Implementation of the code from the paper "Diffusion Models for Handwriting Generation": https://arxiv.org/abs/2011.06704

A number of errors have been found, most of which are related to how the Tensorflow has changed (2.3 vs. 2.12). Internal mathematical functions have been changed, due to which we observe a discrepancy in the final sizes of tensors.

The model was launched on a HSE supercomputer, via a connection to Mountain Duck.

Results of the generation: without weights:

Requirements from the authors: python 3.7, Tensorflow 2.3 My requirements: Python 3.11, TF 2.12.

I rewrited and trained the model. Weights are uploaded via the link to my GDrive (too large for repo):

https://drive.google.com/file/d/1Cvkd1Iq-r-rvH9HO8vgCap4Lzt7aAi-r/view?usp=share_link

To get an inference, run inference.py (with arguments, see the file). To retrain model run train.py. Arguments can be changed.

IAM Dataset from this project can be downloaded here:

https://fki.tic.heia-fr.ch/databases/download-the-iam-on-line-handwriting-database

We need the following files:

data/lineStrokes-all.tar.gz - the stroke xml for the online dataset data/lineImages-all.tar.gz - the images for the offline dataset ascii-all.tar.gz - the text labels for the dataset

After that, we extracting these contents and put them in the ./data directory.

The authors calculated the FID metric, I rewrote it (library incompatibility prompted this + in authors version the images for FID are first plotted on a graph, then the graph is converted to an image, our team used a different method) and added the Inseption, CLIP calculation.