feat(metadata): collect generation params from Krea two/three stage samplers

py/metadata_collector/metadata_processor.py

@@ -214,6 +214,24 @@ class MetadataProcessor:
                     max_denoise = denoise
                     primary_sampler = sampler_info
                     primary_sampler_id = node_id
+
+        # Last resort: any registered sampler. Samplers without a denoise or
+        # add_noise parameter (e.g. multi-stage samplers like KreaTwoStageSampler)
+        # are not caught by the criteria above. Prefer execution order so the
+        # first executed sampler wins, matching the downstream_id branch.
+        if primary_sampler is None:
+            sampler_ids = [
+                node_id
+                for node_id, sampler_info in metadata.get(SAMPLING, {}).items()
+                if sampler_info.get(IS_SAMPLER, False)
+            ]
+            if sampler_ids:
+                if downstream_id and "execution_order" in metadata:
+                    for node_id in metadata["execution_order"]:
+                        if node_id in sampler_ids:
+                            return node_id, metadata[SAMPLING][node_id]
+                primary_sampler_id = sampler_ids[0]
+                primary_sampler = metadata[SAMPLING][sampler_ids[0]]
                 
         return primary_sampler_id, primary_sampler
     

py/metadata_collector/node_extractors.py

@@ -861,6 +861,65 @@ class TSCKSamplerAdvancedExtractor(KSamplerAdvancedExtractor, TSCSamplerBaseExtr
 
     # Update method is inherited from TSCSamplerBaseExtractor
 
+class KreaTwoStageSamplerExtractor(BaseSamplerExtractor):
+    """Extractor for Krea Two/Three Stage Samplers (Auryg/Krea-2-Two-Stage-Sampler).
+
+    The node samples in two (or three) stages with per-stage settings
+    (stage1_steps/stage2_steps, stage1_cfg/stage2_cfg, ...). The canonical
+    metadata fields consumed by ``extract_generation_params`` (steps, cfg,
+    sampler_name, scheduler) are derived from the base stage (stage 1; the
+    three-stage variant reuses stage 1 settings for stage 3), while the full
+    per-stage breakdown is preserved in the raw parameters.
+    """
+
+    # All per-stage parameter keys present on both node variants.
+    _STAGE_PARAM_KEYS = (
+        "stage1_steps", "stage1_cfg", "stage1_sampler_name", "stage1_scheduler",
+        "stage2_steps", "stage2_cfg", "stage2_sampler_name", "stage2_scheduler",
+    )
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        BaseSamplerExtractor.extract_sampling_params(
+            node_id,
+            inputs,
+            metadata,
+            ("seed", "handoff_percent", "stage3_handoff_percent")
+            + KreaTwoStageSamplerExtractor._STAGE_PARAM_KEYS,
+        )
+
+        # Derive the canonical fields expected by extract_generation_params.
+        sampling_params = metadata[SAMPLING][node_id]["parameters"]
+        if "stage1_steps" in sampling_params or "stage2_steps" in sampling_params:
+            sampling_params["steps"] = (
+                (sampling_params.get("stage1_steps") or 0)
+                + (sampling_params.get("stage2_steps") or 0)
+            )
+        if "stage1_cfg" in sampling_params:
+            sampling_params["cfg"] = sampling_params["stage1_cfg"]
+        if "stage1_sampler_name" in sampling_params:
+            sampling_params["sampler_name"] = sampling_params["stage1_sampler_name"]
+        if "stage1_scheduler" in sampling_params:
+            sampling_params["scheduler"] = sampling_params["stage1_scheduler"]
+
+        BaseSamplerExtractor.extract_conditioning(node_id, inputs, metadata)
+
+        # Prefer the final generation resolution; latent dims are the fallback.
+        BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
+        final_width = inputs.get("final_width")
+        final_height = inputs.get("final_height")
+        if final_width and final_height:
+            if SIZE not in metadata:
+                metadata[SIZE] = {}
+            metadata[SIZE][node_id] = {
+                "width": final_width,
+                "height": final_height,
+                "node_id": node_id,
+            }
+
 class LoraLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -901,6 +960,37 @@ class ImageSizeExtractor(NodeMetadataExtractor):
             "node_id": node_id
         }
 
+class KreaDualResolutionSelectorExtractor(NodeMetadataExtractor):
+    """Extract base resolution from Krea Dual Resolution Selector outputs
+    (Auryg/Krea-2-Two-Stage-Sampler).
+
+    The node computes base/final dimensions at runtime from aspect ratio and
+    megapixel settings, so the values are only available in the update phase
+    (outputs: base_width, base_height, final_width, final_height, seed).
+    """
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        # Dimensions are computed at runtime; nothing to do here.
+        pass
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        output_tuple = _first_output_tuple(outputs)
+        if not output_tuple or len(output_tuple) < 2:
+            return
+        width, height = output_tuple[0], output_tuple[1]
+        if not isinstance(width, int) or not isinstance(height, int):
+            return
+
+        if SIZE not in metadata:
+            metadata[SIZE] = {}
+        metadata[SIZE][node_id] = {
+            "width": width,
+            "height": height,
+            "node_id": node_id,
+        }
+
 class RgthreePowerLoraLoaderExtractor(NodeMetadataExtractor):
     """Extract LoRA metadata from rgthree Power Lora Loader.
 
