add layer filtering

.gitignore

@@ -1,3 +1,7 @@
+# Repo specific
+AGENTS.MD
+custom_nodes.examples.md
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

docs/merger_2_model_modes.md

@@ -39,4 +39,49 @@
 **Parameters:**
 - **Alpha:** The Rank (dimension) for the LoRA's standard layers.
 - **Beta:** The Rank (dimension) specifically for 3x3 convolution layers.
-- **Gamma:** The clamp quantile for weight values. `0.99` is a typical value to prevent outlier weights from dominating.
+- **Gamma:** The clamp quantile for weight values. `0.99` is a typical value to prevent outlier weights from dominating.
+
+---
+
+## Layer Mismatch Handling
+
+When merging models with different layer structures (e.g., fine-tuned models with added/removed layers, or LoRAs with partial layer coverage), the `mismatch_mode` parameter controls behavior:
+
+| Mode | Behavior |
+|------|----------|
+| `skip` | Missing layers in B use A's values **(default)** |
+| `zeros` | Missing layers in B are treated as zeros |
+| `error` | Fail if any layer is missing |
+
+**Common scenarios:**
+- Merging a fine-tuned model with added/removed layers
+- Combining LoRA-extracted differences with base models
+- Cross-architecture experiments
+
+**Note:** Extra layers in Model B that don't exist in Model A are currently ignored. The output will always have the same layer structure as Model A.
+
+---
+
+## Layer Filtering (Regex Patterns)
+
+Use regex patterns to control which layers are merged:
+
+### `exclude_patterns`
+Layers matching any pattern will **keep Model A's values only** (no merge).
+
+### `discard_patterns`
+Layers matching any pattern will be **removed entirely** from the output.
+
+**Pattern format:**
+- Whitespace-separated regex patterns
+- Patterns use **substring matching** (not full match)
+- Example: `text_model lora` matches any key containing "text_model" OR "lora"
+- Example: `layer\.[0-5]\.` matches layers 0-5 using regex syntax
+
+**Pattern Examples:**
+| Pattern | Matches |
+|---------|---------|
+| `text_model` | All text encoder layers |
+| `\.norm` | All normalization layers |
+| `attn\.(q\|k\|v)` | Query, key, value attention weights |
+| `block\.[0-9]\.` | Blocks 0-9 |

docs/merger_3_model_modes.md

@@ -40,4 +40,44 @@
 **Parameters:**
 - **Alpha:** Weights the comparison between Model B (`0.0`) and Model C (`1.0`).
 - **Beta:** A multiplier for the final extracted dissimilar features.
-- **Gamma:** A bias exponent for the similarity calculation.
+- **Gamma:** A bias exponent for the similarity calculation.
+
+---
+
+## Layer Mismatch Handling
+
+When merging models with different layer structures, the `mismatch_mode` parameter controls behavior:
+
+| Mode | Behavior |
+|------|----------|
+| `skip` | Missing layers in B/C use A's values **(default)** |
+| `zeros` | Missing layers in B/C are treated as zeros |
+| `error` | Fail if any layer is missing |
+
+**Special case for B - C subtraction (Add-Difference, Train-Difference):**
+- If **both** B and C have the layer: Normal subtraction is performed
+- If **either** B or C is missing the layer: The difference is treated as `None` (no contribution from this layer), and Model A's value is used
+
+**Common scenarios:**
+- Merging a fine-tuned model with added/removed layers
+- Extracting style differences between models with slightly different architectures
+- Combining partial LoRAs or adapters
+
+**Note:** Extra layers in Models B/C that don't exist in Model A are currently ignored. The output will always have the same layer structure as Model A.
+
+---
+
+## Layer Filtering (Regex Patterns)
+
+Use regex patterns to control which layers are merged:
+
+### `exclude_patterns`
+Layers matching any pattern will **keep Model A's values only** (no merge).
+
+### `discard_patterns`
+Layers matching any pattern will be **removed entirely** from the output.
+
+**Pattern format:**
+- Whitespace-separated regex patterns
+- Patterns use **substring matching** (not full match)
+- Example: `text_model lora` matches any key containing "text_model" OR "lora"

nodes/merger.py

@@ -1,11 +1,13 @@
 import os
+import re
 import torch
 import folder_paths
 import comfy.utils
 from safetensors.torch import save_file
-from .merger_ops import TWO_MODEL_MODES, THREE_MODEL_MODES
+from .merger_ops import TWO_MODEL_MODES, THREE_MODEL_MODES, MissingTensorBehavior, MissingTensorError
 from .merger_utils import MemoryEfficientSafeOpen
 
