i+1, None, :, :], 
            size=(h, w), 
            mode='nearest-exact'
        )
        
        # Flatten to patch sequence
        patch_masks[i] = mask_resized[0][0]
    for i in range(pad):
        patch_masks[i + N] = torch.ones((h, w), device=masks.device, dtype=masks.dtype)
    return patch_masks, h, w

def match_condition(reference_conditions: List, target_condition: List) -> torch.Tensor:
    """
    Match reference conditions to target condition using dot product similarity.
    
    Args:
        reference_conditions: List of N conditioning tensors (one per reference subject)
        target_condition: Conditioning tensor for target images (shared)
        
    Returns:
        conditions_mask: 2D tensor [num_references, max_embedding_seq_length] 
                     with 1s where reference embeddings match target subsequences
    """
    # Extract embeddings
    ref_embeds = [cond[0][0] for cond in reference_conditions]  # text embeddings
    target_embed = target_condition[0][0]  # [1, seq_len, embed_dim]
    
    num_references = len(ref_embeds)
    target_seq_len = target_embed.shape[1]
    
    # Initialize mask tensor
    conditions_mask = torch.zeros(num_references, target_seq_len, dtype=target_embed.dtype, device=target_embed.device)
    
    # Process each reference embedding
    for i, ref_embed in enumerate(ref_embeds):
        ref_seq_len = ref_embed.shape[1]
        
        # If reference is longer than target, skip (shouldn't happen per spec)
        if ref_seq_len > target_seq_len:
            continue
            
        # Get reference embedding (remove batch dimension)
        ref_vec = ref_embed[0]  # [ref_seq_len, embed_dim]
        
        # Use unfold to get all subsequences of target with length ref_seq_len
        # target_embed[0] is [target_seq_len, embed_dim]
        # unfolded will be [target_seq_len - ref_seq_len + 1, ref_seq_len, embed_dim]
        unfolded = target_embed[0].unfold(0, ref_seq_len, 1).permute(0,2,1)  # [num_subseq, ref_seq_len, embed_dim]
        
        # Normalize for cosine similarity
        ref_normalized = F.normalize(ref_vec, p=2, dim=-1)  # [ref_seq_len, embed_dim]
        target_normalized = F.normalize(unfolded, p=2, dim=-1)  # [num_subseq, ref_seq_len, embed_dim]
        
        # Compute cosine similarity for all subsequences at once
        # Result: [num_subseq]
        similarities = torch.mean(torch.sum(ref_normalized.unsqueeze(0) * target_normalized, dim=-1), dim=-1)
        
        # Find the best matching subsequence
        best_match_idx = torch.argmax(similarities)
        
        # Mask the best matching subsequence
        start_idx = best_match_idx.item()
        conditions_mask[i, start_idx:start_idx + ref_seq_len] = 1.0
    
    return conditions_mask

def prepare_batched_conditions(reference_conditions: List, target_condition: List, num_targets) -> tuple:
    """
    Prepare the batched conditions for reference and target images.
    
    Args:
        reference_conditions: List of N conditioning tensors (one per reference subject)
        target_condition: Conditioning tensor for target images (shared)
        
    Returns:
        extended_condition: Batched conditioning tensor [B, num_reference + 1, max_seq, 4096]
        conditions_mask: Mask tensor [B, num_references + 1, max_seq]
        pooled_output: CLIP pooled output (None for Chroma, tensor for Flux)
    """
    # Extract text embeddings from conditions
    ref_embeds = []
    ref_pooled = []
    seq_lens = []
    for cond in reference_conditions:
        if cond is None:
            continue
        ref_embeds.append(cond[0][0])  # text embedding
        ref_pooled.append(cond[0][1].get('pooled_output', None))
    
    # Target condition
    target_embed = target_condition[0][0]
    target_pooled = target_condition[0][1].get('pooled_output', None)
    
    # Calculate max sequence length
    all_embeds = [target_embed] + ref_embeds
    max_seq_len = max(embed.shape[1] for embed in all_embeds)
    
    # Get batch size and embedding dimension
    embed_dim = target_embed.shape[2]  # Should be 4096
    
    # Create padded tensors with shape [B, num_reference + 1, max_seq, 4096]
    num_references = len(ref_embeds)
    padded_embeds = torch.zeros(num_references + num_targets, max_seq_len, embed_dim, dtype=target_embed.dtype, device=target_embed.device)
    conditions_mask = torch.zeros(num_references + num_targets, max_seq_len, dtype=target_embed.dtype, device=target_embed.device)
    
