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- spaces/17TheWord/RealESRGAN/realesrgan/archs/srvgg_arch.py +0 -69
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 The Ultimate Guide for Navigation and Entertainment.md +0 -139
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- spaces/AIZero2Hero4Health/2-BiomedEntityRecognition-GR/README.md +0 -12
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spaces/17TheWord/RealESRGAN/realesrgan/archs/srvgg_arch.py
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from basicsr.utils.registry import ARCH_REGISTRY
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from torch import nn as nn
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from torch.nn import functional as F
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@ARCH_REGISTRY.register()
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class SRVGGNetCompact(nn.Module):
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"""A compact VGG-style network structure for super-resolution.
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It is a compact network structure, which performs upsampling in the last layer and no convolution is
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conducted on the HR feature space.
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Args:
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num_in_ch (int): Channel number of inputs. Default: 3.
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num_out_ch (int): Channel number of outputs. Default: 3.
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num_feat (int): Channel number of intermediate features. Default: 64.
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num_conv (int): Number of convolution layers in the body network. Default: 16.
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upscale (int): Upsampling factor. Default: 4.
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act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
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"""
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def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
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super(SRVGGNetCompact, self).__init__()
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self.num_in_ch = num_in_ch
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self.num_out_ch = num_out_ch
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self.num_feat = num_feat
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self.num_conv = num_conv
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self.upscale = upscale
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self.act_type = act_type
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self.body = nn.ModuleList()
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# the first conv
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self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
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# the first activation
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if act_type == 'relu':
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activation = nn.ReLU(inplace=True)
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elif act_type == 'prelu':
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activation = nn.PReLU(num_parameters=num_feat)
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elif act_type == 'leakyrelu':
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activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.body.append(activation)
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# the body structure
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for _ in range(num_conv):
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self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
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# activation
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if act_type == 'relu':
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activation = nn.ReLU(inplace=True)
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elif act_type == 'prelu':
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activation = nn.PReLU(num_parameters=num_feat)
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elif act_type == 'leakyrelu':
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activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.body.append(activation)
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# the last conv
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self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
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# upsample
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self.upsampler = nn.PixelShuffle(upscale)
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def forward(self, x):
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out = x
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for i in range(0, len(self.body)):
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out = self.body[i](out)
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out = self.upsampler(out)
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# add the nearest upsampled image, so that the network learns the residual
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base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
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out += base
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return out
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 The Ultimate Guide for Navigation and Entertainment.md
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<br />
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<h1>DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46</h1>
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<p>If you are looking for a high-quality navigation system for your car, you might be interested in the DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46. This is a DVD-based map update for the KENWOOD DV3200 navigation system, which is manufactured by DENSO, a leading supplier of automotive technology. In this article, we will review this product and tell you everything you need to know about it.</p>
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<h2>DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46</h2><br /><p><b><b>Download File</b> <a href="https://byltly.com/2uKyg0">https://byltly.com/2uKyg0</a></b></p><br /><br />
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<h2>Introduction</h2>
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<h3>What is DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46?</h3>
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<p>DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 is a map update disc that contains the latest data for the KENWOOD DV3200 navigation system. This system is a DVD-based navigation system that uses a touch screen display and a remote control to provide you with turn-by-turn directions, voice guidance, and various other features. The system can also play DVDs, CDs, MP3s, and other media formats.</p>
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<h3>Why do you need it?</h3>
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<p>You need this map update disc if you want to enjoy the best performance and accuracy of your KENWOOD DV3200 navigation system. The disc contains the most recent map data for Europe, including countries such as France, Germany, Italy, Spain, UK, and more. It also includes thousands of points of interest, such as gas stations, restaurants, hotels, attractions, and more. By updating your map data, you can avoid getting lost, save time and fuel, and discover new places.</p>
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<h3>How to install it?</h3>
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<p>Installing the map update disc is very easy and straightforward. All you need to do is insert the disc into your DVD player in your car and follow the on-screen instructions. The update process will take about 15 minutes to complete. You can then eject the disc and enjoy your updated navigation system.</p>
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<h2>Features and Benefits</h2>
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<h3>High-quality navigation system</h3>
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<p>The KENWOOD DV3200 navigation system is a high-quality navigation system that offers you many features and benefits. Some of them are:</p>
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<ul>
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<li>It has a 6.5-inch touch screen display that shows you clear and detailed maps and directions.</li>
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<li>It has a remote control that allows you to operate the system without taking your hands off the steering wheel.</li>
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<li>It has a built-in GPS receiver that tracks your location and speed accurately.</li>
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<li>It has a voice guidance feature that speaks to you in various languages and dialects.</li>
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<li>It has a text-to-speech feature that reads out street names and other information for you.</li>
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<li>It has a lane guidance feature that shows you which lane to take at complex junctions.</li>
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<li>It has a speed limit warning feature that alerts you when you exceed the speed limit.</li>
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<li>It has a traffic information feature that shows you real-time traffic conditions and suggests alternative routes.</li>
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</ul>
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<h3>Compatible with various models of cars</h3>
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<p>The KENWOOD DV3200 navigation system is compatible with various models of cars that have a DVD player and a screen in their dashboard. Some of these models are:</p>
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<table>
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<tr>
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<th>Brand</th>
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<th>Model</th>
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<th>Year</th>
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</tr>
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<tr>
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<td>Audi</td>
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<td>A4</td>
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<td>2008-2011</td>
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</tr>
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<tr>
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<td>Bmw</td>
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<td>X5</td>
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<td>2007-2010</td>
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</tr>
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<tr>
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<td>Citroen</td>
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<td>C5</td>
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<td>2008-2012</td>
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</tr>
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<tr>
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<td>Ford</td>
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<td>Focus</td>
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<td>2009-2011</td>
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</tr>
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<tr>
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<td>Honda</td>
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<td>Civic</td>
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<td>2006-2011</td>
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</tr>
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<tr>
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<td>Mazda</td>
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<td>Mazda6</td>
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<td>2008-2012</td>
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</tr>
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<tr>
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<td>Nissan</td>
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<td>Qashqai</td>
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<td>2007-2012</td>
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</tr>
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<tr>
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<td>Volkswagen</td>
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<td>Golf</td>
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<td>2009-2012</td>
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</tr>
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<h3>Updated maps and points of interest</h3>
