lopho

#!/bin/bash

# Raspberry Pi ZRAM script
# Tuned for quad core, 1 GB RAM models
# put me in /etc/init.d/zram.sh and make me executable
# then run "sudo update-rc.d zram.sh defaults"

modprobe zram
echo 3 >/sys/devices/virtual/block/zram0/max_comp_streams
echo lz4 >/sys/devices/virtual/block/zram0/comp_algorithm

lopho

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

lopho

#include "OpenSimplexNoise.hpp"
#include <cmath> // sqrt
#include <numeric> // std::iota
#include <algorithm> // std::shuffle

const OpenSimplexNoise::value_t
OpenSimplexNoise::STRETCH_CONST = (sqrt(3) - 3) / 6,
OpenSimplexNoise::SQUISH_CONST = (sqrt(3) - 1) / 2;

const std::array<OpenSimplexNoise::gradient, 8> OpenSimplexNoise::GRADIENTS =

lopho

• normal(explicitly specified start AND end)

  curl -v -X GET -H "range: bytes=1-8" http://localhost:8080/bbb/test

• specified ONLY start(end will be specified at the end of file)

  curl -v -X GET -H "range: bytes=10-" http://localhost:8080/bbb/test

• specified ONLY one negative value(last N bytes of file will be retrieved)

lopho

import torch
import numpy as np
import k_diffusion as K

from PIL import Image
from torch import autocast
from einops import rearrange, repeat

def pil_img_to_torch(pil_img, half=False):
    image = np.array(pil_img).astype(np.float32) / 255.0

lopho

# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
# Written by jachiam

import argparse
import os.path as osp

import torch