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Comfyui_CXH_FluxLoraMerge 2 | 3 | flux lora merge 4 | 5 | 自动保存到lora目录：auto save to lora dir 6 | 7 | Merge type: 8 | 9 | 1.自适应合并（使用张量范数和权重） adaptive Merge (uses tensor norms and weight) 10 | 11 | 2.手动合并（使用您指定的固定权重） manual Merge (uses fixed weights you specify) 12 | 13 | 3.加法合并（使用第一个的100%并加上第二个的一定百分比） additive Merge (uses 100% of the first and adds a percentage of the second) 14 | 15 | ![65ff0b9e18bad7d98f426bbb99c04f8](https://github.com/user-attachments/assets/839d1603-f4a1-4fb5-9354-0a578e21271e) 16 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | from .mergeNode import CXH_Lora_Merge 4 | 5 | NODE_CLASS_MAPPINGS = { 6 | "CXH_Lora_Merge":CXH_Lora_Merge 7 | } 8 | 9 | NODE_DISPLAY_NAME_MAPPINGS = { 10 | "CXH_Lora_Merge":"CXH_Lora_Merge" 11 | } -------------------------------------------------------------------------------- /mergeNode.py: -------------------------------------------------------------------------------- 1 | # merge_lora.py 2 | import os 3 | import time 4 | import sys 5 | import torch 6 | from tqdm import tqdm 7 | from safetensors.torch import load_file, save_file 8 | import comfy.utils 9 | import comfy.sd 10 | import folder_paths 11 | 12 | def merge_loras_mix(main_lora_model, merge_lora_model, weight_percentages, merge_type): 13 | """Merges two LoRA models using multiple weight percentages.""" 14 | merged_models = [] 15 | for weight in weight_percentages: 16 | merged_model = merge_loras_weighted(main_lora_model, merge_lora_model, weight / 100, merge_type) 17 | merged_models.append((weight / 100, merged_model)) 18 | return merged_models 19 | 20 | 21 | def merge_loras_weighted(main_lora_model, merge_lora_model, main_weight, merge_type='adaptive'): 22 | """Merges two LoRA models using adaptive or manual merge with a specified main weight.""" 23 | merged_model = {} 24 | all_keys = set(main_lora_model.keys()).union(set(merge_lora_model.keys())) 25 | 26 | with tqdm(total=len(all_keys), desc="Merging LoRA models", unit="layer") as pbar: 27 | for key in all_keys: 28 | if key in main_lora_model and key in merge_lora_model: 29 | if merge_type == 'adaptive': 30 | merged_model[key] = adaptive_merge(main_lora_model[key], merge_lora_model[key], main_weight) 31 | else: 32 | merged_model[key] = manual_merge(main_lora_model[key], merge_lora_model[key], main_weight) 33 | elif key in main_lora_model: 34 | merged_model[key] = main_lora_model[key] 35 | else: 36 | merged_model[key] = merge_lora_model[key] 37 | pbar.update(1) 38 | 39 | return merged_model 40 | 41 | 42 | def additive_merge(main_lora_model, merge_lora_model, add_weight): 43 | """Always use 100% of the first model and add the second model at a specified percentage.""" 44 | merged_model = {} 45 | all_keys = set(main_lora_model.keys()).union(set(merge_lora_model.keys())) 46 | 47 | with tqdm(total=len(all_keys), desc="Additive Merging LoRA models", unit="layer") as pbar: 48 | for key in all_keys: 49 | if key in main_lora_model and key in merge_lora_model: 50 | tensor1 = main_lora_model[key] 51 | tensor2 = merge_lora_model[key] 52 | if tensor1.size() != tensor2.size(): 53 | tensor1, tensor2 = pad_tensors(tensor1, tensor2) 54 | merged_model[key] = tensor1 + (add_weight * tensor2) 55 | elif key in main_lora_model: 56 | merged_model[key] = main_lora_model[key] 57 | else: 58 | merged_model[key] = add_weight * merge_lora_model[key] 59 | pbar.update(1) 60 | 61 | return merged_model 62 | 63 | 64 | def adaptive_merge(tensor1, tensor2, main_weight): 65 | """Merges two tensors using adaptive weights based on their L2 norms.""" 