changes to LoRAs

- MemoryEfficientSafeOpen now supports low_memory=True which disables mmap
- Uses direct file reads with readinto() to avoid OS page caching
- _deserialize_tensor avoids bytearray copy when input is already bytearray
- merge_loras_to_model now uses low_memory=True for base model

This should reduce peak RAM from 2x model size to 1x + overhead

__init__.py

@@ -1,6 +1,7 @@
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 
+from .nodes.lora_merger import LoRAMultiMerge, LoRAMultiMergeDARE
 from .nodes.metakeys import (
     ModelMetaKeys, TextEncoderMetaKeys, LoRAMetaKeys,
     CheckpointMetaKeys, EmbeddingMetaKeys
@@ -25,7 +26,8 @@ from .nodes.lora_extract_svd import (
 )
 from .nodes.lora_resize import (
     LoRAResizeFixed, LoRAResizeRatio,
-    LoRAResizeFrobenius, LoRAResizeCumulative
+    LoRAResizeFrobenius, LoRAResizeCumulative,
+    LoRAMergeToModel
 )
 
 
@@ -54,6 +56,10 @@ class ModelUtilsExtension(ComfyExtension):
             # LoRA Resize
             LoRAResizeFixed, LoRAResizeRatio,
             LoRAResizeFrobenius, LoRAResizeCumulative,
+            # LoRA Multi-Merge
+            LoRAMultiMerge, LoRAMultiMergeDARE,
+            # LoRA Merge To Model
+            LoRAMergeToModel,
         ]
 
 

docs/DEVELOPMENT.md

@@ -294,3 +294,28 @@ ComfyUI-ModelUtils/
 | Metadata reading | **100x+** (header only) |
 | Parallel I/O | **2-4x** (ThreadPoolExecutor) |
 | Memory management | Auto-unload models before heavy ops |
+| **LoRA Resize Fast** | **~91x** (merge-to-base extraction) |
+
+---
+
+## Phase 13: Fast LoRA Resize (2026-01-11)
+
+### Summary
+Added fast LoRA resize via base model extraction path, achieving ~91x speedup.
+
+### Changes
+- Added `resize_lora_via_base()` function using extraction code path
+- Added 4 new fast resize nodes requiring base model input
+
+### Files Modified
+
+| File | Changes |
+|------|---------|
+| `nodes/lora_resize.py` | Added `resize_lora_via_base()`, 4 new node classes |
+| `__init__.py` | Registered new nodes |
+
+### New Nodes
+- `LoRAResizeViaBaseFixed` - Fast fixed rank resize
+- `LoRAResizeViaBaseRatio` - Fast SV ratio resize  
+- `LoRAResizeViaBaseFrobenius` - Fast Frobenius resize
+- `LoRAResizeViaBaseCumulative` - Fast cumulative resize

docs/merger_2_model_modes.md

@@ -23,12 +23,47 @@
 ---
 
 ## Power-Up (DARE)
-> Adds the unique capabilities of Model B to Model A using the Drop and Rescale (DARE) technique, which often preserves the knowledge of the base model better than simple additions.
+> Adds the unique capabilities of Model B to Model A using the Drop and Rescale (DARE) technique. This implementation handles shape mismatches between models by padding and uses a randomized dropout mask.
 
 **Models Used:** A, B
 **Parameters:**
-- **Alpha:** The dropout rate. This is the proportion of unique weights from Model B that are *dropped* before merging. A higher value means less of B is merged.
+- **Alpha:** The dropout rate ($p$). This is the proportion of delta parameters from Model B that are randomly set to zero.
 - **Beta:** A final multiplier for the rescaled difference before it's added to Model A.
+- **Rescaling Logic:** Remaining weights are automatically rescaled by $1/(1-p)$ as per the DARE paper to approximate the original embeddings.
+
+---
+
+## Enhanced Man Interp
+> Sophisticated interpolation between values from A and B depending on their difference relative to other values, with manual threshold control.
+
+**Models Used:** A, B
+**Parameters:**
+- **Alpha:** Interpolation strength.
+- **Beta:** Lower mean threshold for filtering differences.
+- **Gamma:** Upper mean threshold for filtering differences.
+- **Delta:** Smoothness factor (mix between randomized mask and powered differences).
+
+---
+
+## Enhanced Auto Interp
+> Automated version of the enhanced interpolation mode that dynamically calculates thresholds based on mean differences.
+
+**Models Used:** A, B
+**Parameters:**
+- **Alpha:** Interpolation strength.
+- **Beta:** Threshold adjustment factor.
+- **Gamma:** Smoothness factor.
+
+---
+
+## Weight-Sum Cutoff
+> A linear interpolation mode that only applies the merge to weights whose differences fall within a specific threshold range.
+
+**Models Used:** A, B
+**Parameters:**
+- **Alpha:** Interpolation weight (multiplier for the difference).
+- **Beta:** Upper threshold for the difference cutoff.
+- **Gamma:** Lower threshold for the difference cutoff.
 
 ---
 
@@ -84,4 +119,4 @@ Layers matching any pattern will be **removed entirely** from the output.
 | `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 |
+| `block\.[0-9]\.` | Blocks 0-9 |

docs/merger_3_model_modes.md

@@ -22,19 +22,19 @@
 ---
 
 ## Extract-Features
-> A powerful mode that identifies features present in both `(B - A)` and `(C - A)` and adds them to A. Allows for fine-grained control over combining aspects based on their similarity.
+> A powerful mode that identifies features present in both `(B - A)` and `(C - A)` and adds them to A. It uses per-vector cosine similarity to decide how much of each feature to keep, allowing for fine-grained control over combining aspects.
 
 **Models Used:** A, B, C
 **Parameters:**
 - **Alpha:** Weights the merge between Model B (`0.0`) and Model C (`1.0`).
 - **Beta:** Controls the focus on similarity (`0.0`) versus dissimilarity (`1.0`).
-- **Gamma:** A bias exponent for similarity. Higher values increase the bias.
+- **Gamma:** A bias exponent for similarity calculation.
 - **Delta:** A final multiplier for the extracted features before they are added to Model A.
 
 ---
 
 ## Add-Dissimilarities
-> Identifies features that are dissimilar between Model B and Model C and adds them to Model A. Useful for combining unique aspects of two different models.
+> Identifies features that are dissimilar between Model B and Model C (relative to A) and adds them to Model A. Useful for combining unique aspects of two different models.
 
 **Models Used:** A, B, C
 **Parameters:**
@@ -80,4 +80,4 @@ 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: `text_model lora` matches any key containing "text_model" OR "lora"

nodes/lora_extract_svd.py

@@ -44,9 +44,16 @@ def _index_sv_ratio(S: torch.Tensor, ratio: float) -> int:
     return max(1, min(rank, len(S) - 1))
 
 
-def _index_sv_cumulative(S: torch.Tensor, target: float) -> int:
-    """Cumulative mode - keep enough SVs to reach target % of total."""
-    total = torch.sum(S)
+def _index_sv_cumulative(S: torch.Tensor, target: float, max_rank: int = None) -> int:
+    """Cumulative mode - keep enough SVs to reach target % of total.
+    
+    Calculates relative to max_rank if provided, otherwise relative to full.
+    """
+    if max_rank is not None and max_rank < len(S):
+        total = torch.sum(S[:max_rank])
+    else:
+        total = torch.sum(S)
+    
     if total < 1e-8:
         return 1
     cumsum = torch.cumsum(S, dim=0) / total
@@ -54,13 +61,26 @@ def _index_sv_cumulative(S: torch.Tensor, target: float) -> int:
     return max(1, min(rank, len(S) - 1))
 
 
-def _index_sv_fro(S: torch.Tensor, target: float) -> int:
-    """Frobenius norm mode - preserve target fraction of Frobenius norm."""
-    S_sq = S.pow(2)
-    total_sq = torch.sum(S_sq)
+def _index_sv_fro(S: torch.Tensor, target: float, max_rank: int = None) -> int:
+    """Frobenius norm mode - preserve target fraction of Frobenius norm.
+    
+    Calculates relative to max_rank if provided, otherwise relative to full.
+    This means "retain target% of what's achievable within max_rank".
+    """
+    if max_rank is not None and max_rank < len(S):
+        # Calculate relative to what's achievable within max_rank
+        S_capped = S[:max_rank]
+        S_sq = S_capped.pow(2)
+        total_sq = torch.sum(S_sq)
+    else:
+        S_sq = S.pow(2)
+        total_sq = torch.sum(S_sq)
+    
     if total_sq < 1e-8:
         return 1
-    cumsum = torch.cumsum(S_sq, dim=0) / total_sq
+    
+    # Cumsum of all S (not capped) to find where we reach target
+    cumsum = torch.cumsum(S.pow(2), dim=0) / total_sq
     rank = int(torch.searchsorted(cumsum, target ** 2).item()) + 1
     return max(1, min(rank, len(S) - 1))
 
@@ -122,9 +142,9 @@ def _compute_rank(S: torch.Tensor, mode: str, mode_param: float,
     elif mode == "ratio":
         rank = _index_sv_ratio(S, mode_param)
     elif mode == "quantile" or mode == "sv_cumulative":
-        rank = _index_sv_cumulative(S, mode_param)
+        rank = _index_sv_cumulative(S, mode_param, max_rank)
     elif mode == "sv_fro":
-        rank = _index_sv_fro(S, mode_param)
+        rank = _index_sv_fro(S, mode_param, max_rank)
     elif mode == "sv_knee":
         rank = _index_sv_knee(S)
     elif mode == "sv_cumulative_knee":
@@ -403,7 +423,6 @@ def extract_lora_from_files(
     mode: str,
     linear_param: float,
     conv_param: float,
-    prefix: str,
     device: str,
     save_dtype: str,
     linear_max_rank: int = None,
@@ -424,7 +443,6 @@ def extract_lora_from_files(
         mode: Rank selection mode
         linear_param: Mode parameter for linear layers
         conv_param: Mode parameter for conv layers
-        prefix: LoRA key prefix (e.g. "lora_unet_")
         device: Computation device
         save_dtype: Output dtype
         linear_max_rank: Max rank for linear layers
@@ -464,8 +482,7 @@ def extract_lora_from_files(
         stats = {"extracted": 0, "full": 0, "skipped": 0, "chunked": 0}
 
         for key in tqdm(weight_keys, desc="Extracting LoRA", unit="layers"):
-            lora_key = key[:-7]
-            lora_name = prefix + lora_key.replace(".", "_")
+            lora_name = _format_lora_key(key)
 
             if _matches_any_pattern(key, skip_patterns):
                 stats["skipped"] += 1
@@ -547,7 +564,7 @@ def extract_lora_from_files(
                         if lora_up is not None:
                             output_sd[f"{lora_name}.lora_up.weight"] = lora_up.to(save_torch_dtype).cpu().contiguous()
                             output_sd[f"{lora_name}.lora_down.weight"] = lora_down.to(save_torch_dtype).cpu().contiguous()
-                            output_sd[f"{lora_name}.alpha"] = torch.tensor([rank]).to(save_torch_dtype)
+                            output_sd[f"{lora_name}.alpha"] = torch.tensor(rank).to(save_torch_dtype)
                             stats["chunked"] += 1
                             del weight_diff
                             pbar.update(1)
@@ -567,7 +584,7 @@ def extract_lora_from_files(
                 lora_down, lora_up, _ = result
                 output_sd[f"{lora_name}.lora_down.weight"] = lora_down.to(save_torch_dtype).cpu().contiguous()
                 output_sd[f"{lora_name}.lora_up.weight"] = lora_up.to(save_torch_dtype).cpu().contiguous()
-                output_sd[f"{lora_name}.alpha"] = torch.tensor([lora_down.shape[0]]).to(save_torch_dtype)
+                output_sd[f"{lora_name}.alpha"] = torch.tensor(lora_down.shape[0]).to(save_torch_dtype)
                 stats["extracted"] += 1
 
             del weight_diff
@@ -612,6 +629,84 @@ def _get_model_inputs():
     ]
 