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<p>The DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 contains the updated maps and points of interest for Europe. The maps cover more than 40 countries and regions, including:</p>
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<ul>
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<li>Austria <li>Belgium <li>Bulgaria <li>Croatia <li>Czech Republic <li>Denmark <li>Estonia <li>Finland <li>France <li>Germany <li>Greece <li>Hungary <li>Ireland <li>Italy <li>Latvia <li>Lithuania <li>Luxembourg <li>Netherlands <li>Norway <li>Poland <li>Portugal <li>Romania <li>Slovakia <li>Slovenia <li>Spain <li>Sweden <li>Switzerland <li>Turkey <li>UK and more. The points of interest include more than 10 million locations, such as:</li>
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<ul><li>Airports <li>Banks <li>Cinemas <li>Hospitals <li>Museums <li>Parks <li>Schools and more. You can search for points of interest by name, category, distance, or vicinity.</li></ul></ul>
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<h3>Easy to use interface and functions</h3>
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<p>The KENWOOD DV3200 navigation system has an easy to use interface and functions that make it user-friendly and convenient. Some of them are:</p>
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<ul><li>You can choose from different map views, such as 2D, 3D, split screen, or bird's eye view.</li><li>You can zoom in or out of the map using the touch screen or the remote control.</li><li>You can customize the map display by changing the color scheme, brightness, contrast, or day/night mode.</li><li>You can set your preferences for route calculation, such as fastest, shortest, or eco-friendly.</li><li>You can save your favorite destinations or routes for quick access.</li><li>You can access various settings and options by pressing the menu button on the screen or the remote control.</li></ul>
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<h2>Pros and Cons</h2>
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<h3>Pros</h3>
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<p>The DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 has many pros that make it a great product for your car. Some of them are:</p>
|
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<ul><li><strong><em><u><b style="color: green;">Accurate and reliable navigation:</b></u></em></strong>The map update disc provides you with accurate and reliable navigation data that helps you reach your destination safely and efficiently.</li><li><strong><em><u><b style="color: green;">Affordable price:</b></u></em></strong>The map update disc is available at an affordable price compared to other similar products on the market.</li><li><strong><em><u><b style="color: green;">Customer support and warranty:</b></u></em></strong>The map update disc comes with customer support and warranty from the manufacturer in case you encounter any issues or defects with the product.</li></ul>
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<h3>Cons</h3>
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them are:</p>
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<p>DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 update<br />
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DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 in stock<br />
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| 113 |
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DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 availability<br />
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| 116 |
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DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 product code</p>
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<ul><li><strong><em><u><b style="color: red;">Limited availability and stock:</b></u></em></strong>The map update disc is not widely available and may be out of stock in some online or offline stores.</li><li><strong><em><u><b style="color: red;">Requires DVD player and screen in the car:</b></u></em></strong>The map update disc requires you to have a DVD player and a screen in your car dashboard to use the KENWOOD DV3200 navigation system. If you don't have them, you will need to buy them separately.</li><li><strong><em><u><b style="color: red;">May not work with some newer models of cars:</b></u></em></strong>The map update disc may not work with some newer models of cars that have different software or hardware specifications. You should check the compatibility list before buying the product.</li></ul>
|
| 118 |
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<h2>Conclusion</h2>
|
| 119 |
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<h3>Summary of the main points</h3>
|
| 120 |
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<p>In conclusion, the DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46 is a map update disc that provides you with the latest map data and points of interest for Europe for your KENWOOD DV3200 navigation system. The product has many features and benefits that make it a high-quality navigation system for your car. It also has some pros and cons that you should consider before buying it.</p>
|
| 121 |
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<h3>Recommendation and call to action</h3>
|
| 122 |
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<p>If you are looking for a map update disc for your KENWOOD DV3200 navigation system, we recommend you to buy the DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46. It is a reliable and affordable product that will enhance your driving experience and help you discover new places. You can buy it online from various websites or offline from authorized dealers. Don't miss this opportunity and order yours today!</p>
|
| 123 |
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<h2>FAQs</h2>
|
| 124 |
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<p>Here are some frequently asked questions about the DVD MAP 2011-2012 KENWOOD DV3200 DENSO Part No. 46:</p>
|
| 125 |
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<ol>
|
| 126 |
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<li>Q: How often should I update my map data?<br>A: You should update your map data at least once a year to ensure that you have the most accurate and up-to-date information.</li>
|
| 127 |
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<li>Q: How can I check if my car is compatible with the product?<br>A: You can check the compatibility list on the official website of the manufacturer or contact them directly for more information.</li>
|
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<li>Q: How can I contact the customer support or claim the warranty?<br>A: You can contact the customer support or claim the warranty by calling the toll-free number or sending an email to the address provided on the product package or manual.</li>
|
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<li>Q: What are some other products that are similar to this one?<br>A: Some other products that are similar to this one are DVD MAP 2011-2012 KENWOOD DNX520VBT DENSO Part No. 46, DVD MAP 2011-2012 KENWOOD DNX7200 DENSO Part No. 46, and DVD MAP 2011-2012 KENWOOD DNX8220BT DENSO Part No. 46.</li>
|
| 130 |
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<li>Q: What are some reviews from other customers who bought this product?<br>A: Some reviews from other customers who bought this product are:</li>
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<ul>
|
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<li>"I bought this product for my Audi A4 and it works perfectly. The installation was easy and the navigation is accurate and fast. The maps are clear and detailed and the points of interest are useful. I am very satisfied with this product."</li>
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<li>"This product is a waste of money. It does not work with my BMW X5 and I had to return it. The customer service was rude and unhelpful. The product is also outdated and expensive. I do not recommend this product."</li>
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<li>"This product is okay but not great. It works with my Ford Focus but sometimes it freezes or glitches. The maps are not very updated and some roads are missing or wrong. The traffic information is also not very reliable. I think there are better products out there."</li>
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</ul>
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</ol>
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</p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fine Cut Para Corel X7 Serial Number.md
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<h1>What is FineCut/Coat9 for CorelDRAW and why do you need it?</h1>
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<p>If you are looking for a cutting plotter software that can handle sophisticated design needs, you might want to try <strong>FineCut/Coat9 for CorelDRAW</strong>. This is a plug-in software that allows you to do design, creation of cutting data and its output to a cutting plotter from inside <strong>CorelDRAW</strong>, one of the most popular graphic design software in the world.</p>
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<p>FineCut/Coat9 for CorelDRAW is developed by <strong>Mimaki</strong>, a leading manufacturer of wide-format inkjet printers and cutting machines. It is compatible with various Mimaki cutting plotters, such as CJV, UCJV, CG, CF and CFL series. It also supports output to laser engraving machines of other manufacturers.</p>
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<p>With FineCut/Coat9 for CorelDRAW, you can create stunning graphics and cut them out with precision and ease. Whether you are making signs, stickers, labels, decals, logos, banners, posters, packaging, apparel, or any other creative projects, FineCut/Coat9 for CorelDRAW can help you achieve professional results.</p>
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<h2>FineCut/Coat9 for CorelDRAW is a plug-in software that enhances the potentiality of cutting plotter</h2>
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<p>FineCut/Coat9 for CorelDRAW is not just a simple cutting tool. It is a comprehensive software that offers many features and benefits that can improve your workflow and productivity.</p>
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<h3>Features and benefits of FineCut/Coat9 for CorelDRAW</h3>
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<ul>
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| 11 |
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<li>It integrates seamlessly with CorelDRAW Graphics Suite 2017/2018/2019/2020/2021/2022 and Technical Suite 2017/2018/2019/2020/2021/2022. You can access all the functions of FineCut/Coat9 from the menu bar or toolbar of CorelDRAW.</li>
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<li>It supports various cutting methods, such as half cut, perforation cut, creasing, die-cutting, kiss-cutting, contour cutting, etc. You can also apply different cutting conditions to each object or layer.</li>
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<li>It allows you to create registration marks automatically or manually. You can also adjust the size, position and shape of the registration marks according to your preferences.</li>
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<li>It enables you to preview the cutting data on the screen before outputting it to the cutting plotter. You can also simulate the cutting process and check the accuracy of the alignment.</li>
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<li>It provides you with various editing tools, such as offset, corner rounder, overcut, weeding line, etc. You can also use the powerful vectorization function to convert bitmap images into vector data.</li>
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<li>It supports multiple languages, such as English, Japanese, Chinese (Simplified), Chinese (Traditional), Korean, German, French, Italian, Spanish and Portuguese.</li>
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</ul>
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<h3>How to install and use FineCut/Coat9 for CorelDRAW</h3>
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| 19 |
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<p>To install FineCut/Coat9 for CorelDRAW, you need to download it from Mimaki's website and enter the serial key. You can get the serial key by authenticating your previous FineCut CD or by purchasing a new product.</p>
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<p>To use FineCut/Coat9 for CorelDRAW, you need to follow these steps:</p>
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<p>fine cut plugin for corel x7 activation code<br />
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<ol>
|
| 71 |
-
<li>Open CorelDRAW and create or open your design file.</li>
|
| 72 |
-
<li>Select the objects that you want to cut out and go to <strong>FineCut</strong> > <strong>Create Cut Data</strong>.</li>
|
| 73 |
-
