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Mods

1. Core helpers

1. Antibodies
2. Skill Recovery Journal
3. [B41/B42] Homebound: Spawn Point

2. Maps / Radar 4. MinimapZ

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Detailed Guide for Running Hunyuan Video T2V on Your M4 MacBook

This guide shows you how to run the Hunyuan Video text‑to‑video (T2V) workflow on your M4 MacBook (128 GB RAM with a top‑end GPU). We’ll use the pre‑built dmg file to install ComfyUI (avoiding the need to compile C++ code), set up the workflow in “fast video” mode (reducing inference steps from ~20 to 6–8), and cover downloading and organizing the required model files.

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1. Overview

You will install ComfyUI using its pre‑built dmg installer, then configure the Hunyuan Video workflow from the available collection. We focus on using T2V (text‑to‑video) with the “fast video” mode to achieve faster iteration times. With your M4 MacBook’s ample resources, you can experiment confidently while monitoring system performance.

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import hashlib
import json
import random
from datetime import datetime

import diffusers
import piexif
import torch
from diffusers import FluxPipeline
from PIL import Image

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import mailbox
import csv
import email.utils
from collections import defaultdict
import argparse
from pathlib import Path
from bs4 import BeautifulSoup
from tqdm import tqdm

# Constants for field names

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"""
Fixes https://github.com/scikit-learn/scikit-learn/issues/12052
CalibratedClassifierGroupCV is a drop in replacment for CalibratedClassifierCV that supports GroupKFold cv.

This is based off of https://github.com/scikit-learn/scikit-learn/blob/0.24.1/sklearn/calibration.py.
If you are using a different version of sklearn, you can make similar modifications to your version.

Example usage:

```

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initial_susceptible # defaults to 3,600,000 https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/presentation.py#L200-L206
initial_infected # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L25-L27
initial_recovered # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L34
beta = # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L42-L45
gamma = # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L39
n_days = # User input, default to 60 or something

def sir(s, i, r, beta, gama, n):
    """The SIR model, one time step."""
    s_n = (-beta * s * i) + s

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{"date":{"4":1576826679362,"5":1576826680953,"6":1576826682705,"7":1576826715094,"8":1576826738398,"9":1576826749536,"10":1576826964746,"11":1576827009901,"12":1576827049302,"13":1576827049369,"14":1576827067127,"15":1576827067174,"16":1576827067715,"17":1576827071028,"18":1576827128560,"19":1576827181988,"20":1576827228449,"21":1576827233823,"22":1576827236225,"23":1576827244532,"24":1576827326470,"25":1576827331045,"26":1576827338079,"27":1576827342801,"28":1576827342887,"29":1576827362202,"30":1576827369175,"31":1576827406098,"32":1576827475226,"33":1576827479353,"34":1576827479381,"35":1576827481299,"36":1576827481300,"37":1576827484089,"38":1576827484095,"39":1576827495704,"40":1576827501289,"41":1576827508178,"42":1576827515849,"154":1576832407342,"155":1576832407392,"156":1576832428810,"157":1576832428828,"158":1576832429440,"179":1576840413638,"180":1576840413746,"181":1576840440551,"182":1576840440565,"183":1576840441194,"184":1576841274254,"185":1576841274351,"186":1576841285635,"187":1576841285658,

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from os import getpid, kill
from time import sleep
import re
import signal

from notebook.notebookapp import list_running_servers
from requests import get
from requests.compat import urljoin
import ipykernel
import json

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import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt


class _MiniBoxPlotter(sns.categorical._ViolinPlotter):

    def draw_violins(self, ax):
        """Draw the violins onto `ax`."""
        for i, group_data in enumerate(self.plot_data):

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from sklearn import metrics

def binary_cv_metrics(y, preds, m):
    ACC = metrics.accuracy_score(y,preds)
    cm = metrics.confusion_matrix(y,preds)

    m['confusion_matrix'] = cm
    m['Accuracy'] = ACC
    m['F1 score'] = metrics.f1_score(y,preds)
    m['FPR'] = cm[0,1]/(cm[0,:].sum()*1.0)