madebyollin

#!/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))

madebyollin

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;

madebyollin

# 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

madebyollin

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

madebyollin

#!/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})"

madebyollin

madebyollin

List of good image generator training logs

A list of public training logs from neural network image generation models, since I think they're interesting.

The Criteria

• Publicly accessible link
• Losses plotted every so often
• Samples generated every so often
• Nontrivial dataset (i.e. not MNIST - 64x64 output RGB or better)

madebyollin

# ------------------------------------------------------------------
# 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

madebyollin

#!/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

madebyollin

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.
    """