Support CR Apply ControlNet conditioning passthrough

smart_metadata_reader/adapters/conditioning.py

@@ -4,12 +4,16 @@ from ..models import NodeRecord
 
 
 ROLE_FIELD_ORDER = {
-    "positive": ("positive", "positive_conditioning", "base_positive"),
-    "negative": ("negative", "negative_conditioning", "base_negative"),
+    "positive": ("conditioning", "positive", "positive_conditioning", "base_positive"),
+    "negative": ("negative", "negative_conditioning", "base_negative", "conditioning"),
 }
 
 KNOWN_CONDITIONING_MARKERS = (
     "controlnetapply",
+    "controlnet",
+    "control_net",
+    "apply controlnet",
+    "comfyroll",
     "inpaintmodelconditioning",
     "conditioningcombine",
     "conditioningconcat",
@@ -24,7 +28,9 @@ KNOWN_CONDITIONING_MARKERS = (
 class ConditioningAdapter:
     def matches(self, node: NodeRecord) -> bool:
         lowered = node.class_type.lower()
-        return any(marker in lowered for marker in KNOWN_CONDITIONING_MARKERS)
+        if any(marker in lowered for marker in KNOWN_CONDITIONING_MARKERS):
+            return any(_is_conditioning_field(field) for field in node.inputs)
+        return False
 
     def fields_for_role(self, node: NodeRecord, role: str) -> list[str]:
         role_fields = [

tests/test_conditioning_resolver.py

@@ -64,6 +64,75 @@ def test_resolves_controlnet_apply_advanced_passthrough():
     assert segment_texts(resolver.resolve_positive(sampler)) == ["advanced positive"]
 
 
+def test_resolves_cr_apply_controlnet_conditioning_passthrough_without_parameters():
+    prompt = {
+        "1": {"class_type": "KSampler", "inputs": {"positive": ["150", 0]}},
+        "2": {"class_type": "CLIPTextEncode", "inputs": {"text": "real positive prompt"}},
+        "3": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": "control_net"}},
+        "150": {
+            "class_type": "CR Apply ControlNet",
+            "inputs": {
+                "conditioning": ["2", 0],
+                "image": "control_image.png",
+                "strength": 0.3,
+                "control_net": ["3", 0],
+            },
+        },
+    }
+
+    resolver, sampler = resolver_for(prompt)
+    segments = resolver.resolve_positive(sampler)
+    positive = merge_prompt_segments(segments)
+
+    assert positive == "real positive prompt"
+    assert "control_image.png" not in positive
+    assert "0.3" not in positive
+    assert "control_net" not in positive
+    assert "CR Apply ControlNet 150" in resolver.debug_trace
+
+
+def test_resolves_cr_apply_controlnet_complex_upstream_text_chain():
+    prompt = {
+        "1": {"class_type": "KSampler", "inputs": {"positive": ["150", 0]}},
+        "2": {"class_type": "CLIPTextEncode", "inputs": {"text": ["3", 0]}},
+        "3": {
+            "class_type": "StringFunction|pysssss",
+            "inputs": {
+                "action": "append",
+                "text_a": "role_lora_trigger, character_name,",
+                "text_b": ["4", 0],
+                "text_c": ["5", 0],
+            },
+        },
+        "4": {
+            "class_type": "ShowText|pysssss",
+            "inputs": {"text_0": "1girl, solo, stage lights"},
+        },
+        "5": {
+            "class_type": "DeepTranslatorTextNode",
+            "inputs": {"text": "nude, huge breasts"},
+        },
+        "150": {
+            "class_type": "CR Apply ControlNet",
+            "inputs": {
+                "conditioning": ["2", 0],
+                "image": "control_image.png",
+                "strength": 0.3,
+            },
+        },
+    }
+
+    resolver, sampler = resolver_for(prompt)
+    positive = merge_prompt_segments(resolver.resolve_positive(sampler))
+
+    assert "role_lora_trigger" in positive
+    assert "character_name" in positive
+    assert "1girl, solo, stage lights" in positive
+    assert "nude, huge breasts" in positive
+    assert "control_image.png" not in positive
+    assert "0.3" not in positive
+
+
 def test_resolves_inpaint_model_conditioning_passthrough():
     prompt = {
         "1": {"class_type": "KSampler", "inputs": {"positive": ["3", 0]}},