Add ability to adjust paramaters on a ler layer basis using regex/substring for layer colon separated with parameter=float for specific parameters

Feature/preview meta wf loader

__init__.py

@@ -29,6 +29,14 @@ from .nodes.lora_resize import (
     LoRAResizeFrobenius, LoRAResizeCumulative,
     LoRAMergeToModel
 )
+from .nodes.downloader_nodes import (
+    CheckpointInfoMetaDownloader, DiffusionModelInfoMetaDownloader, LoRAInfoMetaDownloader, EmbeddingInfoMetaDownloader,
+    VAEInfoMetaDownloader, ControlNetInfoMetaDownloader, ManualPathInfoMetaDownloader
+)
+from .nodes.model_info_nodes import (
+    CheckpointInfoLoader, LoRAInfoLoader, EmbeddingInfoLoader,
+    VAEInfoLoader, ControlNetInfoLoader, DiffusionModelInfoLoader
+)
 
 
 class ModelUtilsExtension(ComfyExtension):
@@ -60,6 +68,12 @@ class ModelUtilsExtension(ComfyExtension):
             LoRAMultiMerge, LoRAMultiMergeDARE,
             # LoRA Merge To Model
             LoRAMergeToModel,
+            # Downloaders
+            CheckpointInfoMetaDownloader, DiffusionModelInfoMetaDownloader, LoRAInfoMetaDownloader, EmbeddingInfoMetaDownloader,
+            VAEInfoMetaDownloader, ControlNetInfoMetaDownloader, ManualPathInfoMetaDownloader,
+            # Info Loaders
+            CheckpointInfoLoader, LoRAInfoLoader, EmbeddingInfoLoader,
+            VAEInfoLoader, ControlNetInfoLoader, DiffusionModelInfoLoader,
         ]
 
 

docs/merger_2_model_modes.md

@@ -33,6 +33,30 @@
 
 ---
 
+## Power-Up (DARE+TIES)
+> Combines DARE (Drop and Rescale) with TIES (Trim, Elect Sign) — the current best-practice algorithm for merging fine-tuned models, particularly LoRA-tuned ones. DARE sparsifies the task vector via random dropout and rescaling; TIES then removes residual low-magnitude noise and enforces sign consistency. The result is a cleaner, more targeted capability transfer from B to A with less interference.
+
+**Models Used:** A, B
+**Parameters:**
+- **Alpha:** DARE drop rate. Fraction of delta parameters randomly zeroed. Higher values produce a sparser, more targeted delta. Typical range: `0.5`–`0.9`.
+- **Beta:** TIES trim quantile. Fraction of the smallest-magnitude delta parameters zeroed after DARE, removing residual noise. `0.0` disables trimming. Typical range: `0.1`–`0.3`.
+- **Gamma:** Lambda scale. Final multiplier applied to the merged delta before adding to Model A. Equivalent to the task arithmetic scaling coefficient. Typical range: `0.5`–`1.5`.
+- **Seed:** Random seed for the DARE dropout mask. Controls reproducibility.
+
+**Algorithm:**
+1. Compute task vector `δ = B − A`
+2. **DARE:** apply random binary mask with keep probability `1 − alpha`; rescale survivors by `1/(1−alpha)`
+3. **TIES trim:** zero out parameters below the `beta`-quantile magnitude threshold
+4. **TIES elect:** determine dominant sign per position; zero out disagreeing parameters
+5. Return `A + gamma × δ_filtered`
+
+**Guidance:**
+- Start with `alpha=0.9, beta=0.2, gamma=1.0` for LoRA-derived models
+- Lower `alpha` (e.g. `0.5`) for full fine-tunes where more delta mass should be retained
+- Increase `gamma` beyond `1.0` to amplify B's influence; decrease below `1.0` to soften it
+
+---
+
 ## Enhanced Man Interp
 > Sophisticated interpolation between values from A and B depending on their difference relative to other values, with manual threshold control.
 

nodes/downloader_nodes.py

@@ -0,0 +1,341 @@
+import os
+import folder_paths
+from comfy_api.latest import io
+from .downloader_utils import scan_and_process
+
+def get_subdirectories(folder_name: str) -> list[str]:
+    """Helper to get a list of subdirectories for a given model folder name."""
+    paths = folder_paths.get_folder_paths(folder_name)
+    if not paths:
+        return ["/"]
+
+    subdirs = set()
+    for base_path in paths:
+        if not os.path.exists(base_path):
+            continue
+        for root, dirs, _ in os.walk(base_path):
+            # Ignore hidden directories directly during walk
+            dirs[:] = [d for d in dirs if not d.startswith('.')]
+
+            rel_path = os.path.relpath(root, base_path)
+            if rel_path == ".":
+                subdirs.add("/")
+            else:
+                # Store with forward slashes for consistency
+                subdirs.add("/" + rel_path.replace("\\", "/"))
+
+    return sorted(list(subdirs))
+
+class BaseInfoMetaDownloaderNode(io.ComfyNode):
+    CATEGORY = "ModelUtils/Downloader"
+    OUTPUT_NODE = True
+
+    @classmethod
+    def get_common_inputs(cls):
+        return {
+            "recursive": io.Boolean.Input("recursive", default=True, tooltip="Whether to scan subdirectories recursively."),
+            "nsfw_level": io.Combo.Input("nsfw_level", options=["None", "Soft", "Mature", "X", "XXX", "All"], default="All", tooltip="Filter models based on NSFW level in metadata."),
+            "max_examples": io.Int.Input("max_examples", default=0, min=0, max=100, tooltip="Maximum number of examples to process."),
+            "threads": io.Int.Input("threads", default=4, min=1, max=32, tooltip="Number of threads to use for processing."),
+            "api_key": io.String.Input("api_key", default="", tooltip="API key for accessing the model repository.")
+        }
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name=cls.__name__.replace("Downloader", " Downloader"),
+            category=cls.CATEGORY,
+            inputs=list(cls.get_common_inputs().values()),
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+            description=("Scans specified directories for models, extracts metadata, generates previews, and optionally compresses files.")
+        )
+
+    @classmethod
+    def execute(cls, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        scan_dirs = cls.get_scan_dirs(**kwargs)
+        if not scan_dirs:
+            return io.NodeOutput("No directories to scan.")
+
+        # Call the scan_and_process function
+        stats = scan_and_process(
+            scan_dirs=scan_dirs,
+            recursive=recursive,
+            nsfw_level=nsfw_level,
+            max_examples=max_examples,
+            api_key=api_key if api_key else None,
+            threads=threads
+        )
+
+        mb_saved = stats.get("space_saved", 0) / (1024 * 1024)
+
+        output_msg = (
+            f"Scan Complete for {len(scan_dirs)} directory/directories.\n"
+            f"Total Models Processed: {stats.get('total_processed', 0)}\n"
+            f"New Models Hashed: {stats.get('new_hashes', 0)}\n"
+            f"Images Processed: {stats.get('images_processed', 0)}\n"
+            f"Workflows Extracted: {stats.get('workflows_extracted', 0)}\n"
+            f"Space Saved via Compression: {mb_saved:.2f} MB"
+        )
+
+        return io.NodeOutput(output_msg)
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs) -> list[str]:
+        return []
+
+class CheckpointInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.insert(0, io.Combo.Input("subdirectory", options=get_subdirectories("checkpoints"), default="/"))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Checkpoint Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, subdirectory, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        return super().execute(
+            recursive=recursive, nsfw_level=nsfw_level, max_examples=max_examples,
+            threads=threads, api_key=api_key, subdirectory=subdirectory, **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        base_paths = folder_paths.get_folder_paths("checkpoints")
+        if not base_paths:
+            return []
+        sub = kwargs.get("subdirectory", "/")
+        if sub == "/":
+            return base_paths
+
+        target_paths = []
+        for bp in base_paths:
+            target_path = os.path.join(bp, sub.lstrip("/"))
+            if os.path.exists(target_path):
+                target_paths.append(target_path)
+        return target_paths
+
+class DiffusionModelInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.insert(0, io.Combo.Input("subdirectory", options=get_subdirectories("diffusion_models"), default="/"))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Diffusion Model Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, subdirectory, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        return super().execute(
+            recursive=recursive, nsfw_level=nsfw_level, max_examples=max_examples,
+            threads=threads, api_key=api_key, subdirectory=subdirectory, **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        base_paths = folder_paths.get_folder_paths("diffusion_models")
+        if not base_paths:
+            return []
+        sub = kwargs.get("subdirectory", "/")
+        if sub == "/":
+            return base_paths
+
+        target_paths = []
+        for bp in base_paths:
+            target_path = os.path.join(bp, sub.lstrip("/"))
+            if os.path.exists(target_path):
+                target_paths.append(target_path)
+        return target_paths
+
+class LoRAInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.insert(0, io.Combo.Input("subdirectory", options=get_subdirectories("loras"), default="/"))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="LoRA Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, subdirectory, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        return super().execute(
+            recursive=recursive, nsfw_level=nsfw_level, max_examples=max_examples,
+            threads=threads, api_key=api_key, subdirectory=subdirectory, **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        base_paths = folder_paths.get_folder_paths("loras")
+        if not base_paths:
+            return []
+        sub = kwargs.get("subdirectory", "/")
+        if sub == "/":
+            return base_paths
+
+        target_paths = []
+        for bp in base_paths:
+            target_path = os.path.join(bp, sub.lstrip("/"))
+            if os.path.exists(target_path):
+                target_paths.append(target_path)
+        return target_paths
+
+class EmbeddingInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.insert(0, io.Combo.Input("subdirectory", options=get_subdirectories("embeddings"), default="/"))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Embedding Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, subdirectory, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        return super().execute(
+            recursive=recursive, nsfw_level=nsfw_level, max_examples=max_examples,
+            threads=threads, api_key=api_key, subdirectory=subdirectory, **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        base_paths = folder_paths.get_folder_paths("embeddings")
+        if not base_paths:
+            return []
+        sub = kwargs.get("subdirectory", "/")
+        if sub == "/":
+            return base_paths
+
+        target_paths = []
+        for bp in base_paths:
+            target_path = os.path.join(bp, sub.lstrip("/"))
+            if os.path.exists(target_path):
+                target_paths.append(target_path)
+        return target_paths
+
+class VAEInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.insert(0, io.Combo.Input("subdirectory", options=get_subdirectories("vae"), default="/"))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="VAE Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, subdirectory, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        return super().execute(
+            recursive=recursive, nsfw_level=nsfw_level, max_examples=max_examples,
+            threads=threads, api_key=api_key, subdirectory=subdirectory, **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        base_paths = folder_paths.get_folder_paths("vae")
+        if not base_paths:
+            return []
+        sub = kwargs.get("subdirectory", "/")
+        if sub == "/":
+            return base_paths
+
+        target_paths = []
+        for bp in base_paths:
+            target_path = os.path.join(bp, sub.lstrip("/"))
+            if os.path.exists(target_path):
+                target_paths.append(target_path)
+        return target_paths
+
+class ControlNetInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.insert(0, io.Combo.Input("subdirectory", options=get_subdirectories("controlnet"), default="/"))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="ControlNet Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, subdirectory, recursive, nsfw_level, max_examples, threads, api_key, **kwargs):
+        return super().execute(
+            recursive=recursive, nsfw_level=nsfw_level, max_examples=max_examples,
+            threads=threads, api_key=api_key, subdirectory=subdirectory, **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        base_paths = folder_paths.get_folder_paths("controlnet")
+        if not base_paths:
+            return []
+        sub = kwargs.get("subdirectory", "/")
+        if sub == "/":
+            return base_paths
+
+        target_paths = []
+        for bp in base_paths:
+            target_path = os.path.join(bp, sub.lstrip("/"))
+            if os.path.exists(target_path):
+                target_paths.append(target_path)
+        return target_paths
+
+class ManualPathInfoMetaDownloader(BaseInfoMetaDownloaderNode):
+    @classmethod
+    def define_schema(cls):
+        inputs = list(cls.get_common_inputs().values())
+        inputs.append(io.String.Input("scan_dir", default=""))
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Manual Path Downloader",
+            category=cls.CATEGORY,
+            inputs=inputs,
+            outputs=[io.String.Output(display_name="status")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, recursive, nsfw_level, max_examples, threads, api_key, scan_dir, **kwargs):
+        return super().execute(
+            recursive=recursive,
+            nsfw_level=nsfw_level,
+            max_examples=max_examples,
+            threads=threads,
+            api_key=api_key,
+            scan_dir=scan_dir,
+            **kwargs
+        )
+
+    @classmethod
+    def get_scan_dirs(cls, **kwargs):
+        scan_dir = kwargs.get("scan_dir", "")
+        if scan_dir.strip():
+            return [scan_dir.strip()]
+        return []

