From 629bc791a6735658b848e378aeb0cc3c97c649c4 Mon Sep 17 00:00:00 2001 From: Dariusz L Date: Sun, 5 Jul 2026 04:22:25 +0200 Subject: [PATCH] feat: extract richer local metadata from safetensors headers, improve base-model inference, and preserve local data when remote lookups fail --- __init__.py | 116 ++++- core/path_utils.py | 638 +++++++++++++++++++++++-- core/sources/civitai.py | 208 +++++++- metadata/huggingface-author-index.json | 2 +- pyproject.toml | 6 +- 5 files changed, 900 insertions(+), 70 deletions(-) diff --git a/__init__.py b/__init__.py index a0679fb..ea77786 100644 --- a/__init__.py +++ b/__init__.py @@ -1588,6 +1588,34 @@ class ModelResolverExtension: clean_name = _os.path.splitext(filename)[0] + local_metadata_fields = ( + "author", + "creator", + "license", + "usage_hint", + "usage_tips", + "preview_url", + "metadata_source", + "from_safetensors_header", + "local_metadata_available", + "header_metadata_keys", + "metadata_summary", + "base_model_raw", + "sha256_source", + "hashes", + "hash_status", + ) + + def add_local_metadata_fields(payload, result): + if not isinstance(payload, dict) or not isinstance(result, dict): + return payload + for key in local_metadata_fields: + value = result.get(key) + if value in (None, "", [], {}): + continue + payload[key] = value + return payload + # Get the file path to hash file_path = resolved_path if resolved_path else None file_location = "" @@ -1625,7 +1653,7 @@ class ModelResolverExtension: or infer_model_type_from_category(category) ) - return { + response = { "filename": result.get("filename") or filename, "category": category, "file_path": result.get("file_path") or file_path or "", @@ -1653,6 +1681,8 @@ class ModelResolverExtension: "sha256": result.get("sha256") or provided_hash, "size": size_value, "base_model": result.get("base_model"), + "base_model_source": result.get("base_model_source"), + "base_model_inferred": bool(result.get("base_model_inferred")), "tags": result.get("tags", []), "trained_words": result.get("trained_words", []), "images": result.get("images", []), @@ -1664,6 +1694,7 @@ class ModelResolverExtension: "metadata_checked": bool(civitai_checked), "civitai_checked": bool(civitai_checked), } + return add_local_metadata_fields(response, result) def extract_result_sha256(result): return extract_sha256_from_metadata(result) @@ -1763,6 +1794,20 @@ class ModelResolverExtension: pass return result + def result_has_remote_identity(result): + if not isinstance(result, dict): + return False + return bool( + result.get("url") + or result.get("version_url") + or result.get("download_url") + or result.get("platform_url") + or result.get("repo_id") + or result.get("model_id") + or result.get("version_id") + or result.get("from_metadata") + ) + def remote_link_is_marked_dead(item): if not isinstance(item, dict): return False @@ -1911,6 +1956,8 @@ class ModelResolverExtension: "sha256": result.get("sha256") or provided_hash, "size": result.get("size"), "base_model": result.get("base_model"), + "base_model_source": result.get("base_model_source"), + "base_model_inferred": bool(result.get("base_model_inferred")), "tags": result.get("tags", []), "trained_words": result.get("trained_words", []), "images": result.get("images", []), @@ -1935,6 +1982,7 @@ class ModelResolverExtension: "path": result.get("path"), }, } + add_local_metadata_fields(metadata_payload, result) metadata_path = write_lora_manager_metadata( file_path, metadata_payload, @@ -2011,6 +2059,8 @@ class ModelResolverExtension: "sha256": result.get("sha256"), "size": result.get("size"), "base_model": result.get("base_model"), + "base_model_source": result.get("base_model_source"), + "base_model_inferred": bool(result.get("base_model_inferred")), "tags": result.get("tags", []), "trained_words": result.get("trained_words", []), "images": result.get("images", []), @@ -2034,6 +2084,7 @@ class ModelResolverExtension: "imported_from": source_metadata_path, }, } + add_local_metadata_fields(metadata_payload, result) metadata_path = write_lora_manager_metadata( file_path, metadata_payload, @@ -2094,15 +2145,24 @@ class ModelResolverExtension: or result.get("version_url") or result.get("from_metadata") or result.get("trained_words") + or