 
+def _reconstruct_dots(key: str) -> str:
+    """Reconstruct dot structure from underscored key using heuristics."""
+    # Block indices: input_blocks_1 -> input_blocks.1
+    blocks = [
+        "input_blocks", "output_blocks", "middle_block",
+        "transformer_blocks", "single_transformer_blocks",
+        "double_blocks", "single_blocks",
+        "down_blocks", "up_blocks", "mid_block",
+        "attentions", "resnets", "upsamplers", "downsamplers"
+    ]
+    for b in blocks:
+        key = re.sub(f"{b}_(\\d+)", f"{b}.\\1", key)
+
+    # Sequential numbers: .1_1 -> .1.1
+    key = re.sub(r"(\.\d+)_(\d+)", r"\1.\2", key)
+
+    # Separators that should be preceded by dot
+    separators = [
+        "transformer_blocks", "single_transformer_blocks",
+        "attn1", "attn2", "attn",
+        "img_attn", "txt_attn",
+        "to_q", "to_k", "to_v", "to_out",
+        "q_proj", "k_proj", "v_proj", "out_proj",
+        "qkv", "proj", "ff", "net",
+        "norm1", "norm2", "norm3", "norm",
+        "time_emb_proj"
+    ]
+    for s in separators:
+        key = key.replace(f"_{s}", f".{s}")
+
+    # Special case: to_out_0 -> to_out.0
+    key = re.sub(r"to_out_(\d+)", r"to_out.\1", key)
+
+    return key
+
+
+def _format_lora_key(key: str) -> str:
+    """
+    Format the key for LoRA saving.
+    Standardizes to 'diffusion_model.' or 'transformer.' prefix.
+    """
+    if key.endswith(".weight"):
+        key = key[:-7]
+
+    # Handle ComfyUI Checkpoint wrapper
+    if key.startswith("model.diffusion_model."):
+        # Check if it's a Diffusers-format transformer inside a checkpoint
+        inner_key = key[22:]  # len("model.diffusion_model.")
+        if inner_key.startswith("transformer_blocks") or inner_key.startswith("single_transformer_blocks"):
+            return f"transformer.{inner_key}"
+        # Standard checkpoint or Original Flux
+        return f"diffusion_model.{inner_key}"
+
+    # Handle direct Diffusers keys
+    if key.startswith("transformer_blocks") or key.startswith("single_transformer_blocks"):
+        return f"transformer.{key}"
+
+    # Handle legacy lora_unet_ prefix (for resizing without base)
+    if key.startswith("lora_unet_"):
+        core = key[10:]
+        dotted = _reconstruct_dots(core)
+        # Check if reconstructed key implies transformer format
+        if dotted.startswith("transformer_blocks") or dotted.startswith("single_transformer_blocks"):
+            return f"transformer.{dotted}"
+        return f"diffusion_model.{dotted}"
+
+    # Handle already correct prefixes
+    if key.startswith("diffusion_model.") or key.startswith("transformer."):
+        return key
+    
+    # Handle known Unet blocks (Diffusers format UNet)
+    if any(key.startswith(p) for p in ["down_blocks", "up_blocks", "mid_block", "conv_in", "conv_out", "time_embedding", "class_embedding"]):
+        return f"diffusion_model.{key}"
+
+    # Default fallback to diffusion_model
+    return f"diffusion_model.{key}"
+
+
 def _get_common_inputs():
     return [
         io.Boolean.Input("chunk_large_layers", default=False,
@@ -663,7 +758,7 @@ class LoRAExtractFixed(io.ComfyNode):
 
         output_sd = extract_lora_from_files(
             model_a_path, model_b_path, "fixed", linear_dim, conv_dim,
-            "lora_unet_", device, save_dtype, linear_dim, conv_dim,
+            device, save_dtype, linear_dim, conv_dim,
             clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers, svd_niter
         )
 
@@ -686,9 +781,9 @@ class LoRAExtractRatio(io.ComfyNode):
                               tooltip="Ratio threshold for linear layers (higher = more SVs kept)"),
                 io.Float.Input("conv_ratio", default=2.0, min=1.0, max=100.0, step=0.1,
                               tooltip="Ratio threshold for conv layers (higher = more SVs kept)"),
-                io.Int.Input("linear_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("linear_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for linear layers"),
-                io.Int.Input("conv_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("conv_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for conv layers"),
                 *_get_common_inputs(),
             ],
@@ -706,7 +801,7 @@ class LoRAExtractRatio(io.ComfyNode):
 
         output_sd = extract_lora_from_files(
             model_a_path, model_b_path, "ratio", linear_ratio, conv_ratio,
-            "lora_unet_", device, save_dtype, linear_max_rank, conv_max_rank,
+            device, save_dtype, linear_max_rank, conv_max_rank,
             clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers
         )
 
@@ -729,9 +824,9 @@ class LoRAExtractQuantile(io.ComfyNode):
                               tooltip="Target cumulative % for linear layers"),
                 io.Float.Input("conv_quantile", default=0.9, min=0.0, max=1.0, step=0.01,
                               tooltip="Target cumulative % for conv layers"),
-                io.Int.Input("linear_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("linear_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for linear layers"),
-                io.Int.Input("conv_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("conv_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for conv layers"),
                 *_get_common_inputs(),
             ],
@@ -749,7 +844,7 @@ class LoRAExtractQuantile(io.ComfyNode):
 
         output_sd = extract_lora_from_files(
             model_a_path, model_b_path, "quantile", linear_quantile, conv_quantile,
-            "lora_unet_", device, save_dtype, linear_max_rank, conv_max_rank,
+            device, save_dtype, linear_max_rank, conv_max_rank,
             clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers
         )
 
@@ -770,9 +865,9 @@ class LoRAExtractKnee(io.ComfyNode):
                 *_get_model_inputs(),
                 io.Combo.Input("knee_method", options=["sv_knee", "sv_cumulative_knee"],
                               default="sv_knee", tooltip="Knee detection method"),
-                io.Int.Input("linear_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("linear_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for linear layers"),
-                io.Int.Input("conv_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("conv_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for conv layers"),
                 *_get_common_inputs(),
             ],
@@ -790,7 +885,7 @@ class LoRAExtractKnee(io.ComfyNode):
 
         output_sd = extract_lora_from_files(
             model_a_path, model_b_path, knee_method, 0, 0,
-            "lora_unet_", device, save_dtype, linear_max_rank, conv_max_rank,
+            device, save_dtype, linear_max_rank, conv_max_rank,
             clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers
         )
 
@@ -813,9 +908,9 @@ class LoRAExtractFrobenius(io.ComfyNode):
                               tooltip="Target Frobenius norm fraction for linear"),
                 io.Float.Input("conv_target", default=0.9, min=0.0, max=1.0, step=0.01,
                               tooltip="Target Frobenius norm fraction for conv"),
-                io.Int.Input("linear_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("linear_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for linear layers"),
-                io.Int.Input("conv_max_rank", default=128, min=1, max=1024,
+                io.Int.Input("conv_max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum rank for conv layers"),
                 *_get_common_inputs(),
             ],
@@ -833,7 +928,7 @@ class LoRAExtractFrobenius(io.ComfyNode):
 
         output_sd = extract_lora_from_files(
             model_a_path, model_b_path, "sv_fro", linear_target, conv_target,
-            "lora_unet_", device, save_dtype, linear_max_rank, conv_max_rank,
+            device, save_dtype, linear_max_rank, conv_max_rank,
             clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers
         )
 