+
 def load_documentation_from_file(filename):
     """Loads documentation from a markdown file in the ../docs/ directory."""
     current_dir = os.path.dirname(os.path.realpath(__file__))
@@ -16,6 +18,33 @@ def load_documentation_from_file(filename):
     except FileNotFoundError:
         return f"# Documentation File Not Found\n\nPlease ensure `{filename}` exists in the `docs` directory of the custom node."
 
+
+def _compile_patterns(pattern_string):
+    """Compiles whitespace-separated regex patterns into a list of compiled patterns.
+    
+    Returns empty list if pattern_string is empty or whitespace-only.
+    Raises ValueError if any pattern is invalid regex.
+    """
+    if not pattern_string or not pattern_string.strip():
+        return []
+    
+    patterns = []
+    for pattern in pattern_string.split():
+        try:
+            patterns.append(re.compile(pattern))
+        except re.error as e:
+            raise ValueError(f"Invalid regex pattern '{pattern}': {e}")
+    return patterns
+
+
+def _matches_any_pattern(key, patterns):
+    """Returns True if key matches any of the compiled regex patterns (substring search)."""
+    for pattern in patterns:
+        if pattern.search(key):
+            return True
+    return False
+
+
 # --- UI Dictionary Helper Functions ---
 def _create_two_model_inputs(model_folder_name):
     return {"required": {
@@ -23,6 +52,7 @@ def _create_two_model_inputs(model_folder_name):
         "model_a": (["None"] + folder_paths.get_filename_list(model_folder_name),),
         "model_b": (["None"] + folder_paths.get_filename_list(model_folder_name),),
         "calc_mode": ([m.name for m in TWO_MODEL_MODES],),
+        "mismatch_mode": (["skip", "zeros", "error"], {"default": "skip"}),
         "alpha": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
         "beta": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
         "gamma": ("FLOAT", {"default": 0.99, "min": 0.0, "max": 1.0, "step": 0.001}),
@@ -30,8 +60,11 @@ def _create_two_model_inputs(model_folder_name):
         "output_filename": ("STRING", {"default": "merged_2_model"}),
         "save_dtype": (["fp32", "fp16", "bf16"],),
         "process_device": (["cuda", "cpu"],),
+        "exclude_patterns": ("STRING", {"default": "", "multiline": True, "placeholder": "Regex patterns (space-separated) - matching layers keep Model A only"}),
+        "discard_patterns": ("STRING", {"default": "", "multiline": True, "placeholder": "Regex patterns (space-separated) - matching layers removed from output"}),
     }}
 
+
 def _create_three_model_inputs(model_folder_name):
     return {"required": {
         "execution_mode": (["MERGE", "DOCUMENTATION ONLY"],),
@@ -39,6 +72,7 @@ def _create_three_model_inputs(model_folder_name):
         "model_b": (["None"] + folder_paths.get_filename_list(model_folder_name),),
         "model_c": (["None"] + folder_paths.get_filename_list(model_folder_name),),
         "calc_mode": ([m.name for m in THREE_MODEL_MODES],),
+        "mismatch_mode": (["skip", "zeros", "error"], {"default": "skip"}),
         "alpha": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
         "beta": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
         "gamma": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 3.0, "step": 0.01}),
@@ -47,8 +81,11 @@ def _create_three_model_inputs(model_folder_name):
         "output_filename": ("STRING", {"default": "merged_3_model"}),
         "save_dtype": (["fp32", "fp16", "bf16"],),
         "process_device": (["cuda", "cpu"],),
+        "exclude_patterns": ("STRING", {"default": "", "multiline": True, "placeholder": "Regex patterns (space-separated) - matching layers keep Model A only"}),
+        "discard_patterns": ("STRING", {"default": "", "multiline": True, "placeholder": "Regex patterns (space-separated) - matching layers removed from output"}),
     }}
 