nodes/downloader_utils.py

@@ -0,0 +1,695 @@
+import os
+import sys
+import hashlib
+import json
+import sqlite3
+import requests
+import glob
+from pathlib import Path
+import threading
+import concurrent.futures
+import codecs
+import shutil
+
+try:
+    from PIL import Image
+    HAS_PIL = True
+except ImportError:
+    HAS_PIL = False
+    print("Warning: Pillow not found. Image workflow extraction disabled.")
+
+try:
+    from mutagen.mp4 import MP4
+    HAS_MUTAGEN = True
+except ImportError:
+    HAS_MUTAGEN = False
+    print("Warning: mutagen not found. MP4 tag extraction will rely entirely on av fallback.")
+
+try:
+    import av
+    HAS_AV = True
+except ImportError:
+    HAS_AV = False
+    print("Warning: av (PyAV) not found. MP4 metadata extraction fallback disabled.")
+
+# Add the reference module to the Python path
+# Assuming downloader_utils.py is inside nodes/, the root is one level up
+root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(root_dir, 'unifiedefficientloader-reference'))
+from unifiedefficientloader import UnifiedSafetensorsLoader
+
+# --- Database Path Logic ---
+try:
+    import folder_paths
+    base_user_dir = folder_paths.get_public_user_directory("default")
+    if not base_user_dir:
+        base_user_dir = root_dir
+except (ImportError, AttributeError):
+    base_user_dir = root_dir
+
+PLUGIN_DATA_DIR = os.path.join(base_user_dir, "ComfyUI-ModelUtils")
+os.makedirs(PLUGIN_DATA_DIR, exist_ok=True)
+DEFAULT_DB_PATH = os.path.join(PLUGIN_DATA_DIR, "cache.db")
+
+
+# --- Workflow Extraction Logic ---
+
+def unescape_and_parse_nested_json(json_string):
+    if not isinstance(json_string, str): return None
+    stripped = json_string.strip()
+    if not (stripped.startswith('{') or stripped.startswith('[')): return None
+    try: return json.loads(stripped)
+    except json.JSONDecodeError: pass
+    try:
+        first_parse = json.loads(stripped)
+        if isinstance(first_parse, str): return json.loads(first_parse)
+    except json.JSONDecodeError: pass
+    try:
+        decoded = codecs.decode(stripped, "unicode_escape")
+        return json.loads(decoded)
+    except (json.JSONDecodeError, UnicodeDecodeError, ValueError): pass
+    try:
+        unescaped = stripped.replace('\\\\"', '\\"').replace('\\"', '"').replace("\\\\", "\\")
+        unescaped = unescaped.replace("\\n", "\n").replace("\\r", "\r").replace("\\t", "\t")
+        return json.loads(unescaped)
+    except json.JSONDecodeError: pass
+    return None
+
+def get_workflow_blob(metadata):
+    for key in ['workflow', 'prompt']:
+        val = metadata.get(key)
+        if val:
+            parsed = unescape_and_parse_nested_json(val)
+            if parsed: return parsed
+    for val in metadata.values():
+        parsed = unescape_and_parse_nested_json(val)
+        if isinstance(parsed, dict) and (any(k.isdigit() for k in parsed.keys()) or 'nodes' in parsed):
+            return parsed
+    return None
+
+def process_image_workflow_and_compress(filepath_str):
+    if not HAS_PIL: return {"images_processed": 0, "workflows_extracted": 0, "space_saved": 0}
+    p = Path(filepath_str)
+    if p.suffix.lower() not in ['.png', '.jpg', '.jpeg']: return {"images_processed": 0, "workflows_extracted": 0, "space_saved": 0}
+
+    workflow = None
+    actual_format = None
+    stats = {"images_processed": 1, "workflows_extracted": 0, "space_saved": 0}
+    try:
+        with Image.open(p) as img:
+            actual_format = img.format
+            # CivitAI sometimes serves PNGs with .jpeg extension in URL
+            if actual_format == 'PNG':
+                workflow = get_workflow_blob(img.info)
+                if workflow:
+                    # Save workflow
+                    json_path = p.with_suffix('.json')
+                    if not json_path.exists():
+                        with open(json_path, 'w', encoding='utf-8') as f:
+                            json.dump(workflow, f, indent=2, ensure_ascii=False)
+                        print(f"Extracted workflow to {json_path}")
+                        stats["workflows_extracted"] = 1
+
+                    # Compress image to WebP
+                    webp_path = p.with_suffix('.webp')
+                    if not webp_path.exists():
+                        orig_size = os.path.getsize(p)
+                        img.save(webp_path, 'WEBP', quality=85, method=4)
+                        new_size = os.path.getsize(webp_path)
+                        stats["space_saved"] = max(0, orig_size - new_size)
+                        print(f"Compressed {p.name} (was actually {actual_format}) to {webp_path.name}")
+
+        # Delete original if we created a webp replacement
+        if workflow and p.with_suffix('.webp').exists() and actual_format == 'PNG':
+            os.remove(p)
+            print(f"Deleted original uncompressed image: {p.name}")
+    except Exception as e:
+        print(f"Failed to process image {p.name}: {e}")
+    return stats
+
+# --- Video Workflow Logic ---
+def get_tag_value(mp4, keys):
+    for key in keys:
+        if key in mp4.tags: return mp4.tags[key][0] if mp4.tags[key] else None
+    for key in keys:
+        search_suffix = key.split(':')[-1].lower()
+        for tag_key, val in mp4.tags.items():
+            if tag_key.lower().endswith(f":{search_suffix}"): return val[0] if val else None
+    return None
+
+def extract_metadata_av(mp4_path):
+    if not HAS_AV: return None
+    try:
+        with av.open(str(mp4_path)) as container:
+            return {k.lower(): v for k, v in container.metadata.items()}
+    except Exception as e:
+        print(f"av extraction error for {mp4_path}: {e}")
+        return None
+
+def process_video_workflow(filepath_str):
+    p = Path(filepath_str)
+    if p.suffix.lower() != '.mp4': return {"images_processed": 0, "workflows_extracted": 0, "space_saved": 0}
+
+    json_path = p.with_suffix('.json')
+    if json_path.exists(): return {"images_processed": 0, "workflows_extracted": 0, "space_saved": 0}
+
+    workflow_data = None
+    mp4 = None
+    stats = {"images_processed": 0, "workflows_extracted": 0, "space_saved": 0}
+    if HAS_MUTAGEN:
+        try: mp4 = MP4(p)
+        except: pass
+
+    if mp4:
+        for tag_keys in [['----:QuickTime:Workflow', 'workflow'], ['----:QuickTime:Prompt', 'prompt'], ['©cmt']]:
+            val = get_tag_value(mp4, tag_keys)
+            if val:
+                if isinstance(val, bytes): val = val.decode('utf-8', errors='ignore')
+                try:
+                    parsed = json.loads(val)
+                    if isinstance(parsed, dict) and ('prompt' in parsed or any(k.isdigit() for k in parsed.keys()) or 'nodes' in parsed):
+                        if 'prompt' in parsed and isinstance(parsed['prompt'], str):
+                            try: workflow_data = json.loads(parsed['prompt'])
+                            except: workflow_data = parsed
+                        else:
+                            workflow_data = parsed
+                        break
+                except json.JSONDecodeError:
+                    # Try more aggressive parsing if it's a nested string
+                    parsed = unescape_and_parse_nested_json(val)
+                    if parsed:
+                        workflow_data = parsed
+                        break
+
+    if not workflow_data:
+        av_data = extract_metadata_av(p)
+        if av_data:
+            for key in ['workflow', 'prompt', 'comment', 'description']:
+                if key in av_data:
+                    val = av_data[key]
+                    try:
+                        parsed = json.loads(val)
+                        if isinstance(parsed, dict):
+                            if 'prompt' in parsed and isinstance(parsed['prompt'], str):
+                                try: workflow_data = json.loads(parsed['prompt'])
+                                except: workflow_data = parsed
+                            else:
+                                workflow_data = parsed
+                            break
+                    except json.JSONDecodeError:
+                        parsed = unescape_and_parse_nested_json(val)
+                        if parsed:
+                            workflow_data = parsed
+                            break
+
+    if workflow_data:
+        try:
+            with open(json_path, 'w', encoding='utf-8') as f:
+                json.dump(workflow_data, f, indent=2, ensure_ascii=False)
+            print(f"Extracted workflow to {json_path.name}")
+            stats["workflows_extracted"] = 1
+        except Exception as e:
+            print(f"Error saving workflow for {p.name}: {e}")
+    return stats
+
+# --- Constants for Civitai NSFW levels ---
+NSFW_LEVELS = {
+    "None": 1,
+    "Soft": 2,
+    "Mature": 4,
+    "X": 8,
+    "XXX": 16,
+    "All": 32  # Anything
+}
+
+class HashCache:
+    def __init__(self, db_path=None):
+        self.db_path = db_path if db_path else DEFAULT_DB_PATH
+        self.is_sqlite = self.db_path.endswith('.db') or self.db_path.endswith('.sqlite')
+        self.lock = threading.Lock()
+        if self.is_sqlite:
+            self._init_sqlite()
+        else:
+            self.cache = self._load_json()
+
+    def _init_sqlite(self):
+        with sqlite3.connect(self.db_path) as conn:
+            cursor = conn.cursor()
+            cursor.execute('''
+                CREATE TABLE IF NOT EXISTS file_hashes (
+                    filepath TEXT PRIMARY KEY,
+                    mtime REAL,
+                    size INTEGER,
+                    hash TEXT
+                )
+            ''')
+            conn.commit()
+
+    def _load_json(self):
+        if os.path.exists(self.db_path):
+            with open(self.db_path, "r", encoding="utf-8") as f:
+                return json.load(f)
+        return {}
+
+    def _save_json(self):
+        with open(self.db_path, "w", encoding="utf-8") as f:
+            json.dump(self.cache, f, indent=4)
+
+    def _get_sqlite_hash(self, filepath, mtime, size):
+        with sqlite3.connect(self.db_path) as conn:
+            cursor = conn.cursor()
+            cursor.execute("SELECT mtime, size, hash FROM file_hashes WHERE filepath=?", (filepath,))
+            row = cursor.fetchone()
+            if row:
+                db_mtime, db_size, db_hash = row
+                if db_mtime == mtime and db_size == size:
+                    return db_hash
+        return None
+
+    def _set_sqlite_hash(self, filepath, mtime, size, file_hash):
+        with sqlite3.connect(self.db_path, timeout=10.0) as conn:
+            cursor = conn.cursor()
+            cursor.execute('''
+                INSERT OR REPLACE INTO file_hashes (filepath, mtime, size, hash)
+                VALUES (?, ?, ?, ?)
+            ''', (filepath, mtime, size, file_hash))
+            conn.commit()
+
+    def get_hash(self, filepath):
+        if not os.path.exists(filepath):
+            return None
+        mtime = os.path.getmtime(filepath)
+        size = os.path.getsize(filepath)
+        key = filepath
+
+        with self.lock:
+            if self.is_sqlite:
+                cached_hash = self._get_sqlite_hash(key, mtime, size)
+                if cached_hash: return cached_hash
+            else:
+                if key in self.cache:
+                    entry = self.cache[key]
+                    if entry["mtime"] == mtime and entry["size"] == size:
+                        return entry["hash"]
+
+        # Calculate new hash (outside the lock to allow parallel hashing)
+        print(f"Calculating SHA256 for: {filepath}")
+        sha256_hash = self.calculate_sha256(filepath)
+
+        with self.lock:
+            if self.is_sqlite:
+                self._set_sqlite_hash(key, mtime, size, sha256_hash)
+            else:
+                self.cache[key] = {
+                    "mtime": mtime,
+                    "size": size,
+                    "hash": sha256_hash
+                }
+                self._save_json()
+
+        return sha256_hash
+
+    @staticmethod
+    def calculate_sha256(filename):
+        sha256_hash = hashlib.sha256()
+        with open(filename, "rb") as f:
+            # Read and update hash string value in blocks of 4K
+            for byte_block in iter(lambda: f.read(4096), b""):
+                sha256_hash.update(byte_block)
+        return sha256_hash.hexdigest()
+
+class CivitaiAPI:
+    def __init__(self, api_key=None):
+        self.api_key = api_key
+        self.base_url = "https://civitai.com/api/v1"
+
+    def get_headers(self):
+        headers = {}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+        return headers
+
+    def get_model_info_by_hash(self, file_hash):
+        url = f"{self.base_url}/model-versions/by-hash/{file_hash}"
+        response = requests.get(url, headers=self.get_headers())
+        if response.status_code == 200:
+            return response.json()
+        elif response.status_code == 404:
+            return None
+        else:
+            response.raise_for_status()
+
+class Downloader:
+    @staticmethod
