venetanji

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler, AutoencoderTiny
import numpy as np
import torch
import cv2
from PIL import Image

# load control net and stable diffusion v1-5
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "lykon/dreamshaper-8-lcm", controlnet=controlnet, torch_dtype=torch.float16, safety_checker = None

venetanji

#!/bin/bash

# Step 0: Ensure .cache and .local link are symlinks
if [ ! -L ~/.cache ]; then
    # create the directory if it doesn't exist
    mkdir -p /vast/${USER}/.cache
    ln -s /vast/${USER}/.cache ~/.cache
fi

if [ ! -L ~/.local ]; then

venetanji

# Define the installation path
INSTALL_PATH="/vast/${USER}/comfyui/"

# create directory if it doesn't exist
mkdir -p "${INSTALL_PATH}"

# create comfyui_models.txt if it doesn't exist
if [ ! -f "${INSTALL_PATH}/comfyui_models.txt" ]; then
    # get the default modles file from gist: https://gist.githubusercontent.com/venetanji/7be194b3f943ff0597fa11f37d14fc1b/raw/881b73837a643db008fc13f503464a746b931eb5/comfyui_models.txt
    curl -s https://gist.githubusercontent.com/venetanji/7be194b3f943ff0597fa11f37d14fc1b/raw/881b73837a643db008fc13f503464a746b931eb5/comfyui_models.txt > "${INSTALL_PATH}/comfyui_models.txt"

venetanji

# Stable Cascade

#https://huggingface.co/stabilityai/stable-cascade/resolve/main/comfyui_checkpoints/stable_cascade_stage_c.safetensors
#  dir=models/checkpoints
#  out=stable_cascade_stage_c.safetensors
#https://huggingface.co/stabilityai/stable-cascade/resolve/main/comfyui_checkpoints/stable_cascade_stage_b.safetensors
#  dir=models/checkpoints
#  out=stable_cascade_stage_b.safetensors
#https://huggingface.co/stabilityai/stable-cascade/resolve/main/controlnet/canny.safetensors
#  dir=models/controlnet

venetanji

import sys
import numpy as np
import cv2 as cv
import NDIlib as ndi
import re
import asyncio
from pythonosc import udp_client
from facenet_pytorch import MTCNN
from hsemotion.facial_emotions import HSEmotionRecognizer
import torch

venetanji

import cv2
import asyncio
import time
# open camera and get video capture object
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
# set camera resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
size=10
cap.set(cv2.CAP_PROP_BUFFERSIZE,size)

venetanji

/*
	Esp8266 Websockets Client

	This sketch:
        1. Connects to a WiFi network
        2. Connects to a Websockets server
        3. Sends the websockets server a message ("Hello Server")
        4. Prints all incoming messages while the connection is open

	Hardware:

venetanji

const tf = require('@tensorflow/tfjs-node-gpu');
const cv = require("@u4/opencv4nodejs");
const posenet = require("@tensorflow-models/posenet")

let camera = new cv.VideoCapture(0);
camera.set(cv.CAP_PROP_FRAME_WIDTH, 640);
camera.set(cv.CAP_PROP_FRAME_HEIGHT, 480);

async function loadPosenet() {
    net = await posenet.load({

venetanji

#include <Arduino.h>
#ifdef ESP32
  #include <WiFi.h>
  #include "SPIFFS.h"
#else
  #include <ESP8266WiFi.h>
#endif
#include "AudioFileSourceSPIFFS.h"
#include "AudioGeneratorMP3.h"
#include "AudioOutputI2S.h"

venetanji

#include  <ESP8266WiFi.h>
#include <ArduinoWebsockets.h>

// include wpa2 enterprise code
extern "C" {
  #include "user_interface.h"
  #include "wpa2_enterprise.h"
}

// SSID, Username and password. Update with yours!