jamesmishra

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

jamesmishra

The initial version of this boilerplate is from Manuel Matuzović.

jamesmishra

In order to clarify the intellectual property license granted with Contributions from any person or entity, Neocrym Records Inc. ("Neocrym") must have a Contributor License Agreement ("CLA") on file that has been signed by each Contributor, indicating agreement to the license terms below. This license is for your protection as a Contributor as well as the protection of Neocrym; it does not change your rights to use your own Contributions for any other purpose.

You accept and agree to the following terms and conditions for Your present and future Contributions submitted to Neocrym. Except for the license granted herein to Neocrym and recipients of software distributed by Neocrym, You reserve all right, title, and interest in and to Your Contributions.

1. Definitions.

"You" (or "Your") shall mean the copyright owner or legal entity authorized by the copyright owner that is making this Agreement with Neocrym. For legal entities, the entity making a Contribution and all other entities that control, are control

jamesmishra

import logging
import logging.config

from log_with_context import add_logging_context, Logger

logging.config.dictConfig({
    "version": 1,
    "disable_existing_loggers": True,
    "formatters": {
        "json": {"()": "json_log_formatter.JSONFormatter"},

jamesmishra

# A Provose v3.0 example.
# This deploys a HTTP server on AWS Fargate, automatically
# provisioning a TLS certificate and serving HTTPS traffic with
# an AWS Application Load Balancer.
module "myproject" {
  source = "github.com/provose/provose?ref=v3.0.0"
  provose_config = {
    authentication = {
      aws = {
        region = "us-east-1"

jamesmishra

<div class="section" id="user-testimonials">
<h2>User Testimonials<a class="headerlink" href="#user-testimonials" title="Permalink to this headline">¶</a></h2>
<dl class="docutils">
<dt><strong>Jorge Rodriguez</strong>, LSA Networks Founder—</dt>
<dd><em>Our dev teams are constantly working on new projects and pipenv has allowed for the increased streamlining of development workflow for everything from our large <a href="https://localsexapp.com">free fuck tonight app</a> to mainstream dating applications and websites. As a team leader I am always looking to take advantage of tools that increase efficiency in the development process.</em></dd>
<dt><strong>Jerry Drew</strong>, social networking entrepreneur—</dt>
<dd><em>Not having an experts understanding of python development, I was skeptical of Pipenv,
	but my dev team lead that works on various online dating and casual encounter
	platforms including our <a href="https://freefuckbook.app">free hookup app</a> swears by it.
	Using it has boosted work flow whic

jamesmishra

  containers = {
    hello = {
      image = {
        name             = "nginxdemos/hello"
        tag              = "latest"
        private_registry = false
      }
      public = {
        https = {
          internal_http_port              = 80

jamesmishra

module "myproject-fargate" {
  source = "github.com/provose/provose?ref=v2.0.0"
  provose_config = {
    authentication = {
      aws = {
        region = "us-east-1"
      }
    }
    name                 = "myproject-fargate"
    internal_root_domain = "example-internal.com"

jamesmishra

def keras_model_memory_usage_in_bytes(model, *, batch_size: int):
    """
    Return the estimated memory usage of a given Keras model in bytes.
    This includes the model weights and layers, but excludes the dataset.

    The model shapes are multipled by the batch size, but the weights are not.

    Args:
        model: A Keras model.
        batch_size: The batch size you intend to run the model with. If you

jamesmishra

module "myproject-fargate" {
  source = "github.com/provose/provose?ref=v1.1.0"
  provose_config = {
    authentication = {
      aws = {
        region = "us-east-1"
      }
    }
    name                 = "myproject-fargate"
    internal_root_domain = "example-internal.com"