@@ -1302,6 +1392,8 @@ NODE_EXTRACTORS = {
     "ClownsharKSampler_Beta": SamplerExtractor,
     "TSC_KSampler": TSCKSamplerExtractor,   # Efficient Nodes
     "TSC_KSamplerAdvanced": TSCKSamplerAdvancedExtractor,  # Efficient Nodes
+    "KreaTwoStageSampler": KreaTwoStageSamplerExtractor,  # Auryg/Krea-2-Two-Stage-Sampler
+    "KreaThreeStageSampler": KreaTwoStageSamplerExtractor,  # Auryg/Krea-2-Two-Stage-Sampler
     "KSamplerBasicPipe": KSamplerBasicPipeExtractor,    # comfyui-impact-pack
     "KSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipeExtractor,    # comfyui-impact-pack
     "KSampler_inspire_pipe": KSamplerBasicPipeExtractor,    # comfyui-inspire-pack
@@ -1353,6 +1445,7 @@ NODE_EXTRACTORS = {
     "GetNode": GetNodeExtractor,
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
+    "KreaDualResolutionSelector": KreaDualResolutionSelectorExtractor,  # Auryg/Krea-2-Two-Stage-Sampler
     # Flux
     "FluxGuidance": FluxGuidanceExtractor,      # Add FluxGuidance
     "CFGGuider": CFGGuiderExtractor,            # Add CFGGuider

py/services/recipe_scanner.py

@@ -36,6 +36,11 @@ logger = logging.getLogger(__name__)
 # explicitly to "diffusion_model" (mirrors Oracle R2-F1).
 _CHECKPOINT_MODEL_TYPE_ALIASES = {"diffusionmodel": "diffusion_model"}
 
+# Known weight-file extensions stripped by _normalize_filename_key. Names are
+# stored extensionless on both sides, so splitext would misread dotted stems
+# ("my.mix" -> "my") and silently collide distinct models.
+_WEIGHT_FILE_EXTS = (".safetensors", ".ckpt", ".pt", ".pth", ".gguf", ".bin", ".safebin", ".sft")
+
 
 class RecipeScanner:
     """Service for scanning and managing recipe images"""
@@ -116,6 +121,12 @@ class RecipeScanner:
             self._rematch_autov3_cache: dict[str, dict[str, Any]] | None = None
             self._rematch_autov3_versions: tuple[int, int] | None = None
             self._rematch_autov3_lock = asyncio.Lock()
+            # Normalized filename -> [items] map for the L4 rematch fallback,
+            # rebuilt only when either model scanner's cache_version changes.
+            # Mirrors the build_local_hash_cache version pattern.
+            self._local_filename_cache: dict[str, list[dict[str, Any]]] | None = None
+            self._local_filename_cache_versions: tuple[int, int] | None = None
+            self._local_filename_cache_lock = asyncio.Lock()
             self._initialized = True
 
     async def build_local_hash_cache(self) -> dict[str, dict[str, Any]]:
@@ -162,6 +173,70 @@ class RecipeScanner:
             self._local_hash_cache_versions = versions
             return cache
 
+    @staticmethod
+    def _normalize_filename_key(name: str) -> str:
+        """Normalize a file name to a lookup key (basename, lowercase).
+
+        Only known weight-file extensions are stripped — names are stored
+        extensionless on both sides, so splitext would misread dotted stems
+        ("my.mix" -> "my") and collide distinct models.
+        """
+        if not name:
+            return ""
+        basename = os.path.basename(name.replace("\\", "/"))
+        lower = basename.lower()
+        for ext in _WEIGHT_FILE_EXTS:
+            if lower.endswith(ext):
+                basename = basename[: -len(ext)]
+                break
+        return basename.strip().lower()
+
+    async def _build_local_filename_cache(self) -> dict[str, list[dict[str, Any]]]:
+        """Build a version-cached map of normalized file names to local items.
+
+        Keys are lowercase basenames without extension. Values are lists of
+        items (lora + checkpoint, type-blind) sharing that name. Only items
+        with a sha256 are indexed — matching a pending or failed download
+        (empty sha256) would leave the entry without a usable hash. The dict
+        is reused while both scanners' cache_version values are unchanged;
+        concurrent callers share a single build via the lock.
+        """
+        async with self._local_filename_cache_lock:
+            lora_scanner = self._lora_scanner
+            checkpoint_scanner = self._checkpoint_scanner
+            versions = (
+                lora_scanner.cache_version if lora_scanner is not None else 0,
+                checkpoint_scanner.cache_version
+                if checkpoint_scanner is not None
+                else 0,
+            )
+            if (
+                self._local_filename_cache is not None
+                and self._local_filename_cache_versions == versions
+            ):
+                return self._local_filename_cache
+
+            cache: dict[str, list[dict[str, Any]]] = {}
+            for scanner in (lora_scanner, checkpoint_scanner):
+                if scanner is None:
+                    continue
+                data = await scanner.get_cached_data()
+                for item in data.raw_data:
+                    if not isinstance(item, dict):
+                        continue
+                    if not (item.get("sha256") or "").lower():
+                        continue
+                    file_path = item.get("file_path") or ""
+                    file_name = item.get("file_name") or ""
+                    key = self._normalize_filename_key(file_name or file_path)
+                    if not key:
+                        continue
+                    cache.setdefault(key, []).append(item)
+
+            self._local_filename_cache = cache
+            self._local_filename_cache_versions = versions
+            return cache
+
     def _is_rematch_candidate(self, entry: dict[str, Any]) -> bool:
         """Return True when a recipe entry is eligible for local re-matching."""
         if not isinstance(entry, dict):
@@ -170,7 +245,10 @@ class RecipeScanner:
             entry.get("isDeleted") or not entry.get("hash") or not entry.get("file_name")
         )
         has_identifier = (
-            entry.get("hash") or entry.get("modelVersionId") or entry.get("id")
+            entry.get("hash")
+            or entry.get("modelVersionId")
+            or entry.get("id")
+            or entry.get("file_name")
         )
         return bool(unresolved and has_identifier)
 
