diff --git a/spaces/0x90e/ESRGAN-MANGA/ESRGAN_plus/block.py b/spaces/0x90e/ESRGAN-MANGA/ESRGAN_plus/block.py
deleted file mode 100644
index 5c9cd2fbc2334080fa4b1a85bd1a37769a396d55..0000000000000000000000000000000000000000
--- a/spaces/0x90e/ESRGAN-MANGA/ESRGAN_plus/block.py
+++ /dev/null
@@ -1,287 +0,0 @@
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-
-####################
-# Basic blocks
-####################
-
-
-def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1):
- # helper selecting activation
- # neg_slope: for leakyrelu and init of prelu
- # n_prelu: for p_relu num_parameters
- act_type = act_type.lower()
- if act_type == 'relu':
- layer = nn.ReLU(inplace)
- elif act_type == 'leakyrelu':
- layer = nn.LeakyReLU(neg_slope, inplace)
- elif act_type == 'prelu':
- layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
- else:
- raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
- return layer
-
-
-def norm(norm_type, nc):
- # helper selecting normalization layer
- norm_type = norm_type.lower()
- if norm_type == 'batch':
- layer = nn.BatchNorm2d(nc, affine=True)
- elif norm_type == 'instance':
- layer = nn.InstanceNorm2d(nc, affine=False)
- else:
- raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
- return layer
-
-
-def pad(pad_type, padding):
- # helper selecting padding layer
- # if padding is 'zero', do by conv layers
- pad_type = pad_type.lower()
- if padding == 0:
- return None
- if pad_type == 'reflect':
- layer = nn.ReflectionPad2d(padding)
- elif pad_type == 'replicate':
- layer = nn.ReplicationPad2d(padding)
- else:
- raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
- return layer
-
-
-def get_valid_padding(kernel_size, dilation):
- kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
- padding = (kernel_size - 1) // 2
- return padding
-
-
-class ConcatBlock(nn.Module):
- # Concat the output of a submodule to its input
- def __init__(self, submodule):
- super(ConcatBlock, self).__init__()
- self.sub = submodule
-
- def forward(self, x):
- output = torch.cat((x, self.sub(x)), dim=1)
- return output
-
- def __repr__(self):
- tmpstr = 'Identity .. \n|'
- modstr = self.sub.__repr__().replace('\n', '\n|')
- tmpstr = tmpstr + modstr
- return tmpstr
-
-
-class ShortcutBlock(nn.Module):
- #Elementwise sum the output of a submodule to its input
- def __init__(self, submodule):
- super(ShortcutBlock, self).__init__()
- self.sub = submodule
-
- def forward(self, x):
- output = x + self.sub(x)
- return output
-
- def __repr__(self):
- tmpstr = 'Identity + \n|'
- modstr = self.sub.__repr__().replace('\n', '\n|')
- tmpstr = tmpstr + modstr
- return tmpstr
-
-
-def sequential(*args):
- # Flatten Sequential. It unwraps nn.Sequential.
- if len(args) == 1:
- if isinstance(args[0], OrderedDict):
- raise NotImplementedError('sequential does not support OrderedDict input.')
- return args[0] # No sequential is needed.
- modules = []
- for module in args:
- if isinstance(module, nn.Sequential):
- for submodule in module.children():
- modules.append(submodule)
- elif isinstance(module, nn.Module):
- modules.append(module)
- return nn.Sequential(*modules)
-
-
-def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True, \
- pad_type='zero', norm_type=None, act_type='relu', mode='CNA'):
- '''
- Conv layer with padding, normalization, activation
- mode: CNA --> Conv -> Norm -> Act
- NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
- '''
- assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [{:s}]'.format(mode)
- padding = get_valid_padding(kernel_size, dilation)
- p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
- padding = padding if pad_type == 'zero' else 0
-
- c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, \
- dilation=dilation, bias=bias, groups=groups)
- a = act(act_type) if act_type else None
- if 'CNA' in mode:
- n = norm(norm_type, out_nc) if norm_type else None
- return sequential(p, c, n, a)
- elif mode == 'NAC':
- if norm_type is None and act_type is not None:
- a = act(act_type, inplace=False)
- # Important!
- # input----ReLU(inplace)----Conv--+----output
- # |________________________|
- # inplace ReLU will modify the input, therefore wrong output
- n = norm(norm_type, in_nc) if norm_type else None
- return sequential(n, a, p, c)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
- """1x1 convolution"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class GaussianNoise(nn.Module):
- def __init__(self, sigma=0.1, is_relative_detach=False):
- super().__init__()
- self.sigma = sigma
- self.is_relative_detach = is_relative_detach
- self.noise = torch.tensor(0, dtype=torch.float).to(torch.device('cuda'))
-
- def forward(self, x):
- if self.training and self.sigma != 0:
- scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
- sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
- x = x + sampled_noise
- return x
-
-
-####################
-# Useful blocks
-####################
-
-
-class ResNetBlock(nn.Module):
- '''
- ResNet Block, 3-3 style
- with extra residual scaling used in EDSR
- (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
- '''
-
- def __init__(self, in_nc, mid_nc, out_nc, kernel_size=3, stride=1, dilation=1, groups=1, \
- bias=True, pad_type='zero', norm_type=None, act_type='relu', mode='CNA', res_scale=1):
- super(ResNetBlock, self).__init__()
- conv0 = conv_block(in_nc, mid_nc, kernel_size, stride, dilation, groups, bias, pad_type, \
- norm_type, act_type, mode)
- if mode == 'CNA':
- act_type = None
- if mode == 'CNAC': # Residual path: |-CNAC-|
- act_type = None
- norm_type = None
- conv1 = conv_block(mid_nc, out_nc, kernel_size, stride, dilation, groups, bias, pad_type, \
- norm_type, act_type, mode)
- # if in_nc != out_nc:
- # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \
- # None, None)
- # print('Need a projecter in ResNetBlock.')
- # else:
- # self.project = lambda x:x
- self.res = sequential(conv0, conv1)
- self.res_scale = res_scale
-
- def forward(self, x):
- res = self.res(x).mul(self.res_scale)
- return x + res
-
-
-class ResidualDenseBlock_5C(nn.Module):
- '''
- Residual Dense Block
- style: 5 convs
- The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
- '''
-
- def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
- norm_type=None, act_type='leakyrelu', mode='CNA', noise_input=True):
- super(ResidualDenseBlock_5C, self).__init__()
- # gc: growth channel, i.e. intermediate channels
- self.noise = GaussianNoise() if noise_input else None
- self.conv1x1 = conv1x1(nc, gc)
- self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
- norm_type=norm_type, act_type=act_type, mode=mode)
- self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
- norm_type=norm_type, act_type=act_type, mode=mode)
- self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
- norm_type=norm_type, act_type=act_type, mode=mode)
- self.conv4 = conv_block(nc+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
- norm_type=norm_type, act_type=act_type, mode=mode)
- if mode == 'CNA':
- last_act = None
- else:
- last_act = act_type
- self.conv5 = conv_block(nc+4*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \
- norm_type=norm_type, act_type=last_act, mode=mode)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv2(torch.cat((x, x1), 1))
- x2 = x2 + self.conv1x1(x)
- x3 = self.conv3(torch.cat((x, x1, x2), 1))
- x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
- x4 = x4 + x2
- x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
- return self.noise(x5.mul(0.2) + x)
-
-
-class RRDB(nn.Module):
- '''
- Residual in Residual Dense Block
- (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
- '''
-
- def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
- norm_type=None, act_type='leakyrelu', mode='CNA'):
- super(RRDB, self).__init__()
- self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
- norm_type, act_type, mode)
- self.RDB2 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
- norm_type, act_type, mode)
- self.RDB3 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
- norm_type, act_type, mode)
- self.noise = GaussianNoise()
-
- def forward(self, x):
- out = self.RDB1(x)
- out = self.RDB2(out)
- out = self.RDB3(out)
- return self.noise(out.mul(0.2) + x)
-
-
-####################
-# Upsampler
-####################
-
-
-def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, \
- pad_type='zero', norm_type=None, act_type='relu'):
- '''
- Pixel shuffle layer
- (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
- Neural Network, CVPR17)
- '''
- conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias, \
- pad_type=pad_type, norm_type=None, act_type=None)
- pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
- n = norm(norm_type, out_nc) if norm_type else None
- a = act(act_type) if act_type else None
- return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_blcok(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True, \
- pad_type='zero', norm_type=None, act_type='relu', mode='nearest'):
- # Up conv
- # described in https://distill.pub/2016/deconv-checkerboard/
- upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
- conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias, \
- pad_type=pad_type, norm_type=norm_type, act_type=act_type)
- return sequential(upsample, conv)
diff --git a/spaces/1368565466ki/ZSTRD/modules.py b/spaces/1368565466ki/ZSTRD/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/1368565466ki/ZSTRD/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2012 Crack Kickass Torre The Best Way to Enjoy Autocad without Paying a Dime.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2012 Crack Kickass Torre The Best Way to Enjoy Autocad without Paying a Dime.md
deleted file mode 100644
index 9ac136cc1517bcdca90db110d4510abc5d325672..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2012 Crack Kickass Torre The Best Way to Enjoy Autocad without Paying a Dime.md
+++ /dev/null
@@ -1,108 +0,0 @@
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Autocad 2012 Crack Kickass Torre: What You Need to Know
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If you are looking for a way to get Autocad 2012, a popular software for designing and drafting, for free, you might have come across the term "Autocad 2012 Crack Kickass Torre". But what does it mean and how can you use it? In this article, we will explain everything you need to know about Autocad 2012 Crack Kickass Torre, including what it is, how it works, and what are the risks and benefits of using it. We will also provide you with a step-by-step guide on how to download and install Autocad 2012 Crack from Kickass Torre, as well as some tips and tricks for using it successfully.
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What is Autocad 2012?
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Autocad 2012 is a software developed by Autodesk that allows you to create and edit 2D and 3D designs. It is widely used by architects, engineers, designers, and other professionals who need to create accurate and detailed drawings. Autocad 2012 has many features and benefits that make it a powerful and versatile tool for design and drafting.
Some of the features and benefits of Autocad 2012 are:
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It has a user-friendly interface that lets you access various tools and commands easily.
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It supports multiple file formats, such as DWG, DXF, DWF, PDF, JPG, PNG, etc.
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It allows you to create complex shapes and objects with the help of advanced modeling tools.
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It enables you to add dimensions, annotations, text, hatches, tables, leaders, etc. to your drawings.
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It allows you to customize your workspace with different layouts, views, styles, palettes, etc.
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It has a built-in rendering engine that lets you create realistic images and animations of your designs.
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It has a cloud service that lets you store, share, and access your files online.
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It has a collaboration feature that lets you work with other users on the same project.
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It has a parametric drawing feature that lets you define relationships between objects and modify them automatically.
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It has an array feature that lets you create multiple copies of objects in a pattern.
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To run Autocad 2012 smoothly on your computer, you need to meet the following system requirements:
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Operating system: Windows XP SP3 or later (32-bit or 64-bit), Windows Vista SP1 or later (32-bit or 64-bit), Windows 7 (32-bit or 64-bit)
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Processor: Intel Pentium 4 or AMD Athlon dual-core processor with SSE2 technology (3 GHz or higher)
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Memory: 2 GB RAM (4 GB recommended)
-
Disk space: 6 GB free disk space for installation
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Display: 1024 x 768 resolution with true color (1600 x 1050 recommended)
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Graphics card: Microsoft DirectX®-compatible graphics card with hardware acceleration (256 MB or higher)
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Internet connection: Required for online services
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To use Autocad 2012 on your computer, you also need to have a valid license key that activates the software. A license key is a unique code that proves that you have purchased the software legally from Autodesk or an authorized reseller. A license key can be obtained by paying a subscription fee or buying a perpetual license. However, some people may not be able or willing to pay for the software, so they look for alternative ways to get it for free. One of these ways is using a crack.
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What is a Crack?
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A crack is a program or file that modifies or bypasses the original software's security features. A crack can be used to remove the license verification process or generate fake license keys that trick the software into thinking that it is activated. A crack can also be used to unlock additional features or functions that are not available in the original software. A crack can be applied to various types of software, such as games, applications, operating systems, etc.
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How Cracks Work and Why People Use Them
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A crack works by altering the code or data of the original software in some way. For example, a crack may change some values in the registry or memory that control the license verification process. A crack may also replace some files or folders in the installation directory that contain the security features. A crack may also inject some code into the running process of the software that disables the security features. A crack may also create some fake files or folders that mimic the original ones but contain different information.
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People use cracks for various reasons. Some of the common reasons are:
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To save money by not paying for the software.
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To test the software before buying it.
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To access additional features or functions that are not available in the original software.
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To bypass regional restrictions or censorship that prevent them from using the software.
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To avoid updating or registering the software.
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Risks and Drawbacks of Using Cracks
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While using cracks may seem tempting or convenient for some people, there are also many risks and drawbacks associated with them. Some of the common risks and drawbacks are:
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You may violate the intellectual property rights of the software developer or owner.
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You may expose your computer to viruses, malware, spyware, ransomware, etc. that may be hidden in the cracks.
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You may damage your computer system or files by installing corrupted or incompatible cracks.
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You may lose access to online services or updates that require an authentic license key.
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You may experience performance issues or errors due to conflicts between the cracks and the original software.
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You may compromise your personal information or data by using cracks from untrusted sources.
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Therefore, using cracks is not recommended as it may cause more harm than good. If you want to use Autocad 2012 legally and safely, you should buy it from Autodesk or an authorized reseller. However, if you still insist on using a crack for Autocad 2012, you should be aware of where to get it from and how to use it properly. One of the sources where people get cracks from is Kickass Torre.
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What is Kickass Torre?
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Kickass Torre is a website that hosts torrent files for various types of content, such as movies, music, games, software, etc. A torrent file is a small file that contains information about the content and how to download it from other users who have the content on their computers. A torrent file does not contain the content itself, but rather the metadata that helps to locate and download it. To download a torrent file, you need a torrent client, such as BitTorrent, uTorrent, or qBittorrent, that can read the torrent file and connect to other users who have the content on their computers. The process of downloading a torrent file is called seeding, and the process of uploading a torrent file is called leeching. The more seeders and le
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Elysian cracked roblox download 13 Full lua loadstrings scripts and more.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Elysian cracked roblox download 13 Full lua loadstrings scripts and more.md
deleted file mode 100644
index 500c6a317d9cbb74efc48e415d4dd322597588ab..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Elysian cracked roblox download 13 Full lua loadstrings scripts and more.md
+++ /dev/null
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Elysian Cracked Roblox Download 13: Everything You Need to Know
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Roblox is one of the most popular online gaming platforms in the world, with millions of players creating and exploring various games every day. However, some players may want to enhance their gaming experience by using exploits, which are tools that can modify the game's code and give them an advantage over other players. One of the most powerful and sought-after exploits for Roblox is Elysian, which can execute any script and bypass any anti-cheat system. In this article, we will tell you everything you need to know about Elysian cracked Roblox download 13, including what it is, how to get it, why use it, how to use it, and some tips and tricks for using it safely and effectively.
Elysian is a premium exploit for Roblox that can run any script with full Lua support, loadstrings, and more. It can also bypass any anti-exploit system that Roblox has, such as Filtering Enabled, Remote Spy, Script Dumper, etc. This means that you can use Elysian to execute any script that you want without getting detected or banned by Roblox. Some of the scripts that you can use with Elysian are aimbot, fly, noclip, speed, teleport, kill all, admin commands, and more.
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Features of Elysian
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Some of the features that make Elysian one of the best exploits for Roblox are:
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Full Lua support: You can run any Lua script with Elysian, including those that require loadstrings or require functions.
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Script hub: You can access a huge library of scripts that are compatible with Elysian from its built-in script hub. You can also add your own scripts or import scripts from other sources.
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Custom UI: You can customize the appearance and functionality of Elysian's user interface according to your preferences. You can change the theme, color, font, size, position, transparency, etc. of the UI elements.
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Auto-update: You don't have to worry about updating Elysian manually every time Roblox updates its game engine. Elysian has an auto-update feature that will automatically download and install the latest version of the exploit whenever it is available.
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Support: You can get help and support from the developers and the community of Elysian through its official website, Discord server, YouTube channel, etc.
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How to get Elysian cracked version
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Elysian is a premium exploit that costs $20 to buy from its official website. However, some people may not be able or willing to pay for it. In that case, they may look for a cracked version of Elysian that they can download for free from other sources. A cracked version is a modified version of the original exploit that bypasses its security features and allows anyone to use it without paying.
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However, getting a cracked version of Elysian is not easy or safe. Most of the websites or videos that claim to offer a free download link for Elysian cracked are fake or malicious. They may contain viruses, malware, spyware, ransomware, or other harmful programs that can infect your computer or steal your personal information. They may also ask you to complete surveys, download apps, enter your email or password, or perform other tasks that are scams or phishing attempts.
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Elysian is one of the best exploits for Roblox because it can give you many advantages and benefits over other players. Some of the reasons why you may want to use Elysian for Roblox are:
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Benefits of using Elysian
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You can have more fun and enjoyment by using different scripts that can enhance your gameplay or let you do things that are normally impossible in Roblox.
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You can have more control and freedom over your game by modifying its code and changing its rules according to your wishes.
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You can have more power and dominance over other players by using scripts that can give you unfair advantages such as aimbot, fly, speed, kill all, etc.
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You can have more creativity and expression by using scripts that can let you create or customize your own game modes, maps, items, characters, etc.
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You can have more challenge and learning by using scripts that can test your skills or teach you new things about coding or game development.
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Risks of using Elysian
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You may get banned by Roblox if they detect that you are using an exploit or violating their terms of service. Although Elysian has a strong anti-detection system that can prevent most bans, there is still a small chance that you may get caught or reported by other players or moderators.
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You may get infected by malware or scammed by fake websites or videos if you download a cracked version of Elysian from untrusted sources. As we mentioned earlier, most of the links that offer a free download of Elysian cracked are fake or malicious and can harm your computer or steal your information.
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You may get bored or frustrated by using an exploit if you abuse it too much or use it for the wrong reasons. If you use an exploit to cheat or ruin other people's games without their consent or enjoyment, you may lose the fun and challenge of playing Roblox normally. You may also face backlash or hate from other players who dislike exploits or cheaters.
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How to use Elysian for Roblox?
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If you have bought Elysian from its official website and want to use it for Roblox, here are the steps that you need to follow:
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Step 1: Download and install Elysian
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After purchasing Elysian from its website , you will receive an email with a download link for the exploit. Click on the link and download the zip file containing the exploit files. Extract the zip file to a folder on your computer and run the setup.exe file as administrator. Follow the instructions on the screen to install Elysian on your computer.
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Step 2: Run Elysian and select a script
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After installing Elysian on your computer
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Excel 2013 Free Download A Step-by-Step Guide for Windows 10 Users.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Excel 2013 Free Download A Step-by-Step Guide for Windows 10 Users.md
deleted file mode 100644
index 433650f65897f324e58d4a38c6d4a4e06e9f65f8..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Excel 2013 Free Download A Step-by-Step Guide for Windows 10 Users.md
+++ /dev/null
@@ -1,38 +0,0 @@
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-
How to Get Excel 2013 Free Download for Windows 10
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Excel 2013 is one of the most popular spreadsheet applications in the world. It allows you to create, edit, and analyze data in various formats, such as tables, charts, graphs, and pivot tables. Excel 2013 also has many advanced features, such as formulas, functions, macros, and data analysis tools. If you want to use Excel 2013 on your Windows 10 PC, you might be wondering how to get it for free. In this article, we will show you some ways to do that.
The easiest way to get Excel 2013 for free is to use the Office 365 trial. Office 365 is a subscription service that gives you access to the latest versions of Microsoft Office applications, including Excel. You can sign up for a free trial of Office 365 for one month and use Excel 2013 on your Windows 10 PC. Here are the steps to use the Office 365 trial:
Sign in with your Microsoft account or create a new one.
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Enter your payment details and confirm your order. You will not be charged until the end of the trial period.
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Download and install Office 365 on your Windows 10 PC.
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Launch Excel 2013 and enjoy using it for free for one month.
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Method 2: Use the Excel Online App
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Another way to get Excel 2013 for free is to use the Excel Online app. This is a web-based version of Excel that lets you create and edit spreadsheets online. You can access it from any browser on your Windows 10 PC. You can also save your files to OneDrive or download them to your PC. Here are the steps to use the Excel Online app:
Click on the New blank workbook button or choose from the templates available.
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Create and edit your spreadsheet online using the familiar Excel interface and features.
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Save your file to OneDrive or download it to your PC.
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Method 3: Use a Third-Party Software
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A third way to get Excel 2013 for free is to use a third-party software that can open and edit Excel files. There are many free alternatives to Excel that offer similar functionality and compatibility. Some of them are:
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LibreOffice Calc: A free and open source software that is part of the LibreOffice suite. It can open and edit Excel files as well as other spreadsheet formats.
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Apache OpenOffice Calc: Another free and open source software that is part of the Apache OpenOffice suite. It can also open and edit Excel files as well as other spreadsheet formats.
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WPS Office Spreadsheets: A free software that is part of the WPS Office suite. It can open and edit Excel files as well as other spreadsheet formats. It also has a cloud version that lets you access your files online.
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To use a third-party software, follow these steps:
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Download and install the software of your choice on your Windows 10 PC.
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Launch the software and open an Excel file or create a new one.
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Edit your spreadsheet using the software's features and interface.
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Save your file in Excel format or another format of your choice.
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Far Cry 3 Blood Dragon Crack Only Enjoy the Kick- Action with No-DVD [FTS] or [Reloaded].md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Far Cry 3 Blood Dragon Crack Only Enjoy the Kick- Action with No-DVD [FTS] or [Reloaded].md
deleted file mode 100644
index 183253d1c91f71a9c1627227543b7ca5685f5046..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Far Cry 3 Blood Dragon Crack Only Enjoy the Kick- Action with No-DVD [FTS] or [Reloaded].md
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@@ -1,118 +0,0 @@
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Far Cry 3 Blood Dragon Crack Only: How to Play the Game for Free
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Introduction
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Do you love shooting games with a twist of humor and nostalgia? Do you want to experience a cyberpunk adventure in a neon-lit world full of mutants, cyborgs, and blood dragons? Do you want to do all that without spending a dime? If you answered yes to any of these questions, then you might be interested in Far Cry 3 Blood Dragon Crack Only.
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Far Cry 3 Blood Dragon is a standalone expansion of the popular first-person shooter game Far Cry 3. It was released in 2013 by Ubisoft as a homage to the 1980s sci-fi and action movies. The game is set in an alternate version of 2007, where you play as Sergeant Rex "Power" Colt, a cybernetic commando who has to stop a rogue colonel from unleashing a nuclear war.
The game features a retro-futuristic aesthetic, with neon colors, pixelated graphics, synth music, and cheesy dialogue. The game also parodies many tropes and cliches of the genre, such as hacking minigames, training montages, one-liners, and boss fights. The game is full of references and easter eggs to popular movies, games, and shows of the era, such as Terminator, RoboCop, Aliens, Rambo, Predator, Tron, Blade Runner, Star Wars, and more.
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A crack is a modified version of a game's executable file or other files that bypasses the copy protection or digital rights management (DRM) system of the game. A crack allows you to play a game without having to purchase it or activate it online. A crack can also enable features that are otherwise locked or restricted by the game's developer or publisher.
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You might need a crack for various reasons. Maybe you want to try out a game before buying it. Maybe you don't have enough money to buy it. Maybe you don't have an internet connection or don't want to deal with online activation. Maybe you want to mod the game or play it with custom settings. Whatever your reason is, a crack can help you enjoy a game for free.
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The third thing you need to do is to download Far Cry 3 Blood Dragon Crack Only from a reliable site. You can find many sites that offer cracks for games, but not all of them are safe or working. Some of them might contain malware, viruses, adware, spyware, or other unwanted programs that can harm your PC or steal your data. Some of them might not work properly or cause errors or crashes in the game.
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To avoid these problems, you should download Far Cry 3 Blood Dragon Crack Only from a reputable site like MegaGames.com , which offers various fixes and patches for games. You can also check other sites like GameCopyWorld.com or SkidrowReloaded.com for alternative cracks.
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Table: Comparison of different cracks for Far Cry 3 Blood Dragon
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Crack name
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Uploader
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Size
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Date
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Features
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Ratings
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FAR.CRY.3.BD.V1.02.ALL.FTS.NODVD.ZIP
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FTS
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689 KB
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July 8, 2013
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Bypasses Ubisoft DRM; includes update v1.02; credits RELOADED for uplay wrapper
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4/5 stars; 174 votes; positive comments
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FAR.CRY.3.BD.V1.02.ALL.RELOADED.NODVD.ZIP
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Reloaded
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689 KB
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July 9, 2013
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Bypasses Ubisoft DRM; includes update v1.02; uses own uplay wrapper
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4/5 stars; 98 votes; positive comments
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FAR.CRY.3.BD.V1.0.ALL.RELOADED.NODVD.ZIP
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Reloaded
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689 KB
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May 1, 2019
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Bypasses Ubisoft DRM; does not include update v1.02; uses own uplay wrapper
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4/5 stars; 67 votes; positive comments
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FAR.CRY.3.BD.V1.0.ALL.FTS.NODVD.ZIP
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FTS
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689 KB
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May 1,2019
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Bypasses Ubisoft DRM; does not include update v1.02; credits RELOADED for uplay wrapper
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4/5 stars; 54 votes; positive comments
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FAR.CRY.3.BD.V1.0.ALL.FTS.NODVD.ZIP
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FTS
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689 KB
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May 1 ,2019
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Bypasses Ubisoft DRM; does not include update v1.02; credits RELOADED for uplay wrapper
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4/5 stars; 54 votes; positive comments
Note: The table above is based on information available at MegaGames.com as of May ,2020 . The information may change over time.
: https://megagames.com/fixes/far-cry-3-blood-dragon-v102-all-no-dvd-fts [^2]: https://megagames.com/download/316635/0 : https://skidrowreloaded.com/f 0a6ba089eb
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Astro Vision Lifesign With Remedies 12.5 Free Download ((FREE)).rar.md b/spaces/1gistliPinn/ChatGPT4/Examples/Astro Vision Lifesign With Remedies 12.5 Free Download ((FREE)).rar.md
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Astro Vision Lifesign with Remedies 12.5: A Comprehensive Software for Vedic Astrology
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If you are interested in Vedic astrology and want to get accurate horoscopes and predictions based on your birth details, you might want to try Astro Vision Lifesign with Remedies 12.5. This is a software that allows you to create and analyze your own horoscope, as well as get remedies for any problems or obstacles you might face in life.
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What is Astro Vision Lifesign with Remedies 12.5?
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Astro Vision Lifesign with Remedies 12.5 is a software that is based on the principles of Vedic astrology, also known as Jyotish or Hindu astrology. Vedic astrology is an ancient system of knowledge that uses the positions of the planets and stars at the time of your birth to reveal your personality, destiny, and karma.
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astro vision lifesign with remedies 12.5 free download.rar
Astro Vision Lifesign with Remedies 12.5 can help you to generate your own horoscope, which is a graphical representation of the sky at the moment of your birth. The horoscope shows the placement of the 12 zodiac signs, the 9 planets, and the 27 lunar constellations or nakshatras in the 12 houses or bhavas. Each of these elements has a specific meaning and influence on your life.
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Astro Vision Lifesign with Remedies 12.5 can also help you to interpret your horoscope and get detailed predictions for various aspects of your life, such as career, education, marriage, health, wealth, family, etc. The software uses various methods of analysis, such as dasa system, ashtakavarga system, transit system, yogas, etc., to give you accurate and reliable results.
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What are the benefits of Astro Vision Lifesign with Remedies 12.5?
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Astro Vision Lifesign with Remedies 12.5 is not just a software for creating and reading horoscopes. It is also a software that can help you to overcome any difficulties or challenges you might face in life. The software can suggest remedies or parihara for any doshas or afflictions in your horoscope that might cause problems or delays in your life.
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The remedies are based on the principles of Vedic astrology and can include various types of solutions, such as mantras, yantras, gemstones, rudrakshas, donations, fasting, etc. The software can also recommend auspicious times or muhurthas for performing any important activities or events in your life.
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By using Astro Vision Lifesign with Remedies 12.5, you can get a better understanding of yourself and your life purpose. You can also get guidance and support for achieving your goals and fulfilling your potential.
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How to download Astro Vision Lifesign with Remedies 12.5 for free?
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If you want to try Astro Vision Lifesign with Remedies 12.5 for yourself, you can download it for free from the internet. However, you need to be careful about the source of the download, as some websites might offer fake or corrupted files that might harm your computer or compromise your privacy.
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One of the safest and easiest ways to download Astro Vision Lifesign with Remedies 12.5 for free is to use a file sharing platform like Peatix or SoundCloud. These platforms allow users to upload and download files without any restrictions or fees. You can find the link to download Astro Vision Lifesign with Remedies 12.5 for free from these platforms below:
Once you have downloaded the file, you need to extract it using a software like WinRAR or 7-Zip. Then you need to install the software by following the instructions on the screen. You might need to enter a serial number or a crack code to activate the software.
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Conclusion
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Astro Vision Lifesign with Remedies 12.5 is a software that can help you to explore the fascinating world of Vedic astrology and get insights into your life and future. It can also help you to find solutions and remedies for any problems or obstacles you might encounter in life.
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If you want to download Astro Vision Lifesign with Remedies 12.5 for free, you can use the links provided above and enjoy the benefits of this software.
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What are the features of Astro Vision Lifesign with Remedies 12.5?
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Astro Vision Lifesign with Remedies 12.5 is a software that has many features and options to suit your needs and preferences. Some of the features of this software are:
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It supports multiple languages, such as English, Hindi, Tamil, Telugu, Malayalam, Kannada, Marathi, Bengali, Oriya, etc.
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It allows you to choose from various chart styles, such as North Indian, South Indian, East Indian, Kerala, etc.
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It provides detailed reports on various aspects of your life, such as personality, education, career, marriage, children, health, wealth, etc.
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It gives you predictions for the current year and the next 25 years based on the dasa system.
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It gives you remedies or parihara for any doshas or afflictions in your horoscope that might cause problems or delays in your life.
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It gives you auspicious times or muhurthas for performing any important activities or events in your life.
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It gives you compatibility analysis or porutham for marriage or partnership.
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It gives you gemstone recommendations based on your horoscope.
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It gives you panchanga or almanac information for any date and time.
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It gives you ayanamsa options such as Lahiri ayanamsa, Raman ayanamsa, Krishnamurthy ayanamsa, etc.
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What are the reviews of Astro Vision Lifesign with Remedies 12.5?
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Astro Vision Lifesign with Remedies 12.5 is a software that has received positive reviews from many users and experts. Some of the reviews of this software are:
-
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"I have been using Astro Vision Lifesign with Remedies 12.5 for a long time and I am very satisfied with it. It is very accurate and easy to use. It has helped me to understand myself better and to make better decisions in life. It has also helped me to find solutions and remedies for any problems I faced in life. I would recommend this software to anyone who is interested in Vedic astrology." - S S Gopalakrishnan
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-
"Astro Vision Lifesign with Remedies 12.5 is a comprehensive software for Vedic astrology. It has everything you need to create and analyze your own horoscope and get predictions and remedies for various aspects of your life. It is very user-friendly and customizable. It supports multiple languages and chart styles. It is also very affordable and reliable. I have been using this software for years and I have never been disappointed." - Rajesh Kumar
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"Astro Vision Lifesign with Remedies 12.5 is a software that can help you to explore the fascinating world of Vedic astrology and get insights into your life and future. It can also help you to find solutions and remedies for any problems or obstacles you might encounter in life. It is a software that can change your life for the better." - Priya Sharma
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-
Conclusion
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Astro Vision Lifesign with Remedies 12.5 is a software that can help you to create and analyze your own horoscope based on the principles of Vedic astrology. It can also help you to get predictions and remedies for various aspects of your life. It is a software that can help you to overcome any difficulties or challenges you might face in life.
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If you want to download Astro Vision Lifesign with Remedies 12.5 for free, you can use the links provided above and enjoy the benefits of this software.
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How to use Astro Vision Lifesign with Remedies 12.5?
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Astro Vision Lifesign with Remedies 12.5 is a software that is easy to use and user-friendly. You can follow these simple steps to use this software:
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Download Astro Vision Lifesign with Remedies 12.5 for free from the links provided above and extract the file using a software like WinRAR or 7-Zip.
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Install the software by following the instructions on the screen. You might need to enter a serial number or a crack code to activate the software.
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Launch the software and enter your name, date of birth, time of birth, and place of birth. You can also choose your preferred language, chart style, ayanamsa, etc.
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Click on the Generate Horoscope button and wait for a few seconds. The software will create your horoscope and display it on the screen.
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Click on the different tabs and buttons to view various reports and predictions for your life. You can also get remedies or parihara for any doshas or afflictions in your horoscope.
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Click on the Print or Save button to print or save your horoscope and reports for future reference.
-
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Why choose Astro Vision Lifesign with Remedies 12.5?
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Astro Vision Lifesign with Remedies 12.5 is a software that has many advantages and benefits over other astrology software. Some of the reasons why you should choose this software are:
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It is based on the principles of Vedic astrology, which is an ancient and authentic system of knowledge that can reveal your true nature and destiny.
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It is accurate and reliable, as it uses advanced mathematical calculations and algorithms to generate your horoscope and predictions.
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It is comprehensive and detailed, as it covers various aspects of your life, such as personality, education, career, marriage, health, wealth, family, etc.
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It is helpful and supportive, as it suggests remedies or parihara for any problems or obstacles you might face in life.
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It is affordable and accessible, as you can download it for free from the internet and use it on any Windows PC.
-
-
-
Conclusion
-
-
Astro Vision Lifesign with Remedies 12.5 is a software that can help you to create and analyze your own horoscope based on the principles of Vedic astrology. It can also help you to get predictions and remedies for various aspects of your life. It is a software that can help you to overcome any difficulties or challenges you might face in life.
-
-
If you want to download Astro Vision Lifesign with Remedies 12.5 for free, you can use the links provided above and enjoy the benefits of this software.
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Conclusion
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-
Astro Vision Lifesign with Remedies 12.5 is a software that can help you to create and analyze your own horoscope based on the principles of Vedic astrology. It can also help you to get predictions and remedies for various aspects of your life. It is a software that can help you to overcome any difficulties or challenges you might face in life.
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If you want to download Astro Vision Lifesign with Remedies 12.5 for free, you can use the links provided above and enjoy the benefits of this software.
3cee63e6c2
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Athlean X Workout PDF Learn from the Expert Jeff Cavaliere.md b/spaces/1gistliPinn/ChatGPT4/Examples/Athlean X Workout PDF Learn from the Expert Jeff Cavaliere.md
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full body workout, ATHLEAN-X workout. These are exercises that lend themselves to increased strength at a faster pace and also help to coordinate multiple muscle groups into one action, making them more athletically based and functionality ...full body workout, ATHLEAN-X workout. These are exercises that lend themselves to increased strength at a faster pace and also help to coordinate multiple muscle groups into one action, making them more athletically based and functionality. Given the fact that science will support the fact that stimulation and re-stimulation of a muscle ever 48 hours produces the mostvamound of muscle growth with the least amount of wasted time, you will find this workouts taking advantage of this. Read more
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Digikam Handbuch Deutsch Pdf 205.md b/spaces/1gistliPinn/ChatGPT4/Examples/Digikam Handbuch Deutsch Pdf 205.md
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Digikam Handbuch Deutsch Pdf 205: Ein umfassender Ratgeber für Fotofreunde
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Digikam ist eine leistungsstarke und vielseitige Anwendung für die Verwaltung, Bearbeitung und Präsentation von digitalen Fotos. Mit Digikam können Sie Ihre Bilder organisieren, bewerten, kommentieren, verschlagworten, suchen und filtern. Sie können auch Ihre Kamera mit Digikam verbinden und Fotos direkt von dort importieren. Digikam bietet Ihnen außerdem zahlreiche Werkzeuge und Effekte zur Verbesserung Ihrer Bilder, wie z.B. Schärfen, Farbkorrektur, Belichtungskorrektur, Rote-Augen-Entfernung und vieles mehr. Sie können Ihre Bilder auch drucken, per E-Mail versenden oder in Online-Galerien hochladen.
Um alle Funktionen und Möglichkeiten von Digikam zu nutzen, empfehlen wir Ihnen, das Digikam Handbuch Deutsch Pdf 205 zu lesen. Dieses Handbuch ist eine umfassende und aktuelle Dokumentation zu Digikam, die Ihnen Schritt für Schritt erklärt, wie Sie die Anwendung einrichten und verwenden können. Das Handbuch enthält auch viele Tipps und Tricks für Profis, die ihre Fotos noch weiter optimieren wollen.
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Was Sie im Digikam Handbuch Deutsch Pdf 205 finden
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Das Digikam Handbuch Deutsch Pdf 205 ist in vier Hauptteile gegliedert:
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Einleitung: Hier erfahren Sie mehr über die Hintergründe und Ziele von Digikam, wie Sie Probleme melden oder Support erhalten können, wie Sie sich an der Entwicklung beteiligen können und welche Bildformate und Module Digikam unterstützt.
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Digikam benutzen: Hier lernen Sie die Grundlagen der Bedienung von Digikam kennen, wie z.B. das Hauptfenster, die Albenliste, die Stichwortliste, die Datumsliste und die Suchliste. Sie erfahren auch, wie Sie eine Digitalkamera mit Digikam verwenden können, wie Sie Ihre Bilder bearbeiten können und wie Sie Ihre Bilder drucken oder teilen können.
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Beschreibung der Menüs: Hier erhalten Sie eine detaillierte Beschreibung aller Menüs und Optionen von Digikam, sowohl im Hauptfenster als auch in der Bildbearbeitung.
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Mitwirkende und Lizenz: Hier finden Sie Informationen über die Autoren und Übersetzer des Handbuchs sowie die Lizenzbedingungen für Digikam.
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Darüber hinaus enthält das Handbuch einen Anhang mit Informationen zur Installation von Digikam sowie ein Abbildungsverzeichnis mit allen Screenshots aus dem Handbuch.
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Wie Sie das Digikam Handbuch Deutsch Pdf 205 lesen können
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Sie haben mehrere Möglichkeiten, das Digikam Handbuch Deutsch Pdf 205 zu lesen:
Sie können es im EPUB-Format herunterladen hier und es auf Ihrem E-Reader oder Tablet lesen.
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Sie können es als PDF-Datei herunterladen hier und es auf Ihrem Computer oder Drucker ausdrucken.