    # Fill in target embeds (first position)
    target_seq_len = target_embed.shape[1]

    
    # Fill in reference embeds
    for i, ref_embed in enumerate(ref_embeds):
        ref_seq_len = ref_embed.shape[1]
        padded_embeds[i, :ref_seq_len, :] = ref_embed[0]
        conditions_mask[i, :ref_seq_len] = 1.0
        seq_lens.append(ref_seq_len)
    
    for i in range(num_targets):
        padded_embeds[i + num_references, :target_seq_len, :] = target_embed[0]
        conditions_mask[i + num_references, :target_seq_len] = 1.0        
        seq_lens.append(target_seq_len)
    # Handle pooled output (only used by Flux)
    if target_pooled is not None:
        # For Flux, use the target pooled output
        pooled_output = target_pooled
    else:
        pooled_output = None
    
    extended_condition = [[
        padded_embeds,
        {'pooled_output': pooled_output}
    ]]
    
    return extended_condition, conditions_mask, max_seq_len, seq_lens


class ReferenceConditionCombine:
    RETURN_TYPES = ("CONDITIONING", "MASK")
    RETURN_NAMES = ("condition", "condition_mask")
    FUNCTION = "go"
    
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "num_targets": ("INT",),
            },
            "optional": {
                "reference_condition1": ("CONDITIONING",),
                "reference_condition2": ("CONDITIONING",),
                "reference_condition3": ("CONDITIONING",),
                "reference_condition4": ("CONDITIONING",),
                "reference_condition5": ("CONDITIONING",),
                "target_condition": ("CONDITIONING",),
            }
        }
    @classmethod
    def go(cls, *,
           num_targets, reference_condition1=None, reference_condition2=None,
           reference_condition3=None, reference_condition4=None, reference_condition5=None,
           target_condition=None):
        
        reference_conditions = [reference_condition1, reference_condition2, reference_condition3, 
                        reference_condition4, reference_condition5]
        extended_condition, conditions_mask, max_seq_len, seq_lens = prepare_batched_conditions(reference_conditions, target_condition, num_targets)
        return extended_condition, conditions_mask.unsqueeze(0)
    
class BatchedMaskedReferenceGen:
    RETURN_TYPES = ("MODEL",)
    RETURN_NAMES = ("model",)
    FUNCTION = "go"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "source_patch_masks": ("MASK",),
                "target_patch_masks": ("MASK",),
                "conditions_mask": ("MASK",),
                "conditions_ref_mask":  ("MASK",),
            },
        }
    @classmethod
    def go(cls, *, model: object, source_patch_masks: torch.Tensor, target_patch_masks: torch.Tensor,
           conditions_mask: torch.Tensor, conditions_ref_mask=torch.Tensor):
        
        print(f'BatchedMaskedReferenceGen - Processing with model: {model}')
        if conditions_mask.dim() == 3:
            conditions_mask = conditions_mask[0]
        if conditions_ref_mask.dim() == 3:
            conditions_ref_mask = conditions_ref_mask[0]
        # Detect model type
        model_type = "flux"
        if model.model.__class__.__name__ == "Chroma":
            model_type = "chroma"
        print(f'Detected model type: {model_type}')

        num_references = conditions_ref_mask.shape[0]
        num_targets = conditions_mask.shape[0] - num_references

        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        attn_dtype = torch.bfloat16 if model_management.should_use_bf16(device=device) else torch.float16
        w = source_patch_masks.shape[-1]
        h = source_patch_masks.shape[1]
        patch_count = h * w
        source_patch_masks = source_patch_masks.flatten(start_dim=1)
        target_patch_masks = target_patch_masks.flatten(start_dim=1)
        reference_seq_mask = torch.log(torch.cat([conditions_mask[:num_references], torch.ones((num_references, patch_count), dtype=attn_dtype)], dim=1))
        source_seq_masks = torch.log(torch.cat([conditions_mask[:num_references+1], source_patch_masks], dim=1))
        target_seq_masks = torch.log(torch.cat([conditions_mask[num_references].unsqueeze(0).repeat(num_references+1,1), target_patch_masks], dim=1))

        def masked_attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor,  reference_seq_mask, target_seq_masks, source_seq_masks) -> Tensor:
            """
            Implementation using PyTorch's scaled_dot_product_attention with custom masking.
            
            Args:
                q: Query tensor of shape [B, H, L, D]
                k: Key tensor of shape [B, H, L, D]
                v: Value tensor of shape [B, H, L, D]
                pe: Positional encoding tensor
                attn_mask: Optional attention mask of shape [B, H, L, L]
                
            Returns:
                Output tensor of shape [B, L, H*D]
            """
            target_count = q.shape[0] - num_references
            # Apply rotary positional encoding for Flux models
            if pe is not None:
                q, k = flux_math.apply_rope(q, k, pe)
            
            # Get actual sequence length from QKV tensors
            actual_seq_len = q.shape[2]
            
            #if attn_mask is None or attn_mask.shape[-2] != actual_seq_len:
            #    this_source = torch.cat([torch.zeros([num_references, text_len]), source_patch_masks], dim=-1)
            #    this_target = torch.cat([torch.zeros([num_references, text_len]), source_patch_masks], dim=-1)
            #    attn_mask = torch.stack(torch.min(this_source.unsqueeze(1), this_target.unsqueeze(2)), dim=-1).to(device)
                
            reference_seq_mask_ = reference_seq_mask.to(device, dtype=attn_dtype)
            reference_attn_mask = torch.min(reference_seq_mask_.unsqueeze(1), reference_seq_mask_.unsqueeze(2))
            target_attn_mask = rearrange(torch.min(source_seq_masks.to(device, dtype=attn_dtype).unsqueeze(1), target_seq_masks.to(device, dtype=attn_dtype).unsqueeze(2)), "B H W -> 1 W (B H)")

            h = torch.zeros(q.shape, device=device)
                # PyTorch's scaled_dot_product_attention
            h[:num_references] = F.scaled_dot_product_attention(
                q[:num_references], k[:num_references], v[:num_references],
                attn_mask=reference_attn_mask
            )
                
