Jason Antic

(11) [Consistent Rendering Quality] Finally- With the above, I was finally able to narrow down a scheme to make it so that the hunt to find the best version of what the model can render is a lot less tedious. Basically, it amounts to providing a render_factor (int) by the user

(12) and multiplying it by a base size multiplier of 16. This, combined with the square rendering, plays well together. It means that you get predictable behavior of rendering as you increase and decrease render_factor, without too many surprise glitches.

(13) Increase render_factor: Get more details right. Decrease: Still looks good but might miss some details. Simple! So you're no longer going to deal with a clumsy sz factor. Bonus: The memory usage is consistent and predictable so you just have to

(14) figure out the render_factor that works for your gpu once and forget about it. I'll probably try to make that render_factor determination automatic eventually but this should be a big improvement in the meantime.

OH and p.s- Your'e not losing any image anymore with padding issues. That's solved as a biproduct. I wish all weeks were as productive as this!

Also also- I added a new generic filter interface that replaces the visualizer dealing with models directly. The visualizer loops through these filters that you provide as a list. They don't have to be backed by deep learning models- they can be any image modification you want!

Also also also- Need to give a shout out to MayeulC on Hacker News, who was the one who came up with the Chrominance optimization- a huge contribution. Original thread is here: https://news.ycombinator.com/item?id=18363870 …