feat: Introduce generation parameter merging from request, Civitai, and embedded image metadata, and enhance ComfyUI metadata parsing.

py/recipes/merger.py

@@ -0,0 +1,50 @@
+from typing import Any, Dict, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+class GenParamsMerger:
+    """Utility to merge generation parameters from multiple sources with priority."""
+
+    BLACKLISTED_KEYS = {"id", "url", "userId", "username", "createdAt", "updatedAt", "hash"}
+
+    @staticmethod
+    def merge(
+        request_params: Optional[Dict[str, Any]] = None,
+        civitai_meta: Optional[Dict[str, Any]] = None,
+        embedded_metadata: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        Merge generation parameters from three sources.
+        
+        Priority: request_params > civitai_meta > embedded_metadata
+        
+        Args:
+            request_params: Params provided directly in the import request
+            civitai_meta: Params from Civitai Image API 'meta' field
+            embedded_metadata: Params extracted from image EXIF/embedded metadata
+            
+        Returns:
+            Merged parameters dictionary
+        """
+        result = {}
+
+        # 1. Start with embedded metadata (lowest priority)
+        if embedded_metadata:
+            # If it's a full recipe metadata, we use its gen_params
+            if "gen_params" in embedded_metadata and isinstance(embedded_metadata["gen_params"], dict):
+                result.update(embedded_metadata["gen_params"])
+            else:
+                # Otherwise assume the dict itself contains gen_params
+                result.update(embedded_metadata)
+
+        # 2. Layer Civitai meta (medium priority)
+        if civitai_meta:
+            result.update(civitai_meta)
+
+        # 3. Layer request params (highest priority)
+        if request_params:
+            result.update(request_params)
+
+        # Filter out blacklisted keys
+        return {k: v for k, v in result.items() if k not in GenParamsMerger.BLACKLISTED_KEYS}

py/recipes/parsers/comfy.py

@@ -36,9 +36,6 @@ class ComfyMetadataParser(RecipeMetadataParser):
             # Find all LoraLoader nodes
             lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
             
-            if not lora_nodes:
-                return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
-            
             # Process each LoraLoader node
             for node_id, node in lora_nodes.items():
                 if 'inputs' not in node or 'lora_name' not in node['inputs']:

py/routes/handlers/recipe_handlers.py

@@ -24,6 +24,8 @@ from ...services.recipes import (
 )
 from ...services.metadata_service import get_default_metadata_provider
 from ...utils.civitai_utils import rewrite_preview_url
+from ...utils.exif_utils import ExifUtils
+from ...recipes.merger import GenParamsMerger
 
 Logger = logging.Logger
 EnsureDependenciesCallable = Callable[[], Awaitable[None]]
@@ -552,7 +554,41 @@ class RecipeManagementHandler:
                     metadata["base_model"] = base_model_from_metadata
 
             tags = self._parse_tags(params.get("tags"))
-            image_bytes, extension = await self._download_remote_media(image_url)
+            image_bytes, extension, civitai_meta = await self._download_remote_media(image_url)
+
+            # Extract embedded metadata from the downloaded image
+            embedded_metadata = None
+            try:
+                with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_img:
+                    temp_img.write(image_bytes)
+                    temp_img_path = temp_img.name
+                
+                try:
+                    raw_embedded = ExifUtils.extract_image_metadata(temp_img_path)
+                    if raw_embedded:
+                        # Try to parse it using standard parsers if it looks like a recipe
+                        parser = self._analysis_service._recipe_parser_factory.create_parser(raw_embedded)
+                        if parser:
+                            parsed_embedded = await parser.parse_metadata(raw_embedded, recipe_scanner=recipe_scanner)
+                            embedded_metadata = parsed_embedded
+                        else:
+                            # Fallback to raw string if no parser matches (might be simple params)
+                            embedded_metadata = {"gen_params": {"raw_metadata": raw_embedded}}
+                finally:
+                    if os.path.exists(temp_img_path):
+                        os.unlink(temp_img_path)
+            except Exception as exc:
+                self._logger.warning("Failed to extract embedded metadata during import: %s", exc)
+
+            # Merge gen_params from all sources
+            merged_gen_params = GenParamsMerger.merge(
+                request_params=gen_params,
+                civitai_meta=civitai_meta,
+                embedded_metadata=embedded_metadata
+            )
+
+            if merged_gen_params:
+                metadata["gen_params"] = merged_gen_params
 
             result = await self._persistence_service.save_recipe(
                 recipe_scanner=recipe_scanner,
@@ -900,7 +936,7 @@ class RecipeManagementHandler:
                 extension = ".webp" # Default to webp if unknown
 
