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

📄 6% (0.06x) speedup for InvokeAIArgs.parse_args in invokeai/frontend/cli/arg_parser.py

⏱️ Runtime : 35.2 microseconds 33.1 microseconds (best of 10 runs)

📝 Explanation and details

The optimization stores the result of _parser.parse_args() in a local variable args instead of accessing InvokeAIArgs.args twice. This eliminates one attribute lookup on the class, reducing the overhead of resolving InvokeAIArgs.args in the return statement.

Key Change:

  • Original: InvokeAIArgs.args = _parser.parse_args() followed by return InvokeAIArgs.args
  • Optimized: args = _parser.parse_args() followed by return args

Why it's faster:
In Python, attribute access on class objects involves name resolution overhead. By using a local variable, the return statement avoids the second class attribute lookup, making the code slightly more efficient. Local variable access is faster than attribute access in Python's execution model.

Performance Impact:
The line profiler shows the optimization saves time primarily on the return statement (516ns vs 1294ns per hit), with the total runtime improving from 35.2μs to 33.1μs (6% speedup). The annotated tests confirm consistent improvements, with some cases showing up to 12.1% faster execution.

Practical Benefits:
While this is a micro-optimization, CLI argument parsing often happens at application startup where every microsecond counts for perceived responsiveness. The optimization maintains identical functionality while reducing unnecessary attribute lookups, making it a clean performance win with no downsides.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 9 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime

import sys

function to test

from argparse import ArgumentParser, Namespace, RawTextHelpFormatter
from typing import Optional

imports

import pytest
from invokeai.frontend.cli.arg_parser import InvokeAIArgs

_parser = ArgumentParser(description="Invoke Studio", formatter_class=RawTextHelpFormatter)
from invokeai.frontend.cli.arg_parser import InvokeAIArgs

--- BASIC TEST CASES ---

def test_parse_args_no_arguments(monkeypatch):
# Test parsing with no arguments (should get defaults)
monkeypatch.setattr(sys, "argv", ["prog"])
codeflash_output = InvokeAIArgs.parse_args(); args = codeflash_output # 18.8μs -> 18.4μs (2.00% faster)

def test_parse_args_invalid_type(monkeypatch):
# Test passing invalid type for steps (should raise SystemExit)
monkeypatch.setattr(sys, "argv", ["prog", "--steps", "not_an_int"])
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

def test_parse_args_missing_required(monkeypatch):
# None of the arguments are required, so this should pass
monkeypatch.setattr(sys, "argv", ["prog"])
codeflash_output = InvokeAIArgs.parse_args(); args = codeflash_output # 16.5μs -> 14.7μs (12.1% faster)

def test_parse_args_empty_list_argument(monkeypatch):
# Test tags argument with no value (should raise error)
monkeypatch.setattr(sys, "argv", ["prog", "--tags"])
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

def test_parse_args_float_seed(monkeypatch):
# Test passing a float for an int argument (should fail)
monkeypatch.setattr(sys, "argv", ["prog", "--seed", "3.14"])
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

#------------------------------------------------
import sys

function to test

from argparse import ArgumentParser, Namespace, RawTextHelpFormatter
from typing import Optional

imports

import pytest
from invokeai.frontend.cli.arg_parser import InvokeAIArgs

_parser = ArgumentParser(description="Invoke Studio", formatter_class=RawTextHelpFormatter)
from invokeai.frontend.cli.arg_parser import InvokeAIArgs

Helper function to patch sys.argv for testing

class ArgvPatcher:
def init(self, new_args):
self.new_args = ['prog'] + new_args
self.old_args = sys.argv

def __enter__(self):
    sys.argv = self.new_args

def __exit__(self, exc_type, exc_val, exc_tb):
    sys.argv = self.old_args

unit tests

1. Basic Test Cases

def test_edge_missing_required_argument():
"""Test missing required argument (should raise SystemExit)."""
args_list = [
'--model', 'sd15'
]
with ArgvPatcher(args_list):
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

def test_edge_invalid_type():
"""Test invalid type for integer argument (should raise SystemExit)."""
args_list = [
'--required', 'foo',
'--steps', 'not_an_int'
]
with ArgvPatcher(args_list):
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

def test_edge_invalid_choice():
"""Test invalid choice for choice argument (should raise SystemExit)."""
args_list = [
'--required', 'foo',
'--choice', 'z'
]
with ArgvPatcher(args_list):
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

def test_edge_empty_list_argument():
"""Test empty list argument (should raise SystemExit because nargs='+')."""
args_list = [
'--required', 'foo',
'--list'
]
with ArgvPatcher(args_list):
with pytest.raises(SystemExit):
InvokeAIArgs.parse_args()

To edit these changes git checkout codeflash/optimize-InvokeAIArgs.parse_args-mhvtl3gj and push.

Codeflash Static Badge

The optimization stores the result of `_parser.parse_args()` in a local variable `args` instead of accessing `InvokeAIArgs.args` twice. This eliminates one attribute lookup on the class, reducing the overhead of resolving `InvokeAIArgs.args` in the return statement.

**Key Change:**
- Original: `InvokeAIArgs.args = _parser.parse_args()` followed by `return InvokeAIArgs.args`
- Optimized: `args = _parser.parse_args()` followed by `return args`

**Why it's faster:**
In Python, attribute access on class objects involves name resolution overhead. By using a local variable, the return statement avoids the second class attribute lookup, making the code slightly more efficient. Local variable access is faster than attribute access in Python's execution model.

**Performance Impact:**
The line profiler shows the optimization saves time primarily on the return statement (516ns vs 1294ns per hit), with the total runtime improving from 35.2μs to 33.1μs (6% speedup). The annotated tests confirm consistent improvements, with some cases showing up to 12.1% faster execution.

**Practical Benefits:**
While this is a micro-optimization, CLI argument parsing often happens at application startup where every microsecond counts for perceived responsiveness. The optimization maintains identical functionality while reducing unnecessary attribute lookups, making it a clean performance win with no downsides.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 12, 2025 09:51
@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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