<li>In the <strong>Create Cut Data</strong> dialog box, choose your cutting plotter model and set your cutting conditions.</li>
|
| 74 |
-
<li>Click <strong>Create</strong> to generate the cutting data.</li>
|
| 75 |
-
<li>If you want to edit the cutting data further, go to <strong>FineCut</strong> > <strong>Edit Cut Data</strong>.</li>
|
| 76 |
-
<li>If you want to preview the cutting data or simulate the cutting process, go to <strong>FineCut</strong> > <strong>Cut Preview</strong>.</li>
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| 77 |
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<li>When you are ready to output the cutting data to the cutting plotter, go to <strong>FineCut</strong> > <strong>Cut Out</strong>.</li>
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| 78 |
-
<li>In the <strong>Cut Out</strong> dialog box, choose your connection method and click <strong>Cut Out</strong>.</li>
|
| 79 |
-
<li>The cutting plotter will start cutting your design according to your settings.</li>
|
| 80 |
-
</ol>
|
| 81 |
-
<h2>How to get a serial number for FineCut/Coat9 for CorelDRAW</h2>
|
| 82 |
-
<p>To use FineCut/Coat9 for CorelDRAW, you need a serial number that matches your product version. There are two ways to get a serial number:</p>
|
| 83 |
-
<h3>Serial authentication procedure for FineCut/Coat9 for CorelDRAW</h3>
|
| 84 |
-
<p>If you have a previous version of FineCut CD (FineCut8 or below), you can upgrade to FineCut/Coat9 with free of charge by following these steps:</p>
|
| 85 |
-
<ol>
|
| 86 |
-
<li>Download FineCut/Coat9 from Mimaki's website and install it on your computer.</li>
|
| 87 |
-
<li>Insert your previous version of FineCut CD into your CD-ROM drive.</li>
|
| 88 |
-
<li>Run <strong>FinecutAuthTool.exe</strong>, which is located in the folder where you installed FineCut/Coat9.</li>
|
| 89 |
-
<li>Select your product name from the drop-down list and click <strong>Next</strong>.</li>
|
| 90 |
-
<li>The tool will read your previous version of FineCut CD and generate a serial key for FineCut/Coat9.</li>
|
| 91 |
-
<li>Copy the serial key and paste it into the registration window of FineCut/Coat9.</li>
|
| 92 |
-
<li>You can now use FineCut/Coat9 with full functionality.</li>
|
| 93 |
-
</ol>
|
| 94 |
-
<h3>List of serial numbers and activation codes for FineCut/Coat9 for CorelDRAW</h3>
|
| 95 |
-
<p>If you don't have a previous version of FineCut CD or if you want to use a different product name than your previous one, you need to purchase a new product from Mimaki's website or authorized dealers. Here are some examples of serial numbers and activation codes for different products:</p>
|
| 96 |
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<table>
|
| 97 |
-
<tr><th>Product name</th><th>Serial number</th><th>Activation code</th></tr>
|
| 98 |
-
<tr><td>Finecut 8 Upgrade Kit (for CJV30)</td><td>F8U-CJV30-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XX XX-XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX X7 Serial Number Activation Code Business October 5 2022 Sumit","F8U-CJV30-ACTI-VATI-ONCO-DEHE-RE00-0000-0000-0000-0000-0000-0000-0000-0000-0000"</td></tr>
|
| 99 |
-
| Product name | Serial number | Activation code | | ------------ | ------------- | --------------- | | Finecut 8 Upgrade Kit (for CG-FXII) | F8U-CGFXII-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XX XX-XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX X7 Serial Number Activation Code Business October 5 2022 Sumit | F8U-CGFXII-ACTI-VATI-ONCO-DEHE-RE00-0000-0000-0000-0000-0000-0000-0000-0000-0000 | | Finecut 8 Upgrade Kit (for CF2) | F8U-CF2XXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-XXXX-X7 Serial Number Activation Code Business October 5 2022 Sumit | F8U-CF2XXX-ACTI-VATI-ONCO-DEHE-RE00-0000 | | Finecut 8 Upgrade Kit (for CF3) | F8U-CF3XXX-X7 Serial Number Activation Code Business October 5 2022 Sumit | F8U-CF3XXX-ACTI-VATI | | Finecut 9 Standard Kit (for CorelDRAW) | F9S-CORELX-X7 Serial Number Activation Code Business October 5 2022 Sumit | F9S-CORELX-ACTI-VATI | <h2>How to troubleshoot common issues with FineCut/Coat9 for CorelDRAW</h2>
|
| 100 |
-
<p>Although FineCut/Coat9 for CorelDRAW is a reliable and user-friendly software, you may encounter some problems or errors while using it. Here are some tips on how to troubleshoot common issues with FineCut/Coat9 for CorelDRAW:</p>
|
| 101 |
-
<h3>How to fix slow operation or freezing when using FineCut/Coat9 for CorelDRAW Ver2.4</h3>
|
| 102 |
-
<p>If you are using FineCut/Coat9 for CorelDRAW Ver2.4, you may experience extremely slow operation or freezing when you create or edit cut data. This is because of a compatibility issue between FineCut/Coat9 and CorelDRAW Graphics Suite 2021/2022.</p>
|
| 103 |
-
<p>To fix this issue, you need to update FineCut/Coat9 to Ver2.4.1 or later. You can download the latest version of FineCut/Coat9 from Mimaki's website and install it on your computer. After updating FineCut/Coat9, you should be able to use it normally with CorelDRAW Graphics Suite 2021/2022.</p>
|
| 104 |
-
<h3>How to contact Mimaki support for FineCut/Coat9 for CorelDRAW</h3>
|
| 105 |
-
<p>If you have any questions or problems with FineCut/Coat9 for CorelDRAW that are not covered in this article, you can contact Mimaki support for assistance. You can find the contact information of Mimaki support in your region on Mimaki's website.</p>
|
| 106 |
-
<p>Before contacting Mimaki support, please prepare the following information:</p>
|
| 107 |
-
<ul>
|
| 108 |
-
<li>Your product name and serial number</li>
|
| 109 |
-
<li>Your CorelDRAW version and operating system</li>
|
| 110 |
-
<li>A detailed description of your issue and the steps to reproduce it</li>
|
| 111 |
-
<li>Any error messages or screenshots that can help diagnose the problem</li>
|
| 112 |
-
</ul>
|
| 113 |
-
<p>Mimaki support will try to help you resolve your issue as soon as possible.</p>
|
| 114 |
-
<h2>Conclusion</h2>
|
| 115 |
-
<h3>Summary of the main points</h3>
|
| 116 |
-
<p>In this article, we have learned about FineCut/Coat9 for CorelDRAW, a plug-in software that enhances the potentiality of cutting plotter. We have discussed its features and benefits, how to install and use it, how to get a serial number for it, and how to troubleshoot common issues with it.</p>
|
| 117 |
-
<p>We have also provided some examples of serial numbers and activation codes for different products of FineCut/Coat9 for CorelDRAW. You can use these serial numbers and activation codes to activate your product and enjoy its full functionality.</p>
|
| 118 |
-
<h3>Call to action and recommendation</h3>
|
| 119 |
-
<p>If you are interested in FineCut/Coat9 for CorelDRAW, you can download it from Mimaki's website and try it for free for 30 days. You can also purchase a new product or upgrade from a previous version of FineCut CD.</p>
|
| 120 |
-
<p>We recommend you to use FineCut/Coat9 for CorelDRAW with Mimaki cutting plotters, such as CJV, UCJV, CG, CF and CFL series. These cutting plotters are designed to work seamlessly with FineCut/Coat9 and offer high-quality and versatile cutting performance.</p>
|
| 121 |
-
<p>Thank you for reading this article. We hope you have found it useful and informative. If you have any feedback or questions, please feel free to contact us or leave a comment below.</p>
|
| 122 |
-
<h4>FAQs</h4>
|
| 123 |
-
<ul>
|
| 124 |
-
<li><strong>Q: What is the difference between FineCut/Coat9 and FineCut8?</strong></li>
|
| 125 |
-
<li>A: The main difference between FineCut/Coat9 and FineCut8 is that FineCut/Coat9 supports output to laser engraving machines of other manufacturers. It also has some new and improved functions, such as offset correction function, cut preview function, vectorization function, etc.</li>
|
| 126 |
-
<li><strong>Q: Can I use FineCut/Coat9 with CorelDRAW in Macintosh version?</strong></li>
|
| 127 |
-
<li>A: No, FineCut/Coat9 does not support CorelDRAW in Macintosh version. You can only use it with CorelDRAW in Windows version.</li>
|
| 128 |
-
<li><strong>Q: Can I use FineCut/Coat9 with other graphic design software besides CorelDRAW?</strong></li>
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| 129 |
-
<li>A: Yes, you can use FineCut/Coat9 with Adobe Illustrator as well. You can download FineCut/Coat9 for Illustrator from Mimaki's website.</li>
|
| 130 |
-
<li><strong>Q: How can I update my FineCut/Coat9 to the latest version?</strong></li>
|
| 131 |
-
<li>A: You can update your FineCut/Coat9 to the latest version by downloading it from Mimaki's website and installing it on your computer. You don't need to enter a new serial key if you already have one.</li>
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| 132 |
-
<li><strong>Q: How can I get more information about FineCut/Coat9?</strong></li>
|
| 133 |
-
<li>A: You can get more information about FineCut/Coat9 by visiting Mimaki's website or reading the user manual that comes with the software.</li>
|
| 134 |
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</ul>
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</p> 0a6ba089eb<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ARK Survival Evolved Steam Activation Code - Get Your License Key.txt Now.md
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<table>
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<tr>
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<h1>Ark: Survival Evolved - A Guide for Beginners</h1></td>
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</tr>
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<tr>
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<td><h2>What is Ark: Survival Evolved?</h2></td>
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</tr>
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<tr>
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<td><h3>A brief overview of the game's premise, genre, and features</h3></td>
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</tr>
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<tr>
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<td><p>Ark: Survival Evolved is an action-adventure survival game that was released in 2017 by Studio Wildcard. In this game, you play as a human who wakes up on a mysterious island full of dinosaurs, mythical creatures, natural hazards, and potentially hostile human players. Your goal is to survive by gathering resources, crafting tools, building shelters, taming creatures, exploring the island, and fighting against enemies. You can play solo or with other players in various modes, such as PvE (player versus environment), PvP (player versus player), or PvX (a mix of both). You can also customize your game experience by using mods that add new features, content, and options to the game.</p></td>
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</tr>
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<tr>
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<td><h2>How to get started in Ark: Survival Evolved</h2></td>
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</tr>
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<tr>
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<td><h3>The basics of survival: gathering resources, crafting tools, building shelters, and taming creatures</h3></td>
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</tr>
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<tr>
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<tr>
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<td><h4>Tips and tricks for finding and harvesting resources</h4></td>
|
| 24 |
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</tr>
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<tr>
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<td><p>Resources are essential for your survival in Ark: Survival Evolved, as they allow you to craft items, build structures, tame creatures, and more. However, resources are not evenly distributed across the map, and some are more rare and valuable than others. Here are some tips and tricks for finding and harvesting resources:</p>
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| 28 |
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<ul>
|
| 29 |
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<li>Use your map and explorer notes to locate resource-rich areas, such as mountains, caves, rivers, and swamps. You can also use a GPS or a Compass to navigate more easily.</li>
|
| 30 |
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<li>Use the right tool for the right resource. For example, use a pick to get more flint from rocks, or a hatchet to get more wood from trees. You can also use specialized tools, such as a Metal Pick or a Chainsaw, to get more resources faster and more efficiently.</li>
|
| 31 |
-
<li>Use tamed creatures to help you gather resources. Some creatures have special abilities or bonuses for harvesting certain resources, such as the Ankylosaurus for metal, the Doedicurus for stone, the Castoroides for wood, or the Therizinosaur for fiber. You can also use flying creatures, such as the Argentavis or the Quetzal, to transport large amounts of resources.</li>
|
| 32 |
-
<li>Be aware of the weight limit of your inventory and your creatures. If you exceed the weight limit, you will move slower and consume more stamina. You can increase your weight capacity by leveling up your Weight stat or by using items such as a Backpack or a Parachute.</li>
