A Pytorch Reproduction of Masked Generative Image Transformer
This is an incremental reproduction that facilitates reproducibility and accessibility of masked generative models for the research community.
The authors reproduced MaskGIT, a masked generative image transformer, using PyTorch, achieving similar FID scores (e.g., 7.59 vs. 7.32 at 512x512 resolution) and minor improvements (e.g., 7.26 FID) with hyperparameter tweaks.
In this technical report, we present a reproduction of MaskGIT: Masked Generative Image Transformer, using PyTorch. The approach involves leveraging a masked bidirectional transformer architecture, enabling image generation with only few steps (8~16 steps) for 512 x 512 resolution images, i.e., ~64x faster than an auto-regressive approach. Through rigorous experimentation and optimization, we achieved results that closely align with the findings presented in the original paper. We match the reported FID of 7.32 with our replication and obtain 7.59 with similar hyperparameters on ImageNet at resolution 512 x 512. Moreover, we improve over the official implementation with some minor hyperparameter tweaking, achieving FID of 7.26. At the lower resolution of 256 x 256 pixels, our reimplementation scores 6.80, in comparison to the original paper's 6.18. To promote further research on Masked Generative Models and facilitate their reproducibility, we released our code and pre-trained weights openly at https://github.com/valeoai/MaskGIT-pytorch/