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Wir hoffen, dass Ihnen das Digikam Handbuch Deutsch Pdf 205 gefällt und Ihnen hilft, das Beste aus Ihren Fotos herauszuholen. Wenn Sie Fragen oder Anregungen haben, zögern Sie nicht, uns zu kontaktieren. Wir freuen uns über Ihr Feedback!
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Wie Sie Digikam installieren und einrichten
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Um Digikam zu installieren, müssen Sie zunächst die passende Version für Ihr Betriebssystem herunterladen. Sie können Digikam für Windows, Linux oder Mac OS X von der offiziellen Website hier beziehen. Folgen Sie dann den Anweisungen auf dem Bildschirm, um die Installation abzuschließen.
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Nach der Installation müssen Sie Digikam einrichten, um Ihre Fotos zu verwalten. Dazu müssen Sie zunächst ein Album erstellen, in dem Sie Ihre Fotos speichern wollen. Sie können mehrere Alben für verschiedene Themen oder Projekte anlegen. Um ein Album zu erstellen, klicken Sie auf das Symbol "Neues Album" in der Albenliste oder wählen Sie "Album -> Neues Album" aus dem Menü. Geben Sie dann einen Namen und einen Speicherort für das Album an und klicken Sie auf "OK".
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Als nächstes müssen Sie Ihre Fotos in das Album importieren. Sie können dies auf verschiedene Weisen tun:
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Sie können Fotos von Ihrer Festplatte oder einem externen Speichermedium in das Album ziehen und ablegen.
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Sie können Fotos von Ihrer Digitalkamera direkt in das Album importieren, indem Sie Ihre Kamera mit Ihrem Computer verbinden und auf das Symbol "Kamera" in der Werkzeugleiste klicken oder "Kamera -> Kamera hinzufügen" aus dem Menü wählen.
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Sie können Fotos aus einer Online-Galerie wie Flickr, Google Photos oder Facebook in das Album importieren, indem Sie auf das Symbol "Online-Speicher" in der Werkzeugleiste klicken oder "Extras -> Online-Speicher" aus dem Menü wählen.
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Nachdem Sie Ihre Fotos in das Album importiert haben, können Sie sie nach verschiedenen Kriterien sortieren, filtern und durchsuchen. Sie können auch Stichworte, Kommentare, Bewertungen und andere Metadaten zu Ihren Fotos hinzufügen, um sie besser zu organisieren und zu finden.
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Wie Sie Digikam zur Bildbearbeitung verwenden
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Digikam bietet Ihnen eine Reihe von Werkzeugen und Effekten zur Verbesserung Ihrer Fotos. Um ein Foto zu bearbeiten, müssen Sie es zunächst aus dem Album auswählen und doppelklicken oder auf das Symbol "Bildbearbeitung" in der Werkzeugleiste klicken oder "Bild -> Bildbearbeitung" aus dem Menü wählen. Dies öffnet ein neues Fenster mit dem Foto und verschiedenen Optionen zur Bearbeitung.
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In der Bildbearbeitung können Sie folgende Aktionen ausführen:
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Sie können das Foto zuschneiden, drehen, spiegeln oder skalieren, indem Sie die entsprechenden Symbole in der Werkzeugleiste anklicken oder die Optionen unter dem Menü "Transformieren" wählen.
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Sie können die Schärfe, den Kontrast, die Helligkeit, die Sättigung, die Farbbalance oder die Belichtung des Fotos anpassen, indem Sie die entsprechenden Schieberegler im Reiter "Korrigieren" verwenden oder die Optionen unter dem Menü "Korrigieren" wählen.
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Sie können verschiedene Spezialeffekte und Filter auf das Foto anwenden, wie z.B. Schwarz-Weiß, Sepia, Ölgemälde, Cartoon oder Rauschreduzierung, indem Sie die entsprechenden Symbole im Reiter "Filter" anklicken oder die Optionen unter dem Menü "Filter" wählen.
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Sie können auch weitere Funktionen wie Rote-Augen-Entfernung, Perspektivenkorrektur, Weißabgleich oder Histogramm anzeigen lassen, indem Sie die entsprechenden Symbole im Reiter "Extras" anklicken oder die Optionen unter dem Menü "Extras" wählen.
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Nachdem Sie Ihre Änderungen vorgenommen haben, können Sie das Foto speichern, indem Sie auf das Symbol "Speichern" in der Werkzeugleiste klicken oder "Datei -> Speichern" aus dem Menü wählen. Sie können auch eine Kopie des Fotos speichern oder das Originalfoto wiederherstellen.
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Wie Sie Digikam zur Präsentation Ihrer Fotos verwenden
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Digikam bietet Ihnen auch verschiedene Möglichkeiten, Ihre Fotos zu präsentieren und zu teilen. Sie können Ihre Fotos als Diashow anzeigen lassen, indem Sie auf das Symbol "Diashow" in der Werkzeugleiste klicken oder "Bild -> Diashow" aus dem Menü wählen. Sie können dabei verschiedene Einstellungen wie die Übergangseffekte, die Anzeigedauer oder die Hintergrundmusik anpassen.
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Sie können Ihre Fotos auch als HTML-Galerie exportieren, indem Sie auf das Symbol "HTML-Galerie" in der Werkzeugleiste klicken oder "Extras -> HTML-Galerie" aus dem Menü wählen. Sie können dabei verschiedene Vorlagen, Farben und Schriftarten für Ihre Galerie auswählen und sie auf Ihrer Festplatte speichern oder per FTP auf einen Server hochladen.
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Sie können Ihre Fotos auch in eine Online-Galerie wie Flickr, Google Photos oder Facebook hochladen, indem Sie auf das Symbol "Online-Speicher" in der Werkzeugleiste klicken oder "Extras -> Online-Speicher" aus dem Menü wählen. Sie müssen sich dazu zunächst mit Ihrem Konto bei dem jeweiligen Dienst anmelden und die Berechtigungen für Digikam erteilen. Dann können Sie die Fotos auswählen, die Sie hochladen wollen, und die entsprechenden Einstellungen wie den Titel, die Beschreibung, die Stichworte oder die Privatsphäre angeben.
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Wie Sie Digikam aktualisieren und erweitern können
-
-
Um Digikam auf dem neuesten Stand zu halten und von den neuesten Funktionen und Verbesserungen zu profitieren, empfehlen wir Ihnen, regelmäßig nach Updates zu suchen. Sie können dies tun, indem Sie auf das Symbol "Aktualisieren" in der Werkzeugleiste klicken oder "Einstellungen -> Aktualisieren" aus dem Menü wählen. Digikam wird dann überprüfen, ob eine neuere Version verfügbar ist, und Ihnen gegebenenfalls einen Download-Link anbieten.
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Um Digikam zu erweitern und neue Funktionen hinzuzufügen, können Sie verschiedene Plugins installieren. Plugins sind kleine Erweiterungen, die zusätzliche Werkzeuge oder Effekte für Digikam bereitstellen. Sie können Plugins für Digikam von der offiziellen Website hier herunterladen oder aus dem Menü "Einstellungen -> Plugins" installieren. Um ein Plugin zu verwenden, müssen Sie es zunächst aktivieren und dann die entsprechende Option im Menü oder in der Werkzeugleiste auswählen.
-
Wie Sie Digikam zur Scannung und Konvertierung Ihrer Fotos verwenden
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Digikam ermöglicht Ihnen auch, Ihre analogen Fotos zu scannen und zu konvertieren. Sie können Ihre Fotos von einem Flachbettscanner oder einem Filmscanner importieren, indem Sie auf das Symbol "Scannen" in der Werkzeugleiste klicken oder "Extras -> Scannen" aus dem Menü wählen. Sie müssen dazu zunächst Ihren Scanner mit Ihrem Computer verbinden und die entsprechenden Treiber installieren.
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Nachdem Sie Ihren Scanner ausgewählt haben, können Sie die Scan-Einstellungen wie die Auflösung, den Farbmodus oder den Scan-Bereich anpassen. Sie können auch eine Vorschau des Scans anzeigen lassen und die Bildqualität überprüfen. Wenn Sie mit den Einstellungen zufrieden sind, können Sie den Scan starten und das Foto in einem Album Ihrer Wahl speichern.
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Sie können Ihre Fotos auch von einem anderen Dateiformat in ein anderes konvertieren, indem Sie auf das Symbol "Konvertieren" in der Werkzeugleiste klicken oder "Extras -> Konvertieren" aus dem Menü wählen. Sie können dabei mehrere Fotos gleichzeitig konvertieren und die Zielgröße, das Zielverzeichnis und das Zielformat angeben. Digikam unterstützt verschiedene Dateiformate wie JPEG, PNG, TIFF, RAW oder PDF.
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Wie Sie Digikam zur Sicherung und Wiederherstellung Ihrer Fotos verwenden
-
-
Digikam bietet Ihnen auch verschiedene Möglichkeiten, Ihre Fotos zu sichern und wiederherzustellen. Sie können Ihre Fotos auf eine CD oder DVD brennen, indem Sie auf das Symbol "Brennen" in der Werkzeugleiste klicken oder "Extras -> Brennen" aus dem Menü wählen. Sie müssen dazu zunächst einen leeren Datenträger in Ihr Laufwerk einlegen und die Brenn-Einstellungen wie die Geschwindigkeit, das Dateisystem oder die Beschriftung anpassen.
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Sie können Ihre Fotos auch auf eine externe Festplatte oder einen USB-Stick kopieren, indem Sie auf das Symbol "Kopieren" in der Werkzeugleiste klicken oder "Extras -> Kopieren" aus dem Menü wählen. Sie müssen dazu zunächst Ihr Speichermedium mit Ihrem Computer verbinden und den Speicherort auswählen.
-
-
Sie können Ihre Fotos auch aus einer Sicherung wiederherstellen, indem Sie auf das Symbol "Wiederherstellen" in der Werkzeugleiste klicken oder "Extras -> Wiederherstellen" aus dem Menü wählen. Sie müssen dazu zunächst Ihr Sicherungsmedium mit Ihrem Computer verbinden und die Quelle auswählen. Dann können Sie die Fotos auswählen, die Sie wiederherstellen wollen, und den Zielort angeben.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Burger Place Mod APK 0.15.0 Review Rating and Download Link.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Burger Place Mod APK 0.15.0 Review Rating and Download Link.md
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Burger Place Mod APK 0.15.0: A Fun and Addictive Cooking Game
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Do you love burgers? Do you want to run your own burger shop? Do you want to have unlimited money and no ads while playing? If you answered yes to any of these questions, then you should download Burger Place Mod APK 0.15.0 right now!
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Burger Place Mod APK 0.15.0 is a modified version of the original game Burger Please, which is a fun and addictive cooking game where you have to make burgers for your hungry customers. You can choose from different ingredients, toppings, sauces, breads, and sides to create your own unique burger recipes.
But that's not all! With Burger Place Mod APK 0.15.0, you can also enjoy some amazing features that will make your gameplay more enjoyable and rewarding. Here are some of the features of Burger Place Mod APK 0.15.0:
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Features of Burger Place Mod APK 0.15.0
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Unlimited money to buy ingredients and upgrades
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With Burger Place Mod APK 0.15.0, you don't have to worry about running out of money to buy ingredients and upgrades for your burger shop. You can start with a huge amount of money and spend it as you wish. You can buy more ingredients, upgrade your equipment, expand your menu, and decorate your shop to attract more customers.
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No ads to interrupt your gameplay
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Another great feature of Burger Place Mod APK 0.15.0 is that it removes all the annoying ads that pop up in the original game. You can play without any interruptions or distractions from ads. You can focus on making burgers and satisfying your customers.
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Various levels and challenges to test your skills
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Burger Place Mod APK 0.15.0 also offers you various levels and challenges to test your skills as a burger chef. You can serve different types of customers with different preferences and personalities. You can also face different scenarios and situations that will require you to think fast and act smart. You can earn stars and coins for completing each level and challenge.
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Customizable burger shop and character
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Another fun feature of Burger Place Mod APK 0.15.0 is that it allows you to customize your burger shop and character. You can choose from different styles and themes for your shop, such as retro, modern, or futuristic. You can also change the appearance of your character, such as the hair, clothes, and accessories.
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Easy controls and graphics
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Burger Place Mod APK 0.15.0 also has easy controls and graphics that make the game simple and enjoyable to play. You can use the touch screen to drag and drop ingredients, swipe to serve customers, and tap to collect money. The game also has colorful and cartoonish graphics that make the game look appealing and lively.
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How to Download and Install Burger Place Mod APK 0.15.0
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If you are interested in downloading and installing Burger Place Mod APK 0.15.0, you can follow these simple steps:
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Download the APK file from a trusted source
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The first step is to download the APK file from a trusted source, such as this link. Make sure you have enough storage space on your device before downloading the file.
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Enable unknown sources on your device
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The next step is to enable unknown sources on your device, which will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on.
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Install the APK file and launch the game
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The final step is to install the APK file and launch the game. To do this, locate the downloaded file in your file manager or downloads folder, then tap on it to install it. Once the installation is done, you can launch the game from your app drawer or home screen.
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Enjoy your unlimited money and no ads
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Now you can enjoy your unlimited money and no ads while playing Burger Place Mod APK 0.15.0. You can buy ingredients and upgrades, serve customers, complete levels and challenges, customize your shop and character, and have fun making burgers.
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Tips and Tricks for Playing Burger Place Mod APK 0.15.0
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If you want to master Burger Place Mod APK 0.15.0, you can use these tips and tricks:
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Upgrade your ingredients and equipment regularly
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One of the best ways to improve your gameplay is to upgrade your ingredients and equipment regularly. This will allow you to make better burgers, serve more customers, earn more money, and unlock more items.
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Serve your customers quickly and accurately
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Another important tip is to serve your customers quickly and accurately. This will increase their satisfaction level, which will affect their tips and ratings. You can also earn bonuses for serving customers in a row without any mistakes.
-
Use boosters and power-ups wisely
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Another useful tip is to use boosters and power-ups wisely. These are special items that can help you in different ways, such as speeding up your cooking time, freezing the customer's patience level, or doubling your earnings.
-
Complete daily tasks and achievements for extra rewards
-
Another helpful tip is to complete daily tasks and achievements for extra rewards. These are specific goals that you can accomplish by playing the game, such as serving a certain number of customers, making a certain amount of money, or using a certain booster. You can earn coins, stars, and gems for completing these tasks and achievements.
-
Have fun and experiment with different burger combinations
-
The last tip is to have fun and experiment with different burger combinations. You can try different ingredients, toppings, sauces, breads, and sides to create your own unique burger recipes. You can also see how your customers react to your creations and get feedback from them.
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Conclusion
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Burger Place Mod APK 0.15.0 is a fun and addictive cooking game that lets you run your own burger shop. You can make burgers for your hungry customers, buy ingredients and upgrades, customize your shop and character, and enjoy unlimited money and no ads. You can also challenge yourself with various levels and tasks, use boosters and power-ups, and have fun experimenting with different burger combinations.
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If you are looking for a game that will keep you entertained and engaged for hours, then you should download Burger Place Mod APK 0.15.0 today. You will not regret it!
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FAQs
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Is Burger Place Mod APK 0.15.0 safe to download?
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Yes, Burger Place Mod APK 0.15.0 is safe to download as long as you download it from a trusted source, such as this link. You should also scan the file with an antivirus program before installing it.
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What are the minimum requirements for playing Burger Place Mod APK 0.15.0?
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The minimum requirements for playing Burger Place Mod APK 0.15.0 are:
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Android 4.4 or higher
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At least 100 MB of free storage space
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A stable internet connection
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How can I get more money in Burger Place Mod APK 0.15.0?
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You can get more money in Burger Place Mod APK 0.15.0 by:
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Serving more customers and earning tips and ratings
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Completing levels and challenges and earning stars and coins
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Completing daily tasks and achievements and earning coins, stars, and gems
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Using boosters and power-ups that double your earnings
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Downloading the modded version that gives you unlimited money
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How can I unlock more levels and items in Burger Place Mod APK 0.15.0?
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You can unlock more levels and items in Burger Place Mod APK 0.15.0 by:
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Earning enough stars to unlock new levels
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Earning enough coins to buy new ingredients, equipment, and decorations
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Earning enough gems to buy premium items and boosters
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Downloading the modded version that unlocks everything
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How can I contact the developer of Burger Place Mod APK 0.15.0?
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You can contact the developer of Burger Place Mod APK 0.15.0 by:
Leaving a review or rating on the Google Play Store or App Store
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diff --git a/spaces/1phancelerku/anime-remove-background/Clash Mini APK Hack Download and Install Unlimited Money and Gems Mod.md b/spaces/1phancelerku/anime-remove-background/Clash Mini APK Hack Download and Install Unlimited Money and Gems Mod.md
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index db34d8efc79823c038d582c19f957b605d094f18..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Clash Mini APK Hack Download and Install Unlimited Money and Gems Mod.md
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Clash Mini APK Hack Mediafıre: How to Get Unlimited Gems for Free
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If you are a fan of strategy games, you might have heard of Clash Mini, the latest spin-off from the popular Clash of Clans franchise. In this game, you can create your own team of miniatures and battle against other players in fast-paced matches. However, to unlock more characters, skins, and items, you will need gems, the premium currency of the game. Gems are hard to come by, and they can be quite expensive to buy with real money. That's why many players are looking for ways to get free gems, such as using clash mini apk hack mediafıre.
In this article, we will tell you everything you need to know about clash mini apk hack mediafıre, including what it is, how to download and install it, how to use it, and what are its pros and cons. By the end of this article, you will be able to decide whether this hack is worth trying or not.
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What is Clash Mini?
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Clash Mini is a strategy game developed by Supercell, the same company behind Clash of Clans, Clash Royale, Brawl Stars, and Hay Day. It is set in the same universe as Clash of Clans, but with a different gameplay style. Instead of building your own base and raiding others, you can collect and upgrade various miniatures that represent different characters from the Clash world. You can then use them to form your own team and compete against other players in turn-based battles.
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Why do you need gems in Clash Mini?
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Gems are the premium currency of Clash Mini. You can use them to buy chests that contain random miniatures, skins, and items. You can also use them to speed up the upgrading process of your miniatures, or to unlock special offers and events. Gems can help you progress faster in the game and gain an edge over your opponents.
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What is clash mini apk hack mediafıre?
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Clash mini apk hack mediafıre is a modified version of the original Clash Mini app that allows you to generate unlimited gems for free. It is not an official app from Supercell, but rather a third-party app that has been hacked by some developers. It is usually hosted on mediafıre, a file-sharing platform that lets you download files without any registration or payment.
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How to download and install clash mini apk hack mediafıre
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If you want to try clash mini apk hack mediafıre, you will need to follow these steps:
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Step 1: Find a reliable source for the apk file
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The first thing you need to do is to find a trustworthy website that offers the clash mini apk hack mediafıre file. You can search for it on Google or any other search engine, but be careful not to click on any suspicious or malicious links that might harm your device or steal your personal information. You can also check the reviews and ratings of the website to see if other users have had a positive or negative experience with it.
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Step 2: Enable unknown sources on your device
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Before you can install the clash mini apk hack mediafıre file, you will need to enable unknown sources on your device. This is a security feature that prevents you from installing apps that are not from the official app store. To enable unknown sources, you will need to go to your device settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". You might also need to confirm this action by tapping on "OK" or "Yes".
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Step 3: Download and install the apk file
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Once you have enabled unknown sources, you can proceed to download and install the clash mini apk hack mediafıre file. To do this, you will need to go to the website where you found the file, then click on the download button or link. You might have to wait for a few seconds or minutes for the download to complete, depending on your internet speed and the size of the file. After the download is done, you can open the file and tap on "install". You might also have to agree to some permissions and terms of service before the installation is complete.
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How to use clash mini apk hack mediafıre
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After you have successfully installed the clash mini apk hack mediafıre app, you can start using it to get free gems. Here are the steps you need to follow:
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Step 1: Launch the app and log in with your account
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The first thing you need to do is to launch the clash mini apk hack mediafıre app and log in with your existing Clash Mini account. If you don't have one, you can create one for free by entering your email and password. You can also use your Facebook or Google account to log in.
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Step 2: Choose the amount of gems you want to generate
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Once you have logged in, you will see a simple interface that shows your current balance of gems and a slider that lets you choose how many gems you want to generate. You can choose from 1000 to 99999 gems per day. The more gems you choose, the longer it will take for the hack to work.
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Step 3: Wait for the verification process and enjoy your free gems
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After you have chosen the amount of gems you want, you will need to wait for the verification process to complete. This is a security measure that prevents bots and spammers from abusing the hack. You might have to complete a human verification test, such as a captcha, a survey, or an offer. This should not take more than a few minutes. Once you have passed the verification, you will receive your free gems in your Clash Mini account. You can then use them to buy chests, upgrade your miniatures, and enjoy the game.
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Pros and cons of clash mini apk hack mediafıre
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Like any other hack, clash mini apk hack mediafıre has its advantages and disadvantages. Here are some of them:
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Pros
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Unlimited gems for free
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The main benefit of using clash mini apk hack mediafıre is that you can get unlimited gems for free without spending any real money. Gems are very useful in Clash Mini, as they can help you unlock more characters, skins, and items. They can also help you progress faster in the game and gain an edge over your opponents.
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No root or jailbreak required
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Another advantage of using clash mini apk hack mediafıre is that you don't need to root or jailbreak your device to use it. Rooting or jailbreaking is a process that gives you full access to your device's system, but it also voids your warranty and exposes your device to security risks. With clash mini apk hack mediafıre, you don't have to worry about any of that.
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Easy to use and safe to download
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The last advantage of using clash mini apk hack mediafıre is that it is very easy to use and safe to download. You don't need any technical skills or knowledge to use it. All you need is a device with an internet connection and a Clash Mini account. The app is also virus-free and malware-free, as long as you download it from a reliable source.
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Cons
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Not compatible with the official version of Clash Mini
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The first drawback of using clash mini apk hack mediafıre is that it is not compatible with the official version of Clash Mini. This means that you cannot play with other players who are using the original app, or access the official features and updates of the game. You can only play with other players who are using the same hack as you, which might limit your options and fun.
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May contain malware or viruses
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The second drawback of using clash mini apk hack mediafıre is that it may contain malware or viruses that can harm your device or steal your personal information. Even though we said that the app is safe to download from a reliable source, there is no guarantee that the source is always trustworthy or secure. There might be some hidden codes or scripts that can infect your device or compromise your data. Therefore, you should always scan the file before installing it, and use a reputable antivirus software to protect your device.
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May get banned by the game developers
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The last drawback of using clash mini apk hack mediafıre is that you may get banned by the game developers for violating their terms of service. Using hacks or cheats is considered unfair and unethical by most game developers, and they have the right to ban or suspend any account that is found to be using them. If you get banned, you will lose all your progress and achievements in the game, and you might not be able to play it again. Therefore, you should use this hack at your own risk, and be careful not to get caught by the game's anti-cheat system.
-
-
Conclusion
-
Clash mini apk hack mediafıre is a hack that allows you to get unlimited gems for free in Clash Mini, a strategy game developed by Supercell. It is a modified version of the original app that is hosted on mediafıre, a file-sharing platform. To use this hack, you need to download and install the apk file on your device, then launch the app and log in with your account. You can then choose the amount of gems you want to generate, and wait for the verification process to complete. You can use the gems to buy chests, upgrade your miniatures, and enjoy the game.
-
However, this hack also has some drawbacks, such as being not compatible with the official version of Clash Mini, containing malware or viruses, and getting banned by the game developers. Therefore, you should weigh the pros and cons carefully before deciding whether to use this hack or not. You should also be aware of the risks and consequences of using hacks or cheats in any game.
-
FAQs
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-
Is clash mini apk hack mediafıre legal?
-
No, clash mini apk hack mediafıre is not legal, as it violates the terms of service of Clash Mini and Supercell. It also infringes on their intellectual property rights and copyrights. Using this hack can result in legal actions from the game developers or authorities.
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Is clash mini apk hack mediafıre safe?
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Not necessarily, clash mini apk hack mediafıre may not be safe, as it may contain malware or viruses that can harm your device or steal your personal information. It may also expose your account to security risks or hacking attempts. Therefore, you should always scan the file before installing it, and use a reputable antivirus software to protect your device.
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Does clash mini apk hack mediafıre work?
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Yes, clash mini apk hack mediafıre works, as it can generate unlimited gems for free in Clash Mini. However, it may not work with the latest version of Clash Mini or with other devices or platforms. It may also stop working if the game developers patch or update their anti-cheat system.
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Where can I download clash mini apk hack mediafıre?
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You can download clash mini apk hack mediafıre from various websites that offer it for free. However, you should be careful not to download it from any suspicious or malicious links that might harm your device or steal your personal information. You should also check the reviews and ratings of the website to see if other users have had a positive or negative experience with it.
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Can I use clash mini apk hack mediafıre with my existing Clash Mini account?
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Yes, you can use clash mini apk hack mediafıre with your existing Clash Mini account, as long as you log in with it when you launch the app. However, you should be aware that using this hack can put your account at risk of being banned or suspended by the game developers for violating their terms of service. If you get banned, you will lose all your progress and achievements in the game, and you might not be able to play it again. Therefore, you should use this hack at your own risk, and be careful not to get caught by the game's anti-cheat system.
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-
I hope this article has helped you understand more about clash mini apk hack mediafıre and how to use it. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and have a great day!
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Demolition Derby 3 Hack Mod APK with Unlimited Money Feature.md b/spaces/1phancelerku/anime-remove-background/Download Demolition Derby 3 Hack Mod APK with Unlimited Money Feature.md
deleted file mode 100644
index d3e140efc37bd1433c70ab8b53dbf48bf9859895..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Demolition Derby 3 Hack Mod APK with Unlimited Money Feature.md
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@@ -1,80 +0,0 @@
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Demolition Derby 3 Mod APK Hack Unlimited Money: A Review
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If you are a fan of racing games, especially the ones that involve smashing and crashing other cars, then you might have heard of Demolition Derby 3. This is a popular game that lets you experience the thrill and excitement of demolition derby racing. But what if you want to enjoy the game without any limitations or restrictions? Well, that's where Demolition Derby 3 Mod APK comes in. In this article, we will review the game and its modded version, and show you how to download and install it on your device.
Demolition Derby 3 is a racing game developed by Beer Money Games, the same studio that created Demolition Derby 2 and Demolition Derby Multiplayer. The game is a sequel to the previous titles, and it offers more features, cars, maps, and modes than ever before. The game is available for free on Google Play Store and App Store, but it also contains in-app purchases and ads.
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Features of Demolition Derby 3
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Demolition Derby 3 has many features that make it one of the best racing games on the market. Here are some of them:
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The game uses a realistic physics engine that simulates the damage and destruction of the cars. You can see the parts flying off, the windows shattering, the tires bursting, and the smoke rising as you collide with other vehicles. The game also has stunning graphics that create a immersive environment for the races. You can see the details of the cars, the tracks, the weather effects, and the lighting.
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The game offers various game modes that suit different preferences and play styles. You can choose from Career Mode, Quick Play Mode, Multiplayer Mode, Free Drive Mode, and Custom Events Mode. Each mode has its own objectives, challenges, rewards, and leaderboards. The game also has over 40 maps that range from arenas, stadiums, dirt tracks, highways, deserts, forests, and more. Each map has its own obstacles, hazards, ramps, loops, and shortcuts.
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The game has over 80 cars that you can unlock, buy, and customize. You can choose from different categories such as muscle cars, sports cars, trucks, buses, vans, and even tanks. You can also modify your car's appearance by changing its color, paint job, decals, wheels, spoilers, bumpers, hoods, roofs, and more. You can also upgrade your car's performance by improving its engine, transmission, suspension, brakes, tires, armor, nitro, and more.
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What is Demolition Derby 3 Mod APK?
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Demolition Derby 3 Mod APK is a modified version of the original game that gives you access to unlimited money and coins. This means that you can buy any car you want, unlock any map you want, and upgrade your car to the fullest without spending any real money. The mod apk also removes all the ads from the game and does not require root access to work.
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Demolition Derby 3 Mod APK has many benefits that make it a better choice than the original game. Here are some of them:
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- Unlimited money and coins
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With Demolition Derby 3 Mod APK, you don't have to worry about running out of money or coins. You can use them to buy any car you want, unlock any map you want, and upgrade your car to the fullest. You can also use them to buy boosters, power-ups, and skins that can enhance your gameplay. You can enjoy the game without any limitations or restrictions.
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- All cars and maps unlocked
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With Demolition Derby 3 Mod APK, you don't have to wait for hours or days to unlock new cars and maps. You can access all of them from the start. You can choose from over 80 cars and over 40 maps that suit your taste and preference. You can also switch between them anytime you want. You can explore the game's content without any hassle or boredom.
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- No ads and no root required
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With Demolition Derby 3 Mod APK, you don't have to deal with annoying ads that pop up every few minutes. You can play the game without any interruptions or distractions. You can also play the game without rooting your device. You don't have to risk damaging your device or voiding your warranty. You can play the game safely and smoothly.
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How to download and install Demolition Derby 3 Mod APK?
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If you are interested in downloading and installing Demolition Derby 3 Mod APK, you can follow these simple steps:
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Steps to download and install Demolition Derby 3 Mod APK
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- Step 1: Download the mod apk file from a trusted source
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The first step is to download the mod apk file from a reliable source. You can use the link below to download the latest version of Demolition Derby 3 Mod APK. The file size is about 100 MB, so make sure you have enough storage space on your device.
- Step 2: Enable unknown sources on your device settings
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The second step is to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on.
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- Step 3: Install the mod apk file and launch the game
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The third step is to install the mod apk file and launch the game. To do this, locate the downloaded file on your device, tap on it, and follow the instructions on the screen. Once the installation is complete, open the game and enjoy.
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Conclusion
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Demolition Derby 3 is a fun and exciting racing game that lets you smash and crash other cars in various modes and maps. However, if you want to enjoy the game without any limitations or restrictions, you should try Demolition Derby 3 Mod APK. This is a modified version of the game that gives you unlimited money and coins, all cars and maps unlocked, no ads, and no root required. You can download and install it easily by following the steps above. So what are you waiting for? Download Demolition Derby 3 Mod APK now and unleash your inner racer.
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Here are some frequently asked questions about Demolition Derby 3 Mod APK:
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Is Demolition Derby 3 Mod APK safe?
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Yes, Demolition Derby 3 Mod APK is safe to use. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a trusted source and scan it with an antivirus before installing it.
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Is Demolition Derby 3 Mod APK compatible with my device?
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Demolition Derby 3 Mod APK is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may experience performance issues or crashes due to hardware limitations or compatibility issues.
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Can I play Demolition Derby 3 Mod APK online?
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Yes, you can play Demolition Derby 3 Mod APK online with other players around the world. However, you may encounter some problems such as lagging, disconnecting, or banning due to server issues or anti-cheat measures.
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Can I update Demolition Derby 3 Mod APK?
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Yes, you can update Demolition Derby 3 Mod APK whenever a new version is available. However, you may lose your modded features and progress if you update it from the Google Play Store. To avoid this, you should always update it from the same source where you downloaded it.
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Can I use Demolition Derby 3 Mod APK with my existing account?
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Yes, you can use Demolition Derby 3 Mod APK with your existing account. However, you may risk losing your account or getting banned if the game detects that you are using a modded version. To avoid this, you should always backup your account data and use a different account for the modded version.
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Can I request a feature or report a bug for Demolition Derby 3 Mod APK?
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Yes, you can request a feature or report a bug for Demolition Derby 3 Mod APK by contacting the developer or the source where you downloaded it. However, there is no guarantee that your request or report will be addressed or resolved.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Green Farm 3 and Experience Farming Adventure on Your PC.md b/spaces/1phancelerku/anime-remove-background/Download Green Farm 3 and Experience Farming Adventure on Your PC.md
deleted file mode 100644
index 783abb48fec543dc5262833044b4186532565b39..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Green Farm 3 and Experience Farming Adventure on Your PC.md
+++ /dev/null
@@ -1,142 +0,0 @@
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-
Download Green Farm 3: A Fun and Relaxing Farming Adventure
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Do you love farming games? Do you want to experience the joy of restoring an old manor and turning it into a thriving farm? If yes, then you should download Green Farm 3, a simulation game developed by Gameloft. In this game, you will inherit a rundown estate from your uncle and embark on a series of missions to bring it back to life. You will also have to manage your farm with the help of friends and neighbors, and enjoy a fresh new atmosphere, easier controls, and a rich storyline with new characters to meet and play with. In this article, we will tell you what Green Farm 3 is, how to download it, and some tips and tricks for playing it.
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What is Green Farm 3?
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Green Farm 3 is a casual simulation game that lets you rediscover farming adventure in a new and exciting way. It is the third installment of the popular Green Farm series, which has been downloaded by millions of players around the world. The game offers many new features and improvements over the previous versions, such as:
In Green Farm 3, you will play as a young farmer who inherits an old manor from your uncle. Your uncle was a famous adventurer who traveled around the world and collected many exotic items and animals. However, he also neglected his farm, which became dilapidated and abandoned. Your task is to restore the manor to its former glory, by completing various missions that involve farming, harvesting, crafting, and more. You will also have to deal with some challenges and surprises along the way, such as a mysterious rival who wants to sabotage your efforts, or a friendly ghost who haunts the manor.
-
The features and the benefits
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Green Farm 3 has many features that make it fun and enjoyable to play, such as:
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-
A fresh new atmosphere, with more colorful environments and cooler characters to meet.
-
Easier controls, that make managing your farm simpler and more fun.
-
A rich storyline, with new characters to meet and play with, each with their own personality and backstory.
-
Many missions to complete, each with different objectives and rewards.
-
A variety of crops, animals, buildings, decorations, and products to grow and produce on your farm.
-
A social aspect, that allows you to connect with friends and neighbors, help each other out, trade goods, or compete in events.
-
A customization option, that lets you personalize your farm with your own style and preferences.
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-
By playing Green Farm 3, you will not only have fun, but also benefit from some positive effects, such as:
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-
Relaxing your mind and body, by immersing yourself in a peaceful and soothing environment.
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Stimulating your creativity and imagination, by designing your own farm layout and products.
-
Improving your time management and planning skills, by organizing your tasks and resources efficiently.
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Learning about farming and nature, by discovering different types of plants and animals.
-
Making new friends and socializing with others, by interacting with other players online.
-
-
How to download Green Farm 3?
-
If you are interested in playing Green Farm 3, you can download it for free from various platforms. Here are the steps for downloading it for different devices:
-
For Android devices
-
-
Go to the Google Play Store app on your device.
-
Search for "Green Farm 3" in the search bar.
-
Select the game from the list of results and tap on "Install".
-
Wait for the game to download and install on your device.
-
Open the game and enjoy playing it.
-
-
For iOS devices
-
-
Go to the App Store app on your device.
-
Search for "Green Farm 3" in the search bar.
-
Select the game from the list of results and tap on "Get".
-
Enter your Apple ID and password if prompted.
-
Wait for the game to download and install on your device.
Click on "Games" and select "Green Farm 3" from the list of games.
-
Click on "Download" and choose your preferred platform (Windows or Mac).
-
Follow the instructions on the screen to download and install the game on your computer.
-
Open the game and enjoy playing it.
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Tips and tricks for playing Green Farm 3
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Now that you have downloaded Green Farm 3, you might be wondering how to play it well and make the most out of it. Here are some tips and tricks that will help you succeed in your farming adventure:
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Manage your resources wisely
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In Green Farm 3, you will need various resources to run your farm, such as coins, cash, energy, seeds, water, feed, fertilizer, etc. You can earn these resources by completing missions, harvesting crops, selling products, or watching ads. However, you should also be careful not to waste them or run out of them. Here are some ways to manage your resources wisely:
-
-
Plan ahead and prioritize your tasks. For example, plant crops that match your mission objectives, or craft products that have high demand in the market.
-
Use your energy efficiently. Energy is used for almost every action in the game, such as planting, harvesting, feeding, etc. You can replenish your energy by waiting over time, eating food, or using cash. However, you should also avoid wasting energy by doing unnecessary actions or leaving crops unharvested.
-
Save your cash for important purchases. Cash is the premium currency in the game, which can be used to buy special items, speed up processes, or unlock new features. You can earn cash by leveling up, completing achievements, or spending real money. However, you should also save your cash for important purchases, such as expanding your land, hiring workers, or buying rare animals.
-
-
Complete missions and achievements
-
In Green Farm 3, you will have many missions and achievements to complete, which will guide you through the game and reward you with various prizes. Missions are tasks that are given by different characters in the game, such as your uncle, your friends, or your rivals. Achievements are goals that are set by yourself, such as reaching a certain level, collecting a certain amount of products, or visiting a certain number of neighbors. Here are some benefits of completing missions and achievements:
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-
You will progress faster in the game and unlock new features and content.
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You will earn more coins, cash, energy, and other resources.
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You will learn more about the story and the characters of the game.
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You will have more fun and challenge yourself.
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Connect with friends and neighbors
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In Green Farm 3, you can also connect with other players online and become friends or neighbors. You can visit their farms, help them out with their tasks, trade goods with them, or chat with them. You can also join events and competitions with them and win exclusive rewards. Here are some advantages of connecting with friends and neighbors:
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You will make new friends and socialize with others who share your interest in farming games.
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You will get more help and support for your farm activities.
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You will get more opportunities to earn coins, cash, energy, and other resources.
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You will get more inspiration and ideas for your own farm design.
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Conclusion
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To sum up, Green Farm 3 is a fun and relaxing farming adventure that you can download for free on various platforms. It has many features and benefits that make it enjoyable to play, such as a fresh new atmosphere, easier controls, a rich storyline, many missions to complete, many crops, animals, buildings, decorations, and products to grow and produce, a social aspect, a customization option, and more. You can also download it easily for your Android, iOS, or PC and Mac devices. Moreover, you can improve your farming skills and have more fun by following some tips and tricks, such as managing your resources wisely, completing missions and achievements, and connecting with friends and neighbors. If you are looking for a farming game that is fun, relaxing, and rewarding, you should definitely download Green Farm 3 and start your adventure today.
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FAQs
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Here are some frequently asked questions about Green Farm 3:
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How can I get more cash in Green Farm 3?
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You can get more cash in Green Farm 3 by leveling up, completing achievements, watching ads, or spending real money. You can also get some cash by visiting your friends' farms and helping them out.
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How can I expand my land in Green Farm 3?
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You can expand your land in Green Farm 3 by using cash or coins. You will need to clear some obstacles first before you can buy more land. You can also unlock new areas by completing certain missions.
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How can I hire workers in Green Farm 3?
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You can hire workers in Green Farm 3 by using cash or coins. Workers can help you with various tasks on your farm, such as planting, harvesting, feeding, etc. You can also upgrade your workers to make them more efficient.