+
 # --- Base Classes for Backend Logic ONLY ---
 class MergerLogic:
     def _execute_merge(self, model_names, calc_mode, all_modes, recipe_params, model_type):
@@ -56,26 +93,108 @@ class MergerLogic:
         if not calc_mode_class: raise ValueError(f"Calc mode '{calc_mode}' not found.")
         primary_model_name = model_names.get('model_a')
         if not primary_model_name or primary_model_name == "None": raise ValueError("Model A is required to run the merge.")
+        
         handlers = {}
         for name in model_names.values():
             if name and name != "None":
                 path = folder_paths.get_full_path(model_type, name)
                 if not path: raise FileNotFoundError(f"Model '{name}' not found.")
                 handlers[name] = MemoryEfficientSafeOpen(path)
-        all_keys = handlers[primary_model_name].keys()
-        metadata = handlers[primary_model_name].header.get("__metadata__", {})
+        
+        primary_handler = handlers[primary_model_name]
+        all_keys = primary_handler.keys()
+        metadata = primary_handler.header.get("__metadata__", {})
+        
+        # Convert mismatch_mode string to enum
+        mismatch_mode_str = recipe_params.get('mismatch_mode', 'skip')
+        mismatch_mode = MissingTensorBehavior(mismatch_mode_str)
+        recipe_params['mismatch_mode'] = mismatch_mode
+        
+        # Compile filter patterns
+        exclude_patterns = _compile_patterns(recipe_params.get('exclude_patterns', ''))
+        discard_patterns = _compile_patterns(recipe_params.get('discard_patterns', ''))
+        
+        # Pre-compute key differences for logging
+        primary_keys = set(all_keys)
+        for name, handler in handlers.items():
+            if name != primary_model_name:
+                secondary_keys = set(handler.keys())
+                missing = primary_keys - secondary_keys
+                extra = secondary_keys - primary_keys
+                if missing:
+                    print(f"[Merger] {name} is missing {len(missing)} keys present in Model A")
+                if extra:
+                    print(f"[Merger] {name} has {len(extra)} extra keys not in Model A (ignored)")
+        
         merged_state_dict = {}
         pbar = comfy.utils.ProgressBar(len(all_keys))
         save_dtype = recipe_params.pop('save_dtype')
         save_torch_dtype = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}.get(save_dtype)
-        recipe_params.update({"handlers": handlers}) # Add handlers, device and dtype are already in
+        recipe_params.update({"handlers": handlers})
+        process_device = recipe_params.get('device', 'cpu')
+        process_dtype = recipe_params.get('dtype', torch.float32)
+        
+        skipped_keys = 0
+        excluded_keys = 0
+        discarded_keys = 0
+        error_keys = []
+        
         for key in all_keys:
-            recipe = calc_mode_class.create_recipe(key=key, **recipe_params)
-            result = recipe.merge()
+            # Check discard patterns first - skip entirely
+            if _matches_any_pattern(key, discard_patterns):
+                discarded_keys += 1
+                pbar.update(1)
+                continue
+            
+            # Pre-load Model A's tensor
+            tensor_a = primary_handler.get_tensor(key).to(device=process_device, dtype=process_dtype)
+            
+            # Check exclude patterns - use Model A only, no merge
+            if _matches_any_pattern(key, exclude_patterns):
+                merged_state_dict[key] = tensor_a.to(save_torch_dtype).cpu().clone()
+                excluded_keys += 1
+                pbar.update(1)
+                continue
+            
+            # Pass tensor_a metadata to recipes for zeros mode and fallback
+            recipe_params['_tensor_a'] = tensor_a
+            recipe_params['_tensor_a_shape'] = tensor_a.shape
+            recipe_params['_tensor_a_dtype'] = tensor_a.dtype
+            
+            try:
+                recipe = calc_mode_class.create_recipe(key=key, **recipe_params)
+                result = recipe.merge()
+            except MissingTensorError as e:
+                if mismatch_mode == MissingTensorBehavior.ERROR:
+                    raise ValueError(f"Layer mismatch error (mismatch_mode='error'): {e}")
+                result = None
+                error_keys.append(key)
+            
+            # Handle None result (mismatch occurred with skip mode)
+            if result is None:
+                result = tensor_a
+                skipped_keys += 1
+            
             if isinstance(result, dict):
-                for r_key, r_tensor in result.items(): merged_state_dict[r_key] = r_tensor.to(save_torch_dtype).cpu().clone()
-            else: merged_state_dict[key] = result.to(save_torch_dtype).cpu().clone()
+                for r_key, r_tensor in result.items():
+                    merged_state_dict[r_key] = r_tensor.to(save_torch_dtype).cpu().clone()
+            else:
+                merged_state_dict[key] = result.to(save_torch_dtype).cpu().clone()
+            
+            # Clean up tensor_a reference to allow GC
+            del recipe_params['_tensor_a']
             pbar.update(1)
+        
+        # Log summary
+        if excluded_keys > 0:
+            print(f"[Merger] Excluded {excluded_keys} keys from merge (kept Model A only)")
+        if discarded_keys > 0:
+            print(f"[Merger] Discarded {discarded_keys} keys from output")
+        if skipped_keys > 0:
+            print(f"[Merger] Used Model A's values for {skipped_keys} keys due to mismatches")
+        if error_keys:
+            print(f"[Merger] Unexpected errors on {len(error_keys)} keys: {error_keys[:5]}{'...' if len(error_keys) > 5 else ''}")
+        
         for handler in handlers.values(): handler.__exit__(None, None, None)
         output_folder = "loras" if calc_mode == "SVD LoRA Extraction" else model_type
         output_dir = folder_paths.get_folder_paths(output_folder)[0]
@@ -85,44 +204,51 @@ class MergerLogic:
         save_file(merged_state_dict, output_path, metadata=metadata)
         return (f"{output_filename}.safetensors",)
 