+    def download_file(url, target_path, headers=None):
+        if headers is None:
+            headers = {}
+
+        temp_path = target_path + ".downloading"
+
+        downloaded_size = 0
+        if os.path.exists(temp_path):
+            downloaded_size = os.path.getsize(temp_path)
+
+        if downloaded_size > 0:
+            headers["Range"] = f"bytes={downloaded_size}-"
+
+        response = requests.get(url, headers=headers, stream=True)
+
+        if response.status_code == 416: # Range not satisfiable, file probably changed or completed
+            os.remove(temp_path)
+            downloaded_size = 0
+            if "Range" in headers:
+                del headers["Range"]
+            response = requests.get(url, headers=headers, stream=True)
+
+        if response.status_code not in [200, 206]:
+            print(f"Failed to download from {url}, status code: {response.status_code}")
+            return False
+
+        mode = "ab" if response.status_code == 206 else "wb"
+
+        total_size = int(response.headers.get('content-length', 0)) + downloaded_size
+
+        print(f"Downloading: {target_path} ({downloaded_size}/{total_size} bytes)")
+
+        with open(temp_path, mode) as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+
+        os.rename(temp_path, target_path)
+        return True
+
+def get_potential_preview_files(base_path):
+    exts = ["png", "jpg", "jpeg", "webp", "mp4"]
+    files = []
+    for ext in exts:
+        files.append(f"{base_path}.preview.{ext}")
+        files.append(f"{base_path}.{ext}")
+    return files
+
+def preview_exists(base_path):
+    previews = get_potential_preview_files(base_path)
+    for p in previews:
+        if os.path.exists(p):
+            return True
+    return False
+
+def next_example_image_path(base_path, ext):
+    i = 0
+    while len(glob.glob(f"{base_path}.example.{i}.*")) > 0:
+        i += 1
+    return f"{base_path}.example.{i}.{ext}"
+
+def filter_images_by_nsfw(images, threshold_level):
+    valid_images = []
+    for img in images:
+        rating = img.get("nsfwLevel", 1)
+        if rating <= threshold_level:
+            valid_images.append(img)
+    return valid_images
+
+def extract_safetensors_metadata(filepath):
+    try:
+        # Use low_memory=True to only read the header and metadata without loading tensors
+        loader = UnifiedSafetensorsLoader(filepath, low_memory=True)
+        metadata = loader.metadata()
+        loader.close()
+        return metadata if metadata else None
+    except Exception as e:
+        print(f"Failed to read safetensors metadata from {filepath} using unifiedefficientloader: {e}")
+        return None
+
+def process_file(filepath, cache, api, nsfw_threshold, max_examples):
+    filepath_str = str(filepath)
+    base_path = os.path.splitext(filepath_str)[0]
+    is_safetensors = filepath_str.lower().endswith(".safetensors")
+
+    stats = {"new_hashes": 0, "images_processed": 0, "workflows_extracted": 0, "space_saved": 0}
+
+    # Check if all targets already exist
+    info_path = f"{base_path}.civitai.info"
+    st_metadata_path = f"{base_path}.metadata.json"
+
+    has_info = os.path.exists(info_path)
+    has_preview = preview_exists(base_path)
+    has_st_metadata = not is_safetensors or os.path.exists(st_metadata_path)
+
+    # Process existing images/videos for workflow extraction & compression BEFORE we possibly skip the file
+    for p in get_potential_preview_files(base_path):
+        if os.path.exists(p):
+            ext_lower = p.lower()
+            if ext_lower.endswith(('.png', '.jpg', '.jpeg')):
+                res = process_image_workflow_and_compress(p)
+                for k in stats: stats[k] += res.get(k, 0)
+            elif ext_lower.endswith('.mp4'):
+                res = process_video_workflow(p)
+                for k in stats: stats[k] += res.get(k, 0)
+
+    for ex in glob.glob(f"{base_path}.example.*"):
+        ext_lower = ex.lower()
+        if ext_lower.endswith(('.png', '.jpg', '.jpeg')):
+            res = process_image_workflow_and_compress(ex)
+            for k in stats: stats[k] += res.get(k, 0)
+        elif ext_lower.endswith('.mp4'):
+            res = process_video_workflow(ex)
+            for k in stats: stats[k] += res.get(k, 0)
+
+    # If everything exists and we don't want examples or already have enough examples, we might skip API logic
+    if has_info and has_preview and has_st_metadata and max_examples == 0:
+        print(f"Skipping fully populated model: {filepath_str}")
+        return stats
+
+    print(f"Processing model: {filepath_str}")
+
+    # Safetensors built-in metadata extraction
+    if is_safetensors and not has_st_metadata:
+        metadata = extract_safetensors_metadata(filepath_str)
+        if metadata:
+            with open(st_metadata_path, "w", encoding="utf-8") as f:
+                json.dump(metadata, f, indent=4)
+            print(f"Saved safetensors metadata to {st_metadata_path}")
+
+    mtime = os.path.getmtime(filepath_str)
+    size = os.path.getsize(filepath_str)
+
+    # We want to know if it's a NEW hash or cached
+    is_cached = False
+    with cache.lock:
+        if cache.is_sqlite:
+            cached_hash = cache._get_sqlite_hash(filepath_str, mtime, size)
+            if cached_hash: is_cached = True
+        else:
+            if filepath_str in cache.cache:
+                entry = cache.cache[filepath_str]
+                if entry["mtime"] == mtime and entry["size"] == size:
+                    is_cached = True
+
+    file_hash = cache.get_hash(filepath_str)
+    if not file_hash:
+        return stats
+
+    if not is_cached:
+        stats["new_hashes"] += 1
+
+    try:
+        model_info = api.get_model_info_by_hash(file_hash)
+    except Exception as e:
+        print(f"API Error for {filepath_str}: {e}")
+        return stats
+
+    if not model_info:
+        print(f"Model not found on Civitai: {filepath_str}")
+        return stats
+
+    # 1. Save Info
+    if not has_info:
+        with open(info_path, "w", encoding="utf-8") as f:
+            json.dump(model_info, f, indent=4)
+        print(f"Saved metadata to {info_path}")
+
+    images = model_info.get("images", [])
+    valid_images = filter_images_by_nsfw(images, nsfw_threshold)
+
+    if not valid_images:
+        print(f"No valid images found matching NSFW criteria for {filepath_str}")
+        return stats
+
+    # 2. Save Preview
+    if not has_preview and len(valid_images) > 0:
+        preview_img = valid_images[0]
+        url = preview_img.get("url")
+        ext = "jpeg" # Default fallback
+        if ".png" in url.lower(): ext = "png"
+        elif ".mp4" in url.lower(): ext = "mp4"
+        elif ".jpg" in url.lower() or ".jpeg" in url.lower(): ext = "jpeg"
+
+        preview_path = f"{base_path}.preview.{ext}"
+        if Downloader.download_file(url, preview_path):
+            if ext in ("png", "jpg", "jpeg"):
+                res = process_image_workflow_and_compress(preview_path)
+                for k in stats: stats[k] += res.get(k, 0)
+            elif ext == "mp4":
+                res = process_video_workflow(preview_path)
+                for k in stats: stats[k] += res.get(k, 0)
+
+    # 3. Save Examples
+    if max_examples > 0:
+        existing_examples_count = len(glob.glob(f"{base_path}.example.*"))
+        # We divide by 2 roughly if there are json files, let's just count unique example indices
+        existing_indices = set()
+        for f in glob.glob(f"{base_path}.example.*"):
+            parts = f.split('.example.')
+            if len(parts) > 1:
+                idx = parts[1].split('.')[0]
+                if idx.isdigit(): existing_indices.add(idx)
+
+        needed_examples = max_examples - len(existing_indices)
+
+        if needed_examples > 0:
+            example_images = valid_images[1:max_examples+1]
+            # Skip images we probably already downloaded by skipping the first N
+            example_images_to_dl = example_images[len(existing_indices):]
+
+            for img in example_images_to_dl:
+                url = img.get("url")
+                ext = "jpeg"
+                if ".png" in url.lower(): ext = "png"
+                elif ".mp4" in url.lower(): ext = "mp4"
+                elif ".jpg" in url.lower() or ".jpeg" in url.lower(): ext = "jpeg"
+
+                example_path = next_example_image_path(base_path, ext)
+                if Downloader.download_file(url, example_path):
+                    if ext in ("png", "jpg", "jpeg"):
+                        res = process_image_workflow_and_compress(example_path)
+                        for k in stats: stats[k] += res.get(k, 0)
+                    elif ext == "mp4":
+                        res = process_video_workflow(example_path)
+                        for k in stats: stats[k] += res.get(k, 0)
+    return stats
+
+import numpy as np
+from PIL import Image, ImageOps
+import torch
+
+def load_image_tensor(path):
+    if not os.path.exists(path):
+        return None
+    try:
+        img = Image.open(path)
+        img = ImageOps.exif_transpose(img)
+        image = img.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        return image
+    except Exception as e:
+        print(f"Error loading image {path}: {e}")
+        return None
+
+def get_model_workflows(base_path):
+    workflows = []
+    # Main workflow
+    main_json = f"{base_path}.json"
+    if os.path.exists(main_json):
+        workflows.append(main_json)
+
+    # Example workflows
+    # They are named like base_path.example.N.json
+    # or base_path.preview.json (if extracted from preview)
+    preview_json = f"{base_path}.preview.json"
+    if os.path.exists(preview_json) and preview_json not in workflows:
+        workflows.append(preview_json)
+
+    example_jsons = glob.glob(f"{base_path}.example.*.json")
+    # Sort them by index
+    example_jsons.sort()
+    for ej in example_jsons:
+        if ej not in workflows:
+            workflows.append(ej)
+
+    return workflows
+
+def get_model_metadata_file(base_path):
+    # Prefer civitai.info if available
+    civitai_info = f"{base_path}.civitai.info"
+    if os.path.exists(civitai_info):
+        return civitai_info
+
+    # Fallback to metadata.json (safetensors metadata)
+    metadata_json = f"{base_path}.metadata.json"
+    if os.path.exists(metadata_json):
+        return metadata_json
+
+    return None
+
+def scan_and_process(scan_dirs, recursive=True, db_path=None, nsfw_level="All", max_examples=0, api_key=None, threads=4):
+    """
+    Scans directories for models and fetches their metadata/previews.
+    Adapted for direct Python API calls rather than argparse CLI.
+    """
+    db_path = db_path if db_path else DEFAULT_DB_PATH
+    cache = HashCache(db_path)
+    api = CivitaiAPI(api_key)
+
+    model_exts = [".safetensors", ".ckpt", ".pt"]
+
+    files_to_process = []
+    if isinstance(scan_dirs, str):
+        scan_dirs = [scan_dirs]
+
+    for scan_dir in scan_dirs:
+        path = Path(scan_dir)
+        if recursive:
+            for ext in model_exts:
+                # rglob includes all directories, so we must filter out hidden ones
+                for f in path.rglob(f"*{ext}"):
+                    if not any(part.startswith('.') for part in f.parts if part != '.' and part != '..'):
+                        files_to_process.append(f)
+        else:
+            for ext in model_exts:
+                for f in path.glob(f"*{ext}"):
+                    if not any(part.startswith('.') for part in f.parts if part != '.' and part != '..'):
+                        files_to_process.append(f)
+
+    nsfw_threshold = NSFW_LEVELS.get(nsfw_level, NSFW_LEVELS["All"])
+
+    print(f"Found {len(files_to_process)} models to process.")
+
+    global_stats = {
+        "total_processed": len(files_to_process),
+        "new_hashes": 0,
+        "images_processed": 0,
+        "workflows_extracted": 0,
+        "space_saved": 0
+    }
+
+    # Import and use ComfyUI's ProgressBar
+    try:
+        import comfy.utils
+        pbar = comfy.utils.ProgressBar(len(files_to_process))
+    except ImportError:
+        pbar = None
+
+    if threads > 1:
+        print(f"Starting {threads} threads...")
+        with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
+            futures = [executor.submit(process_file, fp, cache, api, nsfw_threshold, max_examples) for fp in files_to_process]
+            for future in concurrent.futures.as_completed(futures):
+                try:
+                    res = future.result()
+                    if res:
+                        for k in global_stats: global_stats[k] += res.get(k, 0)
+                except Exception as e:
+                    print(f"Error in thread: {e}")
+                if pbar:
+                    pbar.update(1)
+    else:
+        for filepath in files_to_process:
+            res = process_file(filepath, cache, api, nsfw_threshold, max_examples)
+            if res:
+                for k in global_stats: global_stats[k] += res.get(k, 0)
+            if pbar:
+                pbar.update(1)
+
+    return global_stats