            B, H, L, D = k.shape
            k = k.permute(1, 0, 2, 3).reshape(1, H, B * L, D)
            B, H, L, D = v.shape
            v = v.permute(1, 0, 2, 3).reshape(1, H, B * L, D)

            for i in range(target_count):
                target_idx = i + num_references
                if i > 0:
                    k[:, :, num_references*actual_seq_len : num_references*actual_seq_len + actual_seq_len] = k[:, :, target_idx*actual_seq_len : target_idx*actual_seq_len + actual_seq_len]
                    v[:, :, num_references*actual_seq_len : num_references*actual_seq_len + actual_seq_len] = v[:, :, target_idx*actual_seq_len : target_idx*actual_seq_len + actual_seq_len]
                
                h[target_idx:target_idx + 1] = F.scaled_dot_product_attention(
                    q[target_idx: target_idx + 1], 
                    k[:, :, :num_references*actual_seq_len + actual_seq_len], 
                    v[:, :, :num_references*actual_seq_len + actual_seq_len], attn_mask=target_attn_mask)
            
            # Reshape back to the expected output format
            h = rearrange(h, "B H L D -> B L (H D)")
            return h

        # Create the attention override function
        def batched_masked_attention_override(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask, transformer_options,  reference_seq_mask, target_seq_masks, source_seq_masks) -> Tensor:
            if q.shape[0] != num_references + num_targets or reference_seq_mask is None or reference_seq_mask.shape[-1] != q.shape[2]:
                return orig_attention(q, k, v, pe)
            else:
                return masked_attention(q, k, v, pe,  reference_seq_mask, target_seq_masks, source_seq_masks)
        
        # Apply the attention override based on model type
        overrider = partial(batched_masked_attention_override, reference_seq_mask=reference_seq_mask, target_seq_masks=target_seq_masks, source_seq_masks=source_seq_masks)
        if model_type == "flux":
            flux_math.attention = overrider
        else:  # chroma
            chroma_layers.attention = overrider
        
        # Clean up
        #del source_patch_masks, target_patch_masks
        gc.collect()
        torch.cuda.empty_cache()
        
        print('BatchedMaskedReferenceGen processing completed')
        
        return (model,)


class ConditioningMatchMask:
    RETURN_TYPES = ("MASK",)
    RETURN_NAMES = ("mask",)
    FUNCTION = "match"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
            },
            "optional": {
                "reference_condition1": ("CONDITIONING",),
                "reference_condition2": ("CONDITIONING",),
                "reference_condition3": ("CONDITIONING",),
                "reference_condition4": ("CONDITIONING",),
                "reference_condition5": ("CONDITIONING",),
                "target_condition": ("CONDITIONING",),
            }
        }

    def match(self, reference_condition1=None, reference_condition2=None, reference_condition3=None, 
              reference_condition4=None, reference_condition5=None, target_condition=None):
        # Collect reference conditions
        reference_conditions = []
        ref_conditions = [reference_condition1, reference_condition2, reference_condition3, 
                         reference_condition4, reference_condition5]
        
        for cond in ref_conditions:
            if cond is not None:
                reference_conditions.append(cond)
        
        if not reference_conditions:
            raise ValueError("At least one reference condition must be provided")
        
        if target_condition is None:
            raise ValueError("Target condition must be provided")
        
        # Call the match_condition function
        conditions_mask = match_condition(reference_conditions, target_condition)
        
        return (conditions_mask.unsqueeze(0),)


class MasksToPatches:
    RETURN_TYPES = ("MASK", "INT", "INT")
    RETURN_NAMES = ("patch_masks", "h", "w")
    FUNCTION = "convert"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "masks": ("MASK",),
                "target_count": ("INT",{"default":1,"min":0}),
            }
        }

    def convert(self, masks, target_count):
        # Call the convert_masks_to_patches function
        patch_masks, h, w = convert_masks_to_patches(masks, target_count)
        print(f'Patch dimensions: {h}x{w}, Total patches: {h*w}')
        return (patch_masks, h, w)

class PreviewSubjectMask:
    RETURN_TYPES = ("MASK","LATENT",)
    RETURN_NAMES = ("mask","latent",)
    FUNCTION = "go"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "latent": ("LATENT",),
                "subject": ("ATTN",),
            },
        }
    @classmethod
    def go(cls, latent, subject):
        # Iterate through subject[0] and gather all fg_masks
        fg_masks = []
        for s in subject:
            # Now s is a dict where keys are step ids
            for step_id, step_data in s.items():
                if "fg_mask" in step_data:
                    fg_masks.append(step_data["fg_mask"])
        
