As quixel is being removed, all items are free to aquire. This script is to automate the process to add items to your account (As of writing, a total of 18874 items)
Note: This script only tested in the latest version of Chrome.
run.js)
First, here is my SimpleTuner LoRA config, so you can get started with it. I used an 80GB A100 (thanks @bghira!).
LoRA repository:
| #!/usr/bin/env python3 | |
| """ | |
| Very simple HTTP server in python for logging requests | |
| Usage:: | |
| ./server.py [<port>] | |
| """ | |
| import json | |
| from http.server import BaseHTTPRequestHandler, HTTPServer | |
| import requests |
| # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. | |
| # *Only* converts the UNet, VAE, and Text Encoder. | |
| # Does not convert optimizer state or any other thing. | |
| # Written by jachiam | |
| import argparse | |
| import os.path as osp | |
| import torch |
| # ffmpeg -i foo.m4a foo.wav | |
| from pyannote.audio import Pipeline | |
| pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization") | |
| diarization = pipeline("foo.wav") | |
| # RTTM format https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf | |
| with open("foo.rttm", "w") as rttm: | |
| diarization.write_rttm(rttm) |
# Download Alpine Linux and install it on disk
cd /
wget https://dl-cdn.alpinelinux.org/alpine/v3.16/releases/x86_64/alpine-virt-3.16.2-x86_64.iso
| def normalize_latent(x, max_val, quantile_val): | |
| x = x.detach().clone() | |
| for i in range(x.shape[0]): | |
| if x[[i], :].std() > 1.0: | |
| x[[i], :] = x[[i], :] / x[[i], :].std() | |
| s = torch.quantile(torch.abs(x[[i], :]), quantile_val) | |
| s = torch.maximum(s, torch.ones_like(s) * max_val) | |
| x[[i], :] = x[[i], :] / (s / max_val) | |
| return x |
UPDATE: A faster (20x) approach for running Stable Diffusion using MLIR/Vulkan/IREE is available on Windows:
conda create --name sd39 python=3.9 -y
| """ | |
| 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 |
| /*============================================================================= | |
| ReShade 5 effect file | |
| github.com/martymcmodding | |
| Author: Pascal Gilcher / Marty McFly | |
| ReShade Motion Estimation Shader for dense 2D UV-space motion vectors | |
| Based on ReShade Motion Estimation by Jakob Wapenhensch | |
| (https://github.com/JakobPCoder/ReshadeMotionEstimation) |