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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 |
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#!/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 |
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# 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" |
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# 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 |
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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 |
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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) |
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/* | |
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: |
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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({ |
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#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" |
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#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! |
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