        # Stack all fg_masks into a single tensor
        if fg_masks:
            # Stack masks to create a tensor with shape [B, H, W]
            combined_mask = torch.cat(fg_masks, dim=0)
        else:
            # If no fg_masks found, return empty tensor
            combined_mask = torch.empty(0)
            
        return (combined_mask, latent, )

class MaskToPatchMask:
    RETURN_TYPES = ("MASK",)
    FUNCTION = "set_mask"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "mask": ("MASK",),
                "patch_size": ("INT",{"default": 16}),
                "threshold": ("FLOAT",{"default": 1.0}),
            }
        }

    @classmethod
    def set_mask(cls, mask: torch.Tensor, patch_size, threshold):
        # Get the dimensions of the input mask
        B, H, W = mask.shape
        
        # Calculate patch dimensions (divide by 16)
        h_patches = H // patch_size
        w_patches = W // patch_size
        
        # Reshape mask to [B, H//16, 16, W//16, 16] for processing 16x16 blocks
        mask_reshaped = mask.view(B, h_patches, patch_size, w_patches, patch_size)
        
        # Check if any pixel in each 16x16 block is non-zero
        # Take max over the 16x16 blocks, then convert to boolean
        patch_mask = mask_reshaped.mean(dim=2).mean(dim=3) >= threshold
        
        # Convert to boolean tensor
        patch_mask = patch_mask.bool()
        
        return (patch_mask,)
def hook_math(hooked_attention):
    save_freq = None
    def hook_apply_rope1(x, freqs_cis):
        nonlocal save_freq
        save_freq = freqs_cis
        return x
    def hook_optimized_attention(q, k, v, heads, mask=None, skip_reshape = False, skip_output_reshape=False, transformer_options={}, **kwargs) -> Tensor:
        nonlocal save_freq
        if "cond_or_uncond" in transformer_options and transformer_options["cond_or_uncond"][-1]: # negative prompt, ignore
            q = orig_apply_rope1(q, save_freq)
            k = orig_apply_rope1(k, save_freq)
            return orig_attention(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, transformer_options=transformer_options, **kwargs)
        if not skip_reshape: # not B H L D but B L (H D), we must reshape to B H L D
            B, L, HD = q.shape
            D = HD // heads
            q = q.view(B, L, heads, D).permute(0, 2, 1, 3)
            B, L, HD = k.shape
            D = HD // heads
            k = k.view(B, L, heads, D).permute(0, 2, 1, 3)
            B, L, HD = v.shape
            D = HD // heads
            v = v.view(B, L, heads, D).permute(0, 2, 1, 3)
        output = hooked_attention(q, k, v, save_freq, mask, **kwargs) # no reshape for hooked attention
        if skip_output_reshape: # because we reshaped output to B L (H D) before, we need to reshape back
            output = rearrange(output, "B L (H D) -> B H L D", H=heads)
        return output

    flux_math.apply_rope1 = hook_apply_rope1
    flux_math.optimized_attention = hook_optimized_attention
    qwen_image_model.apply_rope1 = hook_apply_rope1
    qwen_image_model.optimized_attention_masked = hook_optimized_attention
    
def unhook_math():
    flux_math.apply_rope1 = orig_apply_rope1
    flux_math.optimized_attention = orig_attention
    qwen_image_model.apply_rope1 = orig_apply_rope1
    qwen_image_model.optimized_attention_masked = orig_attention

def hook_prepare_callback():
    unhook_math()
    def prepare_callback(model, max_steps, x0_output_dict=None):
        if "prepare_callback" in model.model_options:
            return model.model_options["prepare_callback"](model, max_steps, x0_output_dict)
        else:
            return orig_prepare_callback(model, max_steps, x0_output_dict) 
    latent_preview.prepare_callback = prepare_callback

def get_shared_fg_mask(id_fg_mask, curr_fg_mask, curr2id_argmax_indices, curr2id_valid_mask):
    curr_valid_mask = curr2id_valid_mask.flatten()
    id_fg_mask = id_fg_mask.flatten().to(curr2id_argmax_indices.device)
    curr_fg_mask = curr_fg_mask.flatten().to(curr2id_argmax_indices.device)
    rearrange_id_fg_mask = id_fg_mask[curr2id_argmax_indices]
    share_fg_mask = curr_fg_mask & rearrange_id_fg_mask & curr_valid_mask
    id_share_fg_indices = curr2id_argmax_indices[share_fg_mask]
    curr_share_fg_indices = torch.nonzero(share_fg_mask).squeeze()
    return id_share_fg_indices, curr_share_fg_indices

def get_fg_attn_weights(text_len, image_start, q, k, fg_condition_mask):
    attn_weights = torch.matmul(q[:, :, image_start:], k[:, :, :text_len].transpose(-2, -1)).to(torch.float16)
    attn_weights = attn_weights.softmax(-1).sum(1, keepdim=True)
    fg_condition_mask_gpu = fg_condition_mask
    bg_condition_mask_gpu = 1.0 - fg_condition_mask_gpu
    masks = torch.nn.functional.normalize(torch.stack([fg_condition_mask_gpu, bg_condition_mask_gpu], dim=1), p=1, dim=-1)
    attn_weights = (attn_weights * masks).sum(-1)
    return attn_weights