nodes/lora_extract_svd.py

@@ -13,12 +13,15 @@ import comfy.utils
 from tqdm import tqdm
 from comfy_api.latest import io
 from safetensors.torch import save_file
-from .merger_utils import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
 from .device_utils import (
     estimate_model_size, prepare_for_large_operation,
     cleanup_after_operation, get_device_capabilities
 )
 
+from unifiedefficientloader import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
+
+
+
 
 # =============================================================================
 # Rank Index Functions - Determine LoRA rank from singular values
@@ -689,9 +692,13 @@ def _format_lora_key(key: str) -> str:
         key = key[:-7]
 
     # Handle ComfyUI Checkpoint wrapper
-    if key.startswith("model.diffusion_model."):
+    if key.startswith("model.diffusion_model.") or key.startswith("net."):
         # Check if it's a Diffusers-format transformer inside a checkpoint
-        inner_key = key[22:]  # len("model.diffusion_model.")
+        if key.startswith("model.diffusion_model."):
+            inner_key = key[22:]  # len("model.diffusion_model.")
+        else:
+            inner_key = key[4:]   # len("net.")
+
         if inner_key.startswith("transformer_blocks") or inner_key.startswith("single_transformer_blocks"):
             return f"transformer.{inner_key}"
         # Standard checkpoint or Original Flux

nodes/lora_merger.py

@@ -10,8 +10,8 @@ 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 .merger_utils import parse_custom_weights, get_custom_parameters
 from .lora_resize import (
     detect_lora_format,
     extract_lora_pairs,
@@ -21,6 +21,10 @@ from .lora_resize import (
     _format_lora_key
 )
 
+from unifiedefficientloader import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
+
+
+
 def merge_multi_loras(
     lora_paths: List[str],
     lora_weights: List[float],
@@ -28,6 +32,7 @@ def merge_multi_loras(
     device: str,
     save_dtype: torch.dtype,
     output_filename: str,
+    custom_weights: str = "",
     verbose: bool = True,
 ) -> str:
     """
@@ -48,6 +53,8 @@ def merge_multi_loras(
 
     prepare_for_large_operation(total_size_gb * 2.5, torch.device(device))
 
+    parsed_custom_weights = parse_custom_weights(custom_weights)
+
     handlers = [MemoryEfficientSafeOpen(p, low_memory=True) for p in lora_paths]
 
     try:
@@ -90,6 +97,9 @@ def merge_multi_loras(
         # 2. Merge each core layer
         with torch.no_grad():
             for core, indices in tqdm(layer_map.items(), desc="Merging layers", unit="layers"):
+                # Apply per-layer overrides (regex/substring matching)
+                layer_overrides = get_custom_parameters(core, parsed_custom_weights, {})
+
                 downs = []
                 ups = []
                 is_conv = False
@@ -97,7 +107,14 @@ def merge_multi_loras(
 