result.get("base_model_inferred") + or result.get("local_metadata_available") + or result.get("from_safetensors_header") ) and ( result.get("from_metadata") or not provided_hash or result_hash_matches(result) ): - result = prepare_remote_result(result, "civitai") + result_source = ( + "local" + if result.get("source") == "local" + and not result_has_remote_identity(result) + else "civitai" + ) + result = prepare_remote_result(result, result_source) metadata_path, metadata_saved = save_remote_metadata( result, - "civitai", + result_source, ) return web.json_response( @@ -2214,25 +2274,59 @@ class ModelResolverExtension: f"{hf_attempt['label']} metadata lookup error: {e}" ) - # No result found - response = build_info_response(None, civitai_checked=True) + # No remote result found. Keep any useful local safetensors + # header metadata, including base-model fingerprints, model + # titles, descriptions, tags, trigger words, and author data. + local_fallback_result = None + if file_path and _os.path.exists(file_path): + try: + from .core.sources.civitai import get_model_info_for_file + + local_candidate = get_model_info_for_file( + file_path, + local_only=True, + ) + if ( + isinstance(local_candidate, dict) + and ( + local_candidate.get("base_model") + or local_candidate.get("local_metadata_available") + or local_candidate.get("from_safetensors_header") + ) + ): + local_fallback_result = local_candidate + except Exception as local_fallback_error: + self.logger.debug( + f"Local base model inference fallback failed: {local_fallback_error}" + ) + + response = build_info_response( + local_fallback_result, + civitai_checked=True, + local_payload=bool(local_fallback_result), + ) response["url"] = None if force_refresh and file_path and _os.path.exists(file_path): try: metadata_payload = { "filename": filename, "category": category, - "model_name": clean_name, - "name": clean_name, - "model_type": infer_model_type_from_category(category), - "sha256": provided_hash, - "size": _os.path.getsize(file_path), + "model_name": response.get("model_name") or clean_name, + "name": response.get("model_name") or clean_name, + "model_type": response.get("model_type") + or infer_model_type_from_category(category), + "sha256": response.get("sha256") or provided_hash, + "size": response.get("size") or _os.path.getsize(file_path), + "base_model": response.get("base_model"), + "base_model_source": response.get("base_model_source"), + "base_model_inferred": bool(response.get("base_model_inferred")), "civitai_deleted": True, "civitai_checked": True, "remote_metadata_missing": True, "source": "local", - "details_source": "", + "details_source": response.get("details_source") or "", } + add_local_metadata_fields(metadata_payload, response) metadata_path = write_lora_manager_metadata( file_path, metadata_payload, diff --git a/core/path_utils.py b/core/path_utils.py index ae35d51..178f9ac 100644 --- a/core/path_utils.py +++ b/core/path_utils.py @@ -8,7 +8,8 @@ and directory matching to be shared across all modules. import os import json import hashlib -from typing import Any, Tuple, Optional, Callable, Iterable, List +import re +from typing import Any, Tuple, Optional, Callable, Iterable, List, Dict _log = None @@ -277,9 +278,19 @@ _NON_SHA256_HASH_KEY_PARTS = ( "crc32", "md5", "sha1", + "ss_new_sd_model_hash", + "ss_sd_model_hash", + "sshs_model_hash", + "sshs_legacy_hash", + "autov3", "autov1", "autov2", ) +_BASE_MODEL_ALIAS_CACHE: Optional[List[Tuple[str, str]]] = None +SAFETENSORS_METADATA_TAG_LIMIT = 40 +SAFETENSORS_METADATA_KEY_LIMIT = 80 +SAFETENSORS_METADATA_TEXT_MAX_CHARS = 20000 +SAFETENSORS_METADATA_THUMBNAIL_MAX_CHARS = 512 * 1024 def _normalize_header_sha256(value: Any) -> str: @@ -306,11 +317,11 @@ def _normalize_header_sha256(value: Any) -> str: return candidate.lower() -def extract_safetensors_header_sha256( +def read_safetensors_header( file_path: str, max_header_size: int = SAFETENSORS_HEADER_MAX_BYTES, -) -> Optional[str]: - """Return an embedded SHA256 from a safetensors metadata header when present.""" +) -> Optional[Dict[str, Any]]: + """Read and parse a safetensors JSON header without touching tensor payloads.""" if not file_path or not str(file_path).lower().endswith(".safetensors"): return