diff --git a/__init__.py b/__init__.py index 87b6f47..f565dc6 100644 --- a/__init__.py +++ b/__init__.py @@ -138,6 +138,52 @@ class ModelResolverExtension: ) download_available = False + # ==================== BASE MODELS CONFIG ROUTE ==================== + + @routes.get("/model_resolver/base-models") + async def get_base_models(request): + """Return the base models config from metadata/base-models.json. + + Returns {base_models: [{name, aliases}]} so the frontend + dropdown can populate correctly via baseModels.base_models. + """ + try: + from .core.sources.popular import get_base_models_config + data = get_base_models_config() + return web.json_response(data) + except Exception as e: + self.logger.error( + f"Model Resolver base-models error: {e}", exc_info=True + ) + return web.json_response({"error": str(e)}, status=500) + + @routes.get("/model_resolver/base-models/status") + async def get_base_models_status_route(request): + """Get local and optional remote base models status.""" + try: + check_remote = request.query.get("check_remote") == "1" + from .core.sources.popular import get_base_models_status + status = await asyncio.to_thread(get_base_models_status, check_remote) + return web.json_response(status) + except Exception as e: + self.logger.error( + f"Model Resolver base-models status error: {e}", exc_info=True + ) + return web.json_response({"error": str(e)}, status=500) + + @routes.post("/model_resolver/base-models/update") + async def update_base_models_route(request): + """Update base models list from CivitAI.""" + try: + from .core.sources.popular import update_base_models_from_remote + status = await asyncio.to_thread(update_base_models_from_remote) + return web.json_response(status) + except Exception as e: + self.logger.error( + f"Model Resolver base-models update error: {e}", exc_info=True + ) + return web.json_response({"error": str(e)}, status=500) + # ==================== ANALYZE ROUTES ==================== @routes.post("/model_resolver/analyze") diff --git a/core/sources/civarchive.py b/core/sources/civarchive.py index 92d4d8d..11c6029 100644 --- a/core/sources/civarchive.py +++ b/core/sources/civarchive.py @@ -486,6 +486,34 @@ def _calculate_confidence( return round(best * 100, 1) +def _normalize_base_model(value: str) -> str: + return re.sub(r"[^a-z0-9]+", "", str(value or "").lower()) + + +def _base_model_matches(candidate: str, preferred: Optional[str]) -> bool: + preferred_norm = _normalize_base_model(preferred or "") + if not preferred_norm: + return True + + candidate_norm = _normalize_base_model(candidate or "") + if not candidate_norm: + return False + + from .popular import load_base_model_aliases + aliases = load_base_model_aliases() + preferred_tokens = aliases.get(preferred_norm, [preferred_norm]) + return any( + token and (token in candidate_norm or candidate_norm in token) + for token in preferred_tokens + ) + + +def _base_model_score(candidate: str, preferred: Optional[str]) -> float: + if not preferred: + return 0.0 + return 1000.0 if _base_model_matches(candidate, preferred) else -1000.0 + + def _build_result_from_payload( payload: Dict[str, Any], query: str = "", @@ -751,6 +779,7 @@ def search_civarchive( def search_civarchive_for_file( filename: str, model_type: Optional[str] = None, + base_model_context: Optional[str] = None, exact_only: bool = False, limit: int = DEFAULT_CIVARCHIVE_CANDIDATE_LIMIT, ) -> Optional[Dict[str, Any]]: @@ -762,8 +791,9 @@ def search_civarchive_for_file( return None limit = max(1, min(int(limit), MAX_CIVARCHIVE_CANDIDATE_LIMIT)) + base_model_key = _normalize_base_model(base_model_context or "") cache_key = ( - f"file::{normalized_filename.lower()}::{model_type or ''}::{exact_only}::{limit}" + f"file::{normalized_filename.lower()}::{model_type or ''}::{base_model_key}::{exact_only}::{limit}" ) if cache_key in _search_cache: return _search_cache[cache_key] @@ -780,6 +810,7 @@ def search_civarchive_for_file( best_match = None best_confidence = 0.0 + best_rank = -9999.0 seen = set() try: @@ -810,19 +841,29 @@ def search_civarchive_for_file( resolved["confidence"] = confidence resolved["match_type"] = "exact" if confidence == 100.0 else "similar" + base_model_matches = _base_model_matches( + resolved.get("base_model"), base_model_context + ) + rank = confidence + _base_model_score( + resolved.get("base_model"), base_model_context + ) - if confidence > best_confidence: + if rank > best_rank: + best_rank = rank best_confidence = confidence best_match = resolved - if confidence == 100.0: + if confidence == 100.0 and base_model_matches: _search_cache[cache_key] = best_match return best_match if len(seen) >= limit: break - if best_match: + if best_match and ( + not base_model_context + or _base_model_matches(best_match.get("base_model"), base_model_context) + ): break except CivArchiveSearchError: raise @@ -832,6 +873,10 @@ def search_civarchive_for_file( if best_match and best_confidence < 40: best_match = None + if best_match and base_model_context and not _base_model_matches( + best_match.get("base_model"), base_model_context + ): + best_match = None _search_cache[cache_key] = best_match return best_match diff --git a/core/sources/civitai.py b/core/sources/civitai.py index 3faf7ed..79be7b8 100644 --- a/core/sources/civitai.py +++ b/core/sources/civitai.py @@ -261,6 +261,34 @@ def _calculate_filename_confidence(target_filename: str, candidate_filename: str return round(max(similarity, similarity_no_ext) * 100, 1) +def _normalize_base_model(value: str) -> str: + return re.sub(r"[^a-z0-9]+", "", str(value or "").lower()) + + +def _base_model_matches(candidate: str, preferred: Optional[str]) -> bool: + preferred_norm = _normalize_base_model(preferred or "") + if not preferred_norm: + return True + + candidate_norm = _normalize_base_model(candidate or "") + if not candidate_norm: + return False + + from .popular import load_base_model_aliases + aliases = load_base_model_aliases() + preferred_tokens = aliases.get(preferred_norm, [preferred_norm]) + return any( + token and (token in candidate_norm or candidate_norm in token) + for token in preferred_tokens + ) + + +def _base_model_score(candidate: str, preferred: Optional[str]) -> float: + if not preferred: + return 0.0 + return 1000.0 if _base_model_matches(candidate, preferred) else -1000.0 + + def _find_matching_file_in_versions( versions: List[Dict[str, Any]], filename: str, @@ -502,6 +530,7 @@ def _find_civitai_file_in_model( api_key: Optional[str] = None, exact_only: bool = False, preferred_version_id: Optional[int] = None, + base_model_context: Optional[str] = None, ) -> Optional[Dict[str, Any]]: """Load one CivitAI model and search all its versions for the requested file.""" filename_lower = filename.lower() @@ -554,7 +583,9 @@ def _find_civitai_file_in_model( if preferred_version_id is not None: resolved = resolve_urn(model_id, preferred_version_id, api_key) if resolved: - if resolved_version_matches(resolved): + if resolved_version_matches(resolved) and _base_model_matches( + resolved.get("base_model"), base_model_context + ): return build_result_from_resolved_version(resolved, preferred_version_id) best_resolved_result = None @@ -589,17 +620,22 @@ def _find_civitai_file_in_model( resolved = resolve_urn(model_id, version_id, api_key) if resolved: - if resolved_version_matches(resolved): + if resolved_version_matches(resolved) and _base_model_matches( + resolved.get("base_model"), base_model_context + ): return build_result_from_resolved_version(resolved, version_id) if not exact_only: expected_filename = resolved.get("expected_filename", "") confidence = _calculate_filename_confidence(filename, expected_filename) + ranking_score = confidence + _base_model_score( + resolved.get("base_model"), base_model_context + ) log_debug( f"CivitAI resolved version confidence: model_id={model_id}, version_id={version_id}, expected_filename={expected_filename}, confidence={confidence}" ) - if confidence > best_resolved_confidence: - best_resolved_confidence = confidence + if ranking_score > best_resolved_confidence: + best_resolved_confidence = ranking_score best_resolved_result = build_result_from_resolved_version( resolved, version_id ) @@ -639,7 +675,10 @@ def _find_civitai_file_in_model( log_info( f"CivitAI version-list probable match: model_id={model_id}, version_id={result.get('version_id')}, filename={result.get('filename')}, confidence={result['confidence']}" ) - return result + if _base_model_matches(result.get("base_model"), base_model_context): + return result + if not base_model_context: + return result return None @@ -716,6 +755,7 @@ def search_civitai_for_file( api_key: Optional[str] = None, exact_only: bool = False, model_type: Optional[str] = None, + base_model_context: Optional[str] = None, session_token: Optional[str] = None, candidate_limit: int = DEFAULT_CIVITAI_CANDIDATE_LIMIT, use_trpc_search: bool = True, @@ -739,9 +779,10 @@ def search_civitai_for_file( candidate_limit = max(1, min(int(candidate_limit), MAX_CIVITAI_CANDIDATE_LIMIT)) session_key = "session" if session_token else "anon" model_type_key = str(model_type or "").lower() + base_model_key = _normalize_base_model(base_model_context or "") methods_key = f"trpc{int(bool(use_trpc_search))}_html{int(bool(use_html_fallback))}" cache_key = ( - f"civit_{filename}_exact{exact_only}_type{model_type_key}_session{session_key}_limit{candidate_limit}_{methods_key}" + f"civit_{filename}_exact{exact_only}_type{model_type_key}_base{base_model_key}_session{session_key}_limit{candidate_limit}_{methods_key}" ) if cache_key in _search_cache: log_debug(f"CivitAI search cache hit for {filename} (exact_only={exact_only})") @@ -798,6 +839,7 @@ def search_civitai_for_file( api_key=api_key, exact_only=exact_only, preferred_version_id=version_id, + base_model_context=base_model_context, ) if result: confidence = float(result.get("confidence") or 0.0) diff --git a/core/sources/lora_manager_archive.py b/core/sources/lora_manager_archive.py index 4cc8ab7..968c738 100644 --- a/core/sources/lora_manager_archive.py +++ b/core/sources/lora_manager_archive.py @@ -1,4 +1,4 @@ -""" +""" LoRA Manager Archive Source Module Search archived CivitAI metadata stored by comfyui-lora-manager. @@ -153,6 +153,34 @@ def _calculate_confidence( return round(best * 100, 1) +def _normalize_base_model(value: str) -> str: + return re.sub(r"[^a-z0-9]+", "", str(value or "").lower()) + + +def _base_model_matches(candidate: str, preferred: Optional[str]) -> bool: + preferred_norm = _normalize_base_model(preferred or "") + if not preferred_norm: + return True + + candidate_norm = _normalize_base_model(candidate or "") + if not candidate_norm: + return False + + from .popular import load_base_model_aliases + aliases = load_base_model_aliases() + preferred_tokens = aliases.get(preferred_norm, [preferred_norm]) + return any( + token and (token in candidate_norm or candidate_norm in token) + for token in preferred_tokens + ) + + +def _base_model_score(candidate: str, preferred: Optional[str]) -> float: + if not preferred: + return 0.0 + return 1000.0 if _base_model_matches(candidate, preferred) else -1000.0 + + def _extract_search_tokens(query: str) -> Dict[str, List[str]]: """Split filename/model query into useful content/version tokens.""" base_query = os.path.splitext(os.path.basename(query or ""))[0] @@ -513,7 +541,10 @@ def _build_result_from_row( def search_lora_manager_archive( - query: str, model_type: Optional[str] = None, limit: int = 10 + query: str, + model_type: Optional[str] = None, + base_model_context: Optional[str] = None, + limit: int = 10, ) -> List[Dict[str, Any]]: """ Search the local comfyui-lora-manager CivitAI archive by model name. @@ -531,7 +562,10 @@ def search_lora_manager_archive( return [] normalized_type = _normalize_model_type(model_type) - cache_key = f"search::{normalized_query.lower()}::{normalized_type}::{limit}" + base_model_key = _normalize_base_model(base_model_context or "") + cache_key = ( + f"search::{normalized_query.lower()}::{normalized_type}::{base_model_key}::{limit}" + ) if cache_key in _search_cache: return list(_search_cache[cache_key]) @@ -562,22 +596,27 @@ def search_lora_manager_archive( ) if confidence < 40: continue - scored_candidates.append((confidence, row)) + rank = confidence + _base_model_score( + row["base_model"], base_model_context + ) + scored_candidates.append((rank, confidence, row)) scored_candidates.sort( key=lambda item: ( item[0], - 1 if item[1]["version_name"] else 0, + item[1], + 1 if item[2]["version_name"] else 0, ), reverse=True, ) selected_version_ids = [ - row["version_id"] for _, row in scored_candidates[: max(limit * 3, limit)] + row["version_id"] + for _, _, row in scored_candidates[: max(limit * 3, limit)] ] full_rows = _load_full_rows_for_versions(conn, selected_version_ids) results = [] - for _, candidate_row in scored_candidates: + for _, _, candidate_row in scored_candidates: version_id = candidate_row["version_id"] full_row = full_rows.get(version_id) if not full_row: @@ -585,10 +624,15 @@ def search_lora_manager_archive( result = _build_result_from_row(conn, full_row, normalized_query) if not result or result["confidence"] < 40: continue + if base_model_context and not _base_model_matches( + result.get("base_model"), base_model_context + ): + continue results.append(result) results.sort( key=lambda item: ( + _base_model_score(item.get("base_model"), base_model_context), item.get("confidence", 0), 1 if item.get("match_type") == "exact" else 0, ), @@ -611,6 +655,7 @@ def search_lora_manager_archive( def search_lora_manager_archive_for_file( filename: str, model_type: Optional[str] = None, + base_model_context: Optional[str] = None, exact_only: bool = False, limit: int = 10, ) -> Optional[Dict[str, Any]]: @@ -621,29 +666,43 @@ def search_lora_manager_archive_for_file( if not search_query: return None + base_model_key = _normalize_base_model(base_model_context or "") cache_key = ( - f"file::{filename.lower()}::{_normalize_model_type(model_type)}::{exact_only}::{limit}" + f"file::{filename.lower()}::{_normalize_model_type(model_type)}::{base_model_key}::{exact_only}::{limit}" ) if cache_key in _search_cache: return _search_cache[cache_key] candidates = search_lora_manager_archive( - search_query, model_type=model_type, limit=limit + search_query, + model_type=model_type, + base_model_context=base_model_context, + limit=limit, ) best_match = None best_confidence = 0.0 + best_rank = -9999.0 for candidate in candidates: candidate_filename = candidate.get("filename", "") confidence = _calculate_confidence(filename, candidate.get("name", ""), candidate.get("version_name", ""), candidate_filename) if exact_only and confidence < 100.0: continue - if confidence > best_confidence: + base_model_matches = _base_model_matches( + candidate.get("base_model"), base_model_context + ) + if base_model_context and not base_model_matches: + continue + rank = confidence + _base_model_score( + candidate.get("base_model"), base_model_context + ) + if rank > best_rank: + best_rank = rank best_confidence = confidence best_match = dict(candidate) best_match["confidence"] = confidence best_match["match_type"] = "exact" if confidence == 100.0 else "similar" - if confidence == 100.0: + if confidence == 100.0 and base_model_matches: break if best_match is None and exact_only: diff --git a/core/sources/popular.py b/core/sources/popular.py index b34f43d..eb4b2cf 100644 --- a/core/sources/popular.py +++ b/core/sources/popular.py @@ -6,6 +6,10 @@ Curated list of common models with known download URLs. import os import json +import re +import shutil +import tempfile +from datetime import datetime, timezone from typing import Dict, Any, Optional, List from ..log_system.log_funcs import ( @@ -22,10 +26,12 @@ METADATA_DIR = os.path.join( ) POPULAR_MODELS_FILE = os.path.join(METADATA_DIR, "popular-models.json") MODEL_ALIASES_FILE = os.path.join(METADATA_DIR, "model-aliases.json") +BASE_MODELS_FILE = os.path.join(METADATA_DIR, "base-models.json") # Cache for loaded data _popular_models_cache: Optional[Dict] = None _model_aliases_cache: Optional[Dict] = None +_base_models_aliases_cache: Optional[Dict[str, List[str]]] = None def _load_popular_models() -> Dict[str, Any]: @@ -68,6 +74,71 @@ def _load_model_aliases() -> Dict[str, List[str]]: return _model_aliases_cache +def _normalize_base_model(value: str) -> str: + import re + return re.sub(r"[^a-z0-9]+", "", str(value or "").lower()) + + +def load_base_model_aliases() -> Dict[str, List[str]]: + """Load and normalize base model aliases from base-models.json.""" + global _base_models_aliases_cache + + if _base_models_aliases_cache is not None: + return _base_models_aliases_cache + + aliases_dict = {} + try: + if os.path.exists(BASE_MODELS_FILE): + with open(BASE_MODELS_FILE, "r", encoding="utf-8") as f: + data = json.load(f) + base_models = data.get("base_models", []) + for model in base_models: + name = model.get("name", "") + normalized_name = _normalize_base_model(name) + if not normalized_name: + continue + + aliases = model.get("aliases", []) + # Normalize each alias token and deduplicate + normalized_tokens = [] + seen_tokens = set() + for alias in aliases: + if not alias: + continue + normalized_alias = _normalize_base_model(alias) + if normalized_alias and normalized_alias not in seen_tokens: + seen_tokens.add(normalized_alias) + normalized_tokens.append(normalized_alias) + + # Ensure normalized name itself is in preferred tokens if not already + if normalized_name not in seen_tokens: + normalized_tokens.append(normalized_name) + + aliases_dict[normalized_name] = normalized_tokens + except Exception as e: + log_error(f"Error loading base model aliases: {e}") + + # Fallback to hardcoded defaults if file is missing/corrupted + if not aliases_dict: + aliases_dict = { + "zimage": ["zimage", "zimageturbo"], + "pony": ["pony", "ponyxl"], + "illustrious": ["illustrious", "illustriousxl"], + "sdxl10": ["sdxl", "sdxl10", "sdxl100", "sdxl1"], + "sd15": ["sd15", "sd1", "stablediffusion15"], + "flux1d": ["flux", "flux1", "flux1d", "fluxdev"], + "flux1s": ["flux1s", "fluxschnell"], + "qwenimage": ["qwenimage"], + "hunyuan1": ["hunyuan", "hunyuan1"], + "wanvideo": ["wan", "wanvideo"], + "noobai": ["noobai"], + "hidream": ["hidream"], + } + + _base_models_aliases_cache = aliases_dict + return _base_models_aliases_cache + + def get_popular_model_url(filename: str) -> Optional[Dict[str, Any]]: """ Look up a model filename in the popular models database. @@ -136,8 +207,241 @@ def get_all_popular_models() -> Dict[str, Any]: def reload_databases(): """Force reload of all databases.""" - global _popular_models_cache, _model_aliases_cache + global _popular_models_cache, _model_aliases_cache, _base_models_aliases_cache _popular_models_cache = None _model_aliases_cache = None + _base_models_aliases_cache = None _load_popular_models() _load_model_aliases() + load_base_model_aliases() + + +BASE_MODELS_META_FILE = os.path.join(METADATA_DIR, "base-models.meta.json") + + +def get_base_models_config() -> Dict[str, Any]: + """Return the raw base-models config (base_models array) for the frontend dropdown.""" + return _read_base_models_file() + + +def _read_base_models_file() -> Dict[str, Any]: + try: + if os.path.exists(BASE_MODELS_FILE): + with open(BASE_MODELS_FILE, "r", encoding="utf-8") as f: + return json.load(f) + except Exception as e: + log_warn(f"Error reading base-models.json: {e}") + return {"base_models": []} + + +def _read_base_models_meta() -> Dict[str, Any]: + try: + if os.path.exists(BASE_MODELS_META_FILE): + with open(BASE_MODELS_META_FILE, "r", encoding="utf-8") as f: + return json.load(f) + except Exception as e: + log_warn(f"Error reading base-models.meta.json: {e}") + return {} + + +def generate_aliases(name: str) -> List[str]: + aliases = {name.lower()} + + # Replace dots, dashes, underscores with spaces, then normalize spaces + normalized = re.sub(r"[\._\-]+", " ", name.lower()) + normalized = re.sub(r"\s+", " ", normalized).strip() + aliases.add(normalized) + + # Remove all spaces/dashes/dots/underscores + collapsed = re.sub(r"[\s\._\-]+", "", name.lower()) + aliases.add(collapsed) + + # If there are dots/dashes/underscores, add specific clean replacements + if "." in name: + aliases.add(name.lower().replace(".", "")) + aliases.add(name.lower().replace(".", " ")) + if "-" in name: + aliases.add(name.lower().replace("-", "")) + aliases.add(name.lower().replace("-", " ")) + if "_": + aliases.add(name.lower().replace("_", "")) + aliases.add(name.lower().replace("_", " ")) + + # Extract first word if it has multiple words + words = name.lower().split() + if len(words) > 1: + first_word = words[0] + if len(first_word) > 2 and first_word not in {"stable", "flux", "pony", "sdxl"}: + aliases.add(first_word) + + return sorted(list({a for a in aliases if len(a) > 1})) + + +def get_base_models_status(check_remote: bool = False) -> Dict[str, Any]: + """Return local base-models metadata and optionally compare with CivitAI.""" + local_data = _read_base_models_file() + base_models = local_data.get("base_models", []) + meta = _read_base_models_meta() + + local_count = len(base_models) + local_updated_at = meta.get("updated_at") or "" + + status = { + "local_count": local_count, + "local_updated_at": local_updated_at, + "remote_checked_at": meta.get("last_checked_at") or "", + "update_available": False, + } + + if check_remote: + try: + import requests + # Fetch from CivitAI API + response = requests.get("https://civitai.com/api/v1/enums", timeout=15) + if response.status_code == 200: + enums = response.json() + remote_models = enums.get("BaseModel", []) + + # Check if there are new models by comparing normalized names and aliases + all_existing_normalized = set() + for m in base_models: + all_existing_normalized.add(_normalize_base_model(m.get("name", ""))) + for alias in m.get("aliases", []): + all_existing_normalized.add(_normalize_base_model(alias)) + + new_models_found = False + for remote_name in remote_models: + norm_remote = _normalize_base_model(remote_name) + if norm_remote and norm_remote not in all_existing_normalized: + new_models_found = True + break + + status.update({ + "remote_checked_at": datetime.now(timezone.utc).replace(microsecond=0).isoformat(), + "update_available": new_models_found + }) + except Exception as e: + log_warn(f"Error checking remote base models: {e}") + + return status + + +def update_base_models_from_remote() -> Dict[str, Any]: + """Fetch live BaseModel enums from CivitAI and merge into base-models.json.""" + import requests + response = requests.get("https://civitai.com/api/v1/enums", timeout=15) + if response.status_code != 200: + raise ValueError(f"CivitAI enums endpoint returned HTTP {response.status_code}") + + enums = response.json() + remote_names = enums.get("BaseModel", []) + if not remote_names or not isinstance(remote_names, list): + raise ValueError("CivitAI enums did not return a valid BaseModel list") + + local_data = _read_base_models_file() + base_models = local_data.get("base_models", []) + + # 1. Build a set of all normalized aliases currently in base-models.json + all_known_normalized = set() + for m in base_models: + for alias in m.get("aliases", []): + all_known_normalized.add(_normalize_base_model(alias)) + all_known_normalized.add(_normalize_base_model(m.get("name", ""))) + + updated_models = list(base_models) + new_added_count = 0 + + for name in remote_names: + norm_name = _normalize_base_model(name) + if not norm_name: + continue + + # If the normalized name is already a known alias or name, skip it + if norm_name in all_known_normalized: + continue + + # Generate candidate aliases + candidates = generate_aliases(name) + + # Filter out aliases that are already registered/known + filtered_aliases = [] + for alias in candidates: + norm_alias = _normalize_base_model(alias) + if norm_alias not in all_known_normalized: + filtered_aliases.append(alias) + all_known_normalized.add(norm_alias) + + # If we have valid aliases, add the new base model entry + if filtered_aliases: + if name.lower() not in [a.lower() for a in filtered_aliases]: + filtered_aliases.insert(0, name) + all_known_normalized.add(norm_name) + + updated_models.append({ + "name": name, + "aliases": filtered_aliases + }) + new_added_count += 1 + + # Write updated models to base-models.json atomically + os.makedirs(METADATA_DIR, exist_ok=True) + + # Backup existing + if os.path.exists(BASE_MODELS_FILE): + shutil.copy2(BASE_MODELS_FILE, f"{BASE_MODELS_FILE}.bak") + + # Write atomically + fd, tmp_path = tempfile.mkstemp( + prefix=f".{os.path.basename(BASE_MODELS_FILE)}.", + suffix=".tmp", + dir=os.path.dirname(BASE_MODELS_FILE), + text=True, + ) + try: + with os.fdopen(fd, "w", encoding="utf-8") as f: + json.dump({"base_models": updated_models}, f, indent=2, ensure_ascii=False) + f.write("\n") + os.replace(tmp_path, BASE_MODELS_FILE) + except Exception: + try: + os.remove(tmp_path) + except OSError: + pass + raise + + # Write meta file + now_str = datetime.now(timezone.utc).replace(microsecond=0).isoformat() + meta = { + "updated_at": now_str, + "last_checked_at": now_str, + "local_count": len(updated_models), + "new_models_added": new_added_count + } + + fd_meta, tmp_meta_path = tempfile.mkstemp( + prefix=f".{os.path.basename(BASE_MODELS_META_FILE)}.", + suffix=".tmp", + dir=os.path.dirname(BASE_MODELS_META_FILE), + text=True, + ) + try: + with os.fdopen(fd_meta, "w", encoding="utf-8") as f: + json.dump(meta, f, indent=2, ensure_ascii=False) + f.write("\n") + os.replace(tmp_meta_path, BASE_MODELS_META_FILE) + except Exception: + try: + os.remove(tmp_meta_path) + except OSError: + pass + raise + + # Force reload database in-memory cache + reload_databases() + + return { + "local_count": len(updated_models), + "local_updated_at": now_str, + "new_models_added": new_added_count, + "updated": True + } diff --git a/metadata/base-models.json b/metadata/base-models.json new file mode 100644 index 0000000..c05b9c2 --- /dev/null +++ b/metadata/base-models.json @@ -0,0 +1,627 @@ +{ + "base_models": [ + { + "name": "Z-Image", + "aliases": [ + "zimage", + "z image", + "z-image", + "z_image", + "zImageTurbo", + "z image turbo" + ] + }, + { + "name": "Pony", + "aliases": [ + "pony", + "ponyxl", + "pony diffusion", + "pony realism" + ] + }, + { + "name": "Illustrious", + "aliases": [ + "illustrious", + "illustriousxl", + "illustrious xl" + ] + }, + { + "name": "SDXL 1.0", + "aliases": [ + "sdxl", + "sdxl10", + "sdxl 1.0", + "stable diffusion xl", + "sdxl100", + "sdxl1" + ] + }, + { + "name": "SD 1.5", + "aliases": [ + "sd15", + "sd 1.5", + "sd1.5", + "stable diffusion 1.5", + "sd1", + "stablediffusion15" + ] + }, + { + "name": "Flux.1 D", + "aliases": [ + "flux", + "flux1", + "flux.1", + "flux dev", + "flux.1 d", + "flux1d", + "fluxdev" + ] + }, + { + "name": "Flux.1 S", + "aliases": [ + "flux schnell", + "flux.1 s", + "flux1s", + "fluxschnell" + ] + }, + { + "name": "Qwen Image", + "aliases": [ + "qwen image", + "qwenimage", + "qwen-image" + ] + }, + { + "name": "Hunyuan 1", + "aliases": [ + "hunyuan", + "hunyuan1" + ] + }, + { + "name": "WAN Video", + "aliases": [ + "wan", + "wan video", + "wanvideo" + ] + }, + { + "name": "NoobAI", + "aliases": [ + "noobai", + "noob ai" + ] + }, + { + "name": "HiDream", + "aliases": [ + "hidream", + "hi dream" + ] + }, + { + "name": "Anima", + "aliases": [ + "anima" + ] + }, + { + "name": "AuraFlow", + "aliases": [ + "auraflow" + ] + }, + { + "name": "Chroma", + "aliases": [ + "chroma" + ] + }, + { + "name": "CogVideoX", + "aliases": [ + "cogvideox" + ] + }, + { + "name": "Ernie", + "aliases": [ + "ernie" + ] + }, + { + "name": "Flux.1 Krea", + "aliases": [ + "Flux.1 Krea", + "flux 1 krea" + ] + }, + { + "name": "Flux.1 Kontext", + "aliases": [ + "Flux.1 Kontext", + "flux 1 kontext" + ] + }, + { + "name": "Flux.2 D", + "aliases": [ + "Flux.2 D", + "flux 2 d", + "flux.2" + ] + }, + { + "name": "Flux.2 Klein 9B", + "aliases": [ + "Flux.2 Klein 9B", + "flux 2 klein 9b" + ] + }, + { + "name": "Flux.2 Klein 9B-base", + "aliases": [ + "Flux.2 Klein 9B-base", + "flux 2 klein 9b base" + ] + }, + { + "name": "Flux.2 Klein 4B", + "aliases": [ + "Flux.2 Klein 4B", + "flux 2 klein 4b" + ] + }, + { + "name": "Flux.2 Klein 4B-base", + "aliases": [ + "Flux.2 Klein 4B-base", + "flux 2 klein 4b base" + ] + }, + { + "name": "Grok", + "aliases": [ + "grok" + ] + }, + { + "name": "HappyHorse", + "aliases": [ + "happyhorse" + ] + }, + { + "name": "HiDream-O1", + "aliases": [ + "HiDream-O1", + "hidream o1" + ] + }, + { + "name": "Hunyuan Video", + "aliases": [ + "hunyuan video" + ] + }, + { + "name": "Imagen4", + "aliases": [ + "imagen4" + ] + }, + { + "name": "Kolors", + "aliases": [ + "kolors" + ] + }, + { + "name": "Krea 2", + "aliases": [ + "krea", + "krea 2" + ] + }, + { + "name": "LTXV", + "aliases": [ + "ltxv" + ] + }, + { + "name": "LTXV2", + "aliases": [ + "ltxv2" + ] + }, + { + "name": "LTXV 2.3", + "aliases": [ + "LTXV 2.3", + "ltxv 2 3" + ] + }, + { + "name": "Lens", + "aliases": [ + "lens" + ] + }, + { + "name": "Lumina", + "aliases": [ + "lumina" + ] + }, + { + "name": "MAI", + "aliases": [ + "mai" + ] + }, + { + "name": "Mochi", + "aliases": [ + "mochi" + ] + }, + { + "name": "Nano Banana", + "aliases": [ + "nano", + "nano banana" + ] + }, + { + "name": "ODOR", + "aliases": [ + "odor" + ] + }, + { + "name": "OpenAI", + "aliases": [ + "openai" + ] + }, + { + "name": "Upscaler", + "aliases": [ + "upscaler" + ] + }, + { + "name": "Other", + "aliases": [ + "other" + ] + }, + { + "name": "PixArt a", + "aliases": [ + "pixart", + "pixart a" + ] + }, + { + "name": "PixArt E", + "aliases": [ + "pixart e" + ] + }, + { + "name": "Playground v2", + "aliases": [ + "playground", + "playground v2" + ] + }, + { + "name": "Pony V7", + "aliases": [ + "pony v7" + ] + }, + { + "name": "Qwen", + "aliases": [ + "qwen" + ] + }, + { + "name": "Qwen 2", + "aliases": [ + "qwen 2" + ] + }, + { + "name": "Stable Cascade", + "aliases": [ + "stable cascade" + ] + }, + { + "name": "SD 1.4", + "aliases": [ + "SD 1.4", + "sd 1 4" + ] + }, + { + "name": "SD 1.5 LCM", + "aliases": [ + "SD 1.5 LCM", + "sd 1 5 lcm" + ] + }, + { + "name": "SD 1.5 Hyper", + "aliases": [ + "SD 1.5 Hyper", + "sd 1 5 hyper" + ] + }, + { + "name": "SD 2.0", + "aliases": [ + "SD 2.0", + "sd 2 0" + ] + }, + { + "name": "SD 2.0 768", + "aliases": [ + "SD 2.0 768", + "sd 2 0 768" + ] + }, + { + "name": "SD 2.1", + "aliases": [ + "SD 2.1", + "sd 2 1" + ] + }, + { + "name": "SD 2.1 768", + "aliases": [ + "SD 2.1 768", + "sd 2 1 768" + ] + }, + { + "name": "SD 2.1 Unclip", + "aliases": [ + "SD 2.1 Unclip", + "sd 2 1 