Add docs to the requantize(...) function explaining why it was copied from optimum-quanto.

Author: RyanJDick

invokeai/backend/requantize.py

@@ -3,19 +3,21 @@
 import torch
 from optimum.quanto.quantize import _quantize_submodule
 
-# def custom_freeze(model: torch.nn.Module):
-#     for name, m in model.named_modules():
-#         if isinstance(m, QModuleMixin):
-#             m.weight =
-#             m.freeze()
-
 
 def requantize(
     model: torch.nn.Module,
     state_dict: Dict[str, Any],
     quantization_map: Dict[str, Dict[str, str]],
-    device: torch.device = None,
+    device: torch.device | None = None,
 ):
+    """This function was initially copied from:
+    https://github.com/huggingface/optimum-quanto/blob/832f7f5c3926c91fe4f923aaaf037a780ac3e6c3/optimum/quanto/quantize.py#L101
+
+    The function was modified to remove the `freeze()` call. The `freeze()` call is very slow and unnecessary when the
+    weights are about to be loaded from a state_dict.
+
+    TODO(ryand): Unless I'm overlooking something, this should be contributed upstream to the `optimum-quanto` library.
+    """
     if device is None:
         device = next(model.parameters()).device
         if device.type == "meta":
@@ -45,6 +47,7 @@ def move_tensor(t, device):
             setattr(m, name, torch.nn.Parameter(move_tensor(param, "cpu")))
         for name, param in m.named_buffers(recurse=False):
             setattr(m, name, move_tensor(param, "cpu"))
+
     # Freeze model and move to target device
     # freeze(model)
     # model.to(device)