Arkoniak

import torch
from diffusers import StableDiffusionPipeline

access_token = "<access token>"

# make sure you're logged in with `huggingface-cli login`
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=access_token)

pipe = pipe.to("cuda")

Arkoniak

Принятие заявок (CFP) на JuliaCon 2022 открыто: https://t.co/T9LrFCVJfH

Если вы хотите выступить с докладом или виртуальным постером, провести минисимпозиум, практикум или 
просто поделиться опытом, то обязательно перейдите по ссылке выше!
#JuliaLang #JuliaCon

Arkoniak

function foo()
    ts = Task[]
    begin
        for i in 1:20
            t = @task begin
                sleep(i/10)
                println(i)
                @assert i != 5
            end
            push!(ts, t)

Arkoniak

using CSV
using DataPipes
using SplitApplyCombine

csv = """
x,y,z
1,3,5
1,2,4
2,4,1
"""

Arkoniak

0.19345484517173928
0.11384864375977188
0.1844963245314597
0.11030110979110863
0.6292936087454175
0.11452167250127743
0.06513420967099504
0.9024443886795332
0.19154452909475705
0.34650274437026796

Arkoniak

function f1(v)
    v1 = deepcopy(v)
    stats = @timed sort!(v1)
    return stats.time*1.0e6
end

function f2(v)
    v1 = deepcopy(v)
    v2 = @views v1[500:1499]
    stats = @timed sort!(v2)

Arkoniak

"""
This is an implementation of the NBabel N-body problem.
See nbabel.org for more information.

This is 'naive & native' Julia with type and loop annotations for
fastmath and vectorization, because this is really is no effort.

Without annotations the code will be as slow as naive Python/IDL.

    include("nbabel.jl")

Arkoniak

"""
This is an implementation of the NBabel N-body problem.
See nbabel.org for more information.

This is 'naive & native' Julia with type and loop annotations for
fastmath and vectorization, because this is really is no effort.

Without annotations the code will be as slow as naive Python/IDL.

    include("nbabel.jl")

Arkoniak

"""
This is an implementation of the NBabel N-body problem.
See nbabel.org for more information.

This is 'naive & native' Julia with type and loop annotations for
fastmath and vectorization, because this is really is no effort.

Without annotations the code will be as slow as naive Python/IDL.

    include("nbabel.jl")

Arkoniak

using StableRNGs

function validate2(res, n)
    cnt = 0
    for i in res
        for j in res
            cnt += (i != j) & (i + j == n)
        end
    end
    cnt == 2