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citnaj's profile
Jason Antic
Jason Antic
Jason Antic
@citnaj

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Jason Antic

@citnaj

Obsessively pursuing the perfection of image and video colorization/restoration using deep learning. Creator of DeOldify.

Ocean Beach, San Diego
deoldify.ai
Joined January 2010

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    1. Jason Antic‏ @citnaj 15 Nov 2018
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      (6) Before, I was having the model render the image at the same size as the end result image that you saw. So you maxed out around 550px (maybe) because the GPU couldn't handle anymore. Now? Colors can be rendered at say a tiny 272x272 (as the image above), then the color part

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    2. Jason Antic‏ @citnaj 15 Nov 2018
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      (7) of the model output is simply resized and stretched to map over the much higher resolution original images's luminance portion (we already have that!). So the end result looks fantastic, because your eyes can't tell the difference with the color anyway!

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    3. Jason Antic‏ @citnaj 15 Nov 2018
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      (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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    4. Jason Antic‏ @citnaj 15 Nov 2018
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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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    5. Jason Antic‏ @citnaj 15 Nov 2018
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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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    6. Jason Antic‏ @citnaj 15 Nov 2018
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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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    7. Jason Antic‏ @citnaj 15 Nov 2018
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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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    8. Jason Antic‏ @citnaj 15 Nov 2018
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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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    9. Jason Antic‏ @citnaj 15 Nov 2018
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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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    10. Jason Antic‏ @citnaj 15 Nov 2018
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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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      Jason Antic‏ @citnaj 15 Nov 2018
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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!

      6:38 PM - 15 Nov 2018
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        2. Jason Antic‏ @citnaj 15 Nov 2018
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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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        3. Jason Antic‏ @citnaj 15 Nov 2018
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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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        4. Jason Antic‏ @citnaj 15 Nov 2018
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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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        5. End of conversation

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