class ExtractAttn:
    RETURN_TYPES = ("MODEL","ATTN",)
    FUNCTION = "go"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "kv_start_step":  ("INT",{"default":1,"min":1}),
                "kv_end_step":  ("INT",{"default":9,"min":1}),
                "x_start_step":  ("INT",{"default":1,"min":1}),
                "x_end_step":  ("INT",{"default":9,"min":1}),
                "mask_start_step": ("INT",{"default":8,"min":1}),
                "mask_end_step": ("INT",{"default":9,"min":1}),
            },
            "optional":{
                "fg_condition_mask": ("MASK",),
            }
        }
    @classmethod
    def go(cls, model, kv_start_step, kv_end_step, x_start_step, x_end_step, mask_start_step, mask_end_step, fg_condition_mask = None):
        
        attn_data = [{}]
        index = len(attn_data) - 1
        def prepare_callback(model, max_steps, x0_output_dict=None):
            nonlocal attn_data, fg_condition_mask
            attn_data[index] = {}
            sampled_step = -1
            sampling_step = -1
            layer = 0
            w = 0
            h = 0
            #text_len = fg_condition_mask.shape[-1]
            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
            attn_dtype = torch.bfloat16 if model_management.should_use_bf16(device=device) else torch.float16
            fg_bg_mask = None
            if fg_condition_mask is not None:
                fg_condition_mask = fg_condition_mask.to(device)
            def masked_attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask: Tensor) -> Tensor:
                """
                Implementation using PyTorch's scaled_dot_product_attention with custom masking.
                
                Args:
                    q: Query tensor of shape [B, H, L, D]
                    k: Key tensor of shape [B, H, L, D]
                    v: Value tensor of shape [B, H, L, D]
                    pe: Positional encoding tensor
                    attn_mask: Optional attention mask of shape [B, H, L, L]
                    
                Returns:
                    Output tensor of shape [B, L, H*D]
                """
                nonlocal sampling_step, sampled_step, layer, attn_data, w, h, fg_bg_mask
                text_len = q.shape[2] - w * h
                if sampling_step != sampled_step + 1:
                    layer = 0
                    sampling_step = sampled_step + 1
                # Initialize step in attn_data dict if not exists
                if sampling_step not in attn_data[index]:
                    attn_data[index][sampling_step] = {"layers": {}}
                # Store k, v, and x in a dict with layer id as key
                if sampling_step >= kv_start_step and sampling_step < kv_end_step:
                    attn_data[index][sampling_step]["layers"][layer] = {"k": k[:, :, text_len:,:].cpu(), "v": v[:, :, text_len:,:].cpu()}
                # Apply rotary positional encoding for Flux models
                if pe is not None:
                    q = orig_apply_rope1(q, pe)
                    k = orig_apply_rope1(k, pe)
                
                if fg_condition_mask is not None and sampling_step >= mask_start_step and sampling_step < mask_end_step:
                    if fg_bg_mask is None:
                        fg_bg_mask = get_fg_attn_weights(text_len, text_len, q, k, fg_condition_mask)
                    else:
                        fg_bg_mask += get_fg_attn_weights(text_len, text_len, q, k, fg_condition_mask)

                x = F.scaled_dot_product_attention(
                    q, k, v, attn_mask=mask
                )
                # Reshape back to the expected output format
                x = rearrange(x, "B H L D -> B L (H D)")
                # Store attention result x along with k and v
                if sampling_step >= x_start_step and sampling_step < x_end_step:
                    if not layer in attn_data[index][sampling_step]["layers"]:
                        attn_data[index][sampling_step]["layers"][layer] = {}
                    attn_data[index][sampling_step]["layers"][layer]["x"] = x[:, text_len:,:].cpu()
                layer += 1
                return x
   
            hook_math(masked_attention)
            origin_callback = orig_prepare_callback(model, max_steps, x0_output_dict)
            def callback_change_sampled_step(step, x0, x, total_steps):
                nonlocal max_steps
                nonlocal sampling_step, sampled_step, layer, fg_bg_mask, model, w, h
                
                w = x0.shape[-1] // model.model.diffusion_model.patch_size
                h = x0.shape[-2] // model.model.diffusion_model.patch_size
                sampled_step = step
                if fg_condition_mask is not None and sampling_step >= mask_start_step and sampling_step < mask_end_step:
                    bitmask = fg_bg_mask[:,0] >= fg_bg_mask[:,1]
                    bitmask = rearrange(bitmask, "b (h w) -> b h w", h=h, w=w)
                    fg_bg_mask = None
                    # Store fg_mask in a dictionary with step id as key
                    if sampling_step not in attn_data[index]:
                        attn_data[index][sampling_step] = {"layers": {}}
                    attn_data[index][sampling_step]["fg_mask"] = bitmask.cpu()
                origin_callback(step, x0, x, total_steps)
                #if step == total_steps - 1:
                #    latent_preview.prepare_callback = orig_prepare_callback
            return callback_change_sampled_step
        model = copy.copy(model)
        model.model_options = copy.copy(model.model_options)
        model.model_options["prepare_callback"] = prepare_callback
        hook_prepare_callback()        
        return (model, attn_data,)

class GetCrossSim:
    RETURN_TYPES = ("CROSSSIM",)
    RETURN_NAMES = ("cross_sim",)
    FUNCTION = "compute_cross_sim"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "subject_attn": ("ATTN",),
                "curr_attn": ("ATTN",), 
                "id_mask": ("MASK",),
                "curr_mask": ("MASK",),
                "cross_sim_step": ("INT",),
                "sim_threshold": ("FLOAT", {"default":0.5,"min":0,"max":2}),
            }
        }

