How to draw dicks
How to draw dicks
-insert what here- — how do you draw dicks that don’t look like green.
Для этого и создано приложение.
-insert what here-
Аноним asked:
luwha answered:
Every PENIS is different, some looks like mushrooms and some look like green bananas, but it’s not about preferences! They’re different and i suggest you to look for more than one reference, different types, even dildos can be used.
Seriously, don’t be shy.
They have diffrent thickness, sizes, as you can see with this simple overline. Foreskin or not is also a thing to consider. Try to make them easy forms:
LOOKS DUMB, but you have now a reference of your OWN memory, something you as an artist can easily identify. You’re creating your own reference now.
Study over their forms, move them, use different angles, practice is the key for everything and drawing penis is not different. This is the best part, because you can turn your study in your style, see how it will works or not and adapt it to your drawings.
EVERYONE DRAW PENIS IN DIFFERENT WAYS. Some focus on lines, others on colors, it’s good to check because you can learn different ways to make your penis more attractive and interesting on the drawings. Then take reffs and repeat all the steps!
How to draw dick butt
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How to Draw a Braid
wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. To create this article, 21 people, some anonymous, worked to edit and improve it over time.
This article has been viewed 165,553 times.
Braids are a challenge that have frustrated many artists. The good news is that it often becomes easier with practice.
Tip: Use the elbow and wrist to keep your strokes loose. Stay loose and relaxed. There’s no need to be precise; a little chaos is a good thing when it comes to drawing hair.
Tip: Your highlight color will be a little less saturated than your main hair color, and your shadow color will be a little more saturated. This keeps it more lifelike than shading with black and white.
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dickrnn/dickrnn.github.io
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README.md
A recurrent neural network trained to draw dicks.
This project is based on the original sketch-rnn demo, and is a fork of sketch-rnn-js, but customized for dicks.
The methodology is described in this paper: https://arxiv.org/abs/1704.03477
Dataset used for training is based on Quickdraw-appendix.
“Mean Time To Dick is a key measure of any given human or machine intelligence system.” — Elon Musk, on dick-rnn.
Articles about dick-rnn around the world, in The Next Web, Boing Boing, PC Gamer, Mashable, Dlisted, 9GAG, Código Espagueti and HD Tecnologia (Spanish), Feber (Swedish), dobreprogramy (Polish), futuretech (Austrian German), 4Gamers (Traditional Chinese), Gigazine and Karapaia (Japanese).
From Studio Moniker’s Quickdraw-appendix project:
In 2018 Google open-sourced the Quickdraw data set. “The world’s largest doodling data set”. The set consists of 345 categories and over 50 million drawings. For obvious reasons the data set was missing a few specific categories that people seem to enjoy drawing. This made us at Moniker think about the moral reality big tech companies are imposing on our global community and that most people willingly accept this. Therefore we decided to publish an appendix to the Google Quickdraw data set.
I also believe that “Doodling a penis is a light-hearted symbol for a rebellious act” and also “think our moral compasses should not be in the hands of big tech”.
Example Dicks from Main Demo
Examples of sharable generated dick doodles:
This recurrent neural network was trained on a dataset of roughly 10,000 dick doodles.
The Quickdraw-appendix dataset was processed via incremental RDP epsilons to fit most dicks within 200 steps. Note that I used the raw version, not their simplified version, since the dicks were more detailed. The processed dataset that is compatable with sketch-rnn’s strokes (no pun) is in this repo as dataset/dicks.npz and can be loaded this way:
Training samples from the dataset:
For best results, train with default sketch-rnn settings, but use a dropout keep probability of 80%. Early stopping was performed on the validation set. To maximize samples used for training/validation, no test set is used, and the test set is just set to the same 496 validation samples to be compatable with the data format expected by the existing code.
Command used to train the TensorFlow sketch-rnn model:
Update (4/27/2020) The Quickdraw-appendix dataset was updated, and there are now 25K examples, up from the earlier 10K. I processed the newer dataset as dicksv2.npz with a proper train/valid/test split of 23000/1500/693 samples. The Main Demo has been updated to use a larger, but slightly slower model trained on the revised dataset containing more training examples.
Original license for each file is indicated in the header comment for each source file, or referenced in the URL stated in the source file. Creative Commons License for datasets also indicated accordingly.
MIT License for my specific additional work.