             with open(temp_path, "rb") as file_obj:
-                return file_obj.read(), extension
+                return file_obj.read(), extension, image_info.get("meta") if civitai_match and image_info else None
         except RecipeDownloadError:
             raise
         except RecipeValidationError:

tests/routes/test_recipe_routes.py

@@ -91,6 +91,7 @@ class StubAnalysisService:
         self.remote_calls: List[Optional[str]] = []
         self.local_calls: List[Optional[str]] = []
         self.result = SimpleNamespace(payload={"loras": []}, status=200)
+        self._recipe_parser_factory = None
         StubAnalysisService.instances.append(self)
 
     async def analyze_uploaded_image(self, *, image_bytes: bytes | None, recipe_scanner) -> SimpleNamespace:  # noqa: D401 - mirrors real signature
@@ -527,3 +528,69 @@ async def test_share_and_download_recipe(monkeypatch, tmp_path: Path) -> None:
         assert body == b"stub"
 
         download_path.unlink(missing_ok=True)
+async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path) -> None:
+    # 1. Mock Metadata Provider
+    class Provider:
+        async def get_model_version_info(self, model_version_id):
+            return {"baseModel": "Flux Provider"}, None
+
+    async def fake_get_default_metadata_provider():
+        return Provider()
+
+    monkeypatch.setattr(recipe_handlers, "get_default_metadata_provider", fake_get_default_metadata_provider)
+
+    # 2. Mock ExifUtils to return some embedded metadata
+    class MockExifUtils:
+        @staticmethod
+        def extract_image_metadata(path):
+            return "Recipe metadata: " + json.dumps({
+                "gen_params": {"prompt": "from embedded", "seed": 123}
+            })
+
+    monkeypatch.setattr(recipe_handlers, "ExifUtils", MockExifUtils)
+
+    # 3. Mock Parser Factory for StubAnalysisService
+    class MockParser:
+        async def parse_metadata(self, raw, recipe_scanner=None):
+            return json.loads(raw[len("Recipe metadata: "):])
+
+    class MockFactory:
+        def create_parser(self, raw):
+            if raw.startswith("Recipe metadata: "):
+                return MockParser()
+            return None
+
+    # 4. Setup Harness and run test
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        harness.analysis._recipe_parser_factory = MockFactory()
+        
+        # Civitai meta via image_info
+        harness.civitai.image_info["1"] = {
+            "id": 1,
+            "url": "https://example.com/images/1.jpg",
+            "meta": {"prompt": "from civitai", "cfg": 7.0}
+        }
+
+        resources = []
+        response = await harness.client.get(
+            "/api/lm/recipes/import-remote",
+            params={
+                "image_url": "https://civitai.com/images/1",
+                "name": "Merged Recipe",
+                "resources": json.dumps(resources),
+                "gen_params": json.dumps({"prompt": "from request", "steps": 25}),
+            },
+        )
+
+        payload = await response.json()
+        assert response.status == 200
+        
+        call = harness.persistence.save_calls[-1]
+        metadata = call["metadata"]
+        gen_params = metadata["gen_params"]
+        
+        # Priority: request (prompt=request, steps=25) > civitai (prompt=civitai, cfg=7.0) > embedded (prompt=embedded, seed=123)
+        assert gen_params["prompt"] == "from request"
+        assert gen_params["steps"] == 25
+        assert gen_params["cfg"] == 7.0
+        assert gen_params["seed"] == 123

