diff --git a/invokeai/backend/lora/conversions/flux_control_lora_utils.py b/invokeai/backend/lora/conversions/flux_control_lora_utils.py
index b52ad47a0b..1f3222a1be 100644
--- a/invokeai/backend/lora/conversions/flux_control_lora_utils.py
+++ b/invokeai/backend/lora/conversions/flux_control_lora_utils.py
@@ -14,7 +14,7 @@ from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 #   guidance_in.in_layer.lora_B.bias
 #   single_blocks.0.linear1.lora_A.weight
 #   double_blocks.0.img_attn.norm.key_norm.scale
-FLUX_CONTROL_TRANSFORMER_KEY_REGEX = r"(final_layer|vector_in|txt_in|time_in|img_in|guidance_in|\w+_blocks)(\.(\d+))?\.(lora_(A|B)|(in|out)_layer|adaLN_modulation|img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear|linear1|linear2|modulation|norm)\.?(.*)"
+FLUX_CONTROL_TRANSFORMER_KEY_REGEX = r"(\w+\.)+(lora_A\.weight|lora_B\.weight|lora_B\.bias|scale)"
 
 
 def is_state_dict_likely_flux_control(state_dict: Dict[str, Any]) -> bool:
@@ -23,7 +23,23 @@ def is_state_dict_likely_flux_control(state_dict: Dict[str, Any]) -> bool:
     This is intended to be a high-precision detector, but it is not guaranteed to have perfect precision. (A
     perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
     """
-    return all(re.match(FLUX_CONTROL_TRANSFORMER_KEY_REGEX, k) for k in state_dict.keys())
+
+    all_keys_match = all(re.match(FLUX_CONTROL_TRANSFORMER_KEY_REGEX, k) for k in state_dict.keys())
+
+    # Check the shape of the img_in weight, because this layer shape is modified by FLUX control LoRAs.
+    lora_a_weight = state_dict.get("img_in.lora_A.weight", None)
+    lora_b_bias = state_dict.get("img_in.lora_B.bias", None)
+    lora_b_weight = state_dict.get("img_in.lora_B.weight", None)
+
+    return (
+        all_keys_match
+        and lora_a_weight is not None
+        and lora_b_bias is not None
+        and lora_b_weight is not None
+        and lora_a_weight.shape[1] == 128
+        and lora_b_weight.shape[0] == 3072
+        and lora_b_bias.shape[0] == 3072
+    )
 
 
 def lora_model_from_flux_control_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py
index 268c94f410..b03367147b 100644
--- a/invokeai/backend/model_manager/probe.py
+++ b/invokeai/backend/model_manager/probe.py
@@ -259,17 +259,8 @@ class ModelProbe(object):
         ckpt = checkpoint if checkpoint else read_checkpoint_meta(model_path, scan=True)
         ckpt = ckpt.get("state_dict", ckpt)
 
-        if isinstance(ckpt, dict) and "img_in.lora_A.weight" in ckpt and "img_in.lora_B.weight" in ckpt:
-            tensor_a, tensor_b = ckpt["img_in.lora_A.weight"], ckpt["img_in.lora_B.weight"]
-            if (
-                tensor_a is not None
-                and isinstance(tensor_a, torch.Tensor)
-                and tensor_a.shape[1] == 128
-                and tensor_b is not None
-                and isinstance(tensor_b, torch.Tensor)
-                and tensor_b.shape[0] == 3072
-            ):
-                return ModelType.ControlLoRa
+        if isinstance(ckpt, dict) and is_state_dict_likely_flux_control(ckpt):
+            return ModelType.ControlLoRa
 
         for key in [str(k) for k in ckpt.keys()]:
             if key.startswith(