unclip" + ] + }, + { + "name": "SD 3", + "aliases": [ + "sd 3" + ] + }, + { + "name": "SD 3.5", + "aliases": [ + "SD 3.5", + "sd 3 5" + ] + }, + { + "name": "SD 3.5 Large", + "aliases": [ + "SD 3.5 Large", + "sd 3 5 large" + ] + }, + { + "name": "SD 3.5 Large Turbo", + "aliases": [ + "SD 3.5 Large Turbo", + "sd 3 5 large turbo" + ] + }, + { + "name": "SD 3.5 Medium", + "aliases": [ + "SD 3.5 Medium", + "sd 3 5 medium" + ] + }, + { + "name": "SDXL 0.9", + "aliases": [ + "SDXL 0.9", + "sdxl 0 9" + ] + }, + { + "name": "SDXL 1.0 LCM", + "aliases": [ + "SDXL 1.0 LCM", + "sdxl 1 0 lcm" + ] + }, + { + "name": "SDXL Lightning", + "aliases": [ + "sdxl lightning" + ] + }, + { + "name": "SDXL Hyper", + "aliases": [ + "sdxl hyper" + ] + }, + { + "name": "SDXL Turbo", + "aliases": [ + "sdxl turbo" + ] + }, + { + "name": "SDXL Distilled", + "aliases": [ + "sdxl distilled" + ] + }, + { + "name": "Seedream", + "aliases": [ + "seedream" + ] + }, + { + "name": "SVD", + "aliases": [ + "svd" + ] + }, + { + "name": "SVD XT", + "aliases": [ + "svd xt" + ] + }, + { + "name": "Sora 2", + "aliases": [ + "sora", + "sora 2" + ] + }, + { + "name": "Veo 3", + "aliases": [ + "veo", + "veo 3" + ] + }, + { + "name": "Wan Video 1.3B t2v", + "aliases": [ + "Wan Video 1.3B t2v", + "wan video 1 3b t2v" + ] + }, + { + "name": "Wan Video 14B t2v", + "aliases": [ + "wan video 14b t2v" + ] + }, + { + "name": "Wan Video 14B i2v 480p", + "aliases": [ + "wan video 14b i2v 480p" + ] + }, + { + "name": "Wan Video 14B i2v 720p", + "aliases": [ + "wan video 14b i2v 720p" + ] + }, + { + "name": "Wan Video 2.2 TI2V-5B", + "aliases": [ + "Wan Video 2.2 TI2V-5B", + "wan video 2 2 ti2v 5b" + ] + }, + { + "name": "Wan Video 2.2 I2V-A14B", + "aliases": [ + "Wan Video 2.2 I2V-A14B", + "wan video 2 2 i2v a14b" + ] + }, + { + "name": "Wan Video 2.2 T2V-A14B", + "aliases": [ + "Wan Video 2.2 T2V-A14B", + "wan video 2 2 t2v a14b" + ] + }, + { + "name": "Wan Video 2.5 T2V", + "aliases": [ + "Wan Video 2.5 T2V", + "wan video 2 5 t2v" + ] + }, + { + "name": "Wan Video 2.5 I2V", + "aliases": [ + "Wan Video 2.5 I2V", + "wan video 2 5 i2v" + ] + }, + { + "name": "Wan Image 2.7", + "aliases": [ + "Wan Image 2.7", + "wan image 2 7" + ] + }, + { + "name": "Wan Video 2.7", + "aliases": [ + "Wan Video 2.7", + "wan video 2 7" + ] + }, + { + "name": "ZImageBase", + "aliases": [ + "zimagebase" + ] + }, + { + "name": "Vidu Q1", + "aliases": [ + "vidu", + "vidu q1" + ] + }, + { + "name": "Hailuo by MiniMax", + "aliases": [ + "hailuo", + "hailuo by minimax" + ] + }, + { + "name": "Kling", + "aliases": [ + "kling" + ] + }, + { + "name": "Seedance", + "aliases": [ + "seedance" + ] + }, + { + "name": "ACE Audio", + "aliases": [ + "ace", + "ace audio" + ] + } + ] +} diff --git a/metadata/base-models.meta.json b/metadata/base-models.meta.json new file mode 100644 index 0000000..c3b7b86 --- /dev/null +++ b/metadata/base-models.meta.json @@ -0,0 +1,6 @@ +{ + "updated_at": "2026-06-06T17:28:12+00:00", + "last_checked_at": "2026-06-06T17:28:12+00:00", + "local_count": 91, + "new_models_added": 79 +} diff --git a/metadata/huggingface-author-index.json b/metadata/huggingface-author-index.json index 4e71c98..e0dea9d 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/web/css/resolver-main.css b/web/css/resolver-main.css index 78d643c..ed3e59e 100644 --- a/web/css/resolver-main.css +++ b/web/css/resolver-main.css @@ -1822,7 +1822,7 @@ } .mr-search-source-bar { display: grid; - grid-template-columns: auto minmax(178px, 1fr); + grid-template-columns: auto minmax(150px, 0.85fr) minmax(150px, 1fr); align-items: center; gap: 10px; margin-bottom: 0; @@ -1863,7 +1863,8 @@ text-transform: uppercase; white-space: nowrap; } -.mr-search-source-select { +.mr-search-source-select, +.mr-search-base-select { width: 100%; height: var(--mr-download-control-height); min-height: var(--mr-download-control-height); @@ -1878,16 +1879,19 @@ appearance: none; box-sizing: border-box; } -.mr-missing-browser .mr-search-source-select { +.mr-missing-browser .mr-search-source-select, +.mr-missing-browser .mr-search-base-select { background: var(--mr-missing-control-bg, rgba(255,255,255,0.035)); border-color: var(--mr-missing-control-border, var(--mr-missing-line-strong, rgba(255,255,255,0.12))); border-radius: var(--mr-missing-control-radius, 7px); } -.mr-missing-browser .mr-search-source-select:focus { +.mr-missing-browser .mr-search-source-select:focus, +.mr-missing-browser .mr-search-base-select:focus { border-color: var(--mr-missing-control-focus-border, rgba(114,211,138,0.4)); box-shadow: 0 0 0 2px var(--mr-missing-control-focus-ring, rgba(114,211,138,0.12)); } -.mr-search-source-select:focus { +.mr-search-source-select:focus, +.mr-search-base-select:focus { border-color: rgba(114,211,138,0.4); box-shadow: 0 0 0 3px rgba(114,211,138,0.12); } @@ -4362,6 +4366,7 @@ } #model-resolver-modal .mr-missing-detail-pane .mr-combo-input, #model-resolver-modal .mr-missing-detail-pane .mr-search-source-select, +#model-resolver-modal .mr-missing-detail-pane .mr-search-base-select, #model-resolver-modal .mr-missing-detail-pane .mr-download-target-input, #model-resolver-modal .mr-missing-detail-pane .mr-download-target-select { min-height: var(--mr-missing-field-height, 38px); @@ -4374,6 +4379,7 @@ } #model-resolver-modal .mr-missing-detail-pane .mr-combo-input:focus, #model-resolver-modal .mr-missing-detail-pane .mr-search-source-select:focus, +#model-resolver-modal .mr-missing-detail-pane .mr-search-base-select:focus, #model-resolver-modal .mr-missing-detail-pane .mr-download-target-input:focus, #model-resolver-modal .mr-missing-detail-pane .mr-download-target-select:focus { background: var(--mr-missing-control-bg-hover, rgba(255,255,255,0.045)); diff --git a/web/resolver/actions/resolve_download_methods.js b/web/resolver/actions/resolve_download_methods.js index 3964278..bdbc110 100644 --- a/web/resolver/actions/resolve_download_methods.js +++ b/web/resolver/actions/resolve_download_methods.js @@ -846,6 +846,7 @@ export const resolveDownloadMethods = { } const selectedSourceLabel = this.getSearchSourceLabel(selectedSource); const sourceIds = this.getSearchSourcesForSelection(selectedSource, missing); + const baseModelContext = this.getSearchBaseModelContext(missing); // For URNs, use the resolved file/model name for searching instead of the URN itself // and pass the URN type as category (CivitAI expects specific type names) @@ -907,6 +908,7 @@ export const resolveDownloadMethods = { state.lastAttemptSources = sourceIds; state.lastAttemptFound = null; state.lastAttemptError = null; + state.lastAttemptBaseModelContext = baseModelContext; state.sourceProgress = {}; for (const source of sourceIds) { this.setSourceProgress(state, source, { @@ -944,6 +946,7 @@ export const resolveDownloadMethods = { filename, category, is_urn: isUrn, + base_model_context: baseModelContext, civitai_session_token: tokens.civitai_session_token, civitai_candidate_limit: tokens.civitai_candidate_limit, civitai_use_trpc_search: tokens.civitai_use_trpc_search, @@ -991,6 +994,9 @@ export const resolveDownloadMethods = { if (!this.isBackgroundSearchRunActive(workflowKey, missingSearchKey, searchRunId)) { return { source, stale: true }; } + if (baseModelContext !== this.getSearchBaseModelContext(missing)) { + return { source, stale: true }; + } const responseSources = Array.isArray(data.searched_sources) && data.searched_sources.length ? data.searched_sources @@ -1300,6 +1306,53 @@ export const resolveDownloadMethods = { this.syncSearchSourceUi(missing, container); } + const baseSelect = container.querySelector(`#search-base-select-${missing.node_id}-${missing.widget_index}`); + const baseList = container.querySelector(`#search-base-list-${missing.node_id}-${missing.widget_index}`); + if (baseSelect && baseList && baseSelect.dataset.mlSearchBaseBound !== 'true') { + baseSelect.dataset.mlSearchBaseBound = 'true'; + + const renderBaseOptions = () => { + const options = this.getKnownBaseModelOptions(); + baseList.innerHTML = options + .map(option => { + const label = option.value === 'auto' + ? this.getSearchBaseModelLabel('auto') + : option.label; + return `