tests/services/test_comfy_metadata_parser.py

@@ -0,0 +1,113 @@
+import pytest
+import json
+from py.recipes.parsers.comfy import ComfyMetadataParser
+
+@pytest.mark.asyncio
+async def test_parse_metadata_without_loras(monkeypatch):
+    checkpoint_info = {
+        "id": 2224012,
+        "modelId": 1908679,
+        "model": {"name": "SDXL Checkpoint", "type": "checkpoint"},
+        "name": "v1.0",
+        "images": [{"url": "https://image.civitai.com/checkpoints/original=true"}],
+        "baseModel": "sdxl",
+        "downloadUrl": "https://civitai.com/api/download/checkpoint",
+    }
+
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_version_info(self, version_id):
+                assert version_id == "2224012"
+                return checkpoint_info, None
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.comfy.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = ComfyMetadataParser()
+
+    # User provided metadata
+    metadata_json = {
+        "resource-stack": {
+            "class_type": "CheckpointLoaderSimple",
+            "inputs": {"ckpt_name": "urn:air:sdxl:checkpoint:civitai:1908679@2224012"}
+        },
+        "6": {
+            "class_type": "smZ CLIPTextEncode",
+            "inputs": {"text": "Positive prompt content"},
+            "_meta": {"title": "Positive"}
+        },
+        "7": {
+            "class_type": "smZ CLIPTextEncode",
+            "inputs": {"text": "Negative prompt content"},
+            "_meta": {"title": "Negative"}
+        },
+        "11": {
+            "class_type": "KSampler",
+            "inputs": {
+                "sampler_name": "euler_ancestral",
+                "scheduler": "normal",
+                "seed": 904124997,
+                "steps": 35,
+                "cfg": 6,
+                "denoise": 0.1,
+                "model": ["resource-stack", 0],
+                "positive": ["6", 0],
+                "negative": ["7", 0],
+                "latent_image": ["21", 0]
+            },
+            "_meta": {"title": "KSampler"}
+        },
+        "extraMetadata": json.dumps({
+            "prompt": "One woman, (solo:1.3), ...",
+            "negativePrompt": "lowres, worst quality, ...",
+            "steps": 35,
+            "cfgScale": 6,
+            "sampler": "euler_ancestral",
+            "seed": 904124997,
+            "width": 1024,
+            "height": 1024
+        })
+    }
+
+    result = await parser.parse_metadata(json.dumps(metadata_json))
+
+    assert "error" not in result
+    assert result["loras"] == []
+    assert result["checkpoint"] is not None
+    assert int(result["checkpoint"]["modelId"]) == 1908679
+    assert int(result["checkpoint"]["id"]) == 2224012
+    assert result["gen_params"]["prompt"] == "One woman, (solo:1.3), ..."
+    assert result["gen_params"]["steps"] == 35
+    assert result["gen_params"]["size"] == "1024x1024"
+    assert result["from_comfy_metadata"] is True
+
+@pytest.mark.asyncio
+async def test_parse_metadata_without_extra_metadata(monkeypatch):
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_version_info(self, version_id):
+                return {"model": {"name": "Test"}, "id": version_id}, None
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.comfy.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = ComfyMetadataParser()
+
+    metadata_json = {
+        "node_1": {
+            "class_type": "CheckpointLoaderSimple",
+            "inputs": {"ckpt_name": "urn:air:sdxl:checkpoint:civitai:123@456"}
+        }
+    }
+
+    result = await parser.parse_metadata(json.dumps(metadata_json))
+
+    assert "error" not in result
+    assert result["loras"] == []
+    assert result["checkpoint"]["id"] == "456"

tests/services/test_gen_params_merger.py

@@ -0,0 +1,59 @@
+import pytest
+from py.recipes.merger import GenParamsMerger
+
+def test_merge_priority():
+    request_params = {"prompt": "from request", "steps": 20}
+    civitai_meta = {"prompt": "from civitai", "cfg": 7.0}
+    embedded_metadata = {"gen_params": {"prompt": "from embedded", "seed": 123}}
+    
+    merged = GenParamsMerger.merge(request_params, civitai_meta, embedded_metadata)
+    
+    assert merged["prompt"] == "from request"
+    assert merged["steps"] == 20
+    assert merged["cfg"] == 7.0
+    assert merged["seed"] == 123
+
+def test_merge_no_request_params():
+    civitai_meta = {"prompt": "from civitai", "cfg": 7.0}
+    embedded_metadata = {"gen_params": {"prompt": "from embedded", "seed": 123}}
+    
+    merged = GenParamsMerger.merge(None, civitai_meta, embedded_metadata)
+    
+    assert merged["prompt"] == "from civitai"
+    assert merged["cfg"] == 7.0
+    assert merged["seed"] == 123
+
+def test_merge_only_embedded():
+    embedded_metadata = {"gen_params": {"prompt": "from embedded", "seed": 123}}
+    
+    merged = GenParamsMerger.merge(None, None, embedded_metadata)
+    
+    assert merged["prompt"] == "from embedded"
+    assert merged["seed"] == 123
+
+def test_merge_raw_embedded():
+    # Test when embedded metadata is just the gen_params themselves
+    embedded_metadata = {"prompt": "from raw embedded", "seed": 456}
+    
+    merged = GenParamsMerger.merge(None, None, embedded_metadata)
+    
+    assert merged["prompt"] == "from raw embedded"
+    assert merged["seed"] == 456
+
+def test_merge_none_values():
+    merged = GenParamsMerger.merge(None, None, None)
+    assert merged == {}
+
+def test_merge_filters_blacklisted_keys():
+    request_params = {"prompt": "test", "id": "should-be-removed"}
+    civitai_meta = {"cfg": 7, "url": "remove-me"}
+    embedded_metadata = {"seed": 123, "hash": "remove-also"}
+    
+    merged = GenParamsMerger.merge(request_params, civitai_meta, embedded_metadata)
+    
+    assert "prompt" in merged
+    assert "cfg" in merged
+    assert "seed" in merged
+    assert "id" not in merged
+    assert "url" not in merged
+    assert "hash" not in merged