nodes/lora_merger.py

@@ -0,0 +1,549 @@
+"""
+LoRA Multi-Merge - Merge multiple LoRAs into a single LoRA file.
+Resolves different naming conventions and ranks via concatenation.
+"""
+import os
+import torch
+import folder_paths
+import comfy.utils
+from tqdm import tqdm
+from comfy_api.latest import io
+from safetensors.torch import save_file
+from typing import List, Dict, Tuple, Optional
+from .merger_utils import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
+from .device_utils import estimate_model_size, prepare_for_large_operation, cleanup_after_operation
+from .lora_resize import (
+    detect_lora_format, 
+    extract_lora_pairs, 
+    detect_lora_rank,
+    _compile_patterns,
+    _matches_any_pattern,
+    _format_lora_key
+)
+
+def merge_multi_loras(
+    lora_paths: List[str],
+    lora_weights: List[float],
+    merge_mode: str,
+    device: str,
+    save_dtype: torch.dtype,
+    output_filename: str,
+    verbose: bool = True,
+) -> str:
+    """
+    Merge multiple LoRAs into a single LoRA file.
+    
+    Modes:
+    - concatenate: Mathematically sound merge by stacking ranks.
+                   New rank = sum(input ranks). Alpha set to new rank.
+    - weighted_sum: Weighted sum of A and B weights (Kohya style).
+                    If ranks differ, smaller ones are zero-padded to match the largest rank.
+                    New rank = max(input ranks).
+    """
+    # Estimate memory
+    total_size_gb = sum(estimate_model_size(p) for p in lora_paths)
+    if verbose:
+        print(f"[LoRA Multi-Merge] Preparing memory for {total_size_gb:.2f}GB operation...")
+        print(f"[LoRA Multi-Merge] Mode: {merge_mode}")
+    
+    prepare_for_large_operation(total_size_gb * 2.5, torch.device(device))
+
+    handlers = [MemoryEfficientSafeOpen(p) for p in lora_paths]
+    
+    try:
+        # 1. Analyze all LoRAs
+        layer_map = {}
+        lora_infos = []
+        
+        for i, handler in enumerate(handlers):
+            keys = handler.keys()
+            fmt = detect_lora_format(keys)
+            pairs = extract_lora_pairs(keys, fmt)
+            rank, alpha = detect_lora_rank(handler, pairs)
+            
+            info = {
+                "name": os.path.basename(lora_paths[i]),
+                "format": fmt,
+                "pairs": pairs,
+                "rank": rank,
+                "alpha": alpha,
+                "weight": lora_weights[i],
+                "scale": alpha / rank if rank > 0 else 1.0
+            }
+            lora_infos.append(info)
+            
+            if verbose:
+                print(f"[LoRA Multi-Merge] LoRA {i+1} ({info['name']}): {len(pairs)} layers, dim={rank}, format={fmt['format']}")
+            
+            for block_name in pairs.keys():
+                core = _format_lora_key(block_name)
+                if core not in layer_map:
+                    layer_map[core] = []
+                layer_map[core].append(i)
+
+        if verbose:
+            print(f"[LoRA Multi-Merge] Total unique layers after resolving naming: {len(layer_map)}")
+
+        output_sd = {}
+        pbar = comfy.utils.ProgressBar(len(layer_map))
+
+        # 2. Merge each core layer
+        with torch.no_grad():
+            for core, indices in tqdm(layer_map.items(), desc="Merging layers", unit="layers"):
+                downs = []
+                ups = []
+                is_conv = False
+                max_rank = 0
+                
+                # First pass: load and determine max rank
+                for idx in indices:
+                    info = lora_infos[idx]
+                    handler = handlers[idx]
+                    orig_block = next(b for b in info["pairs"].keys() if _format_lora_key(b) == core)
+                    block_keys = info["pairs"][orig_block]
+                    
+                    if "down" not in block_keys or "up" not in block_keys:
+                        continue
+                        
+                    t_down = handler.get_tensor(block_keys["down"])
+                    t_up = handler.get_tensor(block_keys["up"])
+                    
+                    if device == 'cuda':
+                        t_down = transfer_to_gpu_pinned(t_down, device, torch.float32)
+                        t_up = transfer_to_gpu_pinned(t_up, device, torch.float32)
+                    else:
+                        t_down = t_down.to(device=device, dtype=torch.float32)
+                        t_up = t_up.to(device=device, dtype=torch.float32)
+                    
+                    # Store with original info for padding/weighting
+                    is_conv = len(t_down.shape) == 4
+                    current_rank = t_down.shape[0]
+                    max_rank = max(max_rank, current_rank)
+                    
+                    # Apply scale immediately for concatenate mode, or keep for later
+                    if merge_mode == "concatenate":
+                        effective_weight = info["weight"] * info["scale"]
+                        t_up = t_up * effective_weight
+                    
+                    downs.append((t_down, info))
+                    ups.append((t_up, info))
+
+                if not downs:
+                    pbar.update(1)
+                    continue
+
+                if merge_mode == "concatenate":
+                    # Stack ranks
+                    merged_down = torch.cat([d[0] for d in downs], dim=0)
+                    merged_up = torch.cat([u[0] for u in ups], dim=1)
+                    new_rank = merged_down.shape[0]
+                    new_alpha = float(new_rank)
+                else:
+                    # weighted_sum (Kohya style)
+                    # Result = sum( weight_i * Padded(B_i) ), sum( weight_i * Padded(A_i) )
+                    # Note: We apply scale to weights here to normalize different alphas
+                    merged_down = torch.zeros_like(downs[0][0])
+                    # Need to handle different ranks via padding
+                    target_down_shape = list(downs[0][0].shape)
+                    target_down_shape[0] = max_rank
+                    target_up_shape = list(ups[0][0].shape)
+                    target_up_shape[1] = max_rank
+                    
+                    merged_down = torch.zeros(target_down_shape, device=device, dtype=torch.float32)
+                    merged_up = torch.zeros(target_up_shape, device=device, dtype=torch.float32)
+                    
+                    for (t_d, info_d), (t_u, info_u) in zip(downs, ups):
+                        r = t_d.shape[0]
+                        w = info_d["weight"]
+                        # We also apply sqrt(scale) to both to distribute the scale factor?
+                        # Kohya just uses weights. But to be safe with different alphas,
+                        # we apply scale to the final delta.
+                        # For direct weight merge, we'll just use weights.
+                        
+                        if r < max_rank:
+                            # Pad down: [r, in...] -> [max_rank, in...]
+                            pad_d = [0] * (len(t_d.shape) * 2)
+                            pad_d[-1] = max_rank - r # last dim in pad is first dim in tensor (reversed)
+                            # Wait, F.pad uses reverse order of dims.
+                            # For [r, in], padding is (0,0, 0, max_rank-r)
+                            padding_d = [0, 0] * (len(t_d.shape) - 1) + [0, max_rank - r]
+                            t_d = torch.nn.functional.pad(t_d, tuple(padding_d))
+                            
+                            # Pad up: [out, r...] -> [out, max_rank...]
+                            # For [out, r], padding is (0, max_rank-r, 0, 0)
+                            padding_u = [0, max_rank - r] + [0, 0] * (len(t_u.shape) - 1)
+                            t_u = torch.nn.functional.pad(t_u, tuple(padding_u))
+                        
+                        merged_down += w * t_d
+                        merged_up += w * t_u
+                    
+                    new_rank = max_rank
+                    # Alpha is usually max of alphas or same as rank
+                    new_alpha = float(max_rank)
+
+                # Store
+                output_sd[f"{core}.lora_down.weight"] = merged_down.to(save_dtype).cpu().contiguous()
+                output_sd[f"{core}.lora_up.weight"] = merged_up.to(save_dtype).cpu().contiguous()
+                output_sd[f"{core}.alpha"] = torch.tensor(new_alpha, dtype=save_dtype)
+                
+                # Cleanup
+                del merged_down, merged_up
+                for d, _ in downs: del d
+                for u, _ in ups: del u
+                
+                pbar.update(1)
+
+        # 3. Final Summary
+        if verbose:
+            matched_stats = {i: 0 for i in range(len(lora_paths))}
+            for indices in layer_map.values():
+                for idx in indices:
+                    matched_stats[idx] += 1
+            
+            print(f"[LoRA Multi-Merge] --- Merge Summary ---")
+            for i, info in enumerate(lora_infos):
+                print(f"[LoRA Multi-Merge] LoRA {i+1}: {matched_stats[i]}/{len(info['pairs'])} layers used in merge")
+            print(f"[LoRA Multi-Merge] Output state dict has {len(output_sd)} tensors")
+
+        # 4. Metadata
+        metadata = {
+            "ss_training_comment": f"Merged {len(lora_paths)} LoRAs via concatenation",
+            "ss_network_module": "networks.lora",
+        }
+        
+        # 4. Save
+        output_dir = folder_paths.get_folder_paths("loras")[0]
+        os.makedirs(output_dir, exist_ok=True)
+        output_path = os.path.join(output_dir, f"{output_filename.strip()}.safetensors")
+        
+        save_file(output_sd, output_path, metadata)
+        if verbose:
+            print(f"[LoRA Multi-Merge] Saved merged LoRA to {output_path}")
+            
+        return output_path
+
+    finally:
+        for h in handlers:
+            h.__exit__(None, None, None)
+        cleanup_after_operation()
+
+
+class LoRAMultiMerge(io.ComfyNode):
+    """Merge multiple LoRAs into a single LoRA file."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoRAMultiMerge",
+            display_name="LoRA Multi-Merge",
+            category="ModelUtils/LoRA/Merge",
+            description="Merge 1-8 LoRAs into a single LoRA file. This resolves different ranks and naming conventions.",
+            inputs=[
+                io.Combo.Input("lora_count", options=[str(i) for i in range(1, 9)], default="2",
+                              tooltip="Number of LoRAs to merge"),
+                # LoRA 1
+                io.Combo.Input("lora_1", options=folder_paths.get_filename_list("loras"), tooltip="First LoRA"),
+                io.Float.Input("weight_1", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 2
+                io.Combo.Input("lora_2", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_2", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 3
+                io.Combo.Input("lora_3", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_3", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 4
+                io.Combo.Input("lora_4", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_4", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 5
+                io.Combo.Input("lora_5", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_5", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 6
+                io.Combo.Input("lora_6", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_6", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 7
+                io.Combo.Input("lora_7", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_7", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # LoRA 8
+                io.Combo.Input("lora_8", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_8", default=1.0, min=-10.0, max=10.0, step=0.01),
+                
+                io.Combo.Input("merge_mode", options=["concatenate", "weighted_sum"], default="concatenate",
+                              tooltip="concatenate: safe, increases rank. weighted_sum: fixed rank (to max input rank), mathematically lossy but standard in Kohya."),
+                io.String.Input("output_filename", default="merged_lora"),
+                io.Combo.Input("save_dtype", options=["fp16", "bf16", "fp32"], default="fp16"),
+                io.Combo.Input("device", options=["cuda", "cpu"], default="cuda"),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, lora_count,
+                lora_1, weight_1, lora_2, weight_2, lora_3, weight_3, lora_4, weight_4,
+                lora_5, weight_5, lora_6, weight_6, lora_7, weight_7, lora_8, weight_8,
+                merge_mode, output_filename, save_dtype, device) -> io.NodeOutput:
+        
+        count = int(lora_count)
+        names = [lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8]
+        weights = [weight_1, weight_2, weight_3, weight_4, weight_5, weight_6, weight_7, weight_8]
+        
+        valid_paths = []
+        valid_weights = []
+        
+        for i in range(count):
+            if names[i] and names[i] != "None":
+                path = folder_paths.get_full_path_or_raise("loras", names[i])
+                valid_paths.append(path)
+                valid_weights.append(weights[i])
+                
+        if not valid_paths:
+            raise ValueError("No LoRAs selected for merging.")
+            
+        dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
+        
+        path = merge_multi_loras(
+            lora_paths=valid_paths,
+            lora_weights=valid_weights,
+            merge_mode=merge_mode,
+            device=device,
+            save_dtype=dtype,
+            output_filename=output_filename
+        )
+        
+        return io.NodeOutput(path)
+
+
+class LoRAMultiMergeDARE(io.ComfyNode):
+    """Merge multiple LoRAs into a single LoRA file using DARE-Ties method."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoRAMultiMergeDARE",
+            display_name="LoRA Multi-Merge (DARE-Ties)",
+            category="ModelUtils/LoRA/Merge",
+            description="Merge 1-8 LoRAs into a single LoRA file using DARE-Ties. Drops small values and resolves sign conflicts.",
+            inputs=[
+                io.Combo.Input("lora_count", options=[str(i) for i in range(1, 9)], default="2"),
+                # LoRA 1
+                io.Combo.Input("lora_1", options=folder_paths.get_filename_list("loras"), tooltip="First LoRA"),
+                io.Float.Input("weight_1", default=1.0, min=-10.0, max=10.0, step=0.01),
+                # ...
+                io.Combo.Input("lora_2", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_2", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Combo.Input("lora_3", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_3", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Combo.Input("lora_4", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_4", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Combo.Input("lora_5", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_5", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Combo.Input("lora_6", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_6", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Combo.Input("lora_7", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_7", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Combo.Input("lora_8", options=["None"] + folder_paths.get_filename_list("loras"), default="None"),
+                io.Float.Input("weight_8", default=1.0, min=-10.0, max=10.0, step=0.01),
+
+                io.Float.Input("drop_rate", default=0.1, min=0.0, max=1.0, step=0.01, tooltip="DARE drop rate"),
+                io.Float.Input("trim_quantile", default=0.2, min=0.0, max=1.0, step=0.01, tooltip="TIES trim quantile (drops smallest values)"),
+                io.Int.Input("seed", default=42, min=0, max=0xffffffffffffffff),
+                io.String.Input("output_filename", default="merged_lora_dare"),
+                io.Combo.Input("save_dtype", options=["fp16", "bf16", "fp32"], default="fp16"),
+                io.Combo.Input("device", options=["cuda", "cpu"], default="cuda"),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, lora_count,
+                lora_1, weight_1, lora_2, weight_2, lora_3, weight_3, lora_4, weight_4,
+                lora_5, weight_5, lora_6, weight_6, lora_7, weight_7, lora_8, weight_8,
+                drop_rate, trim_quantile, seed, output_filename, save_dtype, device) -> io.NodeOutput:
+        
+        count = int(lora_count)
+        names = [lora_1, lora_2, lora_3, lora_4, lora_5, lora_6, lora_7, lora_8]
+        weights = [weight_1, weight_2, weight_3, weight_4, weight_5, weight_6, weight_7, weight_8]
+        
+        valid_paths = []
+        valid_weights = []
+        
+        for i in range(count):
+            if names[i] and names[i] != "None":
+                path = folder_paths.get_full_path_or_raise("loras", names[i])
+                valid_paths.append(path)
+                valid_weights.append(weights[i])
+                
+        if not valid_paths:
+            raise ValueError("No LoRAs selected for merging.")
+            
+        dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
+        
+        path = merge_multi_loras_dare(
+            lora_paths=valid_paths,
+            lora_weights=valid_weights,
+            drop_rate=drop_rate,
+            trim_quantile=trim_quantile,
+            seed=seed,
+            device=device,
+            save_dtype=dtype,
+            output_filename=output_filename
+        )
+        
+        return io.NodeOutput(path)
+
+def merge_multi_loras_dare(
+    lora_paths: List[str],
+    lora_weights: List[float],
+    drop_rate: float,
+    trim_quantile: float,
+    seed: int,
+    device: str,
+    save_dtype: torch.dtype,
+    output_filename: str,
+    verbose: bool = True,
+) -> str:
+    """
+    Merge multiple LoRAs using DARE-Ties method applied to weights.
+    Different ranks are handled via zero-padding to the max rank.
+    """
+    total_size_gb = sum(estimate_model_size(p) for p in lora_paths)
+    if verbose:
+        print(f"[LoRA Multi-Merge DARE] Preparing memory for {total_size_gb:.2f}GB operation...")
+        print(f"[LoRA Multi-Merge DARE] Drop rate: {drop_rate}, Trim quantile: {trim_quantile}")
+
+    prepare_for_large_operation(total_size_gb * 2.5, torch.device(device))
+    handlers = [MemoryEfficientSafeOpen(p) for p in lora_paths]
+    
+    rng = torch.Generator(device=device).manual_seed(seed)
+
+    try:
+        # 1. Analyze
+        layer_map = {}
+        lora_infos = []
+        for i, handler in enumerate(handlers):
+            keys = handler.keys()
+            fmt = detect_lora_format(keys)
+            pairs = extract_lora_pairs(keys, fmt)
+            rank, alpha = detect_lora_rank(handler, pairs)
+            info = {
+                "name": os.path.basename(lora_paths[i]),
+                "pairs": pairs,
+                "rank": rank,
+                "alpha": alpha,
+                "weight": lora_weights[i]
+            }
+            lora_infos.append(info)
+            
+            if verbose:
+                print(f"[LoRA Multi-Merge DARE] LoRA {i+1} ({info['name']}): {len(pairs)} layers, dim={rank}")
+
+            for block_name in pairs.keys():
+                core = _format_lora_key(block_name)
+                if core not in layer_map: layer_map[core] = []
+                layer_map[core].append(i)
+
+        if verbose:
+            print(f"[LoRA Multi-Merge DARE] Total unique layers after resolving naming: {len(layer_map)}")
+
+        output_sd = {}
+        pbar = comfy.utils.ProgressBar(len(layer_map))
+
+        # 2. Merge
+        with torch.no_grad():
+            for core, indices in tqdm(layer_map.items(), desc="Merging layers (DARE)", unit="layers"):
+                downs = []
+                ups = []
+                max_rank = 0
+                
+                for idx in indices:
+                    info = lora_infos[idx]
+                    orig_block = next(b for b in info["pairs"].keys() if _format_lora_key(b) == core)
+                    block_keys = info["pairs"][orig_block]
+                    if "down" not in block_keys or "up" not in block_keys: continue
+                    
+                    t_d = handlers[idx].get_tensor(block_keys["down"]).to(device=device, dtype=torch.float32)
+                    t_u = handlers[idx].get_tensor(block_keys["up"]).to(device=device, dtype=torch.float32)
+                    
+                    max_rank = max(max_rank, t_d.shape[0])
+                    downs.append((t_d, info["weight"]))
+                    ups.append((t_u, info["weight"]))
+
+                if not downs:
+                    pbar.update(1)
+                    continue
+
+                def process_ties_dare(tensors, weights, dim_to_pad):
+                    # Pad all to max_rank
+                    padded = []
+                    for t, w in tensors:
+                        if t.shape[dim_to_pad] < max_rank:
+                            padding = [0] * (len(t.shape) * 2)
+                            # dim_to_pad 0 (down) -> last pair in pad
+                            # dim_to_pad 1 (up) -> second to last pair in pad
+                            rev_dim = len(t.shape) - 1 - dim_to_pad
+                            padding[rev_dim*2 + 1] = max_rank - t.shape[dim_to_pad]
+                            t = torch.nn.functional.pad(t, tuple(padding))
+                        padded.append(t * w)
+                    
+                    # DARE
+                    if drop_rate > 0:
+                        for i in range(len(padded)):
+                            mask = (torch.rand(padded[i].shape, generator=rng, device=device) > drop_rate).float()
+                            padded[i] = (padded[i] * mask) / (1 - drop_rate)
+                    
+                    # TIES
+                    # 1. Trim
+                    if trim_quantile > 0:
+                        for i in range(len(padded)):
+                            flat = padded[i].abs().flatten()
+                            k = int(len(flat) * trim_quantile)
+                            if k > 0:
+                                threshold = torch.kthvalue(flat, k).values
+                                padded[i] = torch.where(padded[i].abs() < threshold, torch.zeros_like(padded[i]), padded[i])
+                    
+                    # 2. Elect & Merge
+                    stacked = torch.stack(padded) # [N, ...]
+                    signs = torch.sign(stacked)
+                    sum_signs = signs.sum(dim=0)
+                    dominant_sign = torch.sign(sum_signs)
+                    
+                    # Filter those matching dominant sign
+                    mask = (signs == dominant_sign) & (dominant_sign != 0)
+                    filtered = torch.where(mask, stacked, torch.zeros_like(stacked))
+                    
+                    # Average matching signs
+                    count = mask.sum(dim=0)
+                    result = filtered.sum(dim=0) / torch.clamp(count, min=1.0)
+                    return result
+
+                merged_down = process_ties_dare(downs, [1.0]*len(downs), 0)
+                merged_up = process_ties_dare(ups, [1.0]*len(ups), 1)
+
+                output_sd[f"{core}.lora_down.weight"] = merged_down.to(save_dtype).cpu().contiguous()
+                output_sd[f"{core}.lora_up.weight"] = merged_up.to(save_dtype).cpu().contiguous()
+                output_sd[f"{core}.alpha"] = torch.tensor(float(max_rank), dtype=save_dtype)
+                
+                pbar.update(1)
+
+        # Final Summary
+        if verbose:
+            matched_stats = {i: 0 for i in range(len(lora_paths))}
+            for indices in layer_map.values():
+                for idx in indices:
+                    matched_stats[idx] += 1
+            
+            print(f"[LoRA Multi-Merge DARE] --- Merge Summary ---")
+            for i, info in enumerate(lora_infos):
+                print(f"[LoRA Multi-Merge DARE] LoRA {i+1}: {matched_stats[i]}/{len(info['pairs'])} layers used in merge")
+            print(f"[LoRA Multi-Merge DARE] Output state dict has {len(output_sd)} tensors")
+
+        # Save
+        output_dir = folder_paths.get_folder_paths("loras")[0]
+        os.makedirs(output_dir, exist_ok=True)
+        output_path = os.path.join(output_dir, f"{output_filename.strip()}.safetensors")
+        save_file(output_sd, output_path, {"ss_training_comment": "Merged via DARE-Ties"})
+        return output_path
+
+    finally:
+        for h in handlers: h.__exit__(None, None, None)
+        cleanup_after_operation()

nodes/lora_resize.py

@@ -22,6 +22,11 @@ from .lora_extract_svd import (
     _index_sv_ratio,
     _index_sv_cumulative,
     _index_sv_fro,
+    _svd_extract_linear,
+    _svd_extract_conv,
+    _compile_patterns,
+    _matches_any_pattern,
+    _format_lora_key,
 )
 