None @@ -345,42 +356,599 @@ def extract_safetensors_header_sha256( return None header_json = json.loads(header_bytes.decode("utf-8")) - if not isinstance(header_json, dict): - return None - - metadata = header_json.get("__metadata__") - if not isinstance(metadata, dict): - return None - - for key in ( - "modelspec.hash.sha256", - "modelspec.hash_sha256", - "sha256", - "SHA256", - ): - sha256 = _normalize_header_sha256(metadata.get(key)) - if sha256: - return sha256 - - for key, value in metadata.items(): - key_lower = str(key).lower() - if not ( - "sha256" in key_lower - or "hash" in key_lower - or "civitai" in key_lower - ): - continue - if any(part in key_lower for part in _NON_SHA256_HASH_KEY_PARTS): - continue - sha256 = _normalize_header_sha256(value) - if sha256: - return sha256 + if isinstance(header_json, dict): + return header_json except Exception as e: - _get_log().debug(f"Could not read safetensors header hash for {file_path}: {e}") + _get_log().debug(f"Could not read safetensors header for {file_path}: {e}") return None +def extract_safetensors_header_sha256( + file_path: str, + max_header_size: int = SAFETENSORS_HEADER_MAX_BYTES, +) -> Optional[str]: + """Return an embedded SHA256 from a safetensors metadata header when present.""" + header_json = read_safetensors_header(file_path, max_header_size=max_header_size) + if not isinstance(header_json, dict): + return None + + metadata = header_json.get("__metadata__") + if not isinstance(metadata, dict): + return None + + for key in ( + "modelspec.hash.sha256", + "modelspec.hash_sha256", + "sha256", + "SHA256", + ): + sha256 = _normalize_header_sha256(metadata.get(key)) + if sha256: + return sha256 + + for key, value in metadata.items(): + key_lower = str(key).lower() + if not ( + "sha256" in key_lower + or "hash" in key_lower + or "civitai" in key_lower + ): + continue + if any(part in key_lower for part in _NON_SHA256_HASH_KEY_PARTS): + continue + sha256 = _normalize_header_sha256(value) + if sha256: + return sha256 + + return None + + +def _normalize_base_model_token(value: Any) -> str: + return re.sub(r"[^a-z0-9]+", "", str(value or "").lower()) + + +def _load_base_model_aliases() -> List[Tuple[str, str]]: + """Return normalized base-model aliases ordered from most specific first.""" + global _BASE_MODEL_ALIAS_CACHE + if _BASE_MODEL_ALIAS_CACHE is not None: + return _BASE_MODEL_ALIAS_CACHE + + aliases: List[Tuple[str, str]] = [] + try: + data = read_json_safe(os.path.join(METADATA_DIR, "base-models.json"), {}) + for entry in data.get("base_models", []) if isinstance(data, dict) else []: + if not isinstance(entry, dict): + continue + name = str(entry.get("name") or "").strip() + if not name: + continue + raw_aliases = entry.get("aliases") or [] + if isinstance(raw_aliases, str): + raw_aliases = [raw_aliases] + if not isinstance(raw_aliases, (list, tuple, set)): + raw_aliases = [] + for raw_alias in [name, *raw_aliases]: + alias = _normalize_base_model_token(raw_alias) + if alias: + aliases.append((alias, name)) + except Exception as e: + _get_log().debug(f"Could not load base model aliases: {e}") + + aliases.sort(key=lambda item: len(item[0]), reverse=True) + _BASE_MODEL_ALIAS_CACHE = aliases + return aliases + + +def _canonical_base_model_from_text(value: Any) -> str: + normalized = _normalize_base_model_token(value) + if not normalized: + return "" + + for alias, name in _load_base_model_aliases(): + if normalized == alias or (len(alias) >= 4 and alias in normalized): + return name + return "" + + +def infer_safetensors_base_model( + file_path: str, + max_tensor_keys: int = 500, +) -> str: + """Infer a likely base model from safetensors metadata and tensor names.""" + header_json = read_safetensors_header(file_path) + if not isinstance(header_json, dict): + return "" + + metadata = header_json.get("__metadata__") + if not isinstance(metadata, dict): + metadata = {} + + architecture = str( + metadata.get("modelspec.architecture") + or metadata.get("architecture") + or metadata.get("ss_base_model_version") + or "" + ).lower() + + if architecture: + if "stable-diffusion-xl" in architecture or "sdxl" in architecture: + return "SDXL 1.0" + if ( + "stable-diffusion-v1" in architecture + or "stable-diffusion-1" in architecture + or "runwayml/stable-diffusion-v1-5" in architecture + or "sd1.5" in architecture + or "sd 1.5" in architecture + ): + return "SD 1.5" + if "stable-diffusion-v2" in architecture or "sd2" in architecture: + return "SD 2.1" + if ( + "stable-diffusion-3" in architecture + or "stable-diffusion-v3" in architecture + or "sd3" in architecture + ): + return "SD 3" + if "flux" in architecture: + if "krea" in architecture: + return "Flux.1 Krea" + if "schnell" in architecture: + return "Flux.1 S" + if "kontext" in architecture: + return "Flux.1 Kontext" + return "Flux.1 D" + alias_match = _canonical_base_model_from_text(architecture) + if alias_match: + return alias_match + + tensor_keys = [ + str(key) + for key in header_json.keys() + if key != "__metadata__" + ][:max_tensor_keys] + keys_text = " ".join(tensor_keys) + + if "double_blocks.0.img_attn" in keys_text or "img_in.weight" in keys_text: + return "Flux.1 D" + if "joint_blocks.0.x_block" in keys_text: + return "SD 3" + if ( + "conditioner.embedders.1.model" in keys_text + or "label_emb.0.0.weight" in keys_text + ): + return "SDXL 1.0" + if ( + "cond_stage_model.transformer.text_model" in keys_text + or "model.diffusion_model.input_blocks.0.0.weight" in keys_text + ): + return "SD 1.5" + + return "" + + +def _parse_safetensors_metadata_value(value: Any) -> Any: + if not isinstance(value, str): + return value + + text = value.strip() + if not text: + return "" + if text[:1] in {"{", "["}: + try: + return json.loads(text) + except Exception: + return text + return text + + +def _metadata_get(metadata: Dict[str, Any], *keys: str) -> Any: + if not isinstance(metadata, dict): + return None + + for key in keys: + if key in metadata: + return metadata.get(key) + + lowered = {str(key).lower(): key for key in metadata.keys()} + for key in keys: + original_key = lowered.get(str(key).lower()) + if original_key is not None: + return metadata.get(original_key) + return None + + +def _metadata_text( + metadata: Dict[str, Any], + *keys: str, + max_chars: int = SAFETENSORS_METADATA_TEXT_MAX_CHARS, +) -> str: + value = _parse_safetensors_metadata_value(_metadata_get(metadata, *keys)) + if value is None or isinstance(value, (dict, list, tuple, set)): + return "" + + text = str(value).strip() + if not text or text.lower() in {"none", "null", "undefined"}: + return "" + if max_chars and len(text) > max_chars: + return text[:max_chars].rstrip() + return text + + +def _dedupe_strings(values: Iterable[Any], limit: int = 0) -> List[str]: + result: List[str] = [] + seen = set() + for value in values: + text = str(value or "").strip() + if not text: + continue + key = text.lower() + if key in seen: + continue + seen.add(key) + result.append(text) + if limit and len(result) >= limit: + break + return result + + +def _metadata_string_list(value: Any, limit: int = SAFETENSORS_METADATA_TAG_LIMIT) -> List[str]: + parsed = _parse_safetensors_metadata_value(value) + items: List[Any] = [] + + def collect(item: Any) -> None: + item = _parse_safetensors_metadata_value(item) + if item is None: + return + if isinstance(item, dict): + for key in ( + "tags", + "model_tags", + "modelTags", + "trained_words", + "trainedWords", + "trigger", + "trigger_phrase", + "values", + "words", + ): + if key in item: + collect(item.get(key)) + return + for value_item in item.values(): + collect(value_item) + return + if isinstance(item, (list, tuple, set)): + for value_item in item: + collect(value_item) + return + if isinstance(item, str): + parts = re.split(r"[\n,|;]+", item) + items.extend(part.strip() for part in parts if part.strip()) + return + items.append(item) + + collect(parsed) + return _dedupe_strings(items, limit=limit) + + +def _collect_tag_frequency_tags(value: Any, limit: int = 20) -> List[str]: + parsed = _parse_safetensors_metadata_value(value) + counts: Dict[str, float] = {} + + def add_tag(tag: Any, count: Any) -> None: + text = str(tag or "").strip() + if ( + not text + or len(text) > 80 + or "/" in text + or "\\" in text + or text.lower().endswith((".safetensors", ".ckpt", ".pt")) + ): + return + try: + amount = float(count) + except (TypeError, ValueError): + amount = 1.0 + counts[text] = counts.get(text, 0.0) + max(amount, 