How To Install Divide Et Impera


Divide et Impera. There is place only for one kingdom! To go back to the menu while you are in game you can surrender by destroying your own palace!

How To Install Divide Et Impera

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Ancient Empires: Divide et Impera is a submod for DeI that brings with it a host of new features and options. It is meant to be run with DeI as an official submod. We would like to thank the DeI team once again for partnering with us and giving us the opportunity to build our Rome 2 work around their wonderful mod. Nov 26, 2020 Ludus Magnus Studio is raising funds for D.E.I. Divide et Impera on Kickstarter! - Divide et Impera is a competitive game based on conquests of resources, set in a world devastated by a New Ice Age.

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DreamPower is a fork of the DeepNude algorithm that generates better fake nudes and puts at your disposal a command line interface.

It consists of several algorithms that together create a fake nude from a photo.

If you don’t have experience using command line applications you can download DreamTime which offers you a friendly user interface.


Command-line interface✔️
NVIDIA GPU Support✔️
Automatic Scale✔️
GIF Support✔️
Video Support✔️
Body Customization✔️
Custom Masks✔️
Active Development✔️


  • 64 bits operating system:
    • Windows 7 or superior.
    • Ubuntu 16.04+
    • macOS Catalina or superior.
  • 12 GB of RAM.

GPU (Optional)

  • NVIDIA GPU (AMD GPU’s are not supported)
  • Minimum 3.5 CUDA compute capability. (GeForce GTX 780+)
  • Latest NVIDIA drivers.
  • 6 GB of GPU VRAM.
  • 8 GB of RAM.


In the command line terminal run:

This will print out help on the parameters the algorithm accepts.

The input image should be 512px * 512px in size (parameters are provided to auto resize/scale your input).



DreamPower is an open-source project that will be free forever. The project is kept in development thanks to the support of our incredible backers, you can also help keep the project alive in different ways:

Dei rome 2

How To Install Divide Et Impera Meaning

DreamPower uses an interesting method to solve a typical AI problem, so it could be useful for researchers and developers working in other fields such as fashion, cinema and visual effects.

Divide Et Impera Rome 2

The algorithm uses a slightly modified version of the pix2pixHD GAN architecture. If you are interested in the details of the network you can study this amazing project provided by NVIDIA.

A GAN network can be trained using both paired and unpaired dataset. Paired datasets get better results and are the only choice if you want to get photorealistic results, but there are cases in which these datasets do not exist and they are impossible to create. A database in which a person appears both naked and dressed, in the same position, is extremely difficult to achieve, if not impossible.

We overcome the problem using a divide-et-impera approach. Instead of relying on a single network, we divided the problem into 3 simpler sub-problems:

    1. Generation of a mask that selects clothes
    1. Generation of a abstract representation of anatomical attributes
    1. Generation of the fake nude photo

Original problem:

How To Install Divide Et Impera C++

Divide-et-impera problem:

This approach makes the construction of the sub-datasets accessible and feasible. Web scrapers can download thousands of images from the web, dressed and nude, and through photoshop you can apply the appropriate masks and details to build the dataset that solve a particular sub problem. Working on stylized and abstract graphic fields the construction of these datasets becomes a mere problem of hours working on photoshop to mask photos and apply geometric elements. Although it is possible to use some automations, the creation of these datasets still require great and repetitive manual effort.

Computer Vision Optimization

To optimize the result, simple computer vision transformations are performed before each GAN phase, using OpenCV. The nature and meaning of these transformations are not very important, and have been discovered after numerous trial and error attempts.

Divide Et Impera Adalah

Considering these additional transformations, the phases of the algorithm are the following:

  • dress -> correct [OPENCV]
  • correct -> mask [GAN]
  • mask -> maskref [OPENCV]
  • maskref -> maskdet [GAN]
  • maskdet -> maskfin [OPENCV]
  • maskfin -> nude [GAN]
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