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How can I join events and competitions in Green Farm 3?
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You can join events and competitions in Green Farm 3 by tapping on the event icon on the top right corner of the screen. Events and competitions are time-limited challenges that require you to complete certain objectives or compete with other players. You can win exclusive rewards by participating in them.
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How can I contact the support team of Green Farm 3?
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You can contact the support team of Green Farm 3 by tapping on the settings icon on the top left corner of the screen. Then, tap on "Help & Support" and choose the option that suits your issue. You can also visit the official website of Gameloft at https://www.gameloft.com/en/ for more information.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Nulls Brawl Mod APK 49.194 with Unlimited Money and Infinite Gems.md b/spaces/1phancelerku/anime-remove-background/Download Nulls Brawl Mod APK 49.194 with Unlimited Money and Infinite Gems.md
deleted file mode 100644
index 500ebc65015fc3b05f37f9db5b7f41e96dc1ae39..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Nulls Brawl Mod APK 49.194 with Unlimited Money and Infinite Gems.md
+++ /dev/null
@@ -1,82 +0,0 @@
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-
What is Nulls Brawl Mod APK?
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If you are a fan of fast-paced multiplayer games, you might have heard of Brawl Stars, a popular game developed by Supercell, the makers of Clash of Clans and Clash Royale. Brawl Stars is a 3v3 shooter game where you can choose from dozens of unique characters, called brawlers, each with their own abilities, gadgets, and star powers. You can play with your friends or solo across various game modes, such as Gem Grab, Showdown, Brawl Ball, Bounty, Heist, and more. You can also collect skins, pins, trophies, and other rewards as you progress in the game.
However, if you want to experience more features and fun in Brawl Stars, you might want to try Nulls Brawl Mod APK, a modified version of the game that offers many advantages over the original one. Nulls Brawl Mod APK is developed by Nulls Gg, a team of modders who create custom versions of popular games. With Nulls Brawl Mod APK, you can enjoy:
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Infinite gems: Gems are the premium currency in Brawl Stars that you can use to buy skins, pins, brawl boxes, brawl passes, and other items. Normally, you have to spend real money or complete quests to get gems, but with Nulls Brawl Mod APK, you can get unlimited gems for free.
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A lot of money: Money, or coins, are the basic currency in Brawl Stars that you can use to upgrade your brawlers and unlock their star powers. Normally, you have to win matches or open brawl boxes to get coins, but with Nulls Brawl Mod APK, you can get a lot of money for free.
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All characters unlocked: Characters, or brawlers, are the main attraction of Brawl Stars. There are over 40 brawlers in the game, each with their own personality, style, and skills. Normally, you have to open brawl boxes or reach certain milestones to unlock new brawlers, but with Nulls Brawl Mod APK, you can access all characters from the start.
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As you can see, Nulls Brawl Mod APK is a great way to enhance your gaming experience and have more fun in B
How to download Nulls Brawl Mod APK?
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Now that you know what Nulls Brawl Mod APK is and why you should download it, you might be wondering how to do it. Well, don't worry, because it's very easy and simple. Just follow these steps and you'll be playing Nulls Brawl Mod APK in no time.
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Step 1: Enable unknown sources
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The first thing you need to do is to enable unknown sources on your Android device. This will allow you to install apps that are not from the Google Play Store, such as Nulls Brawl Mod APK. To do this, go to your device's settings, then security, then unknown sources, and turn it on. You might see a warning message, but just ignore it and tap OK.
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Step 2: Download the APK file
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The next thing you need to do is to download the APK file of Nulls Brawl Mod APK. This is the file that contains the modded version of the game. You can find it on various websites, but make sure you choose a reputable and safe one. For example, you can use this link to download the latest version of Nulls Brawl Mod APK. Just click on the download button and wait for the file to be downloaded.
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Step 3: Install the APK file
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The last thing you need to do is to install the APK file on your device. To do this, locate the file in your device's storage, either using a file manager app or by going to your downloads folder. Then, tap on the file and follow the instructions on the screen. You might see a pop-up asking for permissions, but just allow them and continue. Once the installation is done, you can open the game and enjoy Nulls Brawl Mod APK.
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How to play Nulls Brawl Mod APK?
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Now that you have downloaded and installed Nulls Brawl Mod APK, you might be wondering how to play it. Well, it's very similar to the original Brawl Stars game, but with more features and fun. Here are some of the things you can do in Nulls Brawl Mod APK.
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Choose your brawler
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The first thing you need to do is to choose your brawler. As mentioned before, Nulls Brawl Mod APK gives you access to all brawlers from the start, so you can pick any one you like. You can also customize your brawler's appearance by changing their skin or pin. To do this, go to the brawlers menu and tap on the brawler you want to use. Then, tap on the skin or pin icon and select the one you want.
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Join a game mode
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The next thing you need to do is to join a game mode. Nulls Brawl Mod APK offers all the game modes that are available in the original Brawl Stars game, such as Gem Grab, Showdown, Brawl Ball, Bounty, Heist, and more. You can also play special events or friendly matches with your friends or club members. To join a game mode, go to the home screen and tap on the play button. Then, choose the game mode you want to play and tap on it.
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Battle with other players
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The last thing you need to do is to battle with other players. Nulls Brawl Mod APK lets you play with or against other players who are also using the modded version of the game. You can use your brawler's abilities, gadgets, and star powers to win matches and earn rewards. To battle with other players, just follow the instructions on the screen and have fun.
Tips and tricks for Nulls Brawl Mod APK
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If you want to get the most out of Nulls Brawl Mod APK, you might want to follow some tips and tricks that can help you improve your gameplay and have more fun. Here are some of them:
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Experiment with different brawlers
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One of the best things about Nulls Brawl Mod APK is that you can use any brawler you want, without having to unlock them or spend gems or coins. This means you can experiment with different brawlers and find the ones that suit your playstyle and preference. You can also learn their strengths and weaknesses, and how to counter them in battles. To experiment with different brawlers, just go to the brawlers menu and tap on the random button. You will be assigned a random brawler that you can use in the next match.
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Team up with your friends
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Another great thing about Nulls Brawl Mod APK is that you can team up with your friends or other players who are also using the modded version of the game. This can make the game more fun and exciting, as you can communicate and coordinate with your teammates, and use strategies and tactics to win matches. To team up with your friends, just go to the social menu and tap on the invite button. You can then send an invitation to your friends or club members, or join an existing team.
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Upgrade your brawlers
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Even though Nulls Brawl Mod APK gives you unlimited gems and coins, you still need to use them wisely to upgrade your brawlers and unlock their star powers and gadgets. Upgrading your brawlers can make them more powerful and effective in battles, as they can deal more damage, have more health, and use more skills. To upgrade your brawlers, just go to the brawlers menu and tap on the brawler you want to upgrade. Then, tap on the upgrade button and spend the required amount of coins.
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Conclusion
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Nulls Brawl Mod APK is a modified version of Brawl Stars that offers many benefits over the original game, such as unlimited gems, money, and characters. It also lets you play with or against other players who are using the same modded version of the game. You can download and install Nulls Brawl Mod APK easily by following the steps mentioned above. You can also play Nulls Brawl Mod APK by choosing your brawler, joining a game mode, and battling with other players. You can also follow some tips and tricks to improve your gameplay and have more fun.
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If you are looking for a way to spice up your gaming experience and have more fun in Brawl Stars, you should definitely try Nulls Brawl Mod APK. It is a free, safe, and easy way to enjoy more features and fun in one of the most popular multiplayer games in the world. So what are you waiting for? Download Nulls Brawl Mod APK today and start brawling!
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FAQs
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Here are some of the frequently asked questions and answers about Nulls Brawl Mod APK:
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Q: Is Nulls Brawl Mod APK safe?
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A: Yes, Nulls Brawl Mod APK is safe to download and use. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a reputable source and scan it before installing it.
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Q: Is Nulls Brawl Mod APK legal?
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A: No, Nulls Brawl Mod APK is not legal. It is a modified version of Brawl Stars that violates the terms of service of Supercell, the developer of the game. Using Nulls Brawl Mod APK can result in a ban from the original game or legal action from Supercell.
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Q: Can I play Nulls Brawl Mod APK with players who are using the original game?
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A: No, you cannot play Nulls Brawl Mod APK with players who are using the original game. Nulls Brawl Mod APK uses a different server than the original game, so you can only play with or against other players who are using the same modded version of the game.
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Q: Can I update Nulls Brawl Mod APK?
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A: Yes, you can update Nulls Brawl Mod APK whenever there is a new version available. However, you cannot update it from the Google Play Store or the original game. You have to download the new version from a reputable source and install it manually.
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Q: Can I transfer my progress from Nulls B
Q: Can I transfer my progress from Nulls Brawl Mod APK to the original game?
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A: No, you cannot transfer your progress from Nulls Brawl Mod APK to the original game. Nulls Brawl Mod APK uses a different account and data system than the original game, so you cannot sync or transfer your progress between them.
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197e85843d
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Stick War Legacy MOD APK for Android - Unlimited Gems and More.md b/spaces/1phancelerku/anime-remove-background/Download Stick War Legacy MOD APK for Android - Unlimited Gems and More.md
deleted file mode 100644
index e5272ab292f6be3391ecc43505af5063d7790bac..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Stick War Legacy MOD APK for Android - Unlimited Gems and More.md
+++ /dev/null
@@ -1,116 +0,0 @@
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Download Stick War Legacy Mod APK Android: A Guide for Beginners
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Are you a fan of strategy games? Do you want to experience a thrilling and addictive game that will challenge your skills and creativity? If yes, then you should try Stick War Legacy, one of the most popular and fun games on Android devices. And if you want to enjoy the game even more, you should download Stick War Legacy mod apk android, which will give you unlimited gems, gold, and other features that will make your gameplay easier and more enjoyable. In this article, we will tell you everything you need to know about Stick War Legacy and its mod apk version, including what it is, how to play it, why you should download it, and some tips and tricks to help you win. Let's get started!
Stick War Legacy is a strategy game developed by Max Games Studios, where you control an army of stick figures in a world called Inamorta. In this world, different nations have their own unique weapons and technologies, such as archers, spears, swords, mages, and even giants. Your goal is to conquer all the territories and become the ruler of Inamorta. You can do this by building your own fort, recruiting and training your units, and attacking your enemies with different strategies. You can also play in different modes, such as campaign mode, survival mode, tournament mode, and endless mode.
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Features of Stick War Legacy
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Stick War Legacy has many features that make it an exciting and addictive game. Some of them are:
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Stunning graphics and animations that bring the stick figures to life.
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Simple and intuitive controls that allow you to command your army with ease.
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A variety of units and skills that you can use to customize your army and strategy.
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A challenging and rewarding gameplay that tests your skills and creativity.
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A lot of content and modes that you can explore and enjoy.
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How to play Stick War Legacy
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Playing Stick War Legacy is easy and fun. Here are the basic steps that you need to follow:
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Choose a mode that you want to play. You can start with the campaign mode to learn the basics of the game.
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Select a difficulty level that suits your preference. You can choose from normal, hard, or insane.
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Start the game and build your fort. You can mine gold from the gold statue near your base. You can also build other structures, such as barracks, archery range, mage tower, etc.
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Recruit and train your units. You can choose from miners, swordwrath, archidons, speartons, magikill, or giants. Each unit has its own strengths and weaknesses.
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Attack your enemy's base. You can either control each unit individually or use the attack button to send all your units at once. You can also use special skills, such as heal, rage, summon minions, etc.
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Destroy your enemy's statue and win the game. You can also capture their gold statue to get more resources.
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Why download Stick War Legacy mod apk android?
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If you love playing Stick War Legacy but want to have more fun and convenience, then you should download Stick War Legacy mod apk android. This is a modified version of the game that gives you access to unlimited gems, gold, and other features that will enhance your gameplay. With Stick War Legacy mod apk android, you can:
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Benefits of Stick War Legacy mod apk android
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Get unlimited gems and gold that you can use to buy and upgrade anything you want in the game. You can also unlock all the skins and weapons for your units.
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Enjoy unlimited stamina and health for your units. You can make them invincible and unstoppable in the battlefield.
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Remove all the ads and pop-ups that may interrupt your gameplay. You can play the game without any distractions or annoyances.
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Access all the features and modes of the game without any restrictions. You can play any level, mode, or difficulty that you want.
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How to download and install Stick War Legacy mod apk android
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Downloading and installing Stick War Legacy mod apk android is easy and fast. Here are the steps that you need to follow:
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Click on the link below to download the Stick War Legacy mod apk file. The file size is about 100 MB, so make sure you have enough space on your device.
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After downloading the file, go to your device settings and enable the installation of apps from unknown sources. This will allow you to install the mod apk file.
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Locate the downloaded file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for a few seconds.
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Once the installation is complete, you can open the game and enjoy Stick War Legacy mod apk android. You will see that you have unlimited gems, gold, and other features in the game.
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Tips and tricks for Stick War Legacy mod apk android
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Now that you have downloaded and installed Stick War Legacy mod apk android, you may wonder how to play it better and win more games. Here are some tips and tricks that you can use:
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Choose your strategy wisely
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Stick War Legacy is a strategy game, so you need to plan your moves carefully and adapt to different situations. You can choose from different strategies, such as offensive, defensive, balanced, or creative. Each strategy has its own advantages and disadvantages, so you need to consider your enemy's strengths and weaknesses, as well as your own resources and goals. For example, if you want to attack quickly and aggressively, you can use an offensive strategy with swordwrath and archidons. But if you want to defend your base and wait for an opportunity, you can use a defensive strategy with speartons and magikill.
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Upgrade your units and skills
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One of the best ways to improve your gameplay is to upgrade your units and skills. You can do this by using gems and gold that you get from winning games or from Stick War Legacy mod apk android. You can upgrade your units' health, damage, speed, armor, etc. You can also upgrade your skills' cooldown, duration, effect, etc. Upgrading your units and skills will make them more powerful and efficient in combat.
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Use gems and gold wisely
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Gems and gold are the main currencies in Stick War Legacy. You can use them to buy and upgrade anything you want in the game. However, even though you have unlimited gems and gold from Stick War Legacy mod apk android, you should still use them wisely. You should not waste them on unnecessary things or buy everything at once. You should prioritize what you need most and save some for later. You should also balance your spending between units and skills, as well as between offense and defense.
-
Conclusion
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Stick War Legacy is a fun and addictive strategy game that will keep you entertained for hours. You can control an army of stick figures in a world of war and conquer all the territories. You can also download Stick War Legacy mod apk android to get unlimited gems, gold, and other features that will make your gameplay easier and more enjoyable. In this article, we have told you everything you need to know about Stick War Legacy and its mod apk version, including what it is, how to play it, why you should download it, and some tips and tricks to help you win. We hope that this article has been helpful and informative for you. Now go ahead and download Stick War Legacy mod apk android and have fun!
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Summary of the article
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This article is a guide for beginners who want to download Stick War Legacy mod apk android. It covers the following topics:
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What is Stick War Legacy?
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Why download Stick War Legacy mod apk android?
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How to download and install Stick War Legacy mod apk android?
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Tips and tricks for Stick War Legacy mod apk android?
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FAQs
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Here are some frequently asked questions about Stick War Legacy mod apk android:
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Is Stick War Legacy mod apk android safe to use?
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Yes, Stick War Legacy mod apk android is safe to use, as long as you download it from a trusted source. However, you should always be careful when downloading and installing any mod apk file, as some of them may contain viruses or malware that can harm your device. You should also backup your data before installing any mod apk file, in case something goes wrong.
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Does Stick War Legacy mod apk android work on all devices?
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Stick War Legacy mod apk android works on most Android devices that support the original version of the game. However, some devices may not be compatible with the mod apk file, or may experience some glitches or errors. You should check the compatibility of your device before downloading and installing Stick War Legacy mod apk android.
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Can I play Stick War Legacy mod apk android online?
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No, Stick War Legacy mod apk android is not an online game. You can only play it offline, on your own device. You cannot play it with other players or connect it to any social media platforms. You also cannot update the game or access any online features.
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Can I uninstall Stick War Legacy mod apk android?
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Yes, you can uninstall Stick War Legacy mod apk android anytime you want. You can do this by going to your device settings and finding the app in the list of installed apps. Then, you can tap on the app and select the uninstall option. You can also delete the mod apk file from your device storage.
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You can download Stick War Legacy mod apk android from the link below. This is a trusted and reliable source that provides the latest and working version of the mod apk file. You can also find more information and reviews about the mod apk file on this site.
- 401be4b1e0
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\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy the Best Gaming Experience with Clash of Clans Mod Menu APK Latest Version.md b/spaces/1phancelerku/anime-remove-background/Enjoy the Best Gaming Experience with Clash of Clans Mod Menu APK Latest Version.md
deleted file mode 100644
index fc892701f0396ebe5b57e043d6bc62c1849d3add..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy the Best Gaming Experience with Clash of Clans Mod Menu APK Latest Version.md
+++ /dev/null
@@ -1,151 +0,0 @@
-
-
Clash of Clans Mod Menu APK Latest Version: Everything You Need to Know
-
If you are a fan of strategy games, you have probably heard of Clash of Clans, one of the most popular and addictive games in the world. But did you know that there is a way to make your gaming experience even more fun and exciting? Yes, we are talking about Clash of Clans Mod Menu APK, a modified version of the game that gives you unlimited resources and features. In this article, we will tell you everything you need to know about Clash of Clans Mod Menu APK latest version, including what it is, how to download and install it, how to use it, and more. So, let's get started!
-
What is Clash of Clans?
-
A brief introduction to the game
-
Clash of Clans is a freemium mobile strategy game developed and published by Supercell, a Finnish game company. The game was released in 2012 for iOS and in 2013 for Android devices. The game has over 500 million downloads on Google Play Store and over 10 million ratings on App Store.
The game is set in a fantasy world where you have to build your own village, train your troops, and fight against other players online. You can also join or create clans with other players and participate in clan wars, clan games, and special events. The game is updated regularly with new content and features.
-
The main features of the game
-
Some of the main features of Clash of Clans are:
-
-
Build your own village with various buildings, defenses, traps, and decorations.
-
Train different types of troops with unique abilities and upgrade them with elixir or dark elixir.
-
Collect resources such as gold, elixir, dark elixir, gems, and clan points by raiding other villages or completing tasks.
-
Use spells and siege machines to support your attacks or defend your village.
-
Compete with other players in various leagues and tournaments for trophies and rewards.
-
Join or create clans with other players and chat, donate troops, request reinforcements, and cooperate in clan wars, clan games, and special events.
-
Explore new areas such as the Builder Base, the Town Hall 13, and the Super Troops.
-
-
What is Clash of Clans Mod Menu APK?
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A modified version of the game with unlimited resources and features
-
Clash of Clans Mod Menu APK is a modified version of the original game that gives you access to unlimited resources and features. With this mod menu apk, you can enjoy the game without any limitations or restrictions. You can build your village as you wish, train any troops you want, attack any base you like, and join any clan you prefer.
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Some of the unlimited resources and features that you can get with Clash of Clans Mod Menu APK are:
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-
Unlimited gold, elixir, dark el
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-
The benefits of using the mod menu apk
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Some of the benefits of using Clash of Clans Mod Menu APK are:
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You can save your time and money. You don't have to spend hours or dollars to progress in the game. You can get everything you need for free and instantly.
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You can have more fun and excitement. You don't have to worry about running out of resources or losing battles. You can experiment with different strategies and tactics and enjoy the game to the fullest.
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You can challenge yourself and others. You don't have to settle for the easy or boring levels. You can try the harder or more interesting ones and test your skills and creativity. You can also compete with other players who use the mod menu apk and see who is the best.
-
-
How to download and install Clash of Clans Mod Menu APK latest version?
-
The steps to download and install the mod menu apk
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If you want to download and install Clash of Clans Mod Menu APK latest version, you need to follow these steps:
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-
Go to a trusted website that provides the mod menu apk file, such as [clashofclansmodapk.net].
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Click on the download button and wait for the file to be downloaded on your device.
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Go to your device settings and enable the installation of apps from unknown sources.
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Locate the downloaded file in your file manager and tap on it to start the installation process.
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Follow the instructions on the screen and wait for the installation to be completed.
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Launch the game and enjoy the mod menu apk.
-
-
The precautions to take before installing the mod menu apk
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Before you install Clash of Clans Mod Menu APK latest version, you need to take some precautions to avoid any problems or risks. Here are some of them:
-
-
Make sure that your device has enough storage space and battery life for the installation process.
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Make sure that you have a stable internet connection for the download process.
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Make sure that you download the mod menu apk file from a trusted and secure website, not from a random or suspicious one.
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Make sure that you backup your original game data before installing the mod menu apk, in case you want to switch back to the official version later.
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Make sure that you do not use your real account or personal information when playing with the mod menu apk, as it may result in a ban or a hack.
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How to use Clash of Clans Mod Menu APK latest version?
The main options and settings of the mod menu apk
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Once you launch the game with the mod menu apk, you will see a floating icon on the screen that gives you access to the mod menu. By tapping on this icon, you can open the mod menu and choose from various options and settings. Some of the main options and settings of the mod menu apk are:
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Resources: You can adjust the amount of gold, elixir, dark elixir, gems, and clan points that you have in your account. You can also refill your resources whenever you want.
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Troops: You can select any troops that you want to train, even if they are not available in your town hall level or barracks. You can also change the level and quantity of your troops.
-
Spells: You can select any spells that you want to use, even if they are not available in your town hall level or spell factory. You can also change the level and quantity of your spells.
-
Siege Machines: You can select any siege machines that you want to use, even if they are not available in your town hall level or workshop. You can also change the level and quantity of your siege machines.
-
Builder Base: You can access the Builder Base without any requirements or restrictions. You can also adjust the amount of gold, elixir, gems, and builder trophies that you have in your Builder Base account.
-
Town Hall 13: You can access the Town Hall 13 without any requirements or restrictions. You can also upgrade your town hall, buildings, troops, spells, and siege machines to level 13.
-
Super Troops: You can access the Super Troops without any requirements or restrictions. You can also select any Super Troops that you want to use and change their level and quantity.
-
Customization: You can change your village name, clan name, clan badge, profile picture, and chat color as many times as you want.
-
Cheats and Hacks: You can enable or disable various cheats and hacks that can enhance your gaming experience, such as auto-attack, auto-collect, auto-train, auto-upgrade, anti-ban, and more.
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-
The tips and tricks to enjoy the mod menu apk
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Here are some tips and tricks that can help you enjoy Clash of Clans Mod Menu APK latest version:
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Use the mod menu apk for fun and entertainment only. Do not use it for malicious or illegal purposes.
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Do not abuse the mod menu apk or spam other players with it. Be respectful and fair to other players.
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Do not share your mod menu apk account or personal information with anyone. Keep your account safe and secure.
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Do not update the game from the official sources or uninstall the mod menu apk without backing up your data. You may lose your progress or data.
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Do not play with the mod menu apk on public or unsecured networks. You may expose your device or account to viruses or hackers.
-
-
Conclusion
-
A summary of the main points of the article
-
In conclusion, Clash of Clans Mod Menu APK latest version is a modified version of the original game that gives you unlimited resources and features. With this mod menu apk, you can enjoy the game without any limitations or restrictions. You can build your village as you wish, train any troops you want, attack any base you like, and join any clan you prefer. You can also explore new areas such as the Builder Base, the Town Hall 13, and the Super Troops. You can also customize your village name, clan name, clan badge, profile picture, and chat color as many times as you want. You can also use various cheats and hacks to enhance your gaming experience.
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A call to action for the readers
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If you are interested in trying out Clash of Clans Mod Menu APK latest version, you can download it from [clashofclansmodapk.net] and follow the steps to install it on your device. However, before you do that, make sure that you take some precautions to avoid any problems or risks. Also, make sure that you use the mod menu apk for fun and entertainment only and not for malicious or illegal purposes. And remember to be respectful and fair to other players who play with the official version of the game.
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We hope that this article has given you all the information you need to know about Clash of Clans Mod Menu APK latest version. If you have any questions or feedback, feel free to leave a comment below. And if you liked this article, please share it with your friends who might be interested in Clash of Clans Mod Menu APK. And don't forget to check out our other articles on similar topics. Thank you for reading and happy gaming!
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FAQs
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Here are some of the frequently asked questions about Clash of Clans Mod Menu APK latest version:
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Question
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Answer
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Is Clash of Clans Mod Menu APK safe to use?
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Clash of Clans Mod Menu APK is safe to use as long as you download it from a trusted and secure website, such as [clashofclansmodapk.net]. However, you should always be careful when installing apps from unknown sources and take some precautions to protect your device and account.
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Is Clash of Clans Mod Menu APK legal to use?
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Clash of Clans Mod Menu APK is not legal to use, as it violates the terms and conditions of the original game. Using the mod menu apk may result in a ban or a hack from the official game servers. Therefore, you should use the mod menu apk at your own risk and responsibility.
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Can I play with the mod menu apk online?
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Yes, you can play with the mod menu apk online, as it connects to the same servers as the official game. However, you may encounter some problems or errors when playing with the mod menu apk online, such as lagging, crashing, or mismatching. Also, you may face some backlash or complaints from other players who play with the official version of the game.
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Can I play with the mod menu apk offline?
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Yes, you can play with the mod menu apk offline, as it does not require an internet connection to run. However, you may miss some features or updates that are only available online, such as clan wars, clan games, and special events.
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Can I switch back to the official version of the game after using the mod menu apk?
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Yes, you can switch back to the official version of the game after using the mod menu apk, as long as you backup your original game data before installing the mod menu apk. To switch back to the official version of the game, you need to uninstall the mod menu apk and reinstall the original game from the official sources.
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-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets_537227KB.py b/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets_537227KB.py
deleted file mode 100644
index 1ceac4a470ca311d594818d52e5f96919cfddb26..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets_537227KB.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import torch
-import numpy as np
-from torch import nn
-import torch.nn.functional as F
-
-from uvr5_pack.lib_v5 import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
- def __init__(self, nin, ch, dilations=(4, 8, 16)):
- super(BaseASPPNet, self).__init__()
- self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
- self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
- self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
- self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
- self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
- self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
- self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
- self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
- self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
- def __call__(self, x):
- h, e1 = self.enc1(x)
- h, e2 = self.enc2(h)
- h, e3 = self.enc3(h)
- h, e4 = self.enc4(h)
-
- h = self.aspp(h)
-
- h = self.dec4(h, e4)
- h = self.dec3(h, e3)
- h = self.dec2(h, e2)
- h = self.dec1(h, e1)
-
- return h
-
-
-class CascadedASPPNet(nn.Module):
- def __init__(self, n_fft):
- super(CascadedASPPNet, self).__init__()
- self.stg1_low_band_net = BaseASPPNet(2, 64)
- self.stg1_high_band_net = BaseASPPNet(2, 64)
-
- self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
- self.stg2_full_band_net = BaseASPPNet(32, 64)
-
- self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
- self.stg3_full_band_net = BaseASPPNet(64, 128)
-
- self.out = nn.Conv2d(128, 2, 1, bias=False)
- self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
- self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
- self.max_bin = n_fft // 2
- self.output_bin = n_fft // 2 + 1
-
- self.offset = 128
-
- def forward(self, x, aggressiveness=None):
- mix = x.detach()
- x = x.clone()
-
- x = x[:, :, : self.max_bin]
-
- bandw = x.size()[2] // 2
- aux1 = torch.cat(
- [
- self.stg1_low_band_net(x[:, :, :bandw]),
- self.stg1_high_band_net(x[:, :, bandw:]),
- ],
- dim=2,
- )
-
- h = torch.cat([x, aux1], dim=1)
- aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
- h = torch.cat([x, aux1, aux2], dim=1)
- h = self.stg3_full_band_net(self.stg3_bridge(h))
-
- mask = torch.sigmoid(self.out(h))
- mask = F.pad(
- input=mask,
- pad=(0, 0, 0, self.output_bin - mask.size()[2]),
- mode="replicate",
- )
-
- if self.training:
- aux1 = torch.sigmoid(self.aux1_out(aux1))
- aux1 = F.pad(
- input=aux1,
- pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
- mode="replicate",
- )
- aux2 = torch.sigmoid(self.aux2_out(aux2))
- aux2 = F.pad(
- input=aux2,
- pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
- mode="replicate",
- )
- return mask * mix, aux1 * mix, aux2 * mix
- else:
- if aggressiveness:
- mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
- mask[:, :, : aggressiveness["split_bin"]],
- 1 + aggressiveness["value"] / 3,
- )
- mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
- mask[:, :, aggressiveness["split_bin"] :],
- 1 + aggressiveness["value"],
- )
-
- return mask * mix
-
- def predict(self, x_mag, aggressiveness=None):
- h = self.forward(x_mag, aggressiveness)
-
- if self.offset > 0:
- h = h[:, :, :, self.offset : -self.offset]
- assert h.size()[3] > 0
-
- return h
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/_explorers.py b/spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/_explorers.py
deleted file mode 100644
index 334836b72559a120feb8a15eef3fe96ce88a4edb..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/grids/musicgen/_explorers.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-import treetable as tt
-
-from .._base_explorers import BaseExplorer
-
-
-class LMExplorer(BaseExplorer):
- eval_metrics: tp.List[str] = []
-
- def stages(self) -> tp.List[str]:
- return ['train', 'valid']
-
- def get_grid_metrics(self):
- """Return the metrics that should be displayed in the tracking table."""
- return [
- tt.group(
- 'train',
- [
- tt.leaf('epoch'),
- tt.leaf('duration', '.1f'), # duration in minutes
- tt.leaf('ping'),
- tt.leaf('ce', '.4f'), # cross entropy
- tt.leaf("ppl", '.3f'), # perplexity
- ],
- align='>',
- ),
- tt.group(
- 'valid',
- [
- tt.leaf('ce', '.4f'),
- tt.leaf('ppl', '.3f'),
- tt.leaf('best_ppl', '.3f'),
- ],
- align='>',
- ),
- ]
-
- def process_sheep(self, sheep, history):
- parts = super().process_sheep(sheep, history)
-
- track_by = {'ppl': 'lower'} # values should be in ['lower', 'higher']
- best_metrics = {k: (1 if v == 'lower' else -1) * float('inf') for k, v in track_by.items()}
-
- def comparator(mode, a, b):
- return a < b if mode == 'lower' else a > b
-
- for metrics in history:
- for key, sub in metrics.items():
- for metric in track_by:
- # for the validation set, keep track of best metrics (ppl in this example)
- # this is so we can conveniently compare metrics between runs in the grid
- if key == 'valid' and metric in sub and comparator(
- track_by[metric], sub[metric], best_metrics[metric]
- ):
- best_metrics[metric] = sub[metric]
-
- if 'valid' in parts:
- parts['valid'].update({f'best_{k}': v for k, v in best_metrics.items()})
- return parts
-
-
-class GenerationEvalExplorer(BaseExplorer):
- eval_metrics: tp.List[str] = []
-
- def stages(self) -> tp.List[str]:
- return ['evaluate']
-
- def get_grid_metrics(self):
- """Return the metrics that should be displayed in the tracking table."""
