Add support for diff/full lora layers

invokeai/backend/model_management/lora.py

@@ -325,6 +325,43 @@ class LoKRLayer(LoRALayerBase):
             self.t2 = self.t2.to(device=device, dtype=dtype)
 
 
+class FullLayer(LoRALayerBase):
+    # weight: torch.Tensor
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: dict,
+    ):
+        super().__init__(layer_key, values)
+
+        self.weight = values["diff"]
+
+        if len(values.keys()) > 1:
+            _keys = list(values.keys())
+            _keys.remove("diff")
+            raise NotImplementedError(f"Unexpected keys in lora diff layer: {_keys}")
+
+        self.rank = None  # unscaled
+
+    def get_weight(self):
+        return self.weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        model_size += self.weight.nelement() * self.weight.element_size()
+        return model_size
+
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        super().to(device=device, dtype=dtype)
+
+        self.weight = self.weight.to(device=device, dtype=dtype)    
+
+
 class LoRAModel:  # (torch.nn.Module):
     _name: str
     layers: Dict[str, LoRALayer]
@@ -412,10 +449,13 @@ class LoRAModel:  # (torch.nn.Module):
             elif "lokr_w1_b" in values or "lokr_w1" in values:
                 layer = LoKRLayer(layer_key, values)
 
+            elif "diff" in values:
+                layer = FullLayer(layer_key, values)
+
             else:
-                # TODO: diff/ia3/... format
-                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key}")
-                return
+                # TODO: ia3/... format
+                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
+                raise Exception("Unknown lora format!")
 
             # lower memory consumption by removing already parsed layer values
             state_dict[layer_key].clear()