ContraCLIP/models/genforce/MODEL_ZOO.md

Model Zoo

Pre-trained Models

First of all, we thank the following repositories for their work on high-quality image synthesis

• PGGAN
• StyleGAN
• StyleGAN2

Please download the models you need and save them to checkpoints/.

PGGAN Official
Face
celebahq-1024x1024
Indoor Scene
bedroom-256x256	livingroom-256x256	diningroom-256x256	kitchen-256x256
Outdoor Scene
churchoutdoor-256x256	tower-256x256	bridge-256x256
Other Scene
restaurant-256x256	classroom-256x256	conferenceroom-256x256
Animal
person-256x256	cat-256x256	dog-256x256	bird-256x256
horse-256x256	sheep-256x256	cow-256x256
Transportation
car-256x256	bicycle-256x256	motorbike-256x256	bus-256x256
train-256x256	boat-256x256	airplane-256x256
Furniture
bottle-256x256	chair-256x256	pottedplant-256x256	tvmonitor-256x256
diningtable-256x256	sofa-256x256

StyleGAN Official
Model (Dataset)	Training Samples	Training Duration (K Images)	FID
ffhq-1024x1024	70,000	25,000	4.40
celebahq-1024x1024	30,000	25,000	5.06
bedroom-256x256	3,033,042	70,000	2.65
cat-256x256	1,657,266	70,000	8.53
car-512x384	5,520,756	46,000	3.27

StyleGAN Ours
Model (Dataset)	Training Samples	Training Duration (K Images)	FID
Face ("partial" means faces are not fully aligned to center)
celeba_partial-256x256	103,706	50,000	7.03
ffhq-256x256	70,000	25,000	5.70
ffhq-512x512	70,000	25,000	5.15
LSUN Indoor Scene
livingroom-256x256	1,315,802	30,000	5.16
diningroom-256x256	657,571	25,000	4.13
kitchen-256x256	1,000,000	30,000	5.06
LSUN Indoor Scene Mixture
apartment-256x256	4 * 200,000	60,000	4.18
LSUN Outdoor Scene
church-256x256	126,227	30,000	4.82
tower-256x256	708,264	30,000	5.99
bridge-256x256	818,687	25,000	6.42
LSUN Other Scene
restaurant-256x256	626,331	50,000	4.03
classroom-256x256	168,103	50,000	10.10
conferenceroom-256x256	229,069	50,000	6.20

StyleGAN Third-Party
Model (Dataset)	Source
animeface-512x512	link
animeportrait-512x512	link
artface-512x512	link

StyleGAN2 Official
Model (Dataset)	Training Samples	Training Duration (K Images)	FID
ffhq-1024x1024	70,000	25,000	2.84
church-256x256	126,227	48,000	3.86
cat-256x256	1,657,266	88,000	6.93
horse-256x256	2,000,340	100,000	3.43
car-512x384	5,520,756	57,000	2.32

Training Datasets

• MNIST (60,000 training samples and 10,000 test samples on 10 digital numbers)
• SVHN (73,257 training samples, 26,032 testing samples, and 531,131 additional samples on 10 digital numbers)
• CIFAR10 (50,000 training samples and 10,000 test samples on 10 classes)
• CIFAR100 (50,000 training samples and 10,000 test samples on 100 classes)
• ImageNet (1,281,167 training samples, 50,000 validation samples, and 100,100 testing samples on 1000 classes)
• CelebA (202,599 samples from 10,177 identities, with 5 landmarks and 40 binary facial attributes)
• CelebA-HQ (30,000 samples)
• FF-HQ (70,000 samples)
• LSUN (see statistical information below)
• Places (around 1.8M training samples covering 365 classes)
• Cityscapes (2,975 training samples, 19998 extra training samples (one broken), 500 validation samples, and 1,525 test samples)
• Streetscapes

Statistical information of LSUN dataset is summarized as follows:

LSUN Datasets Stats
Name	Number of Samples	Size
Scenes
bedroom (train)	3,033,042	43G
bridge (train)	818,687	15G
churchoutdoor (train)	126,227	2G
classroom (train)	168,103	3G
conferenceroom (train)	229,069	4G
diningroom (train)	657,571	11G
kitchen (train)	2,212,277	33G
livingroom (train)	1,315,802	21G
restaurant (train)	626,331	13G
tower (train)	708,264	11G
Objects
airplane	1,530,696	34G
bicycle	3,347,211	129G
bird	2,310,362	65G
boat	2,651,165	86G
bottle	3,202,760	64G
bus	695,891	24G
car	5,520,756	173G
cat	1,657,266	42G
chair	5,037,807	116G
cow	377,379	15G
diningtable	1,537,123	48G
dog	5,054,817	145G
horse	2,000,340	69G
motorbike	1,194,101	42G
person	18,890,816	477G
pottedplant	1,104,859	43G
sheep	418,983	18G
sofa	2,365,870	56G
train	1,148,020	43G
tvmonitor	2,463,284	46G