sekstini

laksjdjf

'''
https://gist.github.com/kohya-ss/3f774da220df102548093a7abc8538ed
1. put this file in ComfyUI/custom_nodes
2. load node from <loaders>
'''

import torch
from comfy.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, timestep_embedding, th

def apply_control(h, control, name):

karpathy

"""
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

rom1504

Install

git clone https://github.com/mlfoundations/open_clip.git
cd open_clip
python3.8 -m venv .env
source .env/bin/activate
pip install -U pip
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip install -e .

rahulrajpl

tjjfvi

; acceleration_enabled = {acceleration_enabled}
; acceleration_infill = {acceleration_infill}
; acceleration_ironing = {acceleration_ironing}
; acceleration_layer_0 = {acceleration_layer_0}
; acceleration_prime_tower = {acceleration_prime_tower}
; acceleration_print = {acceleration_print}
; acceleration_print_layer_0 = {acceleration_print_layer_0}
; acceleration_roofing = {acceleration_roofing}
; acceleration_skirt_brim = {acceleration_skirt_brim}
; acceleration_support = {acceleration_support}

gilrosenthal

!pip install fastai
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python
import cv2
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())

accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'

!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision

tomfa

# This file is your Lambda function
import json
import boto3


def save_to_bucket(event, context):
    AWS_BUCKET_NAME = 'my-bucket-name'
    s3 = boto3.resource('s3')
    bucket = s3.Bucket(AWS_BUCKET_NAME)
    path = 'my-path-name.txt'

shagunsodhani

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Introduction

• The paper introduces a novel technique to explain the predictions of any classifier in an interpretable and faithful manner.
• It also proposes a method to explain models by obtaining representative individual predictions and their explanations.
• Link to the paper
• Demo

Desired Characteristics for Explanations

thomasp85

library(ggraph)
library(gganimate)
library(igraph)
# Data from http://konect.uni-koblenz.de/networks/sociopatterns-infectious
infect <- read.table('out.sociopatterns-infectious', skip = 2, sep = ' ', stringsAsFactors = FALSE)
infect$V3 <- NULL
names(infect) <- c('from', 'to', 'time')
infect$timebins <- as.numeric(cut(infect$time, breaks = 100))

# We want that nice fading effect so we need to add extra data for the trailing