diff --git a/invokeai/backend/model_manager/configs/lora.py b/invokeai/backend/model_manager/configs/lora.py
index 1d7b4b304e9..ce273fe5bac 100644
--- a/invokeai/backend/model_manager/configs/lora.py
+++ b/invokeai/backend/model_manager/configs/lora.py
@@ -227,6 +227,42 @@ class LoRA_LyCORIS_ZImage_Config(LoRA_LyCORIS_Config_Base, Config_Base):
 
     base: Literal[BaseModelType.ZImage] = Field(default=BaseModelType.ZImage)
 
+    @classmethod
+    def _validate_looks_like_lora(cls, mod: ModelOnDisk) -> None:
+        """Z-Image LoRAs have different key patterns than SD/SDXL LoRAs.
+
+        Z-Image LoRAs use keys like:
+        - diffusion_model.layers.X.attention.to_k.lora_down.weight (DoRA format)
+        - diffusion_model.layers.X.attention.to_k.lora_A.weight (PEFT format)
+        - diffusion_model.layers.X.attention.to_k.dora_scale (DoRA scale)
+        """
+        state_dict = mod.load_state_dict()
+
+        # Check for Z-Image specific LoRA patterns
+        has_z_image_lora_keys = state_dict_has_any_keys_starting_with(
+            state_dict,
+            {
+                "diffusion_model.layers.",  # Z-Image S3-DiT layer pattern
+            },
+        )
+
+        # Also check for LoRA weight suffixes (various formats)
+        has_lora_suffix = state_dict_has_any_keys_ending_with(
+            state_dict,
+            {
+                "lora_A.weight",
+                "lora_B.weight",
+                "lora_down.weight",
+                "lora_up.weight",
+                "dora_scale",
+            },
+        )
+
+        if has_z_image_lora_keys and has_lora_suffix:
+            return
+
+        raise NotAMatchError("model does not match Z-Image LoRA heuristics")
+
     @classmethod
     def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
         """Z-Image LoRAs are identified by their diffusion_model.layers structure.
diff --git a/invokeai/backend/patches/lora_conversions/z_image_lora_conversion_utils.py b/invokeai/backend/patches/lora_conversions/z_image_lora_conversion_utils.py
index 84e5a21c022..e248f9cfc4d 100644
--- a/invokeai/backend/patches/lora_conversions/z_image_lora_conversion_utils.py
+++ b/invokeai/backend/patches/lora_conversions/z_image_lora_conversion_utils.py
@@ -140,16 +140,50 @@ def _get_lora_layer_values(layer_dict: dict[str, torch.Tensor], alpha: float | N
 
 
 def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:
-    """Groups the keys in the state dict by layer."""
+    """Groups the keys in the state dict by layer.
+
+    Z-Image LoRAs have keys like:
+    - diffusion_model.layers.17.attention.to_k.alpha
+    - diffusion_model.layers.17.attention.to_k.dora_scale
+    - diffusion_model.layers.17.attention.to_k.lora_down.weight
+    - diffusion_model.layers.17.attention.to_k.lora_up.weight
+
+    We need to group these by the full layer path (e.g., diffusion_model.layers.17.attention.to_k)
+    and extract the suffix (alpha, dora_scale, lora_down.weight, lora_up.weight).
+    """
     layer_dict: dict[str, dict[str, torch.Tensor]] = {}
+
+    # Known suffixes that indicate the end of a layer name
+    known_suffixes = [
+        ".lora_A.weight",
+        ".lora_B.weight",
+        ".lora_down.weight",
+        ".lora_up.weight",
+        ".dora_scale",
+        ".alpha",
+    ]
+
     for key in state_dict:
         if not isinstance(key, str):
             continue
-        # Split the 'lora_A.weight' or 'lora_B.weight' suffix from the layer name.
-        parts = key.rsplit(".", maxsplit=2)
-        layer_name = parts[0]
-        key_name = ".".join(parts[1:])
+
+        # Try to find a known suffix
+        layer_name = None
+        key_name = None
+        for suffix in known_suffixes:
+            if key.endswith(suffix):
+                layer_name = key[: -len(suffix)]
+                key_name = suffix[1:]  # Remove leading dot
+                break
+
+        if layer_name is None:
+            # Fallback to original logic for unknown formats
+            parts = key.rsplit(".", maxsplit=2)
+            layer_name = parts[0]
+            key_name = ".".join(parts[1:])
+
         if layer_name not in layer_dict:
             layer_dict[layer_name] = {}
         layer_dict[layer_name][key_name] = state_dict[key]
+
     return layer_dict