@@ -221,6 +299,97 @@ class RecipeScanner:
             self._rematch_autov3_versions = versions
             return cache
 
+    def _is_type_compatible(self, item: dict[str, Any], *, is_checkpoint: bool) -> bool:
+        """Return True when a local item's type matches the entry kind.
+
+        The L1 hash cache and the L4 filename cache merge lora and checkpoint
+        items and are type-blind, so a match must be verified against the
+        entry kind before it is accepted.
+        """
+        sub_type = (item.get("sub_type") or "").lower()
+        if sub_type:
+            valid = (
+                VALID_CHECKPOINT_SUB_TYPES if is_checkpoint else VALID_LORA_TYPES
+            )
+            return sub_type in valid
+
+        civitai_type = (
+            (item.get("civitai") or {}).get("model", {}) or {}
+        ).get("type", "")
+        if civitai_type:
+            normalized = civitai_type.lower()
+            if is_checkpoint:
+                normalized = _CHECKPOINT_MODEL_TYPE_ALIASES.get(
+                    normalized, normalized
+                )
+                valid = VALID_CHECKPOINT_SUB_TYPES
+            else:
+                valid = VALID_LORA_TYPES
+            return normalized in valid
+        return True
+
+    @staticmethod
+    def _has_positive_type_evidence(item: dict[str, Any]) -> bool:
+        """Return True when the item carries an explicit type marker.
+
+        Lora raw items rarely carry ``sub_type`` (it is only written when
+        metadata provides it), while checkpoint items always do — so for
+        checkpoint slots a type-less candidate is a red flag, not the norm.
+        """
+        if (item.get("sub_type") or "").lower():
+            return True
+        civitai_type = (
+            (item.get("civitai") or {}).get("model", {}) or {}
+        ).get("type", "")
+        return bool(civitai_type)
+
+    def _match_rematch_entry_filename(
+        self,
+        entry: dict[str, Any],
+        recipe_base_model: Optional[str],
+        filename_cache: dict[str, list[dict[str, Any]]],
+        *,
+        is_checkpoint: bool,
+    ) -> Tuple[Optional[dict[str, Any]], Optional[str]]:
+        """Match a recipe entry against local models by file name (L4).
+
+        Conservative fallback used only after the hash (L1), version-index
+        (L2) and computed-autov3 (L3) tiers all failed. Candidates share the
+        entry's normalized file name; a candidate is accepted only when BOTH
+        the recipe base model and the candidate's base model are known and
+        equal (unknown on either side rejects — never guess on missing
+        metadata), the type gate passes, and exactly one candidate survives
+        (ambiguity is a miss). Checkpoint slots additionally require positive
+        type evidence: lora raw items often lack ``sub_type`` while
+        checkpoints always carry it, so a type-less candidate is a red flag
+        there — an unknown-type lora must not be bound into a checkpoint
+        slot.
+
+        Returns:
+            Tuple of (matched item, "L4") — or ``(None, None)``.
+        """
+        entry_name = self._normalize_filename_key(entry.get("file_name") or "")
+        if not entry_name:
+            return (None, None)
+
+        recipe_base = (recipe_base_model or "").strip().lower()
+        matched: list[dict[str, Any]] = []
+        for candidate in filename_cache.get(entry_name, []):
+            candidate_base = (candidate.get("base_model") or "").strip().lower()
+            if not recipe_base or not candidate_base:
+                continue
+            if recipe_base != candidate_base:
+                continue
+            if is_checkpoint and not self._has_positive_type_evidence(candidate):
+                continue
+            if not self._is_type_compatible(candidate, is_checkpoint=is_checkpoint):
+                continue
+            matched.append(candidate)
+
+        if len(matched) != 1:
+            return (None, None)
+        return (matched[0], "L4")
+
     async def _match_rematch_entry(
         self,
         entry: dict[str, Any],
@@ -247,19 +416,23 @@ class RecipeScanner:
         autov3_cache: dict[str, Any],
         *,
         is_checkpoint: bool,
+        filename_cache: Optional[dict[str, list[dict[str, Any]]]] = None,
+        recipe_base_model: Optional[str] = None,
     ) -> Tuple[Optional[dict[str, Any]], Optional[str]]:
-        """Match a recipe entry against local models across three levels.
+        """Match a recipe entry against local models across four levels.
 
         L1 looks the stored hash up in the type-blind local hash cache; L2
         falls back to the version index via ``modelVersionId`` or ``id``; L3
-        resolves 12-char hashes through the computed AutoV3 cache. Matched
-        items are type-verified against the entry kind before being returned.
+        resolves 12-char hashes through the computed AutoV3 cache; L4
+        (conservative) falls back to the file name when a filename cache is
+        provided. Matched items are type-verified against the entry kind
+        before being returned.
 