    @classmethod
    def compute_cross_sim(cls, subject_attn, curr_attn, id_mask, curr_mask, cross_sim_step, sim_threshold):
        cross_sim = {}
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        attn_dtype = torch.bfloat16 if model_management.should_use_bf16(device=device) else torch.float16
        
        # Validate inputs
        if not subject_attn or len(subject_attn) == 0:
            raise ValueError("subject_attn cannot be empty")
        if not curr_attn or len(curr_attn) == 0:
            raise ValueError("curr_attn cannot be empty")
        
        # Use the first (and only) current image attention data
        curr_attn_data = curr_attn[0]
        
        # Check if the specified step exists in current image attention data
        if cross_sim_step not in curr_attn_data:
            raise ValueError(f"cross_sim_step {cross_sim_step} not found in curr_attn")
        
        # Get current attention layers for the specified step
        curr_layers = curr_attn_data[cross_sim_step].get("layers", {})
        if not curr_layers:
            raise ValueError(f"No layers found for step {cross_sim_step} in curr_attn")
        
        # Initialize accumulators
        all_cross_sim = []
        all_max_sim = []
        all_argmax_indices = []
        all_id_fg_inds = []
        all_curr_fg_inds = []

        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        attn_dtype = torch.bfloat16 if model_management.should_use_bf16(device=device) else torch.float16
       # Iterate through each ID subject
        for id_idx, id_attn_data in enumerate(subject_attn):
            # Check if the specified step exists in this ID subject
            if cross_sim_step not in id_attn_data:
                continue  # Skip this subject if step doesn't exist
            
            # Get ID subject layers for the specified step
            id_layers = id_attn_data[cross_sim_step].get("layers", {})
            if not id_layers:
                continue  # Skip this subject if no layers
            
            id_fg_mask = id_mask[id_idx].to(device).flatten()
            curr_fg_mask = curr_mask[id_idx].to(device).flatten() # todo multiple images in 1 batch
            # Accumulate cross similarity across all layers
            layer_sim = None
            for layer_idx in curr_layers:
                if layer_idx not in id_layers:
                    continue  # Skip layer if not present in both subjects
                
                # Get attention vectors for this layer
                curr_x = F.normalize(curr_layers[layer_idx]["x"].to(device))  # [B, D, L] format
                id_x = F.normalize(id_layers[layer_idx]["x"].to(device))      # [B, D, L] format
                
                # Compute cross similarity: current @ ID^T
                # curr_x is [B, D, L], id_x is [B, D, L]
                # We want: curr_x^T @ id_x -> [L, L]
                similarity = torch.matmul(curr_x, id_x.transpose(-2, -1))  # [B, L, L]
                
                if layer_sim is None:
                    layer_sim = similarity
                else:
                    layer_sim += similarity

            if layer_sim is not None:
                # Average across layers
                avg_sim = layer_sim / len(curr_layers)
                max_sim, argmax_indices = torch.max(avg_sim, dim=-1)
                id_fg_inds, curr_fg_inds = get_shared_fg_mask( # todo multiple batch gen.
                    id_fg_mask, 
                    curr_fg_mask, 
                    argmax_indices[0],
                    max_sim[0]>sim_threshold
                )
                all_id_fg_inds.append(id_fg_inds.cpu())
                all_curr_fg_inds.append(curr_fg_inds.cpu())
                all_max_sim.append(max_sim)
                all_argmax_indices.append(argmax_indices)
                all_cross_sim.append(avg_sim)
        
        cross_sim["id_fg_inds"] = all_id_fg_inds
        cross_sim["curr_fg_inds"] = all_curr_fg_inds
        cross_sim["max_sim"] = torch.cat(all_max_sim).cpu()
        cross_sim["argmax_indices"] = torch.cat(all_argmax_indices).cpu()
        cross_sim["debug_mask"] = torch.cat(all_cross_sim).cpu()
        cross_sim["id_mask"] = id_mask
        cross_sim["curr_mask"] = curr_mask
        return (cross_sim,)

class CrossSimMaskExtract:
    RETURN_TYPES = ("MASK", "MASK")
    RETURN_NAMES = ("cross_mask", "fg_mask")
    FUNCTION = "extract"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "cross_sim": ("CROSSSIM",),
                "latent": ("LATENT",),
            }
        }

    def extract(self, cross_sim, latent):
        # Extract fg_mask directly from cross_sim
        fg_mask = cross_sim.get("fg_mask", None)
        
