huggingface / flux-fast Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Conditional Complexity Overall

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 18 most complex units

Unit	# lines	McCabe index	# params
def use_export_aoti() in utils/pipeline_utils.py	61	14	4
def cudagraph() in utils/pipeline_utils.py	24	13	1
def optimize() in utils/pipeline_utils.py	37	10	2
def main() in run_benchmark.py	43	5	1
def flash_attn_func() in utils/pipeline_utils.py	17	3	0
def use_compile() in utils/pipeline_utils.py	14	3	1
def create_parser() in utils/benchmark_utils.py	36	3	0
def __init__() in utils/pipeline_utils.py	7	2	1
def load_package() in utils/pipeline_utils.py	5	2	1
def load_pipeline() in utils/pipeline_utils.py	6	2	1
def _determine_pipe_call_kwargs() in run_benchmark.py	6	2	1
def _() in utils/pipeline_utils.py	3	1	4
def __call__() in utils/pipeline_utils.py	7	1	0
def download_hosted_file() in utils/pipeline_utils.py	4	1	2
def annotate() in utils/benchmark_utils.py	6	1	2
def set_rand_seeds() in gen_image.py	3	1	1
def main() in gen_image.py	7	1	1
def set_rand_seeds() in run_benchmark.py	3	1	1