0.0) + + def walk(item: Any) -> None: + item = _parse_safetensors_metadata_value(item) + if isinstance(item, dict): + numeric_values = True + for value_item in item.values(): + try: + float(value_item) + except (TypeError, ValueError): + numeric_values = False + break + if numeric_values: + for key, value_item in item.items(): + add_tag(key, value_item) + return + for value_item in item.values(): + walk(value_item) + elif isinstance(item, (list, tuple, set)): + for value_item in item: + walk(value_item) + + walk(parsed) + ordered = sorted(counts.items(), key=lambda item: (-item[1], item[0].lower())) + return [tag for tag, _count in ordered[:limit]] + + +def _limited_metadata_value(value: Any, *, max_chars: int = 1000, max_items: int = 8) -> Any: + parsed = _parse_safetensors_metadata_value(value) + if isinstance(parsed, str): + text = parsed.strip() + return text[:max_chars].rstrip() if len(text) > max_chars else text + if isinstance(parsed, (bool, int, float)) or parsed is None: + return parsed + if isinstance(parsed, dict): + limited: Dict[str, Any] = {} + for index, (key, item_value) in enumerate(parsed.items()): + if index >= max_items: + limited["_truncated"] = True + break + limited[str(key)] = _limited_metadata_value( + item_value, + max_chars=max_chars, + max_items=max_items, + ) + return limited + if isinstance(parsed, (list, tuple, set)): + values = list(parsed) + limited_list = [ + _limited_metadata_value(item, max_chars=max_chars, max_items=max_items) + for item in values[:max_items] + ] + if len(values) > max_items: + limited_list.append({"_truncated": True}) + return limited_list + return str(parsed) + + +def _metadata_preview_url(metadata: Dict[str, Any]) -> str: + value = _metadata_get( + metadata, + "modelspec.thumbnail", + "thumbnail", + "thumbnail_url", + "thumbnailUrl", + "preview_url", + "previewUrl", + "ssmd_cover_images", + ) + parsed = _parse_safetensors_metadata_value(value) + + def extract(item: Any) -> str: + item = _parse_safetensors_metadata_value(item) + if isinstance(item, str): + text = item.strip() + if not text or len(text) > SAFETENSORS_METADATA_THUMBNAIL_MAX_CHARS: + return "" + if text.startswith(("http://", "https://", "data:image/")): + return text + return "" + if isinstance(item, dict): + for key in ("url", "src", "image", "thumbnail", "preview", "data"): + preview = extract(item.get(key)) + if preview: + return preview + if isinstance(item, (list, tuple)): + for value_item in item: + preview = extract(value_item) + if preview: + return preview + return "" + + return extract(parsed) + + +def _metadata_number_or_text(metadata: Dict[str, Any], *keys: str) -> Any: + value = _metadata_text(metadata, *keys, max_chars=100) + if not value: + return None + try: + number = int(value) + if str(number) == value: + return number + except (TypeError, ValueError): + pass + return value + + +def extract_safetensors_header_metadata(file_path: str) -> Dict[str, Any]: + """Extract displayable local model metadata from a safetensors header.""" + header_json = read_safetensors_header(file_path) + if not isinstance(header_json, dict): + return {} + + metadata = header_json.get("__metadata__") + if not isinstance(metadata, dict): + metadata = {} + + result: Dict[str, Any] = {} + metadata_keys = sorted(str(key) for key in metadata.keys()) + if metadata_keys: + result["header_metadata_keys"] = metadata_keys[:SAFETENSORS_METADATA_KEY_LIMIT] + + title = _metadata_text( + metadata, + "modelspec.title", + "ssmd_display_name", + "ss_output_name", + "title", + "name", + "model_name", + max_chars=300, + ) + if title: + result["model_name"] = title + + description = _metadata_text( + metadata, + "modelspec.description", + "ssmd_description", + "description", + "model_description", + "ss_training_comment", + ) + if description: + result["description"] = description + result["model_description"] = description + + author = _metadata_text( + metadata, + "modelspec.author", + "ssmd_author", + "author", + "creator", + max_chars=200, + ) + if author: + result["author"] = author + result["creator"] = {"username": author, "name": author} + + license_value = _metadata_text(metadata, "modelspec.license", "license", max_chars=200) + if license_value: + result["license"] = license_value + + usage_hint = _metadata_text( + metadata, + "modelspec.usage_hint", + "usage_hint", + "usage_tips", + max_chars=2000, + ) + if usage_hint: + result["usage_hint"] = usage_hint + result["usage_tips"] = usage_hint + + explicit_tags: List[str] = [] + for key in ("modelspec.tags", "ssmd_tags", "tags", "model_tags"): + explicit_tags.extend(_metadata_string_list(_metadata_get(metadata, key))) + explicit_tags.extend(_collect_tag_frequency_tags(_metadata_get(metadata, "ss_tag_frequency"))) + tags = _dedupe_strings(explicit_tags, limit=SAFETENSORS_METADATA_TAG_LIMIT) + if tags: + result["tags"] = tags + + trained_words: List[str] = [] + for key in ( + "modelspec.trigger_phrase", + "modelspec.trigger_phrases", + "trigger_phrase", + "trigger_phrases", + "trigger", + "trained_words", + "trainedWords", + "ssmd_trained_words", + ): + trained_words.extend(_metadata_string_list(_metadata_get(metadata, key), limit=20)) + trained_words = _dedupe_strings(trained_words, limit=20) + if trained_words: + result["trained_words"] = trained_words + + preview_url = _metadata_preview_url(metadata) + if preview_url: + result["preview_url"] = preview_url + result["images"] = [ + { + "url": preview_url, + "metadata": {"source": "safetensors_header"}, + } + ] + + clip_skip = _metadata_number_or_text(metadata, "ss_clip_skip", "clip_skip", "clipSkip") + if clip_skip not in (None, ""): + result["clip_skip"] = clip_skip + + sha256 = extract_safetensors_header_sha256(file_path) + if sha256: + result["sha256"] = sha256 + result["hash"] = sha256 + result["hashes"] = {"SHA256": sha256} + result["sha256_source"] = "safetensors_header" + result["hash_status"] = "completed" + + base_model = infer_safetensors_base_model(file_path) + if base_model: + result["base_model"] = base_model + result["base_model_source"] = "safetensors_header" + result["base_model_inferred"] = True + + raw_base = _metadata_text( + metadata, + "ss_base_model_version", + "modelspec.architecture", + "architecture", + max_chars=300, + ) + if raw_base: + result["base_model_raw"] = raw_base + + if _metadata_get(metadata, "ss_network_module"): + result["model_type"] = "LORA" + + summary_fields = { + "architecture": ("modelspec.architecture", "architecture"), + "base_model_version": ("ss_base_model_version",), + "resolution": ("modelspec.resolution", "ss_resolution"), + "prediction_type": ("modelspec.prediction_type", "prediction_type"), + "date": ("modelspec.date", "date"), + "implementation": ("modelspec.implementation",), + "network_module": ("ss_network_module",), + "network_dim": ("ss_network_dim",), + "network_alpha": ("ss_network_alpha",), + "model_hash": ("sshs_model_hash",), + "legacy_model_hash": ("sshs_legacy_hash",), + "training_model": ("ss_sd_model_name", "ss_new_sd_model_name"), + "vae": ("ss_vae_name",), + "encoder_layer": ("modelspec.encoder_layer",), + "merged_from": ("modelspec.merged_from",), + "merge_recipe": ("sd_merge_recipe",), + "merge_models": ("sd_merge_models",), + } + metadata_summary: Dict[str, Any] = {} + for output_key, source_keys in summary_fields.items(): + value = _metadata_get(metadata, *source_keys) + if value in (None, ""): + continue + limited_value = _limited_metadata_value(value) + if limited_value not in (None, "", [], {}): + metadata_summary[output_key] = limited_value + + if _metadata_get(metadata, "workflow") not in (None, ""): + metadata_summary["has_embedded_workflow"] = True + if _metadata_get(metadata, "prompt") not in (None, ""): + metadata_summary["has_embedded_prompt"] = True + + if metadata_summary: + result["metadata_summary"] = metadata_summary + + meaningful_keys = ( + "model_name", + "description", + "author", + "license", + "usage_hint", + "tags", + "trained_words", + "preview_url", + "sha256", + "base_model", + "metadata_summary", + ) + if any(result.get(key) for key in meaningful_keys): + result["from_safetensors_header"] = True + result["metadata_source"] = "safetensors_header" + result["details_source"] = "safetensors_header" + result["local_metadata_available"] = True + return result + + return {} + + def calculate_file_sha256( file_path: str, chunk_size: int = 131072, diff --git a/core/sources/civitai.py b/core/sources/civitai.py index