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#!/usr/bin/env python | |
filename = "output.txt" | |
with open(filename, 'w') as fout: | |
x = 0 | |
for x in range(5): | |
fout.write(""" def function_number{}(): \n | |
""".format(x)) |
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function addCompressor(el) { | |
var AudioContext = window.AudioContext || window.webkitAudioContext; | |
var context = new AudioContext(); | |
var source = context.createMediaElementSource(el); | |
var compressor = context.createDynamicsCompressor(); | |
var makeupGain = context.createGain(); | |
// Set compressor params here | |
compressor.threshold.value = -40; | |
compressor.knee.value = 40; |
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# PyTorch <=2.0 doesn't support bfloat16 F.interpolate natively. | |
# so, we have to do things the old fashioned way. | |
import torch | |
import torch.nn as nn | |
# functional implementation | |
def nearest_neighbor_upsample(x: torch.Tensor, scale_factor: int): | |
"""Upsample {x} (NCHW) by scale factor {scale_factor} using nearest neighbor interpolation.""" | |
s = scale_factor |
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def summarize_tensor(x): | |
return f"\033[34m{str(tuple(x.shape)).ljust(24)}\033[0m (\033[31mmin {x.min().item():+.4f}\033[0m / \033[32mmean {x.mean().item():+.4f}\033[0m / \033[33mmax {x.max().item():+.4f}\033[0m)" | |
class ModelActivationPrinter: | |
def __init__(self, module, submodules_to_log): | |
self.id_to_name = { | |
id(module): str(name) for name, module in module.named_modules() | |
} | |
self.submodules = submodules_to_log |
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#!/usr/bin/env python3 | |
from pathlib import Path | |
from safetensors.torch import load_file | |
def summarize_tensor(x): | |
if x is None: | |
return "None" | |
x = x.float() | |
return f"({x.min().item():.3f}, {x.mean().item():.3f}, {x.max().item():.3f})" |
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# ------------------------------------------------------------------ | |
# EDIT: I eventually found a faster way to run SD on macOS, via MPSGraph (~0.8s / step on M1 Pro): | |
# https://github.com/madebyollin/maple-diffusion | |
# The original CoreML-related code & discussion is preserved below :) | |
# ------------------------------------------------------------------ | |
# you too can run stable diffusion on the apple silicon GPU (no ANE sadly) | |
# | |
# quick test portraits (each took 50 steps x 2s / step ~= 100s on my M1 Pro): | |
# * https://i.imgur.com/5ywISvm.png |
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#!/usr/bin/env python | |
import numpy as np | |
import cv2 | |
from scipy.signal import convolve2d | |
from skimage import color, data, restoration | |
import console | |
# read input | |
frame = cv2.imread("input.jpg").astype(np.float32) / 255.0 |
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def add_profiling_markers(model): | |
"""Monkey-patch profiling markers into an nn.Module. | |
Args: | |
model: an nn.Module | |
Effect: | |
all model.named_module() forward calls get wrapped in their | |
own profiling scope, making traces easier to understand. | |
""" |
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#!/usr/bin/env python3 | |
""" | |
To use: | |
1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client | |
2. install pandoc and pypandoc, also tqdm | |
3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials | |
4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub | |
""" | |
import re | |
import sys |
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