|
| 33 |
-
<li>Be careful of the dangers that lurk in resource-rich areas. Some areas may have hostile creatures, environmental hazards, or enemy players that may attack you while you are gathering resources. Always be prepared for a fight or a flight, and have an escape plan in case things go wrong.</li>
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| 34 |
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</ul></td>
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| 35 |
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</tr>
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| 36 |
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<tr>
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<td><h4>Tips and tricks for crafting useful items and weapons</h4></td>
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| 38 |
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</tr>
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<tr>
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| 40 |
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<td><p>Crafting is one of the core mechanics of Ark: Survival Evolved, as it allows you to create items and weapons that can help you survive and thrive in the game. However, crafting is not always straightforward, and you may need to learn some tips and tricks to craft more effectively. Here are some tips and tricks for crafting useful items and weapons:</p>
|
| 41 |
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<ul>
|
| 42 |
-
<li>Learn new Engrams as you level up. Engrams are blueprints that unlock new crafting recipes for items and weapons. You can choose which Engrams to learn by spending Engram Points that you earn by leveling up. You can also find Engrams in loot crates, explorer notes, or by defeating bosses.</li>
|
| 43 |
-
<li>Use different crafting stations to craft different items and weapons. Some items and weapons can be crafted in your inventory, such as a Stone Pick or a Spear. However, some items and weapons require a specific crafting station, such as a Mortar and Pestle, a Smithy, a Fabricator, or a Tek Replicator. You can also use crafting stations to repair your items and weapons.</li>
|
| 44 |
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<li>Use blueprints to craft higher quality items and weapons. Blueprints are special versions of Engrams that can produce items and weapons with better stats and durability than normal ones. You can find blueprints in loot crates, explorer notes, or by defeating bosses. Blueprints have different quality levels, from Primitive to Ascendant.</li>
|
| 45 |
-
<li>Use mods to add new items and weapons to the game. Mods are user-created content that can enhance your game experience by adding new features, content, and customization options to the game. You can find mods on Steam Workshop or on other websites. Some of the most popular mods for Ark: Survival Evolved are Structures Plus (S+), Super Structures (SS), Awesome Spyglass!, Classic Flyers, Eco's Decor Mods, Primal Fear, Ark Eternal, Gaia, Extinction Core, Annunaki Genesis, Ark Additions: The Collection!, Pyria: Mythos Evolved ,and many more.</li>
|
| 46 |
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<li>Use cheats or admin commands to spawn items and weapons instantly. Cheats or admin commands are special codes that can alter the game settings or give you access to items and weapons without having to craft them. However, using cheats or admin commands may disable achievements or affect your game balance. You can find a list of cheats or admin commands on this wiki or on other websites.</li>
|
| 47 |
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</ul></td>
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| 48 |
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</tr>
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<tr>
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<td><h4>Tips and tricks for building and defending your base</h4></td>
|
| 51 |
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</tr>
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<tr>
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| 53 |
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<td><p>Building and defending your base is another important aspect of Ark: Survival Evolved, as it provides you with a safe place to store your items, craft your weapons, breed your creatures, and more. However, building and defending your base is not easy, and you may need to learn some tips and tricks to do it well. Here are some tips and tricks for building and defending your base:</p>
|
| 54 |
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<ul>
|
| 55 |
-
<li>Choose a good location for your base. You want to find a spot that has access to resources, water, and flat land, but also has some natural defenses, such as cliffs, caves, or waterfalls. You also want to avoid areas that are too crowded, too dangerous, or too exposed to enemy attacks.</li>
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| 56 |
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<li>Use different materials and structures to build your base. You can start with basic materials, such as Thatch or Wood, but you should upgrade to stronger materials, such as Stone, Metal, or Tek, as soon as possible. You can also use different structures, such as Foundations, Walls, Ceilings, Doors, Windows, Stairs, Ramps, Pillars, Fences, Gates, Turrets, Traps, and more.</li>
|
| 57 |
-
<li>Use different designs and layouts to build your base. You can build your base in any shape or size you want, but you should consider some factors, such as functionality, aesthetics, and security. You can also use different designs and layouts, such as a Tower, a Castle, a Bunker, a Treehouse, a Cave Base, a Raft Base, a Platform Base, or a Floating Base.</li>
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| 58 |
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<li>Use different strategies and tactics to defend your base. You can use passive defenses, such as Walls, Gates, Turrets, Traps, and Spikes. You can also use active defenses, such as tamed creatures, weapons, explosives, or allies. You can also use stealth defenses, such as camouflage, decoys, or hidden entrances.</li>
|
| 59 |
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<li>Use different modes and settings to protect your base. You can play on PvE servers or single-player mode if you don't want to deal with other players. You can also use settings such as Offline Raid Protection (ORP), Structure Resistance (SR), Structure Decay (SD), or Tribe Governance (TG) to adjust the rules and options for your base.</li>
|
| 60 |
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</ul></td>
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| 61 |
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</tr>
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| 62 |
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<tr>
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| 63 |
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<td><h4>Tips and tricks for taming and riding dinosaurs and other creatures</h4></td>
|
| 64 |
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</tr>
|
| 65 |
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<tr>
|
| 66 |
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<td><p>Taming and riding dinosaurs and other creatures is one of the most fun and rewarding aspects of Ark: Survival Evolved, as it allows you to have loyal companions that can help you in various ways. However, taming and riding dinosaurs and other creatures is not easy, and you may need to learn some tips and tricks to do it well. Here are some tips and tricks for taming and riding dinosaurs and other creatures:</p>
|
| 67 |
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<ul>
|
| 68 |
-
<li>Use different methods and items to tame dinosaurs and other creatures. There are two main methods of taming: passive taming and knockout taming. Passive taming involves feeding the creature its preferred food while avoiding its aggression. Knockout taming involves knocking out the creature with tranquilizers or other means and then feeding it its preferred food while keeping it unconscious. You can also use items such as Bolas, Nets, Cages, Traps, Lassos, or Taming Pens to immobilize or capture the creature.</li>
|
| 69 |
-
<li>Use different foods and kibbles to tame dinosaurs and other creatures faster and more efficiently. Different creatures have different preferences for food and kibble. Food is any edible item that can fill the creature's hunger bar. Kibble is a special type of food that is made from eggs and other ingredients in a Cooking Pot or an Industrial Cooker. Kibble has higher taming effectiveness than regular food.</li>
|
| 70 |
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<li>Use different saddles and platforms to ride dinosaurs and other creatures more comfortably and securely. Saddles are items that allow you to ride certain creatures once they are tamed. Saddles have different quality levels from Primitive to Ascendant that affect their armor value and durability. Platforms are special types of saddles that allow you to build structures on top of certain creatures.</li>
|
| 71 |
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/Disable Group/Set Aggression Level/Set Turret Mode and more. Settings are options that you can adjust for your tamed creatures by using the radial menu or the options menu. Settings include Enable/Disable Ally Looking/Enable/Disable Resource Harvesting/Enable/Disable Victim Item Collection/Enable/Disable Allow Anyone To Imprint/Enable/Disable Public Seating/Enable/Disable Auto-Courtesy/Enable/Disable Auto-Engage and more.</li>
|
| 72 |
-
<li>Use different stats and mutations to breed dinosaurs and other creatures more effectively and selectively. Stats are numerical values that represent the attributes of your tamed creatures, such as Health, Stamina, Oxygen, Food, Weight, Melee Damage, Movement Speed, Torpidity, and more. Mutations are random changes that occur in the stats or colors of your offspring when you breed two tamed creatures. Mutations can be beneficial or detrimental, and they can be inherited or not by the next generation.</li>
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| 73 |
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</ul></td>
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| 74 |
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</tr>
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| 75 |
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<tr>
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| 76 |
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<td><h2>How to enhance your experience in Ark: Survival Evolved</h2></td>
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</tr>
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<tr>
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| 79 |
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<td><h3>The benefits of playing with other players: forming tribes, trading, and cooperating</h3></td>
|
| 80 |
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</tr>
|
| 81 |
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<tr>
|
| 82 |
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<td><p>Playing with other players can enhance your experience in Ark: Survival Evolved by adding more fun, challenge, and diversity to the game. You can play with other players in various ways, such as forming tribes, trading, and cooperating. Here are some benefits of playing with other players:</p>
|
| 83 |
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<ul>
|
| 84 |
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<li>Forming tribes can help you survive and thrive in the game by sharing resources, items, structures, creatures, and responsibilities with your tribe mates. You can also access tribe-only features, such as tribe chat, tribe log, tribe rank, tribe alliances, tribe wars, and more.</li>
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| 85 |
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<li>Trading can help you acquire resources, items, creatures, or services that you may not have or need in exchange for something that you have or don't need. You can trade with other players by using chat, voice chat, signs, mailboxes, trading posts, vending machines, or other means.</li>
|
| 86 |
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<li>Cooperating can help you achieve goals that you may not be able to achieve alone or with your tribe mates. You can cooperate with other players by joining forces to explore the map, tame creatures, build structures, fight enemies, complete missions, defeat bosses, or participate in events.</li>
|
| 87 |
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</ul></td>
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| 88 |
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</tr>
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| 89 |
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<tr>
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| 90 |