- return [
- tt.group(
- 'evaluate',
- [
- tt.leaf('epoch', '.3f'),
- tt.leaf('duration', '.1f'),
- tt.leaf('ping'),
- tt.leaf('ce', '.4f'),
- tt.leaf('ppl', '.3f'),
- tt.leaf('fad', '.3f'),
- tt.leaf('kld', '.3f'),
- tt.leaf('text_consistency', '.3f'),
- tt.leaf('chroma_cosine', '.3f'),
- ],
- align='>',
- ),
- ]
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192.py
deleted file mode 100644
index 7db9e0f047b8714df07776fc77880c9f5ca09478..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192.py
+++ /dev/null
@@ -1,2861 +0,0 @@
-default_scope = 'mmpose'
-default_hooks = dict(
- timer=dict(type='IterTimerHook'),
- logger=dict(type='LoggerHook', interval=50),
- param_scheduler=dict(type='ParamSchedulerHook'),
- checkpoint=dict(
- type='CheckpointHook', interval=10, save_best='PCK', rule='greater'),
- sampler_seed=dict(type='DistSamplerSeedHook'),
- visualization=dict(type='PoseVisualizationHook', enable=False))
-custom_hooks = [dict(type='SyncBuffersHook')]
-env_cfg = dict(
- cudnn_benchmark=False,
- mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
- dist_cfg=dict(backend='nccl'))
-vis_backends = [dict(type='LocalVisBackend')]
-visualizer = dict(
- type='PoseLocalVisualizer',
- vis_backends=[dict(type='LocalVisBackend'),
- dict(type='WandbVisBackend')],
- name='visualizer')
-log_processor = dict(
- type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
-log_level = 'INFO'
-load_from = None
-resume = False
-backend_args = dict(backend='local')
-train_cfg = dict(by_epoch=True, max_epochs=120, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-colors = dict(
- sss=[255, 128, 0],
- lss=[255, 0, 128],
- sso=[128, 0, 255],
- lso=[0, 128, 255],
- vest=[0, 128, 128],
- sling=[0, 0, 128],
- shorts=[128, 128, 128],
- trousers=[128, 0, 128],
- skirt=[64, 128, 128],
- ssd=[64, 64, 128],
- lsd=[128, 64, 0],
- vd=[128, 64, 255],
- sd=[128, 64, 0])
-dataset_info = dict(
- dataset_name='deepfashion2',
- paper_info=dict(
- author=
- 'Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo',
- title=
- 'DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images',
- container=
- 'Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)',
- year='2019',
- homepage='https://github.com/switchablenorms/DeepFashion2'),
- keypoint_info=dict({
- 0:
- dict(name='sss_kpt1', id=0, color=[255, 128, 0], type='', swap=''),
- 1:
- dict(
- name='sss_kpt2',
- id=1,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt6'),
- 2:
- dict(
- name='sss_kpt3',
- id=2,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt5'),
- 3:
- dict(name='sss_kpt4', id=3, color=[255, 128, 0], type='', swap=''),
- 4:
- dict(
- name='sss_kpt5',
- id=4,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt3'),
- 5:
- dict(
- name='sss_kpt6',
- id=5,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt2'),
- 6:
- dict(
- name='sss_kpt7',
- id=6,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt25'),
- 7:
- dict(
- name='sss_kpt8',
- id=7,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt24'),
- 8:
- dict(
- name='sss_kpt9',
- id=8,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt23'),
- 9:
- dict(
- name='sss_kpt10',
- id=9,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt22'),
- 10:
- dict(
- name='sss_kpt11',
- id=10,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt21'),
- 11:
- dict(
- name='sss_kpt12',
- id=11,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt20'),
- 12:
- dict(
- name='sss_kpt13',
- id=12,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt19'),
- 13:
- dict(
- name='sss_kpt14',
- id=13,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt18'),
- 14:
- dict(
- name='sss_kpt15',
- id=14,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt17'),
- 15:
- dict(name='sss_kpt16', id=15, color=[255, 128, 0], type='', swap=''),
- 16:
- dict(
- name='sss_kpt17',
- id=16,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt15'),
- 17:
- dict(
- name='sss_kpt18',
- id=17,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt14'),
- 18:
- dict(
- name='sss_kpt19',
- id=18,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt13'),
- 19:
- dict(
- name='sss_kpt20',
- id=19,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt12'),
- 20:
- dict(
- name='sss_kpt21',
- id=20,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt11'),
- 21:
- dict(
- name='sss_kpt22',
- id=21,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt10'),
- 22:
- dict(
- name='sss_kpt23',
- id=22,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt9'),
- 23:
- dict(
- name='sss_kpt24',
- id=23,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt8'),
- 24:
- dict(
- name='sss_kpt25',
- id=24,
- color=[255, 128, 0],
- type='',
- swap='sss_kpt7'),
- 25:
- dict(name='lss_kpt1', id=25, color=[255, 0, 128], type='', swap=''),
- 26:
- dict(
- name='lss_kpt2',
- id=26,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt6'),
- 27:
- dict(
- name='lss_kpt3',
- id=27,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt5'),
- 28:
- dict(name='lss_kpt4', id=28, color=[255, 0, 128], type='', swap=''),
- 29:
- dict(
- name='lss_kpt5',
- id=29,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt3'),
- 30:
- dict(
- name='lss_kpt6',
- id=30,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt2'),
- 31:
- dict(
- name='lss_kpt7',
- id=31,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt33'),
- 32:
- dict(
- name='lss_kpt8',
- id=32,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt32'),
- 33:
- dict(
- name='lss_kpt9',
- id=33,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt31'),
- 34:
- dict(
- name='lss_kpt10',
- id=34,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt30'),
- 35:
- dict(
- name='lss_kpt11',
- id=35,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt29'),
- 36:
- dict(
- name='lss_kpt12',
- id=36,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt28'),
- 37:
- dict(
- name='lss_kpt13',
- id=37,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt27'),
- 38:
- dict(
- name='lss_kpt14',
- id=38,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt26'),
- 39:
- dict(
- name='lss_kpt15',
- id=39,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt25'),
- 40:
- dict(
- name='lss_kpt16',
- id=40,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt24'),
- 41:
- dict(
- name='lss_kpt17',
- id=41,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt23'),
- 42:
- dict(
- name='lss_kpt18',
- id=42,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt22'),
- 43:
- dict(
- name='lss_kpt19',
- id=43,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt21'),
- 44:
- dict(name='lss_kpt20', id=44, color=[255, 0, 128], type='', swap=''),
- 45:
- dict(
- name='lss_kpt21',
- id=45,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt19'),
- 46:
- dict(
- name='lss_kpt22',
- id=46,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt18'),
- 47:
- dict(
- name='lss_kpt23',
- id=47,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt17'),
- 48:
- dict(
- name='lss_kpt24',
- id=48,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt16'),
- 49:
- dict(
- name='lss_kpt25',
- id=49,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt15'),
- 50:
- dict(
- name='lss_kpt26',
- id=50,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt14'),
- 51:
- dict(
- name='lss_kpt27',
- id=51,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt13'),
- 52:
- dict(
- name='lss_kpt28',
- id=52,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt12'),
- 53:
- dict(
- name='lss_kpt29',
- id=53,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt11'),
- 54:
- dict(
- name='lss_kpt30',
- id=54,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt10'),
- 55:
- dict(
- name='lss_kpt31',
- id=55,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt9'),
- 56:
- dict(
- name='lss_kpt32',
- id=56,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt8'),
- 57:
- dict(
- name='lss_kpt33',
- id=57,
- color=[255, 0, 128],
- type='',
- swap='lss_kpt7'),
- 58:
- dict(name='sso_kpt1', id=58, color=[128, 0, 255], type='', swap=''),
- 59:
- dict(
- name='sso_kpt2',
- id=59,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt26'),
- 60:
- dict(
- name='sso_kpt3',
- id=60,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt5'),
- 61:
- dict(
- name='sso_kpt4',
- id=61,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt6'),
- 62:
- dict(
- name='sso_kpt5',
- id=62,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt3'),
- 63:
- dict(
- name='sso_kpt6',
- id=63,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt4'),
- 64:
- dict(
- name='sso_kpt7',
- id=64,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt25'),
- 65:
- dict(
- name='sso_kpt8',
- id=65,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt24'),
- 66:
- dict(
- name='sso_kpt9',
- id=66,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt23'),
- 67:
- dict(
- name='sso_kpt10',
- id=67,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt22'),
- 68:
- dict(
- name='sso_kpt11',
- id=68,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt21'),
- 69:
- dict(
- name='sso_kpt12',
- id=69,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt20'),
- 70:
- dict(
- name='sso_kpt13',
- id=70,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt19'),
- 71:
- dict(
- name='sso_kpt14',
- id=71,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt18'),
- 72:
- dict(
- name='sso_kpt15',
- id=72,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt17'),
- 73:
- dict(
- name='sso_kpt16',
- id=73,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt29'),
- 74:
- dict(
- name='sso_kpt17',
- id=74,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt15'),
- 75:
- dict(
- name='sso_kpt18',
- id=75,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt14'),
- 76:
- dict(
- name='sso_kpt19',
- id=76,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt13'),
- 77:
- dict(
- name='sso_kpt20',
- id=77,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt12'),
- 78:
- dict(
- name='sso_kpt21',
- id=78,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt11'),
- 79:
- dict(
- name='sso_kpt22',
- id=79,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt10'),
- 80:
- dict(
- name='sso_kpt23',
- id=80,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt9'),
- 81:
- dict(
- name='sso_kpt24',
- id=81,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt8'),
- 82:
- dict(
- name='sso_kpt25',
- id=82,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt7'),
- 83:
- dict(
- name='sso_kpt26',
- id=83,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt2'),
- 84:
- dict(
- name='sso_kpt27',
- id=84,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt30'),
- 85:
- dict(
- name='sso_kpt28',
- id=85,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt31'),
- 86:
- dict(
- name='sso_kpt29',
- id=86,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt16'),
- 87:
- dict(
- name='sso_kpt30',
- id=87,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt27'),
- 88:
- dict(
- name='sso_kpt31',
- id=88,
- color=[128, 0, 255],
- type='',
- swap='sso_kpt28'),
- 89:
- dict(name='lso_kpt1', id=89, color=[0, 128, 255], type='', swap=''),
- 90:
- dict(
- name='lso_kpt2',
- id=90,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt6'),
- 91:
- dict(
- name='lso_kpt3',
- id=91,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt5'),
- 92:
- dict(
- name='lso_kpt4',
- id=92,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt34'),
- 93:
- dict(
- name='lso_kpt5',
- id=93,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt3'),
- 94:
- dict(
- name='lso_kpt6',
- id=94,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt2'),
- 95:
- dict(
- name='lso_kpt7',
- id=95,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt33'),
- 96:
- dict(
- name='lso_kpt8',
- id=96,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt32'),
- 97:
- dict(
- name='lso_kpt9',
- id=97,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt31'),
- 98:
- dict(
- name='lso_kpt10',
- id=98,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt30'),
- 99:
- dict(
- name='lso_kpt11',
- id=99,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt29'),
- 100:
- dict(
- name='lso_kpt12',
- id=100,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt28'),
- 101:
- dict(
- name='lso_kpt13',
- id=101,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt27'),
- 102:
- dict(
- name='lso_kpt14',
- id=102,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt26'),
- 103:
- dict(
- name='lso_kpt15',
- id=103,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt25'),
- 104:
- dict(
- name='lso_kpt16',
- id=104,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt24'),
- 105:
- dict(
- name='lso_kpt17',
- id=105,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt23'),
- 106:
- dict(
- name='lso_kpt18',
- id=106,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt22'),
- 107:
- dict(
- name='lso_kpt19',
- id=107,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt21'),
- 108:
- dict(
- name='lso_kpt20',
- id=108,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt37'),
- 109:
- dict(
- name='lso_kpt21',
- id=109,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt19'),
- 110:
- dict(
- name='lso_kpt22',
- id=110,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt18'),
- 111:
- dict(
- name='lso_kpt23',
- id=111,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt17'),
- 112:
- dict(
- name='lso_kpt24',
- id=112,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt16'),
- 113:
- dict(
- name='lso_kpt25',
- id=113,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt15'),
- 114:
- dict(
- name='lso_kpt26',
- id=114,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt14'),
- 115:
- dict(
- name='lso_kpt27',
- id=115,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt13'),
- 116:
- dict(
- name='lso_kpt28',
- id=116,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt12'),
- 117:
- dict(
- name='lso_kpt29',
- id=117,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt11'),
- 118:
- dict(
- name='lso_kpt30',
- id=118,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt10'),
- 119:
- dict(
- name='lso_kpt31',
- id=119,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt9'),
- 120:
- dict(
- name='lso_kpt32',
- id=120,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt8'),
- 121:
- dict(
- name='lso_kpt33',
- id=121,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt7'),
- 122:
- dict(
- name='lso_kpt34',
- id=122,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt4'),
- 123:
- dict(
- name='lso_kpt35',
- id=123,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt38'),
- 124:
- dict(
- name='lso_kpt36',
- id=124,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt39'),
- 125:
- dict(
- name='lso_kpt37',
- id=125,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt20'),
- 126:
- dict(
- name='lso_kpt38',
- id=126,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt35'),
- 127:
- dict(
- name='lso_kpt39',
- id=127,
- color=[0, 128, 255],
- type='',
- swap='lso_kpt36'),
- 128:
- dict(name='vest_kpt1', id=128, color=[0, 128, 128], type='', swap=''),
- 129:
- dict(
- name='vest_kpt2',
- id=129,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt6'),
- 130:
- dict(
- name='vest_kpt3',
- id=130,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt5'),
- 131:
- dict(name='vest_kpt4', id=131, color=[0, 128, 128], type='', swap=''),
- 132:
- dict(
- name='vest_kpt5',
- id=132,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt3'),
- 133:
- dict(
- name='vest_kpt6',
- id=133,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt2'),
- 134:
- dict(
- name='vest_kpt7',
- id=134,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt15'),
- 135:
- dict(
- name='vest_kpt8',
- id=135,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt14'),
- 136:
- dict(
- name='vest_kpt9',
- id=136,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt13'),
- 137:
- dict(
- name='vest_kpt10',
- id=137,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt12'),
- 138:
- dict(name='vest_kpt11', id=138, color=[0, 128, 128], type='', swap=''),
- 139:
- dict(
- name='vest_kpt12',
- id=139,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt10'),
- 140:
- dict(name='vest_kpt13', id=140, color=[0, 128, 128], type='', swap=''),
- 141:
- dict(
- name='vest_kpt14',
- id=141,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt8'),
- 142:
- dict(
- name='vest_kpt15',
- id=142,
- color=[0, 128, 128],
- type='',
- swap='vest_kpt7'),
- 143:
- dict(name='sling_kpt1', id=143, color=[0, 0, 128], type='', swap=''),
- 144:
- dict(
- name='sling_kpt2',
- id=144,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt6'),
- 145:
- dict(
- name='sling_kpt3',
- id=145,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt5'),
- 146:
- dict(name='sling_kpt4', id=146, color=[0, 0, 128], type='', swap=''),
- 147:
- dict(
- name='sling_kpt5',
- id=147,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt3'),
- 148:
- dict(
- name='sling_kpt6',
- id=148,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt2'),
- 149:
- dict(
- name='sling_kpt7',
- id=149,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt15'),
- 150:
- dict(
- name='sling_kpt8',
- id=150,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt14'),
- 151:
- dict(
- name='sling_kpt9',
- id=151,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt13'),
- 152:
- dict(
- name='sling_kpt10',
- id=152,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt12'),
- 153:
- dict(name='sling_kpt11', id=153, color=[0, 0, 128], type='', swap=''),
- 154:
- dict(
- name='sling_kpt12',
- id=154,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt10'),
- 155:
- dict(
- name='sling_kpt13',
- id=155,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt9'),
- 156:
- dict(
- name='sling_kpt14',
- id=156,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt8'),
- 157:
- dict(
- name='sling_kpt15',
- id=157,
- color=[0, 0, 128],
- type='',
- swap='sling_kpt7'),
- 158:
- dict(
- name='shorts_kpt1',
- id=158,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt3'),
- 159:
- dict(
- name='shorts_kpt2',
- id=159,
- color=[128, 128, 128],
- type='',
- swap=''),
- 160:
- dict(
- name='shorts_kpt3',
- id=160,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt1'),
- 161:
- dict(
- name='shorts_kpt4',
- id=161,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt10'),
- 162:
- dict(
- name='shorts_kpt5',
- id=162,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt9'),
- 163:
- dict(
- name='shorts_kpt6',
- id=163,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt8'),
- 164:
- dict(
- name='shorts_kpt7',
- id=164,
- color=[128, 128, 128],
- type='',
- swap=''),
- 165:
- dict(
- name='shorts_kpt8',
- id=165,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt6'),
- 166:
- dict(
- name='shorts_kpt9',
- id=166,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt5'),
- 167:
- dict(
- name='shorts_kpt10',
- id=167,
- color=[128, 128, 128],
- type='',
- swap='shorts_kpt4'),
- 168:
- dict(
- name='trousers_kpt1',
- id=168,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt3'),
- 169:
- dict(
- name='trousers_kpt2',
- id=169,
- color=[128, 0, 128],
- type='',
- swap=''),
- 170:
- dict(
- name='trousers_kpt3',
- id=170,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt1'),
- 171:
- dict(
- name='trousers_kpt4',
- id=171,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt14'),
- 172:
- dict(
- name='trousers_kpt5',
- id=172,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt13'),
- 173:
- dict(
- name='trousers_kpt6',
- id=173,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt12'),
- 174:
- dict(
- name='trousers_kpt7',
- id=174,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt11'),
- 175:
- dict(
- name='trousers_kpt8',
- id=175,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt10'),
- 176:
- dict(
- name='trousers_kpt9',
- id=176,
- color=[128, 0, 128],
- type='',
- swap=''),
- 177:
- dict(
- name='trousers_kpt10',
- id=177,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt8'),
- 178:
- dict(
- name='trousers_kpt11',
- id=178,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt7'),
- 179:
- dict(
- name='trousers_kpt12',
- id=179,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt6'),
- 180:
- dict(
- name='trousers_kpt13',
- id=180,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt5'),
- 181:
- dict(
- name='trousers_kpt14',
- id=181,
- color=[128, 0, 128],
- type='',
- swap='trousers_kpt4'),
- 182:
- dict(
- name='skirt_kpt1',
- id=182,
- color=[64, 128, 128],
- type='',
- swap='skirt_kpt3'),
- 183:
- dict(
- name='skirt_kpt2', id=183, color=[64, 128, 128], type='', swap=''),
- 184:
- dict(
- name='skirt_kpt3',
- id=184,
- color=[64, 128, 128],
- type='',
- swap='skirt_kpt1'),
- 185:
- dict(
- name='skirt_kpt4',
- id=185,
- color=[64, 128, 128],
- type='',
- swap='skirt_kpt8'),
- 186:
- dict(
- name='skirt_kpt5',
- id=186,
- color=[64, 128, 128],
- type='',
- swap='skirt_kpt7'),
- 187:
- dict(
- name='skirt_kpt6', id=187, color=[64, 128, 128], type='', swap=''),
- 188:
- dict(
- name='skirt_kpt7',
- id=188,
- color=[64, 128, 128],
- type='',
- swap='skirt_kpt5'),
- 189:
- dict(
- name='skirt_kpt8',
- id=189,
- color=[64, 128, 128],
- type='',
- swap='skirt_kpt4'),
- 190:
- dict(name='ssd_kpt1', id=190, color=[64, 64, 128], type='', swap=''),
- 191:
- dict(
- name='ssd_kpt2',
- id=191,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt6'),
- 192:
- dict(
- name='ssd_kpt3',
- id=192,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt5'),
- 193:
- dict(name='ssd_kpt4', id=193, color=[64, 64, 128], type='', swap=''),
- 194:
- dict(
- name='ssd_kpt5',
- id=194,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt3'),
- 195:
- dict(
- name='ssd_kpt6',
- id=195,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt2'),
- 196:
- dict(
- name='ssd_kpt7',
- id=196,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt29'),
- 197:
- dict(
- name='ssd_kpt8',
- id=197,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt28'),
- 198:
- dict(
- name='ssd_kpt9',
- id=198,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt27'),
- 199:
- dict(
- name='ssd_kpt10',
- id=199,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt26'),
- 200:
- dict(
- name='ssd_kpt11',
- id=200,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt25'),
- 201:
- dict(
- name='ssd_kpt12',
- id=201,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt24'),
- 202:
- dict(
- name='ssd_kpt13',
- id=202,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt23'),
- 203:
- dict(
- name='ssd_kpt14',
- id=203,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt22'),
- 204:
- dict(
- name='ssd_kpt15',
- id=204,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt21'),
- 205:
- dict(
- name='ssd_kpt16',
- id=205,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt20'),
- 206:
- dict(
- name='ssd_kpt17',
- id=206,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt19'),
- 207:
- dict(name='ssd_kpt18', id=207, color=[64, 64, 128], type='', swap=''),
- 208:
- dict(
- name='ssd_kpt19',
- id=208,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt17'),
- 209:
- dict(
- name='ssd_kpt20',
- id=209,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt16'),
- 210:
- dict(
- name='ssd_kpt21',
- id=210,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt15'),
- 211:
- dict(
- name='ssd_kpt22',
- id=211,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt14'),
- 212:
- dict(
- name='ssd_kpt23',
- id=212,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt13'),
- 213:
- dict(
- name='ssd_kpt24',
- id=213,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt12'),
- 214:
- dict(
- name='ssd_kpt25',
- id=214,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt11'),
- 215:
- dict(
- name='ssd_kpt26',
- id=215,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt10'),
- 216:
- dict(
- name='ssd_kpt27',
- id=216,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt9'),
- 217:
- dict(
- name='ssd_kpt28',
- id=217,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt8'),
- 218:
- dict(
- name='ssd_kpt29',
- id=218,
- color=[64, 64, 128],
- type='',
- swap='ssd_kpt7'),
- 219:
- dict(name='lsd_kpt1', id=219, color=[128, 64, 0], type='', swap=''),
- 220:
- dict(
- name='lsd_kpt2',
- id=220,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt6'),
- 221:
- dict(
- name='lsd_kpt3',
- id=221,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt5'),
- 222:
- dict(name='lsd_kpt4', id=222, color=[128, 64, 0], type='', swap=''),
- 223:
- dict(
- name='lsd_kpt5',
- id=223,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt3'),
- 224:
- dict(
- name='lsd_kpt6',
- id=224,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt2'),
- 225:
- dict(
- name='lsd_kpt7',
- id=225,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt37'),
- 226:
- dict(
- name='lsd_kpt8',
- id=226,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt36'),
- 227:
- dict(
- name='lsd_kpt9',
- id=227,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt35'),
- 228:
- dict(
- name='lsd_kpt10',
- id=228,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt34'),
- 229:
- dict(
- name='lsd_kpt11',
- id=229,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt33'),
- 230:
- dict(
- name='lsd_kpt12',
- id=230,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt32'),
- 231:
- dict(
- name='lsd_kpt13',
- id=231,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt31'),
- 232:
- dict(
- name='lsd_kpt14',
- id=232,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt30'),
- 233:
- dict(
- name='lsd_kpt15',
- id=233,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt29'),
- 234:
- dict(
- name='lsd_kpt16',
- id=234,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt28'),
- 235:
- dict(
- name='lsd_kpt17',
- id=235,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt27'),
- 236:
- dict(
- name='lsd_kpt18',
- id=236,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt26'),
- 237:
- dict(
- name='lsd_kpt19',
- id=237,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt25'),
- 238:
- dict(
- name='lsd_kpt20',
- id=238,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt24'),
- 239:
- dict(
- name='lsd_kpt21',
- id=239,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt23'),
- 240:
- dict(name='lsd_kpt22', id=240, color=[128, 64, 0], type='', swap=''),
- 241:
- dict(
- name='lsd_kpt23',
- id=241,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt21'),
- 242:
- dict(
- name='lsd_kpt24',
- id=242,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt20'),
- 243:
- dict(
- name='lsd_kpt25',
- id=243,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt19'),
- 244:
- dict(
- name='lsd_kpt26',
- id=244,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt18'),
- 245:
- dict(
- name='lsd_kpt27',
- id=245,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt17'),
- 246:
- dict(
- name='lsd_kpt28',
- id=246,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt16'),
- 247:
- dict(
- name='lsd_kpt29',
- id=247,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt15'),
- 248:
- dict(
- name='lsd_kpt30',
- id=248,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt14'),
- 249:
- dict(
- name='lsd_kpt31',
- id=249,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt13'),
- 250:
- dict(
- name='lsd_kpt32',
- id=250,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt12'),
- 251:
- dict(
- name='lsd_kpt33',
- id=251,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt11'),
- 252:
- dict(
- name='lsd_kpt34',
- id=252,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt10'),
- 253:
- dict(
- name='lsd_kpt35',
- id=253,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt9'),
- 254:
- dict(
- name='lsd_kpt36',
- id=254,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt8'),
- 255:
- dict(
- name='lsd_kpt37',
- id=255,
- color=[128, 64, 0],
- type='',
- swap='lsd_kpt7'),
- 256:
- dict(name='vd_kpt1', id=256, color=[128, 64, 255], type='', swap=''),
- 257:
- dict(
- name='vd_kpt2',
- id=257,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt6'),
- 258:
- dict(
- name='vd_kpt3',
- id=258,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt5'),
- 259:
- dict(name='vd_kpt4', id=259, color=[128, 64, 255], type='', swap=''),
- 260:
- dict(
- name='vd_kpt5',
- id=260,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt3'),
- 261:
- dict(
- name='vd_kpt6',
- id=261,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt2'),
- 262:
- dict(
- name='vd_kpt7',
- id=262,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt19'),
- 263:
- dict(
- name='vd_kpt8',
- id=263,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt18'),
- 264:
- dict(
- name='vd_kpt9',
- id=264,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt17'),
- 265:
- dict(
- name='vd_kpt10',
- id=265,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt16'),
- 266:
- dict(
- name='vd_kpt11',
- id=266,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt15'),
- 267:
- dict(
- name='vd_kpt12',
- id=267,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt14'),
- 268:
- dict(name='vd_kpt13', id=268, color=[128, 64, 255], type='', swap=''),
- 269:
- dict(
- name='vd_kpt14',
- id=269,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt12'),
- 270:
- dict(
- name='vd_kpt15',
- id=270,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt11'),
- 271:
- dict(
- name='vd_kpt16',
- id=271,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt10'),
- 272:
- dict(
- name='vd_kpt17',
- id=272,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt9'),
- 273:
- dict(
- name='vd_kpt18',
- id=273,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt8'),
- 274:
- dict(
- name='vd_kpt19',
- id=274,
- color=[128, 64, 255],
- type='',
- swap='vd_kpt7'),
- 275:
- dict(name='sd_kpt1', id=275, color=[128, 64, 0], type='', swap=''),
- 276:
- dict(
- name='sd_kpt2',
- id=276,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt6'),
- 277:
- dict(
- name='sd_kpt3',
- id=277,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt5'),
- 278:
- dict(name='sd_kpt4', id=278, color=[128, 64, 0], type='', swap=''),
- 279:
- dict(
- name='sd_kpt5',
- id=279,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt3'),
- 280:
- dict(
- name='sd_kpt6',
- id=280,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt2'),
- 281:
- dict(
- name='sd_kpt7',
- id=281,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt19'),
- 282:
- dict(
- name='sd_kpt8',
- id=282,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt18'),
- 283:
- dict(
- name='sd_kpt9',
- id=283,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt17'),
- 284:
- dict(
- name='sd_kpt10',
- id=284,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt16'),
- 285:
- dict(
- name='sd_kpt11',
- id=285,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt15'),
- 286:
- dict(
- name='sd_kpt12',
- id=286,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt14'),
- 287:
- dict(name='sd_kpt13', id=287, color=[128, 64, 0], type='', swap=''),
- 288:
- dict(
- name='sd_kpt14',
- id=288,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt12'),
- 289:
- dict(
- name='sd_kpt15',
- id=289,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt11'),
- 290:
- dict(
- name='sd_kpt16',
- id=290,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt10'),
- 291:
- dict(
- name='sd_kpt17',
- id=291,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt9'),
- 292:
- dict(
- name='sd_kpt18',
- id=292,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt8'),
- 293:
- dict(
- name='sd_kpt19',
- id=293,
- color=[128, 64, 0],
- type='',
- swap='sd_kpt7')
- }),
- skeleton_info=dict({
- 0:
- dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]),
- 1:
- dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]),
- 2:
- dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]),
- 3:
- dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]),
- 4:
- dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]),
- 5:
- dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]),
- 6:
- dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]),
- 7:
- dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]),
- 8:
- dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]),
- 9:
- dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]),
- 10:
- dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]),
- 11:
- dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]),
- 12:
- dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]),
- 13:
- dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]),
- 14:
- dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]),
- 15:
- dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]),
- 16:
- dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]),
- 17:
- dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]),
- 18:
- dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]),
- 19:
- dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]),
- 20:
- dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]),
- 21:
- dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]),
- 22:
- dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]),
- 23:
- dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]),
- 24:
- dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]),
- 25:
- dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]),
- 26:
- dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]),
- 27:
- dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]),
- 28:
- dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]),
- 29:
- dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]),
- 30:
- dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]),
- 31:
- dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]),
- 32:
- dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]),
- 33:
- dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]),
- 34:
- dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]),
- 35:
- dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]),
- 36:
- dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]),
- 37:
- dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]),
- 38:
- dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]),
- 39:
- dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]),
- 40:
- dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]),
- 41:
- dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]),
- 42:
- dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]),
- 43:
- dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]),
- 44:
- dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]),
- 45:
- dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]),
- 46:
- dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]),
- 47:
- dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]),
- 48:
- dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]),
- 49:
- dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]),
- 50:
- dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]),
- 51:
- dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]),
- 52:
- dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]),
- 53:
- dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]),
- 54:
- dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]),
- 55:
- dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]),
- 56:
- dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]),
- 57:
- dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]),
- 58:
- dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]),
- 59:
- dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]),
- 60:
- dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]),
- 61:
- dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]),
- 62:
- dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]),
- 63:
- dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]),
- 64:
- dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]),
- 65:
- dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]),
- 66:
- dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]),
- 67:
- dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]),
- 68:
- dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]),
- 69:
- dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]),
- 70:
- dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]),
- 71:
- dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]),
- 72:
- dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]),
- 73:
- dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]),
- 74:
- dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]),
- 75:
- dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]),
- 76:
- dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]),
- 77:
- dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]),
- 78:
- dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]),
- 79:
- dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]),
- 80:
- dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]),
- 81:
- dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]),
- 82:
- dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]),
- 83:
- dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]),
- 84:
- dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]),
- 85:
- dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]),
- 86:
- dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]),
- 87:
- dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]),
- 88:
- dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]),
- 89:
- dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]),
- 90:
- dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]),
- 91:
- dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]),
- 92:
- dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]),
- 93:
- dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]),
- 94:
- dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]),
- 95:
- dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]),
- 96:
- dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]),
- 97:
- dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]),
- 98:
- dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]),
- 99:
- dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]),
- 100:
- dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]),
- 101:
- dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]),
- 102:
- dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]),
- 103:
- dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]),
- 104:
- dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]),
- 105:
- dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]),
- 106:
- dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]),
- 107:
- dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]),
- 108:
- dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]),
- 109:
- dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]),
- 110:
- dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]),
- 111:
- dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]),
- 112:
- dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]),
- 113:
- dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]),
- 114:
- dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]),
- 115:
- dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]),
- 116:
- dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]),
- 117:
- dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]),
- 118:
- dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]),
- 119:
- dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]),
- 120:
- dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]),
- 121:
- dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]),
- 122:
- dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]),
- 123:
- dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]),
- 124:
- dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]),
- 125:
- dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]),
- 126:
- dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]),
- 127:
- dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]),
- 128:
- dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]),
- 129:
- dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]),
- 130:
- dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]),
- 131:
- dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]),
- 132:
- dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]),
- 133:
- dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]),
- 134:
- dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]),
- 135:
- dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]),
- 136:
- dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]),
- 137:
- dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]),
- 138:
- dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]),
- 139:
- dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]),
- 140:
- dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]),
- 141:
- dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]),
- 142:
- dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]),
- 143:
- dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]),
- 144:
- dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]),
- 145:
- dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]),
- 146:
- dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]),
- 147:
- dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]),
- 148:
- dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]),
- 149:
- dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]),
- 150:
- dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]),
- 151:
- dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]),
- 152:
- dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]),
- 153:
- dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]),
- 154:
- dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]),
- 155:
- dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]),
- 156:
- dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]),
- 157:
- dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]),
- 158:
- dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]),
- 159:
- dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]),
- 160:
- dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]),
- 161:
- dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]),
- 162:
- dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]),
- 163:
- dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]),
- 164:
- dict(
- link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128,
- 128]),
- 165:
- dict(
- link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128,
- 128]),
- 166:
- dict(
- link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128,
- 128]),
- 167:
- dict(
- link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128,
- 128]),
- 168:
- dict(
- link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128,
- 128]),
- 169:
- dict(
- link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128,
- 128]),
- 170:
- dict(
- link=('shorts_kpt9', 'shorts_kpt10'),
- id=170,
- color=[128, 128, 128]),
- 171:
- dict(
- link=('shorts_kpt10', 'shorts_kpt3'),
- id=171,
- color=[128, 128, 128]),
- 172:
- dict(
- link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128,
- 128]),
- 173:
- dict(
- link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128,
- 128]),
- 174:
- dict(
- link=('trousers_kpt1', 'trousers_kpt4'),
- id=174,
- color=[128, 0, 128]),
- 175:
- dict(
- link=('trousers_kpt4', 'trousers_kpt5'),
- id=175,
- color=[128, 0, 128]),
- 176:
- dict(
- link=('trousers_kpt5', 'trousers_kpt6'),
- id=176,
- color=[128, 0, 128]),
- 177:
- dict(
- link=('trousers_kpt6', 'trousers_kpt7'),
- id=177,
- color=[128, 0, 128]),
- 178:
- dict(
- link=('trousers_kpt7', 'trousers_kpt8'),
- id=178,
- color=[128, 0, 128]),
- 179:
- dict(
- link=('trousers_kpt8', 'trousers_kpt9'),
- id=179,
- color=[128, 0, 128]),
- 180:
- dict(
- link=('trousers_kpt9', 'trousers_kpt10'),
- id=180,
- color=[128, 0, 128]),
- 181:
- dict(
- link=('trousers_kpt10', 'trousers_kpt11'),
- id=181,
- color=[128, 0, 128]),
- 182:
- dict(
- link=('trousers_kpt11', 'trousers_kpt12'),
- id=182,
- color=[128, 0, 128]),
- 183:
- dict(
- link=('trousers_kpt12', 'trousers_kpt13'),
- id=183,
- color=[128, 0, 128]),
- 184:
- dict(
- link=('trousers_kpt13', 'trousers_kpt14'),
- id=184,
- color=[128, 0, 128]),
- 185:
- dict(
- link=('trousers_kpt14', 'trousers_kpt3'),
- id=185,
- color=[128, 0, 128]),
- 186:
- dict(
- link=('trousers_kpt3', 'trousers_kpt2'),
- id=186,
- color=[128, 0, 128]),
- 187:
- dict(
- link=('trousers_kpt2', 'trousers_kpt1'),
- id=187,
- color=[128, 0, 128]),
- 188:
- dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]),
- 189:
- dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]),
- 190:
- dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]),
- 191:
- dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]),
- 192:
- dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]),
- 193:
- dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]),
- 194:
- dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]),
- 195:
- dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]),
- 196:
- dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]),
- 197:
- dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]),
- 198:
- dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]),
- 199:
- dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]),
- 200:
- dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]),
- 201:
- dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]),
- 202:
- dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]),
- 203:
- dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]),
- 204:
- dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]),
- 205:
- dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]),
- 206:
- dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]),
- 207:
- dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]),
- 208:
- dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]),
- 209:
- dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]),
- 210:
- dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]),
- 211:
- dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]),
- 212:
- dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]),
- 213:
- dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]),
- 214:
- dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]),
- 215:
- dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]),
- 216:
- dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]),
- 217:
- dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]),
- 218:
- dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]),
- 219:
- dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]),
- 220:
- dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]),
- 221:
- dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]),
- 222:
- dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]),
- 223:
- dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]),
- 224:
- dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]),
- 225:
- dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]),
- 226:
- dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]),
- 227:
- dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]),
- 228:
- dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]),
- 229:
- dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]),
- 230:
- dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]),
- 231:
- dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]),
- 232:
- dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]),
- 233:
- dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]),
- 234:
- dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]),
- 235:
- dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]),
- 236:
- dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]),
- 237:
- dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]),
- 238:
- dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]),
- 239:
- dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]),
- 240:
- dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]),
- 241:
- dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]),
- 242:
- dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]),
- 243:
- dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]),
- 244:
- dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]),
- 245:
- dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]),
- 246:
- dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]),
- 247:
- dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]),
- 248:
- dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]),
- 249:
- dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]),
- 250:
- dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]),
- 251:
- dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]),
- 252:
- dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]),
- 253:
- dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]),
- 254:
- dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]),
- 255:
- dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]),
- 256:
- dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]),
- 257:
- dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]),
- 258:
- dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]),
- 259:
- dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]),
- 260:
- dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]),
- 261:
- dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]),
- 262:
- dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]),
- 263:
- dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]),
- 264:
- dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]),
- 265:
- dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]),
- 266:
- dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]),
- 267:
- dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]),
- 268:
- dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]),
- 269:
- dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]),
- 270:
- dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]),
- 271:
- dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]),
- 272:
- dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]),
- 273:
- dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]),
- 274:
- dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]),
- 275:
- dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]),
- 276:
- dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]),
- 277:
- dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]),
- 278:
- dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]),
- 279:
- dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]),
- 280:
- dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]),
- 281:
- dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]),
- 282:
- dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]),
- 283:
- dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]),
- 284:
- dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]),
- 285:
- dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]),
- 286:
- dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]),
- 287:
- dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]),
- 288:
- dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]),
- 289:
- dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]),
- 290:
- dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]),
- 291:
- dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]),
- 292:
- dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]),
- 293:
- dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]),
- 294:
- dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]),
- 295:
- dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]),
- 296:
- dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]),
- 297:
- dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]),
- 298:
- dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]),
- 299:
- dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]),
- 300:
- dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]),
- 301:
- dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]),
- 302:
- dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]),
- 303:
- dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0])
- }),
- joint_weights=[
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
- ],
- sigmas=[])
-param_scheduler = [
- dict(
- type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False),
- dict(
- type='MultiStepLR',
- begin=0,
- end=120,
- milestones=[80, 100],
- gamma=0.1,
- by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005))
-auto_scale_lr = dict(base_batch_size=512)
-dataset_type = 'DeepFashion2Dataset'
-data_mode = 'topdown'
-data_root = 'data/deepfashion2/'
-codec = dict(
- type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
-train_pipeline = [
- dict(type='LoadImage'),
- dict(type='GetBBoxCenterScale'),
- dict(type='RandomFlip', direction='horizontal'),
- dict(
- type='RandomBBoxTransform',
- shift_prob=0,
- rotate_factor=60,
- scale_factor=(0.75, 1.25)),
- dict(type='TopdownAffine', input_size=(192, 256)),
- dict(
- type='GenerateTarget',
- encoder=dict(
- type='MSRAHeatmap',
- input_size=(192, 256),
- heatmap_size=(48, 64),
- sigma=2)),
- dict(type='PackPoseInputs')
-]
-val_pipeline = [
- dict(type='LoadImage', backend_args=dict(backend='local')),
- dict(type='GetBBoxCenterScale'),
- dict(type='TopdownAffine', input_size=(192, 256)),
- dict(type='PackPoseInputs')
-]
-train_dataloader = dict(
- batch_size=64,
- num_workers=6,
- persistent_workers=True,
- sampler=dict(type='DefaultSampler', shuffle=True),
- dataset=dict(
- type='DeepFashion2Dataset',
- data_root='data/deepfashion2/',
- data_mode='topdown',
- ann_file='train/deepfashion2_sling.json',
- data_prefix=dict(img='train/image/'),
- pipeline=[
- dict(type='LoadImage'),
- dict(type='GetBBoxCenterScale'),
- dict(type='RandomFlip', direction='horizontal'),
- dict(
- type='RandomBBoxTransform',
- shift_prob=0,
- rotate_factor=60,
- scale_factor=(0.75, 1.25)),
- dict(type='TopdownAffine', input_size=(192, 256)),
- dict(
- type='GenerateTarget',
- encoder=dict(
- type='MSRAHeatmap',
- input_size=(192, 256),
- heatmap_size=(48, 64),
- sigma=2)),
- dict(type='PackPoseInputs')
- ]))
-val_dataloader = dict(
- batch_size=32,
- num_workers=6,
- persistent_workers=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=dict(
- type='DeepFashion2Dataset',
- data_root='data/deepfashion2/',
- data_mode='topdown',
- ann_file='validation/deepfashion2_sling.json',
- data_prefix=dict(img='validation/image/'),
- test_mode=True,
- pipeline=[
- dict(type='LoadImage', backend_args=dict(backend='local')),
- dict(type='GetBBoxCenterScale'),
- dict(type='TopdownAffine', input_size=(192, 256)),
- dict(type='PackPoseInputs')
- ]))
-test_dataloader = dict(
- batch_size=32,
- num_workers=6,
- persistent_workers=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=dict(
- type='DeepFashion2Dataset',
- data_root='data/deepfashion2/',
- data_mode='topdown',
- ann_file='validation/deepfashion2_sling.json',
- data_prefix=dict(img='validation/image/'),
- test_mode=True,
- pipeline=[
- dict(type='LoadImage', backend_args=dict(backend='local')),
- dict(type='GetBBoxCenterScale'),
- dict(type='TopdownAffine', input_size=(192, 256)),
- dict(type='PackPoseInputs')
- ]))
-channel_cfg = dict(
- num_output_channels=294,
- dataset_joints=294,
- dataset_channel=[[
- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
- 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
- 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
- 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
- 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
- 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
- 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
- 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
- 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
- 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
- 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
- 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
- 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
- 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
- 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
- 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
- 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
- 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
- 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
- 290, 291, 292, 293
- ]],
- inference_channel=[
- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
- 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
- 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
- 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
- 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
- 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
- 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
- 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
- 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
- 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
- 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
- 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
- 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
- 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
- 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
- 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
- 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
- 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
- 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
- 290, 291, 292, 293
- ])
-model = dict(
- type='TopdownPoseEstimator',
- data_preprocessor=dict(
- type='PoseDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True),
- backbone=dict(
- type='ResNet',
- depth=50,
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
- head=dict(
- type='HeatmapHead',
- in_channels=2048,
- out_channels=294,
- loss=dict(type='KeypointMSELoss', use_target_weight=True),
- decoder=dict(
- type='MSRAHeatmap',
- input_size=(192, 256),
- heatmap_size=(48, 64),
- sigma=2)),
- test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True))
-val_evaluator = [
- dict(type='PCKAccuracy', thr=0.2),
- dict(type='AUC'),
- dict(type='EPE')
-]
-test_evaluator = [
- dict(type='PCKAccuracy', thr=0.2),
- dict(type='AUC'),
- dict(type='EPE')
-]
-launcher = 'pytorch'
-work_dir = './work_dirs/td_hm_res50_4xb64-120e_deepfashion2_sling_256x192'
diff --git a/spaces/AUBADA-ALARABI/poetry2023/app.py b/spaces/AUBADA-ALARABI/poetry2023/app.py
deleted file mode 100644
index 5b6654d5a405778ddbc9ca5fa5d041aff535f3b5..0000000000000000000000000000000000000000
--- a/spaces/AUBADA-ALARABI/poetry2023/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import gc
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
- ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
- ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
- ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
- ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
- ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
- ,1.0, 50, 1.0, 1.0, 114 ],['.'
- ,1.0, 50, 1.0, 1.0, 114]]
-
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long)
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859)
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
- if not int(seed) >= 0: seed=114
- set_seed(seed)
- gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
- min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
- num_beams=5, num_return_sequences=1)[0]["generated_text"]
- poetry =""
- for line in gen.split('.')[:-1]:
- poetry += line #+ "\n"
- return poetry
-poetry = gr.Interface(fn=sayPoetry,
- inputs=[
- gr.Textbox(label="Enter short prompt or select from examples:"),
- gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control temperature'),
- gr.Slider(25, 100, step=1,value=50, label='control top k'),
- gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control top p'),
- gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control penalty'),
- gr.Number(value=139750, precision=0, label='Seed'),
- ],
- outputs=[gr.Textbox(label="Generated Poetry:")],
-
- allow_flagging='never',
- title='Arabic Poetry Generation Demo (updated Jan. 2023)',
- description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
- examples=samples,
- cache_examples=False,
- article = notes)
-poetry.launch() # show_error = True, debug=True
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/requests.py b/spaces/AchyuthGamer/OpenGPT/g4f/requests.py
deleted file mode 100644
index f238062e3e3832d79a5e12b1d3f96c11d708cb0e..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/requests.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from __future__ import annotations
-
-import warnings
-import json
-import asyncio
-from functools import partialmethod
-from asyncio import Future, Queue
-from typing import AsyncGenerator, Union, Optional
-
-from curl_cffi.requests import AsyncSession, Response
-import curl_cffi
-
-is_newer_0_5_8: bool = hasattr(AsyncSession, "_set_cookies") or hasattr(curl_cffi.requests.Cookies, "get_cookies_for_curl")
-is_newer_0_5_9: bool = hasattr(curl_cffi.AsyncCurl, "remove_handle")
-is_newer_0_5_10: bool = hasattr(AsyncSession, "release_curl")
-
-
-class StreamResponse:
- def __init__(self, inner: Response, queue: Queue[bytes]) -> None:
- self.inner: Response = inner
- self.queue: Queue[bytes] = queue
- self.request = inner.request
- self.status_code: int = inner.status_code
- self.reason: str = inner.reason
- self.ok: bool = inner.ok
- self.headers = inner.headers
- self.cookies = inner.cookies
-
- async def text(self) -> str:
- content: bytes = await self.read()
- return content.decode()
-
- def raise_for_status(self) -> None:
- if not self.ok:
- raise RuntimeError(f"HTTP Error {self.status_code}: {self.reason}")
-
- async def json(self, **kwargs) -> dict:
- return json.loads(await self.read(), **kwargs)
-
- async def iter_lines(
- self, chunk_size: Optional[int] = None, decode_unicode: bool = False, delimiter: Optional[str] = None
- ) -> AsyncGenerator[bytes, None]:
- """
- Copied from: https://requests.readthedocs.io/en/latest/_modules/requests/models/
- which is under the License: Apache 2.0
- """
-
- pending: bytes = None
-
- async for chunk in self.iter_content(
- chunk_size=chunk_size, decode_unicode=decode_unicode
- ):
- if pending is not None:
- chunk = pending + chunk
- if delimiter:
- lines = chunk.split(delimiter)
- else:
- lines = chunk.splitlines()
- if lines and lines[-1] and chunk and lines[-1][-1] == chunk[-1]:
- pending = lines.pop()
- else:
- pending = None
-
- for line in lines:
- yield line
-
- if pending is not None:
- yield pending
-
- async def iter_content(
- self, chunk_size: Optional[int] = None, decode_unicode: bool = False
- ) -> AsyncGenerator[bytes, None]:
- if chunk_size:
- warnings.warn("chunk_size is ignored, there is no way to tell curl that.")