${this.escapeHtml(label)}
`; + }) + .join(''); + baseList.style.display = 'block'; + baseList.querySelectorAll('.mr-download-target-option').forEach(optionEl => { + optionEl.addEventListener('mousedown', (event) => { + event.preventDefault(); + const value = decodeURIComponent(optionEl.dataset.value || ''); + const label = decodeURIComponent(optionEl.dataset.label || optionEl.dataset.value || ''); + this.setDropdownValue(baseSelect, value, label); + this.setSearchBaseModel(missing, value, container); + baseList.style.display = 'none'; + }); + }); + }; + + const hideBaseList = () => { + setTimeout(() => { + baseList.style.display = 'none'; + }, 150); + }; + + this.enableWheelScrollChaining(baseList); + baseSelect.addEventListener('focus', () => renderBaseOptions()); + baseSelect.addEventListener('click', () => renderBaseOptions()); + baseSelect.addEventListener('keydown', (event) => { + if (event.key === 'Enter' || event.key === ' ' || event.key === 'ArrowDown') { + event.preventDefault(); + renderBaseOptions(); + } + }); + baseSelect.addEventListener('blur', hideBaseList); + this.syncSearchSourceUi(missing, container); + } + this.wireDownloadTargetAutocomplete(container, missing); }, diff --git a/web/resolver/resolver_dialog.js b/web/resolver/resolver_dialog.js index 39d7ce3..cf2a6ff 100644 --- a/web/resolver/resolver_dialog.js +++ b/web/resolver/resolver_dialog.js @@ -1,4 +1,4 @@ -import { $el, ComfyDialog } from "../../../../scripts/ui.js"; +import { $el, ComfyDialog } from "../../../../scripts/ui.js"; import { dialogShellMethods } from "./shell/dialog_shell_methods.js"; import { lifecycleGraphMethods } from "./shell/lifecycle_graph_methods.js"; import { workflowStateMethods } from "./shell/workflow_state_methods.js"; @@ -21,6 +21,7 @@ export class ResolverManagerDialog extends ComfyDialog { this.allModels = null; // list of all available models for dropdown this.downloadDirectories = null; this.capabilities = null; + this.baseModels = null; this.downloadSubfolders = new Map(); this.downloadTargetSelections = new Map(); // missing model key -> user/suggested download target this.pendingResolutions = []; @@ -85,21 +86,21 @@ export class ResolverManagerDialog extends ComfyDialog { $el("div.mr-context-menu-item", { onclick: () => this.handleContextMenuAction('showInfo') }, [ - $el("span.mr-context-menu-item-icon", { textContent: "ℹ" }), + $el("span.mr-context-menu-item-icon", { textContent: "?" }), $el("span", { textContent: "Show Info" }) ]), $el("div.mr-context-menu-divider"), $el("div.mr-context-menu-item", { onclick: () => this.handleContextMenuAction('civitai') }, [ - $el("span.mr-context-menu-item-icon", { textContent: "🌐" }), + $el("span.mr-context-menu-item-icon", { textContent: "??" }), $el("span", { textContent: "Open in CivitAI" }) ]), $el("div.mr-context-menu-divider"), $el("div.mr-context-menu-item", { onclick: () => this.handleContextMenuAction('openFolder') }, [ - $el("span.mr-context-menu-item-icon", { textContent: "📁" }), + $el("span.mr-context-menu-item-icon", { textContent: "??" }), $el("span", { textContent: "Open Containing Folder" }) ]) ]); diff --git a/web/resolver/search/download_target_methods.js b/web/resolver/search/download_target_methods.js index e7bfa96..2a90564 100644 --- a/web/resolver/search/download_target_methods.js +++ b/web/resolver/search/download_target_methods.js @@ -50,7 +50,21 @@ export const downloadTargetMethods = { } }, + async ensureBaseModelsLoaded() { + if (this.baseModels) return; + try { + const resp = await api.fetchApi('/model_resolver/base-models'); + if (!resp.ok) throw new Error(`HTTP ${resp.status}`); + const data = await resp.json(); + this.baseModels = data && typeof data === 'object' ? data : {}; + } catch (e) { + console.warn('Model Resolver: could not load base-models config', e); + this.baseModels = {}; + } + }, + isSourceAvailable(source) { + if (!source || ['all', 'local', 'huggingface', 'civitai', 'civarchive'].includes(source)) { return true; } @@ -675,11 +689,13 @@ export const downloadTargetMethods = { this.allModels = null; this.downloadDirectories = null; this.capabilities = null; + this.baseModels = null; this.downloadSubfolders.clear(); this.downloadTargetSelections?.clear(); this._analysisProgressToken = null; await this.ensureCapabilitiesLoaded(); + await this.ensureBaseModelsLoaded(); this.refreshMissingListStats?.(); this.updateBatchFooterButtons?.(); this.updateDownloadAllButtonState?.(); diff --git a/web/resolver/search/search_panel.js b/web/resolver/search/search_panel.js index 636f03b..58fe155 100644 --- a/web/resolver/search/search_panel.js +++ b/web/resolver/search/search_panel.js @@ -1,4 +1,4 @@ -import { app } from "../../../../../scripts/app.js"; +import { app } from "../../../../../scripts/app.js"; import { api } from "../../../../../scripts/api.js"; import { $el } from "../../../../../scripts/ui.js"; import { getSvgIcon } from "../../utils/icon_utils.js"; @@ -29,6 +29,7 @@ export const searchPanelMethods = { createEmptySearchState() { return { selectedSource: 'all', + selectedBaseModel: 'auto', results: { popular: null, model_list: null, @@ -38,6 +39,7 @@ export const searchPanelMethods = { lora_manager_archive: null }, lastAttemptSources: [], + lastAttemptBaseModelContext: '', lastAttemptFound: null, lastAttemptError: null, sourceProgress: {}, @@ -45,6 +47,119 @@ export const searchPanelMethods = { }; }, + getKnownBaseModelOptions() { + const baseModelsList = this.baseModels?.base_models; + if (Array.isArray(baseModelsList) && baseModelsList.length > 0) { + return [ + { value: 'auto', label: 'Auto' }, + { value: 'none', label: 'Any model' }, + ...baseModelsList.map(m => ({ value: m.name, label: m.name })) + ]; + } + // Fallback to hardcoded list + return [ + { value: 'auto', label: 'Auto' }, + { value: 'none', label: 'Any model' }, + { value: 'Z-Image', label: 'Z-Image' }, + { value: 'Pony', label: 'Pony' }, + { value: 'Illustrious', label: 'Illustrious' }, + { value: 'SDXL 1.0', label: 'SDXL 1.0' }, + { value: 'SD 1.5', label: 'SD 1.5' }, + { value: 'Flux.1 D', label: 'Flux.1 D' }, + { value: 'Flux.1 S', label: 'Flux.1 S' }, + { value: 'Qwen Image', label: 'Qwen Image' }, + { value: 'Hunyuan 1', label: 'Hunyuan 1' }, + { value: 'WAN Video', label: 'WAN Video' }, + { value: 'NoobAI', label: 'NoobAI' }, + { value: 'HiDream', label: 'HiDream' } + ]; + }, + + normalizeBaseModelToken(value = '') { + return String(value || '') + .toLowerCase() + .replace(/[^a-z0-9]+/g, ''); + }, + + getBaseModelAliases() { + const baseModelsList = this.baseModels?.base_models; + if (Array.isArray(baseModelsList) && baseModelsList.length > 0) { + return baseModelsList.map(m => ({ value: m.name, aliases: m.aliases || [] })); + } + // Fallback to hardcoded list + return [ + { value: 'Z-Image', aliases: ['zimage', 'z image', 'z-image', 'z_image', 'zImageTurbo', 'z image turbo'] }, + { value: 'Pony', aliases: ['pony', 'ponyxl', 'pony diffusion', 'pony realism'] }, + { value: 'Illustrious', aliases: ['illustrious', 'illustriousxl', 'illustrious xl'] }, + { value: 'SDXL 1.0', aliases: ['sdxl', 'sdxl10', 'sdxl 1.0', 'stable diffusion xl'] }, + { value: 'SD 1.5', aliases: ['sd15', 'sd 1.5', 'sd1.5', 'stable diffusion 1.5'] }, + { value: 'Flux.1 D', aliases: ['flux', 'flux1', 'flux.1', 'flux dev', 'flux.1 d', 'flux1d'] }, + { value: 'Flux.1 S', aliases: ['flux schnell', 'flux.1 s', 'flux1s'] }, + { value: 'Qwen Image', aliases: ['qwen image', 'qwenimage', 'qwen-image'] }, + { value: 'Hunyuan 1', aliases: ['hunyuan', 'hunyuan1'] }, + { value: 'WAN Video', aliases: ['wan', 'wan video', 'wanvideo'] }, + { value: 'NoobAI', aliases: ['noobai', 'noob ai'] }, + { value: 'HiDream', aliases: ['hidream', 'hi dream'] } + ]; + }, + + getWorkflowModelReferenceText() { + const workflow = this.getCurrentWorkflow?.