darthdeus

"""
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

darthdeus

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
                      

darthdeus

// 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)

	}

darthdeus

{
    "type": "NodeCanvas.BehaviourTrees.BehaviourTree",
    "nodes": [
        {
            "_position": {
                "x": 717.0,
                "y": 139.0
            },
            "$type": "NodeCanvas.BehaviourTrees.Sequencer",
            "$id": "0"

darthdeus

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

darthdeus

• Codacy
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• Travis
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• Starhub
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• Greenkeeper
• Dependabot preview
• Hound
• Imgbot

darthdeus

• 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

darthdeus

\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{todonotes}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{amssymb}

\newcommand{\inlinecode}{\texttt}

\title{Státnice - Vyčíslitelnost}

darthdeus

QuantizeImage/Forward/                  : x=[8, 48, 48, 3]	z=[None]	logdet=[8]
SqueezingLayer/Forward/Scale1           : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
ActnormBiasLayer/Forward/               : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
ActnormScaleLayer/Forward/              : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
ChainLayer/Forward/                     : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
ActnormLayer/Forward/                   : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
InvertibleConv1x1Layer/Forward/         : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
AffineCouplingLayer/Forward/            : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
ChainLayer/Forward/Step1                : x=[8, 24, 24, 12]	z=[None]	logdet=[8]
ActnormBiasLayer/Forward/               : x=[8, 24, 24, 12]	z=[None]	logdet=[8]

darthdeus

import numpy as np
import numpy.linalg as linalg
import logging


def jitchol(A, maxtries=6):
    A = np.ascontiguousarray(A)
    diagA = np.diag(A)

    if np.any(diagA <= 0.):