- if decode_unicode:
- raise NotImplementedError()
- while True:
- chunk = await self.queue.get()
- if chunk is None:
- return
- yield chunk
-
- async def read(self) -> bytes:
- return b"".join([chunk async for chunk in self.iter_content()])
-
-
-class StreamRequest:
- def __init__(self, session: AsyncSession, method: str, url: str, **kwargs: Union[bool, int, str]) -> None:
- self.session: AsyncSession = session
- self.loop: asyncio.AbstractEventLoop = session.loop if session.loop else asyncio.get_running_loop()
- self.queue: Queue[bytes] = Queue()
- self.method: str = method
- self.url: str = url
- self.options: dict = kwargs
- self.handle: Optional[curl_cffi.AsyncCurl] = None
-
- def _on_content(self, data: bytes) -> None:
- if not self.enter.done():
- self.enter.set_result(None)
- self.queue.put_nowait(data)
-
- def _on_done(self, task: Future) -> None:
- if not self.enter.done():
- self.enter.set_result(None)
- self.queue.put_nowait(None)
-
- self.loop.call_soon(self.release_curl)
-
- async def fetch(self) -> StreamResponse:
- if self.handle:
- raise RuntimeError("Request already started")
- self.curl: curl_cffi.AsyncCurl = await self.session.pop_curl()
- self.enter: asyncio.Future = self.loop.create_future()
- if is_newer_0_5_10:
- request, _, header_buffer, _, _ = self.session._set_curl_options(
- self.curl,
- self.method,
- self.url,
- content_callback=self._on_content,
- **self.options
- )
- else:
- request, _, header_buffer = self.session._set_curl_options(
- self.curl,
- self.method,
- self.url,
- content_callback=self._on_content,
- **self.options
- )
- if is_newer_0_5_9:
- self.handle = self.session.acurl.add_handle(self.curl)
- else:
- await self.session.acurl.add_handle(self.curl, False)
- self.handle = self.session.acurl._curl2future[self.curl]
- self.handle.add_done_callback(self._on_done)
- # Wait for headers
- await self.enter
- # Raise exceptions
- if self.handle.done():
- self.handle.result()
- if is_newer_0_5_8:
- response = self.session._parse_response(self.curl, _, header_buffer)
- response.request = request
- else:
- response = self.session._parse_response(self.curl, request, _, header_buffer)
- return StreamResponse(response, self.queue)
-
- async def __aenter__(self) -> StreamResponse:
- return await self.fetch()
-
- async def __aexit__(self, *args) -> None:
- self.release_curl()
-
- def release_curl(self) -> None:
- if is_newer_0_5_10:
- self.session.release_curl(self.curl)
- return
- if not self.curl:
- return
- self.curl.clean_after_perform()
- if is_newer_0_5_9:
- self.session.acurl.remove_handle(self.curl)
- elif not self.handle.done() and not self.handle.cancelled():
- self.session.acurl.set_result(self.curl)
- self.curl.reset()
- self.session.push_curl(self.curl)
- self.curl = None
-
-
-class StreamSession(AsyncSession):
- def request(
- self, method: str, url: str, **kwargs
- ) -> StreamRequest:
- return StreamRequest(self, method, url, **kwargs)
-
- head = partialmethod(request, "HEAD")
- get = partialmethod(request, "GET")
- post = partialmethod(request, "POST")
- put = partialmethod(request, "PUT")
- patch = partialmethod(request, "PATCH")
- delete = partialmethod(request, "DELETE")
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/arcadetcrp-plugin.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/arcadetcrp-plugin.js
deleted file mode 100644
index 31aeaa4243d034c5b752da58df010824851137c7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/arcadetcrp-plugin.js
+++ /dev/null
@@ -1,39 +0,0 @@
-import TCRP from './arcadetcrp.js';
-
-const Recorder = TCRP.Recorder;
-const Player = TCRP.Player;
-const StepRunner = TCRP.StepRunner;
-
-class ArcadeTCRPPlugin extends Phaser.Plugins.BasePlugin {
- constructor(pluginManager) {
- super(pluginManager);
- }
-
- start() {
- var eventEmitter = this.game.events;
- eventEmitter.on('destroy', this.destroy, this);
- }
-
- addRecorder(parent, config) {
- return new Recorder(parent, config);
- }
-
- addPlayer(parent, config) {
- return new Player(parent, config);
- }
-
- addStepRunner(parent) {
- return new StepRunner(parent);
- }
-}
-
-var methods = {
- runCommands: TCRP.RunCommands
-}
-
-Object.assign(
- ArcadeTCRPPlugin.prototype,
- methods
-);
-
-export default ArcadeTCRPPlugin;
\ No newline at end of file
diff --git a/spaces/Akmyradov/dost.ai/README.md b/spaces/Akmyradov/dost.ai/README.md
deleted file mode 100644
index 259cc9bbd95d13853371019621f6e34810c787ba..0000000000000000000000000000000000000000
--- a/spaces/Akmyradov/dost.ai/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dost.ai
-emoji: 🦀
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Alashazam/StoryGenerator/README.md b/spaces/Alashazam/StoryGenerator/README.md
deleted file mode 100644
index 069ed0b2085343fddad97c599cecac650adad942..0000000000000000000000000000000000000000
--- a/spaces/Alashazam/StoryGenerator/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: GPT 2 Story Gen
-emoji: 🧙🏻♂️
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
-duplicated_from: merve/GPT-2-story-gen
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Alpaca233/SadTalker/src/utils/init_path.py b/spaces/Alpaca233/SadTalker/src/utils/init_path.py
deleted file mode 100644
index 5f38d11907bd0dc789992062ce7f02d8876c638f..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/utils/init_path.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-import glob
-
-def init_path(checkpoint_dir, config_dir, size=512, old_version=False, preprocess='crop'):
-
- if old_version:
- #### load all the checkpoint of `pth`
- sadtalker_paths = {
- 'wav2lip_checkpoint' : os.path.join(checkpoint_dir, 'wav2lip.pth'),
- 'audio2pose_checkpoint' : os.path.join(checkpoint_dir, 'auido2pose_00140-model.pth'),
- 'audio2exp_checkpoint' : os.path.join(checkpoint_dir, 'auido2exp_00300-model.pth'),
- 'free_view_checkpoint' : os.path.join(checkpoint_dir, 'facevid2vid_00189-model.pth.tar'),
- 'path_of_net_recon_model' : os.path.join(checkpoint_dir, 'epoch_20.pth')
- }
-
- use_safetensor = False
- elif len(glob.glob(os.path.join(checkpoint_dir, '*.safetensors'))):
- print('using safetensor as default')
- sadtalker_paths = {
- "checkpoint":os.path.join(checkpoint_dir, 'SadTalker_V0.0.2_'+str(size)+'.safetensors'),
- }
- use_safetensor = True
- else:
- print("WARNING: The new version of the model will be updated by safetensor, you may need to download it mannully. We run the old version of the checkpoint this time!")
- use_safetensor = False
-
- sadtalker_paths = {
- 'wav2lip_checkpoint' : os.path.join(checkpoint_dir, 'wav2lip.pth'),
- 'audio2pose_checkpoint' : os.path.join(checkpoint_dir, 'auido2pose_00140-model.pth'),
- 'audio2exp_checkpoint' : os.path.join(checkpoint_dir, 'auido2exp_00300-model.pth'),
- 'free_view_checkpoint' : os.path.join(checkpoint_dir, 'facevid2vid_00189-model.pth.tar'),
- 'path_of_net_recon_model' : os.path.join(checkpoint_dir, 'epoch_20.pth')
- }
-
- sadtalker_paths['dir_of_BFM_fitting'] = os.path.join(config_dir) # , 'BFM_Fitting'
- sadtalker_paths['audio2pose_yaml_path'] = os.path.join(config_dir, 'auido2pose.yaml')
- sadtalker_paths['audio2exp_yaml_path'] = os.path.join(config_dir, 'auido2exp.yaml')
- sadtalker_paths['use_safetensor'] = use_safetensor # os.path.join(config_dir, 'auido2exp.yaml')
-
- if 'full' in preprocess:
- sadtalker_paths['mappingnet_checkpoint'] = os.path.join(checkpoint_dir, 'mapping_00109-model.pth.tar')
- sadtalker_paths['facerender_yaml'] = os.path.join(config_dir, 'facerender_still.yaml')
- else:
- sadtalker_paths['mappingnet_checkpoint'] = os.path.join(checkpoint_dir, 'mapping_00229-model.pth.tar')
- sadtalker_paths['facerender_yaml'] = os.path.join(config_dir, 'facerender.yaml')
-
- return sadtalker_paths
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_reference.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_reference.py
deleted file mode 100644
index 364d5d80d721c1483e5b123a3dc92244af88715a..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_reference.py
+++ /dev/null
@@ -1,796 +0,0 @@
-# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-
-from diffusers import StableDiffusionPipeline
-from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg
-from diffusers.utils import PIL_INTERPOLATION, logging, randn_tensor
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```py
- >>> import torch
- >>> from diffusers import UniPCMultistepScheduler
- >>> from diffusers.utils import load_image
-
- >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
-
- >>> pipe = StableDiffusionReferencePipeline.from_pretrained(
- "runwayml/stable-diffusion-v1-5",
- safety_checker=None,
- torch_dtype=torch.float16
- ).to('cuda:0')
-
- >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
-
- >>> result_img = pipe(ref_image=input_image,
- prompt="1girl",
- num_inference_steps=20,
- reference_attn=True,
- reference_adain=True).images[0]
-
- >>> result_img.show()
- ```
-"""
-
-
-def torch_dfs(model: torch.nn.Module):
- result = [model]
- for child in model.children():
- result += torch_dfs(child)
- return result
-
-
-class StableDiffusionReferencePipeline(StableDiffusionPipeline):
- def _default_height_width(self, height, width, image):
- # NOTE: It is possible that a list of images have different
- # dimensions for each image, so just checking the first image
- # is not _exactly_ correct, but it is simple.
- while isinstance(image, list):
- image = image[0]
-
- if height is None:
- if isinstance(image, PIL.Image.Image):
- height = image.height
- elif isinstance(image, torch.Tensor):
- height = image.shape[2]
-
- height = (height // 8) * 8 # round down to nearest multiple of 8
-
- if width is None:
- if isinstance(image, PIL.Image.Image):
- width = image.width
- elif isinstance(image, torch.Tensor):
- width = image.shape[3]
-
- width = (width // 8) * 8 # round down to nearest multiple of 8
-
- return height, width
-
- def prepare_image(
- self,
- image,
- width,
- height,
- batch_size,
- num_images_per_prompt,
- device,
- dtype,
- do_classifier_free_guidance=False,
- guess_mode=False,
- ):
- if not isinstance(image, torch.Tensor):
- if isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- images = []
-
- for image_ in image:
- image_ = image_.convert("RGB")
- image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
- image_ = np.array(image_)
- image_ = image_[None, :]
- images.append(image_)
-
- image = images
-
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = (image - 0.5) / 0.5
- image = image.transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
-
- image_batch_size = image.shape[0]
-
- if image_batch_size == 1:
- repeat_by = batch_size
- else:
- # image batch size is the same as prompt batch size
- repeat_by = num_images_per_prompt
-
- image = image.repeat_interleave(repeat_by, dim=0)
-
- image = image.to(device=device, dtype=dtype)
-
- if do_classifier_free_guidance and not guess_mode:
- image = torch.cat([image] * 2)
-
- return image
-
- def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
- refimage = refimage.to(device=device, dtype=dtype)
-
- # encode the mask image into latents space so we can concatenate it to the latents
- if isinstance(generator, list):
- ref_image_latents = [
- self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
- for i in range(batch_size)
- ]
- ref_image_latents = torch.cat(ref_image_latents, dim=0)
- else:
- ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
- ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
-
- # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
- if ref_image_latents.shape[0] < batch_size:
- if not batch_size % ref_image_latents.shape[0] == 0:
- raise ValueError(
- "The passed images and the required batch size don't match. Images are supposed to be duplicated"
- f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
- " Make sure the number of images that you pass is divisible by the total requested batch size."
- )
- ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
-
- ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
-
- # aligning device to prevent device errors when concating it with the latent model input
- ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
- return ref_image_latents
-
- @torch.no_grad()
- def __call__(
- self,
- prompt: Union[str, List[str]] = None,
- ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- guidance_rescale: float = 0.0,
- attention_auto_machine_weight: float = 1.0,
- gn_auto_machine_weight: float = 1.0,
- style_fidelity: float = 0.5,
- reference_attn: bool = True,
- reference_adain: bool = True,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
- instead.
- ref_image (`torch.FloatTensor`, `PIL.Image.Image`):
- The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
- the type is specified as `Torch.FloatTensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
- also be accepted as an image.
- height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
- to make generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- cross_attention_kwargs (`dict`, *optional*):
- A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
- `self.processor` in
- [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
- guidance_rescale (`float`, *optional*, defaults to 0.7):
- Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
- Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
- [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
- Guidance rescale factor should fix overexposure when using zero terminal SNR.
- attention_auto_machine_weight (`float`):
- Weight of using reference query for self attention's context.
- If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
- gn_auto_machine_weight (`float`):
- Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
- style_fidelity (`float`):
- style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
- elif style_fidelity=0.0, prompt more important, else balanced.
- reference_attn (`bool`):
- Whether to use reference query for self attention's context.
- reference_adain (`bool`):
- Whether to use reference adain.
-
- Examples:
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
-
- # 0. Default height and width to unet
- height, width = self._default_height_width(height, width, ref_image)
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(
- prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
- )
-
- # 2. Define call parameters
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- device = self._execution_device
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 3. Encode input prompt
- text_encoder_lora_scale = (
- cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
- )
- prompt_embeds = self._encode_prompt(
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- lora_scale=text_encoder_lora_scale,
- )
-
- # 4. Preprocess reference image
- ref_image = self.prepare_image(
- image=ref_image,
- width=width,
- height=height,
- batch_size=batch_size * num_images_per_prompt,
- num_images_per_prompt=num_images_per_prompt,
- device=device,
- dtype=prompt_embeds.dtype,
- )
-
- # 5. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- # 6. Prepare latent variables
- num_channels_latents = self.unet.config.in_channels
- latents = self.prepare_latents(
- batch_size * num_images_per_prompt,
- num_channels_latents,
- height,
- width,
- prompt_embeds.dtype,
- device,
- generator,
- latents,
- )
-
- # 7. Prepare reference latent variables
- ref_image_latents = self.prepare_ref_latents(
- ref_image,
- batch_size * num_images_per_prompt,
- prompt_embeds.dtype,
- device,
- generator,
- do_classifier_free_guidance,
- )
-
- # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 9. Modify self attention and group norm
- MODE = "write"
- uc_mask = (
- torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
- .type_as(ref_image_latents)
- .bool()
- )
-
- def hacked_basic_transformer_inner_forward(
- self,
- hidden_states: torch.FloatTensor,
- attention_mask: Optional[torch.FloatTensor] = None,
- encoder_hidden_states: Optional[torch.FloatTensor] = None,
- encoder_attention_mask: Optional[torch.FloatTensor] = None,
- timestep: Optional[torch.LongTensor] = None,
- cross_attention_kwargs: Dict[str, Any] = None,
- class_labels: Optional[torch.LongTensor] = None,
- ):
- if self.use_ada_layer_norm:
- norm_hidden_states = self.norm1(hidden_states, timestep)
- elif self.use_ada_layer_norm_zero:
- norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
- hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
- )
- else:
- norm_hidden_states = self.norm1(hidden_states)
-
- # 1. Self-Attention
- cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
- if self.only_cross_attention:
- attn_output = self.attn1(
- norm_hidden_states,
- encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
- attention_mask=attention_mask,
- **cross_attention_kwargs,
- )
- else:
- if MODE == "write":
- self.bank.append(norm_hidden_states.detach().clone())
- attn_output = self.attn1(
- norm_hidden_states,
- encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
- attention_mask=attention_mask,
- **cross_attention_kwargs,
- )
- if MODE == "read":
- if attention_auto_machine_weight > self.attn_weight:
- attn_output_uc = self.attn1(
- norm_hidden_states,
- encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
- # attention_mask=attention_mask,
- **cross_attention_kwargs,
- )
- attn_output_c = attn_output_uc.clone()
- if do_classifier_free_guidance and style_fidelity > 0:
- attn_output_c[uc_mask] = self.attn1(
- norm_hidden_states[uc_mask],
- encoder_hidden_states=norm_hidden_states[uc_mask],
- **cross_attention_kwargs,
- )
- attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
- self.bank.clear()
- else:
- attn_output = self.attn1(
- norm_hidden_states,
- encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
- attention_mask=attention_mask,
- **cross_attention_kwargs,
- )
- if self.use_ada_layer_norm_zero:
- attn_output = gate_msa.unsqueeze(1) * attn_output
- hidden_states = attn_output + hidden_states
-
- if self.attn2 is not None:
- norm_hidden_states = (
- self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
- )
-
- # 2. Cross-Attention
- attn_output = self.attn2(
- norm_hidden_states,
- encoder_hidden_states=encoder_hidden_states,
- attention_mask=encoder_attention_mask,
- **cross_attention_kwargs,
- )
- hidden_states = attn_output + hidden_states
-
- # 3. Feed-forward
- norm_hidden_states = self.norm3(hidden_states)
-
- if self.use_ada_layer_norm_zero:
- norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-
- ff_output = self.ff(norm_hidden_states)
-
- if self.use_ada_layer_norm_zero:
- ff_output = gate_mlp.unsqueeze(1) * ff_output
-
- hidden_states = ff_output + hidden_states
-
- return hidden_states
-
- def hacked_mid_forward(self, *args, **kwargs):
- eps = 1e-6
- x = self.original_forward(*args, **kwargs)
- if MODE == "write":
- if gn_auto_machine_weight >= self.gn_weight:
- var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
- self.mean_bank.append(mean)
- self.var_bank.append(var)
- if MODE == "read":
- if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
- var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
- std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
- mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
- var_acc = sum(self.var_bank) / float(len(self.var_bank))
- std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
- x_uc = (((x - mean) / std) * std_acc) + mean_acc
- x_c = x_uc.clone()
- if do_classifier_free_guidance and style_fidelity > 0:
- x_c[uc_mask] = x[uc_mask]
- x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
- self.mean_bank = []
- self.var_bank = []
- return x
-
- def hack_CrossAttnDownBlock2D_forward(
- self,
- hidden_states: torch.FloatTensor,
- temb: Optional[torch.FloatTensor] = None,
- encoder_hidden_states: Optional[torch.FloatTensor] = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- encoder_attention_mask: Optional[torch.FloatTensor] = None,
- ):
- eps = 1e-6
-
- # TODO(Patrick, William) - attention mask is not used
- output_states = ()
-
- for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
- hidden_states = resnet(hidden_states, temb)
- hidden_states = attn(
- hidden_states,
- encoder_hidden_states=encoder_hidden_states,
- cross_attention_kwargs=cross_attention_kwargs,
- attention_mask=attention_mask,
- encoder_attention_mask=encoder_attention_mask,
- return_dict=False,
- )[0]
- if MODE == "write":
- if gn_auto_machine_weight >= self.gn_weight:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- self.mean_bank.append([mean])
- self.var_bank.append([var])
- if MODE == "read":
- if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
- mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
- var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
- std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
- hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
- hidden_states_c = hidden_states_uc.clone()
- if do_classifier_free_guidance and style_fidelity > 0:
- hidden_states_c[uc_mask] = hidden_states[uc_mask]
- hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
-
- output_states = output_states + (hidden_states,)
-
- if MODE == "read":
- self.mean_bank = []
- self.var_bank = []
-
- if self.downsamplers is not None:
- for downsampler in self.downsamplers:
- hidden_states = downsampler(hidden_states)
-
- output_states = output_states + (hidden_states,)
-
- return hidden_states, output_states
-
- def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
- eps = 1e-6
-
- output_states = ()
-
- for i, resnet in enumerate(self.resnets):
- hidden_states = resnet(hidden_states, temb)
-
- if MODE == "write":
- if gn_auto_machine_weight >= self.gn_weight:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- self.mean_bank.append([mean])
- self.var_bank.append([var])
- if MODE == "read":
- if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
- mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
- var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
- std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
- hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
- hidden_states_c = hidden_states_uc.clone()
- if do_classifier_free_guidance and style_fidelity > 0:
- hidden_states_c[uc_mask] = hidden_states[uc_mask]
- hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
-
- output_states = output_states + (hidden_states,)
-
- if MODE == "read":
- self.mean_bank = []
- self.var_bank = []
-
- if self.downsamplers is not None:
- for downsampler in self.downsamplers:
- hidden_states = downsampler(hidden_states)
-
- output_states = output_states + (hidden_states,)
-
- return hidden_states, output_states
-
- def hacked_CrossAttnUpBlock2D_forward(
- self,
- hidden_states: torch.FloatTensor,
- res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
- temb: Optional[torch.FloatTensor] = None,
- encoder_hidden_states: Optional[torch.FloatTensor] = None,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- upsample_size: Optional[int] = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- encoder_attention_mask: Optional[torch.FloatTensor] = None,
- ):
- eps = 1e-6
- # TODO(Patrick, William) - attention mask is not used
- for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
- hidden_states = resnet(hidden_states, temb)
- hidden_states = attn(
- hidden_states,
- encoder_hidden_states=encoder_hidden_states,
- cross_attention_kwargs=cross_attention_kwargs,
- attention_mask=attention_mask,
- encoder_attention_mask=encoder_attention_mask,
- return_dict=False,
- )[0]
-
- if MODE == "write":
- if gn_auto_machine_weight >= self.gn_weight:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- self.mean_bank.append([mean])
- self.var_bank.append([var])
- if MODE == "read":
- if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
- mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
- var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
- std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
- hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
- hidden_states_c = hidden_states_uc.clone()
- if do_classifier_free_guidance and style_fidelity > 0:
- hidden_states_c[uc_mask] = hidden_states[uc_mask]
- hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
-
- if MODE == "read":
- self.mean_bank = []
- self.var_bank = []
-
- if self.upsamplers is not None:
- for upsampler in self.upsamplers:
- hidden_states = upsampler(hidden_states, upsample_size)
-
- return hidden_states
-
- def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
- eps = 1e-6
- for i, resnet in enumerate(self.resnets):
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
- hidden_states = resnet(hidden_states, temb)
-
- if MODE == "write":
- if gn_auto_machine_weight >= self.gn_weight:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- self.mean_bank.append([mean])
- self.var_bank.append([var])
- if MODE == "read":
- if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
- var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
- std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
- mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
- var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
- std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
- hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
- hidden_states_c = hidden_states_uc.clone()
- if do_classifier_free_guidance and style_fidelity > 0:
- hidden_states_c[uc_mask] = hidden_states[uc_mask]
- hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
-
- if MODE == "read":
- self.mean_bank = []
- self.var_bank = []
-
- if self.upsamplers is not None:
- for upsampler in self.upsamplers:
- hidden_states = upsampler(hidden_states, upsample_size)
-
- return hidden_states
-
- if reference_attn:
- attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
- attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
-
- for i, module in enumerate(attn_modules):
- module._original_inner_forward = module.forward
- module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
- module.bank = []
- module.attn_weight = float(i) / float(len(attn_modules))
-
- if reference_adain:
- gn_modules = [self.unet.mid_block]
- self.unet.mid_block.gn_weight = 0
-
- down_blocks = self.unet.down_blocks
- for w, module in enumerate(down_blocks):
- module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
- gn_modules.append(module)
-
- up_blocks = self.unet.up_blocks
- for w, module in enumerate(up_blocks):
- module.gn_weight = float(w) / float(len(up_blocks))
- gn_modules.append(module)
-
- for i, module in enumerate(gn_modules):
- if getattr(module, "original_forward", None) is None:
- module.original_forward = module.forward
- if i == 0:
- # mid_block
- module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
- elif isinstance(module, CrossAttnDownBlock2D):
- module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
- elif isinstance(module, DownBlock2D):
- module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
- elif isinstance(module, CrossAttnUpBlock2D):
- module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
- elif isinstance(module, UpBlock2D):
- module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
- module.mean_bank = []
- module.var_bank = []
- module.gn_weight *= 2
-
- # 10. Denoising loop
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # ref only part
- noise = randn_tensor(
- ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
- )
- ref_xt = self.scheduler.add_noise(
- ref_image_latents,
- noise,
- t.reshape(
- 1,
- ),
- )
- ref_xt = self.scheduler.scale_model_input(ref_xt, t)
-
- MODE = "write"
- self.unet(
- ref_xt,
- t,
- encoder_hidden_states=prompt_embeds,
- cross_attention_kwargs=cross_attention_kwargs,
- return_dict=False,
- )
-
- # predict the noise residual
- MODE = "read"
- noise_pred = self.unet(
- latent_model_input,
- t,
- encoder_hidden_states=prompt_embeds,
- cross_attention_kwargs=cross_attention_kwargs,
- return_dict=False,
- )[0]
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- if do_classifier_free_guidance and guidance_rescale > 0.0:
- # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
- noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- if not output_type == "latent":
- image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
- image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
- else:
- image = latents
- has_nsfw_concept = None
-
- if has_nsfw_concept is None:
- do_denormalize = [True] * image.shape[0]
- else:
- do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
- image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
- # Offload last model to CPU
- if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
- self.final_offload_hook.offload()
-
- if not return_dict:
- return (image, has_nsfw_concept)
-
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/dreambooth/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/dreambooth/README.md
deleted file mode 100644
index e450f726e9207a79c1b89f1d3d452d87bc197c5d..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/dreambooth/README.md
+++ /dev/null
@@ -1,743 +0,0 @@
-# DreamBooth training example
-
-[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
-The `train_dreambooth.py` script shows how to implement the training procedure and adapt it for stable diffusion.
-
-
-## Running locally with PyTorch
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd in the example folder and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell e.g. a notebook
-
-```python
-from accelerate.utils import write_basic_config
-write_basic_config()
-```
-
-When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
-
-### Dog toy example
-
-Now let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example.
-
-Let's first download it locally:
-
-```python
-from huggingface_hub import snapshot_download
-
-local_dir = "./dog"
-snapshot_download(
- "diffusers/dog-example",
- local_dir=local_dir, repo_type="dataset",
- ignore_patterns=".gitattributes",
-)
-```
-
-And launch the training using:
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a photo of sks dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --gradient_accumulation_steps=1 \
- --learning_rate=5e-6 \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --max_train_steps=400 \
- --push_to_hub
-```
-
-### Training with prior-preservation loss
-
-Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
-According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --gradient_accumulation_steps=1 \
- --learning_rate=5e-6 \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --num_class_images=200 \
- --max_train_steps=800 \
- --push_to_hub
-```
-
-
-### Training on a 16GB GPU:
-
-With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.
-
-To install `bitsandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --gradient_accumulation_steps=2 --gradient_checkpointing \
- --use_8bit_adam \
- --learning_rate=5e-6 \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --num_class_images=200 \
- --max_train_steps=800 \
- --push_to_hub
-```
-
-
-### Training on a 12GB GPU:
-
-It is possible to run dreambooth on a 12GB GPU by using the following optimizations:
-- [gradient checkpointing and the 8-bit optimizer](#training-on-a-16gb-gpu)
-- [xformers](#training-with-xformers)
-- [setting grads to none](#set-grads-to-none)
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --gradient_accumulation_steps=1 --gradient_checkpointing \
- --use_8bit_adam \
- --enable_xformers_memory_efficient_attention \
- --set_grads_to_none \
- --learning_rate=2e-6 \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --num_class_images=200 \
- --max_train_steps=800 \
- --push_to_hub
-```
-
-
-### Training on a 8 GB GPU:
-
-By using [DeepSpeed](https://www.deepspeed.ai/) it's possible to offload some
-tensors from VRAM to either CPU or NVME allowing to train with less VRAM.
-
-DeepSpeed needs to be enabled with `accelerate config`. During configuration
-answer yes to "Do you want to use DeepSpeed?". With DeepSpeed stage 2, fp16
-mixed precision and offloading both parameters and optimizer state to cpu it's
-possible to train on under 8 GB VRAM with a drawback of requiring significantly
-more RAM (about 25 GB). See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.
-
-Changing the default Adam optimizer to DeepSpeed's special version of Adam
-`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but enabling
-it requires CUDA toolchain with the same version as pytorch. 8-bit optimizer
-does not seem to be compatible with DeepSpeed at the moment.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch --mixed_precision="fp16" train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --sample_batch_size=1 \
- --gradient_accumulation_steps=1 --gradient_checkpointing \
- --learning_rate=5e-6 \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --num_class_images=200 \
- --max_train_steps=800 \
- --push_to_hub
-```
-
-### Fine-tune text encoder with the UNet.
-
-The script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces.
-Pass the `--train_text_encoder` argument to the script to enable training `text_encoder`.
-
-___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --train_text_encoder \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --use_8bit_adam \
- --gradient_checkpointing \
- --learning_rate=2e-6 \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --num_class_images=200 \
- --max_train_steps=800 \
- --push_to_hub
-```
-
-### Using DreamBooth for pipelines other than Stable Diffusion
-
-The [AltDiffusion pipeline](https://huggingface.co/docs/diffusers/api/pipelines/alt_diffusion) also supports dreambooth fine-tuning. The process is the same as above, all you need to do is replace the `MODEL_NAME` like this:
-
-```
-export MODEL_NAME="CompVis/stable-diffusion-v1-4" --> export MODEL_NAME="BAAI/AltDiffusion-m9"
-or
-export MODEL_NAME="CompVis/stable-diffusion-v1-4" --> export MODEL_NAME="BAAI/AltDiffusion"
-```
-
-### Inference
-
-Once you have trained a model using the above command, you can run inference simply using the `StableDiffusionPipeline`. Make sure to include the `identifier` (e.g. sks in above example) in your prompt.
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_id = "path-to-your-trained-model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-prompt = "A photo of sks dog in a bucket"
-image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-
-image.save("dog-bucket.png")
-```
-
-### Inference from a training checkpoint
-
-You can also perform inference from one of the checkpoints saved during the training process, if you used the `--checkpointing_steps` argument. Please, refer to [the documentation](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint) to see how to do it.
-
-## Training with Low-Rank Adaptation of Large Language Models (LoRA)
-
-Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*
-
-In a nutshell, LoRA allows to adapt pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
-- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114)
-- Rank-decomposition matrices have significantly fewer parameters than the original model, which means that trained LoRA weights are easily portable.
-- LoRA attention layers allow to control to which extent the model is adapted towards new training images via a `scale` parameter.
-
-[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in
-the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.
-
-### Training
-
-Let's get started with a simple example. We will re-use the dog example of the [previous section](#dog-toy-example).
-
-First, you need to set-up your dreambooth training example as is explained in the [installation section](#Installing-the-dependencies).
-Next, let's download the dog dataset. Download images from [here](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save them in a directory. Make sure to set `INSTANCE_DIR` to the name of your directory further below. This will be our training data.
-
-Now, you can launch the training. Here we will use [Stable Diffusion 1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [wandb](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training and pass `--report_to="wandb"` to automatically log images.___**
-
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="path-to-save-model"
-```
-
-For this example we want to directly store the trained LoRA embeddings on the Hub, so
-we need to be logged in and add the `--push_to_hub` flag.
-
-```bash
-huggingface-cli login
-```
-
-Now we can start training!
-
-```bash
-accelerate launch train_dreambooth_lora.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a photo of sks dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --gradient_accumulation_steps=1 \
- --checkpointing_steps=100 \
- --learning_rate=1e-4 \
- --report_to="wandb" \
- --lr_scheduler="constant" \
- --lr_warmup_steps=0 \
- --max_train_steps=500 \
- --validation_prompt="A photo of sks dog in a bucket" \
- --validation_epochs=50 \
- --seed="0" \
- --push_to_hub
-```
-
-**___Note: When using LoRA we can use a much higher learning rate compared to vanilla dreambooth. Here we
-use *1e-4* instead of the usual *2e-6*.___**
-
-The final LoRA embedding weights have been uploaded to [patrickvonplaten/lora_dreambooth_dog_example](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example). **___Note: [The final weights](https://huggingface.co/patrickvonplaten/lora/blob/main/pytorch_attn_procs.bin) are only 3 MB in size which is orders of magnitudes smaller than the original model.**
-
-The training results are summarized [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).
-You can use the `Step` slider to see how the model learned the features of our subject while the model trained.
-
-Optionally, we can also train additional LoRA layers for the text encoder. Specify the `--train_text_encoder` argument above for that. If you're interested to know more about how we
-enable this support, check out this [PR](https://github.com/huggingface/diffusers/pull/2918).
-
-With the default hyperparameters from the above, the training seems to go in a positive direction. Check out [this panel](https://wandb.ai/sayakpaul/dreambooth-lora/reports/test-23-04-17-17-00-13---Vmlldzo0MDkwNjMy). The trained LoRA layers are available [here](https://huggingface.co/sayakpaul/dreambooth).
-
-
-### Inference
-
-After training, LoRA weights can be loaded very easily into the original pipeline. First, you need to
-load the original pipeline:
-
-```python
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-import torch
-
-pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.to("cuda")
-```
-
-Next, we can load the adapter layers into the UNet with the [`load_attn_procs` function](https://huggingface.co/docs/diffusers/api/loaders#diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs).
-
-```python
-pipe.unet.load_attn_procs("patrickvonplaten/lora_dreambooth_dog_example")
-```
-
-Finally, we can run the model in inference.
-
-```python
-image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]
-```
-
-If you are loading the LoRA parameters from the Hub and if the Hub repository has
-a `base_model` tag (such as [this](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example/blob/main/README.md?code=true#L4)), then
-you can do:
-
-```py
-from huggingface_hub.repocard import RepoCard
-
-lora_model_id = "patrickvonplaten/lora_dreambooth_dog_example"
-card = RepoCard.load(lora_model_id)
-base_model_id = card.data.to_dict()["base_model"]
-
-pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
-...
-```
-
-If you used `--train_text_encoder` during training, then use `pipe.load_lora_weights()` to load the LoRA
-weights. For example:
-
-```python
-from huggingface_hub.repocard import RepoCard
-from diffusers import StableDiffusionPipeline
-import torch
-
-lora_model_id = "sayakpaul/dreambooth-text-encoder-test"
-card = RepoCard.load(lora_model_id)
-base_model_id = card.data.to_dict()["base_model"]
-
-pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-pipe.load_lora_weights(lora_model_id)
-image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]
-```
-
-Note that the use of [`LoraLoaderMixin.load_lora_weights`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.load_lora_weights) is preferred to [`UNet2DConditionLoadersMixin.load_attn_procs`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs) for loading LoRA parameters. This is because
-`LoraLoaderMixin.load_lora_weights` can handle the following situations:
-
-* LoRA parameters that don't have separate identifiers for the UNet and the text encoder (such as [`"patrickvonplaten/lora_dreambooth_dog_example"`](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example)). So, you can just do:
-
- ```py
- pipe.load_lora_weights(lora_model_path)
- ```
-
-* LoRA parameters that have separate identifiers for the UNet and the text encoder such as: [`"sayakpaul/dreambooth"`](https://huggingface.co/sayakpaul/dreambooth).
-
-## Training with Flax/JAX
-
-For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.
-
-____Note: The flax example don't yet support features like gradient checkpoint, gradient accumulation etc, so to use flax for faster training we will need >30GB cards.___
-
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-```bash
-pip install -U -r requirements_flax.txt
-```
-
-
-### Training without prior preservation loss
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="path-to-save-model"
-
-python train_dreambooth_flax.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a photo of sks dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --learning_rate=5e-6 \
- --max_train_steps=400
-```
-
-
-### Training with prior preservation loss
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-python train_dreambooth_flax.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --learning_rate=5e-6 \
- --num_class_images=200 \
- --max_train_steps=800
-```
-
-
-### Fine-tune text encoder with the UNet.
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="dog"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-python train_dreambooth_flax.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --train_text_encoder \
- --instance_data_dir=$INSTANCE_DIR \
- --class_data_dir=$CLASS_DIR \
- --output_dir=$OUTPUT_DIR \
- --with_prior_preservation --prior_loss_weight=1.0 \
- --instance_prompt="a photo of sks dog" \
- --class_prompt="a photo of dog" \
- --resolution=512 \
- --train_batch_size=1 \
- --learning_rate=2e-6 \
- --num_class_images=200 \
- --max_train_steps=800
-```
-
-### Training with xformers:
-You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.
-
-You can also use Dreambooth to train the specialized in-painting model. See [the script in the research folder for details](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/dreambooth_inpaint).
-
-### Set grads to none
-
-To save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.
-
-More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html
-
-### Experimental results
-You can refer to [this blog post](https://huggingface.co/blog/dreambooth) that discusses some of DreamBooth experiments in detail. Specifically, it recommends a set of DreamBooth-specific tips and tricks that we have found to work well for a variety of subjects.
-
-## IF
-
-You can use the lora and full dreambooth scripts to train the text to image [IF model](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) and the stage II upscaler
-[IF model](https://huggingface.co/DeepFloyd/IF-II-L-v1.0).
-
-Note that IF has a predicted variance, and our finetuning scripts only train the models predicted error, so for finetuned IF models we switch to a fixed
-variance schedule. The full finetuning scripts will update the scheduler config for the full saved model. However, when loading saved LoRA weights, you
-must also update the pipeline's scheduler config.
