OCR-VQGAN: Taming Text-within-Image Generation Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generation remains unexplored. A challenging aspect of generating figures and diagrams is effectively rendering readable texts within the images. To alleviate this problem, we present OCR-VQGAN, an image encoder, and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high-fidelity text and diagram structure. Here we provide the pre-trained model using Paper2Fig100k dataset, which performs a downsampling of factor f=16, using a discrete codebook of 16384 and vectors of dimension 256. Refer to github.com/joanrod/ocr-vqgan/ to see implementation and details. Our paper @WACV2023 presents how we design an OCR perceptual loss to be used in the VQGAN framework (OCR-VQGAN). In the paper, we also define the proposed Paper2Fig100k dataset.