                 # First pass: load and determine max rank
                 for idx in indices:
-                    info = lora_infos[idx]
+                    info = lora_infos[idx].copy()
+                    # Apply overrides: w1, w2, w3... or 'weight'
+                    lora_key = f'w{idx+1}'
+                    if lora_key in layer_overrides:
+                        info["weight"] = layer_overrides[lora_key]
+                    elif "weight" in layer_overrides:
+                        info["weight"] = layer_overrides["weight"]
+
                     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]
@@ -275,6 +292,7 @@ class LoRAMultiMerge(io.ComfyNode):
                 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.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights for each LoRA. Syntax: 'regex:w1,w2,w3...' or 'regex:w1=0.5,w2=1.0'"),
                 io.Combo.Input("save_dtype", options=["fp16", "bf16", "fp32"], default="fp16"),
                 io.Combo.Input("device", options=["cuda", "cpu"], default="cuda"),
             ],
@@ -286,7 +304,7 @@ class LoRAMultiMerge(io.ComfyNode):
     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:
+                merge_mode, output_filename, custom_weights, 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]
@@ -312,7 +330,8 @@ class LoRAMultiMerge(io.ComfyNode):
             merge_mode=merge_mode,
             device=device,
             save_dtype=dtype,
-            output_filename=output_filename
+            output_filename=output_filename,
+            custom_weights=custom_weights
         )
 
         return io.NodeOutput(path)
@@ -353,6 +372,7 @@ class LoRAMultiMergeDARE(io.ComfyNode):
                 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.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights for each LoRA. Syntax: 'regex:w1,w2,w3...' or 'regex:w1=0.5,w2=1.0'"),
                 io.Combo.Input("save_dtype", options=["fp16", "bf16", "fp32"], default="fp16"),
                 io.Combo.Input("device", options=["cuda", "cpu"], default="cuda"),
             ],
@@ -364,7 +384,7 @@ class LoRAMultiMergeDARE(io.ComfyNode):
     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:
+                drop_rate, trim_quantile, seed, output_filename, custom_weights, 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]
@@ -392,7 +412,8 @@ class LoRAMultiMergeDARE(io.ComfyNode):
             seed=seed,
             device=device,
             save_dtype=dtype,
-            output_filename=output_filename
+            output_filename=output_filename,
+            custom_weights=custom_weights
         )
 
         return io.NodeOutput(path)
@@ -406,6 +427,7 @@ def merge_multi_loras_dare(
     device: str,
     save_dtype: torch.dtype,
     output_filename: str,
+    custom_weights: str = "",
     verbose: bool = True,
 ) -> str:
     """
@@ -418,6 +440,9 @@ def merge_multi_loras_dare(
         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))
+
+    parsed_custom_weights = parse_custom_weights(custom_weights)
+
     handlers = [MemoryEfficientSafeOpen(p, low_memory=True) for p in lora_paths]
 
     rng = torch.Generator(device=device).manual_seed(seed)
@@ -457,12 +482,22 @@ def merge_multi_loras_dare(
         # 2. Merge
         with torch.no_grad():
             for core, indices in tqdm(layer_map.items(), desc="Merging layers (DARE)", unit="layers"):
+                # Apply per-layer overrides (regex/substring matching)
+                layer_overrides = get_custom_parameters(core, parsed_custom_weights, {})
+
                 downs = []
                 ups = []
                 max_rank = 0
 
                 for idx in indices:
-                    info = lora_infos[idx]
+                    info = lora_infos[idx].copy()
+                    # Apply overrides: w1, w2, w3... or 'weight'
+                    lora_key = f'w{idx+1}'
+                    if lora_key in layer_overrides:
+                        info["weight"] = layer_overrides[lora_key]
+                    elif "weight" in layer_overrides:
+                        info["weight"] = layer_overrides["weight"]
+
                     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
@@ -491,18 +526,22 @@ def merge_multi_loras_dare(
                             t = torch.nn.functional.pad(t, tuple(padding))
                         padded.append(t * w)
 
+                    # Apply per-layer overrides for DARE/TIES parameters
+                    current_drop_rate = layer_overrides.get('alpha', layer_overrides.get('drop_rate', drop_rate))
+                    current_trim_quantile = layer_overrides.get('beta', layer_overrides.get('trim_quantile', trim_quantile))
+
                     # DARE
-                    if drop_rate > 0:
+                    if current_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)
+                            mask = (torch.rand(padded[i].shape, generator=rng, device=device) > current_drop_rate).float()
+                            padded[i] = (padded[i] * mask) / (1 - current_drop_rate)
 
                     # TIES
                     # 1. Trim
-                    if trim_quantile > 0:
+                    if current_trim_quantile > 0:
                         for i in range(len(padded)):
                             flat = padded[i].abs().flatten()
-                            k = int(len(flat) * trim_quantile)
+                            k = int(len(flat) * current_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])

nodes/lora_resize.py

@@ -14,9 +14,12 @@ from tqdm import tqdm
 from comfy_api.latest import io
 from safetensors.torch import save_file
 from typing import Optional, Dict, Tuple, List
-from .merger_utils import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
 from .device_utils import estimate_model_size, prepare_for_large_operation, cleanup_after_operation
 
+from unifiedefficientloader import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
+
+
+
 # Reuse rank functions from extraction module
 from .lora_extract_svd import (
     _index_sv_ratio,
@@ -189,7 +192,7 @@ def detect_lora_rank(handler: MemoryEfficientSafeOpen, pairs: Dict) -> Tuple[int
         # Get dim from down weight shape
         if network_dim is None:
             down_key = block_keys["down"]
-            shape = handler.header[down_key]["shape"]
+            shape = handler.get_shape(down_key)
             # Linear: [rank, in_features] or Conv: [rank, in_ch, k, k]
             network_dim = shape[0]
 

nodes/merger.py

@@ -7,11 +7,13 @@ from tqdm import tqdm
 from comfy_api.latest import io
 from safetensors.torch import save_file
 from .merger_ops import TWO_MODEL_MODES, THREE_MODEL_MODES, MissingTensorBehavior, MissingTensorError
-from .merger_utils import MemoryEfficientSafeOpen, transfer_to_gpu_pinned
+from .merger_utils import parse_custom_weights, get_custom_parameters
 from .device_utils import (
     estimate_model_size, prepare_for_large_operation, cleanup_after_operation
 )
 
+from unifiedefficientloader import MemoryEfficientSafeOpen, transfer_to_gpu_pinned, IncrementalSafetensorsWriter
+
 
 def load_documentation_from_file(filename):
     """Loads documentation from a markdown file in the ../docs/ directory."""
@@ -76,6 +78,9 @@ class MergerLogic:
             process_device = recipe_params.get('device', 'cpu')
             print(f"[Merger] Preparing memory for {total_size_gb:.2f}GB merge operation...")
             prepare_for_large_operation(total_size_gb * 1.2, torch.device(process_device))
+            flush_threshold_bytes = int(total_size_gb * 1024**3 / 16)
+        else:
+            flush_threshold_bytes = 256 * 1024 * 1024  # 256MB fallback (min 8GB RAM assumed)
 
         handlers = {}
         for name in model_names.values():
@@ -99,6 +104,9 @@ class MergerLogic:
         exclude_patterns = _compile_patterns(recipe_params.get('exclude_patterns', ''))
         discard_patterns = _compile_patterns(recipe_params.get('discard_patterns', ''))
 
+        # Parse custom per-layer weights
+        parsed_custom_weights = parse_custom_weights(recipe_params.get('custom_weights', ''))
+
         # Pre-compute key differences for logging
         primary_keys = set(all_keys)
         for name, handler in handlers.items():
@@ -124,12 +132,42 @@ class MergerLogic:
         discarded_keys = 0
         error_keys = []
 
+        output_folder = "loras" if calc_mode == "SVD LoRA Extraction" else model_type
+        # Use [-1] for diffusion_models to get the actual diffusion_models folder, not legacy unet
+        output_dir = folder_paths.get_folder_paths(output_folder)[-1]
+        os.makedirs(output_dir, exist_ok=True)
+        output_filename = recipe_params.get("output_filename")
+        output_path = os.path.join(output_dir, f"{output_filename}.safetensors")
+
+        writer = None
+
+        writer = IncrementalSafetensorsWriter(output_path, metadata=metadata)
+        writer.__enter__()
+
+        batch_buffer = {}
+        batch_bytes = 0
+        batch_key_count = 0
+
+        def _flush_batch():
+            nonlocal batch_buffer, batch_bytes, batch_key_count
+            if not batch_buffer:
+                return
+            writer.write_dict(batch_buffer)
+            pbar.update(batch_key_count)
+            batch_buffer = {}
+            batch_bytes = 0
+            batch_key_count = 0
+            if recipe_params.get('force_clear_cache', True):
+                import gc
+                gc.collect()
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+
         with torch.no_grad():
             for key in tqdm(all_keys, desc="Merging layers", unit="layers"):
                 # Check discard patterns first - skip entirely
                 if _matches_any_pattern(key, discard_patterns):
                     discarded_keys += 1
-                    pbar.update(1)
                     continue
 
                 # Pre-load Model A's tensor with pinned memory for CUDA
@@ -142,9 +180,15 @@ class MergerLogic:
 
                 # Check exclude patterns - use Model A only, no merge
                 if _matches_any_pattern(key, exclude_patterns):
-                    merged_state_dict[key] = tensor_a.detach().to(save_torch_dtype).cpu().clone()
+                    t = tensor_a.detach().to(save_torch_dtype).cpu()
+
+                    batch_buffer[key] = t
+                    batch_bytes += t.numel() * t.element_size()
+                    batch_key_count += 1
                     excluded_keys += 1
-                    pbar.update(1)
+                    del tensor_a
+                    if (batch_bytes >= flush_threshold_bytes or batch_key_count >= 32):
+                        _flush_batch()
                     continue
 
                 # Pass tensor_a metadata to recipes for zeros mode and fallback
@@ -153,10 +197,15 @@ class MergerLogic:
                 recipe_params['_tensor_a_dtype'] = tensor_a.dtype
 
                 try:
-                    recipe = calc_mode_class.create_recipe(key=key, **recipe_params)
+                    # Apply per-layer parameter overrides (regex/substring matching)
+                    layer_params = get_custom_parameters(key, parsed_custom_weights, recipe_params)
+                    recipe = calc_mode_class.create_recipe(key=key, **layer_params)
                     result = recipe.merge()
                 except MissingTensorError as e:
                     if mismatch_mode == MissingTensorBehavior.ERROR:
+
+                        _flush_batch()
+                        writer.__exit__(None, None, None)
                         raise ValueError(f"Layer mismatch error (mismatch_mode='error'): {e}")
                     result = None
                     error_keys.append(key)
@@ -168,7 +217,10 @@ class MergerLogic:
 
                 if isinstance(result, dict):
                     for r_key, r_tensor in result.items():
-                        merged_state_dict[r_key] = r_tensor.detach().to(save_torch_dtype).cpu().clone()
+                        t = r_tensor.detach().to(save_torch_dtype).cpu()
+
+                        batch_buffer[r_key] = t
+                        batch_bytes += t.numel() * t.element_size()
                 else:
                     # Ensure compatibility with Model A's architecture.
                     # If alignment_mode is 'pad/crop', we crop results that were padded.
@@ -179,7 +231,13 @@ class MergerLogic:
                             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.detach().to(save_torch_dtype).cpu().clone()
+                    t = result.detach().to(save_torch_dtype).cpu()
+
+                    batch_buffer[key] = t
+                    batch_bytes += t.numel() * t.element_size()
+
+
+                batch_key_count += 1
 