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<td><h3>The challenges of playing with other players: competing, raiding, and surviving</h3></td>
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| 91 |
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</tr>
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| 92 |
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<tr>
|
| 93 |
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<td><p>Playing with other players can also pose some challenges in Ark: Survival Evolved by adding more risk, conflict, and uncertainty to the game. You can play with other players in various ways, such as competing, raiding, and surviving. Here are some challenges of playing with other players:</p>
|
| 94 |
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<ul>
|
| 95 |
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<li>Competing can test your skills and abilities in the game by measuring your performance against other players. You can compete with other players by comparing your stats, achievements, trophies, rankings, or scores. You can also participate in competitive modes, such as PvP, PvX, or Survival of the Fittest.</li>
|
| 96 |
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<li>Raiding can challenge your strategy and tactics in the game by attacking or defending against other players' bases. You can raid other players by using weapons, explosives, creatures, or allies to breach their defenses and loot their resources, items, structures, or creatures. You can also defend against raids by using walls, gates, turrets, traps, creatures, or allies to repel their attacks and protect your assets.</li>
|
| 97 |
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<li>Surviving can challenge your endurance and adaptability in the game by facing other players' threats and actions. You can survive with other players by avoiding or dealing with hostile players who may try to kill you, rob you, capture you, enslave you, or troll you. You can also survive in different environments and biomes that may have different weather conditions, temperatures, resources, creatures, or hazards.</li>
|
| 98 |
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</ul></td>
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| 99 |
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</tr>
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| 100 |
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<tr>
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| 101 |
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<td><h3>The fun of playing with mods: adding new features, content, and customization</h3></td>
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| 102 |
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</tr>
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| 103 |
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<tr>
|
| 104 |
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<td><p>Playing with mods can enhance your experience in Ark: Survival Evolved by adding new features, content, and customization to the game. Mods are user-created content that can change or improve the game in various ways. You can play with mods by finding and installing them on Steam Workshop or on other websites. Here are some fun of playing with mods:</p>
|
| 105 |
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<ul>
|
| 106 |
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<li>Adding new features can expand the gameplay and mechanics of the game by introducing new elements or options that are not available in the base game. For example, you can use mods that add new crafting systems, new combat systems, new building systems, new taming systems, new mission systems, new event systems, new difficulty settings, new game modes, and more.</li>
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new weapons, new armor, new vehicles, new structures, new skins, new hairstyles, new tattoos, new music, new sounds, new voiceovers, and more.</li>
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<li>Adding new customization can personalize your game experience by allowing you to modify or adjust the game to your liking. For example, you can use mods that change the graphics, the UI, the controls, the camera, the animations, the effects, the colors, the fonts, the icons, the names, and more.</li>
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| 109 |
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</ul></td>
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| 110 |
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</tr>
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<tr>
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<td><h2>Conclusion</h2></td>
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</tr>
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<tr>
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<td><h3>A summary of the main points of the article</h3></td>
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</tr>
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<tr>
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<td><p>In conclusion, Ark: Survival Evolved is a game that offers a lot of fun and challenge for players who enjoy action-adventure survival games. In this game, you can explore a mysterious island full of dinosaurs and other creatures, gather resources, craft items and weapons, build and defend your base, tame and ride creatures, play with other players or against them, and customize your game with mods. This article has provided you with some tips and tricks on how to get started in Ark: Survival Evolved, how to enhance your experience in Ark: Survival Evolved, and how to download ark survival evolved license key.txt file.</p>
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how to update ark survival evolved license key txt download<br />
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how to remove ark survival evolved license key txt download</p></td>
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</tr>
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<tr>
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<td><h3>A call to action for the readers to try out the game or share their own tips and tricks</h3></td>
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<td><p>If you are interested in trying out Ark: Survival Evolved, you can download it from Steam or other platforms. You can also download ark survival evolved license key.txt file from this link to activate the game. However, be careful of fake or malicious links that may harm your computer or steal your information. Always use a trusted source and scan your files before opening them.</p>
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| 173 |
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<p>If you have already played Ark: Survival Evolved or have your own tips and tricks to share, feel free to leave a comment below or join our community on Discord . We would love to hear from you and learn from your experience. Thank you for reading this article and have fun playing Ark: Survival Evolved!</p></td>
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</tr>
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<tr>
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<td><h2>FAQs</h2></td>
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</tr>
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<td><h3>Q: How do I download ark survival evolved license key.txt file?</h3></td>
|
| 180 |
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</tr>
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<tr>
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<td><p>A: You can download ark survival evolved license key.txt file from this link . However, be careful of fake or malicious links that may harm your computer or steal your information. Always use a trusted source and scan your files before opening them.</p></td>
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</tr>
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<tr>
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<td><h3>Q: How do I install mods for Ark: Survival Evolved?</h3></td>
|
| 186 |
-
</tr>
|
| 187 |
-
<tr>
|
| 188 |
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<td><p>A: You can install mods for Ark: Survival Evolved by using Steam Workshop or other websites. To use Steam Workshop, you need to subscribe to the mod you want to use and then launch the game. The mod will be automatically downloaded and installed. To use other websites, you need to download the mod file and then copy it to the Mods folder in your game directory. You can also use mod managers, such as Ark Server Manager or Ark Mod Downloader, to make the process easier.</p></td>
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</tr>
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<tr>
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| 191 |
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<td><h3>Q: How do I join a tribe in Ark: Survival Evolved?</h3></td>
|
| 192 |
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</tr>
|
| 193 |
-
<tr>
|
| 194 |
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<td><p>A: You can join a tribe in Ark: Survival Evolved by either creating your own tribe or joining an existing one. To create your own tribe, you need to access the Tribe Manager menu and then click on Create New Tribe. You can then invite other players to join your tribe by using the Invite To Tribe option in the radial menu. To join an existing tribe, you need to receive an invitation from a tribe member or leader and then accept it. You can also request to join a tribe by using the Request To Join Tribe option in the radial menu.</p></td>
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| 195 |
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</tr>
|
| 196 |
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<tr>
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| 197 |
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<td><h3>Q: How do I tame a dinosaur in Ark: Survival Evolved?</h3></td>
|
| 198 |
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</tr>
|
| 199 |
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<tr>
|
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<td><p>A: You can tame a dinosaur in Ark: Survival Evolved by either using passive taming or knockout taming. Passive taming involves feeding the dinosaur its preferred food while avoiding its aggression. Knockout taming involves knocking out the dinosaur with tranquilizers or other means and then feeding it its preferred food while keeping it unconscious. You can also use items such as Bolas, Nets, Cages, Traps, Lassos, or Taming Pens to immobilize or capture the dinosaur.</p></td>
|
| 201 |
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</tr>
|
| 202 |
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<tr>
|
| 203 |
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<td><h3>Q: How do I breed dinosaurs in Ark: Survival Evolved?</h3></td>
|
| 204 |
-
</tr>
|
| 205 |
-
<tr>
|
| 206 |
-
<td><p>A: You can breed dinosaurs in Ark: Survival Evolved by mating two tamed dinosaurs of the same species and opposite gender. To mate them, you need to enable mating on both of them and then place them near each other. They will then produce a fertilized egg or a gestation bar, depending on their type. You need to incubate the egg or wait for the gestation bar to fill up until the baby dinosaur is born. You then need to imprint and raise the baby dinosaur until it reaches adulthood.</p></td>
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| 207 |
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</tr></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Brawlhalla APK Chromebook Everything You Need to Know About the Free Platform Fighter.md
DELETED
|
@@ -1,132 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>How to Play Brawlhalla on Your Chromebook</h1>
|
| 3 |
-
<p>If you are looking for a fun and free platform fighting game that supports cross-play with millions of players on various devices, you might want to check out Brawlhalla. Brawlhalla is a 2D game that features over 50 unique characters, various game modes, and frequent updates. In this article, we will show you how to play Brawlhalla on your Chromebook by installing the APK file of the game.</p>
|
| 4 |
-
<h2>brawlhalla apk chromebook</h2><br /><p><b><b>Download</b> ✒ ✒ ✒ <a href="https://urlin.us/2uT1b3">https://urlin.us/2uT1b3</a></b></p><br /><br />
|
| 5 |
-
<h2>What is Brawlhalla?</h2>
|
| 6 |
-
<p>Brawlhalla is a free-to-play game developed by Blue Mammoth Games and published by Ubisoft. It was released in 2017 for Windows, macOS, PlayStation 4, Xbox One, Nintendo Switch, iOS, and Android. It is one of the most popular platform fighting games, with over 100 million players as of 2023.</p>
|
| 7 |
-
<h3>Brawlhalla game features</h3>
|
| 8 |
-
<p>Some of the features that make Brawlhalla stand out are:</p>
|
| 9 |
-
<ul>
|
| 10 |
-
<li>An immersive action game that takes fighting to the next level</li>
|
| 11 |
-
<li>Explore the halls of Valhalla and cherish its splendor</li>
|
| 12 |
-
<li>Supports up to eight players online in a single match</li>
|
| 13 |
-
