dataset.py import os import numpy as np import PIL import torch from PIL import Image from torch.utils.data import Dataset, DataLoader, Sampler from torchvision import transforms from collections import defaultdict from random import shuffle, choices import random import tqdm from modules import devices, shared import re from ldm.modules.distributions.distributions import DiagonalGaussianDistrib..

autocrop.py import cv2 import requests import os import numpy as np from PIL import ImageDraw GREEN = "#0F0" BLUE = "#00F" RED = "#F00" def crop_image(im, settings): """ Intelligently crop an image to the subject matter """ scale_by = 1 if is_landscape(im.width, im.height): scale_by = settings.crop_height / im.height elif is_portrait(im.width, im.height): scale_by = settings.crop_width / im.widt..

You are given an elliptical shaped land and you are asked to choose n arbitrary points on its boundary. Then you connect all these points with one another with straight lines (that’s n ∗ (n − 1)/2 connections for n points). What is the maximum number of pieces of land you will get by choosing the points on the boundary carefully? 주어진 타원형 땅에서 n개의 임의의 점을 경계선 위에 선택하고, 이러한 점들을 직선으로 서로 연결합니다 (n * (n ..

Recall the definition of the Fibonacci numbers: Given two numbers a and b, calculate how many Fibonacci numbers are in the range [a, b]. Input The input contains several test cases. Each test case consists of two non-negative integer numbers a and b. Input is terminated by a = b = 0. Otherwise, a ≤ b ≤ 10100. The numbers a and b are given with no superfluous leading zeros. Output For each test c..

uni_pc.py import torch import math import tqdm class NoiseScheduleVP: def __init__( self, schedule='discrete', betas=None, alphas_cumprod=None, continuous_beta_0=0.1, continuous_beta_1=20., ): """Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. We recommend to use schedule=..

sampler.py """SAMPLING ONLY.""" import torch from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC from modules import shared, devices class UniPCSampler(object): def __init__(self, model, **kwargs): super().__init__() self.model = model to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) self.before_sample = None self.after_sample = None self.register_buffer('alphas_c..

ddpm_edit.py """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ # File mod..

hypernetwork.py import datetime import glob import html import os import sys import traceback import inspect import modules.textual_inversion.dataset import torch import tqdm from einops import rearrange, repeat from ldm.util import default from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint from modules.textual_inversion import textual_inversion..

codeformer_arch.py # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py import math import torch from torch import nn, Tensor import torch.nn.functional as F from typing import Optional from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock from basicsr.utils.registry import ARCH_REGISTRY def calc_mean_std(feat, eps=1e-5): """Calculate me..

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