 
@@ -35,14 +40,16 @@ MIN_SV = 1e-6
 def detect_lora_format(keys: List[str]) -> Dict:
     """
     Detect LoRA format and key patterns from various sources.
-    
+
     Supports:
     - Kohya/A1111: lora_unet_*, lora_te_*
     - Diffusers: *.lora_A.weight, *.lora_B.weight
     - PEFT: base_model.model.*.lora_A/B
     - ComfyUI native: *.lora_down.weight, *.lora_up.weight
+    - HuggingFace: *.lora.down.weight, *.lora.up.weight (dots instead of underscores)
+    - Full diff: *.diff, *.diff_b (full weight differences, not low-rank)
     - Flux/SDXL: Various transformer key patterns
-    
+
     Returns:
         Dict with format info and key patterns
     """
@@ -53,117 +60,153 @@ def detect_lora_format(keys: List[str]) -> Dict:
         "alpha_suffix": ".alpha",
         "has_alpha": False,
         "key_count": 0,
+        "is_full_diff": False,  # New flag for full diff format
     }
-    
+
     # Check for alpha keys
     alpha_keys = [k for k in keys if '.alpha' in k or k.endswith('alpha')]
     format_info["has_alpha"] = len(alpha_keys) > 0
-    
+
     # Detect format by key patterns
-    sample_keys = keys[:20]  # Check first 20 keys
-    
+    sample_keys = keys[:50]  # Check more keys for full_diff detection
+
+    # Full diff format: *.diff (NOT .lora_down, just straight weight diff)
+    if any(k.endswith('.diff') for k in sample_keys):
+        format_info["format"] = "full_diff"
+        format_info["down_suffix"] = ".diff"  # Used for pairing, but no up
+        format_info["up_suffix"] = None  # No up weight in full diff
+        format_info["alpha_suffix"] = None
+        format_info["is_full_diff"] = True
+
     # Kohya/A1111 format: lora_unet_down_blocks_0_*.lora_down.weight
-    if any('lora_unet_' in k or 'lora_te' in k for k in sample_keys):
+    elif any('lora_unet_' in k or 'lora_te' in k for k in sample_keys):
         format_info["format"] = "kohya"
         format_info["down_suffix"] = ".lora_down.weight"
         format_info["up_suffix"] = ".lora_up.weight"
-    
+
     # Diffusers format: transformer.*.lora_A.weight
     elif any('.lora_A.weight' in k for k in sample_keys):
         format_info["format"] = "diffusers"
         format_info["down_suffix"] = ".lora_A.weight"
         format_info["up_suffix"] = ".lora_B.weight"
         format_info["alpha_suffix"] = ".alpha"
-    
+
     # PEFT format: base_model.model.*.lora_A.default.weight
     elif any('base_model.model.' in k and 'lora_' in k for k in sample_keys):
         format_info["format"] = "peft"
         # Handle nested structure
         format_info["down_suffix"] = ".lora_A.default.weight"
         format_info["up_suffix"] = ".lora_B.default.weight"
-    
+
+    # HuggingFace format: transformer.*.lora.down.weight (dots instead of underscores)
+    elif any('.lora.down.weight' in k for k in sample_keys):
+        format_info["format"] = "huggingface"
+        format_info["down_suffix"] = ".lora.down.weight"
+        format_info["up_suffix"] = ".lora.up.weight"
+        format_info["alpha_suffix"] = ".alpha"
+
     # ComfyUI native / standard
     elif any('.lora_down.weight' in k for k in sample_keys):
         format_info["format"] = "comfy"
         format_info["down_suffix"] = ".lora_down.weight"
         format_info["up_suffix"] = ".lora_up.weight"
-    
+
     # Count LoRA layers
     down_keys = [k for k in keys if format_info["down_suffix"] in k]
     format_info["key_count"] = len(down_keys)
-    
+
     return format_info
 
 
+
 def extract_lora_pairs(keys: List[str], format_info: Dict) -> Dict[str, Dict[str, str]]:
     """
     Group LoRA keys into down/up/alpha pairs.
-    
+
+    For full_diff format, groups .diff and .diff_b keys.
+
     Returns:
         Dict[block_name, {"down": key, "up": key, "alpha": key}]
+        For full_diff: {"diff": key, "diff_b": key}
     """
     down_suffix = format_info["down_suffix"]
     up_suffix = format_info["up_suffix"]
     alpha_suffix = format_info["alpha_suffix"]
-    
+    is_full_diff = format_info.get("is_full_diff", False)
+
     pairs = {}
-    
-    for key in keys:
-        if down_suffix in key:
-            block_name = key.replace(down_suffix, "")
-            if block_name not in pairs:
-                pairs[block_name] = {}
-            pairs[block_name]["down"] = key
-        elif up_suffix in key:
-            block_name = key.replace(up_suffix, "")
-            if block_name not in pairs:
-                pairs[block_name] = {}
-            pairs[block_name]["up"] = key
-        elif alpha_suffix in key or key.endswith('.alpha'):
-            # Handle various alpha key formats
-            block_name = key.replace(alpha_suffix, "").replace(".alpha", "")
-            if block_name not in pairs:
-                pairs[block_name] = {}
-            pairs[block_name]["alpha"] = key
-    
+
+    if is_full_diff:
+        # Full diff format: group .diff and .diff_b
+        for key in keys:
+            if key.endswith('.diff'):
+                block_name = key[:-5]  # Remove .diff
+                if block_name not in pairs:
+                    pairs[block_name] = {}
+                pairs[block_name]["diff"] = key
+            elif key.endswith('.diff_b'):
+                block_name = key[:-7]  # Remove .diff_b
+                if block_name not in pairs:
+                    pairs[block_name] = {}
+                pairs[block_name]["diff_b"] = key
+    else:
+        # Standard LoRA format
+        for key in keys:
+            if down_suffix and down_suffix in key:
+                block_name = key.replace(down_suffix, "")
+                if block_name not in pairs:
+                    pairs[block_name] = {}
+                pairs[block_name]["down"] = key
+            elif up_suffix and up_suffix in key:
+                block_name = key.replace(up_suffix, "")
+                if block_name not in pairs:
+                    pairs[block_name] = {}
+                pairs[block_name]["up"] = key
+            elif alpha_suffix and (alpha_suffix in key or key.endswith('.alpha')):
+                # Handle various alpha key formats
+                block_name = key.replace(alpha_suffix, "").replace(".alpha", "")
+                if block_name not in pairs:
+                    pairs[block_name] = {}
+                pairs[block_name]["alpha"] = key
+
     return pairs
 
 
 def detect_lora_rank(handler: MemoryEfficientSafeOpen, pairs: Dict) -> Tuple[int, float]:
     """
     Detect the rank and alpha of an existing LoRA.
-    
+
     Returns:
         (network_dim, network_alpha)
     """
     network_dim = None
     network_alpha = None
-    
+
     for block_name, block_keys in pairs.items():
         if "down" not in block_keys:
             continue
-        
+
         # Get dim from down weight shape
         if network_dim is None:
             down_key = block_keys["down"]
             shape = handler.header[down_key]["shape"]
             # Linear: [rank, in_features] or Conv: [rank, in_ch, k, k]
             network_dim = shape[0]
-        
+
         # Get alpha if present
         if network_alpha is None and "alpha" in block_keys:
             alpha_tensor = handler.get_tensor(block_keys["alpha"])
             network_alpha = float(alpha_tensor.item())
-        
+
         if network_dim is not None and network_alpha is not None:
             break
-    
+
     # Default alpha to dim if not found
     if network_alpha is None:
         network_alpha = float(network_dim) if network_dim else 1.0
     if network_dim is None:
         network_dim = 1
-    
+
     return network_dim, network_alpha
 
 
@@ -183,10 +226,10 @@ def _merge_conv(lora_down: torch.Tensor, lora_up: torch.Tensor, device: str) ->
     in_rank, in_size, kernel_size, k_ = lora_down.shape
     out_size, out_rank, _, _ = lora_up.shape
     assert in_rank == out_rank, f"rank mismatch: {in_rank} vs {out_rank}"
-    
+
     lora_down = lora_down.to(device=device, dtype=torch.float32)
     lora_up = lora_up.to(device=device, dtype=torch.float32)
-    
+
     merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
     return merged.reshape(out_size, in_size, kernel_size, kernel_size)
 
@@ -205,7 +248,7 @@ def _extract_linear(
 ) -> Dict:
     """Extract LoRA from merged linear weight."""
     out_size, in_size = weight.shape
-    
+
     if dynamic_method is None:
         # Fixed rank - use svd_lowrank for 10x speedup
         rank = min(max_rank, min(out_size, in_size) - 1)
@@ -221,10 +264,10 @@ def _extract_linear(
         U = U[:, :new_rank]
         S = S[:new_rank]
         Vh = Vh[:new_rank, :]
-    
+
     lora_up = U @ torch.diag(S)
     lora_down = Vh
-    
+
     return {
         "lora_down": lora_down.cpu().contiguous(),
         "lora_up": lora_up.cpu().contiguous(),
@@ -245,7 +288,7 @@ def _extract_conv(
     """Extract LoRA from merged conv weight."""
     out_ch, in_ch, kh, kw = weight.shape
     mat = weight.reshape(out_ch, -1)
-    
+
     if dynamic_method is None:
         # Fixed rank - use svd_lowrank for 10x speedup
         rank = min(max_rank, min(mat.shape) - 1)
@@ -261,10 +304,10 @@ def _extract_conv(
         U = U[:, :new_rank]
         S = S[:new_rank]
         Vh = Vh[:new_rank, :]
-    
+
     lora_up = (U @ torch.diag(S)).reshape(out_ch, new_rank, 1, 1)
     lora_down = Vh.reshape(new_rank, in_ch, kh, kw)
-    
+
     return {
         "lora_down": lora_down.cpu().contiguous(),
         "lora_up": lora_up.cpu().contiguous(),
@@ -282,40 +325,39 @@ def _compute_resize(
     scale: float
 ) -> Tuple[int, float, Dict]:
     """Compute new rank and alpha based on resize method."""
-    
+
     if dynamic_method == "sv_ratio" and dynamic_param is not None:
-        # Note: _index_sv_ratio uses S[0]*ratio, kohya uses S[0]/ratio
-        # Use kohya convention here for consistency with their tool
+        # Use kohya convention: S[0]/ratio
         min_sv = S[0] / dynamic_param
         new_rank = max(1, int(torch.sum(S > min_sv).item()))
     elif dynamic_method == "sv_cumulative" and dynamic_param is not None:
-        new_rank = _index_sv_cumulative(S, dynamic_param)
+        new_rank = _index_sv_cumulative(S, dynamic_param, max_rank)
     elif dynamic_method == "sv_fro" and dynamic_param is not None:
-        new_rank = _index_sv_fro(S, dynamic_param)
+        new_rank = _index_sv_fro(S, dynamic_param, max_rank)
     else:
         new_rank = max_rank
-    
+
     # Clamp rank
     if S[0] < MIN_SV:
         new_rank = 1
     else:
         new_rank = max(1, min(new_rank, max_rank, len(S) - 1))
-    
+
     new_alpha = float(scale * new_rank)
-    
+
     # Compute retention stats
     s_sum = float(torch.sum(torch.abs(S)))
     s_rank = float(torch.sum(torch.abs(S[:new_rank]))) if new_rank <= len(S) else s_sum
-    
+
     S_sq = S.pow(2)
     s_fro = float(torch.sqrt(torch.sum(S_sq)))
     s_red_fro = float(torch.sqrt(torch.sum(S_sq[:new_rank]))) if new_rank <= len(S) else s_fro
-    
+
     stats = {
         "sum_retained": s_rank / s_sum if s_sum > 0 else 1.0,
         "fro_retained": s_red_fro / s_fro if s_fro > 0 else 1.0,
     }
-    
+
     return new_rank, new_alpha, stats
 
