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@codeflash-ai codeflash-ai bot commented Nov 12, 2025

📄 147% (1.47x) speedup for Unknown_Config.from_model_on_disk in invokeai/backend/model_manager/configs/unknown.py

⏱️ Runtime : 9.51 microseconds 3.85 microseconds (best of 41 runs)

📝 Explanation and details

The optimization replaces Python's deepcopy() with the much faster dict.copy() method when cloning override_fields.

Key Change: Line 19 changed from cloned_override_fields = deepcopy(override_fields) to cloned_override_fields = override_fields.copy().

Why This Works: The function only needs to remove three specific keys ("base", "type", "format") from the cloned dictionary without modifying any nested values. A shallow copy is sufficient since the code doesn't mutate any nested objects within override_fields - it only removes top-level keys.

Performance Impact: The line profiler shows the deepcopy operation took 28.97ms (95.5% of total runtime), while dict.copy() takes only 0.053ms (3.6% of total runtime) - a 540x improvement on that specific line. This translates to an overall 146% speedup (9.51μs → 3.85μs).

Why deepcopy is Slow: deepcopy() recursively traverses the entire object graph, creating new instances of all nested objects even when unnecessary. dict.copy() simply creates a new dictionary with references to the same values, which is exactly what's needed here.

Correctness: The optimization maintains identical behavior since the original override_fields parameter is never mutated after the copy, making the deep vs shallow copy distinction irrelevant for this use case.

This optimization is particularly valuable for model configuration workflows where this method might be called frequently during model loading and discovery operations.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 31 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime

from copy import deepcopy

--- Function to test (copied from prompt, with simulated dependencies) ---

from typing import Any, Literal

imports

import pytest
from invokeai.backend.model_manager.configs.unknown import Unknown_Config
from typing_extensions import Self

--- Simulate dependencies for the function ---

Simulate enums from taxonomy

class BaseModelType:
Unknown = "unknown"

class ModelType:
Unknown = "unknown"

class ModelFormat:
Unknown = "unknown"

Simulate ModelOnDisk class (minimal stub)

class ModelOnDisk:
def init(self, path, metadata=None):
self.path = path
self.metadata = metadata or {}

Simulate Config_Base (minimal stub)

class Config_Base:
pass
from invokeai.backend.model_manager.configs.unknown import Unknown_Config

--- Unit tests ---

1. Basic Test Cases

#------------------------------------------------
from copy import deepcopy

Simulate required enums and classes

from enum import Enum
from typing import Any, Dict

imports

import pytest
from invokeai.backend.model_manager.configs.unknown import Unknown_Config

Simulate the enums used in Unknown_Config

class BaseModelType(str, Enum):
Unknown = "unknown"
Other = "other"

class ModelType(str, Enum):
Unknown = "unknown"
Other = "other"

class ModelFormat(str, Enum):
Unknown = "unknown"
Other = "other"

Simulate ModelOnDisk class

class ModelOnDisk:
# Minimal implementation for testing
def init(self, path: str, metadata: Dict[str, Any] = None):
self.path = path
self.metadata = metadata or {}

Simulate Config_Base

class Config_Base:
def init(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
from invokeai.backend.model_manager.configs.unknown import Unknown_Config

----------------------

UNIT TESTS START HERE

----------------------

1. Basic Test Cases

def test_override_fields_with_non_string_keys():
"""Test that non-string keys in override_fields raise a TypeError."""
mod = ModelOnDisk(path="/models/model7.pt")
override_fields = {1: "integer_key", "foo": "bar"}
with pytest.raises(TypeError):
# The constructor expects keyword arguments as strings, so this should fail
Unknown_Config.from_model_on_disk(mod, override_fields) # 9.51μs -> 3.85μs (147% faster)

To edit these changes git checkout codeflash/optimize-Unknown_Config.from_model_on_disk-mhvtfb8p and push.

Codeflash Static Badge

The optimization replaces Python's `deepcopy()` with the much faster `dict.copy()` method when cloning `override_fields`. 

**Key Change:** Line 19 changed from `cloned_override_fields = deepcopy(override_fields)` to `cloned_override_fields = override_fields.copy()`.

**Why This Works:** The function only needs to remove three specific keys (`"base"`, `"type"`, `"format"`) from the cloned dictionary without modifying any nested values. A shallow copy is sufficient since the code doesn't mutate any nested objects within `override_fields` - it only removes top-level keys.

**Performance Impact:** The line profiler shows the `deepcopy` operation took 28.97ms (95.5% of total runtime), while `dict.copy()` takes only 0.053ms (3.6% of total runtime) - a **540x improvement** on that specific line. This translates to an overall **146% speedup** (9.51μs → 3.85μs).

**Why `deepcopy` is Slow:** `deepcopy()` recursively traverses the entire object graph, creating new instances of all nested objects even when unnecessary. `dict.copy()` simply creates a new dictionary with references to the same values, which is exactly what's needed here.

**Correctness:** The optimization maintains identical behavior since the original `override_fields` parameter is never mutated after the copy, making the deep vs shallow copy distinction irrelevant for this use case.

This optimization is particularly valuable for model configuration workflows where this method might be called frequently during model loading and discovery operations.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 12, 2025 09:46
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Nov 12, 2025
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