openai / guided-diffusion

Intro

For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.

Learn more...

Duplication Overall

22% duplication:

3,896 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
895 duplicated lines

74 duplicates

dependency graphs: 2D graph | 3D graph | 3D graph (with duplicates)...

Duplication per Extension

Duplication per Component (primary)

Longest Duplicates

The list of 50 longest duplicates.

See data for all 74 duplicates...

Size	#	Folders	Files	Lines	Code
83	x 2	guided_diffusion guided_diffusion	unet.py unet.py	481:564 (12%) 739:822 (12%)	view
20	x 2	scripts scripts	image_train.py super_res_train.py	37:57 (32%) 40:60 (27%)	view
18	x 2	scripts scripts	classifier_sample.py image_sample.py	21:42 (17%) 18:39 (21%)	view
17	x 2	scripts scripts	image_nll.py image_sample.py	14:34 (25%) 16:36 (20%)	view
16	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	81:96 (4%) 287:302 (4%)	view
16	x 2	scripts scripts	image_train.py super_res_train.py	60:75 (25%) 75:90 (21%)	view
15	x 2	scripts scripts	classifier_sample.py image_nll.py	21:39 (14%) 16:34 (22%)	view
14	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	498:524 (2%) 671:695 (2%)	view
13	x 2	scripts scripts	super_res_sample.py super_res_train.py	16:32 (14%) 13:30 (17%)	view
12	x 2	guided_diffusion guided_diffusion	unet.py unet.py	427:438 (1%) 690:701 (1%)	view
12	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	626:637 (2%) 660:671 (2%)	view
12	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	116:127 (3%) 320:331 (3%)	view
11	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	442:452 (1%) 488:498 (1%)	view
11	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	116:126 (2%) 214:224 (2%)	view
11	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	214:224 (2%) 320:330 (2%)	view
11	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	104:114 (2%) 309:319 (2%)	view
11	x 2	guided_diffusion guided_diffusion	unet.py unet.py	464:475 (1%) 725:736 (1%)	view
10	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	149:158 (2%) 238:247 (2%)	view
10	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	488:497 (1%) 660:669 (1%)	view
10	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	442:451 (1%) 626:635 (1%)	view
10	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	488:497 (1%) 626:635 (1%)	view
10	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	442:451 (1%) 660:669 (1%)	view
9	x 2	scripts scripts	image_sample.py image_train.py	20:32 (10%) 15:28 (14%)	view
9	x 2	scripts scripts	image_nll.py image_train.py	18:30 (13%) 15:28 (14%)	view
9	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	404:429 (1%) 547:560 (1%)	view
9	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	168:176 (2%) 368:376 (2%)	view
9	x 2	scripts scripts	classifier_sample.py image_train.py	23:35 (8%) 15:28 (14%)	view
9	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	474:482 (1%) 645:653 (1%)	view
9	x 2	scripts scripts	classifier_sample.py image_sample.py	109:119 (8%) 89:99 (10%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	440:447 (1%) 702:709 (1%)	view
8	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	553:560 (1%) 601:608 (1%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	555:562 (1%) 798:805 (1%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	518:525 (1%) 595:602 (1%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	540:547 (1%) 813:820 (1%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	595:602 (1%) 776:783 (1%)	view
8	x 2	scripts scripts	classifier_sample.py super_res_sample.py	35:42 (7%) 32:39 (8%)	view
8	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	396:403 (1%) 538:545 (1%)	view
8	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	206:213 (2%) 229:236 (2%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	455:462 (1%) 717:724 (1%)	view
8	x 2	scripts scripts	classifier_sample.py image_sample.py	72:79 (7%) 47:54 (9%)	view
8	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	188:195 (2%) 229:236 (2%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	798:805 (1%) 813:820 (1%)	view
8	x 2	guided_diffusion guided_diffusion	gaussian_diffusion.py gaussian_diffusion.py	422:429 (1%) 601:608 (1%)	view
8	x 2	guided_diffusion guided_diffusion	script_util.py script_util.py	188:195 (2%) 206:213 (2%)	view
8	x 2	guided_diffusion guided_diffusion	unet.py unet.py	540:547 (1%) 555:562 (1%)	view
8	x 2	scripts scripts	image_sample.py super_res_sample.py	32:39 (9%) 32:39 (8%)	view
7	x 2	scripts scripts	image_sample.py image_train.py	100:108 (8%) 75:83 (11%)	view
7	x 2	scripts scripts	image_train.py super_res_train.py	13:23 (11%) 15:25 (9%)	view
7	x 2	scripts scripts	classifier_sample.py image_sample.py	86:93 (6%) 59:66 (8%)	view
7	x 2	scripts scripts	image_nll.py image_sample.py	88:96 (10%) 100:108 (8%)	view