         Returns:
-            Tuple of (matched item, match level) where level is "L1", "L2" or
-            "L3" — or ``(None, None)`` when no usable match exists. A missing
-            local match is an expected outcome (the model may simply not be
-            present locally), not an error.
+            Tuple of (matched item, match level) where level is "L1", "L2",
+            "L3" or "L4" — or ``(None, None)`` when no usable match exists. A
+            missing local match is an expected outcome (the model may simply
+            not be present locally), not an error.
         """
         entry_hash = (entry.get("hash") or "").lower()
 
@@ -279,33 +452,20 @@ class RecipeScanner:
             item = autov3_cache.get(entry_hash)
             level = "L3" if item is not None else None
 
+        if item is None and filename_cache is not None:
+            item, level = self._match_rematch_entry_filename(
+                entry,
+                recipe_base_model,
+                filename_cache,
+                is_checkpoint=is_checkpoint,
+            )
+            level = "L4" if item is not None else None
+
         if item is None:
             return (None, None)
 
-        # Type gate: the L1 cache merges lora and checkpoint items and is
-        # type-blind, so a match must be verified against the entry kind.
-        sub_type = (item.get("sub_type") or "").lower()
-        if sub_type:
-            valid = (
-                VALID_CHECKPOINT_SUB_TYPES if is_checkpoint else VALID_LORA_TYPES
-            )
-            if sub_type not in valid:
-                return (None, None)
-        else:
-            civitai_type = (
-                (item.get("civitai") or {}).get("model", {}) or {}
-            ).get("type", "")
-            if civitai_type:
-                normalized = civitai_type.lower()
-                if is_checkpoint:
-                    normalized = _CHECKPOINT_MODEL_TYPE_ALIASES.get(
-                        normalized, normalized
-                    )
-                    valid = VALID_CHECKPOINT_SUB_TYPES
-                else:
-                    valid = VALID_LORA_TYPES
-                if normalized not in valid:
-                    return (None, None)
+        if not self._is_type_compatible(item, is_checkpoint=is_checkpoint):
+            return (None, None)
 
         return (item, level)
 
@@ -617,10 +777,11 @@ class RecipeScanner:
     async def _rematch_recipe_by_id(self, recipe_id: str) -> Dict[str, Any]:
         """Rematch a single recipe's deleted lora/checkpoint entries locally.
 
-        Match snapshots (local hash cache + computed autov3 cache) are built
-        BEFORE acquiring the mutation lock — both are read-only snapshots and
-        the version-cached hash dict would otherwise rebuild mid-run if a scan
-        bumps a scanner's cache_version while we hold the lock.
+        Match snapshots (local hash cache, computed autov3 cache, filename
+        cache) are built BEFORE acquiring the mutation lock — all three are
+        read-only snapshots and the version-cached dicts would otherwise
+        rebuild mid-run if a scan bumps a scanner's cache_version while we
+        hold the lock.
 
         Args:
             recipe_id: ID of the recipe to rematch
@@ -636,6 +797,7 @@ class RecipeScanner:
         """
         local_cache = await self.build_local_hash_cache()
         autov3_cache = await self._build_rematch_autov3_cache()
+        filename_cache = await self._build_local_filename_cache()
 
         async with self._mutation_lock:
             # Get raw recipe from cache directly to avoid formatted fields
@@ -649,7 +811,7 @@ class RecipeScanner:
 
             try:
                 rematched, _errors, details = await self._rematch_single_recipe(
-                    recipe, local_cache, autov3_cache
+                    recipe, local_cache, autov3_cache, filename_cache
                 )
             except RecipePersistenceError as exc:
                 logger.error(
@@ -706,6 +868,7 @@ class RecipeScanner:
         recipe: Dict[str, Any],
         local_cache: dict[str, dict[str, Any]],
         autov3_cache: dict[str, dict[str, Any]],
+        filename_cache: Optional[dict[str, list[dict[str, Any]]]] = None,
     ) -> Tuple[int, int, Dict[str, Any]]:
         """Rematch a single recipe's lora/checkpoint entries against local models.
 
@@ -719,6 +882,8 @@ class RecipeScanner:
             recipe: The recipe dictionary to rematch (modified in-place)
             local_cache: L1 hash cache snapshot (build_local_hash_cache)
             autov3_cache: L3 computed-autov3 cache snapshot
+            filename_cache: L4 filename cache snapshot, or None to disable
+                the filename fallback
 
         Returns:
             Tuple of (rematched_entries, errors, details). The errors element
@@ -744,7 +909,13 @@ class RecipeScanner:
                 if not self._is_rematch_candidate(entry):
                     continue
                 item, level = await self._match_rematch_entry_with_level(
-                    entry, local_cache, autov3_cache, is_checkpoint=False
+                    entry,
+                    local_cache,
+                    autov3_cache,
+                    is_checkpoint=False,
+                    filename_cache=filename_cache,
+                    recipe_base_model=entry.get("baseModel")
+                    or recipe.get("base_model"),
                 )
                 if item is None:
                     details["unresolved"].append(
@@ -770,7 +941,13 @@ class RecipeScanner:
         if isinstance(checkpoint, dict):
             if self._is_rematch_candidate(checkpoint):
                 item, level = await self._match_rematch_entry_with_level(
-                    checkpoint, local_cache, autov3_cache, is_checkpoint=True
+                    checkpoint,
+                    local_cache,
+                    autov3_cache,
+                    is_checkpoint=True,
+                    filename_cache=filename_cache,
+                    recipe_base_model=checkpoint.get("baseModel")
+                    or recipe.get("base_model"),
                 )
                 if item is None:
                     details["unresolved"].append(
@@ -832,12 +1009,13 @@ class RecipeScanner:
     ) -> Dict[str, Any]:
         """Rematch every recipe's deleted lora/checkpoint entries locally.
 
