Hugefiver hugefiver

fix convert_original_stable_diffusion_to_diffusers.py for fixing merged models

To fix broken model (mainly merged weights from AUTOMATIC111's no-licensed UI for Invoke AI ,

one can use CKPT -> HF/diffusers -> CKPT route.

Environment setup

🤗 Stable Diffusion for AMD GPUs on Windows using DirectML

Requirements

关于 AUTOMATIC1111/stable-diffusion-webui 的 FAQ

该 FAQ 并非意图一次性提供所有必要信息, 仅仅是提供必要的指路. 最后的杂项部分是宝藏区, 建议都翻翻看啦!

查看最新消息参阅 SD RESOURCE GOLDMINE 2 (English), 或者 sudoskys/StableDiffusionBook (中文)

前言

建议优先阅读官方文档或者 SD RESOURCE GOLDMINE 或者 VOLDY RETARD GUIDE或者としあきdiffusion

Stable Diffusion for AMD GPUs on Windows using DirectML

UPDATE: A faster (20x) approach for running Stable Diffusion using MLIR/Vulkan/IREE is available on Windows:

https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/stable_diffusion_amd.md

Install 🤗 diffusers

conda create --name sd39 python=3.9 -y

This downloads standalone MSVC compiler, linker & other tools, also headers/libraries from Windows SDK into portable folder, without installing Visual Studio. Has bare minimum components - no UWP/Store/WindowsRT stuff, just files & tools for native desktop app development.

Run py.exe portable-msvc.py and it will download output into msvc folder. By default it will download latest available MSVC & Windows SDK - currently v14.40.33807 and v10.0.26100.0.

You can list available versions with py.exe portable-msvc.py --show-versions and then pass versions you want with --msvc-version and --sdk-version arguments.

To use cl.exe/link.exe first run setup_TARGET.bat - after that PATH/INCLUDE/LIB env variables will be updated to use all the tools as usual. You can also use clang-cl.exe with these includes & libraries.

To use clang-cl.exe without running setup.bat, pass extra /winsysroot msvc argument (msvc is folder name where output is stored).

About

I faced bandwidth issues between a WG Peer and a WG server. Download bandwidth when downloading from WG Server to WG peer was reduced significantly and upload bandwidth was practically non existent.
I found a few reddit posts that said that we need to choose the right MTU. So I wrote a script to find an optimal MTU.
Ideally I would have liked to have run all possible MTU configurations for both WG Server and WG Peer but for simplicity I choose to fix the WG Server to the original 1420 MTU and tried all MTUs from 1280 to 1500 for the WG Peer.

Testing

On WG server, I started an iperf3 server
On WG peer, I wrote a script that does the following:
- wg-quick down wg0
Edit MTU in the /etc/wireguard/wg0.conf file

	import ast
	import requests

	class FunctionExtractor(ast.NodeVisitor):
	def __init__(self):
	self.functions = []
	self.function_calls = []

	def visit_FunctionDef(self, node):
	function_code = ast.unparse(node)

	// ==UserScript==
	// @name #相亲相爱一嘉人#
	// @description 在哔站右下角添加嘉然小姐的live2d模型
	// @version 1.0.1
	// @namespace https://github.com/journey-ad
	// @author journey-ad
	// @include /^https:\/\/(www\|live\|space\|t)\.bilibili\.com\/.*$/
	// @icon https://www.google.com/s2/favicons?domain=bilibili.com
	// @license GPL v2
	// @run-at document-end

+/**
+ * MySQL Multi-Line Insert
+ * =======================
+ * See: https://gist.github.com/ProjectCleverWeb/d2362b082af1d7054ebfd464f202ec1b
+ *
+ * Notes:
+ * - I used a output buffer variable since in most languages variables are
+ *   significantly faster than function calls and in this case the syntax for
+ *   using a variable was both shorter and easier to read.
+ * - Table & column names are quoted to help improve compatibility, but in most

	import bisect
	import heapq
	import inspect
	from random import randint, shuffle
	from statistics import mean
	from timeit import default_timer, repeat

	import matplotlib.pyplot as plt
	from bigO import bigO
	from win10toast import ToastNotifier