+
 class BaseTwoMerger(MergerLogic):
     FUNCTION = "merge"; CATEGORY = "ModelUtils/Merging"
     RETURN_TYPES = ("STRING", "STRING",); RETURN_NAMES = ("output_filename", "documentation",)
 
-    def merge(self, execution_mode, model_a, model_b, calc_mode, alpha, beta, gamma, seed, output_filename, save_dtype, process_device):
+    def merge(self, execution_mode, model_a, model_b, calc_mode, mismatch_mode, alpha, beta, gamma, seed, output_filename, save_dtype, process_device, exclude_patterns, discard_patterns):
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
             return ("Documentation mode active. No merge performed.", doc)
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "calc_mode": calc_mode,
+            "mismatch_mode": mismatch_mode,
             "alpha": alpha, "beta": beta, "gamma": gamma, "seed": seed,
             "output_filename": output_filename, "save_dtype": save_dtype,
-            "device": process_device, "dtype": torch.float32
+            "device": process_device, "dtype": torch.float32,
+            "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
         }
         model_names = {"model_a": model_a, "model_b": model_b}
         filename, = self._execute_merge(model_names, calc_mode, TWO_MODEL_MODES, recipe_params, self.MODEL_TYPE)
         return (filename, doc)
 
+
 class BaseThreeMerger(MergerLogic):
     FUNCTION = "merge"; CATEGORY = "ModelUtils/Merging"
     RETURN_TYPES = ("STRING", "STRING",); RETURN_NAMES = ("output_filename", "documentation",)
 