(); + if (!workflow) return ''; + const values = []; + const visit = (value, key = '') => { + if (typeof value === 'string') { + if (/\.(safetensors|ckpt|pt|pth|bin|gguf|onnx)\b/i.test(value) || /model|checkpoint|unet|diffusion/i.test(key)) { + values.push(value); + } + return; + } + if (Array.isArray(value)) { + value.forEach(item => visit(item, key)); + return; + } + if (value && typeof value === 'object') { + Object.entries(value).forEach(([childKey, childValue]) => visit(childValue, childKey)); + } + }; + visit(workflow); + return values.join(' '); + }, + + getDominantWorkflowBaseModel() { + const text = this.getWorkflowModelReferenceText(); + if (!text) return ''; + const normalizedText = this.normalizeBaseModelToken(text); + let best = null; + for (const entry of this.getBaseModelAliases()) { + const score = entry.aliases.reduce((count, alias) => { + const token = this.normalizeBaseModelToken(alias); + return token && normalizedText.includes(token) ? count + 1 : count; + }, 0); + if (score > 0 && (!best || score > best.score)) { + best = { value: entry.value, score }; + } + } + return best?.value || ''; + }, + + getSearchBaseModelLabel(value = 'auto') { + if (value === 'auto') { + const detected = this.getDominantWorkflowBaseModel(); + return detected ? `Auto (${detected})` : 'Auto'; + } + const option = this.getKnownBaseModelOptions().find(item => item.value === value); + return option?.label || value || 'Auto'; + }, + + getSearchBaseModelContext(missing = {}) { + const state = this.getSearchState(missing); + const selected = state.selectedBaseModel || 'auto'; + if (selected === 'none') return ''; + if (selected === 'auto') return this.getDominantWorkflowBaseModel(); + return selected; + }, + getBackgroundSearchJobKey(workflowKey, missingSearchKey) { return `${workflowKey || 'workflow'}\n${missingSearchKey || 'missing'}`; }, @@ -636,6 +751,15 @@ export const searchPanelMethods = { } this.setDropdownValue(selectEl, state.selectedSource, this.getSearchSourceLabel(state.selectedSource)); } + + const baseEl = container.querySelector(`#search-base-select-${missing.node_id}-${missing.widget_index}`); + if (baseEl) { + const options = this.getKnownBaseModelOptions(); + if (!options.some(option => option.value === state.selectedBaseModel)) { + state.selectedBaseModel = 'auto'; + } + this.setDropdownValue(baseEl, state.selectedBaseModel, this.getSearchBaseModelLabel(state.selectedBaseModel)); + } }, /** @@ -648,6 +772,37 @@ export const searchPanelMethods = { this.syncSearchSourceUi(missing, container); }, + setSearchBaseModel(missing, baseModel, container) { + const state = this.getSearchState(missing); + const nextBaseModel = baseModel || 'auto'; + const changed = (state.selectedBaseModel || 'auto') !== nextBaseModel; + if (changed) { + this.clearSearchProgressTimers(state.activeSearchRunId); + state.results = this.createEmptySearchState().results; + state.lastAttemptSources = []; + state.lastAttemptBaseModelContext = ''; + state.lastAttemptFound = null; + state.lastAttemptError = null; + state.sourceProgress = {}; + state.activeSearchRunId = null; + const resultsDiv = container?.querySelector?.(`#search-results-${missing.node_id}-${missing.widget_index}`); + if (resultsDiv) { + resultsDiv.classList.remove('mr-is-visible'); + resultsDiv.classList.add('mr-is-hidden'); + resultsDiv.innerHTML = ''; + } + const searchBtn = container?.querySelector?.(`#search-${missing.node_id}-${missing.widget_index}`); + if (searchBtn) { + searchBtn.disabled = false; + searchBtn.innerHTML = `${this.getSearchIconHtml()} ${this.hasSearchResultsForMissing(missing) ? 'Search Again' : 'Search'}`; + } + } + state.selectedBaseModel = nextBaseModel; + this.persistSearchStateForActiveWorkflow(); + this.syncSearchSourceUi(missing, container); + this.refreshSearchUiForMissing?.(missing, state); + }, + getDropdownValue(el) { return el?.dataset?.value || el?.value || ''; }, @@ -985,8 +1140,11 @@ export const searchPanelMethods = { const searchSourcesId = `search-sources-${missing.node_id}-${missing.widget_index}`; const searchSourceSelectId = `search-source-select-${missing.node_id}-${missing.widget_index}`; const searchSourceListId = `search-source-list-${missing.node_id}-${missing.widget_index}`; + const searchBaseSelectId = `search-base-select-${missing.node_id}-${missing.widget_index}`; + const searchBaseListId = `search-base-list-${missing.node_id}-${missing.widget_index}`; const state = this.getSearchState(missing); const selectedSource = state.selectedSource || 'all'; + const selectedBaseModel = state.selectedBaseModel || 'auto'; const buttonText = options.buttonText || (this.hasSearchResultsForMissing(missing) ? 'Search Again' : 'Search'); @@ -1001,6 +1159,13 @@ export const searchPanelMethods = { html += `
`; html += ``; html += ``; + html += `
`; + html += ``; + html += `
`; + html += ``; + html += `
`; + html += `
`; + html += `
`; html += ``; return html; }, diff --git a/web/resolver/shell/lifecycle_graph_methods.js b/web/resolver/shell/lifecycle_graph_methods.js index 3ce599d..1992f59 100644 --- a/web/resolver/shell/lifecycle_graph_methods.js +++ b/web/resolver/shell/lifecycle_graph_methods.js @@ -32,6 +32,7 @@ export const lifecycleGraphMethods = { // Ensure all models are loaded for dropdown await this.ensureCapabilitiesLoaded(); + await this.ensureBaseModelsLoaded(); await this.ensureAllModelsLoaded(); await this.ensureDownloadDirectoriesLoaded(); diff --git a/web/resolver/shell/workflow_state_methods.js b/web/resolver/shell/workflow_state_methods.js index 1d219f0..b2c4e64 100644 --- a/web/resolver/shell/workflow_state_methods.js +++ b/web/resolver/shell/workflow_state_methods.js @@ -186,10 +186,12 @@ export const workflowStateMethods = { } clone.selectedSource = clone.selectedSource || 'all'; + clone.selectedBaseModel = clone.selectedBaseModel || 'auto'; clone.results = this.mergeSearchResults({}, clone.results || {}); clone.lastAttemptSources = Array.isArray(clone.lastAttemptSources) ? clone.lastAttemptSources : []; + clone.lastAttemptBaseModelContext = clone.lastAttemptBaseModelContext || ''; clone.lastAttemptFound = clone.lastAttemptFound ?? null; clone.lastAttemptError = clone.lastAttemptError || null; clone.sourceProgress = clone.sourceProgress || {}; @@ -229,6 +231,7 @@ export const workflowStateMethods = { isPersistableSearchState(state = {}) { if ((state?.selectedSource || 'all') !== 'all') return true; + if ((state?.selectedBaseModel || 'auto') !== 'auto') return true; if (state?.lastAttemptError) return true; if (this.hasSearchResults(state?.results || {})) return true; diff --git a/web/resolver/views/options_methods.js b/web/resolver/views/options_methods.js index 8ba1448..69548a8 100644 --- a/web/resolver/views/options_methods.js +++ b/web/resolver/views/options_methods.js @@ -136,6 +136,35 @@ export const optionsMethods = { +
+

Base Models Support

+
+
+
+
+
+
+ Base Models + Loading... +
+
+ Last update + Loading... +
+
+ Status + Checking local file... +
+
+
Base Models list maps new CivitAI models to local categories.
