diff --git a/terratorch/models/__init__.py b/terratorch/models/__init__.py
index 3770465f..95bf674a 100644
--- a/terratorch/models/__init__.py
+++ b/terratorch/models/__init__.py
@@ -2,7 +2,6 @@
 
 
 import logging
-
 import terratorch.models.necks  # register necks  # noqa: F401
 from terratorch.models.encoder_decoder_factory import EncoderDecoderFactory
 from terratorch.models.generic_unet_model_factory import GenericUnetModelFactory
@@ -11,24 +10,26 @@
 from terratorch.models.satmae_model_factory import SatMAEModelFactory
 from terratorch.models.smp_model_factory import SMPModelFactory
 from terratorch.models.timm_model_factory import TimmModelFactory
+from terratorch.models.full_model_factory import FullModelFactory
 
 try:
+    granitewcx = True
     from terratorch.models.wxc_model_factory import WxCModelFactory
 except ImportError:
-    import logging
     logging.getLogger("terratorch").debug("granitewcx not installed")
+    granitewcx = False
 
 __all__ = (
     "PrithviModelFactory",
     "ClayModelFactory",
     "SatMAEModelFactory",
-    "ScaleMAEModelFactory",
     "SMPModelFactory",
     "GenericUnetModelFactory",
     "TimmModelFactory",
-    "AuxiliaryHead",
-    "AuxiliaryHeadWithDecoderWithoutInstantiatedHead",
-    "UNet",
     "WxCModelFactory",
-    "EncoderDecoderFactory"
+    "EncoderDecoderFactory",
+    "FullModelFactory",
 )
+
+if granitewcx:
+    __all__.__add__((WxCModelFactory,))
diff --git a/terratorch/models/full_model_factory.py b/terratorch/models/full_model_factory.py
new file mode 100644
index 00000000..83839b2c
--- /dev/null
+++ b/terratorch/models/full_model_factory.py
@@ -0,0 +1,82 @@
+# Copyright contributors to the Terratorch project
+
+import warnings
+from torch import nn
+
+from terratorch.models.model import ModelFactory
+from terratorch.models.peft_utils import get_peft_backbone
+from terratorch.registry import FULL_MODEL_REGISTRY, MODEL_FACTORY_REGISTRY
+
+
+def _get_model(model: str | nn.Module, **model_kwargs) -> nn.Module:
+    if isinstance(model, nn.Module):
+        return model
+    return FULL_MODEL_REGISTRY.build(model, **model_kwargs)
+
+
+def _check_all_args_used(kwargs):
+    if kwargs:
+        msg = f"arguments {kwargs} were passed but not used."
+        raise ValueError(msg)
+
+
+@MODEL_FACTORY_REGISTRY.register
+class FullModelFactory(ModelFactory):
+    def build_model(
+        self,
+        model: str | nn.Module,
+        rescale: bool = True,  # noqa: FBT002, FBT001
+        padding: str = "reflect",
+        peft_config: dict | None = None,
+        **kwargs,
+    ) -> nn.Module:
+        """Generic model factory that wraps any model.
+
+        All kwargs are passed to the model.
+
+        Args:
+            task (str): Task to be performed. Currently supports "segmentation" and "regression".
+            model (str, nn.Module): Model to be used. If a string, will look for such models in the different
+                registries supported (internal terratorch registry, ...). If a torch nn.Module, will use it
+                directly.
+            rescale (bool): Whether to apply bilinear interpolation to rescale the model output if its size
+                is different from the ground truth. Only applicable to pixel wise models
+                (e.g. segmentation, pixel wise regression, reconstruction). Defaults to True.
+            padding (str): Padding method used if images are not divisible by the patch size. Defaults to "reflect".
+            peft_config (dict): Configuration options for using [PEFT](https://huggingface.co/docs/peft/index).
+                The dictionary should have the following keys:
+                - "method": Which PEFT method to use. Should be one implemented in PEFT, a list is available [here](https://huggingface.co/docs/peft/package_reference/peft_types#peft.PeftType).
+                - "replace_qkv": String containing a substring of the name of the submodules to replace with QKVSep.
+                  This should be used when the qkv matrices are merged together in a single linear layer and the PEFT
+                  method should be applied separately to query, key and value matrices (e.g. if LoRA is only desired in
+                  Q and V matrices). e.g. If using Prithvi this should be "qkv"
+                - "peft_config_kwargs": Dictionary containing keyword arguments which will be passed to [PeftConfig](https://huggingface.co/docs/peft/package_reference/config#peft.PeftConfig)
+
+
+        Returns:
+            nn.Module: Full model.
+        """
+
+        model = _get_model(model, **kwargs)
+
+        # If patch size is not provided in the config or by the model, it might lead to errors due to irregular images.
+        patch_size = kwargs.get("patch_size", None)
+
+        if patch_size is None:
+            # Infer patch size from model by checking all backbone modules
+            for module in model.modules():
+                if hasattr(module, "patch_size"):
+                    patch_size = module.patch_size
+                    break
+
+        if peft_config is not None:
+            if not kwargs.get("pretrained", False):
+                msg = (
+                    "You are using PEFT without a pretrained backbone. If you are loading a checkpoint afterwards "
+                    "this is probably fine, but if you are training a model check the backbone_pretrained parameter."
+                )
+                warnings.warn(msg, stacklevel=1)
+
+            model = get_peft_backbone(peft_config, model)
+
+        return model