<li>Advance the ranking ladder by taking on foes alone</li>
|
| 14 |
-
<li>A game training room to hone your skills in fighting</li>
|
| 15 |
-
<li>Over 50 unique characters (and counting) to choose from</li>
|
| 16 |
-
<li>No pay-to-win advantages or in-game purchases</li>
|
| 17 |
-
</ul>
|
| 18 |
-
<h3>Brawlhalla game modes</h3>
|
| 19 |
-
<p>Brawlhalla offers various game modes for different preferences and play styles. Some of the game modes are:</p>
|
| 20 |
-
<ul>
|
| 21 |
-
<li>Online Ranked 1v1: Climb the ranked ladder from Tin up to Platinum and beyond. Fight enemies with swords, axes, hammers, blasters, rocket lances and more.</li>
|
| 22 |
-
<li>Online Ranked 2v2: Team up with a friend or find a partner online. Increase your clan's rank and glory.</li>
|
| 23 |
-
<li>4 Player Online Free for All: Casual matches where four fighters enter, but only one can win.</li>
|
| 24 |
-
<li>Cross-play Custom Rooms: Invite up to 8 friends on PC, Xbox, PS4, Nintendo Switch, iOS or Android to a huge variety of custom matches.</li>
|
| 25 |
-
<li>Many Special Game Modes: Brawlball, Bombsketball, Capture the Flag, Kung-Foot, and many more.</li>
|
| 26 |
-
<li>The Training Room: Practice combos and setups inside the Training Room. Look at detailed frame data, hitboxes, hurtboxes, and sharpen your skills.</li>
|
| 27 |
-
</ul>
|
| 28 |
-
<h2>How to Install Brawlhalla APK on Chromebook</h2>
|
| 29 |
-
<p>To play Brawlhalla on your Chromebook, you will need to install the APK file of the game. APK files are Android application packages that contain all the files and resources needed to run an app on an Android device. However, Chromebooks do not support APK files natively, so you will need to enable some settings and use some tools to install them.</p>
|
| 30 |
-
<h3>Enable developer mode and unknown sources</h3>
|
| 31 |
-
<p>The first step is to enable developer mode on your Chromebook. Developer mode allows you to access more features and settings on your device, such as installing APK files. However, it also removes some security protections and may void your warranty, so proceed with caution. To enable developer mode on your Chromebook, follow these steps:</p>
|
| 32 |
-
<ol>
|
| 33 |
-
<li>Turn off your Chromebook by holding down the power button.</li>
|
| 34 |
-
<li>Press and hold the Esc + Refresh keys (the circular arrow key above the number 3), then press the power button.</li>
|
| 35 |
-
<li>You will see a screen that says "Chrome OS is missing or damaged". Press Ctrl + D to enter developer mode.</li>
|
| 36 |
-
<p>Press Enter to confirm. You will see a screen that says \"To turn OS verification off, press Enter. Your system will reboot and local data will be cleared. To go back, press Esc.\" Press Enter again.</li>
|
| 37 |
-
<li>Your Chromebook will reboot and show a screen that says \"OS verification is off\". Press Ctrl + D to continue.</li>
|
| 38 |
-
<li>Your Chromebook will erase all your local data and prepare for developer mode. This may take a few minutes.</li>
|
| 39 |
-
<li>Once the process is done, your Chromebook will reboot and show the same \"OS verification is off\" screen. Press Ctrl + D again or wait for 30 seconds to boot into developer mode.</li>
|
| 40 |
-
<li>Once you are in developer mode, you will see a screen that says \"Welcome!\". Select your language and keyboard settings, then click \"Let's go\".</li>
|
| 41 |
-
<li>Connect to a network and agree to the terms and conditions. Then, sign in with your Google account or create one if you don't have one.</li>
|
| 42 |
-
<li>You will see a screen that says \"You're almost done\". Click on \"Turn on Google Play Store\".</li>
|
| 43 |
-
<li>You will see a screen that says \"Get your Android apps\". Click on \"More\" and then \"Accept\".</li>
|
| 44 |
-
<li>You will see a screen that says \"Google Play Store is ready to use\". Click on \"Got it\".</li>
|
| 45 |
-
</ol>
|
| 46 |
-
<h3>Download and install APK file</h3>
|
| 47 |
-
<p>The next step is to download and install the APK file of Brawlhalla on your Chromebook. You can use any file manager Android app from the Play Store to do this. We recommend using Solid Explorer, as it is easy to use and has a lot of features. To download and install the APK file of Brawlhalla, follow these steps:</p>
|
| 48 |
-
<ol>
|
| 49 |
-
<li>Open the Play Store app on your Chromebook and search for Solid Explorer. Install the app and open it.</li>
|
| 50 |
-
<li>In Solid Explorer, tap on the hamburger icon on the top left corner and select \"Storage\".</li>
|
| 51 |
-
<li>Tap on \"Downloads\" and you will see all the files you have downloaded on your Chromebook.</li>
|
| 52 |
-
<li>If you have not downloaded the APK file of Brawlhalla yet, you can do so by opening a new tab in Chrome and going to [APKMirror.com](^1^). Search for Brawlhalla and download the latest version of the game.</li>
|
| 53 |
-
<li>Once the download is complete, go back to Solid Explorer and tap on the APK file of Brawlhalla. You will see a pop-up that says \"Do you want to install this application?\" Tap on \"Install\".</li>
|
| 54 |
-
<li>You will see another pop-up that says \"For your security, your phone is not allowed to install unknown apps from this source.\" Tap on \"Settings\".</li>
|
| 55 |
-
<li>You will see a screen that says \"Install unknown apps\". Toggle on the switch next to \"Allow from this source\".</li>
|
| 56 |
-
<li>Go back to Solid Explorer and tap on the APK file of Brawlhalla again. Tap on \"Install\" again.</li>
|
| 57 |
-
<li>You will see a screen that shows the progress of the installation. Wait for it to finish.</li>
|
| 58 |
-
<li>Once the installation is done, you will see a screen that says \"App installed\". Tap on \"Open\" to launch the game.</li>
|
| 59 |
-
</ol>
|
| 60 |
-
<h3>Launch and enjoy the game</h3>
|
| 61 |
-
<p>The final step is to launch and enjoy Brawlhalla on your Chromebook. You can play the game with your keyboard, mouse, or touchpad, or connect a controller if you prefer. To launch and enjoy Brawlhalla, follow these steps:</p>
|
| 62 |
-
<p>brawlhalla free download for chromebook<br />
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|
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brawlhalla developer blog and roadmap for chromebook<br />
|
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|
| 104 |
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brawlhalla wiki and information for chromebook<br />
|
| 105 |
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brawlhalla memes and jokes for chromebook<br />
|
| 106 |
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brawlhalla beginners guide for chromebook</p>
|
| 107 |
-
<ol>
|
| 108 |
-
<li>If you have not opened the game yet, you can do so by going to the launcher app on your Chromebook and finding Brawlhalla under the Play Store section. Tap on it to open it.</li>
|
| 109 |
-
<li>You will see a screen that says \"Brawlhalla requires access to photos/media/files on your device.\" Tap on \"Allow\".</li>
|
| 110 |
-
<li>You will see another screen that says \"Brawlhalla would like to access this device's location.\" Tap on \"Allow only while using the app\".</li>
|
| 111 |
-
<li>You will see a loading screen with the Brawlhalla logo. Wait for it to load.</li>
|
| 112 |
-
<li>You will see a welcome screen with some tips and news about the game. Tap on \"Play Now\".</li>
|
| 113 |
-
<li>You will see a screen that asks you to choose your region. Select the one that is closest to you for better performance.</li>
|
| 114 |
-
<li>You will see a screen that asks you to create or link an Ubisoft account. <p>You can create or link an Ubisoft account to enjoy some benefits, such as saving your progress, unlocking rewards, and accessing exclusive content. You can also skip this step if you want to play as a guest.</li>
|
| 115 |
-
<li>You will see the main menu of the game, where you can choose from various options, such as online play, offline play, training room, store, settings, and more.</li>
|
| 116 |
-
<li>Select the option that suits your preference and start playing Brawlhalla on your Chromebook. Have fun!</li>
|
| 117 |
-
</ol>
|
| 118 |
-
<h2>Conclusion</h2>
|
| 119 |
-
<p>Brawlhalla is a free and fun platform fighting game that you can play on your Chromebook by installing the APK file of the game. You will need to enable developer mode and unknown sources on your device, download and install the APK file using a file manager app, and launch and enjoy the game. You can also create or link an Ubisoft account to get some extra benefits. Brawlhalla supports cross-play with millions of players on various devices, so you can join your friends or make new ones in this epic game.</p>
|
| 120 |
-
<h2>FAQs</h2>
|
| 121 |
-
<h3>Is Brawlhalla safe to play on Chromebook?</h3>
|
| 122 |
-
<p>Yes, Brawlhalla is safe to play on Chromebook as long as you download the APK file from a trusted source, such as [APKMirror.com](^1^). You should also be careful when enabling developer mode and unknown sources on your device, as they may expose your device to some risks.</p>
|
| 123 |
-
<h3>Can I play Brawlhalla with a controller on Chromebook?</h3>
|
| 124 |
-
<p>Yes, you can play Brawlhalla with a controller on Chromebook if you have a compatible controller that can connect to your device via Bluetooth or USB. You can also customize the controller settings in the game menu.</p>
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<h3>How do I update Brawlhalla on Chromebook?</h3>
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<p>To update Brawlhalla on Chromebook, you will need to download and install the latest version of the APK file from [APKMirror.com](^1^) or another trusted source. You can also check for updates in the game menu.</p>
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<p>If you have any issues or questions about Brawlhalla, you can contact Brawlhalla support by visiting their official website at [brawlhalla.com] and clicking on \"Support\" at the bottom of the page. You can also follow them on social media platforms, such as Twitter, Facebook, Instagram, YouTube, Twitch, and Discord.</p> 197e85843d<br />
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<h1>Caribbean Treasures Download: How to Play and Win Big</h1>
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<p>Caribbean Treasures is an online gaming platform that offers two types of games: fish games and slot games. Both types of games are designed to test your skills and luck, and reward you with big treasures. Here are some features of each type of game:</p>
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<p>The fish game is a shooting game where you have to aim and fire at various fish and sea creatures on the screen. The more fish you catch, the more coins you earn. You can also encounter special fish that give you extra coins, bonuses, or multipliers. The fish game has different levels of difficulty, from easy to hard, and different modes, such as single-player or multiplayer. You can also customize your cannon and use different power-ups to enhance your gameplay.</p>
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<p>Caribbean Treasures is an online gaming platform that offers exciting fish games and slot games that you can play anytime, anywhere. You can download the game for free for your mobile device or desktop computer. You can sign up for a free account and start playing with real money or virtual coins. You can use your skills and strategy, take advantage of bonuses and promotions, and join the VIP club to win big with Caribbean Treasures. So what are you waiting for? Download Caribbean Treasures today and discover the hidden treasures of the Caribbean!</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions about Caribbean Treasures:</p>
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<li><b>Is Caribbean Treasures safe and secure?</b></li>
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<p>Yes, Caribbean Treasures is safe and secure. The game uses advanced encryption technology to protect your personal and financial information. The game also follows fair gaming practices and has a random number generator to ensure fair outcomes.</p>
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<p>You can contact the support team of Caribbean Treasures by email, phone, or live chat. The support team is available 24/7 to assist you with any questions or issues you may have.</p>
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<p>Yes, you can play Caribbean Treasures with your friends. You can invite your friends to join the game and play together in the multiplayer mode of the fish game. You can also chat with your friends and other players in the game.</p>
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<tr><td>Android</td><td>4.4 or higher</td><td>1 GB or higher</td></tr>
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spaces/1phancelerku/anime-remove-background/Chess Online APK Download Play with Millions of Players Worldwide.md
DELETED
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<br />
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<h1>Chess Online APK Download: How to Play Chess on Your Android Device</h1>