-        Match snapshots (local hash cache + computed autov3 cache) are built
-        ONCE before the loop — both are read-only and the version-cached hash
-        dict would otherwise rebuild mid-run if a scan bumps a scanner's
-        cache_version while the mutation lock is held. ``_schedule_resort`` is
-        called exactly once after the loop: it spawns an asyncio task per call,
-        so per-recipe calls would race one resort task per recipe.
+        Match snapshots (local hash cache, computed autov3 cache, filename
+        cache) are built ONCE before the loop — all three are read-only and
+        the version-cached dicts would otherwise rebuild mid-run if a scan
+        bumps a scanner's cache_version while the mutation lock is held.
+        ``_schedule_resort`` is called exactly once after the loop: it spawns
+        an asyncio task per call, so per-recipe calls would race one resort
+        task per recipe.
 
         Args:
             progress_callback: Optional callback for progress updates
@@ -858,6 +1036,7 @@ class RecipeScanner:
         # Match snapshots built once and shared by every recipe in the loop.
         local_cache = await self.build_local_hash_cache()
         autov3_cache = await self._build_rematch_autov3_cache()
+        filename_cache = await self._build_local_filename_cache()
 
         async with self._mutation_lock:
             cache = await self.get_cached_data()
@@ -925,7 +1104,7 @@ class RecipeScanner:
                         )
 
                     rematched, _errors, details = await self._rematch_single_recipe(
-                        recipe, local_cache, autov3_cache
+                        recipe, local_cache, autov3_cache, filename_cache
                     )
                     if rematched > 0:
                         matched_recipes += 1

tests/metadata_collector/test_metadata_collector.py

@@ -1613,3 +1613,213 @@ def test_fill_missing_metadata_fills_overwrite_for_muted_node(metadata_registry)
     assert "ow-1" not in metadata.get(OVERWRITE, {})
 