-    def merge(self, execution_mode, model_a, model_b, model_c, calc_mode, alpha, beta, gamma, delta, seed, output_filename, save_dtype, process_device):
+    def merge(self, execution_mode, model_a, model_b, model_c, calc_mode, mismatch_mode, alpha, beta, gamma, delta, seed, output_filename, save_dtype, process_device, exclude_patterns, discard_patterns):
         doc = load_documentation_from_file('merger_3_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
             return ("Documentation mode active. No merge performed.", doc)
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "model_c": model_c, "calc_mode": calc_mode,
+            "mismatch_mode": mismatch_mode,
             "alpha": alpha, "beta": beta, "gamma": gamma, "delta": delta, "seed": seed,
             "output_filename": output_filename, "save_dtype": save_dtype,
-            "device": process_device, "dtype": torch.float32
+            "device": process_device, "dtype": torch.float32,
+            "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
         }
         model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
         filename, = self._execute_merge(model_names, calc_mode, THREE_MODEL_MODES, recipe_params, self.MODEL_TYPE)
         return (filename, doc)
 
+
 # --- Concrete 2-Model Nodes ---
 class CheckpointTwoMerger(BaseTwoMerger):
     MODEL_TYPE = "checkpoints"

nodes/merger_ops.py

@@ -1,6 +1,19 @@
 import torch
 import scipy.ndimage
 import torch.nn.functional as F
+from enum import Enum
+
+
+class MissingTensorBehavior(Enum):
+    """Controls behavior when a tensor key is missing from a model."""
+    ERROR = "error"      # Raise exception (strict mode)
+    SKIP = "skip"        # Return None, let caller handle
+    ZEROS = "zeros"      # Return zeros tensor matching shape from primary model
+
+
+class MissingTensorError(Exception):
+    """Raised when a tensor is missing and behavior is ERROR."""
+    pass
 
 # ################# #
 # MERGE OPERATORS
@@ -34,29 +47,62 @@ class Operation:
         return self.merge_func(self)
 
 class LoadTensor(Operation):
-    def __init__(self, key, model_name, handlers, device, dtype):
+    def __init__(self, key, model_name, handlers, device, dtype,
+                 on_missing=MissingTensorBehavior.ERROR, fallback_shape=None, fallback_dtype=None):
         super().__init__(key)
         self.model_name = model_name
         self.handlers = handlers
         self.device = device
         self.dtype = dtype
+        self.on_missing = on_missing
+        self.fallback_shape = fallback_shape
+        self.fallback_dtype = fallback_dtype
 
     def merge(self) -> torch.Tensor:
         if self.model_name not in self.handlers:
             raise ValueError(f"Model '{self.model_name}' is required for this mode but was not provided.")
-        return self.handlers[self.model_name].get_tensor(self.key).to(device=self.device, dtype=self.dtype)
+        
+        handler = self.handlers[self.model_name]
+        if self.key not in handler.keys():
+            if self.on_missing == MissingTensorBehavior.ERROR:
+                raise MissingTensorError(
+                    f"Key '{self.key}' not found in model '{self.model_name}'"
+                )
+            elif self.on_missing == MissingTensorBehavior.SKIP:
+                return None
+            elif self.on_missing == MissingTensorBehavior.ZEROS:
+                if self.fallback_shape is None:
+                    raise ValueError(
+                        f"Cannot create zeros for '{self.key}' without fallback_shape"
+                    )
+                dtype = self.fallback_dtype if self.fallback_dtype else self.dtype
+                return torch.zeros(self.fallback_shape, device=self.device, dtype=dtype)
+        
+        return handler.get_tensor(self.key).to(device=self.device, dtype=self.dtype)
 
 class Multiply(Operation):
     def __init__(self, key, alpha, *sources):
         super().__init__(key, *sources)
         self.alpha = alpha
     def oper(self, a) -> torch.Tensor:
+        if a is None:
+            return None
         return a * self.alpha
+
+
 class Add(Operation):
     def oper(self, a, b) -> torch.Tensor:
+        if a is None:
+            return b
+        if b is None:
+            return a
         return a + b
+
+
 class Sub(Operation):
     def oper(self, a, b) -> torch.Tensor:
+        if a is None or b is None:
+            return None  # Cannot compute difference with missing operand
         return a - b
 