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<p>Chess is one of the oldest and most popular board games in the world. It is a game of strategy, logic, and skill that can challenge your mind and improve your cognitive abilities. Whether you are a beginner or a master, chess can offer you endless hours of fun and entertainment.</p>
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<p>Chess online apk is an application that allows you to play chess online on your Android device. It is not an official app from any chess organization or website, but rather a third-party app that connects you to various chess servers and platforms. You can download it for free from different sources on the internet, but you need to be careful about the quality and security of the apk file.</p>
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<li>Go to your device settings and look for the security option and tap on it.</li>
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<li>Find the option that says unknown sources or install unknown apps and toggle it on.</li>
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<h3>Step 3: Download and install the apk file</h3>
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<p>The final step is to download and install the apk file. To do this, follow these steps:</p>
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<li>Go to the website where you found the chess online apk file and tap on the download button.</li>
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<h3>Step 1: Create an account or log in with an existing one</h3>
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<p>Before you can play chess online with the app, you need to create an account or log in with an existing one. This will allow you to access all the features and benefits of the app, such as saving your games, joining tournaments, earning ratings, and more. To do this, follow these steps:</p>
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<li>Open the app and tap on the menu icon on the top left corner of the screen.</li>
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<h3>Step 2: Choose a game mode and a skill level</h3>
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<p>After creating an account or logging in, you can choose a game mode and a skill level that suits your preferences. There are four game modes available on the app: play online, play offline, puzzles, and lessons. You can also choose a skill level from beginner to expert, depending on how confident you are in your chess abilities. To do this, follow these steps:</p>
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<li>Tap on the game mode that you want to play. For example, if you want to play online, tap on play online.</li>
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<li>You will see a list of available opponents that match your skill level. You can also filter them by rating, time control, color preference, and more.</li>
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<p>The last step is to start playing and enjoy the game. You will see a chess board with your pieces and your opponent's pieces on it. You can move your pieces by tapping on them and then tapping on the square where you want to move them. You can also chat with your opponent by tapping on the chat icon on the bottom right corner of the screen. To play chess online with the app, follow these steps:</p>
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<li>Make your move by tapping on a piece and then tapping on a square where you want to move it.</li>
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<li>When the game is over, you will see a summary of the game with your rating changes, statistics, analysis, and more. You can also rematch your opponent by tapping on the rematch button or find a new opponent by tapping on the new game button.</li>
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<h3>Play with millions of players from around the world</h3>
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<p>One of the main features and benefits of chess online apk is that you can play with millions of players from around the world, with different skill levels and styles. You can find opponents that match your rating, time control, color preference, and more. You can also join tournaments, clubs, teams, and events that are organized by the app or by other users. You can also challenge your friends or invite them to play with you by using the app's social features.</p>
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<h3>Improve your skills with puzzles, lessons, and analysis tools</h3>
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<p>Another feature and benefit of chess online apk is that you can improve your skills with puzzles, lessons, and analysis tools that are available on the app. You can solve thousands of puzzles that are tailored to your skill level and help you practice different aspects of the game, such as tactics, strategy, endgames, and more. You can also learn from hundreds of lessons that are taught by expert coaches and cover various topics, such as openings, middlegames, endgames, and more. You can also use the app's analysis tools to review your games, find your mistakes, and learn from them. You can also access a powerful chess engine that can evaluate any position and suggest the best moves.</p>
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<h3>Customize your board, pieces, and themes</h3>
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<p>A third feature and benefit of chess online apk is that you can customize your board, pieces, and themes according to your preferences. You can choose from a variety of board styles, piece sets, and themes that suit your taste and mood. You can also adjust the sound effects, the animation speed, the board orientation, and more. You can also create your own custom board and pieces by using the app's editor feature.</p>
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<h4>Summary of the main points</h4>
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<p>In conclusion, chess online apk is an application that allows you to play chess online on your Android device. It is not an official app from any chess organization or website, but rather a third-party app that connects you to various chess servers and platforms. You can download it for free from different sources on the internet, but you need to be careful about the quality and security of the apk file.</p>
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<p>Chess online apk has many features and benefits that make it a great choice for chess lovers. You can play with millions of players from around the world, improve your skills with puzzles, lessons, and analysis tools, and customize your board, pieces, and themes. You can also save your games, join tournaments, chat with your opponents, and more.</p>
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<p>If you are interested in playing chess online on your Android device, you should give chess online apk a try. It is easy to download and install, and it will provide you with endless hours of fun and entertainment. You will also be able to improve your chess skills and meet new friends along the way. So what are you waiting for? Download chess online apk today and start playing chess online!</p>
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<p>Chess online apk is safe to use as long as you download it from a reliable source like [Chess.com], [APKPure], or [APKMirror]. These sources have verified the apk file and ensured that it does not contain any viruses, malware, or spyware that can harm your device or steal your personal information. However, you should always be careful and do some research before downloading anything from the internet, as there may be some fake or malicious sources that try to trick you.</p>
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<p>Chess online apk is updated regularly by the developers to fix bugs, improve performance, and add new features. You can check for updates by opening the app and tapping on the menu icon on the top left corner of the screen. Then, tap on settings and look for the update option. If there is an update available, you can tap on it and download it. Alternatively, you can also check for updates by visiting the website where you downloaded the apk file and looking for the latest version.</p>
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<p>Chess online apk requires an internet connection to play online with other players or access some of the features and benefits of the app. However, you can also play chess online apk offline by choosing the play offline game mode. This will allow you to play against the app's artificial intelligence, which has different skill levels and personalities. You can also access some of the puzzles and lessons that are available offline.</p>
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<p>Chess online apk is designed for Android devices, but you can also play it on other devices that support Android applications. For example, you can play chess online apk on your Windows PC or laptop by using an Android emulator like [BlueStacks](https://www.bluestacks.com/) or [NoxPlayer](https://www.bignox.com/). You can also play chess online apk on your iOS device by using an app like [iAndroid](https://apps.apple.com/us/app/iandroid/id1447357030) or [Appetize.io](https://appetize.io/). However, you may experience some compatibility issues or performance problems when playing chess online apk on other devices.</p>
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<p>A fourth benefit of NBA 2K20 Mod APK Revdl is that it is easy to install and compatible with most Android devices. You do not need to root your device or use any special tools or methods to install the modded game. You just need to download the APK file and the OBB data file from REXDL and follow some simple steps to install them on your device. You can also run the game smoothly on most Android devices without any lag or glitches.</p> <p>As a bonus, we have also prepared a table that compares the features of NBA 2K20 and NBA 2K20 Mod APK Revdl. You can use this table to see the differences and similarities between the two versions of the game.</p>
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| 73 |
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Table 3: Comparison of NBA 2K20 and NBA 2K20 Mod APK Revdl | Feature | NBA 2K20 | NBA 2K20 Mod APK Revdl | | --- | --- | --- | | Graphics | High-quality graphics with realistic animations and effects | Same as NBA 2K20 | | Gameplay | Smooth and responsive gameplay with various modes and options | Same as NBA 2K20 | | Features | 5 new NBA Stories, a new MyCAREER storyline, an all-new Run The Streets mode, 5-on-5 basketball with current or all-time great NBA teams, streetball in Blacktop mode, customization options for players, teams, and courts, online multiplayer and leaderboards | Same as NBA 2K20, plus free shopping for clothes, shoes, accessories, and more, unlimited virtual currency (VC) to upgrade your skills and attributes, no ads or in-app purchases | | Installation | Requires downloading from the Google Play Store or other trusted sources, may require additional data download after installation, may require payment for some features or items | Requires downloading from REXDL website, requires downloading both APK file and OBB data file, requires enabling installation of apps from unknown sources, requires extracting OBB data file to Android/OBB folder, does not require payment for any features or items | | Compatibility | Compatible with most Android devices with Android 4.3 or higher, may require high-end devices for optimal performance | Compatible with most Android devices with Android 4.3 or higher, may require high-end devices for optimal performance | <p>We hope that this table has been useful and informative for you. If you have any questions or feedback, please feel free to leave a comment below.</p>
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<p>Now that you have learned everything you need to know about NBA 2K20 Mod APK Revdl, it is time to download it and enjoy the best basketball game on your Android device. You will not regret it!</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions and answers about NBA 2K20 Mod APK Revdl that you might find helpful.</p>
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<h3>Q: Is NBA 2K20 Mod APK Revdl safe to download and install?</h3>
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<p>A: Yes, NBA 2K20 Mod APK Revdl is safe to download and install. It does not contain any viruses, malware, or spyware that might harm your device or compromise your privacy. However, you should always download it from the official website of REXDL and not from any other sources that might be unreliable or malicious.</p>
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<h3>Q: Is NBA 2K20 Mod APK Revdl legal to use?</h3>
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<p>A: Yes, NBA 2K20 Mod APK Revdl is legal to use. It does not violate any laws or regulations that might prohibit the use of modded games or apps. However, you should always use it at your own risk and discretion. We are not responsible for any consequences that might arise from using NBA 2K20 Mod APK Revdl.</p>
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<h3>Q: Will NBA 2K20 Mod APK Revdl affect my original game progress or data?</h3>
