sberbank-ai/sber-vq-gan

Jupyter Notebook

29

8 commits

updated Jul 5, 2022

See the code

README

Tuned-VQ-GAN

This repository contains information about the Tuned VQ-GAN model.
We've improved the quality of image generation on some domains such as people and text.
We used COCO only validation images. The splitting you can get here.

original

original

original

Models and configs

VAE encoder - VAE decoder
VQ-GAN 16384 - Config 16384
VQ-GAN GUMBELF8 - Config GUMBELF8
Tuned VQ-GAN GUMBELF8 - Config GUMBELF8

Examples

Jupyter with model inference
Jupyter metrics calculation

Metrics calculation results

IS (More - better)

domain/modelVAE16384gumbelf8Tuned-gumbelf8Original
all11.13313.64715.20315.31615.278
indoor9.76910.74411.70711.68811.638
kitchen9.72611.35412.33312.15211.813
appliance5.7056.0246.1546.1995.890
electronic7.8309.5099.7129.6069.497
furniture10.86113.34614.50014.53114.592
outdoor8.1639.52010.66810.29310.451
sports7.4678.5448.8148.8418.962
food7.9548.7259.3909.4349.191
vehicle10.52712.94714.24014.55914.233
animal11.93314.24915.99915.87915.857
accessory9.39911.68713.11713.38813.228
person13.75217.79420.04820.42020.600
face11.90314.98716.98617.48917.584
text14.90218.45721.39621.29221.131

FID (Less - better)

domain/modelVAE16384gumbelf8Tuned-gumbelf8
all59.75338.91230.30430.136
indoor74.73457.92545.43244.686
kitchen66.42447.08636.73536.579
appliance80.35970.60453.22552.064
electronic77.85664.03450.75950.447
furniture53.43838.20429.51029.569
outdoor91.93258.87746.30945.287
sports65.54039.96132.21931.756
food76.97453.10941.01841.413
vehicle60.31834.25926.72126.463
animal64.25041.52032.03932.078
accessory79.84356.31144.66044.454
person40.81020.43015.52315.484
face54.15334.10926.66326.750
text47.29927.65621.30321.148

Images per domain (spliting)

domainimages count
food702
person2652
kitchen904
sports919
indoor645
vehicle1139
furniture1239
appliance316
accessory718
animal1003
electronic589
outdoor553
face1488
text1706

Authors

Denis Karachev
Alex Shonenkov
Denis Dmitrov

sberbank-ai/sber-vq-gan

Jupyter Notebook

29

8 commits

updated Jul 5, 2022

See the code

README

Tuned-VQ-GAN

This repository contains information about the Tuned VQ-GAN model.
We've improved the quality of image generation on some domains such as people and text.
We used COCO only validation images. The splitting you can get here.

original

original

original

Models and configs

VAE encoder - VAE decoder
VQ-GAN 16384 - Config 16384
VQ-GAN GUMBELF8 - Config GUMBELF8
Tuned VQ-GAN GUMBELF8 - Config GUMBELF8

Examples

Jupyter with model inference
Jupyter metrics calculation

Metrics calculation results

IS (More - better)

domain/modelVAE16384gumbelf8Tuned-gumbelf8Original
all11.13313.64715.20315.31615.278
indoor9.76910.74411.70711.68811.638
kitchen9.72611.35412.33312.15211.813
appliance5.7056.0246.1546.1995.890
electronic7.8309.5099.7129.6069.497
furniture10.86113.34614.50014.53114.592
outdoor8.1639.52010.66810.29310.451
sports7.4678.5448.8148.8418.962
food7.9548.7259.3909.4349.191
vehicle10.52712.94714.24014.55914.233
animal11.93314.24915.99915.87915.857
accessory9.39911.68713.11713.38813.228
person13.75217.79420.04820.42020.600
face11.90314.98716.98617.48917.584
text14.90218.45721.39621.29221.131

FID (Less - better)

domain/modelVAE16384gumbelf8Tuned-gumbelf8
all59.75338.91230.30430.136
indoor74.73457.92545.43244.686
kitchen66.42447.08636.73536.579
appliance80.35970.60453.22552.064
electronic77.85664.03450.75950.447
furniture53.43838.20429.51029.569
outdoor91.93258.87746.30945.287
sports65.54039.96132.21931.756
food76.97453.10941.01841.413
vehicle60.31834.25926.72126.463
animal64.25041.52032.03932.078
accessory79.84356.31144.66044.454
person40.81020.43015.52315.484
face54.15334.10926.66326.750
text47.29927.65621.30321.148

Images per domain (spliting)

domainimages count
food702
person2652
kitchen904
sports919
indoor645
vehicle1139
furniture1239
appliance316
accessory718
animal1003
electronic589
outdoor553
face1488
text1706

Authors

Denis Karachev
Alex Shonenkov
Denis Dmitrov