66 | if tensor1.size() != tensor2.size(): 67 | tensor1, tensor2 = pad_tensors(tensor1, tensor2) 68 | 69 | norm1 = torch.norm(tensor1) 70 | norm2 = torch.norm(tensor2) 71 | 72 | adaptive_weight1 = norm1 / (norm1 + norm2) 73 | adaptive_weight2 = norm2 / (norm1 + norm2) 74 | 75 | final_weight1 = adaptive_weight1 * main_weight + (1 - adaptive_weight2) * (1 - main_weight) 76 | final_weight2 = 1 - final_weight1 77 | 78 | return final_weight1 * tensor1 + final_weight2 * tensor2 79 | 80 | 81 | def manual_merge(tensor1, tensor2, main_weight): 82 | """Merges two tensors using fixed weights based on user input.""" 83 | if tensor1.size() != tensor2.size(): 84 | tensor1, tensor2 = pad_tensors(tensor1, tensor2) 85 | 86 | return main_weight * tensor1 + (1 - main_weight) * tensor2 87 | 88 | 89 | def save_merged_lora(merged_model, lora_folder, main_lora_file, merge_lora_file, weight, merge_type): 90 | """Saves the merged LoRA model with an appropriate name.""" 91 | main_name = os.path.splitext(main_lora_file)[0] 92 | merge_name = os.path.splitext(merge_lora_file)[0] 93 | 94 | if merge_type == 'adaptive': 95 | strategy_code = f"A{int(weight * 100)}" 96 | elif merge_type == 'additive': 97 | strategy_code = f"ADDI{int(weight * 100)}" 98 | else: # manual 99 | strategy_code = f"M{int(weight * 100)}" 100 | 101 | merged_lora_name = f"mrg_{main_name}_{strategy_code}_{merge_name}.safetensors" 102 | merged_lora_path = os.path.join(lora_folder, merged_lora_name) 103 | 104 | save_file(merged_model, merged_lora_path) 105 | print(f"Merged LoRA saved as: {merged_lora_name}") 106 | 107 | 108 | from tqdm import tqdm 109 | import torch 110 | 111 | def pad_tensors(tensor1, tensor2): 112 | """Pads tensors to the same size if they differ.""" 113 | max_size = [max(s1, s2) for s1, s2 in zip(tensor1.size(), tensor2.size())] 114 | padded1 = torch.zeros(max_size, device=tensor1.device, dtype=tensor1.dtype) 115 | padded2 = torch.zeros(max_size, device=tensor2.device, dtype=tensor2.dtype) 116 | padded1[tuple(slice(0, s) for s in tensor1.size())] = tensor1 117 | padded2[tuple(slice(0, s) for s in tensor2.size())] = tensor2 118 | return padded1, padded2 119 | 120 | def pad_all_tensors(tensors): 121 | """Pads all tensors in the list to match the maximum size across all tensors.""" 122 | if not tensors: 123 | return [] 124 | 125 | # Determine the max size across all tensors 126 | max_size = [max(t.size(dim) for t in tensors) for dim in range(len(tensors[0].size()))] 127 | 128 | # Pad each tensor to the max size 129 | padded_tensors = [] 130 | for tensor in tensors: 131 | padded_tensor = torch.zeros(max_size, device=tensor.device, dtype=tensor.dtype) 132 | slices = tuple(slice(0, s) for s in tensor.size()) 133 | padded_tensor[slices] = tensor 134 | padded_tensors.append(padded_tensor) 135 | 136 | return padded_tensors 137 | 138 | 139 | def god_mode(lora_folder, merge_strategy='adaptive'): 140 | """ 141 | Merges multiple LoRA models simultaneously using the specified strategy, constrained by available memory. 142 | 143 | Args: 144 | - lora_folder: The folder containing LoRA models to merge. 145 | - merge_strategy: The merging strategy to use ('adaptive', 'additive'). 146 | 147 | Returns: 148 | - Path to the final merged model saved to disk. 149 | """ 150 | # Load all LoRA models from the folder with progress bar 151 | lora_files = [f for f in os.listdir(lora_folder) if f.endswith('.safetensors')] 152 | if not lora_files: 153 | print("No LoRA models found to merge.") 154 | return None 155 | 156 | print(f"Loading {len(lora_files)} LoRA models...") 