SLAPaper Pang SLAPaper
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| // ==UserScript== | |
| // @name Civitai Showcase All | |
| // @namespace http://tampermonkey.net/ | |
| // @version 2025-10-15 | |
| // @description Showcase all items in Civitai user page | |
| // @author SLAPaper | |
| // @match https://civitai.com/user/* | |
| // @match https://civitai.green/user/* | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=civitai.com | |
| // @grant none |
SLAPaper
/ convert_fp8_scaled_stochastic.py
Created
May 20, 2025 01:10
convert_fp8_scaled_stochastic.py from https://huggingface.co/Clybius/Chroma-fp8-scaled/blob/main/convert_fp8_scaled_stochastic.py
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| import argparse | |
| import os | |
| import torch | |
| import numpy as np | |
| from safetensors import safe_open | |
| from safetensors.torch import save_file | |
| from typing import Dict, Tuple | |
| # --- Configuration --- | |
| # Keys containing these substrings will not be quantized if --t5xxl is set |
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| // ==UserScript== | |
| // @name A1111 WebUI fix lobe theme | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.3 | |
| // @description fix Lobe theme broken webui v1.6.0 | |
| // @author SLAPaper, primeinc | |
| // @match http*://localhost:7860/* | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=undefined.localhost | |
| // @grant none | |
| // ==/UserScript== |
SLAPaper
/ Basic API call (TypeScript).EXCEL.yaml
Created
May 12, 2022 18:25
Performs a basic Excel API call using TypeScript.
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| name: Basic API call (TypeScript) | |
| description: Performs a basic Excel API call using TypeScript. | |
| host: EXCEL | |
| api_set: {} | |
| script: | |
| content: | | |
| $("#run").click(() => tryCatch(run)); | |
| async function run() { | |
| await Excel.run(async (context) => { |
SLAPaper
/ chinese-gb7714-2005-numeric-auto-lang.csl
Last active
June 7, 2019 16:45
Chinese GB/T 7714-2005 CSL-M that correctly deal with English citation with more than 3 authors.
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| <?xml version="1.0" encoding="utf-8"?> | |
| <style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only" default-locale="zh-CN"> | |
| <info> | |
| <title>Chinese Std GB/T 7714-2005 (numeric, auto languange)</title> | |
| <id>chinese-gb7714-2005-autolang</id> | |
| <link href="https://gist.github.com/SLAPaper/19497b1cf37bc7c8bb4af9969fe69e9a" rel="self"/> | |
| <link href="http://gradschool.ustc.edu.cn/ylb/material/xw/wdxz/19.pdf" rel="documentation"/> | |
| <author> | |
| <name>SLAPaper</name> | |
| <email>slapaper@pku.edu.cn</email> |
SLAPaper
/ throttle.py
Last active
April 9, 2019 18:22
— forked from ChrisTM/throttle.py
Python decorator for throttling function calls.
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| # https://gist.github.com/SLAPaper/9ef3699e5f76dc80c00bd2d405eef995 | |
| from datetime import datetime, timedelta | |
| from functools import wraps | |
| class throttle: | |
| """ | |
| Decorator that prevents a function from being called more than once every | |
| time period. |
SLAPaper
/ Update-pip.ps1
Created
February 4, 2019 05:18
Pip-Upgrade-All for (Powershell, cmd, bash)
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| Param( | |
| [Parameter(Position = 0)] | |
| [string[]]$python_name = ('python'), | |
| [string[]]$Exclude = (''), | |
| [bool]$NoDeps = $false | |
| ) | |
| foreach ($python in $python_name) { |
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| // ==UserScript== | |
| // @name Save to Zotero | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.1 | |
| // @description Make Zotero's boorkmarklet a user script (click the button 'Zotero!' on bottom right) | |
| // @author SLAPaper | |
| // @match *://*/* | |
| // @grant none | |
| // ==/UserScript== |
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| // ==UserScript== | |
| // @name Juejin max-width setter | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.1 | |
| // @description make the sidebar no longer overlaps the article | |
| // @author You | |
| // @match https://juejin.im/post/* | |
| // @grant none | |
| // @license MIT | |
| // ==/UserScript== |
SLAPaper
/ reuters_mlp_comparison (relu, elu, selu, swish).py
Last active
October 26, 2021 19:41
Compare RELU, ELU, SELU, Swish and Scaled Swish in Reuters MLP (based on Keras' example)
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| '''Compares self-normalizing MLPs with regular MLPs. | |
| Compares the performance of a simple MLP using two | |
| different activation functions: RELU and SELU | |
| on the Reuters newswire topic classification task. | |
| # Reference: | |
| Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017). | |
| Self-Normalizing Neural Networks. arXiv preprint arXiv:1706.02515. | |
| https://arxiv.org/abs/1706.02515 |
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