 
@@ -336,7 +378,7 @@ def resize_lora_file(
 ) -> str:
     """
     Resize a LoRA file to a new rank.
-    
+
     Args:
         lora_path: Path to input LoRA
         new_rank: Target rank (max rank for dynamic methods)
@@ -347,53 +389,53 @@ def resize_lora_file(
         output_filename: Output filename (without extension)
         verbose: Print progress info
         svd_niter: Power iterations for SVD accuracy
-    
+
     Returns:
         Path to saved resized LoRA
     """
     # Prepare memory
     lora_size_gb = estimate_model_size(lora_path)
     prepare_for_large_operation(lora_size_gb * 2, torch.device(device))
-    
+
     handler = MemoryEfficientSafeOpen(lora_path)
-    
+
     try:
         metadata = handler.metadata().copy()
         all_keys = handler.keys()
-        
+
         # Detect format and extract pairs
         format_info = detect_lora_format(all_keys)
         pairs = extract_lora_pairs(all_keys, format_info)
         network_dim, network_alpha = detect_lora_rank(handler, pairs)
-        
+
         scale = network_alpha / network_dim if network_dim > 0 else 1.0
-        
+
         if verbose:
             method_str = f"{dynamic_method}: {dynamic_param}" if dynamic_method else "fixed"
             print(f"[LoRA Resize] Format: {format_info['format']}, layers: {format_info['key_count']}")
             print(f"[LoRA Resize] Original dim={network_dim}, alpha={network_alpha:.1f}, scale={scale:.3f}")
             print(f"[LoRA Resize] Resizing with method={method_str}, max_rank={new_rank}")
-        
+
         output_sd = {}
         fro_list = []
         pbar = comfy.utils.ProgressBar(len(pairs))
-        
+
         with torch.no_grad():
             for block_name, block_keys in tqdm(pairs.items(), desc="Resizing layers", unit="layers"):
                 if "down" not in block_keys or "up" not in block_keys:
                     pbar.update(1)
                     continue
-                
+
                 lora_down = handler.get_tensor(block_keys["down"])
                 lora_up = handler.get_tensor(block_keys["up"])
-                
+
                 is_conv = len(lora_down.shape) == 4
-                
+
                 # Transfer to GPU with pinned memory if available
                 if device == 'cuda':
                     lora_down = transfer_to_gpu_pinned(lora_down, device, torch.float32)
                     lora_up = transfer_to_gpu_pinned(lora_up, device, torch.float32)
-                
+
                 # Merge and re-extract
                 if is_conv:
                     weight = _merge_conv(lora_down, lora_up, device)
@@ -401,28 +443,32 @@ def resize_lora_file(
                 else:
                     weight = _merge_linear(lora_down, lora_up, device)
                     result = _extract_linear(weight, new_rank, dynamic_method, dynamic_param, scale, svd_niter)
-                
+
                 del weight, lora_down, lora_up
-                
+
                 fro_list.append(result['fro_retained'])
-                
+
                 # Store using same format suffixes as input
                 down_suffix = format_info["down_suffix"]
                 up_suffix = format_info["up_suffix"]
                 alpha_suffix = format_info["alpha_suffix"]
-                
-                output_sd[f"{block_name}{down_suffix}"] = result["lora_down"].to(save_dtype)
-                output_sd[f"{block_name}{up_suffix}"] = result["lora_up"].to(save_dtype)
-                output_sd[f"{block_name}{alpha_suffix}"] = torch.tensor([result["new_alpha"]]).to(save_dtype)
-                
+
+                # Standardize prefix using heuristics if possible
+                new_block_name = _format_lora_key(block_name)
+
+                output_sd[f"{new_block_name}{down_suffix}"] = result["lora_down"].to(save_dtype)
+                output_sd[f"{new_block_name}{up_suffix}"] = result["lora_up"].to(save_dtype)
+                if alpha_suffix:
+                    output_sd[f"{new_block_name}{alpha_suffix}"] = torch.tensor(result["new_alpha"]).to(save_dtype)
+
                 pbar.update(1)
-        
+
         if verbose and fro_list:
             import numpy as np
             avg_fro = np.mean(fro_list)
             std_fro = np.std(fro_list)
             print(f"[LoRA Resize] Average Frobenius retention: {avg_fro:.1%} ± {std_fro:.3f}")
-        
+
         # Update metadata
         if dynamic_method:
             metadata["ss_training_comment"] = f"Dynamic resize with {dynamic_method}: {dynamic_param} from dim {network_dim}"
@@ -432,17 +478,17 @@ def resize_lora_file(
             metadata["ss_training_comment"] = f"Resized from dim {network_dim} to {new_rank}"
             metadata["ss_network_dim"] = str(new_rank)
             metadata["ss_network_alpha"] = str(scale * new_rank)
-        
+
         # Save
         output_dir = folder_paths.get_folder_paths("loras")[0]
         os.makedirs(output_dir, exist_ok=True)
         output_path = os.path.join(output_dir, f"{output_filename.strip()}.safetensors")
-        
+
         save_file(output_sd, output_path, metadata)
         print(f"[LoRA Resize] Saved to {output_path}")
-        
+
         return output_path
-        
+
     finally:
         handler.__exit__(None, None, None)
         cleanup_after_operation()
@@ -454,7 +500,7 @@ def resize_lora_file(
 
 class LoRAResizeFixed(io.ComfyNode):
     """Resize LoRA to a fixed rank."""
-    
+
     @classmethod
     def define_schema(cls):
         return io.Schema(
@@ -465,7 +511,7 @@ class LoRAResizeFixed(io.ComfyNode):
             inputs=[
                 io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"),
                               tooltip="LoRA to resize"),
-                io.Int.Input("new_rank", default=64, min=1, max=1024,
+                io.Int.Input("new_rank", default=64, min=1, max=3072,
                             tooltip="Target rank"),
                 io.Int.Input("svd_niter", default=2, min=0, max=10,
                             tooltip="SVD power iterations (higher = more accurate but slower)"),
@@ -476,19 +522,19 @@ class LoRAResizeFixed(io.ComfyNode):
             outputs=[io.String.Output(display_name="output_path")],
             is_output_node=True,
         )
-    
+
     @classmethod
     def execute(cls, lora_name, new_rank, svd_niter, output_filename, save_dtype, device) -> io.NodeOutput:
         lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
         dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
-        
+
         path = resize_lora_file(lora_path, new_rank, None, None, device, dtype, output_filename, svd_niter=svd_niter)
         return io.NodeOutput(path)
 
 
 class LoRAResizeRatio(io.ComfyNode):
     """Resize LoRA keeping SVs above ratio threshold."""
-    
+
     @classmethod
     def define_schema(cls):
         return io.Schema(
@@ -499,7 +545,7 @@ class LoRAResizeRatio(io.ComfyNode):
             inputs=[
                 io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"),
                               tooltip="LoRA to resize"),
-                io.Int.Input("max_rank", default=128, min=1, max=1024,
+                io.Int.Input("max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum allowed rank"),
                 io.Float.Input("ratio", default=2.0, min=1.0, max=100.0, step=0.1,
                               tooltip="Keep SVs where S[i] > S[0]/ratio"),
@@ -510,19 +556,19 @@ class LoRAResizeRatio(io.ComfyNode):
             outputs=[io.String.Output(display_name="output_path")],
             is_output_node=True,
         )
-    
+
     @classmethod
     def execute(cls, lora_name, max_rank, ratio, output_filename, save_dtype, device) -> io.NodeOutput:
         lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
         dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
-        
+
         path = resize_lora_file(lora_path, max_rank, "sv_ratio", ratio, device, dtype, output_filename)
         return io.NodeOutput(path)
 
 
 class LoRAResizeFrobenius(io.ComfyNode):
     """Resize LoRA to preserve Frobenius norm target."""
-    
+
     @classmethod
     def define_schema(cls):
         return io.Schema(
@@ -533,7 +579,7 @@ class LoRAResizeFrobenius(io.ComfyNode):
             inputs=[
                 io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"),
                               tooltip="LoRA to resize"),
-                io.Int.Input("max_rank", default=128, min=1, max=1024,
+                io.Int.Input("max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum allowed rank"),
                 io.Float.Input("target", default=0.9, min=0.1, max=1.0, step=0.01,
                               tooltip="Target Frobenius norm retention (0.9 = 90%)"),
@@ -544,19 +590,19 @@ class LoRAResizeFrobenius(io.ComfyNode):
             outputs=[io.String.Output(display_name="output_path")],
             is_output_node=True,
         )
-    
+
     @classmethod
     def execute(cls, lora_name, max_rank, target, output_filename, save_dtype, device) -> io.NodeOutput:
         lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
         dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
-        
+
         path = resize_lora_file(lora_path, max_rank, "sv_fro", target, device, dtype, output_filename)
         return io.NodeOutput(path)
 
