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#!/usr/bin/env ruby
# encoding: utf-8
#
# This file, gist, is generated code.
# Please DO NOT EDIT or send patches for it.
#
# Please take a look at the source from
# http://github.com/defunkt/gist
# and submit patches against the individual files
# that build gist.
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@darthdeus
darthdeus / stable_diffusion_walk.py
Created August 23, 2022 13:58 — forked from nateraw/stable_diffusion_walk.py
Walk between stable diffusion text prompts
"""
Built on top of this gist by @karpathy:
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
stable diffusion dreaming over text prompts
creates hypnotic moving videos by smoothly walking randomly through the sample space
example way to run this script:
$ python stable_diffusion_walk.py --prompts "['blueberry spaghetti', 'strawberry spaghetti']" --seeds 243,523 --name berry_good_spaghetti
to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i dreams/berry_good_spaghetti/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p berry_good_spaghetti.mp4
nice slerp def from @xsteenbrugge ty
App.ImageView = Ember.View.extend({
tagName: "img",
attributeBindings: ["src"],
failed: false,
didInsertElement: function() {
var self = this;
this.$()[0].onerror = function() {
thread 'main' panicked at 'Matrix index out of bounds.', /Users/darth/.cargo/registry/src/github.com-1ecc6299db9ec823/ncollide2d-0.26.1/src/query/ray/ray_aabb.rs:257:13
stack backtrace:
0: 0x103a862b4 - std::backtrace_rs::backtrace::libunwind::trace::h9b9011e5262e4f26
at /rustc/0edce6f4bbb4514482537f569f0b8ef48e71e0a0/library/std/src/../../backtrace/src/backtrace/libunwind.rs:90:5
1: 0x103a862b4 - std::backtrace_rs::backtrace::trace_unsynchronized::hdb5ec51860531ffc
at /rustc/0edce6f4bbb4514482537f569f0b8ef48e71e0a0/library/std/src/../../backtrace/src/backtrace/mod.rs:66:5
2: 0x103a862b4 - std::sys_common::backtrace::_print_fmt::h8851f8ff0b9f14ac
at /rustc/0edce6f4bbb4514482537f569f0b8ef48e71e0a0/library/std/src/sys_common/backtrace.rs:67:5
3: 0x103a862b4 - <std::sys_common::backtrace::_print::DisplayBacktrace as core::fmt::Display>::fmt::h8afa911bc5282e85
// Made with Amplify Shader Editor
// Available at the Unity Asset Store - http://u3d.as/y3X
Shader "DarkFlame"
{
Properties
{
_Color("Color", Color) = (0.6226415,0.2907618,0.2907618,0)
}
{
"type": "NodeCanvas.BehaviourTrees.BehaviourTree",
"nodes": [
{
"_position": {
"x": 717.0,
"y": 139.0
},
"$type": "NodeCanvas.BehaviourTrees.Sequencer",
"$id": "0"
pkgs: attrs:
with pkgs;
let defaultAttrs = {
builder = "${bash}/bin/bash";
args = [ ./builder.sh ];
baseInputs = [ gnutar gzip gnumake gcc binutils-unwrapped coreutils gawk gnused gnugrep findutils patchelf ];
buildInputs = [];
system = builtins.currentSystem;
};
in
  • Rush, A. M., Chopra, S., and Weston, J. (2015). A neural attention model for abstractive sentence summarization.
  • Chopra, S., Auli, M., and Rush, A. M. (2016). Abstractive sentence summarization with attentive recurrent neural networks.
  • Nallapati, R., Zhou, B., and Ma, M. (2016). Classify or select: Neural architectures for extractive document summarization.
  • Nallapati, R., Zhou, B., dos Santos, C. N., Gülçehre, Ç., and Xiang, B. (2016). Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond.
  • Nallapati, R., Zhai, F., and Zhou, B. (2017). Summarunner: A recurrent neural network based sequence model for extractive summarization of documents.
  • See, A., Liu, P. J., and Manning, C. D. (2017). Get to the point: Summarization with pointer-generator networks.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need.
  • Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz K
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{todonotes}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{amssymb}
\newcommand{\inlinecode}{\texttt}
\title{Státnice - Vyčíslitelnost}