     metadata_registry.clear_metadata()
+
+
+def test_krea_two_stage_sampler_prompt_and_params_collected(
+    metadata_registry, monkeypatch
+):
+    """KreaTwoStageSampler should be recognized as the primary sampler and
+    contribute the prompt, canonical sampling params, and final resolution."""
+    prompt_graph = {
+        "encode_pos": {
+            "class_type": "PromptLM",
+            "inputs": {"text": "krea masterpiece", "clip": ["clip", 0]},
+        },
+        "encode_neg": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": "low quality", "clip": ["clip", 0]},
+        },
+        "sampler": {
+            "class_type": "KreaTwoStageSampler",
+            "inputs": {
+                "seed": 42,
+                "handoff_percent": 16.67,
+                "stage1_steps": 52,
+                "stage1_cfg": 4.0,
+                "stage1_sampler_name": "euler",
+                "stage1_scheduler": "simple",
+                "stage2_steps": 12,
+                "stage2_cfg": 1.0,
+                "stage2_sampler_name": "euler",
+                "stage2_scheduler": "simple",
+                "final_width": 2048,
+                "final_height": 2048,
+                "upscale_method": "bislerp",
+                "positive": ["encode_pos", 0],
+                "negative": ["encode_neg", 0],
+                "latent_image": {
+                    "samples": types.SimpleNamespace(shape=(1, 4, 16, 16))
+                },
+            },
+        },
+    }
+    prompt = SimpleNamespace(original_prompt=prompt_graph)
+
+    pos_conditioning = object()
+    neg_conditioning = object()
+
+    monkeypatch.setattr(metadata_processor, "standalone_mode", False)
+
+    metadata_registry.start_collection("krea-two-stage")
+    metadata_registry.set_current_prompt(prompt)
+
+    metadata_registry.record_node_execution(
+        "encode_pos", "PromptLM", {"text": "krea masterpiece"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_pos", "PromptLM", [(pos_conditioning, "krea masterpiece")]
+    )
+    metadata_registry.record_node_execution(
+        "encode_neg", "CLIPTextEncode", {"text": "low quality"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_neg", "CLIPTextEncode", [(neg_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "sampler",
+        "KreaTwoStageSampler",
+        {
+            "seed": 42,
+            "handoff_percent": 16.67,
+            "stage1_steps": 52,
+            "stage1_cfg": 4.0,
+            "stage1_sampler_name": "euler",
+            "stage1_scheduler": "simple",
+            "stage2_steps": 12,
+            "stage2_cfg": 1.0,
+            "stage2_sampler_name": "euler",
+            "stage2_scheduler": "simple",
+            "final_width": 2048,
+            "final_height": 2048,
+            "upscale_method": "bislerp",
+            "positive": pos_conditioning,
+            "negative": neg_conditioning,
+            "latent_image": {
+                "samples": types.SimpleNamespace(shape=(1, 4, 16, 16))
+            },
+        },
+        None,
+    )
+
+    metadata = metadata_registry.get_metadata("krea-two-stage")
+
+    sampler_data = metadata[SAMPLING]["sampler"]
+    assert sampler_data["is_sampler"] is True
+    parameters = sampler_data["parameters"]
+    assert parameters["seed"] == 42
+    assert parameters["steps"] == 64
+    assert parameters["cfg"] == 4.0
+    assert parameters["sampler_name"] == "euler"
+    assert parameters["scheduler"] == "simple"
+    assert parameters["stage1_steps"] == 52
+    assert parameters["stage2_cfg"] == 1.0
+
+    assert metadata[SIZE]["sampler"] == {
+        "width": 2048,
+        "height": 2048,
+        "node_id": "sampler",
+    }
+
+    prompt_results = MetadataProcessor.match_conditioning_to_prompts(
+        metadata, "sampler"
+    )
+    assert prompt_results["prompt"] == "krea masterpiece"
+    assert prompt_results["negative_prompt"] == "low quality"
+
+    params = MetadataProcessor.extract_generation_params(metadata)
+    assert params["prompt"] == "krea masterpiece"
+    assert params["negative_prompt"] == "low quality"
+    assert params["seed"] == 42
+    assert params["steps"] == 64
+    assert params["cfg_scale"] == 4.0
+    assert params["sampler"] == "euler"
+    assert params["scheduler"] == "simple"
+    assert params["size"] == "2048x2048"
+
+
+def test_krea_three_stage_sampler_uses_stage1_canonical_fields(metadata_registry):
+    """KreaThreeStageSampler reuses stage 1 settings for stage 3, so canonical
+    fields map from stage 1 and the total counts both sampling stages."""
+    metadata_registry.start_collection("krea-three-stage")
+    metadata_registry.set_current_prompt(SimpleNamespace(original_prompt={}))
+
+    metadata_registry.record_node_execution(
+        "sampler",
+        "KreaThreeStageSampler",
+        {
+            "seed": 7,
+            "handoff_percent": 16.67,
+            "stage3_handoff_percent": 83.33,
+            "stage1_steps": 52,
+            "stage1_cfg": 4.0,
+            "stage1_sampler_name": "euler",
+            "stage1_scheduler": "simple",
+            "stage2_steps": 12,
+            "stage2_cfg": 1.0,
+            "stage2_sampler_name": "euler",
+            "stage2_scheduler": "simple",
+            "final_width": 1024,
+            "final_height": 2048,
+            "upscale_method": "bislerp",
+            "positive": object(),
+            "negative": object(),
+            "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 8, 16))},
+        },
+        None,
+    )
+
+    metadata = metadata_registry.get_metadata("krea-three-stage")
+
+    sampler_data = metadata[SAMPLING]["sampler"]
+    assert sampler_data["is_sampler"] is True
+    parameters = sampler_data["parameters"]
+    assert parameters["seed"] == 7
+    assert parameters["stage3_handoff_percent"] == 83.33
+    assert parameters["steps"] == 64
+    assert parameters["cfg"] == 4.0
+    assert parameters["sampler_name"] == "euler"
+    assert parameters["scheduler"] == "simple"
+
+    # Final resolution takes precedence over the latent dimensions (64x128).
+    assert metadata[SIZE]["sampler"] == {
+        "width": 1024,
+        "height": 2048,
+        "node_id": "sampler",
+    }
+
+
+def test_krea_dual_resolution_selector_extracts_size_from_outputs(
+    metadata_registry,
+):
+    """KreaDualResolutionSelector computes dimensions at runtime, so the base
+    resolution is recorded from its outputs in the update phase."""
+    metadata_registry.start_collection("krea-selector")
+    metadata_registry.set_current_prompt(SimpleNamespace(original_prompt={}))
+
+    metadata_registry.record_node_execution(
+        "selector",
+        "KreaDualResolutionSelector",
+        {
+            "aspect_ratio": "1:1",
+            "base_megapixels": 1.0,
+            "final_megapixels": 2.0,
+            "multiple": 16,
+            "random_seed": 123,
+        },
+        None,
+        return_types=("INT", "INT", "INT", "INT", "INT"),
+    )
+    metadata_registry.update_node_execution(
+        "selector",
+        "KreaDualResolutionSelector",
+        [(1024, 1024, 2048, 2048, 123)],
+        return_types=("INT", "INT", "INT", "INT", "INT"),
+    )
+
+    metadata = metadata_registry.get_metadata("krea-selector")
+
+    assert metadata[SIZE]["selector"] == {
+        "width": 1024,
+        "height": 1024,
+        "node_id": "selector",
+    }

tests/services/test_recipe_scanner.py

@@ -1883,9 +1883,10 @@ async def test_is_rematch_candidate_rejects_healthy_entry(tmp_path: Path):
     assert not scanner._is_rematch_candidate({"hash": "abc", "file_name": "m.safetensors"})
 
 
-async def test_is_rematch_candidate_rejects_no_identifier(tmp_path: Path):
+async def test_is_rematch_candidate_file_name_only_is_identifier(tmp_path: Path):
     scanner, _, _ = _make_rematch_scanner([], [], tmp_path)
-    assert not scanner._is_rematch_candidate({"isDeleted": True, "file_name": "m.safetensors"})
+    # file_name alone is now an identifier (enables the L4 filename fallback)
+    assert scanner._is_rematch_candidate({"isDeleted": True, "file_name": "m.safetensors"})
     assert not scanner._is_rematch_candidate({"isDeleted": True})
 
 
@@ -2220,6 +2221,481 @@ async def test_match_rematch_type_gate_lora_accepts_lora_typed_item(tmp_path: Pa
         assert matched is not None
 