+
+ + + Source +
+
+
+
@@ -353,6 +382,12 @@ export const optionsMethods = { const modelListMessageEl = this.contentElement.querySelector('#mr-options-model-list-message'); const modelListCheckBtn = this.contentElement.querySelector('#mr-options-model-list-check'); const modelListUpdateBtn = this.contentElement.querySelector('#mr-options-model-list-update'); + const baseModelsCountEl = this.contentElement.querySelector('#mr-options-base-models-count'); + const baseModelsUpdatedEl = this.contentElement.querySelector('#mr-options-base-models-updated'); + const baseModelsStateEl = this.contentElement.querySelector('#mr-options-base-models-state'); + const baseModelsMessageEl = this.contentElement.querySelector('#mr-options-base-models-message'); + const baseModelsCheckBtn = this.contentElement.querySelector('#mr-options-base-models-check'); + const baseModelsUpdateBtn = this.contentElement.querySelector('#mr-options-base-models-update'); const hfIndexCountEl = this.contentElement.querySelector('#mr-options-hf-index-count'); const hfIndexUpdatedEl = this.contentElement.querySelector('#mr-options-hf-index-updated'); const hfIndexStateEl = this.contentElement.querySelector('#mr-options-hf-index-state'); @@ -397,6 +432,11 @@ export const optionsMethods = { if (modelListUpdateBtn) modelListUpdateBtn.disabled = busy; }; + const setBaseModelsBusy = (busy) => { + if (baseModelsCheckBtn) baseModelsCheckBtn.disabled = busy; + if (baseModelsUpdateBtn) baseModelsUpdateBtn.disabled = busy; + }; + const setHfIndexBusy = (busy) => { if (hfIndexRefreshBtn) hfIndexRefreshBtn.disabled = busy; }; @@ -525,6 +565,89 @@ export const optionsMethods = { } }; + const renderBaseModelsStatus = (data = {}, checkedRemote = false) => { + if (baseModelsCountEl) { + baseModelsCountEl.textContent = Number.isFinite(Number(data.local_count)) + ? Number(data.local_count).toLocaleString() + : '-'; + } + if (baseModelsUpdatedEl) { + baseModelsUpdatedEl.textContent = formatOptionDate(data.local_updated_at); + } + if (baseModelsStateEl) { + if (checkedRemote && data.update_available) { + baseModelsStateEl.textContent = 'Update available'; + } else if (checkedRemote) { + baseModelsStateEl.textContent = 'Up to date'; + } else { + baseModelsStateEl.textContent = data.local_updated_at ? 'Tracked' : 'Bundled'; + } + } + if (baseModelsMessageEl) { + if (checkedRemote && data.update_available) { + baseModelsMessageEl.textContent = `New base models are available from CivitAI.`; + } else if (checkedRemote) { + baseModelsMessageEl.textContent = `Base Models list is current.`; + } else { + baseModelsMessageEl.textContent = `Base Models list maps new CivitAI models to local categories.`; + } + } + }; + + const loadBaseModelsStatus = async (checkRemote = false) => { + try { + setBaseModelsBusy(true); + if (baseModelsStateEl) { + baseModelsStateEl.textContent = checkRemote ? 'Checking CivitAI...' : 'Loading...'; + } + const response = await api.fetchApi(`/model_resolver/base-models/status${checkRemote ? '?check_remote=1' : ''}`); + if (!response.ok) { + throw new Error(`Status failed: ${response.status}`); + } + const data = await response.json(); + renderBaseModelsStatus(data, checkRemote); + return data; + } catch (error) { + console.error('Model Resolver: base-models status error:', error); + if (baseModelsStateEl) baseModelsStateEl.textContent = 'Error'; + if (baseModelsMessageEl) baseModelsMessageEl.textContent = error.message || 'Failed to read Base Models status.'; + return null; + } finally { + setBaseModelsBusy(false); + } + }; + + const updateBaseModels = async () => { + try { + setBaseModelsBusy(true); + if (baseModelsStateEl) baseModelsStateEl.textContent = 'Updating...'; + if (baseModelsMessageEl) baseModelsMessageEl.textContent = 'Downloading latest base models from CivitAI...'; + const response = await api.fetchApi('/model_resolver/base-models/update', { + method: 'POST' + }); + const data = await response.json(); + if (!response.ok) { + throw new Error(data.error || `Update failed: ${response.status}`); + } + await this.clearSearchCaches(); + // Invalidate the in-memory cache so the base model dropdown + // reloads fresh data from the server next time it is opened. + this.baseModels = null; + await this.ensureBaseModelsLoaded(); + renderBaseModelsStatus(data, false); + if (baseModelsStateEl) baseModelsStateEl.textContent = 'Updated'; + if (baseModelsMessageEl) baseModelsMessageEl.textContent = `Base Models updated. ${Number(data.local_count || 0).toLocaleString()} base models loaded.`; + this.showNotification('Base Models updated', 'success'); + } catch (error) { + console.error('Model Resolver: base-models update error:', error); + if (baseModelsStateEl) baseModelsStateEl.textContent = 'Error'; + if (baseModelsMessageEl) baseModelsMessageEl.textContent = error.message || 'Failed to update Base Models.'; + this.showNotification('Base Models update failed', 'error'); + } finally { + setBaseModelsBusy(false); + } + }; + const renderHfIndexStatus = (data = {}) => { if (hfIndexCountEl) { hfIndexCountEl.textContent = Number.isFinite(Number(data.file_count)) @@ -683,6 +806,7 @@ export const optionsMethods = { setVisibleSection(targetId); if (targetId === 'mr-options-section-local-db') { loadModelListStatus(false); + loadBaseModelsStatus(false); } else if (targetId === 'mr-options-section-hf') { loadHfIndexStatus(); } @@ -701,6 +825,18 @@ export const optionsMethods = { }); } + if (baseModelsCheckBtn) { + baseModelsCheckBtn.addEventListener('click', () => { + loadBaseModelsStatus(true); + }); + } + + if (baseModelsUpdateBtn) { + baseModelsUpdateBtn.addEventListener('click', () => { + updateBaseModels(); + }); + } + if (hfIndexRefreshBtn) { hfIndexRefreshBtn.addEventListener('click', () => { refreshHfIndex(); @@ -766,6 +902,7 @@ export const optionsMethods = { } loadModelListStatus(false); + loadBaseModelsStatus(false); loadHfIndexStatus(); if (saveBtn) {