flameSimpleML - Flame Machine Learning Source/Target tool with bespoke training:
Hi guys, I’ve put several scripts together in a package called flameSimpleML:
https://github.com/talosh/flameSimpleML/releases/tag/v0.0.1
This is a “copycat” - style model and scripts that allows training it using your own “source/target” data.
Training script is command-line at the moment. In order to train your model you need to create a folder somewhere for your dataset and then create two more folders named “source” and “target”. Export your training data there as uncompressed exr sequences. The script would assume that both “source” and “target” sequence are of the same dimensions and number of frames and exr’s are uncompressed.
When you run the script it would create a third “preview” folder within dataset folder and you can monitor the progress of model (hopefully) getting smarter there.
Training does not take a lot of GPU ram and can be run in a background so one can continue to use Flame.
One of the simple tests I’ve been using while writing is to give it the same sequence in colour as target and bw as source and teach it to colorise frames.
Trained model data is saved every 1000 iterations into your “homefolder/flameSimpleML_models/” as .pth file
To apply model select it from menu and navigate to that folder to load it. It is possible to use “F1 / F4” to see before / after.
This is a very first release and I’ve been testing it mostly on linux with Flame 2023.3 and I gave it some very limited testing on MacMini M2 using Flame 2025 tech preview. It might work on Intel macs if you sort PyTorch and Numpy dependencies out (give me a shout if you would like to try)
(it has a separate thread here: flameSimpleML - Flame Machine Learning Source/Target tool with bespoke training)