NetHole / sway.shader

Created July 24, 2024 07:11 — forked from mandarinx/sway.shader

Sway shader

	Shader "Toon/Lit Swaying" {
	Properties {
	_Color ("Main Color", Color) = (0.5,0.5,0.5,1)
	_MainTex ("Base (RGB)", 2D) = "white" {}
	_Ramp ("Toon Ramp (RGB)", 2D) = "gray" {}
	_Speed ("MoveSpeed", Range(20,50)) = 25 // speed of the swaying
	_Rigidness("Rigidness", Range(1,50)) = 25 // lower makes it look more "liquid" higher makes it look rigid
	_SwayMax("Sway Max", Range(0, 0.1)) = .005 // how far the swaying goes
	_YOffset("Y offset", float) = 0.5// y offset, below this is no animation

NetHole / Outline.shader

Created July 24, 2024 06:04 — forked from ScottJDaley/Outline.shader

Wide Outlines Renderer Feature for URP and ECS/DOTS/Hybrid Renderer

	// Original shader by @bgolus, modified slightly by @alexanderameye for URP, modified slightly more
	// by @gravitonpunch for ECS/DOTS/HybridRenderer.
	// https://twitter.com/bgolus
	// https://medium.com/@bgolus/the-quest-for-very-wide-outlines-ba82ed442cd9
	// https://alexanderameye.github.io/
	// https://twitter.com/alexanderameye/status/1332286868222775298

	Shader "Hidden/Outline"
	{
	Properties

NetHole / wd1-3-release.md

Created March 13, 2023 06:44 — forked from harubaru/wd1-3-release.md

Official Release Notes for Waifu Diffusion 1.3

Waifu Diffusion 1.3 Release Notes

	#include "InputModeDetector.h"
	#include "Input/Events.h"

	FInputModeDetector::FInputModeDetector()
	{
	// 4 local players should be plenty usually (will expand if necessary)
	LastInputModeByPlayer.Init(EInputMode::Mouse, 4);
	}

	bool FInputModeDetector::HandleKeyDownEvent(FSlateApplication& SlateApp, const FKeyEvent& InKeyEvent)

NetHole / pg-pong.py

Created May 7, 2020 08:24 — forked from karpathy/pg-pong.py

Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

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