 
+# _match_rematch_entry — L4 filename fallback (conservative)
+
+
+async def test_match_rematch_entry_l4_filename_hit(tmp_path: Path):
+    item = _rematch_item(
+        sha256=("T1" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, lora, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert matched is lora._cache.raw_data[0]
+    assert level == "L4"
+
+
+async def test_match_rematch_entry_l4_filename_normalized_key(tmp_path: Path):
+    # case, path and extension differences are normalized on both sides
+    item = _rematch_item(
+        sha256=("T2" * 32).lower(),
+        sub_type="lora",
+        base_model="SDXL",
+        file_name="My_Mix.safetensors",
+    )
+    scanner, lora, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "subdir/my_mix", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="sdxl",
+    )
+
+    assert matched is lora._cache.raw_data[0]
+    assert level == "L4"
+
+
+async def test_match_rematch_entry_l4_dotted_stem_no_collision(tmp_path: Path):
+    # "my.mix" (dotted stem) and "my" are distinct names — splitext-style
+    # stripping would collapse both to "my" and bind the wrong model as a
+    # unique candidate.
+    item = _rematch_item(
+        sha256=("T2A" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="my.mix",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "my", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_extension_bearing_entry_reconciled(tmp_path: Path):
+    # extension-bearing entry names reconcile with extensionless items
+    item = _rematch_item(
+        sha256=("T2B" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="my.mix.v1",
+    )
+    scanner, lora, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "my.mix.v1.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert matched is lora._cache.raw_data[0]
+    assert level == "L4"
+
+
+async def test_match_rematch_entry_l4_base_model_mismatch_rejects(tmp_path: Path):
+    item = _rematch_item(
+        sha256=("T3" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SDXL",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_recipe_base_model_unknown_rejects(tmp_path: Path):
+    item = _rematch_item(
+        sha256=("T4" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_item_base_model_unknown_rejects(tmp_path: Path):
+    item = _rematch_item(
+        sha256=("T5" * 32).lower(), sub_type="lora", file_name="detail.safetensors"
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_ambiguous_same_base_model_rejects(tmp_path: Path):
+    items = [
+        _rematch_item(
+            sha256=("T6" * 32).lower(),
+            sub_type="lora",
+            base_model="SD 1.5",
+            file_name="detail.safetensors",
+        ),
+        _rematch_item(
+            sha256=("T7" * 32).lower(),
+            sub_type="lora",
+            base_model="SD 1.5",
+            file_name="detail.safetensors",
+        ),
+    ]
+    scanner, _, _ = _make_rematch_scanner(items, [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_ambiguity_resolved_by_base_model(tmp_path: Path):
+    sdxl_item = _rematch_item(
+        sha256=("T8" * 32).lower(),
+        sub_type="lora",
+        base_model="SDXL",
+        file_name="detail.safetensors",
+    )
+    sd15_item = _rematch_item(
+        sha256=("T9" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, lora, _ = _make_rematch_scanner([sdxl_item, sd15_item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SDXL",
+    )
+
+    assert matched is lora._cache.raw_data[0]
+    assert level == "L4"
+
+
+async def test_match_rematch_entry_l4_type_gate_rejects(tmp_path: Path):
+    # a checkpoint-typed item with a matching name must not satisfy a lora entry
+    item = _rematch_item(
+        sha256=("TA" * 32).lower(),
+        sub_type="checkpoint",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_checkpoint_slot_rejects_type_less_candidate(
+    tmp_path: Path,
+):
+    # lora raw items often carry no sub_type; an unknown-type candidate must
+    # not be bound into a checkpoint slot
+    item = _rematch_item(
+        sha256=("TA1" * 32).lower(),
+        base_model="SD 1.5",
+        file_name="realistic.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "realistic.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=True,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_checkpoint_slot_accepts_typed_candidate(
+    tmp_path: Path,
+):
+    item = _rematch_item(
+        sha256=("TA2" * 32).lower(),
+        sub_type="checkpoint",
+        base_model="SD 1.5",
+        file_name="realistic.safetensors",
+    )
+    scanner, _, checkpoint = _make_rematch_scanner([], [item], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "realistic.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=True,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert matched is checkpoint._cache.raw_data[0]
+    assert level == "L4"
+
+
+async def test_match_rematch_entry_l4_lora_slot_accepts_type_less_candidate(tmp_path: Path):
+    # asymmetry: lora slots still accept type-less candidates (the norm for
+    # lora raw items); checkpoint items always carry sub_type, so the type
+    # gate alone protects the reverse direction
+    item = _rematch_item(
+        sha256=("TA3" * 32).lower(), base_model="SD 1.5", file_name="detail.safetensors"
+    )
+    scanner, lora, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "detail.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert matched is lora._cache.raw_data[0]
+    assert level == "L4"
+
+
+async def test_rematch_l4_entry_base_model_preferred_over_recipe(tmp_path: Path, monkeypatch):
+    # a Pony lora inside an SD 1.5 recipe matches via its own baseModel
+    item = _rematch_item(
+        sha256=("TB1" * 32).lower(),
+        sub_type="lora",
+        base_model="Pony",
+        file_name="pony.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+    saved, _ = await _spy_rematch_persistence(scanner, monkeypatch)
+    await _spy_fts(scanner, monkeypatch)
+
+    recipe: Dict[str, Any] = {
+        "id": "r1",
+        "base_model": "SD 1.5",
+        "loras": [
+            {"file_name": "pony.safetensors", "isDeleted": True, "baseModel": "Pony"}
+        ],
+    }
+    rematched, _errors, details = await scanner._rematch_single_recipe(
+        recipe, {}, {}, filename_cache
+    )
+
+    assert rematched == 1
+    assert details["matched"][0]["match_level"] == "L4"
+    assert recipe["loras"][0]["hash"] == ("TB1" * 32).lower()
+    assert saved == [recipe]
+
+
+async def test_rematch_l4_entry_base_model_missing_falls_back_to_recipe(
+    tmp_path: Path, monkeypatch
+):
+    # without entry-level baseModel the recipe-level gate governs: a Pony
+    # candidate must not match an SD 1.5 recipe
+    item = _rematch_item(
+        sha256=("TB2" * 32).lower(),
+        sub_type="lora",
+        base_model="Pony",
+        file_name="pony.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+    await _spy_rematch_persistence(scanner, monkeypatch)
+    await _spy_fts(scanner, monkeypatch)
+
+    recipe: Dict[str, Any] = {
+        "id": "r1",
+        "base_model": "SD 1.5",
+        "loras": [{"file_name": "pony.safetensors", "isDeleted": True}],
+    }
+    rematched, _errors, details = await scanner._rematch_single_recipe(
+        recipe, {}, {}, filename_cache
+    )
+
+    assert rematched == 0
+    assert details["unresolved"] == [{"type": "lora", "entry": "pony.safetensors"}]
+
+
+async def test_match_rematch_entry_l4_no_filename_hit(tmp_path: Path):
+    item = _rematch_item(
+        sha256=("TB" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="other.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"file_name": "missing.safetensors", "isDeleted": True},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l4_entry_without_file_name_skipped(tmp_path: Path):
+    item = _rematch_item(
+        sha256=("TC" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, _, _ = _make_rematch_scanner([item], [], tmp_path)
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"isDeleted": True, "hash": ""},
+        {},
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert (matched, level) == (None, None)
+
+
+async def test_match_rematch_entry_l1_wins_over_l4_filename(tmp_path: Path):
+    # a valid stored hash resolves via L1 even when the filename would match
+    sha256 = ("TD" * 32).lower()
+    l1_item = _rematch_item(
+        sha256=sha256, sub_type="lora", base_model="SD 1.5", file_name="l1-item.safetensors"
+    )
+    l4_item = _rematch_item(
+        sha256=("TE" * 32).lower(),
+        sub_type="lora",
+        base_model="SD 1.5",
+        file_name="detail.safetensors",
+    )
+    scanner, lora, _ = _make_rematch_scanner([l1_item, l4_item], [], tmp_path)
+    local_cache = await scanner.build_local_hash_cache()
+    filename_cache = await scanner._build_local_filename_cache()
+
+    matched, level = await scanner._match_rematch_entry_with_level(
+        {"hash": sha256, "file_name": "detail.safetensors", "isDeleted": True},
+        local_cache,
+        {},
+        is_checkpoint=False,
+        filename_cache=filename_cache,
+        recipe_base_model="SD 1.5",
+    )
+
+    assert matched is lora._cache.raw_data[0]
+    assert level == "L1"
+
+
+# _build_local_filename_cache
+
+
+async def test_build_local_filename_cache_normalized_keys_sha256_only(tmp_path: Path):
+    lora_items = [
+        _rematch_item(sha256=("TF" * 32).lower(), file_name="Case.Mix.safetensors"),
+        _rematch_item(sha256="", file_name="no-hash.safetensors"),  # skipped
+    ]
+    checkpoint_items = [
+        _rematch_item(
+            sha256=("TG" * 32).lower(), sub_type="checkpoint", file_name="Base.safetensors"
+        )
+    ]
+    scanner, lora, checkpoint = _make_rematch_scanner(
+        lora_items, checkpoint_items, tmp_path
+    )
+
+    result = await scanner._build_local_filename_cache()
+
+    assert set(result) == {"case.mix", "base"}
+    assert len(result["case.mix"]) == 1
+    assert result["case.mix"][0] is lora._cache.raw_data[0]
+    # checkpoint items are indexed too (type-blind cache)
+    assert result["base"][0] is checkpoint._cache.raw_data[0]
+
+
 # _build_rematch_autov3_cache
 