                 # Clean up references to allow GC immediately.
                 # Local loop variables must be explicitly deleted to prevent PyTorch from keeping tensors in VRAM.
@@ -189,13 +247,12 @@ class MergerLogic:
                 del recipe
                 del result
 
-                if recipe_params.get('force_clear_cache', True):
-                    import gc
-                    gc.collect()
-                    if torch.cuda.is_available():
-                        torch.cuda.empty_cache()
+                if (batch_bytes >= flush_threshold_bytes or batch_key_count >= 32):
+                    _flush_batch()
 
-                pbar.update(1)
+
+        _flush_batch()
+        writer.__exit__(None, None, None)
 
         # Log summary
         if excluded_keys > 0:
@@ -216,10 +273,9 @@ class MergerLogic:
         os.makedirs(output_dir, exist_ok=True)
         output_filename = recipe_params.get("output_filename")
         output_path = os.path.join(output_dir, f"{output_filename}.safetensors")
-        save_file(merged_state_dict, output_path, metadata=metadata)
-
+        if os.path.exists(output_path):
+            print(f"[Merger] Output saved to {output_path}")
         # Cleanup after heavy operation
-        del merged_state_dict
         cleanup_after_operation()
 
         return f"{output_filename}.safetensors"
@@ -243,16 +299,17 @@ class CheckpointTwoMerger(io.ComfyNode):
                 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.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.99, min=-10.0, max=10.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, 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"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -267,6 +324,7 @@ class CheckpointTwoMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -279,6 +337,7 @@ class CheckpointTwoMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b}
@@ -302,16 +361,17 @@ class ModelTwoMerger(io.ComfyNode):
                 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.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.99, min=-10.0, max=10.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, 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"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -326,6 +386,7 @@ class ModelTwoMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -338,6 +399,7 @@ class ModelTwoMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b}
@@ -361,16 +423,17 @@ class TextEncoderTwoMerger(io.ComfyNode):
                 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.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.99, min=-10.0, max=10.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, 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"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -385,6 +448,7 @@ class TextEncoderTwoMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -397,6 +461,7 @@ class TextEncoderTwoMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b}
@@ -420,16 +485,17 @@ class LoRATwoMerger(io.ComfyNode):
                 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.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.99, min=-10.0, max=10.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, 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"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -444,6 +510,7 @@ class LoRATwoMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -456,6 +523,7 @@ class LoRATwoMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b}
@@ -479,16 +547,17 @@ class EmbeddingTwoMerger(io.ComfyNode):
                 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.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.99, min=-10.0, max=10.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, 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"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -503,6 +572,7 @@ class EmbeddingTwoMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_2_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -515,6 +585,7 @@ class EmbeddingTwoMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b}
@@ -541,16 +612,17 @@ class CheckpointThreeMerger(io.ComfyNode):
                 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),
-                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
+                io.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_3_checkpoint"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -565,6 +637,7 @@ class CheckpointThreeMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -577,6 +650,7 @@ class CheckpointThreeMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -601,16 +675,17 @@ class ModelThreeMerger(io.ComfyNode):
                 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),
-                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
+                io.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_3_model"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -625,6 +700,7 @@ class ModelThreeMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -637,6 +713,7 @@ class ModelThreeMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -661,16 +738,17 @@ class TextEncoderThreeMerger(io.ComfyNode):
                 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=0.99, 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.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.99, min=-10.0, max=10.0, step=0.001),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_3_textencoder"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -685,6 +763,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -697,6 +776,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -721,16 +801,17 @@ class LoRAThreeMerger(io.ComfyNode):
                 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),
-                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
+                io.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_3_lora"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -745,6 +826,7 @@ class LoRAThreeMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -757,6 +839,7 @@ class LoRAThreeMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -781,16 +864,17 @@ class EmbeddingThreeMerger(io.ComfyNode):
                 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),
-                io.Float.Input("delta", default=0.5, min=-2.0, max=3.0, step=0.01),
+                io.Float.Input("alpha", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("beta", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("gamma", default=0.5, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("delta", default=0.5, min=-10.0, max=10.0, step=0.01),
                 io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
                 io.String.Input("output_filename", default="merged_3_embedding"),
                 io.Combo.Input("save_dtype", options=["fp32", "fp16", "bf16"]),
                 io.Combo.Input("process_device", options=["cuda", "cpu"]),
                 io.String.Input("exclude_patterns", default="", multiline=True),
                 io.String.Input("discard_patterns", default="", multiline=True),
+                io.String.Input("custom_weights", default="", multiline=True, tooltip="Per-layer weights/params. Syntax: 'regex:weight' or 'regex:param=val,param2=val'"),
                 io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
                 io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
             ],
@@ -805,6 +889,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
                 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,
+                custom_weights: str,
                 lazy_load: bool, force_clear_cache: bool) -> io.NodeOutput:
         doc = load_documentation_from_file('merger_3_model_modes.md')
         if execution_mode == "DOCUMENTATION ONLY":
@@ -817,6 +902,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
             "output_filename": output_filename, "save_dtype": save_dtype,
             "device": process_device, "dtype": torch.float32,
             "exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
+            "custom_weights": custom_weights,
             "lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
         }
         model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}

nodes/merger_ops.py

@@ -4,7 +4,7 @@ import torch.nn.functional as F
 from enum import Enum
 
 
-from .merger_utils import transfer_to_gpu_pinned
+from unifiedefficientloader import transfer_to_gpu_pinned
 
 class MissingTensorBehavior(Enum):
     """Controls behavior when a tensor key is missing from a model."""
@@ -319,6 +319,53 @@ class PowerUpOp(Operation):
         return (m * delta) / keep_prob
 
 
+class DARETIESOp(Operation):
+    """DARE + TIES combined operation on a single task vector (delta = b - a).
+
+    Step 1 — DARE: randomly drop `drop_rate` fraction of delta parameters,
+                   rescale survivors by 1/(1-drop_rate).
+    Step 2 — TIES trim: zero out parameters below the `trim_quantile` magnitude threshold.
+    Step 3 — TIES elect: determine dominant sign per parameter position.
+    Step 4 — TIES merge: zero out parameters that disagree with the dominant sign.
+
+    For a two-model merge the TIES elect/merge step degenerates to a no-op (single vector,
+    sign is always self-consistent) but the trim step still reduces interference from
+    near-zero noisy deltas. The dominant sign filter is included for correctness and
+    forward compatibility with n-model extensions.
+    """
+    def __init__(self, key, alpha, beta, gamma, seed, *sources):
+        super().__init__(key, *sources)
+        # alpha = drop_rate (DARE), beta = trim_quantile (TIES), gamma = lambda scale
+        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 = (b - a).float()
+
+        # --- DARE: drop and rescale ---
+        keep_prob = 1.0 - self.alpha
+        if keep_prob <= 0:
+            return torch.zeros_like(a)
+        rng = torch.Generator(device=delta.device).manual_seed(self.seed)
+        dare_mask = (torch.empty_like(delta).uniform_(0, 1, generator=rng) < keep_prob).float()
+        delta = (delta * dare_mask) / keep_prob
+
+        # --- TIES trim: zero parameters below magnitude threshold ---
+        if self.beta > 0:
+            flat_abs = delta.abs().flatten()
+            k = max(1, int(flat_abs.numel() * self.beta))
+            threshold = torch.kthvalue(flat_abs, k).values
+            delta = torch.where(delta.abs() < threshold, torch.zeros_like(delta), delta)
+
+        # --- TIES elect: dominant sign (trivially self-consistent for single vector) ---
+        dominant_sign = torch.sign(delta)
+        delta = torch.where(torch.sign(delta) == dominant_sign, delta, torch.zeros_like(delta))
+
+        return (a + self.gamma * delta).to(a.dtype)
+
+
 class InterpolateDifference(Operation):
     def __init__(self, key, alpha, beta, gamma, seed, *sources):
         super().__init__(key, *sources)
@@ -710,5 +757,41 @@ class WeightSumCutoffMode(CalcMode):
         return res
 
 
-TWO_MODEL_MODES = [WeightSum(), InterpDifference(), PowerUp(), SVDMode(), ManEnhInterpDifference(), AutoEnhInterpDifference(), WeightSumCutoffMode()]
+class DARETIESMode(CalcMode):
+    name = 'Power-Up (DARE+TIES)'
+    description = 'Adds capabilities of B to A using DARE (Drop and Rescale) followed by TIES (Trim, Elect Sign). Best practice for LoRA-tuned model merging.'
+    models_used = ['A', 'B']
+    param_docs = {
+        'alpha': 'DARE drop rate — fraction of delta parameters randomly zeroed. Higher = more aggressive sparsification.',
+        'beta':  'TIES trim quantile — fraction of smallest-magnitude delta parameters zeroed after DARE. Removes residual noise.',
+        'gamma': 'Lambda — scale multiplier applied to the final merged delta before adding to Model A.',
+        'seed':  'Random seed for the DARE dropout mask.',
+    }
+
+    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)
+
+        op = DARETIESOp(
+            key,
+            kwargs['alpha'],   # drop_rate
+            kwargs['beta'],    # trim_quantile
+            kwargs['gamma'],   # lambda scale
+            kwargs['seed'],
+            a, b,
+        )
+        op.alignment_mode = alignment_mode
+        return op
+
+
+TWO_MODEL_MODES = [WeightSum(), InterpDifference(), PowerUp(), DARETIESMode(), SVDMode(), ManEnhInterpDifference(), AutoEnhInterpDifference(), WeightSumCutoffMode()]
 THREE_MODEL_MODES = [AddDifference(), TrainDifference(), Extract(), AddDisimilarity()]

