Unify metadata workflow cache and category handling

core/services/image_metadata_service.py

@@ -14,6 +14,7 @@ from ..request_utils import (
     read_text_field,
 )
 from ..routes.context import RouteContext
+from ..type_utils import CATEGORY_MAP, normalize_category_token
 from ..workflow.formats import normalize_workflow_payload
 
 MAX_IMAGE_BYTES = 64 * 1024 * 1024
@@ -25,22 +26,6 @@ _CIVITAI_JSON_LABELS = (
     "Civitai metadata",
     "Civitai resources",
 )
-_CIVITAI_CATEGORY_ALIASES = {
-    "checkpoint": "checkpoints",
-    "checkpoints": "checkpoints",
-    "lora": "loras",
-    "loras": "loras",
-    "vae": "vae",
-    "controlnet": "controlnet",
-    "embedding": "embeddings",
-    "embeddings": "embeddings",
-    "textualinversion": "embeddings",
-    "upscale": "upscale_models",
-    "upscaler": "upscale_models",
-    "upscalemodel": "upscale_models",
-}
-
-
 def _decode_exif_user_comment(value: bytes) -> str:
     """Decode the standard EXIF UserComment prefix and payload."""
     if value.startswith(b"UNICODE"):
@@ -268,8 +253,8 @@ def _normalize_civitai_resource(value: Any, index: int) -> Optional[dict[str, An
     resource_type = str(
         _first_mapping_value(value, ("type", "model_type", "modelType")) or ""
     ).strip()
-    category_key = re.sub(r"[^a-z0-9]+", "", resource_type.lower())
-    category = _CIVITAI_CATEGORY_ALIASES.get(category_key, category_key or "unknown")
+    category_key = normalize_category_token(resource_type)
+    category = CATEGORY_MAP.get(category_key, category_key or "unknown")
     model_version_id = _coerce_identifier(
         _first_mapping_value(
             value,

core/type_utils.py

@@ -242,6 +242,7 @@ URN_TYPE_MAP = {
 CATEGORY_MAP = {
     "checkpoints": "checkpoints",
     "checkpoint": "checkpoints",
+    "ckpt": "checkpoints",
     "loras": "loras",
     "lora": "loras",
     "embeddings": "embeddings",
@@ -254,7 +255,9 @@ CATEGORY_MAP = {
     "control_net": "controlnet",
     "vae": "vae",
     "upscaler": "upscale_models",
+    "upscale": "upscale_models",
     "upscale_model": "upscale_models",
+    "upscalemodel": "upscale_models",
     "upscale_models": "upscale_models",
     "latent_upscale_model": "latent_upscale_models",
     "latent_upscale_models": "latent_upscale_models",

core/workflow_transfer.py

@@ -11,33 +11,11 @@ from typing import Any, Dict, List, Optional
 from .contracts import ModelReference, Resolution, ResolvedModel
 from .custom_nodes import get_custom_node_resolution_metadata
 from .path_utils import get_filename_from_path
+from .type_utils import CATEGORY_MAP, normalize_category_token
 from .workflow import dynamic_widgets
 from .workflow.widgets import get_widget_name_candidates, normalize_widget_name
 from .workflow_updater import update_model_path
 
-_CATEGORY_ALIASES = {
-    "checkpoint": "checkpoints",
-    "checkpoints": "checkpoints",
-    "ckpt": "checkpoints",
-    "lora": "loras",
-    "loras": "loras",
-    "text_encoder": "text_encoders",
-    "text_encoders": "text_encoders",
-    "clip": "text_encoders",
-    "vae": "vae",
-    "controlnet": "controlnet",
-    "control_net": "controlnet",
-    "upscale": "upscale_models",
-    "upscaler": "upscale_models",
-    "upscale_model": "upscale_models",
-    "upscale_models": "upscale_models",
-    "diffusion_model": "diffusion_models",
-    "diffusion_models": "diffusion_models",
-    "unet": "diffusion_models",
-    "embedding": "embeddings",
-    "embeddings": "embeddings",
-}
-
 _LORA_MANAGER_NODE_TYPES = {
     "LoraLoaderV2",
     "Lora Loader (LoraManager)",
@@ -64,10 +42,10 @@ _TRANSFER_ACTIVITY_SCOPES = {"all", "active", "inactive"}
 
 def normalize_transfer_category(value: Any) -> str:
     """Normalize the category names used by workflow and metadata payloads."""
-    token = str(value or "").strip().lower().replace("-", "_")
+    token = normalize_category_token(value)
     if not token or token in {"unknown", "none", "null"}:
         return ""
-    return _CATEGORY_ALIASES.get(token, token)
+    return CATEGORY_MAP.get(token, token)
 
 
 def _normalize_identity(value: Any) -> str:
@@ -289,17 +267,6 @@ def _resolution_payload(resolution: Resolution) -> Dict[str, Any]:
     return payload
 
 
-def _assign_sources(
-    sources: Sequence[Mapping[str, Any]],
-    references: Sequence[ModelReference],
-) -> List[tuple[ModelReference, Mapping[str, Any]]]:
-    if not sources or not references:
-        return []
-    if len(sources) == 1:
-        return [(reference, sources[0]) for reference in references]
-    return list(zip(references, sources, strict=False))
-
-
 def _select_source_for_reference(
     category_sources: Sequence[Mapping[str, Any]],
     selector: Mapping[str, Any],