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%pip install omegaconf=2.0.0 pytorch-lightning=1.0.8ĭemo – Taming Transformers via pretrained modelĬonfig_path = "logs/T13-31-51_sflckr/configs/T13-31-51-project.yaml" !mkdir -p logs/T13-31-51_sflckr/checkpoints Some of the results from Taming Transformer Source : Ĭlone the repository via git and download all the required models and configs. The transformer is then trained with this codebook to predict the next indices’ distribution in the given representation, similar to the autoregressive model, to synthesize the required output. This codebook holds the efficient and rich representation of images(instead of pixels) in a compressed form that can be read sequentially. Once this GAN training is over, the model architecture then takes only the decoder part as the input to the transformer architecture, a.k.a codebook. This GAN architecture is used to train the generator to output high-resolution images. The model architecture uses convolutional neural VQGAN, which contains encoder-decoder and adversarial training methods to produce codebook (efficient and rich representations) of images. The Model Architecture of Taming Transformers Utilization of transformers to efficiently model their composition within high-resolution images.They use VQGAN CNNs to effectively learn a codebook of context-rich visual parts.