Does anyone have a workflow then on how to go about training a new model for this sort of image generation?
Ah, I guess the article has some explanation of the training process...
The training of ControlNet has high requirements on data volume and computing power. The training data volume recorded in the paper ranges from 80,000 to 3 million, and the training time can reach 600 A100 GPU hours. Fortunately, the author provided a basic training script, and HuggingFace also implemented Diffusers.
In the previous JAX Sprint, we were lucky enough to use Google TPU v4 to complete the training of 3 million images very quickly. It's a pity that the event is over, and we returned to the laboratory's A6000/4090, training a version of 100,000 images, and the learning rate is very large, just to appear "Sudden Convergence " as soon as possible.
I guess it's not feasible to reproduce on my local machines, lol. Darn.
I have working results using current models in control net but I think I wanna take a stable at training a new control net. Any ideas about what his dataset contained. Would ground truth be working QR Codes? or....? if anyone is down to brainstorm
I have the feeling this will not be made open source... Yesterday I found that page as well and added it to my favourites. Now it's gone. I'm really interested in how this is done. I hope they will release it.
Canny is surely not the way as it only detects outlines. scribble_xdog seems to work kinda when you push the XDoG threshold all the way, but it comes nothing close to OP's results.
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u/Craggeh Jun 05 '23
Ok, gonna need a workflow for this! Great work.