 
 class LoRAResizeCumulative(io.ComfyNode):
     """Resize LoRA to preserve cumulative SV target."""
-    
+
     @classmethod
     def define_schema(cls):
         return io.Schema(
@@ -567,7 +613,7 @@ class LoRAResizeCumulative(io.ComfyNode):
             inputs=[
                 io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"),
                               tooltip="LoRA to resize"),
-                io.Int.Input("max_rank", default=128, min=1, max=1024,
+                io.Int.Input("max_rank", default=128, min=1, max=3072,
                             tooltip="Maximum allowed rank"),
                 io.Float.Input("target", default=0.9, min=0.1, max=1.0, step=0.01,
                               tooltip="Target cumulative SV retention (0.9 = 90%)"),
@@ -578,11 +624,332 @@ class LoRAResizeCumulative(io.ComfyNode):
             outputs=[io.String.Output(display_name="output_path")],
             is_output_node=True,
         )
-    
+
     @classmethod
     def execute(cls, lora_name, max_rank, target, output_filename, save_dtype, device) -> io.NodeOutput:
         lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
         dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
-        
+
         path = resize_lora_file(lora_path, max_rank, "sv_cumulative", target, device, dtype, output_filename)
         return io.NodeOutput(path)
+
+
+
+
+# =============================================================================
+# LoRA Merge To Model (Save merged model, skip extraction)
+# =============================================================================
+
+def merge_loras_to_model(
+    lora_paths: List[str],
+    lora_weights: List[float],
+    base_model_path: str,
+    device: str,
+    save_dtype: torch.dtype,
+    output_filename: str,
+    skip_patterns_str: str = "",
+    verbose: bool = True,
+) -> str:
+    """
+    Merge multiple LoRAs into a base model and save the result directly.
+
+    Unlike merge_multi_loras_via_base, this function:
+    - Does NOT extract the result back to a LoRA
+    - Saves the merged full model to the base model's directory
+
+    Args:
+        lora_paths: List of paths to LoRA files
+        lora_weights: List of weight strengths (0.0-2.0) for each LoRA
+        base_model_path: Path to base model
+        device: Processing device
+        save_dtype: Output dtype
+        output_filename: Output filename (without extension)
+        skip_patterns_str: Regex patterns for layers to skip
+        verbose: Print progress info
+
+    Returns:
+        Path to saved merged model
+    """
+    # Estimate memory and prepare
+    total_size_gb = estimate_model_size(base_model_path)
+    for lp in lora_paths:
+        total_size_gb += estimate_model_size(lp)
+
+    if verbose:
+        print(f"[LoRA Merge To Model] Preparing memory for {total_size_gb:.2f}GB operation...")
+        print(f"[LoRA Merge To Model] Merging {len(lora_paths)} LoRAs with weights: {lora_weights}")
+    prepare_for_large_operation(total_size_gb * 1.5, torch.device(device))
+
+    # Open all files - use low_memory for base model to avoid OS page caching
+    base_handler = MemoryEfficientSafeOpen(base_model_path, low_memory=True)
+    lora_handlers = [MemoryEfficientSafeOpen(lp) for lp in lora_paths]
+
+
+    try:
+        # Detect format and extract pairs for each LoRA
+        lora_infos = []
+
+        for i, handler in enumerate(lora_handlers):
+            keys = handler.keys()
+            format_info = detect_lora_format(keys)
+            pairs = extract_lora_pairs(keys, format_info)
+            network_dim, network_alpha = detect_lora_rank(handler, pairs)
+            lora_infos.append({
+                "handler": handler,
+                "format_info": format_info,
+                "pairs": pairs,
+                "network_dim": network_dim,
+                "network_alpha": network_alpha,
+                "weight": lora_weights[i],
+            })
+            if verbose:
+                print(f"[LoRA Merge To Model] LoRA {i+1}: {format_info['format']}, {len(pairs)} layers, dim={network_dim}")
+
+        # Common prefixes
+        BASE_PREFIXES = ["model.diffusion_model.", "diffusion_model.", "transformer.", "model."]
+        LORA_PREFIXES = [
+            "lora_unet_", "lora_transformer_", "lora_te1_", "lora_te2_", "lora_te_",
+            "lycoris_", "diffusion_model.", "transformer.", "unet."
+        ]
+
+        def extract_core_layer_base(key: str) -> str:
+            result = key
+            if result.endswith(".weight"):
+                result = result[:-7]
+            elif result.endswith(".bias"):
+                result = result[:-5]
+            for prefix in BASE_PREFIXES:
+                if result.startswith(prefix):
+                    result = result[len(prefix):]
+                    break
+            return result
+
+        def extract_core_layer_lora(block_name: str) -> str:
+            result = block_name
+            for prefix in LORA_PREFIXES:
+                if result.startswith(prefix):
+                    result = result[len(prefix):]
+                    break
+            # Strip trailing .lora suffix (HuggingFace format leaves this after suffix removal)
+            if result.endswith(".lora"):
+                result = result[:-5]
+            return result.replace(".", "_")
+
+
+        # Build LoRA lookup: core layer name (underscored) -> list of (info, block_keys)
+        lora_lookup = {}
+        for info in lora_infos:
+            for block_name, block_keys in info["pairs"].items():
+                core = extract_core_layer_lora(block_name)
+                if core not in lora_lookup:
+                    lora_lookup[core] = []
+                lora_lookup[core].append((info, block_keys))
+
+        # Compile skip patterns
+        skip_patterns = _compile_patterns(skip_patterns_str)
+
+        # Preserve metadata from base model
+        base_metadata = base_handler.metadata().copy() if base_handler.metadata() else {}
+        base_metadata["merge_comment"] = f"Merged {len(lora_paths)} LoRAs with weights: {lora_weights}"
+
+        output_sd = {}
+        stats = {"merged": 0, "copied": 0, "skipped": 0}
+        base_keys = list(base_handler.keys())
+        pbar = comfy.utils.ProgressBar(len(base_keys))
+
+        if verbose:
+            print(f"[LoRA Merge To Model] Processing {len(base_keys)} base model keys...")
+
+        with torch.no_grad():
+            for base_key in tqdm(base_keys, desc="Merging to model", unit="keys"):
+                # Check skip patterns
+                if _matches_any_pattern(base_key, skip_patterns):
+                    stats["skipped"] += 1
+                    pbar.update(1)
+                    continue
+
+                # Load base weight
+                cpu_base = base_handler.get_tensor(base_key)
+
+                # Only process weight tensors for LoRA merging
+                if base_key.endswith(".weight"):
+                    core = extract_core_layer_base(base_key)
+                    core_underscored = core.replace(".", "_")
+
+                    # Check if any LoRA contributes to this layer
+                    if core_underscored in lora_lookup:
+                        # Transfer to GPU for computation
+                        if device == 'cuda':
+                            base_weight = transfer_to_gpu_pinned(cpu_base, device, torch.float32)
+                        else:
+                            base_weight = cpu_base.to(device=device, dtype=torch.float32)
+                        del cpu_base
+
+                        # Accumulate deltas from all contributing LoRAs
+                        for info, block_keys in lora_lookup[core_underscored]:
+                            is_full_diff = info["format_info"].get("is_full_diff", False)
+
+                            if is_full_diff:
+                                # Full diff format
+                                if "diff" not in block_keys:
+                                    continue
+                                cpu_diff = info["handler"].get_tensor(block_keys["diff"])
+                                if device == 'cuda':
+                                    delta = transfer_to_gpu_pinned(cpu_diff, device, torch.float32)
+                                else:
+                                    delta = cpu_diff.to(device=device, dtype=torch.float32)
+                                del cpu_diff
+                                effective_scale = info["weight"]
+                            else:
+                                # Standard LoRA format
+                                if "down" not in block_keys or "up" not in block_keys:
+                                    continue
+
+                                cpu_down = info["handler"].get_tensor(block_keys["down"])
+                                cpu_up = info["handler"].get_tensor(block_keys["up"])
+                                if device == 'cuda':
+                                    lora_down = transfer_to_gpu_pinned(cpu_down, device, torch.float32)
+                                    lora_up = transfer_to_gpu_pinned(cpu_up, device, torch.float32)
+                                else:
+                                    lora_down = cpu_down.to(device=device, dtype=torch.float32)
+                                    lora_up = cpu_up.to(device=device, dtype=torch.float32)
+                                del cpu_down, cpu_up
+
+                                # Get alpha
+                                if "alpha" in block_keys:
+                                    alpha_tensor = info["handler"].get_tensor(block_keys["alpha"])
+                                    layer_alpha = float(alpha_tensor.item())
+                                else:
+                                    layer_alpha = float(info["network_dim"])
+                                layer_scale = layer_alpha / info["network_dim"] if info["network_dim"] > 0 else 1.0
+                                effective_scale = layer_scale * info["weight"]
+
+                                # Compute delta
+                                is_conv = len(lora_down.shape) == 4
+                                if is_conv:
+                                    in_rank, in_size, kernel_size, k_ = lora_down.shape
+                                    out_size, out_rank, _, _ = lora_up.shape
+                                    delta = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
+                                    delta = delta.reshape(out_size, in_size, kernel_size, kernel_size)
+                                else:
+                                    delta = lora_up @ lora_down
+                                del lora_down, lora_up
+
+                            # Apply delta to base weight
+                            base_weight = base_weight + effective_scale * delta
+                            del delta
+
+                        # Store merged weight
+                        output_sd[base_key] = base_weight.to(save_dtype).cpu().contiguous()
+                        del base_weight
+                        stats["merged"] += 1
+                    else:
+                        # No LoRA contribution, copy as-is
+                        output_sd[base_key] = cpu_base.to(save_dtype).contiguous()
+                        del cpu_base
+                        stats["copied"] += 1
+                else:
+                    # Non-weight tensor (bias, norm, etc.), copy as-is
+                    output_sd[base_key] = cpu_base.to(save_dtype).contiguous()
+                    del cpu_base
+                    stats["copied"] += 1
+
+
+                pbar.update(1)
+
+        if verbose:
+            print(f"[LoRA Merge To Model] Done: {stats['merged']} merged, {stats['copied']} copied, {stats['skipped']} skipped")
+
+        # Save to base model directory
+        base_dir = os.path.dirname(base_model_path)
+        os.makedirs(base_dir, exist_ok=True)
+        output_path = os.path.join(base_dir, f"{output_filename.strip()}.safetensors")
+
+        save_file(output_sd, output_path, base_metadata)
+        print(f"[LoRA Merge To Model] Saved to {output_path}")
+
+        return output_path
+
+    finally:
+        base_handler.__exit__(None, None, None)
+        for handler in lora_handlers:
+            handler.__exit__(None, None, None)
+        cleanup_after_operation()
+
+
+class LoRAMergeToModel(io.ComfyNode):
+    """Merge multiple LoRAs into base model and save as full model."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoRAMergeToModel",
+            display_name="LoRA Merge To Model",
+            category="ModelUtils/LoRA/Merge",
+            description="Merge 1-4 LoRAs into a base model and save the result. Saves to base model directory.",
+            inputs=[
+                io.Combo.Input("base_model", options=folder_paths.get_filename_list("diffusion_models"),
+                              tooltip="Base model the LoRAs were trained on"),
+                io.Combo.Input("lora_count", options=["1", "2", "3", "4"], default="2",
+                              tooltip="Number of LoRAs to merge"),
+                # LoRA 1
+                io.Combo.Input("lora_1", options=folder_paths.get_filename_list("loras"),
+                              tooltip="First LoRA"),
+                io.Float.Input("weight_1", default=1.0, min=-10.0, max=10.0, step=0.01,
+                              tooltip="Weight strength for LoRA 1"),
+                # LoRA 2
+                io.Combo.Input("lora_2", options=["None"] + folder_paths.get_filename_list("loras"),
+                              default="None", tooltip="Second LoRA"),
+                io.Float.Input("weight_2", default=1.0, min=-10.0, max=10.0, step=0.01,
+                              tooltip="Weight strength for LoRA 2"),
+                # LoRA 3
+                io.Combo.Input("lora_3", options=["None"] + folder_paths.get_filename_list("loras"),
+                              default="None", tooltip="Third LoRA"),
+                io.Float.Input("weight_3", default=1.0, min=-10.0, max=10.0, step=0.01,
+                              tooltip="Weight strength for LoRA 3"),
+                # LoRA 4
+                io.Combo.Input("lora_4", options=["None"] + folder_paths.get_filename_list("loras"),
+                              default="None", tooltip="Fourth LoRA"),
+                io.Float.Input("weight_4", default=1.0, min=-10.0, max=10.0, step=0.01,
+                              tooltip="Weight strength for LoRA 4"),
+                # Settings
+                io.String.Input("skip_patterns", default="", multiline=True,
+                               tooltip="Regex patterns for layers to skip"),
+                io.String.Input("output_filename", default="merged_model"),
+                io.Combo.Input("save_dtype", options=["fp16", "bf16", "fp32"], default="fp16"),
+                io.Combo.Input("device", options=["cuda", "cpu"], default="cuda"),
+            ],
+            outputs=[io.String.Output(display_name="output_path")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, base_model, lora_count,
+                lora_1, weight_1, lora_2, weight_2, lora_3, weight_3, lora_4, weight_4,
+                skip_patterns, output_filename, save_dtype, device) -> io.NodeOutput:
+
+        # Build LoRA list based on count
+        count = int(lora_count)
+        lora_names = [lora_1, lora_2, lora_3, lora_4][:count]
+        lora_weights = [weight_1, weight_2, weight_3, weight_4][:count]
+
+        # Filter out "None" entries
+        valid_loras = [(name, weight) for name, weight in zip(lora_names, lora_weights) if name != "None"]
+        if not valid_loras:
+            raise ValueError("At least one LoRA must be selected")
+
+        lora_names, lora_weights = zip(*valid_loras)
+        lora_paths = [folder_paths.get_full_path_or_raise("loras", name) for name in lora_names]
+        base_path = folder_paths.get_full_path_or_raise("diffusion_models", base_model)
+        dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
+
+        path = merge_loras_to_model(
+            lora_paths=list(lora_paths),
+            lora_weights=list(lora_weights),
+            base_model_path=base_path,
+            device=device,
+            save_dtype=dtype,
+            output_filename=output_filename,
+            skip_patterns_str=skip_patterns,
+        )
+        return io.NodeOutput(path)

nodes/merger.py

@@ -93,6 +93,7 @@ class MergerLogic:
         mismatch_mode_str = recipe_params.get('mismatch_mode', 'skip')
         mismatch_mode = MissingTensorBehavior(mismatch_mode_str)
         recipe_params['mismatch_mode'] = mismatch_mode
+        alignment_mode = recipe_params.get('alignment_mode', 'pad/crop')
 
         # Compile filter patterns
         exclude_patterns = _compile_patterns(recipe_params.get('exclude_patterns', ''))
@@ -168,6 +169,15 @@ class MergerLogic:
                 for r_key, r_tensor in result.items():
                     merged_state_dict[r_key] = r_tensor.to(save_torch_dtype).cpu().clone()
             else:
+                # Ensure compatibility with Model A's architecture.
+                # If alignment_mode is 'pad/crop', we crop results that were padded.
+                # If alignment_mode is 'interpolate', resizing happened during operators.
+                if alignment_mode == 'pad/crop':
+                    target_shape = recipe_params['_tensor_a_shape']
+                    if result.shape != target_shape:
+                        slices = tuple(slice(0, min(res_s, tgt_s)) for res_s, tgt_s in zip(result.shape, target_shape))
+                        result = result[slices]
+
                 merged_state_dict[key] = result.to(save_torch_dtype).cpu().clone()
 
             # Clean up tensor_a reference to allow GC
@@ -219,9 +229,11 @@ class CheckpointTwoMerger(io.ComfyNode):
                 io.Combo.Input("model_b", options=["None"] + folder_paths.get_filename_list("checkpoints")),
                 io.Combo.Input("calc_mode", options=[m.name for m in TWO_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.99, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_2_checkpoint"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
@@ -237,8 +249,8 @@ class CheckpointTwoMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
-                gamma: float, seed: int, output_filename: str, save_dtype: str,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
+                gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -246,8 +258,8 @@ class CheckpointTwoMerger(io.ComfyNode):
 
         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,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
@@ -272,9 +284,11 @@ class ModelTwoMerger(io.ComfyNode):
                 io.Combo.Input("model_b", options=["None"] + folder_paths.get_filename_list("diffusion_models")),
                 io.Combo.Input("calc_mode", options=[m.name for m in TWO_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.99, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_2_model"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
@@ -290,8 +304,8 @@ class ModelTwoMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
-                gamma: float, seed: int, output_filename: str, save_dtype: str,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
+                gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -299,8 +313,8 @@ class ModelTwoMerger(io.ComfyNode):
 
         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,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
@@ -325,9 +339,11 @@ class TextEncoderTwoMerger(io.ComfyNode):
                 io.Combo.Input("model_b", options=["None"] + folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("calc_mode", options=[m.name for m in TWO_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.99, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_2_textencoder"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
@@ -343,8 +359,8 @@ class TextEncoderTwoMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
-                gamma: float, seed: int, output_filename: str, save_dtype: str,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
+                gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -352,8 +368,8 @@ class TextEncoderTwoMerger(io.ComfyNode):
 
         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,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
@@ -378,9 +394,11 @@ class LoRATwoMerger(io.ComfyNode):
                 io.Combo.Input("model_b", options=["None"] + folder_paths.get_filename_list("loras")),
                 io.Combo.Input("calc_mode", options=[m.name for m in TWO_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.99, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_2_lora"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
@@ -396,8 +414,8 @@ class LoRATwoMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
-                gamma: float, seed: int, output_filename: str, save_dtype: str,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
+                gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -405,8 +423,8 @@ class LoRATwoMerger(io.ComfyNode):
 
         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,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
@@ -431,9 +449,11 @@ class EmbeddingTwoMerger(io.ComfyNode):
                 io.Combo.Input("model_b", options=["None"] + folder_paths.get_filename_list("embeddings")),
                 io.Combo.Input("calc_mode", options=[m.name for m in TWO_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.99, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_2_embedding"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
@@ -449,8 +469,8 @@ class EmbeddingTwoMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
-                gamma: float, seed: int, output_filename: str, save_dtype: str,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
+                gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -458,8 +478,8 @@ class EmbeddingTwoMerger(io.ComfyNode):
 