157 | lora_models = [] 158 | largest_file_size = 0 159 | largest_file_name = '' 160 | 161 | with tqdm(total=len(lora_files), desc="Loading LoRA Models", unit="file") as pbar: 162 | for file in lora_files: 163 | file_path = os.path.join(lora_folder, file) 164 | file_size = os.path.getsize(file_path) 165 | if file_size > largest_file_size: 166 | largest_file_size = file_size 167 | largest_file_name = file 168 | try: 169 | lora_model = load_file(file_path) 170 | lora_models.append(lora_model) 171 | except Exception as e: 172 | print(f"Error loading model {file}: {e}") 173 | pbar.update(1) 174 | 175 | if not lora_models: 176 | print("No LoRA models successfully loaded.") 177 | return None 178 | 179 | print(f"Largest input file: {largest_file_name} ({largest_file_size} bytes)") 180 | print(f"Starting merge with {len(lora_models)} LoRA models using {merge_strategy} strategy.") 181 | 182 | # Initialize the merged model with keys from all models 183 | all_keys = set().union(*(model.keys() for model in lora_models)) 184 | merged_model = {key: torch.zeros_like(next(model[key] for model in lora_models if key in model)) 185 | for key in all_keys} 186 | 187 | total_input_tensors = 0 188 | total_merged_tensors = 0 189 | 190 | for key in tqdm(all_keys, desc="Merging tensors", unit="tensor"): 191 | tensors = [model[key] for model in lora_models if key in model] 192 | total_input_tensors += len(tensors) 193 | 194 | if not tensors: 195 | print(f"Warning: No tensors found for key: {key}") 196 | continue 197 | 198 | # print(f"Merging {len(tensors)} tensors for key: {key}") 199 | # print(f"Input tensor sizes: {[t.size() for t in tensors]}") 200 | 201 | try: 202 | padded_tensors = pad_all_tensors(tensors) 203 | if merge_strategy == 'adaptive': 204 | merged_model[key] = adaptive_merge_multiple(padded_tensors) 205 | elif merge_strategy == 'additive': 206 | merged_model[key] = additive_merge_multiple(padded_tensors) 207 | else: 208 | raise ValueError(f"Unknown merge strategy: {merge_strategy}") 209 | 210 | # print(f"Merged tensor size: {merged_model[key].size()}") 211 | total_merged_tensors += 1 212 | 213 | except Exception as e: 214 | print(f"Error merging tensors for key {key}: {e}") 215 | # Instead of skipping, use the tensor from the largest file if available 216 | largest_model_tensor = next((model[key] for model in lora_models if key in model and model is lora_models[0]), None) 217 | if largest_model_tensor is not None: 218 | merged_model[key] = largest_model_tensor 219 | print(f"Using tensor from largest file for key {key}") 220 | else: 221 | print(f"Warning: Skipping key {key} due to errors") 222 | 223 | print(f"Total input tensors: {total_input_tensors}") 224 | print(f"Total merged tensors: {total_merged_tensors}") 225 | 226 | # Determine the strategy code for the filename 227 | strategy_code = 'A' if merge_strategy == 'adaptive' else 'M' 228 | 229 | # Create the filename using the correct naming convention 230 | merged_filename = f"mrg_final_merged_{strategy_code}100_god_mode.safetensors" 231 | merged_file_path = os.path.join(lora_folder, merged_filename) 232 | 233 | # Save the final merged model 234 | try: 235 | save_file(merged_model, merged_file_path) 236 | merged_file_size = os.path.getsize(merged_file_path) 237 | print(f"Merged file saved as: {merged_filename}") 238 | print(f"Merged file size: {merged_file_size} bytes") 239 | 240 | if merged_file_size < largest_file_size: 241 | print("Warning: Merged file is smaller than the largest input file. Some data may have been lost in the process.") 242 | else: 243 | print("Merged file is larger than or equal to the largest input file, as expected.") 