| #!/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) |
| # Attempts to demangle all mangled symbols in the current program using the Rust | |
| # mangling schemes, and replace the default symbol and function signature | |
| # (if applicable) with the demangled symbol. | |
| # | |
| # License: MIT OR Apache-2.0 | |
| #@author Jack Grigg <thestr4d@gmail.com> | |
| #@category Symbol | |
| import string |
Modern versions of Windows support GPU paravirtualization in Hyper-V with normal consumer graphics cards. This is used e.g. for graphics acceleration in Windows Sandbox, as well as WSLg. In some cases, it may be useful to create a normal VM with GPU acceleration using this feature, but this is not officially supported. People already figured out how to do it with Windows guests though, so why not do the same with Linux? It should be easy given that WSLg is open source and reasonably well documented, right?
Well... not quite. I managed to get it to run... but not well.