        # Create cross_mask from max_sim or debug_mask
        # Using debug_mask as it appears to contain the cross attention similarity data
        cross_mask = cross_sim.get("debug_mask", None)
        
        # If debug_mask is not available, try to create from max_sim
        #if cross_mask is None and "max_sim" in cross_sim:
        #    # max_sim is likely a tensor with similarity values
        #    max_sim = cross_sim["max_sim"]
        #    # Normalize to 0-1 range for mask
        #    if max_sim is not None:
        #        cross_mask = (max_sim - max_sim.min()) / (max_sim.max() - max_sim.min() + 1e-8)
        
        # Handle case where neither is available
        #if fg_mask is None:
        #    # Create an empty mask if fg_mask is not available
        #    fg_mask = torch.zeros(1, 1, 1, dtype=torch.float32)
            
        if cross_mask is None:
            # Create an empty mask if cross_mask is not available
            cross_mask = torch.zeros(1, 1, 1, dtype=torch.float32)
            
        return (cross_mask, fg_mask)


class GenConsistent:
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "go"
    CATEGORY = "chara_consist"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "subject": ("ATTN",),
                "cross_sim": ("CROSSSIM",),
                "kv_start_step":  ("INT",{"default":1,"min":1}),
                "kv_end_step":  ("INT",{"default":5,"min":1}),
                "x_start_step":  ("INT",{"default":1,"min":1}),
                "x_end_step":  ("INT",{"default":5,"min":1}),
                "ada_tome_alpha": ("FLOAT",{"default":0.0,"min":0,"max":1,"step":0.1}),
            },
        }
    @classmethod
    def go(cls, model, subject, cross_sim, kv_start_step, kv_end_step, x_start_step, x_end_step, ada_tome_alpha):
        
        def prepare_callback(model, max_steps, x0_output_dict=None):
            nonlocal subject
            sampled_step = -1
            sampling_step = -1
            layer = 0
            w = 0
            h = 0
            
            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
            attn_dtype = torch.bfloat16 if model_management.should_use_bf16(device=device) else torch.float16
            fg_bg_mask = None
            def masked_attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, _: Tensor) -> Tensor:
                """
                Implementation using PyTorch's scaled_dot_product_attention with custom masking.
                
                Args:
                    q: Query tensor of shape [B, H, L, D]
                    k: Key tensor of shape [B, H, L, D]
                    v: Value tensor of shape [B, H, L, D]
                    pe: Positional encoding tensor
                    attn_mask: Optional attention mask of shape [B, H, L, L]
                    
                Returns:
                    Output tensor of shape [B, L, H*D]
                """
                nonlocal sampling_step, sampled_step, layer, subject, w, h, fg_bg_mask
                if sampling_step != sampled_step + 1:
                    layer = 0
                    sampling_step = sampled_step + 1
                
                text_len = q.shape[2] - w*h
                original_seq_len = q.shape[2]
                # Apply rotary positional encoding for Flux models
                if pe is not None:
                    q = orig_apply_rope1(q, pe)
                    k = orig_apply_rope1(k, pe)
                
                # Store original sequence length before K,V concatenation
                original_k_len = k.shape[2]
                
                # Store k, v, and x in a dict with layer id as key
                if sampling_step >= kv_start_step and sampling_step < kv_end_step:
                    # For all other steps, we may apply the key-value expansion algorithm
                    # Get cross-sim data for this layer
                    id_fg_inds = cross_sim.get("id_fg_inds", None)
                    curr_fg_inds = cross_sim.get("curr_fg_inds", None)
                    curr_mask = cross_sim.get("curr_mask", None)
                    
                    # Process all subjects, not just the last one (index = len(subject) - 1)
                    for subj_idx in range(len(subject)):
                        # Check if we have valid cross-sim data and subject data to work with
                        if (id_fg_inds[subj_idx].shape[0] > 0 and
                            curr_fg_inds[subj_idx].shape[0] > 0 and
                            sampling_step in subject[subj_idx] and 
                            layer in subject[subj_idx][sampling_step]["layers"] and
                            "k" in subject[subj_idx][sampling_step]["layers"][layer] and 
                            "v" in subject[subj_idx][sampling_step]["layers"][layer]):
                            
                            ori_saved_key = subject[subj_idx][sampling_step]["layers"][layer]["k"].to(k.device)
                            ori_saved_value = subject[subj_idx][sampling_step]["layers"][layer]["v"].to(v.device)
                            
                            # Ensure we have valid indices within tensor bounds
                            # Handle batch dimension properly - cross_sim tensors have [B, ...] form where B = len(subject)
                            subj_id_fg_inds = id_fg_inds[subj_idx]
                            subj_curr_fg_inds = curr_fg_inds[subj_idx]
                            
                            valid_id_inds = subj_id_fg_inds[subj_id_fg_inds < ori_saved_key.shape[2]]
                            valid_curr_inds = subj_curr_fg_inds[subj_curr_fg_inds < k.shape[2]]
                            
                            if len(valid_id_inds) > 0 and len(valid_curr_inds) > 0:
                                # Apply key-value expansion based on cross-sim data
                                # Extract the saved key-value pairs using valid indices
                                saved_key_fg = ori_saved_key[:, :, valid_id_inds]
                                saved_value_fg = ori_saved_value[:, :, valid_id_inds]
                                