244 | 245 | except Exception as e: 246 | print(f"Error saving merged model: {e}") 247 | return None 248 | 249 | return merged_file_path 250 | 251 | def adaptive_merge_multiple(tensors): 252 | """Merges multiple tensors using adaptive weights based on their L2 norms.""" 253 | try: 254 | norms = [torch.norm(tensor) for tensor in tensors] 255 | total_norm = sum(norms) 256 | weights = [norm / total_norm for norm in norms] 257 | 258 | # Calculate the final merged tensor 259 | merged_tensor = sum(w * t for w, t in zip(weights, tensors)) 260 | return merged_tensor 261 | except Exception as e: 262 | print(f"Error in adaptive_merge_multiple: {e}") 263 | return torch.zeros_like(tensors[0]) 264 | 265 | def additive_merge_multiple(tensors): 266 | """Merges multiple tensors using additive merging with equal weighting.""" 267 | try: 268 | weight = 1.0 / len(tensors) 269 | merged_tensor = sum(weight * tensor for tensor in tensors) 270 | return merged_tensor 271 | except Exception as e: 272 | print(f"Error in additive_merge_multiple: {e}") 273 | return torch.zeros_like(tensors[0]) 274 | 275 | 276 | class CXH_Lora_Merge: 277 | 278 | def __init__(self): 279 | pass 280 | 281 | @classmethod 282 | def INPUT_TYPES(s): 283 | return { 284 | "required": { 285 | "savename": ("STRING", {"multiline": False, "default": ""},), 286 | "main_lora": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the merged LoRA."}), 287 | "merge_lora": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the merged LoRA."}), 288 | "merge_type": (["adaptive", "manual","additive"],), 289 | "weight":("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), 290 | 291 | } 292 | } 293 | 294 | RETURN_TYPES = () 295 | RETURN_NAMES = () 296 | FUNCTION = "gen" 297 | OUTPUT_NODE = True 298 | CATEGORY = "CXH/model" 299 | 300 | def gen(self,savename ,main_lora,merge_lora,merge_type,weight,): 301 | 302 | lora_path_1 = os.path.join(folder_paths.models_dir,"loras",main_lora) 303 | lora_path_2 = os.path.join(folder_paths.models_dir,"loras",merge_lora) 304 | save_lora = os.path.join(folder_paths.models_dir,"loras",savename+".safetensors") 305 | 306 | print(lora_path_1) 307 | main_lora_model = load_file(lora_path_1) 308 | merge_lora_model = load_file(lora_path_2) 309 | 310 | if merge_type == 'adaptive': 311 | merged_models = merge_loras_mix(main_lora_model, merge_lora_model, [weight],merge_type) 312 | elif merge_type == 'additive': 313 | merged_model = additive_merge(main_lora_model, merge_lora_model, weight / 100) 314 | merged_models = [(weight / 100, merged_model)] 315 | else: # Weighted 316 | merged_model = merge_loras_weighted(main_lora_model, merge_lora_model, weight / 100, merge_type) 317 | merged_models = [(weight / 100, merged_model)] 318 | 319 | for weight, merged_model in merged_models: 320 | print(f"Merged LoRA saved as: {save_lora}") 321 | save_file(merged_model, save_lora) 322 | return () 323 | 324 | 325 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui_cxh_fluxloramerge" 3 | description = "flux lora merge.\nadaptive Merge (uses tensor norms and weight), manual Merge (uses fixed weights you specify), additive Merge (uses 100% of the first and adds a percentage of the second)" 4 | version = "1.0.0" 5 | license = {file = "LICENSE"} 6 | dependencies = ["tqdm"] 7 | 8 | [project.urls] 9 | Repository = "https://github.com/StartHua/Comfyui_CXH_FluxLoraMerge" 10 | # Used by Comfy Registry https://comfyregistry.org 11 | 12 | [tool.comfy] 13 | PublisherId = "" 14 | DisplayName = "Comfyui_CXH_FluxLoraMerge" 15 | Icon = "" 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tqdm 2 | --------------------------------------------------------------------------------