diff --git a/invokeai/backend/patches/layers/partial_layer.py b/invokeai/backend/patches/layers/partial_layer.py
index 9d77d92f41..1a3135e58a 100644
--- a/invokeai/backend/patches/layers/partial_layer.py
+++ b/invokeai/backend/patches/layers/partial_layer.py
@@ -4,6 +4,8 @@ import torch
 
 from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
 from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.param_shape_utils import get_param_shape
+from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 
 
 @dataclass
@@ -28,6 +30,18 @@ class PartialLayer(BaseLayerPatch):
         self.range = range
 
     def get_parameters(self, orig_parameters: dict[str, torch.Tensor], weight: float) -> dict[str, torch.Tensor]:
+        # HACK(ryand): If the original parameters are in a quantized format that can't be sliced, we replace them with
+        # dummy tensors on the 'meta' device. This allows sub-layers to access the shapes of the sliced parameters. But,
+        # of course, any sub-layers that need to access the actual values of the parameters will fail.
+        for param_name in orig_parameters.keys():
+            param = orig_parameters[param_name]
+            if type(param) is torch.nn.Parameter and type(param.data) is torch.Tensor:
+                pass
+            elif type(param) is GGMLTensor:
+                pass
+            else:
+                orig_parameters[param_name] = torch.empty(get_param_shape(param), device="meta")
+
         # Slice the original parameters to the specified range.
         sliced_parameters: dict[str, torch.Tensor] = {}
         for param_name, param_weight in orig_parameters.items():
@@ -47,7 +61,9 @@ class PartialLayer(BaseLayerPatch):
         out_params: dict[str, torch.Tensor] = {}
         for param_name, param_weight in params.items():
             orig_param = orig_parameters[param_name]
-            out_params[param_name] = torch.zeros_like(orig_param)
+            out_params[param_name] = torch.zeros(
+                get_param_shape(orig_param), dtype=param_weight.dtype, device=param_weight.device
+            )
 
             if param_name == "weight":
                 out_params[param_name][
diff --git a/invokeai/backend/quantization/gguf/ggml_tensor.py b/invokeai/backend/quantization/gguf/ggml_tensor.py
index 62be2bdb63..d48948dcfa 100644
--- a/invokeai/backend/quantization/gguf/ggml_tensor.py
+++ b/invokeai/backend/quantization/gguf/ggml_tensor.py
@@ -54,7 +54,9 @@ GGML_TENSOR_OP_TABLE = {
     torch.ops.aten.addmm.default: dequantize_and_run,  # pyright: ignore
     torch.ops.aten.mul.Tensor: dequantize_and_run,  # pyright: ignore
     torch.ops.aten.add.Tensor: dequantize_and_run,  # pyright: ignore
+    torch.ops.aten.sub.Tensor: dequantize_and_run,  # pyright: ignore
     torch.ops.aten.allclose.default: dequantize_and_run,  # pyright: ignore
+    torch.ops.aten.slice.Tensor: dequantize_and_run,  # pyright: ignore
 }
 
 if torch.backends.mps.is_available():