ImageNet 256x256 Benchmark This benchmark aims to reproduce the results reported in the papers as closely as possible.
VQGAN (Taming Transformers) paper / config
Downsample ratio
Codebook dim.
Codebook size
Codebook usage↑
PSNR↑
SSIM↑
LPIPS↓
rFID↓
16
256
1024
37.50%
19.9142
0.5052
0.1778
5.8165
️🌱 The PSNR and SSIM are close to the results reported in the paper (19.4 & 0.50).
️🌱 The rFID is even better than the results reported in the paper (7.94).
🎈 The model suffers from the low codebook usage problem.
paper / config
Downsample ratio
Codebook dim.
Codebook size
Codebook usage↑
PSNR↑
SSIM↑
LPIPS↓
rFID↓
16
8
16384
100%
20.7201
0.5509
0.1385
2.1073
️🌱 The PSNR is close to the results reported in the paper (20.79).
️🌱 The rFID is even slightly better than the results reported in the paper (2.19).
paper / project page / config
# tokens
Codebook dim.
Codebook size
Codebook usage↑
PSNR↑
SSIM↑
LPIPS↓
rFID↓
64
12
4096
100%
17.8995
0.4022
0.2681
4.6691
⚠️ The model is trained with a single-stage training strategy, which is different from the paper.⚠️ The results are not good. Reconstructed images contain repeated patterns and artifacts. Need further investigation.