Fix circular import caused by the organization the model size utils.

invokeai/backend/ip_adapter/ip_adapter.py

@@ -11,6 +11,7 @@ from PIL import Image
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionWeights
+from invokeai.backend.model_manager.load.model_size_utils import calc_module_size
 
 from ..raw_model import RawModel
 from .resampler import Resampler
@@ -137,10 +138,7 @@ class IPAdapter(RawModel):
         self.attn_weights.to(device=self.device, dtype=self.dtype, non_blocking=non_blocking)
 
     def calc_size(self):
-        # workaround for circular import
+        return calc_module_size(self._image_proj_model) + calc_module_size(self.attn_weights)
-        from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-
-        return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(self.attn_weights)
 
     def _init_image_proj_model(
         self, state_dict: dict[str, torch.Tensor]

invokeai/backend/model_manager/load/model_size_utils.py

@@ -0,0 +1,79 @@
+import json
+from pathlib import Path
+from typing import Optional
+
+import torch
+
+
+def calc_module_size(model: torch.nn.Module) -> int:
+    """Estimate the size of a torch.nn.Module in bytes."""
+    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
+    mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
+    mem: int = mem_params + mem_bufs  # in bytes
+    return mem
+
+
+def calc_model_size_by_fs(model_path: Path, subfolder: Optional[str] = None, variant: Optional[str] = None) -> int:
+    """Estimate the size of a model on disk in bytes."""
+    if model_path.is_file():
+        return model_path.stat().st_size
+
+    if subfolder is not None:
+        model_path = model_path / subfolder
+
+    # this can happen when, for example, the safety checker is not downloaded.
+    if not model_path.exists():
+        return 0
+
+    all_files = [f for f in model_path.iterdir() if (model_path / f).is_file()]
+
+    fp16_files = {f for f in all_files if ".fp16." in f.name or ".fp16-" in f.name}
+    bit8_files = {f for f in all_files if ".8bit." in f.name or ".8bit-" in f.name}
+    other_files = set(all_files) - fp16_files - bit8_files
+
+    if not variant:  # ModelRepoVariant.DEFAULT evaluates to empty string for compatability with HF
+        files = other_files
+    elif variant == "fp16":
+        files = fp16_files
+    elif variant == "8bit":
+        files = bit8_files
+    else:
+        raise NotImplementedError(f"Unknown variant: {variant}")
+
+    # try read from index if exists
+    index_postfix = ".index.json"
+    if variant is not None:
+        index_postfix = f".index.{variant}.json"
+
+    for file in files:
+        if not file.name.endswith(index_postfix):
+            continue
+        try:
+            with open(model_path / file, "r") as f:
+                index_data = json.loads(f.read())
+            return int(index_data["metadata"]["total_size"])
+        except Exception:
+            pass
+
+    # calculate files size if there is no index file
+    formats = [
+        (".safetensors",),  # safetensors
+        (".bin",),  # torch
+        (".onnx", ".pb"),  # onnx
+        (".msgpack",),  # flax
+        (".ckpt",),  # tf
+        (".h5",),  # tf2
+    ]
+
+    for file_format in formats:
+        model_files = [f for f in files if f.suffix in file_format]
+        if len(model_files) == 0:
+            continue
+
+        model_size = 0
+        for model_file in model_files:
+            file_stats = (model_path / model_file).stat()
+            model_size += file_stats.st_size
+        return model_size
+
+    return 0  # scheduler/feature_extractor/tokenizer - models without loading to gpu

invokeai/backend/model_manager/load/model_util.py

@@ -1,14 +1,11 @@
 # Copyright (c) 2024 The InvokeAI Development Team
 """Various utility functions needed by the loader and caching system."""
 
-import json
-from pathlib import Path
-from typing import Optional
-
 import torch
 from diffusers import DiffusionPipeline
 
 from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.model_manager.load.model_size_utils import calc_module_size
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 
 
@@ -17,7 +14,7 @@ def calc_model_size_by_data(model: AnyModel) -> int:
     if isinstance(model, DiffusionPipeline):
         return _calc_pipeline_by_data(model)
     elif isinstance(model, torch.nn.Module):
-        return _calc_model_by_data(model)
+        return calc_module_size(model)
     elif isinstance(model, IAIOnnxRuntimeModel):
         return _calc_onnx_model_by_data(model)
     else:
@@ -30,84 +27,11 @@ def _calc_pipeline_by_data(pipeline: DiffusionPipeline) -> int:
     for submodel_key in pipeline.components.keys():
         submodel = getattr(pipeline, submodel_key)
         if submodel is not None and isinstance(submodel, torch.nn.Module):
-            res += _calc_model_by_data(submodel)
+            res += calc_module_size(submodel)
     return res
 
 
-def _calc_model_by_data(model: torch.nn.Module) -> int:
-    mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
-    mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
-    mem: int = mem_params + mem_bufs  # in bytes
-    return mem
-
-
 def _calc_onnx_model_by_data(model: IAIOnnxRuntimeModel) -> int:
     tensor_size = model.tensors.size() * 2  # The session doubles this
     mem = tensor_size  # in bytes
     return mem
-
-
-def calc_model_size_by_fs(model_path: Path, subfolder: Optional[str] = None, variant: Optional[str] = None) -> int:
-    """Estimate the size of a model on disk in bytes."""
-    if model_path.is_file():
-        return model_path.stat().st_size
-
-    if subfolder is not None:
-        model_path = model_path / subfolder
-
-    # this can happen when, for example, the safety checker is not downloaded.
-    if not model_path.exists():
-        return 0
-
-    all_files = [f for f in model_path.iterdir() if (model_path / f).is_file()]
-
-    fp16_files = {f for f in all_files if ".fp16." in f.name or ".fp16-" in f.name}
-    bit8_files = {f for f in all_files if ".8bit." in f.name or ".8bit-" in f.name}
-    other_files = set(all_files) - fp16_files - bit8_files
-
-    if not variant:  # ModelRepoVariant.DEFAULT evaluates to empty string for compatability with HF
-        files = other_files
-    elif variant == "fp16":
-        files = fp16_files
-    elif variant == "8bit":
-        files = bit8_files
-    else:
-        raise NotImplementedError(f"Unknown variant: {variant}")
-
-    # try read from index if exists
-    index_postfix = ".index.json"
-    if variant is not None:
-        index_postfix = f".index.{variant}.json"
-
-    for file in files:
-        if not file.name.endswith(index_postfix):
-            continue
-        try:
-            with open(model_path / file, "r") as f:
-                index_data = json.loads(f.read())
-            return int(index_data["metadata"]["total_size"])
-        except Exception:
-            pass
-
-    # calculate files size if there is no index file
-    formats = [
-        (".safetensors",),  # safetensors
-        (".bin",),  # torch
-        (".onnx", ".pb"),  # onnx
-        (".msgpack",),  # flax
-        (".ckpt",),  # tf
-        (".h5",),  # tf2
-    ]
-
-    for file_format in formats:
-        model_files = [f for f in files if f.suffix in file_format]
-        if len(model_files) == 0:
-            continue
-
-        model_size = 0
-        for model_file in model_files:
-            file_stats = (model_path / model_file).stat()
-            model_size += file_stats.st_size
-        return model_size
-
-    return 0  # scheduler/feature_extractor/tokenizer - models without loading to gpu