kylemcdonald

#!/usr/bin/env bash

# List all projects
projects=$(gcloud projects list --format="value(projectId)")

# Function to check if Compute Engine API is enabled
is_compute_api_enabled() {
    local project=$1
    gcloud services list --enabled --project="$project" --format="value(config.name)" | grep -q "compute.googleapis.com"
}

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import os
import socket
import time
import struct
from multiprocessing import Process

socket_path = "/tmp/ipc_benchmark_socket"

def run_server():
    if os.path.exists(socket_path):

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#pragma once

#include <vector>
#include "ofImage.h"

class FixedVideoDelay {
private:
    int delayAmount;
    int totalFrames;
    int readPosition;

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import zmq

context = zmq.Context()
socket = context.socket(zmq.PULL)
socket.bind("tcp://*:5558")

print("Collector started... collecting results.")
while True:
    result = socket.recv_json()
    print(f"Collected: {result}")

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import threading
import queue

class ThreadedWorker():
    def __init__(self):
        self.input_queue = queue.Queue()
        self.output_queue = queue.Queue()
        self.should_exit = False
        self.thread = threading.Thread(target=self.run)
        

kylemcdonald

import types
import PIL
import torch
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img import retrieve_latents
from diffusers.utils.torch_utils import randn_tensor

def prepare_latents(
    self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True, fixed_noise=True
):
    if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):

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import numpy as np
import datetime
import re
from dataclasses import dataclass


def point_to_str(point):
    hex =  f"{point & 0xFFFFFF:06x}"
    hex = ("~" + hex) if (point & 0x1000000) else hex
    return hex

kylemcdonald

import argparse
import datetime
import urllib3
import os
from ratelimit import limits, sleep_and_retry
from tqdm import tqdm
import random
import time

domain = "globe.adsbexchange.com"

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