 class SVD(Operation):
@@ -64,6 +110,8 @@ class SVD(Operation):
         super().__init__(key, *sources)
         self.alpha, self.beta, self.gamma = int(alpha), int(beta), gamma
     def oper(self, a, b) -> torch.Tensor:
+        if a is None or b is None:
+            return None
         diff, weights, conv2d = a - b, {}, len(a.size()) == 4
         kernel_size = None if not conv2d else a.size()[2:4]
         conv2d_3x3 = conv2d and kernel_size != (1, 1)
@@ -84,11 +132,15 @@ class SVD(Operation):
 
 class Smooth(Operation):
     def oper(self, a) -> torch.Tensor:
+        if a is None:
+            return None
         filtered = scipy.ndimage.median_filter(a.detach().cpu().to(torch.float32).numpy(), size=3)
         filtered = scipy.ndimage.gaussian_filter(filtered, sigma=1)
         return torch.tensor(filtered, dtype=a.dtype, device=a.device)
 class TrainDiff(Operation):
     def oper(self, a, b, c) -> torch.Tensor:
+        if a is None or b is None or c is None:
+            return None
         if torch.allclose(b.float(), c.float()): return torch.zeros_like(a)
         diff_bc = b.float() - c.float()
         dist_bc, dist_ab = torch.abs(diff_bc), torch.abs(b.float() - a.float())
@@ -99,27 +151,40 @@ class ExtractOp(Operation):
         super().__init__(key, *args)
         self.alpha, self.beta, self.gamma = alpha, beta, gamma
     def oper(self, base: torch.Tensor, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+        if a is None or b is None:
+            return None
         base_f = base.float() if base is not None else 0
         a_f, b_f = a.float() - base_f, b.float() - base_f
         c = torch.cosine_similarity(a_f.flatten(), b_f.flatten(), 0).clamp(-1, 1)
         d = ((c + 1) / 2)**self.gamma
         return (torch.lerp(a_f, b_f, self.alpha) * torch.lerp(d, 1 - d, self.beta)).to(a.dtype)
+
+
 class Similarities(ExtractOp):
-    def oper(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: return super().oper(None, a, b)
+    def oper(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+        if a is None or b is None:
+            return None
+        return super().oper(None, a, b)
 class PowerUpOp(Operation):
     def __init__(self, key, alpha, seed, *sources):
         super().__init__(key, *sources)
         self.alpha, self.seed = alpha, seed
     def oper(self, a, b):
+        if a is None or b is None:
+            return None
         delta = b - a
         rng = torch.Generator(device=delta.device).manual_seed(self.seed)
         m = torch.bernoulli(torch.full_like(delta, 1 - self.alpha), generator=rng)
         return (m * delta) / (1 - self.alpha) if (1 - self.alpha) > 0 else torch.zeros_like(delta)
+
+
 class InterpolateDifference(Operation):
     def __init__(self, key, alpha, beta, gamma, seed, *sources):
         super().__init__(key, *sources)
         self.alpha, self.beta, self.gamma, self.seed = alpha, beta, gamma, seed
     def oper(self, a, b):
+        if a is None or b is None:
+            return None
         alpha, delta = max(self.alpha, 0.001), torch.abs(a - b)
         max_delta = torch.max(delta)
         if max_delta == 0: return a
@@ -135,6 +200,34 @@ class InterpolateDifference(Operation):
 class CalcMode:
     name = 'calcmode'; description = 'description'; models_used = []; param_docs = {}
     def create_recipe(self, key, **kwargs) -> Operation: raise NotImplementedError
+    
+    @staticmethod
+    def _get_secondary_loader_args(kwargs, mismatch_mode):
+        """Helper to build loader args for secondary models (B, C) with fallback support."""
+        base_args = {
+            "handlers": kwargs['handlers'],
+            "device": kwargs['device'],
+            "dtype": kwargs['dtype'],
+            "on_missing": mismatch_mode,
+        }
+        # Add fallback info for zeros mode
+        if '_tensor_a_shape' in kwargs:
+            base_args['fallback_shape'] = kwargs['_tensor_a_shape']
+            base_args['fallback_dtype'] = kwargs.get('_tensor_a_dtype', kwargs['dtype'])
+        return base_args
+
+
+class PreloadedTensor(Operation):
+    """Returns a pre-loaded tensor instead of loading from file.
+    
+    Used when Model A's tensor is pre-loaded in the merge loop.
+    """
+    def __init__(self, key, tensor):
+        super().__init__(key)
+        self.tensor = tensor
+    
+    def merge(self) -> torch.Tensor:
+        return self.tensor
 
