Fix IP-Adapter calculation of memory footprint (#4692)

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:

      
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No


## Description

The IP-Adapter memory footprint was not being calculated correctly.

I think we could put checks in place to catch this type of error in the
future, but for now I'm just fixing the bug.

## QA Instructions, Screenshots, Recordings

I tested manually in a debugger. There are 3 pathways for calculating
the model size. All were tested:
- From file
- From state_dict
- From model weights

## Added/updated tests?

- [ ] Yes
- [x] No : This would require the ability to run tests that depend on
models. I'm working on this in another branch, but not ready quite yet.

Author: RyanJDick

invokeai/backend/ip_adapter/ip_adapter.py

@@ -9,6 +9,8 @@
 from PIL import Image
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
+from invokeai.backend.model_management.models.base import calc_model_size_by_data
+
 from .attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
 from .resampler import Resampler
 
@@ -87,6 +89,20 @@ def to(self, device: torch.device, dtype: Optional[torch.dtype] = None):
         if self._attn_processors is not None:
             torch.nn.ModuleList(self._attn_processors.values()).to(device=self.device, dtype=self.dtype)
 
+    def calc_size(self):
+        if self._state_dict is not None:
+            image_proj_size = sum(
+                [tensor.nelement() * tensor.element_size() for tensor in self._state_dict["image_proj"].values()]
+            )
+            ip_adapter_size = sum(
+                [tensor.nelement() * tensor.element_size() for tensor in self._state_dict["ip_adapter"].values()]
+            )
+            return image_proj_size + ip_adapter_size
+        else:
+            return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(
+                torch.nn.ModuleList(self._attn_processors.values())
+            )
+
     def _init_image_proj_model(self, state_dict):
         return ImageProjModel.from_state_dict(state_dict, self._num_tokens).to(self.device, dtype=self.dtype)
 

invokeai/backend/model_management/models/ip_adapter.py

@@ -13,6 +13,7 @@
     ModelConfigBase,
     ModelType,
     SubModelType,
+    calc_model_size_by_fs,
     classproperty,
 )
 
@@ -30,7 +31,7 @@ def __init__(self, model_path: str, base_model: BaseModelType, model_type: Model
         assert model_type == ModelType.IPAdapter
         super().__init__(model_path, base_model, model_type)
 
-        self.model_size = os.path.getsize(self.model_path)
+        self.model_size = calc_model_size_by_fs(self.model_path)
 
     @classmethod
     def detect_format(cls, path: str) -> str:
@@ -63,10 +64,13 @@ def get_model(
         if child_type is not None:
             raise ValueError("There are no child models in an IP-Adapter model.")
 
-        return build_ip_adapter(
+        model = build_ip_adapter(
             ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
         )
 
+        self.model_size = model.calc_size()
+        return model
+
     @classmethod
     def convert_if_required(
         cls,