fix(parser): merge Lora hashes over empty Hashes JSON values and skip entries without hash

py/recipes/parsers/automatic.py

@@ -123,24 +123,39 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         if model_hash_from_hashes:
                             metadata["model_hash"] = model_hash_from_hashes
                     
-                    # Extract Lora hashes in alternative format
+                    # Extract Lora hashes in alternative format.
+                    # Run unconditionally (not just as fallback) so that
+                    # non-empty hashes from Lora hashes fill in the gaps left
+                    # by empty values in the Hashes JSON dict.  Some WebUI
+                    # builds write real hash values only to Lora hashes and
+                    # leave the Hashes JSON values empty.
                     lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
-                    if not hashes_match and lora_hashes_match:
+                    if lora_hashes_match:
                         try:
                             lora_hashes_str = lora_hashes_match.group(1)
                             lora_hash_entries = lora_hashes_str.split(', ')
-                            
-                            # Initialize hashes dict if it doesn't exist
-                            if "hashes" not in metadata:
-                                metadata["hashes"] = {}
-                                
+
                             # Parse each lora hash entry (format: "name: hash")
                             for entry in lora_hash_entries:
                                 if ': ' in entry:
                                     lora_name, lora_hash = entry.split(': ', 1)
-                                    # Add as lora type in the same format as regular hashes
-                                    metadata["hashes"][f"lora:{lora_name}"] = lora_hash.strip()
-                            
+                                    lora_hash = lora_hash.strip()
+                                    if not lora_hash:
+                                        # Skip entries without a hash value
+                                        continue
+                                    # Initialize hashes dict if it doesn't exist
+                                    if "hashes" not in metadata:
+                                        metadata["hashes"] = {}
+                                    # Add as lora type in the same format as
+                                    # regular hashes.  Only override an
+                                    # existing entry if its value is empty
+                                    # (Lora hashes is the more reliable
+                                    # source when Hashes JSON has blanks).
+                                    key = f"lora:{lora_name}"
+                                    existing = metadata["hashes"].get(key, "")
+                                    if not existing:
+                                        metadata["hashes"][key] = lora_hash
+
                             # Remove lora hashes from params section
                             params_section = params_section.replace(lora_hashes_match.group(0), '')
                         except Exception as e:
@@ -362,6 +377,12 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         # Only process lora or hypernet types
                         if not hash_key.startswith(("lora:", "hypernet:")):
                             continue
+                        
+                        # Skip entries without a hash value — they can't be
+                        # resolved via CivitAI and would only produce a
+                        # useless "Deleted" entry in the recipe.
+                        if not lora_hash:
+                            continue
                             
                         lora_type, lora_name = hash_key.split(':', 1)
                         
@@ -387,11 +408,7 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                         # Try to get info from Civitai
                         if metadata_provider:
                             try:
-                                if lora_hash:
-                                    # If we have hash, use it for lookup
-                                    civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
-                                else:
-                                    civitai_info = None
+                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
                                 
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry, 

tests/services/test_automatic_metadata_parser.py

@@ -64,6 +64,74 @@ async def test_parse_metadata_extracts_checkpoint_from_civitai_resources(monkeyp
     assert result["loras"] == []
 
 
+@pytest.mark.asyncio
+async def test_parse_metadata_merges_lora_hashes_over_empty_hashes_json(monkeypatch):
+    """When Hashes JSON has empty lora hashes but Lora hashes text field has
+    real ones, the real hashes should be used and those LoRAs resolved
+    correctly; entries with empty hashes in both sources should be skipped."""
+    lora_version_info = {
+        "id": 947620,
+        "modelId": 98765,
+        "model": {"name": "cfg_scale_boost", "type": "LORA"},
+        "name": "v1",
+        "images": [{"url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/original=true"}],
+        "baseModel": "illustrious",
+        "downloadUrl": "https://civitai.com/api/download/models/947620",
+        "files": [
+            {
+                "type": "Model",
+                "primary": True,
+                "sizeKB": 1024,
+                "name": "cfg_scale_boost.safetensors",
+                "hashes": {"SHA256": "4605b2de07"},
+            }
+        ],
+    }
+
+    async def fake_metadata_provider():
+        class Provider:
+            async def get_model_by_hash(self, model_hash):
+                assert model_hash == "4605b2de07"
+                return lora_version_info, None
+
+            async def get_model_version_info(self, version_id):
+                raise AssertionError("get_model_version_info should not be called")
+
+        return Provider()
+
+    monkeypatch.setattr(
+        "py.recipes.parsers.automatic.get_default_metadata_provider",
+        fake_metadata_provider,
+    )
+
+    parser = AutomaticMetadataParser()
+
+    metadata_text = (
+        "a cyberpunk portrait <lora:cfg_scale_boost:0.6>\n"
+        "Negative prompt: low quality\n"
+        "Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 123456, Size: 512x768, "
+        "Model hash: abc123, Model: test.safetensors, "
+        'Lora hashes: "cfg_scale_boost: 4605b2de07, EmptyLora: ", '
+        'Hashes: {"model": "abc123", "lora:cfg_scale_boost": "", "lora:EmptyLora": "", "lora:UnusedLora": ""}'
+    )
+
+    result = await parser.parse_metadata(metadata_text)
+
+    # cfg_scale_boost should be resolved (hash from Lora hashes overrode empty Hashes JSON)
+    loras = result.get("loras", [])
+    assert len(loras) == 1, f"Expected 1 LoRA, got {len(loras)}"
+    lora = loras[0]
+    assert lora["name"] == "cfg_scale_boost", f"Expected cfg_scale_boost, got {lora['name']}"
+    assert lora["hash"] == "4605b2de07", f"Expected hash 4605b2de07, got {lora['hash']}"
+    assert lora.get("isDeleted") in (None, False), f"LoRA should not be deleted"
+    assert lora["weight"] == 0.6, f"Expected weight 0.6, got {lora['weight']}"
+
+    # EmptyLora and UnusedLora should be skipped (no hash in either source)
+    lora_names = [l["name"] for l in loras]
+    assert "EmptyLora" not in lora_names, "EmptyLora should have been skipped"
+    assert "UnusedLora" not in lora_names, "UnusedLora should have been skipped"
+
+
 @pytest.mark.asyncio
 async def test_parse_metadata_extracts_checkpoint_from_model_hash(monkeypatch):
     checkpoint_info = {