-
-```py
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0")
-
-pipe.load_lora_weights("")
-
-# Update scheduler config to fixed variance schedule
-pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type="fixed_small")
-```
-
-Additionally, a few alternative cli flags are needed for IF.
-
-`--resolution=64`: IF is a pixel space diffusion model. In order to operate on un-compressed pixels, the input images are of a much smaller resolution.
-
-`--pre_compute_text_embeddings`: IF uses [T5](https://huggingface.co/docs/transformers/model_doc/t5) for its text encoder. In order to save GPU memory, we pre compute all text embeddings and then de-allocate
-T5.
-
-`--tokenizer_max_length=77`: T5 has a longer default text length, but the default IF encoding procedure uses a smaller number.
-
-`--text_encoder_use_attention_mask`: T5 passes the attention mask to the text encoder.
-
-### Tips and Tricks
-We find LoRA to be sufficient for finetuning the stage I model as the low resolution of the model makes representing finegrained detail hard regardless.
-
-For common and/or not-visually complex object concepts, you can get away with not-finetuning the upscaler. Just be sure to adjust the prompt passed to the
-upscaler to remove the new token from the instance prompt. I.e. if your stage I prompt is "a sks dog", use "a dog" for your stage II prompt.
-
-For finegrained detail like faces that aren't present in the original training set, we find that full finetuning of the stage II upscaler is better than
-LoRA finetuning stage II.
-
-For finegrained detail like faces, we find that lower learning rates along with larger batch sizes work best.
-
-For stage II, we find that lower learning rates are also needed.
-
-We found experimentally that the DDPM scheduler with the default larger number of denoising steps to sometimes work better than the DPM Solver scheduler
-used in the training scripts.
-
-### Stage II additional validation images
-
-The stage II validation requires images to upscale, we can download a downsized version of the training set:
-
-```py
-from huggingface_hub import snapshot_download
-
-local_dir = "./dog_downsized"
-snapshot_download(
- "diffusers/dog-example-downsized",
- local_dir=local_dir,
- repo_type="dataset",
- ignore_patterns=".gitattributes",
-)
-```
-
-### IF stage I LoRA Dreambooth
-This training configuration requires ~28 GB VRAM.
-
-```sh
-export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_dog_lora"
-
-accelerate launch train_dreambooth_lora.py \
- --report_to wandb \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a sks dog" \
- --resolution=64 \
- --train_batch_size=4 \
- --gradient_accumulation_steps=1 \
- --learning_rate=5e-6 \
- --scale_lr \
- --max_train_steps=1200 \
- --validation_prompt="a sks dog" \
- --validation_epochs=25 \
- --checkpointing_steps=100 \
- --pre_compute_text_embeddings \
- --tokenizer_max_length=77 \
- --text_encoder_use_attention_mask
-```
-
-### IF stage II LoRA Dreambooth
-
-`--validation_images`: These images are upscaled during validation steps.
-
-`--class_labels_conditioning=timesteps`: Pass additional conditioning to the UNet needed for stage II.
-
-`--learning_rate=1e-6`: Lower learning rate than stage I.
-
-`--resolution=256`: The upscaler expects higher resolution inputs
-
-```sh
-export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_dog_upscale"
-export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
-
-python train_dreambooth_lora.py \
- --report_to wandb \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a sks dog" \
- --resolution=256 \
- --train_batch_size=4 \
- --gradient_accumulation_steps=1 \
- --learning_rate=1e-6 \
- --max_train_steps=2000 \
- --validation_prompt="a sks dog" \
- --validation_epochs=100 \
- --checkpointing_steps=500 \
- --pre_compute_text_embeddings \
- --tokenizer_max_length=77 \
- --text_encoder_use_attention_mask \
- --validation_images $VALIDATION_IMAGES \
- --class_labels_conditioning=timesteps
-```
-
-### IF Stage I Full Dreambooth
-`--skip_save_text_encoder`: When training the full model, this will skip saving the entire T5 with the finetuned model. You can still load the pipeline
-with a T5 loaded from the original model.
-
-`use_8bit_adam`: Due to the size of the optimizer states, we recommend training the full XL IF model with 8bit adam.
-
-`--learning_rate=1e-7`: For full dreambooth, IF requires very low learning rates. With higher learning rates model quality will degrade. Note that it is
-likely the learning rate can be increased with larger batch sizes.
-
-Using 8bit adam and a batch size of 4, the model can be trained in ~48 GB VRAM.
-
-```sh
-export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
-
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_if"
-
-accelerate launch train_dreambooth.py \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a photo of sks dog" \
- --resolution=64 \
- --train_batch_size=4 \
- --gradient_accumulation_steps=1 \
- --learning_rate=1e-7 \
- --max_train_steps=150 \
- --validation_prompt "a photo of sks dog" \
- --validation_steps 25 \
- --text_encoder_use_attention_mask \
- --tokenizer_max_length 77 \
- --pre_compute_text_embeddings \
- --use_8bit_adam \
- --set_grads_to_none \
- --skip_save_text_encoder \
- --push_to_hub
-```
-
-### IF Stage II Full Dreambooth
-
-`--learning_rate=5e-6`: With a smaller effective batch size of 4, we found that we required learning rates as low as
-1e-8.
-
-`--resolution=256`: The upscaler expects higher resolution inputs
-
-`--train_batch_size=2` and `--gradient_accumulation_steps=6`: We found that full training of stage II particularly with
-faces required large effective batch sizes.
-
-```sh
-export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_dog_upscale"
-export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
-
-accelerate launch train_dreambooth.py \
- --report_to wandb \
- --pretrained_model_name_or_path=$MODEL_NAME \
- --instance_data_dir=$INSTANCE_DIR \
- --output_dir=$OUTPUT_DIR \
- --instance_prompt="a sks dog" \
- --resolution=256 \
- --train_batch_size=2 \
- --gradient_accumulation_steps=6 \
- --learning_rate=5e-6 \
- --max_train_steps=2000 \
- --validation_prompt="a sks dog" \
- --validation_steps=150 \
- --checkpointing_steps=500 \
- --pre_compute_text_embeddings \
- --tokenizer_max_length=77 \
- --text_encoder_use_attention_mask \
- --validation_images $VALIDATION_IMAGES \
- --class_labels_conditioning timesteps \
- --push_to_hub
-```
-
-## Stable Diffusion XL
-
-We support fine-tuning of the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with DreamBooth and LoRA via the `train_dreambooth_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/demodata.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/demodata.py
deleted file mode 100644
index feecb693745a47d9f2bebd8af9a217ff4f5cc92b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/demodata.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import numpy as np
-import torch
-
-from mmdet.utils.util_random import ensure_rng
-
-
-def random_boxes(num=1, scale=1, rng=None):
- """Simple version of ``kwimage.Boxes.random``
-
- Returns:
- Tensor: shape (n, 4) in x1, y1, x2, y2 format.
-
- References:
- https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
-
- Example:
- >>> num = 3
- >>> scale = 512
- >>> rng = 0
- >>> boxes = random_boxes(num, scale, rng)
- >>> print(boxes)
- tensor([[280.9925, 278.9802, 308.6148, 366.1769],
- [216.9113, 330.6978, 224.0446, 456.5878],
- [405.3632, 196.3221, 493.3953, 270.7942]])
- """
- rng = ensure_rng(rng)
-
- tlbr = rng.rand(num, 4).astype(np.float32)
-
- tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
- tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
- br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
- br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
-
- tlbr[:, 0] = tl_x * scale
- tlbr[:, 1] = tl_y * scale
- tlbr[:, 2] = br_x * scale
- tlbr[:, 3] = br_y * scale
-
- boxes = torch.from_numpy(tlbr)
- return boxes
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/coco.py b/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/coco.py
deleted file mode 100644
index 3a8e1bcfdd7f2854ca381d4f87788e3a63eb568c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/coco.py
+++ /dev/null
@@ -1,546 +0,0 @@
-import itertools
-import logging
-import os.path as osp
-import tempfile
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-import pycocotools
-from mmcv.utils import print_log
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-from terminaltables import AsciiTable
-
-from mmdet.core import eval_recalls
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class CocoDataset(CustomDataset):
-
- CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
- 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
- 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
- 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
- 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
- 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
- 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
- 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
- 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
- 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
- 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
- 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
- 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
- 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
-
- def load_annotations(self, ann_file):
- """Load annotation from COCO style annotation file.
-
- Args:
- ann_file (str): Path of annotation file.
-
- Returns:
- list[dict]: Annotation info from COCO api.
- """
- if not getattr(pycocotools, '__version__', '0') >= '12.0.2':
- raise AssertionError(
- 'Incompatible version of pycocotools is installed. '
- 'Run pip uninstall pycocotools first. Then run pip '
- 'install mmpycocotools to install open-mmlab forked '
- 'pycocotools.')
-
- self.coco = COCO(ann_file)
- self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
- self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
- self.img_ids = self.coco.get_img_ids()
- data_infos = []
- total_ann_ids = []
- for i in self.img_ids:
- info = self.coco.load_imgs([i])[0]
- info['filename'] = info['file_name']
- data_infos.append(info)
- ann_ids = self.coco.get_ann_ids(img_ids=[i])
- total_ann_ids.extend(ann_ids)
- assert len(set(total_ann_ids)) == len(
- total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
- return data_infos
-
- def get_ann_info(self, idx):
- """Get COCO annotation by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- dict: Annotation info of specified index.
- """
-
- img_id = self.data_infos[idx]['id']
- ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
- ann_info = self.coco.load_anns(ann_ids)
- return self._parse_ann_info(self.data_infos[idx], ann_info)
-
- def get_cat_ids(self, idx):
- """Get COCO category ids by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- list[int]: All categories in the image of specified index.
- """
-
- img_id = self.data_infos[idx]['id']
- ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
- ann_info = self.coco.load_anns(ann_ids)
- return [ann['category_id'] for ann in ann_info]
-
- def _filter_imgs(self, min_size=32):
- """Filter images too small or without ground truths."""
- valid_inds = []
- # obtain images that contain annotation
- ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
- # obtain images that contain annotations of the required categories
- ids_in_cat = set()
- for i, class_id in enumerate(self.cat_ids):
- ids_in_cat |= set(self.coco.cat_img_map[class_id])
- # merge the image id sets of the two conditions and use the merged set
- # to filter out images if self.filter_empty_gt=True
- ids_in_cat &= ids_with_ann
-
- valid_img_ids = []
- for i, img_info in enumerate(self.data_infos):
- img_id = self.img_ids[i]
- if self.filter_empty_gt and img_id not in ids_in_cat:
- continue
- if min(img_info['width'], img_info['height']) >= min_size:
- valid_inds.append(i)
- valid_img_ids.append(img_id)
- self.img_ids = valid_img_ids
- return valid_inds
-
- def _parse_ann_info(self, img_info, ann_info):
- """Parse bbox and mask annotation.
-
- Args:
- ann_info (list[dict]): Annotation info of an image.
- with_mask (bool): Whether to parse mask annotations.
-
- Returns:
- dict: A dict containing the following keys: bboxes, bboxes_ignore,\
- labels, masks, seg_map. "masks" are raw annotations and not \
- decoded into binary masks.
- """
- gt_bboxes = []
- gt_labels = []
- gt_bboxes_ignore = []
- gt_masks_ann = []
- for i, ann in enumerate(ann_info):
- if ann.get('ignore', False):
- continue
- x1, y1, w, h = ann['bbox']
- inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
- inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
- if inter_w * inter_h == 0:
- continue
- if ann['area'] <= 0 or w < 1 or h < 1:
- continue
- if ann['category_id'] not in self.cat_ids:
- continue
- bbox = [x1, y1, x1 + w, y1 + h]
- if ann.get('iscrowd', False):
- gt_bboxes_ignore.append(bbox)
- else:
- gt_bboxes.append(bbox)
- gt_labels.append(self.cat2label[ann['category_id']])
- gt_masks_ann.append(ann.get('segmentation', None))
-
- if gt_bboxes:
- gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
- gt_labels = np.array(gt_labels, dtype=np.int64)
- else:
- gt_bboxes = np.zeros((0, 4), dtype=np.float32)
- gt_labels = np.array([], dtype=np.int64)
-
- if gt_bboxes_ignore:
- gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
- else:
- gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
-
- seg_map = img_info['filename'].replace('jpg', 'png')
-
- ann = dict(
- bboxes=gt_bboxes,
- labels=gt_labels,
- bboxes_ignore=gt_bboxes_ignore,
- masks=gt_masks_ann,
- seg_map=seg_map)
-
- return ann
-
- def xyxy2xywh(self, bbox):
- """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
- evaluation.
-
- Args:
- bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
- ``xyxy`` order.
-
- Returns:
- list[float]: The converted bounding boxes, in ``xywh`` order.
- """
-
- _bbox = bbox.tolist()
- return [
- _bbox[0],
- _bbox[1],
- _bbox[2] - _bbox[0],
- _bbox[3] - _bbox[1],
- ]
-
- def _proposal2json(self, results):
- """Convert proposal results to COCO json style."""
- json_results = []
- for idx in range(len(self)):
- img_id = self.img_ids[idx]
- bboxes = results[idx]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = self.xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = 1
- json_results.append(data)
- return json_results
-
- def _det2json(self, results):
- """Convert detection results to COCO json style."""
- json_results = []
- for idx in range(len(self)):
- img_id = self.img_ids[idx]
- result = results[idx]
- for label in range(len(result)):
- bboxes = result[label]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = self.xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = self.cat_ids[label]
- json_results.append(data)
- return json_results
-
- def _segm2json(self, results):
- """Convert instance segmentation results to COCO json style."""
- bbox_json_results = []
- segm_json_results = []
- for idx in range(len(self)):
- img_id = self.img_ids[idx]
- det, seg = results[idx]
- for label in range(len(det)):
- # bbox results
- bboxes = det[label]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = self.xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = self.cat_ids[label]
- bbox_json_results.append(data)
-
- # segm results
- # some detectors use different scores for bbox and mask
- if isinstance(seg, tuple):
- segms = seg[0][label]
- mask_score = seg[1][label]
- else:
- segms = seg[label]
- mask_score = [bbox[4] for bbox in bboxes]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = self.xyxy2xywh(bboxes[i])
- data['score'] = float(mask_score[i])
- data['category_id'] = self.cat_ids[label]
- if isinstance(segms[i]['counts'], bytes):
- segms[i]['counts'] = segms[i]['counts'].decode()
- data['segmentation'] = segms[i]
- segm_json_results.append(data)
- return bbox_json_results, segm_json_results
-
- def results2json(self, results, outfile_prefix):
- """Dump the detection results to a COCO style json file.
-
- There are 3 types of results: proposals, bbox predictions, mask
- predictions, and they have different data types. This method will
- automatically recognize the type, and dump them to json files.
-
- Args:
- results (list[list | tuple | ndarray]): Testing results of the
- dataset.
- outfile_prefix (str): The filename prefix of the json files. If the
- prefix is "somepath/xxx", the json files will be named
- "somepath/xxx.bbox.json", "somepath/xxx.segm.json",
- "somepath/xxx.proposal.json".
-
- Returns:
- dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
- values are corresponding filenames.
- """
- result_files = dict()
- if isinstance(results[0], list):
- json_results = self._det2json(results)
- result_files['bbox'] = f'{outfile_prefix}.bbox.json'
- result_files['proposal'] = f'{outfile_prefix}.bbox.json'
- mmcv.dump(json_results, result_files['bbox'])
- elif isinstance(results[0], tuple):
- json_results = self._segm2json(results)
- result_files['bbox'] = f'{outfile_prefix}.bbox.json'
- result_files['proposal'] = f'{outfile_prefix}.bbox.json'
- result_files['segm'] = f'{outfile_prefix}.segm.json'
- mmcv.dump(json_results[0], result_files['bbox'])
- mmcv.dump(json_results[1], result_files['segm'])
- elif isinstance(results[0], np.ndarray):
- json_results = self._proposal2json(results)
- result_files['proposal'] = f'{outfile_prefix}.proposal.json'
- mmcv.dump(json_results, result_files['proposal'])
- else:
- raise TypeError('invalid type of results')
- return result_files
-
- def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
- gt_bboxes = []
- for i in range(len(self.img_ids)):
- ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
- ann_info = self.coco.load_anns(ann_ids)
- if len(ann_info) == 0:
- gt_bboxes.append(np.zeros((0, 4)))
- continue
- bboxes = []
- for ann in ann_info:
- if ann.get('ignore', False) or ann['iscrowd']:
- continue
- x1, y1, w, h = ann['bbox']
- bboxes.append([x1, y1, x1 + w, y1 + h])
- bboxes = np.array(bboxes, dtype=np.float32)
- if bboxes.shape[0] == 0:
- bboxes = np.zeros((0, 4))
- gt_bboxes.append(bboxes)
-
- recalls = eval_recalls(
- gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
- ar = recalls.mean(axis=1)
- return ar
-
- def format_results(self, results, jsonfile_prefix=None, **kwargs):
- """Format the results to json (standard format for COCO evaluation).
-
- Args:
- results (list[tuple | numpy.ndarray]): Testing results of the
- dataset.
- jsonfile_prefix (str | None): The prefix of json files. It includes
- the file path and the prefix of filename, e.g., "a/b/prefix".
- If not specified, a temp file will be created. Default: None.
-
- Returns:
- tuple: (result_files, tmp_dir), result_files is a dict containing \
- the json filepaths, tmp_dir is the temporal directory created \
- for saving json files when jsonfile_prefix is not specified.
- """
- assert isinstance(results, list), 'results must be a list'
- assert len(results) == len(self), (
- 'The length of results is not equal to the dataset len: {} != {}'.
- format(len(results), len(self)))
-
- if jsonfile_prefix is None:
- tmp_dir = tempfile.TemporaryDirectory()
- jsonfile_prefix = osp.join(tmp_dir.name, 'results')
- else:
- tmp_dir = None
- result_files = self.results2json(results, jsonfile_prefix)
- return result_files, tmp_dir
-
- def evaluate(self,
- results,
- metric='bbox',
- logger=None,
- jsonfile_prefix=None,
- classwise=False,
- proposal_nums=(100, 300, 1000),
- iou_thrs=None,
- metric_items=None):
- """Evaluation in COCO protocol.
-
- Args:
- results (list[list | tuple]): Testing results of the dataset.
- metric (str | list[str]): Metrics to be evaluated. Options are
- 'bbox', 'segm', 'proposal', 'proposal_fast'.
- logger (logging.Logger | str | None): Logger used for printing
- related information during evaluation. Default: None.
- jsonfile_prefix (str | None): The prefix of json files. It includes
- the file path and the prefix of filename, e.g., "a/b/prefix".
- If not specified, a temp file will be created. Default: None.
- classwise (bool): Whether to evaluating the AP for each class.
- proposal_nums (Sequence[int]): Proposal number used for evaluating
- recalls, such as recall@100, recall@1000.
- Default: (100, 300, 1000).
- iou_thrs (Sequence[float], optional): IoU threshold used for
- evaluating recalls/mAPs. If set to a list, the average of all
- IoUs will also be computed. If not specified, [0.50, 0.55,
- 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
- Default: None.
- metric_items (list[str] | str, optional): Metric items that will
- be returned. If not specified, ``['AR@100', 'AR@300',
- 'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
- used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
- 'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
- ``metric=='bbox' or metric=='segm'``.
-
- Returns:
- dict[str, float]: COCO style evaluation metric.
- """
-
- metrics = metric if isinstance(metric, list) else [metric]
- allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
- for metric in metrics:
- if metric not in allowed_metrics:
- raise KeyError(f'metric {metric} is not supported')
- if iou_thrs is None:
- iou_thrs = np.linspace(
- .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
- if metric_items is not None:
- if not isinstance(metric_items, list):
- metric_items = [metric_items]
-
- result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
-
- eval_results = OrderedDict()
- cocoGt = self.coco
- for metric in metrics:
- msg = f'Evaluating {metric}...'
- if logger is None:
- msg = '\n' + msg
- print_log(msg, logger=logger)
-
- if metric == 'proposal_fast':
- ar = self.fast_eval_recall(
- results, proposal_nums, iou_thrs, logger='silent')
- log_msg = []
- for i, num in enumerate(proposal_nums):
- eval_results[f'AR@{num}'] = ar[i]
- log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
- log_msg = ''.join(log_msg)
- print_log(log_msg, logger=logger)
- continue
-
- if metric not in result_files:
- raise KeyError(f'{metric} is not in results')
- try:
- cocoDt = cocoGt.loadRes(result_files[metric])
- except IndexError:
- print_log(
- 'The testing results of the whole dataset is empty.',
- logger=logger,
- level=logging.ERROR)
- break
-
- iou_type = 'bbox' if metric == 'proposal' else metric
- cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
- cocoEval.params.catIds = self.cat_ids
- cocoEval.params.imgIds = self.img_ids
- cocoEval.params.maxDets = list(proposal_nums)
- cocoEval.params.iouThrs = iou_thrs
- # mapping of cocoEval.stats
- coco_metric_names = {
- 'mAP': 0,
- 'mAP_50': 1,
- 'mAP_75': 2,
- 'mAP_s': 3,
- 'mAP_m': 4,
- 'mAP_l': 5,
- 'AR@100': 6,
- 'AR@300': 7,
- 'AR@1000': 8,
- 'AR_s@1000': 9,
- 'AR_m@1000': 10,
- 'AR_l@1000': 11
- }
- if metric_items is not None:
- for metric_item in metric_items:
- if metric_item not in coco_metric_names:
- raise KeyError(
- f'metric item {metric_item} is not supported')
-
- if metric == 'proposal':
- cocoEval.params.useCats = 0
- cocoEval.evaluate()
- cocoEval.accumulate()
- cocoEval.summarize()
- if metric_items is None:
- metric_items = [
- 'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
- 'AR_m@1000', 'AR_l@1000'
- ]
-
- for item in metric_items:
- val = float(
- f'{cocoEval.stats[coco_metric_names[item]]:.3f}')
- eval_results[item] = val
- else:
- cocoEval.evaluate()
- cocoEval.accumulate()
- cocoEval.summarize()
- if classwise: # Compute per-category AP
- # Compute per-category AP
- # from https://github.com/facebookresearch/detectron2/
- precisions = cocoEval.eval['precision']
- # precision: (iou, recall, cls, area range, max dets)
- assert len(self.cat_ids) == precisions.shape[2]
-
- results_per_category = []
- for idx, catId in enumerate(self.cat_ids):
- # area range index 0: all area ranges
- # max dets index -1: typically 100 per image
- nm = self.coco.loadCats(catId)[0]
- precision = precisions[:, :, idx, 0, -1]
- precision = precision[precision > -1]
- if precision.size:
- ap = np.mean(precision)
- else:
- ap = float('nan')
- results_per_category.append(
- (f'{nm["name"]}', f'{float(ap):0.3f}'))
-
- num_columns = min(6, len(results_per_category) * 2)
- results_flatten = list(
- itertools.chain(*results_per_category))
- headers = ['category', 'AP'] * (num_columns // 2)
- results_2d = itertools.zip_longest(*[
- results_flatten[i::num_columns]
- for i in range(num_columns)
- ])
- table_data = [headers]
- table_data += [result for result in results_2d]
- table = AsciiTable(table_data)
- print_log('\n' + table.table, logger=logger)
-
- if metric_items is None:
- metric_items = [
- 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
- ]
-
- for metric_item in metric_items:
- key = f'{metric}_{metric_item}'
- val = float(
- f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
- )
- eval_results[key] = val
- ap = cocoEval.stats[:6]
- eval_results[f'{metric}_mAP_copypaste'] = (
- f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
- f'{ap[4]:.3f} {ap[5]:.3f}')
- if tmp_dir is not None:
- tmp_dir.cleanup()
- return eval_results
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/custom.py b/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/custom.py
deleted file mode 100644
index 1a2351c217f43d32178053dfc682a2b241f9a3f1..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/custom.py
+++ /dev/null
@@ -1,323 +0,0 @@
-import os.path as osp
-import warnings
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-from mmcv.utils import print_log
-from torch.utils.data import Dataset
-
-from mmdet.core import eval_map, eval_recalls
-from .builder import DATASETS
-from .pipelines import Compose
-
-
-@DATASETS.register_module()
-class CustomDataset(Dataset):
- """Custom dataset for detection.
-
- The annotation format is shown as follows. The `ann` field is optional for
- testing.
-
- .. code-block:: none
-
- [
- {
- 'filename': 'a.jpg',
- 'width': 1280,
- 'height': 720,
- 'ann': {
- 'bboxes': (n, 4) in (x1, y1, x2, y2) order.
- 'labels': (n, ),
- 'bboxes_ignore': (k, 4), (optional field)
- 'labels_ignore': (k, 4) (optional field)
- }
- },
- ...
- ]
-
- Args:
- ann_file (str): Annotation file path.
- pipeline (list[dict]): Processing pipeline.
- classes (str | Sequence[str], optional): Specify classes to load.
- If is None, ``cls.CLASSES`` will be used. Default: None.
- data_root (str, optional): Data root for ``ann_file``,
- ``img_prefix``, ``seg_prefix``, ``proposal_file`` if specified.
- test_mode (bool, optional): If set True, annotation will not be loaded.
- filter_empty_gt (bool, optional): If set true, images without bounding
- boxes of the dataset's classes will be filtered out. This option
- only works when `test_mode=False`, i.e., we never filter images
- during tests.
- """
-
- CLASSES = None
-
- def __init__(self,
- ann_file,
- pipeline,
- classes=None,
- data_root=None,
- img_prefix='',
- seg_prefix=None,
- proposal_file=None,
- test_mode=False,
- filter_empty_gt=True):
- self.ann_file = ann_file
- self.data_root = data_root
- self.img_prefix = img_prefix
- self.seg_prefix = seg_prefix
- self.proposal_file = proposal_file
- self.test_mode = test_mode
- self.filter_empty_gt = filter_empty_gt
- self.CLASSES = self.get_classes(classes)
-
- # join paths if data_root is specified
- if self.data_root is not None:
- if not osp.isabs(self.ann_file):
- self.ann_file = osp.join(self.data_root, self.ann_file)
- if not (self.img_prefix is None or osp.isabs(self.img_prefix)):
- self.img_prefix = osp.join(self.data_root, self.img_prefix)
- if not (self.seg_prefix is None or osp.isabs(self.seg_prefix)):
- self.seg_prefix = osp.join(self.data_root, self.seg_prefix)
- if not (self.proposal_file is None
- or osp.isabs(self.proposal_file)):
- self.proposal_file = osp.join(self.data_root,
- self.proposal_file)
- # load annotations (and proposals)
- self.data_infos = self.load_annotations(self.ann_file)
-
- if self.proposal_file is not None:
- self.proposals = self.load_proposals(self.proposal_file)
- else:
- self.proposals = None
-
- # filter images too small and containing no annotations
- if not test_mode:
- valid_inds = self._filter_imgs()
- self.data_infos = [self.data_infos[i] for i in valid_inds]
- if self.proposals is not None:
- self.proposals = [self.proposals[i] for i in valid_inds]
- # set group flag for the sampler
- self._set_group_flag()
-
- # processing pipeline
- self.pipeline = Compose(pipeline)
-
- def __len__(self):
- """Total number of samples of data."""
- return len(self.data_infos)
-
- def load_annotations(self, ann_file):
- """Load annotation from annotation file."""
- return mmcv.load(ann_file)
-
- def load_proposals(self, proposal_file):
- """Load proposal from proposal file."""
- return mmcv.load(proposal_file)
-
- def get_ann_info(self, idx):
- """Get annotation by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- dict: Annotation info of specified index.
- """
-
- return self.data_infos[idx]['ann']
-
- def get_cat_ids(self, idx):
- """Get category ids by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- list[int]: All categories in the image of specified index.
- """
-
- return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()
-
- def pre_pipeline(self, results):
- """Prepare results dict for pipeline."""
- results['img_prefix'] = self.img_prefix
- results['seg_prefix'] = self.seg_prefix
- results['proposal_file'] = self.proposal_file
- results['bbox_fields'] = []
- results['mask_fields'] = []
- results['seg_fields'] = []
-
- def _filter_imgs(self, min_size=32):
- """Filter images too small."""
- if self.filter_empty_gt:
- warnings.warn(
- 'CustomDataset does not support filtering empty gt images.')
- valid_inds = []
- for i, img_info in enumerate(self.data_infos):
- if min(img_info['width'], img_info['height']) >= min_size:
- valid_inds.append(i)
- return valid_inds
-
- def _set_group_flag(self):
- """Set flag according to image aspect ratio.
-
- Images with aspect ratio greater than 1 will be set as group 1,
- otherwise group 0.
- """
- self.flag = np.zeros(len(self), dtype=np.uint8)
- for i in range(len(self)):
- img_info = self.data_infos[i]
- if img_info['width'] / img_info['height'] > 1:
- self.flag[i] = 1
-
- def _rand_another(self, idx):
- """Get another random index from the same group as the given index."""
- pool = np.where(self.flag == self.flag[idx])[0]
- return np.random.choice(pool)
-
- def __getitem__(self, idx):
- """Get training/test data after pipeline.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- dict: Training/test data (with annotation if `test_mode` is set \
- True).
- """
-
- if self.test_mode:
- return self.prepare_test_img(idx)
- while True:
- data = self.prepare_train_img(idx)
- if data is None:
- idx = self._rand_another(idx)
- continue
- return data
-
- def prepare_train_img(self, idx):
- """Get training data and annotations after pipeline.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- dict: Training data and annotation after pipeline with new keys \
- introduced by pipeline.
- """
-
- img_info = self.data_infos[idx]
- ann_info = self.get_ann_info(idx)
- results = dict(img_info=img_info, ann_info=ann_info)
- if self.proposals is not None:
- results['proposals'] = self.proposals[idx]
- self.pre_pipeline(results)
- return self.pipeline(results)
-
- def prepare_test_img(self, idx):
- """Get testing data after pipeline.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- dict: Testing data after pipeline with new keys introduced by \
- pipeline.
- """
-
- img_info = self.data_infos[idx]
- results = dict(img_info=img_info)
- if self.proposals is not None:
- results['proposals'] = self.proposals[idx]
- self.pre_pipeline(results)
- return self.pipeline(results)
-
- @classmethod
- def get_classes(cls, classes=None):
- """Get class names of current dataset.
-
- Args:
- classes (Sequence[str] | str | None): If classes is None, use
- default CLASSES defined by builtin dataset. If classes is a
- string, take it as a file name. The file contains the name of
- classes where each line contains one class name. If classes is
- a tuple or list, override the CLASSES defined by the dataset.
-
- Returns:
- tuple[str] or list[str]: Names of categories of the dataset.
- """
- if classes is None:
- return cls.CLASSES
-
- if isinstance(classes, str):
- # take it as a file path
- class_names = mmcv.list_from_file(classes)
- elif isinstance(classes, (tuple, list)):
- class_names = classes
- else:
- raise ValueError(f'Unsupported type {type(classes)} of classes.')
-
- return class_names
-
- def format_results(self, results, **kwargs):
- """Place holder to format result to dataset specific output."""
-
- def evaluate(self,
- results,
- metric='mAP',
- logger=None,
- proposal_nums=(100, 300, 1000),
- iou_thr=0.5,
- scale_ranges=None):
- """Evaluate the dataset.
-
- Args:
- results (list): Testing results of the dataset.
- metric (str | list[str]): Metrics to be evaluated.
- logger (logging.Logger | None | str): Logger used for printing
- related information during evaluation. Default: None.
- proposal_nums (Sequence[int]): Proposal number used for evaluating
- recalls, such as recall@100, recall@1000.
- Default: (100, 300, 1000).
- iou_thr (float | list[float]): IoU threshold. Default: 0.5.
- scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
- Default: None.
- """
-
- if not isinstance(metric, str):
- assert len(metric) == 1
- metric = metric[0]
- allowed_metrics = ['mAP', 'recall']
- if metric not in allowed_metrics:
- raise KeyError(f'metric {metric} is not supported')
- annotations = [self.get_ann_info(i) for i in range(len(self))]
- eval_results = OrderedDict()
- iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
- if metric == 'mAP':
- assert isinstance(iou_thrs, list)
- mean_aps = []
- for iou_thr in iou_thrs:
- print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
- mean_ap, _ = eval_map(
- results,
- annotations,
- scale_ranges=scale_ranges,
- iou_thr=iou_thr,
- dataset=self.CLASSES,
- logger=logger)
- mean_aps.append(mean_ap)
- eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
- eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
- elif metric == 'recall':
- gt_bboxes = [ann['bboxes'] for ann in annotations]
- recalls = eval_recalls(
- gt_bboxes, results, proposal_nums, iou_thr, logger=logger)
- for i, num in enumerate(proposal_nums):
- for j, iou in enumerate(iou_thrs):
- eval_results[f'recall@{num}@{iou}'] = recalls[i, j]
- if recalls.shape[1] > 1:
- ar = recalls.mean(axis=1)
- for i, num in enumerate(proposal_nums):
- eval_results[f'AR@{num}'] = ar[i]
- return eval_results
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/github.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/github.py
deleted file mode 100644
index 282267b6be7f3b0371a3fd332f98e38611c9fb9a..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/github.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import subprocess
-from pathlib import Path
-
-new_extensions = set()
-
-
-def clone_or_pull_repository(github_url):
- global new_extensions
-
- repository_folder = Path("extensions")
- repo_name = github_url.rstrip("/").split("/")[-1].split(".")[0]
-
- # Check if the repository folder exists
- if not repository_folder.exists():
- repository_folder.mkdir(parents=True)
-
- repo_path = repository_folder / repo_name
-
- # Check if the repository is already cloned
- if repo_path.exists():
- yield f"Updating {github_url}..."
- # Perform a 'git pull' to update the repository
- try:
- pull_output = subprocess.check_output(["git", "-C", repo_path, "pull"], stderr=subprocess.STDOUT)
- yield "Done."
- return pull_output.decode()
- except subprocess.CalledProcessError as e:
- return str(e)
-
- # Clone the repository
- try:
- yield f"Cloning {github_url}..."
- clone_output = subprocess.check_output(["git", "clone", github_url, repo_path], stderr=subprocess.STDOUT)
- new_extensions.add(repo_name)
- yield f"The extension `{repo_name}` has been downloaded.\n\nPlease close the the web UI completely and launch it again to be able to load it."
- return clone_output.decode()
- except subprocess.CalledProcessError as e:
- return str(e)
diff --git a/spaces/Anonymous-123/ImageNet-Editing/app.py b/spaces/Anonymous-123/ImageNet-Editing/app.py
deleted file mode 100644
index 6a0c9691f3338d0a20157ed7d5bf32b76b3e6cf6..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-import gradio as gr
-import sys
-sys.path.append(".")
-
-#@title Import stuff
-import gc
-
-import subprocess
-import shutil
-from PIL import Image
-import time
-
-import imageio
-
-
-# def run(initial_image, mask, Backgrounds, Backgrounds_complexity, Size, Angle, Steps, num_of_Images):
-def run(source_img, Backgrounds, Backgrounds_complexity, Size, Angle, Steps, num_of_Images):
- print('-------------------starting to process-------------------')
- if os.path.exists('results'):
- shutil.rmtree("results")
- if os.path.exists('tmp'):
- shutil.rmtree("tmp")
- time.sleep(1)
- os.makedirs('results', exist_ok=True)
- os.makedirs('tmp/img', exist_ok=True)
- os.makedirs('tmp/mask', exist_ok=True)
- os.makedirs('tmp/bg', exist_ok=True)
-
- '''
- print('-----initial_image: ', initial_image)
- init_image = Image.open(initial_image)
- mask = Image.open(mask)
- init_image = init_image.resize((256,256))
- mask = mask.resize((256,256))
- init_image.save("tmp/img/input.JPEG")
- mask.save("tmp/mask/input.png")
- '''
- imageio.imwrite("tmp/img/input.JPEG", source_img["image"])
- imageio.imwrite("tmp/mask/input.png", source_img["mask"])
-
- initial_image = Image.open('tmp/img/input.JPEG').resize((256,256))
- initial_image.save('tmp/img/input.JPEG')
- mask = Image.open('tmp/mask/input.png').resize((256,256))
- mask.save('tmp/mask/input.png')
-
-
- if Backgrounds:
- background_specific = Backgrounds
- if background_specific is not None:
- background_specific = Image.open(background_specific).convert('RGB') # Specified background
- background_specific = background_specific.resize((256,256))
- background_specific.save('tmp/bg/bg.png')
- background_specific = '../tmp/bg/bg.png'
- else:
- background_specific = ""
-
- Backgrounds_complexity = Backgrounds_complexity
- Size = Size
- Angle = Angle
- Steps = Steps
- num_of_Images = num_of_Images
- print(Backgrounds_complexity, background_specific, Size, Angle, Steps, num_of_Images)
- p = subprocess.Popen(["sh", "run.sh", str(Backgrounds_complexity), background_specific, str(Size), str(Angle), str(Steps), str(num_of_Images)])
-
- # subprocess.Popen(["cd", "object_removal/TFill/"])
- # subprocess.Popen(["python", "test.py"])
-
- return_code = p.wait()
- print('----return_code: ', return_code)
-
- if os.path.exists('results/edited.png'):
- return Image.open('results/edited.png')
- else:
- return Image.open('tmp/img/input.JPEG')
-
-
-image = gr.outputs.Image(type="pil", label="Your result")
-css = ".output-image{height: 528px !important} .output-carousel .output-image{height:272px !important} a{text-decoration: underline}"
-iface = gr.Interface(fn=run, inputs=[
- # gr.inputs.Image(type="filepath", label='initial_image'),
- gr.Image(source="upload", type="numpy", tool="sketch", elem_id="source_container"),
- # gr.inputs.Image(type="filepath", label='mask - object mask', optional=True),
- gr.inputs.Image(type="filepath", label='Backgrounds - optional, specified backgrounds'),
- gr.inputs.Slider(label="Backgrounds_complexity - How complicated you wish to the generated image to be", default=0, step=1, minimum=-30, maximum=30),
- gr.inputs.Slider(label="Size - Object pixel rates", default=0.1, step=0.02, minimum=0.01, maximum=0.5),
- gr.inputs.Slider(label="Angle - Object angle", default=0, step=10, minimum=-180, maximum=180),
- gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=10,maximum=100,minimum=1,step=1),
- gr.inputs.Slider(label="num_of_Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4),
-
- # gr.inputs.Radio(label="Width", choices=[32,64,128,256],default=256),
- # gr.inputs.Radio(label="Height", choices=[32,64,128,256],default=256),
- # gr.inputs.Textbox(label="Prompt - try adding increments to your prompt such as 'oil on canvas', 'a painting', 'a book cover'",default="chalk pastel drawing of a dog wearing a funny hat"),
- #gr.inputs.Slider(label="ETA - between 0 and 1. Lower values can provide better quality, higher values can be more diverse",default=0.0,minimum=0.0, maximum=1.0,step=0.1),
- ],
- # outputs=[image,gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")],
- outputs=["image"],
- css=css,
- title="Image Editing with Controls of Object Attributes including Backgrounds, Sizes, Positions and Directions",
- description="Demo for Image Editing with Controls of Object Attributes. *** NOTE!!! Due to the requirements of GPU, this demo cannot work on this website currently(it always returns the input image). Please download the codes and run them at your server. ***",
- article="Our code are mostly developed based the codes of `Blended Diffusion for Text-driven Editing of Natural Images' and `TFill'")
-iface.launch(enable_queue=True)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/download.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/download.py
deleted file mode 100644
index 36e947c8c052dc48b2600c29575f5607a5144d5a..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/download.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import logging
-import os
-from optparse import Values
-from typing import List
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.cmdoptions import make_target_python
-from pip._internal.cli.req_command import RequirementCommand, with_cleanup
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.operations.build.build_tracker import get_build_tracker
-from pip._internal.req.req_install import check_legacy_setup_py_options
-from pip._internal.utils.misc import ensure_dir, normalize_path, write_output
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-class DownloadCommand(RequirementCommand):
- """
- Download packages from:
-
- - PyPI (and other indexes) using requirement specifiers.