                                # Apply rotary positional encoding to saved keys (fix missing apply_rope)
                                if pe is not None:
                                    # Extract positional encoding for the saved key positions
                                    # This mimics the approach in attention_processor_characonsist.py
                                    image_rotary_emb_fg = pe[:,:,valid_curr_inds]
                                    saved_key_fg = orig_apply_rope1(saved_key_fg, image_rotary_emb_fg)
                                
                                # Concatenate the expanded key-value pairs
                                k = torch.cat([k, saved_key_fg], dim=2)
                                v = torch.cat([v, saved_value_fg], dim=2)

                x = F.scaled_dot_product_attention(
                    q, k, v, attn_mask=None
                )
                # Reshape back to the expected output format
                x = rearrange(x, "B H L D -> B L (H D)")
                
                # Apply ada_tome blending if within specified step range
                if sampling_step >= x_start_step and sampling_step < x_end_step and ada_tome_alpha > 0:
                    # Get cross-similarity data
                    id_fg_inds = cross_sim.get("id_fg_inds", None)#.to(device)
                    curr_fg_inds = cross_sim.get("curr_fg_inds", None)#.to(device)
                    max_sim = cross_sim.get("max_sim", None).to(device)
                    
                    if (id_fg_inds is not None and curr_fg_inds is not None and max_sim is not None):
                        # Process all subjects for blending
                        for subj_idx in range(len(subject)):
                            # Check if we have valid subject data for this step and layer
                            if (sampling_step in subject[subj_idx] and 
                                layer in subject[subj_idx][sampling_step]["layers"] and
                                "x" in subject[subj_idx][sampling_step]["layers"][layer]):
                                
                                # Get saved hidden states from subject
                                saved_x = subject[subj_idx][sampling_step]["layers"][layer]["x"].to(x.device, dtype=attn_dtype)
                                
                                # Ensure we have valid indices within tensor bounds
                                if subj_idx < len(id_fg_inds) and subj_idx < len(curr_fg_inds) and subj_idx < max_sim.shape[0]:
                                    subj_id_fg_inds = id_fg_inds[subj_idx] 
                                    subj_curr_fg_inds = curr_fg_inds[subj_idx] 
                                    subj_max_sim = max_sim[subj_idx]
                                    
                                    # Validate indices

                                    valid_id_inds = subj_id_fg_inds[subj_id_fg_inds < saved_x.shape[-2]]
                                    valid_curr_inds = subj_curr_fg_inds[subj_curr_fg_inds < x.shape[-2] - text_len]
                                    
                                    if len(valid_id_inds) > 0 and len(valid_curr_inds) > 0:
                                        # Extract vision tokens (after text_len) for blending
                                        vision_hidden_states = x[:, text_len:, :]
                                        
                                        # Get matched hidden states from saved subject and current generation
                                        matched_saved_x = saved_x[:, valid_id_inds, :]  # [B, D, valid_id_inds]
                                        matched_current_x = vision_hidden_states[:, valid_curr_inds, :]  # [B, valid_curr_inds, D]
                                        
                                        # Apply ada_tome blending with similarity-weighted alpha
                                        alpha_tensor = torch.ones(len(valid_curr_inds), dtype=attn_dtype, device=x.device)
                                        alpha_tensor = alpha_tensor * ada_tome_alpha
                                        
                                        # Weight by similarity scores
                                        sim_weight = subj_max_sim.flatten()[valid_curr_inds] if len(valid_curr_inds) <= subj_max_sim.numel() else torch.ones(len(valid_curr_inds), dtype=attn_dtype, device=x.device)
                                        alpha_tensor = alpha_tensor * sim_weight
                                        alpha_tensor = alpha_tensor.view(1, -1, 1)
                                        
                                        # Blend: (1 - alpha) * current + alpha * saved
                                        blended_states = (1 - alpha_tensor) * matched_current_x + alpha_tensor * matched_saved_x
                                        
                                        # Update current vision hidden states with blended values
                                        vision_hidden_states[:, valid_curr_inds, :] = blended_states
                                        
                                        # Reconstruct full hidden states with text + blended vision
                                        x = torch.cat([x[:, :text_len, :], vision_hidden_states], dim=-2)

                layer += 1
                return x

            hook_math(masked_attention)   
            origin_callback = orig_prepare_callback(model, max_steps, x0_output_dict)
            def callback_change_sampled_step(step, x0, x, total_steps):
                nonlocal max_steps
                nonlocal sampling_step, sampled_step, layer, fg_bg_mask, model, w, h
                
                w = x0.shape[-1] // model.model.diffusion_model.patch_size
                h = x0.shape[-2] // model.model.diffusion_model.patch_size
                sampled_step = step
                origin_callback(step, x0, x, total_steps)
            return callback_change_sampled_step
        model = copy.copy(model)
        model.model_options = copy.copy(model.model_options)
        model.model_options["prepare_callback"] = prepare_callback
        hook_prepare_callback()        
        return (model, subject,)