a32036a..39b89e1 100644 --- a/core/sources/civitai.py +++ b/core/sources/civitai.py @@ -35,7 +35,14 @@ from ..type_utils import ( extract_trained_words, ) from ..progress import report_progress, get_progress_reporter -from ..path_utils import calculate_file_sha256, get_filename_from_path, read_json_safe, find_metadata_sidecar_path +from ..path_utils import ( + calculate_file_sha256, + extract_safetensors_header_metadata, + find_metadata_sidecar_path, + get_filename_from_path, + infer_safetensors_base_model, + read_json_safe, +) from ..log_system import create_module_logger log = create_module_logger(__name__) @@ -1601,6 +1608,168 @@ def _format_model_location(file_path: str) -> str: return location +def _has_metadata_value(value: Any) -> bool: + if value is None: + return False + if isinstance(value, str): + return bool(value.strip()) + if isinstance(value, (list, tuple, dict, set)): + return bool(value) + return True + + +def _merge_unique_values(existing: Any, incoming: Any) -> List[Any]: + existing_list = existing if isinstance(existing, list) else ([existing] if existing else []) + incoming_list = incoming if isinstance(incoming, list) else ([incoming] if incoming else []) + result: List[Any] = [] + seen = set() + for item in [*existing_list, *incoming_list]: + if item in (None, ""): + continue + if isinstance(item, dict): + key = json.dumps(item, sort_keys=True, default=str) + else: + key = str(item).strip().lower() + if not key or key in seen: + continue + seen.add(key) + result.append(item) + return result + + +def _apply_safetensors_header_metadata( + result: Dict[str, Any], + file_path: str, +) -> Dict[str, Any]: + if not isinstance(result, dict): + return result + + header_metadata = extract_safetensors_header_metadata(file_path) + if not header_metadata: + return result + + filename = get_filename_from_path(file_path) + stem = os.path.splitext(filename)[0] + header_name = header_metadata.get("model_name") + current_name = str(result.get("model_name") or "").strip() + if header_name and ( + not current_name + or current_name == stem + or current_name == filename + or result.get("source") == "local" + ): + result["model_name"] = header_name + + for key in ( + "description", + "model_description", + "author", + "creator", + "license", + "usage_hint", + "usage_tips", + "preview_url", + "clip_skip", + "sha256", + "hash", + "hashes", + "sha256_source", + "hash_status", + "model_type", + "base_model_raw", + ): + if _has_metadata_value(header_metadata.get(key)) and not _has_metadata_value(result.get(key)): + result[key] = header_metadata[key] + + for key in ("tags", "trained_words", "images"): + if _has_metadata_value(header_metadata.get(key)): + if _has_metadata_value(result.get(key)): + result[key] = _merge_unique_values(result.get(key), header_metadata.get(key)) + else: + result[key] = header_metadata[key] + + if header_metadata.get("base_model") and not result.get("base_model"): + result["base_model"] = header_metadata["base_model"] + result["base_model_source"] = header_metadata.get("base_model_source") + result["base_model_inferred"] = bool(header_metadata.get("base_model_inferred")) + + for key in ( + "metadata_summary", + "header_metadata_keys", + "metadata_source", + "from_safetensors_header", + "local_metadata_available", + ): + if _has_metadata_value(header_metadata.get(key)) and not _has_metadata_value(result.get(key)): + result[key] = header_metadata[key] + + if ( + result.get("source") == "local" + and header_metadata.get("details_source") + ): + result["details_source"] = header_metadata["details_source"] + + return result + + +def _apply_inferred_base_model(result: Dict[str, Any], file_path: str) -> Dict[str, Any]: + if not isinstance(result, dict) or result.get("base_model"): + return result + + inferred_base_model = infer_safetensors_base_model(file_path) + if inferred_base_model: + result["base_model"] = inferred_base_model + result["base_model_inferred"] = True + result["base_model_source"] = "safetensors_header" + log.info( + f"Inferred base model from safetensors tensor fingerprint: " + f"{inferred_base_model} ({file_path})" + ) + return result + + +def _has_local_header_details(result: Optional[Dict[str, Any]]) -> bool: + if not