- - VCS project urls.
- - Local project directories.
- - Local or remote source archives.
-
- pip also supports downloading from "requirements files", which provide
- an easy way to specify a whole environment to be downloaded.
- """
-
- usage = """
- %prog [options] [package-index-options] ...
- %prog [options] -r [package-index-options] ...
- %prog [options] ...
- %prog [options] ...
- %prog [options] ..."""
-
- def add_options(self) -> None:
- self.cmd_opts.add_option(cmdoptions.constraints())
- self.cmd_opts.add_option(cmdoptions.requirements())
- self.cmd_opts.add_option(cmdoptions.no_deps())
- self.cmd_opts.add_option(cmdoptions.global_options())
- self.cmd_opts.add_option(cmdoptions.no_binary())
- self.cmd_opts.add_option(cmdoptions.only_binary())
- self.cmd_opts.add_option(cmdoptions.prefer_binary())
- self.cmd_opts.add_option(cmdoptions.src())
- self.cmd_opts.add_option(cmdoptions.pre())
- self.cmd_opts.add_option(cmdoptions.require_hashes())
- self.cmd_opts.add_option(cmdoptions.progress_bar())
- self.cmd_opts.add_option(cmdoptions.no_build_isolation())
- self.cmd_opts.add_option(cmdoptions.use_pep517())
- self.cmd_opts.add_option(cmdoptions.no_use_pep517())
- self.cmd_opts.add_option(cmdoptions.check_build_deps())
- self.cmd_opts.add_option(cmdoptions.ignore_requires_python())
-
- self.cmd_opts.add_option(
- "-d",
- "--dest",
- "--destination-dir",
- "--destination-directory",
- dest="download_dir",
- metavar="dir",
- default=os.curdir,
- help="Download packages into .",
- )
-
- cmdoptions.add_target_python_options(self.cmd_opts)
-
- index_opts = cmdoptions.make_option_group(
- cmdoptions.index_group,
- self.parser,
- )
-
- self.parser.insert_option_group(0, index_opts)
- self.parser.insert_option_group(0, self.cmd_opts)
-
- @with_cleanup
- def run(self, options: Values, args: List[str]) -> int:
- options.ignore_installed = True
- # editable doesn't really make sense for `pip download`, but the bowels
- # of the RequirementSet code require that property.
- options.editables = []
-
- cmdoptions.check_dist_restriction(options)
-
- options.download_dir = normalize_path(options.download_dir)
- ensure_dir(options.download_dir)
-
- session = self.get_default_session(options)
-
- target_python = make_target_python(options)
- finder = self._build_package_finder(
- options=options,
- session=session,
- target_python=target_python,
- ignore_requires_python=options.ignore_requires_python,
- )
-
- build_tracker = self.enter_context(get_build_tracker())
-
- directory = TempDirectory(
- delete=not options.no_clean,
- kind="download",
- globally_managed=True,
- )
-
- reqs = self.get_requirements(args, options, finder, session)
- check_legacy_setup_py_options(options, reqs)
-
- preparer = self.make_requirement_preparer(
- temp_build_dir=directory,
- options=options,
- build_tracker=build_tracker,
- session=session,
- finder=finder,
- download_dir=options.download_dir,
- use_user_site=False,
- verbosity=self.verbosity,
- )
-
- resolver = self.make_resolver(
- preparer=preparer,
- finder=finder,
- options=options,
- ignore_requires_python=options.ignore_requires_python,
- use_pep517=options.use_pep517,
- py_version_info=options.python_version,
- )
-
- self.trace_basic_info(finder)
-
- requirement_set = resolver.resolve(reqs, check_supported_wheels=True)
-
- downloaded: List[str] = []
- for req in requirement_set.requirements.values():
- if req.satisfied_by is None:
- assert req.name is not None
- preparer.save_linked_requirement(req)
- downloaded.append(req.name)
- if downloaded:
- write_output("Successfully downloaded %s", " ".join(downloaded))
-
- return SUCCESS
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/configuration.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/configuration.py
deleted file mode 100644
index 8fd46c9b8e0dbab8716c33d349f55a494613ba8f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/configuration.py
+++ /dev/null
@@ -1,374 +0,0 @@
-"""Configuration management setup
-
-Some terminology:
-- name
- As written in config files.
-- value
- Value associated with a name
-- key
- Name combined with it's section (section.name)
-- variant
- A single word describing where the configuration key-value pair came from
-"""
-
-import configparser
-import locale
-import os
-import sys
-from typing import Any, Dict, Iterable, List, NewType, Optional, Tuple
-
-from pip._internal.exceptions import (
- ConfigurationError,
- ConfigurationFileCouldNotBeLoaded,
-)
-from pip._internal.utils import appdirs
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.logging import getLogger
-from pip._internal.utils.misc import ensure_dir, enum
-
-RawConfigParser = configparser.RawConfigParser # Shorthand
-Kind = NewType("Kind", str)
-
-CONFIG_BASENAME = "pip.ini" if WINDOWS else "pip.conf"
-ENV_NAMES_IGNORED = "version", "help"
-
-# The kinds of configurations there are.
-kinds = enum(
- USER="user", # User Specific
- GLOBAL="global", # System Wide
- SITE="site", # [Virtual] Environment Specific
- ENV="env", # from PIP_CONFIG_FILE
- ENV_VAR="env-var", # from Environment Variables
-)
-OVERRIDE_ORDER = kinds.GLOBAL, kinds.USER, kinds.SITE, kinds.ENV, kinds.ENV_VAR
-VALID_LOAD_ONLY = kinds.USER, kinds.GLOBAL, kinds.SITE
-
-logger = getLogger(__name__)
-
-
-# NOTE: Maybe use the optionx attribute to normalize keynames.
-def _normalize_name(name: str) -> str:
- """Make a name consistent regardless of source (environment or file)"""
- name = name.lower().replace("_", "-")
- if name.startswith("--"):
- name = name[2:] # only prefer long opts
- return name
-
-
-def _disassemble_key(name: str) -> List[str]:
- if "." not in name:
- error_message = (
- "Key does not contain dot separated section and key. "
- "Perhaps you wanted to use 'global.{}' instead?"
- ).format(name)
- raise ConfigurationError(error_message)
- return name.split(".", 1)
-
-
-def get_configuration_files() -> Dict[Kind, List[str]]:
- global_config_files = [
- os.path.join(path, CONFIG_BASENAME) for path in appdirs.site_config_dirs("pip")
- ]
-
- site_config_file = os.path.join(sys.prefix, CONFIG_BASENAME)
- legacy_config_file = os.path.join(
- os.path.expanduser("~"),
- "pip" if WINDOWS else ".pip",
- CONFIG_BASENAME,
- )
- new_config_file = os.path.join(appdirs.user_config_dir("pip"), CONFIG_BASENAME)
- return {
- kinds.GLOBAL: global_config_files,
- kinds.SITE: [site_config_file],
- kinds.USER: [legacy_config_file, new_config_file],
- }
-
-
-class Configuration:
- """Handles management of configuration.
-
- Provides an interface to accessing and managing configuration files.
-
- This class converts provides an API that takes "section.key-name" style
- keys and stores the value associated with it as "key-name" under the
- section "section".
-
- This allows for a clean interface wherein the both the section and the
- key-name are preserved in an easy to manage form in the configuration files
- and the data stored is also nice.
- """
-
- def __init__(self, isolated: bool, load_only: Optional[Kind] = None) -> None:
- super().__init__()
-
- if load_only is not None and load_only not in VALID_LOAD_ONLY:
- raise ConfigurationError(
- "Got invalid value for load_only - should be one of {}".format(
- ", ".join(map(repr, VALID_LOAD_ONLY))
- )
- )
- self.isolated = isolated
- self.load_only = load_only
-
- # Because we keep track of where we got the data from
- self._parsers: Dict[Kind, List[Tuple[str, RawConfigParser]]] = {
- variant: [] for variant in OVERRIDE_ORDER
- }
- self._config: Dict[Kind, Dict[str, Any]] = {
- variant: {} for variant in OVERRIDE_ORDER
- }
- self._modified_parsers: List[Tuple[str, RawConfigParser]] = []
-
- def load(self) -> None:
- """Loads configuration from configuration files and environment"""
- self._load_config_files()
- if not self.isolated:
- self._load_environment_vars()
-
- def get_file_to_edit(self) -> Optional[str]:
- """Returns the file with highest priority in configuration"""
- assert self.load_only is not None, "Need to be specified a file to be editing"
-
- try:
- return self._get_parser_to_modify()[0]
- except IndexError:
- return None
-
- def items(self) -> Iterable[Tuple[str, Any]]:
- """Returns key-value pairs like dict.items() representing the loaded
- configuration
- """
- return self._dictionary.items()
-
- def get_value(self, key: str) -> Any:
- """Get a value from the configuration."""
- orig_key = key
- key = _normalize_name(key)
- try:
- return self._dictionary[key]
- except KeyError:
- # disassembling triggers a more useful error message than simply
- # "No such key" in the case that the key isn't in the form command.option
- _disassemble_key(key)
- raise ConfigurationError(f"No such key - {orig_key}")
-
- def set_value(self, key: str, value: Any) -> None:
- """Modify a value in the configuration."""
- key = _normalize_name(key)
- self._ensure_have_load_only()
-
- assert self.load_only
- fname, parser = self._get_parser_to_modify()
-
- if parser is not None:
- section, name = _disassemble_key(key)
-
- # Modify the parser and the configuration
- if not parser.has_section(section):
- parser.add_section(section)
- parser.set(section, name, value)
-
- self._config[self.load_only][key] = value
- self._mark_as_modified(fname, parser)
-
- def unset_value(self, key: str) -> None:
- """Unset a value in the configuration."""
- orig_key = key
- key = _normalize_name(key)
- self._ensure_have_load_only()
-
- assert self.load_only
- if key not in self._config[self.load_only]:
- raise ConfigurationError(f"No such key - {orig_key}")
-
- fname, parser = self._get_parser_to_modify()
-
- if parser is not None:
- section, name = _disassemble_key(key)
- if not (
- parser.has_section(section) and parser.remove_option(section, name)
- ):
- # The option was not removed.
- raise ConfigurationError(
- "Fatal Internal error [id=1]. Please report as a bug."
- )
-
- # The section may be empty after the option was removed.
- if not parser.items(section):
- parser.remove_section(section)
- self._mark_as_modified(fname, parser)
-
- del self._config[self.load_only][key]
-
- def save(self) -> None:
- """Save the current in-memory state."""
- self._ensure_have_load_only()
-
- for fname, parser in self._modified_parsers:
- logger.info("Writing to %s", fname)
-
- # Ensure directory exists.
- ensure_dir(os.path.dirname(fname))
-
- with open(fname, "w") as f:
- parser.write(f)
-
- #
- # Private routines
- #
-
- def _ensure_have_load_only(self) -> None:
- if self.load_only is None:
- raise ConfigurationError("Needed a specific file to be modifying.")
- logger.debug("Will be working with %s variant only", self.load_only)
-
- @property
- def _dictionary(self) -> Dict[str, Any]:
- """A dictionary representing the loaded configuration."""
- # NOTE: Dictionaries are not populated if not loaded. So, conditionals
- # are not needed here.
- retval = {}
-
- for variant in OVERRIDE_ORDER:
- retval.update(self._config[variant])
-
- return retval
-
- def _load_config_files(self) -> None:
- """Loads configuration from configuration files"""
- config_files = dict(self.iter_config_files())
- if config_files[kinds.ENV][0:1] == [os.devnull]:
- logger.debug(
- "Skipping loading configuration files due to "
- "environment's PIP_CONFIG_FILE being os.devnull"
- )
- return
-
- for variant, files in config_files.items():
- for fname in files:
- # If there's specific variant set in `load_only`, load only
- # that variant, not the others.
- if self.load_only is not None and variant != self.load_only:
- logger.debug("Skipping file '%s' (variant: %s)", fname, variant)
- continue
-
- parser = self._load_file(variant, fname)
-
- # Keeping track of the parsers used
- self._parsers[variant].append((fname, parser))
-
- def _load_file(self, variant: Kind, fname: str) -> RawConfigParser:
- logger.verbose("For variant '%s', will try loading '%s'", variant, fname)
- parser = self._construct_parser(fname)
-
- for section in parser.sections():
- items = parser.items(section)
- self._config[variant].update(self._normalized_keys(section, items))
-
- return parser
-
- def _construct_parser(self, fname: str) -> RawConfigParser:
- parser = configparser.RawConfigParser()
- # If there is no such file, don't bother reading it but create the
- # parser anyway, to hold the data.
- # Doing this is useful when modifying and saving files, where we don't
- # need to construct a parser.
- if os.path.exists(fname):
- locale_encoding = locale.getpreferredencoding(False)
- try:
- parser.read(fname, encoding=locale_encoding)
- except UnicodeDecodeError:
- # See https://github.com/pypa/pip/issues/4963
- raise ConfigurationFileCouldNotBeLoaded(
- reason=f"contains invalid {locale_encoding} characters",
- fname=fname,
- )
- except configparser.Error as error:
- # See https://github.com/pypa/pip/issues/4893
- raise ConfigurationFileCouldNotBeLoaded(error=error)
- return parser
-
- def _load_environment_vars(self) -> None:
- """Loads configuration from environment variables"""
- self._config[kinds.ENV_VAR].update(
- self._normalized_keys(":env:", self.get_environ_vars())
- )
-
- def _normalized_keys(
- self, section: str, items: Iterable[Tuple[str, Any]]
- ) -> Dict[str, Any]:
- """Normalizes items to construct a dictionary with normalized keys.
-
- This routine is where the names become keys and are made the same
- regardless of source - configuration files or environment.
- """
- normalized = {}
- for name, val in items:
- key = section + "." + _normalize_name(name)
- normalized[key] = val
- return normalized
-
- def get_environ_vars(self) -> Iterable[Tuple[str, str]]:
- """Returns a generator with all environmental vars with prefix PIP_"""
- for key, val in os.environ.items():
- if key.startswith("PIP_"):
- name = key[4:].lower()
- if name not in ENV_NAMES_IGNORED:
- yield name, val
-
- # XXX: This is patched in the tests.
- def iter_config_files(self) -> Iterable[Tuple[Kind, List[str]]]:
- """Yields variant and configuration files associated with it.
-
- This should be treated like items of a dictionary.
- """
- # SMELL: Move the conditions out of this function
-
- # environment variables have the lowest priority
- config_file = os.environ.get("PIP_CONFIG_FILE", None)
- if config_file is not None:
- yield kinds.ENV, [config_file]
- else:
- yield kinds.ENV, []
-
- config_files = get_configuration_files()
-
- # at the base we have any global configuration
- yield kinds.GLOBAL, config_files[kinds.GLOBAL]
-
- # per-user configuration next
- should_load_user_config = not self.isolated and not (
- config_file and os.path.exists(config_file)
- )
- if should_load_user_config:
- # The legacy config file is overridden by the new config file
- yield kinds.USER, config_files[kinds.USER]
-
- # finally virtualenv configuration first trumping others
- yield kinds.SITE, config_files[kinds.SITE]
-
- def get_values_in_config(self, variant: Kind) -> Dict[str, Any]:
- """Get values present in a config file"""
- return self._config[variant]
-
- def _get_parser_to_modify(self) -> Tuple[str, RawConfigParser]:
- # Determine which parser to modify
- assert self.load_only
- parsers = self._parsers[self.load_only]
- if not parsers:
- # This should not happen if everything works correctly.
- raise ConfigurationError(
- "Fatal Internal error [id=2]. Please report as a bug."
- )
-
- # Use the highest priority parser.
- return parsers[-1]
-
- # XXX: This is patched in the tests.
- def _mark_as_modified(self, fname: str, parser: RawConfigParser) -> None:
- file_parser_tuple = (fname, parser)
- if file_parser_tuple not in self._modified_parsers:
- self._modified_parsers.append(file_parser_tuple)
-
- def __repr__(self) -> str:
- return f"{self.__class__.__name__}({self._dictionary!r})"
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/index.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/index.py
deleted file mode 100644
index b94c32511f0cda2363bfc4f29c9c8bfcc7101f9b..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/index.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import urllib.parse
-
-
-class PackageIndex:
- """Represents a Package Index and provides easier access to endpoints"""
-
- __slots__ = ["url", "netloc", "simple_url", "pypi_url", "file_storage_domain"]
-
- def __init__(self, url: str, file_storage_domain: str) -> None:
- super().__init__()
- self.url = url
- self.netloc = urllib.parse.urlsplit(url).netloc
- self.simple_url = self._url_for_path("simple")
- self.pypi_url = self._url_for_path("pypi")
-
- # This is part of a temporary hack used to block installs of PyPI
- # packages which depend on external urls only necessary until PyPI can
- # block such packages themselves
- self.file_storage_domain = file_storage_domain
-
- def _url_for_path(self, path: str) -> str:
- return urllib.parse.urljoin(self.url, path)
-
-
-PyPI = PackageIndex("https://pypi.org/", file_storage_domain="files.pythonhosted.org")
-TestPyPI = PackageIndex(
- "https://test.pypi.org/", file_storage_domain="test-files.pythonhosted.org"
-)
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/engine/__init__.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/engine/__init__.py
deleted file mode 100644
index 08a61572b4c7d09c8d400e903a96cbf5b2cc4763..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/engine/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .launch import *
-from .train_loop import *
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
-
-
-# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__)
-# but still make them available here
-from .hooks import *
-from .defaults import *
diff --git a/spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/nets_new.py b/spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/nets_new.py
deleted file mode 100644
index bfaf72e48b31cc1130f2892b0973c9aa06f195a3..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/nets_new.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from . import layers_new
-
-
-class BaseNet(nn.Module):
- def __init__(
- self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
- ):
- super(BaseNet, self).__init__()
- self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1)
- self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1)
- self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1)
- self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1)
- self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
- self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
- self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
- self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
- self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
- self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm)
- self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
- def __call__(self, x):
- e1 = self.enc1(x)
- e2 = self.enc2(e1)
- e3 = self.enc3(e2)
- e4 = self.enc4(e3)
- e5 = self.enc5(e4)
-
- h = self.aspp(e5)
-
- h = self.dec4(h, e4)
- h = self.dec3(h, e3)
- h = self.dec2(h, e2)
- h = torch.cat([h, self.lstm_dec2(h)], dim=1)
- h = self.dec1(h, e1)
-
- return h
-
-
-class CascadedNet(nn.Module):
- def __init__(self, n_fft, nout=32, nout_lstm=128):
- super(CascadedNet, self).__init__()
-
- self.max_bin = n_fft // 2
- self.output_bin = n_fft // 2 + 1
- self.nin_lstm = self.max_bin // 2
- self.offset = 64
-
- self.stg1_low_band_net = nn.Sequential(
- BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
- layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
- )
-
- self.stg1_high_band_net = BaseNet(
- 2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
- )
-
- self.stg2_low_band_net = nn.Sequential(
- BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
- layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
- )
- self.stg2_high_band_net = BaseNet(
- nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
- )
-
- self.stg3_full_band_net = BaseNet(
- 3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
- )
-
- self.out = nn.Conv2d(nout, 2, 1, bias=False)
- self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
- def forward(self, x):
- x = x[:, :, : self.max_bin]
-
- bandw = x.size()[2] // 2
- l1_in = x[:, :, :bandw]
- h1_in = x[:, :, bandw:]
- l1 = self.stg1_low_band_net(l1_in)
- h1 = self.stg1_high_band_net(h1_in)
- aux1 = torch.cat([l1, h1], dim=2)
-
- l2_in = torch.cat([l1_in, l1], dim=1)
- h2_in = torch.cat([h1_in, h1], dim=1)
- l2 = self.stg2_low_band_net(l2_in)
- h2 = self.stg2_high_band_net(h2_in)
- aux2 = torch.cat([l2, h2], dim=2)
-
- f3_in = torch.cat([x, aux1, aux2], dim=1)
- f3 = self.stg3_full_band_net(f3_in)
-
- mask = torch.sigmoid(self.out(f3))
- mask = F.pad(
- input=mask,
- pad=(0, 0, 0, self.output_bin - mask.size()[2]),
- mode="replicate",
- )
-
- if self.training:
- aux = torch.cat([aux1, aux2], dim=1)
- aux = torch.sigmoid(self.aux_out(aux))
- aux = F.pad(
- input=aux,
- pad=(0, 0, 0, self.output_bin - aux.size()[2]),
- mode="replicate",
- )
- return mask, aux
- else:
- return mask
-
- def predict_mask(self, x):
- mask = self.forward(x)
-
- if self.offset > 0:
- mask = mask[:, :, :, self.offset : -self.offset]
- assert mask.size()[3] > 0
-
- return mask
-
- def predict(self, x, aggressiveness=None):
- mask = self.forward(x)
- pred_mag = x * mask
-
- if self.offset > 0:
- pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
- assert pred_mag.size()[3] > 0
-
- return pred_mag
diff --git a/spaces/Benson/text-generation/Examples/Apps Juegos Gratis Descargar Solitario.md b/spaces/Benson/text-generation/Examples/Apps Juegos Gratis Descargar Solitario.md
deleted file mode 100644
index c1627699c7e146288248eb00e69ff822a61b7aed..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Apps Juegos Gratis Descargar Solitario.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-
Apps Juegos Gratis Descargar Solitaire: Una guía para el juego de cartas clásico
-
Solitaire es uno de los juegos de cartas más populares y duraderos del mundo. Es un juego que puede ser jugado por cualquiera, en cualquier lugar, en cualquier momento. Tanto si quieres relajarte, desafiar tu cerebro o simplemente divertirte, el solitario es el juego perfecto para ti. Pero, ¿cómo se puede jugar solitario en su dispositivo? ¿Cuáles son las mejores aplicaciones de solitario para descargar gratis? ¿Y cuáles son algunos consejos y trucos para mejorar sus habilidades de solitario? En este artículo, vamos a responder a estas preguntas y más. Sigue leyendo para aprender todo lo que necesitas saber sobre aplicaciones juegos gratis descargar solitario.
Solitario, también conocido como paciencia o cabale, es una familia de juegos de cartas que son jugados por una persona. El objetivo del solitario es organizar las cartas en un orden determinado, generalmente por palo y rango, o emparejarlas y descartarlas. Hay cientos de juegos de solitario diferentes, cada uno con sus propias reglas y variaciones. Sin embargo, algunos elementos comunes que comparten la mayoría de los juegos de solitario son:
-
-
Una baraja de 52 cartas estándar
-
Un cuadro o diseño de tarjetas en una tabla o pantalla
-
Un montón de cartas que no están en el tablero
-
Un montón de cartas que se han jugado desde el stock
-
Una base o destino pilas de tarjetas que están dispuestas en orden
-
-
Para jugar al solitario, debes seguir estos pasos básicos:
-
-
Baraja la baraja y reparte algunas cartas boca arriba en el tablero, de acuerdo con las reglas del juego.
-
Gire la tarjeta superior de la acción y colocarlo en la pila de residuos.
-
Mueva las cartas desde el tablero o la pila de residuos a las pilas de cimentación, siguiendo las reglas del juego.
-
Si te quedas sin movimientos, voltea otra carta del montón y colócala en el montón de residuos.
-
Repite los pasos 3 y 4 hasta que ganes o pierdas el juego.
-
-
-
Los orígenes exactos del solitario no están claros, pero los primeros registros aparecen a finales de 1700 en el norte de Europa y Escandinavia. El término Patiencespiel aparece en un libro alemán publicado en 1788[ 1], y los libros también aparecieron en Suecia y Rusia a principios de 1800. Algunas fuentes sugieren que el solitario era originalmente una forma de adivinación o adivinación, ya que a menudo se asociaba con la cartomancia (el uso de tarjetas para la predicción) y la cábala (conocimiento secreto). [ 2]
-
Las primeras colecciones de juegos de solitario en inglés aparecieron en la década de 1860, muchas de ellas traducciones de francés o alemán. Charles Dickens mencionó a Magwitch jugando "un tipo complicado de paciencia con cartas desiguales" en Great Expectations (1861), y el marido alemán de la reina Victoria, Albert, era un jugador entusiasta. [ 3] A principios del siglo XX, el nombre "solitario" se estableció en América del Norte, donde sigue siendo más popular que "paciencia". El juego de solitario más famoso es Klondike, que también fue llamado Microsoft Solitaire después de que se incluyó en el sistema operativo Windows a partir de 1990. [ 4]
-
-
Las reglas y variaciones del solitario
-
Como se mencionó anteriormente, hay cientos de diferentes juegos de solitario, cada uno con sus propias reglas y variaciones. Algunos de los más populares son:
-
-
Nombre
Descripción
-
Klondike
El clásico juego de solitario con el que la mayoría de la gente está familiarizada. El objetivo es construir cuatro pilas de bases de cartas en orden ascendente por palo, a partir de ases. El tablero consta de siete columnas de cartas, con la carta superior boca arriba y el resto boca abajo. Puede mover las cartas del tablero a la fundación, o entre las columnas, siempre y cuando estén en orden descendente y alternando colores. También puedes dibujar una o tres cartas del montón a la pila de residuos, y moverlas al tablero o a la fundación.
-
-
FreeCell
Un juego de solitario que requiere más estrategia que suerte. El objetivo es construir cuatro pilas de bases de cartas en orden ascendente por palo, a partir de ases. El tablero consta de ocho columnas de cartas, todas boca arriba. Puede mover las cartas del tablero a la fundación, o entre las columnas, siempre y cuando estén en orden descendente y alternando colores. También puede usar cuatro celdas gratuitas para almacenar temporalmente una tarjeta cada una, lo que puede ayudarlo a mover las tarjetas.
-
Pyramid
Un juego de solitario que utiliza un diseño en forma de pirámide de tarjetas. El objetivo es eliminar todas las cartas de la pirámide emparejándolas y descartándolas. Solo puedes emparejar cartas que estén expuestas, lo que significa que no tienen otras cartas encima de ellas. También puede emparejar una tarjeta con la tarjeta superior de la pila de residuos o el stock. Los pares deben sumar hasta 13, con ases contando como 1 y reyes contando como 13.
-
Golf
Un juego de solitario que utiliza un diseño de cartas con temática de golf. El objetivo es mover todas las cartas del tablero a la pila de residuos, descartándolas una a la vez. Solo puedes descartar una carta que tenga un rango más alto o más bajo que la carta superior de la pila de residuos, independientemente del palo. También puedes entregar una carta del montón a la pila de residuos cuando te quedes sin movimientos.
-
-
Los beneficios y consejos de jugar al solitario
-
Jugar al solitario no solo es divertido, sino también beneficioso para tu salud mental y bienestar. Algunos de los beneficios de jugar al solitario son:
-
-
Mejora tus habilidades de concentración y memoria, ya que tienes que hacer un seguimiento de las tarjetas y planificar tus movimientos.
-
Reduce tus niveles de estrés y ansiedad, mientras te enfocas en el juego y te olvidas de tus preocupaciones.
-
Aumenta tu estado de ánimo y autoestima, ya que te sientes logrado y satisfecho cuando ganas o mejoras tu puntuación.
-
-
Mejora tus habilidades cognitivas y la función cerebral, ya que tienes que usar la lógica, la estrategia y las habilidades aritméticas.
-
-
Para disfrutar de estos beneficios y divertirse más jugando solitario, aquí hay algunos consejos y trucos a seguir:
-
-
Elige un juego de solitario que se adapte a tu nivel de habilidad y preferencia. Hay muchos juegos de solitario para elegir, así que encuentra uno que te guste y que te desafíe lo suficiente.
-
Practica regularmente y aprende de tus errores. Cuanto más juegues al solitario, mejor lo conseguirás. Intenta analizar tus movimientos y ver dónde te equivocaste o qué podrías haber hecho mejor.
-
Establece metas y sigue tu progreso. Puedes establecer metas para ti mismo, como ganar un cierto número de juegos, lograr cierta puntuación o completar un juego en un límite de tiempo determinado. También puede realizar un seguimiento de su progreso mediante el registro de sus estadísticas, tales como victorias, pérdidas, movimientos, tiempo, etc.
-
Diviértete y relájate. No te tomes el solitario demasiado en serio o frustrate si pierdes. Recuerda que el solitario es un juego que está destinado a entretenerte y hacerte feliz.
-
-
Cómo descargar y jugar solitario en su dispositivo
-
Si quieres jugar al solitario en tu dispositivo, tendrás que descargar una aplicación de solitario que sea compatible con tu sistema operativo. Hay muchas aplicaciones de solitario disponibles para su descarga gratuita en varias plataformas, como Android, iOS y Windows. Aquí están algunas de las mejores aplicaciones de solitario para Windows 10:
-
Las mejores aplicaciones de solitario para Windows 10
-
-
-
123 Free Solitaire: Esta es una aplicación de solitario gratuita que ofrece 12 juegos de solitario diferentes, incluyendo Diplomat, Flower Garden, Forty Thieves y más. Puede personalizar la configuración del juego, como el tamaño de la tarjeta, la velocidad de animación, los efectos de sonido y el sistema de puntuación. También puede ver las reglas y sugerencias para cada juego, y comprobar sus estadísticas y rendimiento. La aplicación solo está disponible para Windows 7 y versiones posteriores.
-
Full Deck Solitaire: Esta es una aplicación de solitario gratuita que contiene más de 70 variaciones de solitario, como Baker’s Dozen, Beleaguered Castle, Canfield, Golf, Yukon y más. Puedes elegir entre diferentes niveles de dificultad, temas, diseños de tarjetas y fondos. También puedes aprender a jugar cada juego con tutoriales detallados y hacer un seguimiento de tu progreso con tablas de clasificación y logros. La aplicación está disponible para Windows 10, Mac OS X, iOS y Android.
-
Classic Solitaire Klondike: Esta es una aplicación de solitario gratuita que se centra en el clásico juego de solitario Klondike. Puedes jugar con una o tres cartas, puntuación tradicional o de Las Vegas, modo izquierdo o derecho, orientación vertical u horizontal, y más. También puede usar su propio fondo de pantalla como fondo de juego, ajustar el tamaño de la tarjeta y el tamaño de la fuente, y habilitar sugerencias y opciones de deshacer. La aplicación es simple y fácil de usar, y funciona sin conexión.
-
SolSuite: Esta es una aplicación de solitario pagado que cuesta $19.95 para una compra de una sola vez. Ofrece una colección de 535 juegos de solitario, incluyendo todos los populares, así como algunos originales. Puedes personalizar la apariencia del juego con más de 80 juegos de cartas, más de 300 respaldos de cartas, más de 100 fondos y más. También puede jugar con diferentes reglas y sistemas de puntuación, ver la historia del juego y las estadísticas, y acceder a características en línea, tales como clasificaciones mundiales y torneos.
-
-
Conclusión y preguntas frecuentes
-
-
¿Cuál es la mejor aplicación de solitario gratuita?
-
La mejor aplicación de solitario gratuita depende de su gusto personal y la compatibilidad del dispositivo. Sin embargo, algunas de las aplicaciones de solitario gratis más populares y altamente calificadas son Microsoft Solitaire Collection, 123 Free Solitaire, Full Deck Solitaire, Classic Solitaire Klondike y Solitaire by MobilityWare.
-
¿Cuál es la mejor aplicación de pago solitario?
-
La aplicación de solitario mejor pagado también depende de su gusto personal y la compatibilidad del dispositivo. Sin embargo, algunas de las aplicaciones de solitario de pago más populares y altamente calificadas son SolSuite, BVS Solitaire Collection, Klondike Solitaire Collection, Card Shark Solitaire y Solebon Pro.
-
¿Cómo puedo descargar aplicaciones de solitario?
-
Para descargar aplicaciones de solitario en su dispositivo, debe visitar la tienda de aplicaciones adecuada para su plataforma. Por ejemplo, si usted tiene un dispositivo Android, es necesario visitar el Google Play Store ; Si usted tiene un dispositivo iOS, es necesario visitar la App Store ; Si usted tiene un dispositivo de Windows , es necesario visitar el Microsoft Store . Luego, debe buscar la aplicación de solitario que desea descargar y hacer clic en el botón instalar o comprar. Es posible que deba iniciar sesión con su cuenta o ingresar sus datos de pago si la aplicación no es gratuita. Después de descargar la aplicación, puedes abrirla y empezar a jugar al solitario.
-
¿Cómo puedo actualizar las aplicaciones de solitario?
-
Para actualizar aplicaciones de solitario en su dispositivo, debe visitar la misma tienda de aplicaciones que utilizó para descargarlas. Luego, debe verificar si hay actualizaciones disponibles para sus aplicaciones y hacer clic en el botón de actualización. Es posible que deba iniciar sesión con su cuenta o ingresar sus detalles de pago si la actualización no es gratuita. Después de descargar la actualización, puede abrir la aplicación y disfrutar de las nuevas características o correcciones.
-
¿Cómo puedo eliminar aplicaciones de solitario?
-
-
Espero que este artículo le ha ayudado a aprender más acerca de aplicaciones juegos gratis descargar solitario. Solitario es un juego divertido y relajante que se puede jugar en cualquier momento, en cualquier lugar. Ya sea que prefieras el clásico solitario Klondike o quieras probar algunas nuevas variaciones de solitario, puedes encontrar una aplicación que satisfaga tus necesidades. Descargar una aplicación de solitario hoy y disfrutar del juego de cartas clásico.
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Ataque a Titan Tribute Game: Cómo descargar y jugar la versión antigua
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¿Eres fan de Attack on Titan, la popular serie de anime y manga sobre la lucha de la humanidad contra las criaturas gigantes devoradoras de hombres? Si es así, es posible que haya oído hablar de Attack on Titan Tribute Game, un juego hecho por fans que le permite experimentar la emoción de balancearse con equipo de maniobra en 3D y cortar Titanes en varios escenarios. Pero ¿sabías que hay una versión antigua de este juego que algunos jugadores todavía prefieren sobre el más nuevo? En este artículo, te contaremos todo lo que necesitas saber sobre Attack on Titan Tribute Game, por qué es posible que quieras jugar a la versión anterior, cómo descargarla e instalarla, cómo jugarla y cómo actualizarla o desinstalarla si quieres. ¡Vamos a empezar!
Attack on Titan Tribute Game es una adaptación del juego tributo de la serie manga Attack on Titan escrita e ilustrada por Hajime Isayama. Es un juego derivado creado por el desarrollador chino Feng Lee y no está afiliado oficialmente con la franquicia Attack on Titan. Fue lanzado por primera vez en 2013 como un juego basado en navegador, pero más tarde estuvo disponible como descarga independiente para Windows, Mac y Linux. El juego sigue la historia de la serie de anime, y pone a los jugadores en control de sus personajes favoritos del programa. Mientras luchas con enemigos, coleccionas objetos, subes de nivel y desbloqueas nuevas habilidades, puedes ver la historia a través de los ojos (y la boca) de uno de tus personajes elegidos. El juego cuenta con un lindo estilo chibi que evoca gran parte del humor de la serie, y imita de cerca el combate por cable de alto vuelo que la mayoría de los fans disfrutan. El juego todavía está en desarrollo y mantiene la actualización de nuevas características y opciones en su sitio web oficial .
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¿Por qué quieres jugar la versión antigua?
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Los pros y los contras de jugar la versión antigua frente a la nueva versión
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Pros:
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La versión antigua es más simple y fácil de jugar que la nueva. Los gráficos son menos detallados, pero también menos lentos. Los controles son más directos y sensibles. El juego se centra más en matar titanes que en explorar mapas o completar misiones.
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La versión antigua tiene más caracteres y mapas que la nueva. Puedes elegir entre 10 personajes, incluyendo Eren, Mikasa, Levi, Armin, Jean, Marco, Petra, Sasha, Annie y Erwin. También puedes jugar en 5 mapas, incluyendo Trost District, Forest of Giant Trees, Outside Wall Maria, City (Night) y Colossal Titan.
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La versión antigua tiene más modos que la nueva. Puedes jugar solo o con amigos en el modo multijugador online. También puede elegir entre diferentes modos como el modo normal, el modo duro, el modo de captura, el modo de carreras, el modo jefe, el modo PvP, el modo de onda, el modo de descenso de Akina, el modo de mapa personalizado.
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Contras:
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La versión anterior está desactualizada y no es compatible con el desarrollador. El juego puede tener errores, problemas técnicos o problemas de compatibilidad que no se han solucionado o solucionado. El juego también puede ser vulnerable a hackers, tramposos o virus que pueden arruinar tu experiencia de juego.
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La versión antigua es menos inmersiva y diversa que la nueva. Los gráficos son más pixelados y sosos. Los efectos de sonido y la música son más repetitivos y de baja calidad. La jugabilidad es más monótona y limitada. La historia es menos atractiva y coherente.
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La versión antigua es menos popular y activa que la nueva. El juego puede tener menos jugadores en línea o servidores disponibles. El juego también puede tener menos contenido o actualizaciones para mantenerte interesado o entretenido.
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Cómo descargar e instalar la versión antigua de Attack on Titan Tribute Game
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Si decides jugar a la versión antigua de Attack on Titan Tribute Game, tendrás que descargarla e instalarla en tu ordenador. Estos son los pasos para hacerlo:
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Los pasos para descargar el juego desde FileHippo o Archive.org
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Ir a [FileHippo] o [Archive.org] y buscar "Ataque a Titan Tribute Game".