 class WeightSum(CalcMode):
     name = 'Weight-Sum'
@@ -142,38 +235,65 @@ class WeightSum(CalcMode):
     models_used = ['A', 'B']
     param_docs = {'alpha': 'Interpolation weight. 0.0 is 100% Model A, 1.0 is 100% Model B.'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        # Use pre-loaded tensor_a if available
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_b = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_b)
+        
         if kwargs['alpha'] >= 1: return b
         if kwargs['alpha'] <= 0: return a
         c = Multiply(key, 1 - kwargs['alpha'], a)
         d = Multiply(key, kwargs['alpha'], b)
         return Add(key, c, d)
+
+
 class AddDifference(CalcMode):
     name = 'Add-Difference'
     description = 'A + (B - C) * α. Applies the difference between B and C to A.'
     models_used = ['A', 'B', 'C']
     param_docs = {'alpha': 'Multiplier for the (B - C) difference.', 'beta': 'Smoothing (0=Off, 1=On).'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
-        c = LoadTensor(key, kwargs['model_c'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_bc = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_bc)
+        c = LoadTensor(key, kwargs['model_c'], **loader_args_bc)
         diff = Sub(key, b, c)
         if kwargs['beta'] == 1: diff = Smooth(key, diff)
         diffm = Multiply(key, kwargs['alpha'], diff)
         return Add(key, a, diffm)
+
+
 class TrainDifference(CalcMode):
     name = 'Train-Difference'
     description = 'A + (B - C) * α, using tensor distances.'
     models_used = ['A', 'B', 'C']
     param_docs = {'alpha': 'Multiplier for the calculated difference.'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
-        c = LoadTensor(key, kwargs['model_c'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_bc = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_bc)
+        c = LoadTensor(key, kwargs['model_c'], **loader_args_bc)
         diff = TrainDiff(key, a, b, c)
         diffm = Multiply(key, kwargs['alpha'], diff)
         return Add(key, a, diffm)
@@ -183,59 +303,106 @@ class InterpDifference(CalcMode):
     models_used = ['A', 'B']
     param_docs = {'alpha': 'Curve strength.', 'beta': 'Style (0=Similarity, 1=Difference).', 'gamma': 'Mix (0=Random, 1=Linear).'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        if key.startswith('cond_stage_model.transformer.text_model.embeddings'): return LoadTensor(key, kwargs['model_a'], **loader_args)
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        # Skip certain keys entirely
+        if key.startswith('cond_stage_model.transformer.text_model.embeddings'):
+            return a
+        
+        loader_args_b = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_b)
         return InterpolateDifference(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], kwargs['seed'], a, b)
+
+
 class Extract(CalcMode):
     name = 'Extract-Features'
     description = 'Adds features from B and C to A, based on their similarity.'
     models_used = ['A', 'B', 'C']
     param_docs = {'alpha': 'Weighting B vs C.', 'beta': 'Similarity vs dissimilarity.', 'gamma': 'Similarity bias.', 'delta': 'Multiplier for final features.'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
-        c = LoadTensor(key, kwargs['model_c'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_bc = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_bc)
+        c = LoadTensor(key, kwargs['model_c'], **loader_args_bc)
         extracted = ExtractOp(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'] * 15, a, b, c)
         multiplied = Multiply(key, kwargs['delta'], extracted)
         return Add(key, a, multiplied)
+
+
 class AddDisimilarity(CalcMode):
     name = 'Add-Dissimilarities'
     description = 'Adds dissimilar features between B and C to model A.'
     models_used = ['A', 'B', 'C']
     param_docs = {'alpha': 'Weighting B vs C.', 'beta': 'Multiplier for final features.', 'gamma': 'Similarity bias.'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
-        c = LoadTensor(key, kwargs['model_c'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_bc = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_bc)
+        c = LoadTensor(key, kwargs['model_c'], **loader_args_bc)
         extracted = Similarities(key, kwargs['alpha'], 1, kwargs['gamma'] * 15, b, c)
         multiplied = Multiply(key, kwargs['beta'], extracted)
         return Add(key, a, multiplied)
+
+
 class PowerUp(CalcMode):
     name = 'Power-Up (DARE)'
     description = 'Adds capabilities of B to A using Drop and Rescale (DARE).'
     models_used = ['A', 'B']
     param_docs = {'alpha': 'Dropout rate (proportion of weights from B to *drop*).', 'beta': 'Multiplier for the final difference.'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_b = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_b)
         deltahat = PowerUpOp(key, kwargs['alpha'], kwargs['seed'], a, b)
         res = Multiply(key, kwargs['beta'], deltahat)
         return Add(key, a, res)
-class SVD(CalcMode):
+
+
+class SVDMode(CalcMode):
     name = 'SVD LoRA Extraction'
     description = 'Extracts the difference between B and A into a LoRA file.'
     models_used = ['A', 'B']
     param_docs = {'alpha': 'Rank for standard layers.', 'beta': 'Rank for 3x3 conv layers.', 'gamma': 'Clamp quantile for weights.'}
     def create_recipe(self, key, **kwargs):
-        loader_args = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
-        a = LoadTensor(key, kwargs['model_a'], **loader_args)
-        b = LoadTensor(key, kwargs['model_b'], **loader_args)
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        
+        if '_tensor_a' in kwargs:
+            a = PreloadedTensor(key, kwargs['_tensor_a'])
+        else:
+            loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']}
+            a = LoadTensor(key, kwargs['model_a'], **loader_args_a)
+        
+        loader_args_b = self._get_secondary_loader_args(kwargs, mismatch_mode)
+        b = LoadTensor(key, kwargs['model_b'], **loader_args_b)
         return SVD(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], a, b)
 