nodes/merger_utils.py

@@ -1,292 +1,8 @@
 """
-Memory-efficient safetensors utilities with pinned memory support.
-Forces direct file reads to minimize OS page cache usage and avoids VRAM accumulation.
+Utilities for model merging operations.
 """
-import os
-import gc
-import json
-import struct
-import torch
-from safetensors import safe_open
-from typing import Dict, List, Optional, Any
-
-# Module-level configuration for pinned transfer
-_verbose = False
-_pinned_transfer_stats = {"pinned": 0, "fallback": 0}
-
-def set_verbose(enabled: bool):
-    """Enable/disable verbose output for pinned transfers."""
-    global _verbose
-    _verbose = enabled
-
-def get_pinned_transfer_stats():
-    """Return pinned transfer statistics."""
-    return _pinned_transfer_stats.copy()
-
-def reset_pinned_transfer_stats():
-    """Reset transfer statistics."""
-    global _pinned_transfer_stats
-    _pinned_transfer_stats = {"pinned": 0, "fallback": 0}
-
-def transfer_to_gpu_pinned(
-    tensor: torch.Tensor,
-    device: str = 'cuda',
-    dtype: Optional[torch.dtype] = None
-) -> torch.Tensor:
-    """Transfer tensor to GPU using pinned memory for faster transfer."""
-    global _pinned_transfer_stats
-
-    if tensor.device.type != 'cpu' or not torch.cuda.is_available():
-        if dtype is not None:
-            return tensor.to(device=device, dtype=dtype)
-        return tensor.to(device=device)
-
-    if not str(device).startswith('cuda'):
-        if dtype is not None:
-            return tensor.to(device=device, dtype=dtype)
-        return tensor.to(device=device)
-
-    try:
-        pinned = tensor.pin_memory()
-        if dtype is not None:
-            result = pinned.to(device=device, dtype=dtype, non_blocking=True)
-        else:
-            result = pinned.to(device=device, non_blocking=True)
-
-        torch.cuda.current_stream().synchronize()
-        _pinned_transfer_stats["pinned"] += 1
-        return result
-    except Exception as e:
-        _pinned_transfer_stats["fallback"] += 1
-        if dtype is not None:
-            return tensor.to(device=device, dtype=dtype)
-        return tensor.to(device=device)
-
-
-class MemoryEfficientSafeOpen:
-    """Memory-efficient safetensors file reader with streaming and pinned memory support.
-    
-    Operates strictly in streaming mode (low memory). Tensors are read byte-by-byte from 
-    their exact offsets without using safe_open or memory mapping, ensuring tensors do not
-    remain persistently in OS page caches or RAM.
-    """
-
-    def __init__(self, filename: str, device: str = 'cpu', **kwargs):
-        # Ignore mmap_mode, low_memory, or any other legacy kwargs. We always stream.
-        self.filename = filename
-        self.device = device
-        self.header, self.header_size = self._read_header()
-        self.file = open(filename, "rb")
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        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__"]
-
-    def metadata(self) -> Dict[str, str]:
-        """Return file metadata."""
-        return self.header.get("__metadata__", {})
-
-    def get_shape(self, key: str) -> tuple:
-        """Get tensor shape without loading tensor data."""
-        if key not in self.header:
-            raise KeyError(f"Tensor '{key}' not found in file")
-        return tuple(self.header[key]["shape"])
-
-    def get_ndim(self, key: str) -> int:
-        """Get tensor ndim without loading tensor data."""
-        return len(self.get_shape(key))
-
-    def keys_sorted_by_offset(self) -> List[str]:
-        keys_with_offsets = []
-        for key in self.keys():
-            offset = self.header[key]["data_offsets"][0]
-            keys_with_offsets.append((key, offset))
-        keys_with_offsets.sort(key=lambda x: x[1])
-        return [k for k, _ in keys_with_offsets]
-
-    def get_tensor(self, key: str) -> torch.Tensor:
-        """Load a single tensor by key directly from file byte offset."""
-        if key not in self.header:
-            raise KeyError(f"Tensor '{key}' not found in the file")
-
-        metadata = self.header[key]
-        offset_start, offset_end = metadata["data_offsets"]
-
-        if offset_start != offset_end:
-            self.file.seek(self.header_size + 8 + offset_start)
-            tensor_bytes = bytearray(offset_end - offset_start)
-            self.file.readinto(tensor_bytes)
-        else:
-            tensor_bytes = None
-
-        return self._deserialize_tensor(tensor_bytes, metadata)
-
-    def get_tensor_to_gpu(
-        self,
-        key: str,
-        device: str = 'cuda',
-        dtype: Optional[torch.dtype] = None
-    ) -> torch.Tensor:
-        cpu_tensor = self.get_tensor(key)
-        return transfer_to_gpu_pinned(cpu_tensor, device, dtype)
-
-    def get_tensors_parallel(
-        self,
-        keys: Optional[List[str]] = None,
-        workers: Optional[int] = None,
-        sort_by_offset: bool = True
-    ) -> Dict[str, torch.Tensor]:
-        if keys is None:
-            keys = self.keys()
-
-        if workers is None:
-            try:
-                from .device_utils import get_optimal_workers, estimate_model_size
-                model_size = estimate_model_size(self.filename)
-                workers = get_optimal_workers(model_size)
-            except ImportError:
-                workers = min(4, os.cpu_count() or 4)
-
-        if sort_by_offset:
-            keys_with_offsets = [(k, self.header[k]["data_offsets"][0]) for k in keys]
-            keys_with_offsets.sort(key=lambda x: x[1])
-            keys = [k for k, _ in keys_with_offsets]
-
-        result = {}
-        with ThreadPoolExecutor(max_workers=workers) as executor:
-            futures = {
-                executor.submit(
-                    _read_tensor_from_file,
-                    self.filename,
-                    key,
-                    self.header[key],
-                    self.header_size
-                ): key
-                for key in keys
-            }
-
-            for future in as_completed(futures):
-                key = futures[future]
-                try:
-                    tensor = future.result()
-                    if tensor is not None:
-                        result[key] = tensor
-                except Exception as e:
-                    print(f"[MemoryEfficientSafeOpen] Failed to load {key}: {e}")
-
-        return result
-
-    def get_tensor_as_dict(self, key: str) -> Dict[str, Any]:
-        if key not in self.header:
-            raise KeyError(f"Tensor '{key}' not found")
-
-        metadata = self.header[key]
-        offset_start, offset_end = metadata["data_offsets"]
-
-        if offset_start == offset_end:
-            return {}
-
-        self.file.seek(self.header_size + 8 + offset_start)
-        tensor_bytes = self.file.read(offset_end - offset_start)
-        return json.loads(tensor_bytes.decode("utf-8"))
-
-    def _read_header(self):
-        with open(self.filename, "rb") as f:
-            header_size = struct.unpack("<Q", f.read(8))[0]
-            header_json = f.read(header_size).decode("utf-8")
-        return json.loads(header_json), header_size
-
-    def _deserialize_tensor(self, tensor_bytes, metadata):
-        dtype_str = metadata["dtype"]
-        shape = metadata["shape"]
-        dtype = self._get_torch_dtype(dtype_str)
-
-        if tensor_bytes is None:
-            byte_tensor = torch.empty(0, dtype=torch.uint8)
-        else:
-            byte_tensor = torch.frombuffer(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 = {
-            "F64": torch.float64, "F32": torch.float32, "F16": torch.float16, "BF16": torch.bfloat16,
-            "I64": torch.int64, "I32": torch.int32, "I16": torch.int16, "I8": torch.int8,
-            "U8": torch.uint8, "BOOL": torch.bool,
-        }
-        if hasattr(torch, "float8_e5m2"):
-            dtype_map["F8_E5M2"] = torch.float8_e5m2
-        if hasattr(torch, "float8_e4m3fn"):
-            dtype_map["F8_E4M3"] = torch.float8_e4m3fn
-
-        dtype = dtype_map.get(dtype_str)
-        if dtype is None:
-            raise ValueError(f"Unsupported dtype: {dtype_str}")
-        return dtype
-
-    @staticmethod
-    def _convert_float8(byte_tensor, dtype_str, shape):
-        if dtype_str == "F8_E5M2" and hasattr(torch, "float8_e5m2"):
-            return byte_tensor.view(torch.float8_e5m2).reshape(shape)
-        elif dtype_str == "F8_E4M3" and hasattr(torch, "float8_e4m3fn"):
-            return byte_tensor.view(torch.float8_e4m3fn).reshape(shape)
-        else:
-            raise ValueError(f"Unsupported float8 type: {dtype_str}")
-
-
-def _read_tensor_from_file(
-    filepath: str,
-    key: str,
-    metadata: Dict,
-    header_size: int
-) -> Optional[torch.Tensor]:
-    """Helper function to read a single tensor from file (for parallel execution).
-
-    Each worker opens its own file handle for thread safety.
-    """
-    offset_start, offset_end = metadata["data_offsets"]
-    dtype_str = metadata["dtype"]
-    shape = metadata["shape"]
-
-    if offset_start == offset_end:
-        return None
-
-    dtype_map = {
-        "F64": torch.float64, "F32": torch.float32, "F16": torch.float16, "BF16": torch.bfloat16,
-        "I64": torch.int64, "I32": torch.int32, "I16": torch.int16, "I8": torch.int8,
-        "U8": torch.uint8, "BOOL": torch.bool,
-    }
-    if hasattr(torch, "float8_e5m2"):
-        dtype_map["F8_E5M2"] = torch.float8_e5m2
-    if hasattr(torch, "float8_e4m3fn"):
-        dtype_map["F8_E4M3"] = torch.float8_e4m3fn
-
-    dtype = dtype_map.get(dtype_str, torch.float32)
-
-    with open(filepath, "rb") as f:
-        f.seek(header_size + 8 + offset_start)
-        tensor_bytes = f.read(offset_end - offset_start)
-
-    byte_tensor = torch.frombuffer(bytearray(tensor_bytes), dtype=torch.uint8)
-
-    if dtype_str in ["F8_E5M2", "F8_E4M3"]:
-        if dtype_str == "F8_E5M2" and hasattr(torch, "float8_e5m2"):
-            return byte_tensor.view(torch.float8_e5m2).reshape(shape)
-        elif dtype_str == "F8_E4M3" and hasattr(torch, "float8_e4m3fn"):
-            return byte_tensor.view(torch.float8_e4m3fn).reshape(shape)
-
-    return byte_tensor.view(dtype).reshape(shape)
-
+from typing import Dict, List, Tuple, Any
+import re
 