         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,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
@@ -487,6 +507,7 @@ class CheckpointThreeMerger(io.ComfyNode):
                 io.Combo.Input("model_c", options=["None"] + folder_paths.get_filename_list("checkpoints")),
                 io.Combo.Input("calc_mode", options=[m.name for m in THREE_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.5, min=-2.0, max=3.0, step=0.01),
@@ -506,7 +527,7 @@ class CheckpointThreeMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str, model_c: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
                 gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
@@ -515,7 +536,7 @@ class CheckpointThreeMerger(io.ComfyNode):
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "model_c": model_c, "calc_mode": calc_mode,
-            "mismatch_mode": mismatch_mode,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
@@ -542,6 +563,7 @@ class ModelThreeMerger(io.ComfyNode):
                 io.Combo.Input("model_c", options=["None"] + folder_paths.get_filename_list("diffusion_models")),
                 io.Combo.Input("calc_mode", options=[m.name for m in THREE_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.5, min=-2.0, max=3.0, step=0.01),
@@ -561,7 +583,7 @@ class ModelThreeMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str, model_c: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
                 gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
@@ -570,7 +592,7 @@ class ModelThreeMerger(io.ComfyNode):
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "model_c": model_c, "calc_mode": calc_mode,
-            "mismatch_mode": mismatch_mode,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
@@ -597,6 +619,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
                 io.Combo.Input("model_c", options=["None"] + folder_paths.get_filename_list("text_encoders")),
                 io.Combo.Input("calc_mode", options=[m.name for m in THREE_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.5, min=-2.0, max=3.0, step=0.01),
@@ -616,7 +639,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str, model_c: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
                 gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
@@ -625,7 +648,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "model_c": model_c, "calc_mode": calc_mode,
-            "mismatch_mode": mismatch_mode,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
@@ -652,6 +675,7 @@ class LoRAThreeMerger(io.ComfyNode):
                 io.Combo.Input("model_c", options=["None"] + folder_paths.get_filename_list("loras")),
                 io.Combo.Input("calc_mode", options=[m.name for m in THREE_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.5, min=-2.0, max=3.0, step=0.01),
@@ -671,7 +695,7 @@ class LoRAThreeMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str, model_c: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
                 gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
@@ -680,7 +704,7 @@ class LoRAThreeMerger(io.ComfyNode):
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "model_c": model_c, "calc_mode": calc_mode,
-            "mismatch_mode": mismatch_mode,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,
@@ -707,6 +731,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
                 io.Combo.Input("model_c", options=["None"] + folder_paths.get_filename_list("embeddings")),
                 io.Combo.Input("calc_mode", options=[m.name for m in THREE_MODEL_MODES]),
                 io.Combo.Input("mismatch_mode", options=["skip", "zeros", "error"], default="skip"),
+                io.Combo.Input("alignment_mode", options=["pad/crop", "interpolate"], default="pad/crop"),
                 io.Float.Input("alpha", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("beta", default=0.5, min=-2.0, max=3.0, step=0.01),
                 io.Float.Input("gamma", default=0.5, min=-2.0, max=3.0, step=0.01),
@@ -726,7 +751,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
 
     @classmethod
     def execute(cls, execution_mode: str, model_a: str, model_b: str, model_c: str,
-                calc_mode: str, mismatch_mode: str, alpha: float, beta: float,
+                calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
                 gamma: float, delta: float, seed: int, output_filename: str, save_dtype: str,
                 process_device: str, exclude_patterns: str, discard_patterns: str) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
@@ -735,7 +760,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
 
         recipe_params = {
             "model_a": model_a, "model_b": model_b, "model_c": model_c, "calc_mode": calc_mode,
-            "mismatch_mode": mismatch_mode,
+            "mismatch_mode": mismatch_mode, "alignment_mode": alignment_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,

nodes/merger_ops.py

@@ -15,6 +15,92 @@ class MissingTensorError(Exception):
     """Raised when a tensor is missing and behavior is ERROR."""
     pass
 
+# ################# #
+# UTILITIES
+# ################# #
+def resize_by_interpolation(tensor1, tensor2):
+    """Resizes tensor2 to match tensor1's shape using interpolation."""
+    if tensor1 is None or tensor2 is None:
+        return tensor1, tensor2
+    if tensor1.shape == tensor2.shape:
+        return tensor1, tensor2
+
+    dims1 = len(tensor1.shape)
+    dims2 = len(tensor2.shape)
+
+    if dims1 != dims2 or dims1 > 4 or dims1 == 0:
+        return tensor1, tensor2
+
+    orig_dtype = tensor2.dtype
+    t2_f = tensor2.float()
+
+    # F.interpolate expects [B, C, ...]
+    # We treat the tensor as [1, 1, ...dims]
+    view_shape = [1, 1] + list(tensor2.shape)
+    t2_f = t2_f.view(view_shape)
+
+    # Select mode based on dimensions
+    if dims1 == 4:
+        # [Out, In, H, W] -> [1, 1, Out, In, H*W] (5D)
+        view_shape = [1, 1, tensor2.shape[0], tensor2.shape[1], tensor2.shape[2] * tensor2.shape[3]]
+        target_size = [tensor1.shape[0], tensor1.shape[1], tensor1.shape[2] * tensor1.shape[3]]
+        t2_f = t2_f.view(view_shape)
+        t2_res = F.interpolate(t2_f, size=target_size, mode='trilinear', align_corners=False)
+        return tensor1, t2_res.view(tensor1.shape).to(orig_dtype)
+
+    if dims1 == 1:
+        mode = 'linear'
+    elif dims1 == 2:
+        mode = 'bilinear'
+    else:
+        mode = 'trilinear'
+
+    t2_res = F.interpolate(t2_f, size=list(tensor1.shape), mode=mode, align_corners=False)
+
+    return tensor1, t2_res.view(tensor1.shape).to(orig_dtype)
+
+
+def resize_tensors(tensor1, tensor2, mode='pad/crop'):
+    """Resizes tensors to match shapes using either padding or interpolation."""
+    if tensor1 is None or tensor2 is None:
+        return tensor1, tensor2
+    if tensor1.shape == tensor2.shape:
+        return tensor1, tensor2
+
+    if mode == 'interpolate':
+        return resize_by_interpolation(tensor1, tensor2)
+
+    dims1 = len(tensor1.shape)
+    dims2 = len(tensor2.shape)
+
+    if dims1 != dims2 or dims1 > 4 or dims1 == 0:
+        return tensor1, tensor2
+
+    padding1 = []
+    padding2 = []
+
+    # F.pad expects: (last_dim_left, last_dim_right, prev_dim_top, prev_dim_bottom, ...)
+    for i in range(dims1 - 1, -1, -1):
+        s1 = tensor1.shape[i]
+        s2 = tensor2.shape[i]
+        if s1 < s2:
+            padding1.extend([0, s2 - s1])
+            padding2.extend([0, 0])
+        elif s2 < s1:
+            padding1.extend([0, 0])
+            padding2.extend([0, s1 - s2])
+        else:
+            padding1.extend([0, 0])
+            padding2.extend([0, 0])
+
+    if any(p > 0 for p in padding1):
+        tensor1 = F.pad(tensor1, tuple(padding1))
+    if any(p > 0 for p in padding2):
+        tensor2 = F.pad(tensor2, tuple(padding2))
+
+    return tensor1, tensor2
+
+
 # ################# #
 # MERGE OPERATORS
 # ################# #
@@ -23,6 +109,7 @@ class Operation:
         self.key = key
         self.sources = tuple(sources)
         self.merge_func = self.recurse
+        self.alignment_mode = 'pad/crop'
         self.alpha = None
         self.beta = None
         self.gamma = None
@@ -96,6 +183,7 @@ class Add(Operation):
             return b
         if b is None:
             return a
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
         return a + b
 
 
@@ -103,15 +191,33 @@ 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
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
         return a - b
 
 class SVD(Operation):
-    def __init__(self, key, alpha, beta, gamma, *sources):
+    def __init__(self, key, alpha, beta, gamma, *sources, target_shape=None):
         super().__init__(key, *sources)
         self.alpha, self.beta, self.gamma = int(alpha), int(beta), gamma
+        self.target_shape = target_shape
+
     def oper(self, a, b) -> torch.Tensor:
         if a is None or b is None:
             return None
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
+
+        # For SVD extraction, we must ensure the diff matches the original Model A shape
+        # so the resulting LoRA weights are compatible with the target architecture.
+        if self.target_shape and a.shape != self.target_shape:
+            if self.alignment_mode == 'interpolate':
+                # Re-align both to match target_shape via interpolation
+                dummy = torch.empty(self.target_shape, device=a.device)
+                _, a = resize_by_interpolation(dummy, a)
+                _, b = resize_by_interpolation(dummy, b)
+            else:
+                slices = tuple(slice(0, min(res_s, tgt_s)) for res_s, tgt_s in zip(a.shape, self.target_shape))
+                a = a[slices]
+                b = b[slices]
+
         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)
@@ -141,6 +247,10 @@ 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
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
+        a, c = resize_tensors(a, c, mode=self.alignment_mode)
+        b, c = resize_tensors(b, c, mode=self.alignment_mode)
+
         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())
@@ -153,11 +263,17 @@ class ExtractOp(Operation):
     def oper(self, base: torch.Tensor, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
         if a is None or b is None:
             return None
+        if base is not None:
+            base, a = resize_tensors(base, a, mode=self.alignment_mode)
+            base, b = resize_tensors(base, b, mode=self.alignment_mode)
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
+
+        dtype = base.dtype if base is not None else a.dtype
         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)
+        c = torch.cosine_similarity(a_f, b_f, -1).clamp(-1, 1).unsqueeze(-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)
+        return (torch.lerp(a_f, b_f, self.alpha) * torch.lerp(d, 1 - d, self.beta)).to(dtype)
 
 
 class Similarities(ExtractOp):
@@ -172,10 +288,14 @@ class PowerUpOp(Operation):
     def oper(self, a, b):
         if a is None or b is None:
             return None
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
         delta = b - a
+        keep_prob = 1 - self.alpha
+        if keep_prob <= 0:
+            return torch.zeros_like(delta)
         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)
+        m = (torch.empty_like(delta).uniform_(0, 1, generator=rng) < keep_prob).to(delta.dtype)
+        return (m * delta) / keep_prob
 
 
 class InterpolateDifference(Operation):
@@ -185,6 +305,7 @@ class InterpolateDifference(Operation):
     def oper(self, a, b):
         if a is None or b is None:
             return None
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
         alpha, delta = max(self.alpha, 0.001), torch.abs(a - b)
         max_delta = torch.max(delta)
         if max_delta == 0: return a
@@ -194,6 +315,65 @@ class InterpolateDifference(Operation):
         mask = torch.lerp(torch.bernoulli(torch.clamp(diff, 0, 1), generator=rng), diff, self.gamma)
         return a * (1 - mask) + b * mask
 
+class ManualEnhancedInterpolateDifferenceOp(Operation):
+    def __init__(self, key, alpha, beta, gamma, delta, seed, *sources):
+        super().__init__(key, *sources)
+        self.alpha, self.beta, self.gamma, self.delta, self.seed = alpha, beta, gamma, delta, seed
+
+    def oper(self, a, b):
+        if a is None or b is None: return None
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
+        delta = torch.abs(a - b)
+        diff = torch.nan_to_num((torch.max(delta) - delta) / torch.max(delta))
+        mean_diff = torch.mean(diff, 0, keepdim=True)
+        mask = torch.logical_and(self.beta < mean_diff, mean_diff < self.gamma)
+        powered_diff = torch.nan_to_num(diff ** (1 / max(self.alpha, 0.001) - 1))
+        masked_diff = powered_diff * mask.float()
+        rng = torch.Generator(device=a.device).manual_seed(self.seed)
+        random_mask = torch.bernoulli(torch.clamp(masked_diff, 0, 1), generator=rng)
+        interpolated_mask = torch.lerp(random_mask, masked_diff, self.delta)
+        return a * (1 - interpolated_mask) + b * interpolated_mask
+
+
+class AutoEnhancedInterpolateDifferenceOp(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
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
+        delta = torch.abs(a - b)
+        max_delta = torch.max(delta)
+        diff = torch.nan_to_num((max_delta - delta) / max_delta)
+        mean_diff = torch.mean(diff)
+        lower_threshold = mean_diff * (1 - self.beta)
+        upper_threshold = mean_diff * (1 + self.beta)
+        mask = torch.logical_and(lower_threshold < diff, diff < upper_threshold)
+        powered_diff = torch.nan_to_num(diff ** (1 / max(self.alpha, 0.001) - 1))
+        masked_diff = powered_diff * mask.float()
+        rng = torch.Generator(device=a.device).manual_seed(self.seed)
+        random_mask = torch.bernoulli(torch.clamp(masked_diff, 0, 1), generator=rng)
+        interpolated_mask = torch.lerp(random_mask, masked_diff, self.gamma)
+        return a * (1 - interpolated_mask) + b * interpolated_mask
+
+
+class WeightSumCutoffOp(Operation):
+    def __init__(self, key, alpha, beta, gamma, *sources):
+        super().__init__(key, *sources)
+        self.alpha, self.beta, self.gamma = alpha, beta, gamma
+
+    def oper(self, a, b):
+        if a is None or b is None: return None
+        a, b = resize_tensors(a, b, mode=self.alignment_mode)
+        delta = torch.abs(a - b)
+        diff = torch.nan_to_num((torch.max(delta) - delta) / torch.max(delta))
+        mean = torch.mean(diff, 0, True)
+        mask = torch.logical_and(mean < self.beta, self.gamma < mean)
+        mul = self.alpha * mask
+        return a * (1 - mul) + b * mul
+
+
 # ################# #
 # CALCULATION MODES
 # ################# #
@@ -236,6 +416,7 @@ class WeightSum(CalcMode):
     param_docs = {'alpha': 'Interpolation weight. 0.0 is 100% Model A, 1.0 is 100% Model B.'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         # Use pre-loaded tensor_a if available
         if '_tensor_a' in kwargs:
@@ -251,7 +432,9 @@ class WeightSum(CalcMode):
         if kwargs['alpha'] <= 0: return a
         c = Multiply(key, 1 - kwargs['alpha'], a)
         d = Multiply(key, kwargs['alpha'], b)
-        return Add(key, c, d)
+        res = Add(key, c, d)
+        res.alignment_mode = alignment_mode
+        return res
 
