Quasimondo

# After a lot of searching and false or complicated leads I found this brilliant method
# that allows to use a numpy array to get direct read/write access to the rpi framebuffer
# https://stackoverflow.com/questions/58772943/how-to-show-an-image-direct-from-memory-on-rpi
# I thought it is worth sharing again since so it might someone else some research time
#
# The only caveat is that you will have to run this as root (sudo python yourscript.py), 
# But you can get around this if you add the current user to the "video" group like this:
# usermod -a -G video [user]
# source: https://medium.com/@avik.das/writing-gui-applications-on-the-raspberry-pi-without-a-desktop-environment-8f8f840d9867
# 

Quasimondo

import torch

#USE AT YOUR OWN RISK

#local path to runwayML SD 1.5 checkpoint (https://huggingface.co/runwayml/stable-diffusion-v1-5)
ckpt_15 = "./v1-5-pruned-emaonly.ckpt"

#local path to StabilityAI finetuned autoencoder (https://huggingface.co/stabilityai/sd-vae-ft-mse)
ckpt_vae = "./vae-ft-mse-840000-ema-pruned.ckpt"

Quasimondo

import json
from urllib.request import urlopen

def get_query_result(query, timeout=10):
    with urlopen(query, timeout=timeout) as request:
        if request.status == 200:
            return json.loads(request.read().decode())

# Get the list of users allowed to vote
hen_users = get_query_result("https://vote.hencommunity.quest/hen-users-snapshot-16-01-2022.json")

Quasimondo

I am attesting that this GitHub handle quasimondo is linked to the Tezos account tz1hb9PiWxQEf6J9xevPsUM6dkuCLnhDMvsp for tzprofiles

sig:edsigtoekSaBkHWoVT4WcedEWrgHS2jfG1tsaqUGH3o9ULoFn8yzEMsAa7JsZbXwu4iKPepv98hKX2QSwi1KMkn1YX8B3tSC4be

Quasimondo

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt upgrade

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda-repo-ubuntu2004-11-1-local_11.1.0-455.23.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-1-local_11.1.0-455.23.05-1_amd64.deb
sudo apt-key add /var/cuda-repo-ubuntu2004-11-1-local/7fa2af80.pub
sudo apt-get update

Quasimondo

import os
import pickle
import requests

#download cached token data here:
#https://drive.google.com/file/d/1g_4w_Re5Y0NmcS2Y55WQzESWDeL2dey6/view?usp=sharing
#and put it into the same folder as this file

cachedTokenData = {"maxTokenID":-1,"knownTokenIds":{},"data":[]}
if os.path.exists("cached_token_data.pickle"):

Quasimondo

import requests
import os
import ipfsApi

api = ipfsApi.Client(host='https://ipfs.infura.io', port=5001)

url = "https://better-call.dev/v1/contract/mainnet/KT1RJ6PbjHpwc3M5rw5s2Nbmefwbuwbdxton/tokens"

r = requests.get(url)
data = r.json()

Quasimondo

'''
Usage:
python quickwatermark.py [path to folder that contains images to waternark]

This will go through all the files in that folder, try to open them and add 
the filename as text on top of the image. The watermarked images will be stored
in a subfolder of the chosen folder called "watermarked"

'''

Quasimondo

This is a dreaded error that seems pop up its ugly head again and again, in particular after upgrading CUDA or Tensorflow.
Typcially it looks like this:

2020-12-30 17:31:40.829615: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2020-12-30 17:31:42.149768: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2020-12-30 17:31:42.150368: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2020-12-30 17:31:42.176643: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

Here is a solution that currently seems to work on my system, 
with Cuda 11.0 and Tensorflow 2.4.0, you can try it if all the 

Quasimondo

import torch
import torch.nn as nn
import torch.nn.functional as F

class LeakySine(nn.Module):
    def __init__(self, w0 =30.0, blend=0.75, slope = 0.2):
        super().__init__()
        self.blend = nn.Parameter(torch.ones(1, 1)*blend)
        self.slope = nn.Parameter(torch.zeros(1, 1)+slope)
        self.w0 = w0