 
@@ -3089,6 +3565,7 @@ async def test_rematch_all_recipes_per_recipe_error_continues_loop(
         recipe: Dict[str, Any],
         local_cache: dict[str, Any],
         autov3_cache: dict[str, Any],
+        filename_cache=None,
     ) -> tuple[int, int, dict[str, Any]]:
         if recipe.get("id") == "boom":
             raise RuntimeError("kaboom")
@@ -3146,12 +3623,13 @@ async def test_rematch_all_recipes_holds_mutation_lock(tmp_path: Path, monkeypat
         recipe: Dict[str, Any],
         local_cache: dict[str, Any],
         autov3_cache: dict[str, Any],
-    ) -> tuple[int, int]:
+        filename_cache=None,
+    ) -> tuple[int, int, dict[str, Any]]:
         nonlocal entered
         if recipe.get("id") == "r0":
             entered = True
             await release.wait()
-        return await original(recipe, local_cache, autov3_cache)
+        return await original(recipe, local_cache, autov3_cache, filename_cache)
 
     monkeypatch.setattr(scanner, "_rematch_single_recipe", blocking_single)
 
@@ -3271,6 +3749,8 @@ async def test_rematch_bulk_generic_exception_continues(tmp_path: Path, monkeypa
         autov3_cache: dict[str, Any],
         *,
         is_checkpoint: bool,
+        filename_cache=None,
+        recipe_base_model=None,
     ) -> Any:
         nonlocal calls
         calls += 1