I provide a python script that concurrently downloads all my (latest) released models into a specified folder, skipping already existing model files in that folder.
The script updates itself on execution, so the user is guaranteed to receive all my latest models.
The user can specify as an input which types of models he wants to download/sync.
Currently the script will download my models as either pth, safetensors or fp32 onnx files, depending on the users choice. Pth files is the recommended option since it will always be the most complete (contain all my models), since that is the output file generated by the training software. Conversions are only partially available.
If there is a demand, i might be able to additionally provide fp16 onnx, fp32 ncnn and fp16 ncnn options in this script.
Previously this release contained (compressed) archive files, but this was superseded by this python script. Not only does the script guarantee the user to receive all my (latest) released models, it is also way simpler for me to update by adding a few lines of code in case of a new release, instead of (re)packing whole archives. It also omits a lot of redundancy (files release duplication plus packing and unpacking archives). Its just a better solution overall. This rework happened thanks to przemoc's input of the current state being suboptimal so I came up with a better solution.
An example use case for this is could be comparing all my models on the same input image. So this script can be used to download all my models into a folder, then chaiNNer can be used to iterate through this models folder, upscaling the image with all my models, and then visually inspecting all the outputs, to find the best model for that image or images of similiar style/type.