-TWO_MODEL_MODES = [WeightSum(), InterpDifference(), PowerUp(), SVD()]
+
+TWO_MODEL_MODES = [WeightSum(), InterpDifference(), PowerUp(), SVDMode()]
 THREE_MODEL_MODES = [AddDifference(), TrainDifference(), Extract(), AddDisimilarity()]

nodes/merger_utils.py

@@ -77,4 +77,30 @@ class MemoryEfficientSafeOpen:
         elif dtype_str == "F8_E4M3" and hasattr(torch, "float8_e4m3fn"):
             return byte_tensor.view(torch.float8_e4m3fn).reshape(shape)
         else:
-            raise ValueError(f"Unsupported float8 type: {dtype_str}. Your PyTorch version may be too old.")
+            raise ValueError(f"Unsupported float8 type: {dtype_str}. Your PyTorch version may be too old.")
+
+
+def get_union_keys(*handlers):
+    """Returns the union of all tensor keys across multiple model handlers.
+    
+    Useful when you want to iterate over all unique keys present in any model.
+    """
+    all_keys = set()
+    for handler in handlers:
+        if handler is not None:
+            all_keys.update(handler.keys())
+    return sorted(all_keys)
+
+
+def get_intersection_keys(*handlers):
+    """Returns only keys present in ALL model handlers.
+    
+    Useful when you want to iterate only over keys that exist in every model.
+    """
+    handlers = [h for h in handlers if h is not None]
+    if not handlers:
+        return []
+    all_keys = set(handlers[0].keys())
+    for handler in handlers[1:]:
+        all_keys &= set(handler.keys())
+    return sorted(all_keys)