From 19bc0472dc2aa815b747cd0afec8f2ec44a3c88f Mon Sep 17 00:00:00 2001 From: silveroxides Date: Thu, 18 Dec 2025 21:43:32 +0100 Subject: [PATCH] add handling of mismatched layers --- docs/merger_2_model_modes.md | 21 +++++- docs/merger_3_model_modes.md | 25 ++++++- nodes/merger.py | 54 +++++++++++++-- nodes/merger_ops.py | 128 ++++++++++++++++++++++++++--------- nodes/merger_utils.py | 28 +++++++- 5 files changed, 214 insertions(+), 42 deletions(-) diff --git a/docs/merger_2_model_modes.md b/docs/merger_2_model_modes.md index 0a6d5f8..c3a15c5 100644 --- a/docs/merger_2_model_modes.md +++ b/docs/merger_2_model_modes.md @@ -39,4 +39,23 @@ **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. \ No newline at end of file +- **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. \ No newline at end of file diff --git a/docs/merger_3_model_modes.md b/docs/merger_3_model_modes.md index 06b273e..a752259 100644 --- a/docs/merger_3_model_modes.md +++ b/docs/merger_3_model_modes.md @@ -40,4 +40,27 @@ **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. \ No newline at end of file +- **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. \ No newline at end of file diff --git a/nodes/merger.py b/nodes/merger.py index 77cf366..9c7046d 100644 --- a/nodes/merger.py +++ b/nodes/merger.py @@ -3,7 +3,7 @@ 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 from .merger_utils import MemoryEfficientSafeOpen def load_documentation_from_file(filename): @@ -23,6 +23,7 @@ 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}), @@ -39,6 +40,7 @@ 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}), @@ -56,26 +58,64 @@ 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__", {}) + + # 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 + + # Log mismatched keys for debugging + primary_keys = set(all_keys) + for name, handler in handlers.items(): + if name != primary_model_name: + secondary_keys = set(handler.keys()) + missing_in_secondary = primary_keys - secondary_keys + extra_in_secondary = secondary_keys - primary_keys + if missing_in_secondary: + print(f"[Merger] {name} is missing {len(missing_in_secondary)} keys present in Model A") + if extra_in_secondary: + print(f"[Merger] {name} has {len(extra_in_secondary)} 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') + + skipped_keys = 0 for key in all_keys: recipe = calc_mode_class.create_recipe(key=key, **recipe_params) result = recipe.merge() + + # Handle None result (mismatch occurred with skip mode) + if result is None: + # Fall back to Model A's value + result = handlers[primary_model_name].get_tensor(key).to( + device=process_device, dtype=torch.float32 + ) + 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() pbar.update(1) + + if skipped_keys > 0: + print(f"[Merger] Used Model A's values for {skipped_keys} keys due to mismatches") + 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] @@ -89,13 +129,14 @@ 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): 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 @@ -108,13 +149,14 @@ 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): 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 diff --git a/nodes/merger_ops.py b/nodes/merger_ops.py index 9664f2e..7dcc748 100644 --- a/nodes/merger_ops.py +++ b/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): @@ -142,9 +188,11 @@ 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_b = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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) @@ -156,10 +204,12 @@ class AddDifference(CalcMode): 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_bc = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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) @@ -170,10 +220,12 @@ class TrainDifference(CalcMode): 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_bc = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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) @@ -183,10 +235,12 @@ 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_b = {**loader_args_a, "on_missing": mismatch_mode} + if key.startswith('cond_stage_model.transformer.text_model.embeddings'): return LoadTensor(key, kwargs['model_a'], **loader_args_a) + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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' @@ -194,10 +248,12 @@ class Extract(CalcMode): 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_bc = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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) @@ -207,10 +263,12 @@ class AddDisimilarity(CalcMode): 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_bc = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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) @@ -220,22 +278,26 @@ class PowerUp(CalcMode): 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_b = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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) + loader_args_a = {"handlers": kwargs['handlers'], "device": kwargs['device'], "dtype": kwargs['dtype']} + loader_args_b = {**loader_args_a, "on_missing": mismatch_mode} + a = LoadTensor(key, kwargs['model_a'], **loader_args_a) + 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()] \ No newline at end of file diff --git a/nodes/merger_utils.py b/nodes/merger_utils.py index e18f872..59a0c4a 100644 --- a/nodes/merger_utils.py +++ b/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.") \ No newline at end of file + 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) \ No newline at end of file