isinstance(result, dict): + return False + return bool( + result.get("local_metadata_available") + or result.get("from_safetensors_header") + or result.get("base_model_inferred") + or result.get("metadata_summary") + ) + + +def _build_local_model_info( + file_path: str, + *, + local_only: bool = False, + require_inferred: bool = False, +) -> Optional[Dict[str, Any]]: + filename = get_filename_from_path(file_path) + stem = os.path.splitext(filename)[0] + result = { + "source": "local", + "details_source": "local", + "filename": filename, + "file_path": file_path, + "resolved_path": file_path, + "model_name": stem, + "model_type": "", + "location": _format_model_location(file_path), + "from_metadata": False, + "local_only": local_only, + } + try: + result["size"] = os.path.getsize(file_path) + except Exception: + pass + + _apply_safetensors_header_metadata(result, file_path) + _apply_inferred_base_model(result, file_path) + if require_inferred and not _has_local_header_details(result): + return None + return result + def _metadata_to_model_info(metadata: Dict[str, Any]) -> Dict[str, Any]: """ @@ -1805,6 +1974,8 @@ def get_model_info_for_file( result["file_path"] = file_path result["metadata_path"] = metadata_path result["location"] = _format_model_location(file_path) + _apply_safetensors_header_metadata(result, file_path) + _apply_inferred_base_model(result, file_path) if not result.get("size"): try: result["size"] = os.path.getsize(file_path) @@ -1822,38 +1993,31 @@ def get_model_info_for_file( log.info(f"Files in {directory}: {metadata_files}") if local_only: - filename = get_filename_from_path(file_path) - stem = os.path.splitext(filename)[0] - result = { - "source": "local", - "filename": filename, - "file_path": file_path, - "resolved_path": file_path, - "model_name": stem, - "model_type": "", - "location": _format_model_location(file_path), - "from_metadata": False, - "local_only": True, - } - try: - result["size"] = os.path.getsize(file_path) - except Exception: - pass - return result + return _build_local_model_info(file_path, local_only=True) # If no metadata file, compute hash and look up on CivitAI file_hash = calculate_file_sha256(file_path) if not file_hash: - return None + return _build_local_model_info(file_path, require_inferred=True) + + try: + result = get_model_info_by_hash(file_hash, api_key) + except Exception as e: + log.warning( + f"CivitAI hash lookup failed; trying local tensor fingerprint fallback: {e}" + ) + return _build_local_model_info(file_path, require_inferred=True) - result = get_model_info_by_hash(file_hash, api_key) if result: result["file_path"] = file_path result["location"] = _format_model_location(file_path) + _apply_safetensors_header_metadata(result, file_path) + _apply_inferred_base_model(result, file_path) if not result.get("size"): try: result["size"] = os.path.getsize(file_path) except Exception: pass + return result - return result + return _build_local_model_info(file_path, require_inferred=True) diff --git a/metadata/huggingface-author-index.json b/metadata/huggingface-author-index.json index 9f5ea34..a988007 100644 --- a/metadata/huggingface-author-index.json +++ b/metadata/huggingface-author-index.json @@ -1 +1 @@ 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\ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index c0a18f2..be8a822 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,15 +3,19 @@ name = "Comfyui-Model-Resolver" description = "ComfyUI extension for automatically resolving missing models in loaded workflows. It features intelligent local fuzzy matching, direct cloud downloads, background download tracking, and automated in-place workflow updating." version = "1.0.0" license = { text = "MIT License" } +requires-python = ">=3.10" dynamic = ["dependencies"] [project.urls] Repository = "https://github.com/Azornes/Comfyui-Model-Resolver" +Documentation = "https://github.com/Azornes/Comfyui-Model-Resolver/wiki" +"Bug Tracker" = "https://github.com/Azornes/Comfyui-Model-Resolver/issues" [tool.setuptools.dynamic] dependencies = { file = ["requirements.txt"] } [tool.comfy] PublisherId = "azornes" -DisplayName = "Comfyui-Model-Resolver" +DisplayName = "Comfyui Model Resolver" Icon = "" +requires-comfyui = ">=0.26.0"