diff --git a/__init__.py b/__init__.py index e29d660..8896830 100644 --- a/__init__.py +++ b/__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, ] diff --git a/nodes/downloader_nodes.py b/nodes/downloader_nodes.py new file mode 100644 index 0000000..f6f15ef --- /dev/null +++ b/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 [] diff --git a/nodes/downloader_utils.py b/nodes/downloader_utils.py new file mode 100644 index 0000000..a27b072 --- /dev/null +++ b/nodes/downloader_utils.py @@ -0,0 +1,702 @@ +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')) +try: + from unifiedefficientloader import UnifiedSafetensorsLoader + HAS_UNIFIED_LOADER = True +except ImportError: + HAS_UNIFIED_LOADER = False + print("Warning: unifiedefficientloader not found. Safetensors metadata extraction disabled.") + +# --- 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): + if not HAS_UNIFIED_LOADER: + return None + 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 diff --git a/nodes/merger_utils.py b/nodes/merger_utils.py index d68e200..dbe56a9 100644 --- a/nodes/merger_utils.py +++ b/nodes/merger_utils.py @@ -65,8 +65,8 @@ def transfer_to_gpu_pinned( 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 + + 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. """ diff --git a/nodes/model_info_nodes.py b/nodes/model_info_nodes.py new file mode 100644 index 0000000..8ade4e1 --- /dev/null +++ b/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]) diff --git a/requirements.txt b/requirements.txt index d2ce485..1faba79 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,5 @@ -# No external dependencies required +requests +Pillow +mutagen +unifiedefficientloader +av