arielreplicate / deoldify_image

Add colours to old images

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DeOldify

The easiest way to colorize images. DeOldify Image Colorization on DeepAI

The most advanced version of DeOldify image colorization is available here, exclusively. Try a few images for free! MyHeritage In Color

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About DeOldify

Simply put, the mission of this project is to colorize and restore old images and film footage. We’ll get into the details in a bit, but first let’s see some pretty pictures and videos!

Something to keep in mind- historical accuracy remains a huge challenge!

About the demo

The model have two available models:

  • Artistic: This model achieves the highest quality results in image coloration, in terms of interesting details and vibrance. The most notable drawback however is that it’s a bit of a pain to fiddle around with to get the best results (you have to adjust the rendering resolution or render_factor to achieve this). Additionally, the model does not do as well as stable in a few key common scenarios- nature scenes and portraits. The model uses a resnet34 backbone on a UNet with an emphasis on depth of layers on the decoder side. This model was trained with 5 critic pretrain/GAN cycle repeats via NoGAN, in addition to the initial generator/critic pretrain/GAN NoGAN training, at 192px. This adds up to a total of 32% of Imagenet data trained once (12.5 hours of direct GAN training).

  • Stable: This model achieves the best results with landscapes and portraits. Notably, it produces less “zombies”- where faces or limbs stay gray rather than being colored in properly. It generally has less weird miscolorations than artistic, but it’s also less colorful in general. This model uses a resnet101 backbone on a UNet with an emphasis on width of layers on the decoder side. This model was trained with 3 critic pretrain/GAN cycle repeats via NoGAN, in addition to the initial generator/critic pretrain/GAN NoGAN training, at 192px. This adds up to a total of 7% of Imagenet data trained once (3 hours of direct GAN training).

License

All code in this repository is under the MIT license as specified by the LICENSE file.