 
 class AddDifference(CalcMode):
@@ -261,6 +444,7 @@ class AddDifference(CalcMode):
     param_docs = {'alpha': 'Multiplier for the (B - C) difference.', 'beta': 'Smoothing (0=Off, 1=On).'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -272,9 +456,12 @@ class AddDifference(CalcMode):
         b = LoadTensor(key, kwargs['model_b'], **loader_args_bc)
         c = LoadTensor(key, kwargs['model_c'], **loader_args_bc)
         diff = Sub(key, b, c)
+        diff.alignment_mode = alignment_mode
         if kwargs['beta'] == 1: diff = Smooth(key, diff)
         diffm = Multiply(key, kwargs['alpha'], diff)
-        return Add(key, a, diffm)
+        res = Add(key, a, diffm)
+        res.alignment_mode = alignment_mode
+        return res
 
 
 class TrainDifference(CalcMode):
@@ -284,6 +471,7 @@ class TrainDifference(CalcMode):
     param_docs = {'alpha': 'Multiplier for the calculated difference.'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -295,8 +483,11 @@ class TrainDifference(CalcMode):
         b = LoadTensor(key, kwargs['model_b'], **loader_args_bc)
         c = LoadTensor(key, kwargs['model_c'], **loader_args_bc)
         diff = TrainDiff(key, a, b, c)
+        diff.alignment_mode = alignment_mode
         diffm = Multiply(key, kwargs['alpha'], diff)
-        return Add(key, a, diffm)
+        res = Add(key, a, diffm)
+        res.alignment_mode = alignment_mode
+        return res
 class InterpDifference(CalcMode):
     name = 'Comparative-Interpolation'
     description = 'Interpolates A and B based on relative tensor value differences.'
@@ -304,6 +495,7 @@ class InterpDifference(CalcMode):
     param_docs = {'alpha': 'Curve strength.', 'beta': 'Style (0=Similarity, 1=Difference).', 'gamma': 'Mix (0=Random, 1=Linear).'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -317,7 +509,9 @@ class InterpDifference(CalcMode):
 
         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)
+        res = InterpolateDifference(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], kwargs['seed'], a, b)
+        res.alignment_mode = alignment_mode
+        return res
 
 
 class Extract(CalcMode):
@@ -327,6 +521,7 @@ class Extract(CalcMode):
     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):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -338,8 +533,11 @@ class Extract(CalcMode):
         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)
+        extracted.alignment_mode = alignment_mode
         multiplied = Multiply(key, kwargs['delta'], extracted)
-        return Add(key, a, multiplied)
+        res = Add(key, a, multiplied)
+        res.alignment_mode = alignment_mode
+        return res
 
 
 class AddDisimilarity(CalcMode):
@@ -349,6 +547,7 @@ class AddDisimilarity(CalcMode):
     param_docs = {'alpha': 'Weighting B vs C.', 'beta': 'Multiplier for final features.', 'gamma': 'Similarity bias.'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -360,8 +559,11 @@ class AddDisimilarity(CalcMode):
         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)
+        extracted.alignment_mode = alignment_mode
         multiplied = Multiply(key, kwargs['beta'], extracted)
-        return Add(key, a, multiplied)
+        res = Add(key, a, multiplied)
+        res.alignment_mode = alignment_mode
+        return res
 
 
 class PowerUp(CalcMode):
@@ -371,6 +573,7 @@ class PowerUp(CalcMode):
     param_docs = {'alpha': 'Dropout rate (proportion of weights from B to *drop*).', 'beta': 'Multiplier for the final difference.'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -381,8 +584,11 @@ class PowerUp(CalcMode):
         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)
+        deltahat.alignment_mode = alignment_mode # Important!
+        multiplied = Multiply(key, kwargs['beta'], deltahat)
+        res = Add(key, a, multiplied)
+        res.alignment_mode = alignment_mode
+        return res
 
 
 class SVDMode(CalcMode):
@@ -392,6 +598,7 @@ class SVDMode(CalcMode):
     param_docs = {'alpha': 'Rank for standard layers.', 'beta': 'Rank for 3x3 conv layers.', 'gamma': 'Clamp quantile for weights.'}
     def create_recipe(self, key, **kwargs):
         mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
 
         if '_tensor_a' in kwargs:
             a = PreloadedTensor(key, kwargs['_tensor_a'])
@@ -401,8 +608,82 @@ class SVDMode(CalcMode):
 
         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)
+        res = SVD(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], a, b, target_shape=kwargs.get('_tensor_a_shape'))
+        res.alignment_mode = alignment_mode
+        return res
 
 
-TWO_MODEL_MODES = [WeightSum(), InterpDifference(), PowerUp(), SVDMode()]
+class ManEnhInterpDifference(CalcMode):
+    name = 'Enhanced Man Interp'
+    description = 'Enhanced interpolation between each pair of values from A and B depending on their difference relative to other values.'
+    models_used = ['A', 'B']
+    param_docs = {'alpha': 'Interpolation strength.', 'beta': 'Lower mean threshold.', 'gamma': 'Upper mean threshold.', 'delta': 'Smoothness factor.'}
+    def create_recipe(self, key, **kwargs):
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
+
+        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)
+
+        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)
+        res = ManualEnhancedInterpolateDifferenceOp(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], kwargs['delta'], kwargs['seed'], a, b)
+        res.alignment_mode = alignment_mode
+        return res
+
+
+class AutoEnhInterpDifference(CalcMode):
+    name = 'Enhanced Auto Interp'
+    description = 'Interpolates between each pair of values from A and B depending on their difference relative to other values.'
+    models_used = ['A', 'B']
+    param_docs = {'alpha': 'Interpolation strength.', 'beta': 'Threshold adjustment factor.', 'gamma': 'Smoothness factor.'}
+    def create_recipe(self, key, **kwargs):
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
+
+        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)
+
+        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)
+        res = AutoEnhancedInterpolateDifferenceOp(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], kwargs['seed'], a, b)
+        res.alignment_mode = alignment_mode
+        return res
+
+
+class WeightSumCutoffMode(CalcMode):
+    name = 'Weight-Sum Cutoff'
+    description = 'Weight-sum with cutoff based on value differences.'
+    models_used = ['A', 'B']
+    param_docs = {'alpha': 'Interpolation weight.', 'beta': 'Upper threshold.', 'gamma': 'Lower threshold.'}
+    def create_recipe(self, key, **kwargs):
+        mismatch_mode = kwargs.get('mismatch_mode', MissingTensorBehavior.SKIP)
+        alignment_mode = kwargs.get('alignment_mode', 'pad/crop')
+
+        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)
+        res = WeightSumCutoffOp(key, kwargs['alpha'], kwargs['beta'], kwargs['gamma'], a, b)
+        res.alignment_mode = alignment_mode
+        return res
+
+
+TWO_MODEL_MODES = [WeightSum(), InterpDifference(), PowerUp(), SVDMode(), ManEnhInterpDifference(), AutoEnhInterpDifference(), WeightSumCutoffMode()]
 THREE_MODEL_MODES = [AddDifference(), TrainDifference(), Extract(), AddDisimilarity()]

nodes/merger_utils.py

@@ -28,23 +28,23 @@ except ImportError:
 
 class PinnedTensor:
     """Context manager for automatic pinned memory management.
-    
+
     Usage:
         with PinnedTensor(cpu_tensor) as pinned:
             gpu_tensor = pinned.to('cuda')
         # Memory automatically unpinned after transfer
     """
-    
+
     def __init__(self, tensor: torch.Tensor, auto_pin: bool = True):
         self.tensor = tensor
         self.is_pinned = False
         self.auto_pin = auto_pin and PINNING_AVAILABLE
-        
+
     def __enter__(self) -> torch.Tensor:
         if self.auto_pin and self.tensor.device.type == 'cpu':
             self.is_pinned = pin_memory(self.tensor)
         return self.tensor
-    
+
     def __exit__(self, exc_type, exc_val, exc_tb):
         if self.is_pinned:
             unpin_memory(self.tensor)
@@ -57,12 +57,12 @@ def transfer_to_gpu_pinned(
     dtype: Optional[torch.dtype] = None
 ) -> torch.Tensor:
     """Transfer tensor to GPU using pinned memory for faster transfer.
-    
+
     Args:
         tensor: CPU tensor to transfer
         device: Target GPU device
         dtype: Optional dtype conversion
-        
+
     Returns:
         Tensor on GPU device
     """
@@ -71,7 +71,7 @@ def transfer_to_gpu_pinned(
         if dtype:
             return tensor.to(device=device, dtype=dtype)
         return tensor.to(device=device)
-    
+
     # Pin memory for faster transfer
     was_pinned = pin_memory(tensor)
     try:
@@ -93,6 +93,7 @@ class MemoryEfficientSafeOpen:
 
     Features:
     - mmap mode: Zero-copy tensor access via memory-mapped file
+    - low_memory mode: Direct file reads to minimize OS page cache usage
     - Parallel loading: Multi-threaded tensor reads for 2-4x speedup
     - Sorted batch reads: Keys sorted by file offset for sequential I/O
     - Auto-optimized workers: Adjusts parallelism based on device capabilities
@@ -102,12 +103,15 @@ class MemoryEfficientSafeOpen:
         filename: Path to safetensors file
         device: Target device (default 'cpu')
         mmap_mode: Use memory-mapped file for zero-copy (default True)
+        low_memory: Use direct file reads to minimize memory (overrides mmap_mode)
     """
 
-    def __init__(self, filename: str, device: str = 'cpu', mmap_mode: bool = True):
+    def __init__(self, filename: str, device: str = 'cpu', mmap_mode: bool = True, low_memory: bool = False):
         self.filename = filename
         self.device = device
-        self.mmap_mode = mmap_mode
+        # low_memory mode forces mmap off to avoid OS page caching
+        self.low_memory = low_memory
+        self.mmap_mode = mmap_mode and not low_memory
         self.header, self.header_size = self._read_header()
         self.file = open(filename, "rb")
         self.mmap_obj = None
@@ -123,6 +127,7 @@ class MemoryEfficientSafeOpen:
             self.mmap_obj.close()
         self.file.close()
 
+
     def keys(self) -> List[str]:
         """Return all tensor keys (excluding metadata)."""
         return [k for k in self.header.keys() if k != "__metadata__"]
@@ -156,13 +161,17 @@ class MemoryEfficientSafeOpen:
             else:
                 tensor_bytes = None
         else:
+            # Non-mmap mode: use pre-allocated bytearray with readinto for minimal copies
             tensor_bytes = None
             if offset_start != offset_end:
                 self.file.seek(self.header_size + 8 + offset_start)
-                tensor_bytes = self.file.read(offset_end - offset_start)
+                # Pre-allocate writable buffer and read directly into it
+                tensor_bytes = bytearray(offset_end - offset_start)
+                self.file.readinto(tensor_bytes)
 
         return self._deserialize_tensor(tensor_bytes, metadata)
-    
+
+
     def get_tensor_to_gpu(
         self,
         key: str,
@@ -170,12 +179,12 @@ class MemoryEfficientSafeOpen:
         dtype: Optional[torch.dtype] = None
     ) -> torch.Tensor:
         """Load a tensor and transfer to GPU using pinned memory for faster transfer.
-        
+
         Args:
             key: Tensor key to load
             device: Target GPU device (default 'cuda')
             dtype: Optional dtype conversion
-            
+
         Returns:
             Tensor on GPU device
         """
@@ -276,13 +285,19 @@ class MemoryEfficientSafeOpen:
         if tensor_bytes is None:
             byte_tensor = torch.empty(0, dtype=torch.uint8)
         else:
-            byte_tensor = torch.frombuffer(bytearray(tensor_bytes), dtype=torch.uint8)
+            # Avoid extra copy if already a bytearray (low_memory mode)
+            if isinstance(tensor_bytes, bytearray):
+                byte_tensor = torch.frombuffer(tensor_bytes, dtype=torch.uint8)
+            else:
+                # mmap memoryview needs copy to create writable tensor
+                byte_tensor = torch.frombuffer(bytearray(tensor_bytes), dtype=torch.uint8)
 
         if dtype_str in ["F8_E5M2", "F8_E4M3"]:
             return self._convert_float8(byte_tensor, dtype_str, shape)
 
         return byte_tensor.view(dtype).reshape(shape)
 
+
     @staticmethod
     def _get_torch_dtype(dtype_str):
         dtype_map = {

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "ComfyUI-ModelUtils"
 description = "[WIP]Custom nodes for handling, inspecting, modifying and creating various model files."
-version = "0.2.0"
+version = "0.2.7"
 license = { file = "LICENSE" }
 
 [project.urls]