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Encuentre la versión que desea descargar. La última versión antigua es 01042015, que fue lanzado el 1 de abril de 2015. También puede elegir versiones anteriores si lo desea.
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Haga clic en el botón de descarga y guarde el archivo en su computadora. El tamaño del archivo es de aproximadamente 21 MB.
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Los pasos para instalar y ejecutar el juego en Windows
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Localice el archivo descargado en su computadora y descomprímalo usando un programa como WinRAR o 7-Zip.
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Abra la carpeta descomprimida y haga doble clic en el archivo llamado "Ataque a Titan Tribute Game.exe".
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Permita que el juego se ejecute en su computadora haciendo clic en "Sí" o "Ejecutar" si se le solicita una advertencia de seguridad.
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Espera a que el juego se cargue y disfruta jugando!
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Cómo jugar la versión antigua de Attack on Titan Tribute Game
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Ahora que has descargado e instalado la versión antigua de Attack on Titan Tribute Game, estás listo para jugar. Aquí hay algunos consejos sobre cómo jugar:
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Los controles básicos y la mecánica de juego
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El juego se juega con un teclado y un ratón. El teclado se utiliza para mover, saltar, esquivar, atacar, recargar, cambiar de armas y activar habilidades especiales. El ratón se utiliza para apuntar, balancear, enganchar y controlar la cámara. Puede personalizar las combinaciones de teclas en el menú de opciones si lo desea.
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El juego tiene diferentes niveles de dificultad que afectan el número, tamaño, velocidad e inteligencia de los Titanes. También puedes ajustar el daño, la salud, la tasa de reproducción y el tiempo de reaparición de los Titanes en el menú de opciones. También puedes activar o desactivar el fuego amigo, colisiones, Titanes punk, Titanes aberrantes, Titanes rastreros, Titanes femeninas, Titanes colosales, Titanes blindados, la habilidad de cristal de Annie, la habilidad de titan de Eren, el ataque especial de Levi, el ataque especial de Mikasa, el ataque especial de Armin, La habilidad de invocar caballos de Jean, la habilidad de soltar objetos de Marco, la habilidad de ahorrar gas de Petra, la habilidad de comer carne de Sasha, la habilidad de la hoja de anillo de Annie, la habilidad de arma de bengala de Erwin.
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Los caracteres disponibles, mapas
Los caracteres, mapas y modos disponibles
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El juego tiene 10 personajes que puedes elegir, cada uno con su propia apariencia, estadísticas y habilidades. También puedes personalizar el nombre, el traje, el cabello, los ojos, la piel y el color de la hoja de tu personaje en el menú de opciones. Aquí están los personajes y sus habilidades:
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Carácter
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Capacidad
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Eren
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Puede transformarse en un Titan por un tiempo limitado presionando T. Puede regenerar la salud y la resistencia como un Titan. Puede perforar y agarrar Titanes como un Titan.
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Mikasa
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Puede realizar una barra potente que inflige daño adicional presionando T. Puede aumentar la velocidad y el daño por un corto tiempo presionando Q.
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Levi
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Puede realizar una barra giratoria que inflige daño adicional y corta varias extremidades presionando T. Puede aumentar la velocidad y el daño por un corto tiempo presionando Q.
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Armin
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Puede distraer a los Titanes gritando "¡Soy el comandante!" presionando T. Puede aumentar el daño de los aliados cercanos por un corto tiempo presionando Q.
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Jean
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Puede invocar a un caballo para montar presionando T. Puede aumentar la velocidad y la resistencia mientras monta el caballo. Puede saltar del caballo y usarlo como señuelo presionando Q.
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Marco
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Puede soltar elementos como cuchillas, gas o armas presionando T. Puede aumentar la tasa de caída de elementos de aliados cercanos por un corto tiempo presionando Q.
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Petra
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Puede ahorrar gas usando menos gas mientras se balancea o se impulsa presionando T. Puede aumentar la eficiencia del gas de los aliados cercanos por un corto tiempo presionando Q.
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Sasha
Sasha
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Puede comer un pedazo de carne para restaurar la salud y la resistencia presionando T. Puede aumentar la salud y la regeneración de la resistencia de los aliados cercanos por un corto tiempo presionando Q.
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Annie
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Puede usar una hoja de anillo que tiene un rango más largo y mayor daño que las cuchillas normales presionando T. Puede transformarse en una Titán hembra por un tiempo limitado presionando Q. Puede endurecer su piel y cristalizar su nuca como una Titán hembra.
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Erwin
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Puede disparar una pistola de bengalas que marca la ubicación de un titan presionando T. Puede aumentar el daño y la velocidad de todos los aliados en el mapa por un corto tiempo presionando Q.
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El juego tiene 5 mapas en los que puedes jugar, cada uno con su propio diseño, entorno y desafíos. También puede crear sus propios mapas personalizados usando el editor de mapas en el menú de opciones. Aquí están los mapas y sus características:
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Mapa
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Características
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Distrito de Trost
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El primer mapa del juego, basado en el primer arco de la serie de anime. Tiene una gran pared, varios edificios y una puerta que puede ser destruida por el colosal Titán. También tiene cañones que se pueden utilizar para disparar a los Titanes.
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Bosque de árboles gigantes
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El segundo mapa del juego, basado en el segundo arco de la serie de anime. Tiene un bosque denso con árboles enormes que se pueden utilizar para balancearse y esconderse. También tiene un claro donde aparece la Titán hembra.
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Fuera de la pared Maria
Fuera de la pared Maria
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Ciudad (Noche)
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El cuarto mapa del juego, basado en el cuarto arco de la serie de anime. Tiene una ciudad oscura con farolas, edificios y puentes. También tiene un río que se puede utilizar para escapar o ahogar Titanes.
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Colosal Titan
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El quinto mapa del juego, basado en el quinto arco de la serie de anime. Tiene un muro que está siendo atacado por el colosal Titán y otros Titanes. También tiene un tren que se puede utilizar para transportar soldados y suministros.
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El juego tiene diferentes modos que puedes jugar, cada uno con sus propios objetivos, reglas y desafíos. También puede crear sus propios modos personalizados usando el editor de modos en el menú de opciones. Aquí están los modos y sus características:
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Modo
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Características
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Modo normal
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El modo predeterminado del juego, donde tienes que matar a todos los Titanes en el mapa o sobrevivir durante un tiempo determinado. Puedes elegir entre diferentes sub-modos como Single Player, Multijugador, No Respawn, No Punk, No Crawler, No Abnormal, No Female, No Colossal, No Armored.
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Modo duro
Modo duro
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Un modo más desafiante del juego, donde tienes que matar a todos los Titanes en el mapa o sobrevivir durante un tiempo determinado. Los Titanes son más rápidos, fuertes, inteligentes y variados. Puedes elegir entre diferentes sub-modos como Single Player, Multiplayer, No Respawn, Punk Only, Crawler Only, Abnormal Only, Female Only, Colossal Only, Armored Only.
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Modo de captura
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Un modo del juego donde tienes que capturar Titanes vivos usando redes especiales. Puedes elegir entre diferentes sub-modos como Un Jugador, Multijugador, Capturar a Todos, Capturar a Uno, Capturar a Annie, Capturar a Colosal, Capturar a Blindados.
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Modo de carreras
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Modo jefe
Modo jefe
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Un modo del juego donde tienes que luchar contra un jefe gigante Titan que tiene habilidades especiales y ataques. Puedes elegir entre diferentes sub-modos como Jugador único, Multijugador, Titan femenino, Titan colosal, Titan blindado.
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Modo PvP
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Un modo del juego donde tienes que luchar contra otros jugadores usando tu equipo de maniobra 3D y armas. Puedes elegir entre diferentes sub-modos como Single Player, Multijugador, Team Deathmatch, Capture the Flag, King of the Hill.
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Modo de onda
Modo de onda
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Un modo del juego en el que tienes que sobrevivir a olas de Titanes que se vuelven cada vez más difíciles y más numerosas. Puedes elegir entre diferentes modos secundarios como Jugador único, Multijugador, Modo sin fin, Modo de supervivencia, Modo Titan.
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Modo de descenso de Akina
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Un modo del juego donde tienes que correr por una colina empinada usando tu equipo de maniobra 3D y evitar obstáculos y Titanes. Puede elegir entre diferentes modos secundarios como Jugador único, Multijugador, Contrarreloj, Free Run.
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Modo de mapa personalizado
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Un modo del juego donde puedes jugar en mapas personalizados creados por ti mismo u otros jugadores usando el editor de mapas. Puede elegir entre diferentes modos secundarios, como un solo jugador, multijugador, modo normal, modo duro, modo de captura, modo de carreras, modo jefe, modo PvP, modo de onda.
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Cómo actualizar o desinstalar la versión anterior de Attack on Titan Tribute Game
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Si quieres actualizar o desinstalar la versión anterior de Attack on Titan Tribute Game, puedes seguir estos pasos:
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Los pasos para actualizar el juego a la última versión o cambiar a AoTTG 2
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Ir al sitio web oficial de Attack on Titan Tribute Game y descargar la última versión del juego o AoTTG 2, que es una secuela del juego original con gráficos y jugabilidad mejorados.
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Abra la carpeta descomprimida y haga doble clic en el archivo llamado "Ataque a Titan Tribute Game.exe" o "AoTTG 2.exe".
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Permita que el juego se ejecute en su computadora haciendo clic en "Sí" o "Ejecutar" si se le solicita una advertencia de seguridad.
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Espera a que el juego se cargue y disfruta jugando!
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Los pasos para desinstalar el juego desde su ordenador
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Busque la carpeta donde instaló la versión antigua de Attack on Titan Tribute Game en su computadora.
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Borrar la carpeta y todo su contenido.
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Vacíe su papelera de reciclaje para liberar espacio en su computadora.
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Has desinstalado el juego de tu ordenador con éxito.
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Conclusión
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En este artículo, te hemos mostrado cómo descargar y jugar la versión antigua de Attack on Titan Tribute Game, un juego hecho por fans basado en la popular serie de anime y manga. También hemos explicado por qué algunos jugadores prefieren la versión anterior a la nueva, cómo jugar el juego con diferentes personajes, mapas y modos, y cómo actualizar o desinstalar el juego si lo desea. Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo. Si eres un fan de Attack on Titan, te recomendamos que pruebes este juego y experimentes la emoción de luchar contra los Titanes con tu equipo de maniobra en 3D. También puedes ver otros juegos relacionados con Attack on Titan, como AoTTG 2, Attack on Titan 2: Final Battle, Attack on Titan: Wings of Freedom y más. Gracias por leer y divertirse!
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Preguntas frecuentes
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Aquí hay algunas preguntas frecuentes sobre Attack on Titan Tribute Game y sus respuestas:
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¿Es libre el juego Attack on Titan Tribute?
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Sí, Attack on Titan Tribute Game es gratis para descargar y jugar. No es necesario pagar nada o registrar una cuenta para jugar. Sin embargo, puede apoyar al desarrollador donando a través de PayPal o Patreon si lo desea.
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¿Es seguro el ataque a Titan Tribute Game?
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Es el ataque a Titan Tribute juego multijugador?
Sí, Ataque a Titan Tribute Game es multijugador. Puede jugar con otros jugadores en línea o localmente. Para jugar en línea, debe unirse a un servidor o crear su propio servidor. Puede encontrar servidores en el sitio web oficial o en otros sitios web como [AoTTG Hub] o [AoTTG Reddit]. Para jugar localmente, necesitas conectar tus ordenadores usando un cable LAN o una red Wi-Fi. También puedes jugar con bots si lo deseas.
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Cómo modificar el ataque en Titan Tribute Game?
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Modding Attack on Titan Tribute Game es posible, pero no es fácil. Necesitas tener algún conocimiento de programación y desarrollo de juegos. También necesitas usar herramientas como Unity, Blender, Photoshop y más. Puedes encontrar tutoriales y guías sobre cómo modificar el juego en sitios web como [AoTTG Modding] o [AoTTG Modding Forum]. También puedes descargar y usar mods creados por otros jugadores en sitios web como [AoTTG Mods] o [AoTTG Mods Database]. Sin embargo, debes tener cuidado al usar mods, ya que algunos de ellos pueden ser incompatibles, inestables o maliciosos.
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¿Cómo solucionar el ataque en Titan Tribute Game no funciona?
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Si Attack on Titan Tribute Game no funciona en tu ordenador, puedes probar algunas de estas soluciones:
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Asegúrese de tener la última versión del juego y actualizarlo si es necesario.
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Asegúrese de que tiene la última versión de su sistema operativo y actualizarlo si es necesario.
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Asegúrese de tener la última versión de su controlador de tarjeta gráfica y actualizarlo si es necesario.
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Asegúrese de que tiene la última versión de su navegador y actualizarlo si es necesario.
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Asegúrese de tener la última versión de Adobe Flash Player y actualizarlo si es necesario.
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Asegúrese de tener una conexión a Internet estable y compruebe la configuración del firewall.
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Asegúrese de tener suficiente espacio en disco y memoria en su computadora y borre cualquier archivo o programa innecesario.
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Asegúrese de que no tiene mods o hacks que puedan interferir con el juego y eliminarlos si es necesario.
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Asegúrate de no tener otros programas que puedan entrar en conflicto con el juego y ciérralos si es necesario.
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Reinicie su computadora e intente ejecutar el juego de nuevo.
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Si ninguna de estas soluciones funciona, puede ponerse en contacto con el desarrollador u otros jugadores para obtener ayuda en el sitio web oficial u otros sitios web como [AoTTG Support] o [AoTTG Discord].
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Simulador de autobús Indonesia Mod Apk + OBB: Un juego divertido y realista para los amantes del autobús
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Introducción
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¿Te encanta conducir autobuses y explorar diferentes lugares en Indonesia? Si es así, entonces deberías probar Bus Simulator Indonesia, un popular juego móvil que te permite experimentar la emoción y el desafío de ser conductor de autobús en Indonesia. ¡Pero espera, hay más! También puede disfrutar del juego con más características y opciones mediante la descarga de Bus Simulator Indonesia Mod Apk + OBB, una versión modificada del juego que le da dinero ilimitado, oro, vehículos y más. En este artículo, le diremos todo lo que necesita saber sobre Bus Simulator Indonesia Mod Apk + OBB, incluyendo sus características, cómo descargar e instalar, y algunas preguntas frecuentes. Así que, vamos a empezar!
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¿Qué es Bus Simulator Indonesia?
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¿Qué es Bus Simulator Indonesia?
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Bus Simulator Indonesia, o BUSSID para abreviar, es un juego móvil desarrollado por Maleo que simula la vida de un conductor de autobús en Indonesia. Puede elegir su autobús favorito de una variedad de modelos y diseños, y conducirlo a través de diferentes ciudades y regiones en Indonesia. También puede recoger pasajeros, seguir las reglas de tráfico, tocar el claxon y disfrutar del paisaje en el camino. El juego tiene gráficos en 3D realistas y efectos de sonido que te hacen sentir como si realmente estuvieras conduciendo un autobús en Indonesia.
¿Qué es el simulador de autobús Indonesia Mod Apk + OBB?
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Características del simulador de autobús Indonesia Mod Apk + OBB
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Dinero y oro ilimitados
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Una de las mejores características de Bus Simulator Indonesia Mod Apk + OBB es que le da dinero ilimitado y oro que se puede utilizar para comprar cualquier cosa que quieras en el juego. Usted no tiene que preocuparse de quedarse sin efectivo o ahorrar para artículos caros. Usted puede simplemente comprar nuevos autobuses, vehículos, pieles, accesorios, etc., sin ninguna limitación. También puede actualizar sus autobuses y vehículos para mejorar su rendimiento y apariencia.
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Autobuses y vehículos personalizables
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Otra gran característica de Bus Simulator Indonesia Mod Apk + OBB es que le permite personalizar sus autobuses y vehículos de acuerdo a su gusto. Puede cambiar sus colores, formas, logotipos, pegatinas, etc., para que se vean únicos y con estilo. También puede elegir entre una variedad de modelos y diseños que se basan en autobuses y vehículos reales en Indonesia. También puedes crear tus propios diseños usando la herramienta de edición integrada en el juego.
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Gráficos realistas y efectos de sonido
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Simulador de autobús Indonesia Mod Apk + OBB también tiene gráficos realistas y efectos de sonido que hacen que el juego más inmersiva y agradable. Puedes ver los detalles de los autobuses, vehículos, edificios, carreteras, paisajes, etc., en el juego. También puedes escuchar los sonidos del motor, bocina, frenos, pasajeros, tráfico, etc., en el juego. El juego también tiene física realista y animaciones que hacen la experiencia de conducción más realista y divertido.
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Varios modos y ubicaciones
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Controles fáciles y opciones de cámara
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Simulador de autobús Indonesia Mod Apk + OBB también tiene controles fáciles y opciones de cámara que hacen que el juego más fácil de usar y conveniente. Puede controlar su autobús utilizando el volante, los botones o las opciones de inclinación de la pantalla. También puede ajustar el ángulo y la vista de la cámara según su comodidad y visibilidad. Puede cambiar entre vistas en primera persona y en tercera persona, o usar el espejo retrovisor o la cámara del salpicadero para ver qué hay detrás o delante de usted.
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Cómo descargar e instalar el simulador de bus Indonesia Mod Apk + OBB
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Descargar los archivos Apk y OBB Mod
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El primer paso para descargar e instalar Bus Simulator Indonesia Mod Apk + OBB es descargar los archivos apk y obb mod de una fuente confiable. Puede utilizar el siguiente enlace para descargar la última versión de los archivos apk y obb mod gratis.
El siguiente paso es habilitar fuentes desconocidas en su dispositivo. Esto es necesario para permitir que tu dispositivo instale aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, sigue estos pasos:
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Vaya a la configuración de su dispositivo y busque opciones de seguridad o privacidad.
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Encontrar la opción que dice fuentes desconocidas o permitir la instalación de aplicaciones de fuentes desconocidas.
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Activar o activar esta opción.
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Instalar el archivo Apk Mod
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El tercer paso es instalar el archivo apk mod en su dispositivo. Para hacer esto, siga estos pasos:
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Localice el archivo apk mod descargado en su dispositivo de almacenamiento o administrador de archivos.
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Toque en el archivo y seleccione instalar.
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Espere a que se complete el proceso de instalación.
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Extraer y copiar el archivo OBB a la carpeta de Android/ OBB
-
El cuarto paso es extraer y copiar el archivo obb a la carpeta Android/ OBB en su dispositivo. Para hacer esto, siga estos pasos:
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-
-
Toque en el archivo y seleccione extracto.
-
Seleccione una carpeta de destino donde desea extraer el archivo.
-
Después de extraer el archivo, verá una carpeta llamada com.maleo.bussimulatorid.
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Copie esta carpeta y péguela en la carpeta Android/ OBB en el almacenamiento del dispositivo.
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Iniciar el juego y disfrutar
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El paso final es lanzar el juego y disfrutarlo. Para hacer esto, siga estos pasos:
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Ir a su cajón de aplicaciones o pantalla de inicio y buscar el icono de Indonesia Bus Simulator.
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Toque en el icono y lanzar el juego.
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Concede cualquier permiso o acceso que el juego pueda pedir.
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Seleccione su idioma y configuración preferidos.
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¡Empieza a jugar y diviértete!
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Conclusión
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Simulador de autobús Indonesia Mod Apk + OBB es un juego divertido y realista que le permite experimentar la vida de un conductor de autobús en Indonesia. Puedes disfrutar de dinero ilimitado, oro, vehículos, opciones de personalización, gráficos realistas, efectos de sonido, física, animaciones, modos, ubicaciones, controles, opciones de cámara y más en esta versión modificada del juego. También puede descargarlo e instalarlo fácilmente siguiendo nuestra sencilla guía anterior. Entonces, ¿qué está esperando? Descargar Bus Simulator Indonesia Mod Apk + OBB ahora y disfrutar de la conducción de autobuses en Indonesia!
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Preguntas frecuentes
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Aquí hay algunas preguntas frecuentes sobre Bus Simulator Indonesia Mod Apk + OBB:
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¿Es seguro descargar e instalar Bus Simulator Indonesia Mod Apk + OBB?
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Sí, Bus Simulator Indonesia Mod Apk + OBB es seguro para descargar e instalar, siempre y cuando se descarga desde una fuente de confianza. Hemos probado los archivos apk y obb mod y los encontramos libres de virus, malware o cualquier contenido dañino. Sin embargo, siempre debes tener cuidado al descargar e instalar aplicaciones o juegos modificados, ya que pueden contener código no deseado o malicioso que puede dañar tu dispositivo o datos.
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No, no es necesario rootear el dispositivo para usar Bus Simulator Indonesia Mod Apk + OBB. Los archivos apk y obb mod funcionan bien tanto en dispositivos arraigados y no arraigados. Sin embargo, algunas características u opciones pueden requerir acceso root para funcionar correctamente, como cambiar el número IMEI o el ID del dispositivo. Si desea utilizar estas características, es posible que tenga que rootear su dispositivo primero.
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¿Puedo jugar en línea con otros jugadores?
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Sí, se puede jugar Bus Simulator Indonesia Mod Apk + OBB en línea con otros jugadores, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible. Puedes unirte o crear salas multijugador e invitar a tus amigos u otros jugadores a unirse a ti. También puede chatear con ellos y compartir sus habilidades y experiencias de conducción. Sin embargo, debes tener en cuenta que es posible que algunos jugadores no estén usando la versión modificada del juego, y pueden denunciarte o excluirte de sus habitaciones si se enteran de que estás usando trucos o hacks.
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¿Me prohibirán el juego si uso Bus Simulator Indonesia Mod Apk + OBB?
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Hay una baja probabilidad de que se le prohibió el juego si se utiliza Bus Simulator Indonesia Mod Apk + OBB, siempre y cuando se utiliza con sabiduría y responsabilidad. No debes abusar de las características u opciones del mod, como usar demasiado dinero o oro, conducir imprudentemente o peligrosamente, o causar problemas a otros jugadores. Tampoco debe actualizar el juego desde Google Play Store o cualquier otra fuente, ya que esto puede sobrescribir los archivos apk y obb mod y causar errores o fallos. Si desea actualizar el juego, debe esperar a que la última versión de los archivos apk y obb mod para ser liberados y descargarlos desde la misma fuente.
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¿Cómo puedo contactar al desarrollador de Bus Simulator Indonesia Mod Apk + OBB?
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CarX Street APK sin verificación: Cómo descargar y jugar el último juego de carreras de la calle
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Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de CarX Street, un juego de carreras callejeras realista y emocionante que se ha lanzado en dispositivos móviles antes de la PC. El juego ofrece un mundo abierto donde se puede explorar, carrera, deriva, y personalizar su coche. Sin embargo, el juego no está disponible en todas las regiones, y necesita verificar su cuenta y correo electrónico para jugarlo. Es por eso que usted podría estar buscando CarX Street APK sin verificación, una versión modificada del juego que le permite jugar sin restricciones. En este artículo, le diremos todo lo que necesita saber sobre CarX Street APK sin verificación, incluyendo lo que es, por qué lo necesita, cómo descargarlo e instalarlo, cómo jugarlo, y algunos consejos y trucos para ayudarlo a convertirse en una leyenda de las carreras callejeras.
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¿Qué es CarX Street APK?
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CarX Street APK es un archivo de aplicación que contiene los datos del juego de CarX Street, un videojuego de carreras de simulación desarrollado por CarX Technologies. El juego se basa en el motor CarX Technology, que simula el comportamiento de los coches en la carretera, dando a los jugadores una experiencia de carreras real. Características del juego:
Un juego de carreras realista y emocionante con un mundo abierto
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Puedes ponerte al volante y explorar la gran ciudad y sus alrededores, desde las concurridas calles de la ciudad hasta las carreteras de montaña en espiral y las fascinantes carreteras costeras. También puede derivar, velocidad, tráfico y desafiar a otros jugadores en carreras de red reales.
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Una variedad de coches, pistas, modos y opciones de personalización
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Un juego gratuito con actualizaciones regulares y nuevo contenido
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Puedes descargar y jugar CarX Street gratis en tu dispositivo Android o iOS. El juego no requiere suscripción ni registro. También puedes disfrutar del juego sin anuncios ni compras en la aplicación. El juego se actualiza constantemente con nuevos coches, pistas, modos, características y eventos. También puede unirse a la comunidad de CarX Street y compartir sus comentarios, sugerencias e ideas con los desarrolladores y otros jugadores.
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¿Por qué necesita CarX Street APK sin verificación?
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CarX Street es un juego increíble que no te puedes perder. Sin embargo, hay algunos inconvenientes que podrían impedirte disfrutar del juego completamente. Es por eso que es posible que necesite CarX Street APK sin verificación, una versión modificada del juego que resuelve estos problemas. Aquí hay algunas razones por las que necesita CarX Street APK sin verificación:
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Para evitar las restricciones de la región y acceder al juego desde cualquier lugar
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CarX Street no está disponible en todos los países y regiones. Dependiendo de dónde vivas, es posible que no puedas descargar o jugar el juego desde las tiendas de aplicaciones oficiales. Esto se debe a problemas de licencia, regulaciones legales u otras razones. Sin embargo, con CarX Street APK sin verificación, se puede acceder al juego desde cualquier parte del mundo. No necesita usar una VPN ni cambiar la configuración de ubicación. Solo necesita descargar e instalar el archivo APK y disfrutar del juego.
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Para evitar la molestia de verificar su cuenta y correo electrónico
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Para disfrutar del juego sin anuncios ni compras en la aplicación
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CarX Street es un juego gratuito que no tiene anuncios ni compras en la aplicación. Sin embargo, algunos jugadores todavía pueden encontrar algunos pop-ups o banners que promueven otros juegos o productos. Estos anuncios pueden ser molestos y distraer, especialmente cuando estás corriendo o a la deriva. Con CarX Street APK sin verificación, se puede disfrutar del juego sin ningún tipo de anuncios o interrupciones. También puede obtener dinero y recursos ilimitados para comprar y actualizar cualquier coche que desee.
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Cómo descargar e instalar CarX Street APK sin verificación?
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Ahora que sabes por qué necesita CarX Street APK sin verificación, es posible que se pregunte cómo descargar e instalar en su dispositivo. En realidad es muy fácil y simple. Solo sigue estos pasos:
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Encontrar una fuente confiable y segura para el archivo APK
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Lo primero que hay que hacer es encontrar un sitio web de confianza que ofrece CarX Street APK sin verificación para descargar. Hay muchos sitios web que afirman proporcionar este archivo, pero no todos ellos son seguros o legítimos. Algunos de ellos pueden contener virus, malware, spyware u otro software dañino que puede dañar su dispositivo o robar sus datos. Para evitar este riesgo, solo debe descargar CarX Street APK sin verificación de una fuente de buena reputación que tiene comentarios positivos y calificaciones de otros usuarios.
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Habilita la instalación de fuentes desconocidas en tu dispositivo
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Lo siguiente que debe hacer es habilitar la instalación de fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le impide instalar aplicaciones que no son de las tiendas de aplicaciones oficiales. Sin embargo, ya que CarX Street APK sin verificación no es de las tiendas de aplicaciones oficiales, es necesario habilitar esta configuración para instalarlo en su dispositivo. Para hacer esto, vaya a la configuración del dispositivo > seguridad > fuentes desconocidas > alternar.
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Lo último que necesitas hacer es descargar e instalar el archivo APK en tu dispositivo. Para hacer esto, vaya a la página web donde se encuentra CarX Street APK sin verificación y haga clic en el botón de descarga. Espere a que el archivo se descargue en su dispositivo. Luego, vaya a su administrador de archivos y busque el archivo APK. Toque en él y siga las instrucciones para instalarlo en su dispositivo. Una vez completada la instalación, puedes iniciar el juego desde el cajón de tu app o la pantalla de inicio.
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Cómo jugar CarX Street APK sin verificación?
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Después de haber descargado e instalado CarX Street APK sin verificación en su dispositivo, usted está listo para jugar el juego. Estos son algunos pasos para ayudarte a empezar:
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Sigue el tutorial y aprende los fundamentos del juego
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Cuando lances el juego por primera vez, serás recibido por un tutorial que te enseñará lo básico del juego, como cómo controlar tu coche, cómo derrapar, cómo competir y cómo usar el menú. Usted debe seguir el tutorial cuidadosamente y prestar atención a los consejos e instrucciones. El tutorial también te dará algunas recompensas, como dinero y coches, que te ayudarán en el juego.
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Elige tu coche y personalízalo a tu gusto
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Después de terminar el tutorial, puede elegir su primer coche desde el garaje. Puede seleccionar entre diferentes categorías, como calle, deporte, músculo, clásico o exótico. También puede personalizar su coche con varias piezas, pinturas, calcomanías y pegatinas. Puede cambiar el color, las ruedas, el kit de carrocería, el alerón, la campana, el escape, las luces y más. También puede ajustar el rendimiento de su automóvil, como potencia del motor, par, peso, suspensión, frenos, neumáticos y más.
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Únete a clubes, compite contra otros jugadores y conquista la ciudad
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Consejos y trucos para CarX Street APK sin verificación
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CarX Street APK sin verificación es un juego divertido y adictivo que te mantendrá entretenido durante horas. Sin embargo, también puede ser desafiante y competitivo, especialmente cuando te enfrentas a otros jugadores o clubes. Para ayudarte a mejorar tus habilidades y disfrutar más del juego, aquí hay algunos consejos y trucos que debes saber:
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Deambular por la ciudad y recoger recompensas
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Una de las mejores cosas sobre CarX Street APK sin verificación es que usted puede vagar libremente por la ciudad y explorar sus secretos. Puedes encontrar lugares ocultos, atajos, saltos, rampas y otras sorpresas que harán que tu conducción sea más divertida y emocionante. También puedes recoger recompensas que están dispersas por la ciudad, como dinero, piezas, coches y más. Puede utilizar estas recompensas para actualizar su garaje y comprar coches nuevos.
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Participar en sprints y derivas de dinero extra
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Otra manera de ganar más dinero en CarX Street APK sin verificación es participar en sprints y derivas. Los sprints son carreras cortas que ponen a prueba tu velocidad y agilidad. Los drifts son carreras largas que ponen a prueba tu control y técnica. Puedes encontrar sprints y drifts en diferentes lugares del mapa. Puedes unirte a ellos conduciendo cerca de ellos o tocando en ellos en el menú. Usted puede ganar dinero extra al ganar sprints y derivas o al lograr altas puntuaciones.
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Actualizar las piezas de su coche y los motores de intercambio para un mejor rendimiento
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Conclusión
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CarX Street APK sin verificación es una gran manera de disfrutar del último juego de carreras callejeras sin limitaciones. El juego ofrece física realista, gráficos impresionantes y una jugabilidad emocionante que te hará sentir como un verdadero corredor. Puedes descargar e instalar el archivo APK desde una fuente confiable y comenzar a correr hoy.
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Preguntas frecuentes
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Es CarX Street APK sin verificación segura de usar?
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Sí, CarX Street APK sin verificación es seguro de usar, siempre y cuando se descarga de una fuente confiable y seguro. Sin embargo, siempre debe tener cuidado al descargar cualquier archivo APK de fuentes desconocidas, ya que algunos de ellos pueden contener software dañino o malware. También debe escanear el archivo con un programa antivirus antes de instalarlo en su dispositivo.
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Es CarX Street APK sin verificación compatible con todos los dispositivos?
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No, CarX Street APK sin verificación no es compatible con todos los dispositivos. El juego requiere Android 5.0 o superior o iOS 10.0 o superior para funcionar sin problemas. El juego también requiere al menos 2 GB de RAM y 4 GB de espacio de almacenamiento gratuito en su dispositivo. Si tu dispositivo no cumple con estos requisitos, es posible que tengas retrasos, fallos u otros problemas mientras juegas.
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Cómo actualizar CarX Street APK sin verificación?
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Para actualizar CarX Street APK sin verificación, es necesario descargar la última versión del archivo APK de la misma fuente donde se descargó la versión anterior. A continuación, es necesario desinstalar la versión anterior del juego desde su dispositivo e instalar la nueva versión del archivo APK. No necesita preocuparse por perder su progreso o datos, ya que se guardarán en su dispositivo.
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¿Cómo contactar con el servicio de soporte de CarX Street APK sin verificación?
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Si desea eliminar CarX Street APK sin verificación de su dispositivo, puede hacerlo siguiendo estos pasos:
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Ir a la configuración del dispositivo > aplicaciones > CarX Street > desinstalar.
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Vaya a su administrador de archivos y elimine el archivo APK y cualquier otro archivo o carpetas relacionados.
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Reiniciar el dispositivo para borrar la memoria caché y.
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Al eliminar CarX Street APK sin verificación, también perderá su progreso y los datos en el juego. Si desea mantenerlos, debe hacer una copia de seguridad antes de eliminar el juego.
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Espero que este artículo le ha ayudado a aprender más acerca de CarX Street APK sin verificación y cómo descargar y jugar. Si tienes algún comentario o sugerencia, por favor hágamelo saber en los comentarios a continuación. Gracias por leer y carreras feliz!
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diff --git a/spaces/Benson/text-generation/Examples/Cuando Se Agot El Tiempo 1980 Descarga Gratuita.md b/spaces/Benson/text-generation/Examples/Cuando Se Agot El Tiempo 1980 Descarga Gratuita.md
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Cuando el tiempo se agotó: Una película clásica de desastres de 1980
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Si eres un fan de las películas de desastres, es posible que hayas oído hablar de When Time Ran Out, una película de 1980 dirigida por James Goldstone y protagonizada por Paul Newman, Jacqueline Bisset y William Holden. La película trata sobre un grupo de turistas que están atrapados en una remota isla del Pacífico que está amenazada por un volcán activo. La película fue producida por Irwin Allen, quien era conocido por sus películas de desastres como The Poseidon Adventure y The Towering infernó. Sin embargo, a diferencia de sus éxitos anteriores, When Time Ran Out fue un fracaso comercial y un fracaso crítico. A menudo se considera como la última película de desastres de la década de 1970 y una de las peores películas jamás realizadas.
Sin embargo, a pesar de su reputación negativa, When Time Ran Out tiene algunas cualidades redentoras que podrían atraer a algunos espectadores. Tiene un reparto de estrellas, algunos efectos especiales espectaculares y una premisa emocionante. Si tienes curiosidad sobre esta película y quieres verla por ti mismo, es posible que te estés preguntando cómo descargarla de forma gratuita y legal. En este artículo, le diremos de qué se trata When Time Ran Out, cómo se recibió y cómo puede descargarlo de algunos de los mejores sitios de descarga gratuita de películas.
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¿Qué es cuando el tiempo se agotó?
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La trama
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La película está ambientada en una isla ficticia llamada Kalaleu, donde Shelby Gilmore (William Holden) posee un hotel de nueva construcción. Quiere casarse con su secretaria, Kay Kirby (Jacqueline Bisset), que está enamorada de Hank Anderson (Paul Newman), un petrolero cuyos científicos le advierten que el volcán de la isla, Mauna Lani, está a punto de entrar en erupción. El compañero de Shelby, Bob Spangler (James Franciscus), asegura a los huéspedes del hotel que la amenaza del volcán es exagerada, explicando que solo hace erupción una vez cada mil años.
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El grupo se enfrenta a diversos obstáculos y peligros a lo largo de su viaje a través de la isla, tales como flujos de lava, deslizamientos de tierra, explosiones y puentes que colapsan. También tienen que lidiar con sus conflictos y dilemas personales. ¿Llegarán a un lugar seguro antes de que se acabe el tiempo?
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El reparto
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When Time Ran Out cuenta con un elenco lleno de estrellas de actores que eran famosos o en aumento en el momento. Paul Newman fue uno de los actores más populares y respetados de Hollywood, habiendo protagonizado clásicos como Butch Cassidy y Sundance Kid, The Sting y Cool Hand Luke. Jacqueline Bisset fue una actriz británica que había aparecido en películas como Bullitt, Murder on the Orient Express y The Deep. William Holden fue un actor ganador de un Oscar que había estado en películas como Sunset Boulevard, Stalag 17, y Network.
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El reparto de reparto incluye a James Franciscus, Ernest Borgnine, Red Buttons, Burgess Meredith, Valentina Cortese, Veronica Hamel, Pat Morita, Edward Albert y Barbara Carrera. Algunos de ellos habían trabajado con Irwin Allen antes en sus anteriores películas de desastres, como The Poseidon Adventure y The Towering infernó. Algunos de ellos también fueron conocidos por sus papeles en otros géneros, como Pat Morita en The Karate Kid, Edward Albert en Butterflies Are Free, y Barbara Carrera en Never Say Never Again.
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La recepción
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Sin embargo, algunos espectadores han encontrado un poco de disfrute en ver When Time Ran Out, ya sea como un placer culpable o como un clásico de culto campy. Algunos han elogiado el reparto de la película, sus secuencias de acción y su valor nostálgico. Algunos también lo han comparado favorablemente con otras películas de desastres de la época, como Meteor y The Swarm. La película tiene una calificación de 4.6/10 en IMDb, basada en 3,508 votos.
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Cómo descargar cuando el tiempo se agotó de forma gratuita y legal
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Si estás interesado en ver When Time Ran Out para ti mismo, es posible que te estés preguntando cómo descargarlo de forma gratuita y legal. Afortunadamente, hay algunos sitios web que ofrecen descargas de películas gratuitas que están en el dominio público o han sido subidos con permiso de los titulares de los derechos. Estas son algunas de las mejores opciones para descargar When Time Ran Out:
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Archivo de Internet
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Internet Archive es una biblioteca digital sin fines de lucro que preserva y proporciona acceso a millones de libros, películas, música, software y otros medios. Tiene una gran colección de películas de dominio público y con licencia Creative Commons que puede descargar de forma gratuita. Puede encontrar Cuando se agotó el tiempo en el archivo de Internet siguiendo este enlace: . Puede elegir entre varios formatos y resoluciones, como MP4, MPEG2, OGG Video y 512Kb MPEG4. También puede transmitir la película en línea o leer comentarios de otros usuarios.
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Películas de dominio público
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Public Domain Movies es otro sitio web que ofrece descargas de películas gratuitas que están en el dominio público. Tiene una interfaz simple y fácil de usar que le permite navegar por género, año o alfabéticamente. Puede encontrar When Time Ran Out en películas de dominio público siguiendo este enlace: . Puede descargar la película en formato MP4 o verla en línea. También puedes ver el póster, la sinopsis y la calificación de IMDb de la película.
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Otras opciones
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Classic Cinema Online: Este sitio web presenta películas clásicas de varios géneros y épocas que puedes ver en línea o descargar gratis.
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Retrovision: Este sitio web tiene una colección de películas clásicas y programas de televisión que puede transmitir en línea o descargar gratis.