 def get_union_keys(*handlers) -> List[str]:
     """Returns the union of all tensor keys across multiple model handlers."""
@@ -305,4 +21,85 @@ def get_intersection_keys(*handlers) -> List[str]:
     all_keys = set(handlers[0].keys())
     for handler in handlers[1:]:
         all_keys &= set(handler.keys())
-    return sorted(all_keys)
+    return sorted(all_keys)
+
+
+def parse_custom_weights(custom_weights_str: str) -> List[Tuple[re.Pattern, Dict[str, float]]]:
+    """Parses multiline string of custom weights.
+    Format: pattern:weight OR pattern:param=value,param2=value
+    Returns list of (compiled_regex, parameter_dict).
+    """
+    if not custom_weights_str or not custom_weights_str.strip():
+        return []
+
+    results = []
+    lines = custom_weights_str.strip().split('\n')
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith('#'):
+            continue
+
+        if ':' not in line:
+            continue
+
+        pattern_str, weight_str = line.rsplit(':', 1)
+        pattern_str = pattern_str.strip()
+        weight_str = weight_str.strip()
+
+        try:
+            pattern = re.compile(pattern_str)
+        except re.error:
+            # Fallback to literal substring if regex invalid
+            pattern = re.compile(re.escape(pattern_str))
+
+        params = {}
+        if '=' in weight_str:
+            # param=value,param2=value
+            parts = weight_str.split(',')
+            for part in parts:
+                if '=' in part:
+                    k, v = part.split('=', 1)
+                    k = k.strip()
+                    # Map common aliases
+                    if k in ['a', 'alpha']: k = 'alpha'
+                    elif k in ['b', 'beta']: k = 'beta'
+                    elif k in ['g', 'gamma']: k = 'gamma'
+                    elif k in ['d', 'delta']: k = 'delta'
+                    elif k in ['w', 'weight']: k = 'weight'
+                    elif re.match(r'^w[1-8]$', k): pass # Keep w1-w8 as is
+
+                    try:
+                        params[k] = float(v.strip())
+                    except ValueError:
+                        pass
+        else:
+            # single float or comma-separated list of floats (for multi-LoRA)
+            if ',' in weight_str:
+                parts = weight_str.split(',')
+                for i, p in enumerate(parts):
+                    try:
+                        params[f'w{i+1}'] = float(p.strip())
+                    except ValueError:
+                        pass
+            else:
+                try:
+                    val = float(weight_str)
+                    params['alpha'] = val # default for merger
+                    params['weight'] = val # default for lora_merger
+                except ValueError:
+                    pass
+
+        if params:
+            results.append((pattern, params))
+
+    return results
+
+
+def get_custom_parameters(key: str, parsed_weights: List[Tuple[re.Pattern, Dict[str, float]]], base_params: Dict[str, Any]) -> Dict[str, Any]:
+    """Returns updated parameters if key matches any pattern."""
+    new_params = base_params.copy()
+    for pattern, custom_dict in parsed_weights:
+        if pattern.search(key):
+            new_params.update(custom_dict)
+            break # First match wins
+    return new_params

nodes/model_info_nodes.py

@@ -0,0 +1,184 @@
+import os
+import folder_paths
+from comfy_api.latest import io
+from .downloader_utils import get_potential_preview_files, load_image_tensor, get_model_workflows, get_model_metadata_file
+
+class BaseModelInfoLoader(io.ComfyNode):
+    CATEGORY = "ModelUtils/Info"
+
+    @classmethod
+    def get_info(cls, category, name, workflow_index):
+        full_path = folder_paths.get_full_path(category, name)
+        if not full_path or not os.path.exists(full_path):
+            return (None, "{}", "{}")
+
+        base_path = os.path.splitext(full_path)[0]
+
+        # 1. Image Preview
+        image_tensor = None
+        previews = get_potential_preview_files(base_path)
+        for p in previews:
+            if os.path.exists(p) and not p.lower().endswith('.mp4'):
+                image_tensor = load_image_tensor(p)
+                if image_tensor is not None:
+                    break
+
+        # 2. Workflow
+        workflow_json = "{}"
+        workflows = get_model_workflows(base_path)
+        if workflows and 0 <= workflow_index < len(workflows):
+            try:
+                with open(workflows[workflow_index], 'r', encoding='utf-8') as f:
+                    workflow_json = f.read()
+            except Exception as e:
+                print(f"Error reading workflow {workflows[workflow_index]}: {e}")
+
+        # 3. Metadata
+        metadata_json = "{}"
+        metadata_file = get_model_metadata_file(base_path)
+        if metadata_file:
+            try:
+                with open(metadata_file, 'r', encoding='utf-8') as f:
+                    metadata_json = f.read()
+            except Exception as e:
+                print(f"Error reading metadata {metadata_file}: {e}")
+
+        return (image_tensor, workflow_json, metadata_json)
+
+class CheckpointInfoLoader(BaseModelInfoLoader):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Checkpoint Info Loader",
+            category=cls.CATEGORY,
+            inputs=[
+                io.Combo.Input("checkpoint", options=folder_paths.get_filename_list("checkpoints")),
+                io.Int.Input("workflow_index", default=0, min=0, max=100)
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.String.Output(display_name="workflow_json"),
+                io.String.Output(display_name="metadata_json")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, checkpoint, workflow_index):
+        res = cls.get_info("checkpoints", checkpoint, workflow_index)
+        return io.NodeOutput(res[0], res[1], res[2])
+
+class LoRAInfoLoader(BaseModelInfoLoader):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="LoRA Info Loader",
+            category=cls.CATEGORY,
+            inputs=[
+                io.Combo.Input("lora", options=folder_paths.get_filename_list("loras")),
+                io.Int.Input("workflow_index", default=0, min=0, max=100)
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.String.Output(display_name="workflow_json"),
+                io.String.Output(display_name="metadata_json")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, lora, workflow_index):
+        res = cls.get_info("loras", lora, workflow_index)
+        return io.NodeOutput(res[0], res[1], res[2])
+
+class EmbeddingInfoLoader(BaseModelInfoLoader):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Embedding Info Loader",
+            category=cls.CATEGORY,
+            inputs=[
+                io.Combo.Input("embedding", options=folder_paths.get_filename_list("embeddings")),
+                io.Int.Input("workflow_index", default=0, min=0, max=100)
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.String.Output(display_name="workflow_json"),
+                io.String.Output(display_name="metadata_json")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, embedding, workflow_index):
+        res = cls.get_info("embeddings", embedding, workflow_index)
+        return io.NodeOutput(res[0], res[1], res[2])
+
+class VAEInfoLoader(BaseModelInfoLoader):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="VAE Info Loader",
+            category=cls.CATEGORY,
+            inputs=[
+                io.Combo.Input("vae", options=folder_paths.get_filename_list("vae")),
+                io.Int.Input("workflow_index", default=0, min=0, max=100)
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.String.Output(display_name="workflow_json"),
+                io.String.Output(display_name="metadata_json")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, vae, workflow_index):
+        res = cls.get_info("vae", vae, workflow_index)
+        return io.NodeOutput(res[0], res[1], res[2])
+
+class ControlNetInfoLoader(BaseModelInfoLoader):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="ControlNet Info Loader",
+            category=cls.CATEGORY,
+            inputs=[
+                io.Combo.Input("controlnet", options=folder_paths.get_filename_list("controlnet")),
+                io.Int.Input("workflow_index", default=0, min=0, max=100)
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.String.Output(display_name="workflow_json"),
+                io.String.Output(display_name="metadata_json")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, controlnet, workflow_index):
+        res = cls.get_info("controlnet", controlnet, workflow_index)
+        return io.NodeOutput(res[0], res[1], res[2])
+
+class DiffusionModelInfoLoader(BaseModelInfoLoader):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id=cls.__name__,
+            display_name="Diffusion Model Info Loader",
+            category=cls.CATEGORY,
+            inputs=[
+                io.Combo.Input("diffusion_model", options=folder_paths.get_filename_list("diffusion_models")),
+                io.Int.Input("workflow_index", default=0, min=0, max=100)
+            ],
+            outputs=[
+                io.Image.Output(display_name="preview"),
+                io.String.Output(display_name="workflow_json"),
+                io.String.Output(display_name="metadata_json")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, diffusion_model, workflow_index):
+        res = cls.get_info("diffusion_models", diffusion_model, workflow_index)
+        return io.NodeOutput(res[0], res[1], res[2])

nodes/prunekeys.py

@@ -7,9 +7,10 @@ from comfy_api.latest import io
 from safetensors.torch import save_file
 from safetensors import safe_open
 from .utils import convert_pt_to_safetensors
-from .merger_utils import MemoryEfficientSafeOpen
 from .device_utils import estimate_model_size, prepare_for_large_operation, cleanup_after_operation
 
+from unifiedefficientloader import MemoryEfficientSafeOpen
+
 
 def _prune_keys(model_name: str, model_type: str, keys_to_prune_str: str,
                 use_regex: bool, output_filename: str) -> str:

nodes/renamekeys.py

@@ -6,9 +6,11 @@ from comfy_api.latest import io
 from safetensors.torch import save_file
 from safetensors import safe_open
 from .utils import convert_pt_to_safetensors
-from .merger_utils import MemoryEfficientSafeOpen
 from .device_utils import estimate_model_size, prepare_for_large_operation, cleanup_after_operation
 
+from unifiedefficientloader import MemoryEfficientSafeOpen
+
+
 
 def _rename_keys(model_name: str, model_type: str, old_keys_str: str,
                  new_keys_str: str, output_filename: str) -> str:

pyproject.toml

@@ -1,9 +1,24 @@
 [project]
 name = "ComfyUI-ModelUtils"
 description = "[WIP]Custom nodes for handling, inspecting, modifying and creating various model files."
-version = "0.2.8"
+version = "0.3.5"
 license = { file = "LICENSE" }
 
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+dependencies = [
+    "unifiedefficientloader==0.4.4"
+]
+
 [project.urls]
 Repository = "https://github.com/silveroxides/ComfyUI-ModelUtils"
 #  Used by Comfy Registry https://comfyregistry.org

requirements.txt

@@ -1 +1,5 @@
-# No external dependencies required
+requests
+Pillow
+mutagen
+unifiedefficientloader==0.4.4
+av