(8)[Graceful Rendering Degradation] With the above, we're now able to generate much more consistently good looking images, even at different color gpu rendering sizes. Basically, you do generally get a better image if you have the model take up more memory with a bigger render
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(9) BUT if you reduce that memory footprint even in half with having the model render a smaller image, the difference in image quality of the end result is often pretty negligible. This effectively means the colorization is usable on a wide variety of machines now!
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(10) i.e. You don't need a GeForce 1080TI to do it anymore. You can get by with much less.
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(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
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(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.
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(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
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(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.
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(15) So that should be it! Code is committed, and again project is here: https://github.com/jantic/DeOldify/blob/master/README.md … and a Colab notebook to get you started immediately is here:https://colab.research.google.com/github/jantic/DeOldify/blob/master/DeOldify_colab.ipynb …
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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!
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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!
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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 …
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Which brings me to another thought- I've already had a lot of great community contributions come in in various forms. It's wonderful! Thank you guys so much.
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