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<p>A: No, NBA 2K20 Mod APK Revdl will not affect your original game progress or data. It will create a separate folder and file for the modded game on your device. You can still play the original game without any interference or conflict with the modded game. You can also switch between the two versions of the game as you wish.</p>
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<h3>Q: Can I play online with other players using NBA 2K20 Mod APK Revdl?</h3>
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<p>A: Yes, you can play online with other players using NBA 2K20 Mod APK Revdl. However, you should be aware that some players might not appreciate playing with someone who has an unfair advantage over them. You might also face some issues or errors while playing online with the modded game. Therefore, we recommend that you play online with other players who are also using NBA 2K20 Mod APK Revdl or play offline with the modded game.</p>
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<h3>Q: Can I update NBA 2K20 Mod APK Revdl to the latest version?</h3>
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<p>A: Yes, you can update NBA 2K20 Mod APK Revdl to the latest version. However, you should always check the official website of REXDL for the latest update and download link. You should also backup your modded game data before updating to avoid losing any progress or settings. You should also follow the same steps as before to install the updated version of the modded game. You should also disable the automatic update of the game from the Google Play Store or other sources to avoid overwriting the modded game.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download for Spider Solitaire The Original and Best Solitaire Game.md
DELETED
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<br />
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<h1>Download for Spider Solitaire: How to Play the Classic Card Game Online and for Free</h1>
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<p>Spider Solitaire is one of the most popular and addictive card games in the world. It is a fun and challenging way to test your logic, patience, and concentration skills. But did you know that you can download and play Spider Solitaire online and for free? In this article, we will tell you everything you need to know about Spider Solitaire, including its history, rules, variations, benefits, tips, tricks, and more. We will also show you the best websites to download and play Spider Solitaire online and for free. So, if you are ready to join millions of other Spider Solitaire fans, read on!</p>
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<h2>What is Spider Solitaire?</h2>
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<p>Spider Solitaire is a type of solitaire game that uses two decks of cards. The goal is to arrange all the cards in the tableau (the playing area) into eight piles of cards, each pile containing cards of the same suit in descending order from King to Ace. Once a pile is completed, it is moved to the foundation (the area at the top) and removed from the game. The game is won when all eight piles are completed.</p>
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<h2>download for spider solitaire</h2><br /><p><b><b>Download File</b> ⏩ <a href="https://jinyurl.com/2uNPfd">https://jinyurl.com/2uNPfd</a></b></p><br /><br />
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<h3>The history and popularity of Spider Solitaire</h3>
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| 8 |
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<p>Spider Solitaire was first introduced in 1949 by F. R. Simon in his book "The Playing Card". It was originally called "Scorpion" and had some different rules. The name "Spider" was given by Thomas Warfield, who created a computer version of the game in 1996. He also added some variations, such as different levels of difficulty based on the number of suits used (one, two, or four). Since then, Spider Solitaire has become one of the most popular solitaire games in the world, especially after it was included in Microsoft Windows in 2001. According to some estimates, more than 100 million people play Spider Solitaire every day.</p>
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<h3>The rules and variations of Spider Solitaire</h3>
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| 10 |
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<p>The basic rules of Spider Solitaire are as follows:</p>
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| 11 |
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<ul>
|
| 12 |
-
<li>The game starts with 54 cards dealt face up into 10 columns in the tableau. The first four columns have six cards each, and the last six columns have five cards each.</li>
|
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<li>The remaining 50 cards are placed face down in the stock (the area at the bottom left). You can deal a new card onto each tableau column by clicking on the stock.</li>
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<li>You can move any card or a sequence of cards that are in descending order and of the same suit from one column to another. For example, you can move a 9 of Spades onto a 10 of Spades, or a 7-6-5 of Hearts onto an 8 of Hearts.</li>
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| 15 |
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<li>You can also move any card or a sequence of cards to an empty column.</li>
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<li>When you have a complete pile of cards from King to Ace of the same suit in a column, it is automatically moved to the foundation and removed from the game.</li>
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<li>You can change the level of difficulty by choosing how many suits you want to play with: one (easy), two (medium), or four (hard).</li>
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</ul>
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<p>There are also some variations of Spider Solitaire that have different rules or features. For example:</p>
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<ul>
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<li>Some versions allow you to undo <p>Some versions allow you to undo your moves, reshuffle the stock, or use hints.</li>
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<li>Some versions have different layouts, themes, backgrounds, or card designs.</li>
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<li>Some versions have different scoring systems, achievements, leaderboards, or statistics.</li>
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<li>Some versions have different modes, such as timed, relaxed, or challenge.</li>
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</ul>
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<h2>How to download and play Spider Solitaire online and for free?</h2>
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<p>If you want to download and play Spider Solitaire online and for free, you have many options to choose from. You can play Spider Solitaire on your computer, smartphone, tablet, or any other device that has an internet connection and a web browser. You can also download Spider Solitaire apps or software for your device if you prefer. Here are some of the benefits of playing Spider Solitaire online and for free:</p>
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<h3>The benefits of playing Spider Solitaire online and for free</h3>
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<ul>
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| 30 |
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<li>You can play Spider Solitaire anytime and anywhere you want, without any limitations or restrictions.</li>
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<li>You can play Spider Solitaire with different levels of difficulty, variations, features, and options, according to your preference and skill level.</li>
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<li>You can play Spider Solitaire with high-quality graphics, sounds, and animations, that enhance your gaming experience and enjoyment.</li>
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<li>You can play Spider Solitaire with other players from around the world, compete with them, chat with them, or learn from them.</li>
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<li>You can play Spider Solitaire without spending any money, downloading any files, or risking any viruses or malware.</li>
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</ul>
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<h3>The best websites to download and play Spider Solitaire online and for free</h3>
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<p>There are many websites that offer Spider Solitaire online and for free, but not all of them are reliable, safe, or fun. To help you find the best websites to download and play Spider Solitaire online and for free, we have selected three of them that we think are the best. Here they are:</p>
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<h4>Spider Solitaire - Play Online</h4>
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<p>This website is one of the most popular and trusted websites to play Spider Solitaire online and for free. It has a simple and user-friendly interface, a smooth and fast gameplay, and a variety of options and features. You can choose from one, two, or four suits, undo your moves, use hints, change the background color, adjust the sound volume, and more. You can also see your score, time, moves, and statistics. You can play Spider Solitaire on this website on any device that has a web browser.</p>
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<h4>Spider Solitaire: free online card game, play full-screen without download</h4>
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<p>This website is another great option to play Spider Solitaire online and for free. It has a beautiful and elegant design, a responsive and fluid gameplay, and a range of options and features. You can choose from one, two, or four suits, undo your moves, use hints, change the card style, switch to full-screen mode, and more. You can also see your score, time, moves, and statistics. You can play Spider Solitaire on this website on any device that has a web browser.</p>
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<h4>Spider Solitaire (2 Suits)</h4>
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<p>This website is a good choice to play Spider Solitaire online and for free if you are looking for a medium level of difficulty. It offers Spider Solitaire with two suits only, which is more challenging than one suit but easier than four suits. It has a clean and simple interface, a smooth and fast gameplay, and some basic options and features. You can undo your moves, use hints, change the background color, and adjust the sound volume. You can also see your score, time, moves, and statistics. You can play Spider Solitaire on this website on any device that has a web browser.</p>
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<h2>How to improve your skills and strategies in Spider Solitaire?</h2>
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<p>Spider Solitaire is not only a game of luck but also a game of skill and strategy. If you want to improve your skills and strategies in Spider Solitaire, you need to practice regularly, learn from your mistakes, and apply some tips and tricks. Here are some of them:</p>
|
| 94 |
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<h3>The tips and tricks for winning Spider Solitaire</h3>
|
| 95 |
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<ul>
|
| 96 |
-
<li>Try to expose the hidden cards as soon as possible, especially the ones in the longer columns.</li>
|
| 97 |
-
<li>Try to create empty columns as soon as possible, as they give you more flexibility and space to move cards around.</li>
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| 98 |
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<li>Try to build piles of cards of the same suit in descending order as much as possible, as they are easier to move and complete.</li>
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<li>Try to avoid moving cards that are not of the same suit unless it is necessary or beneficial.</li>
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<li>Try to plan ahead and anticipate the consequences of your moves before you make them.</li>
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<li>Try to use the stock wisely and only when you are stuck or need new cards.</li>
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| 102 |
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</ul>
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| 103 |
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<h3>The common mistakes and pitfalls to avoid in Spider Solitaire</h3>
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| 104 |
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<ul>
|
| 105 |
-
<li>Don't move cards without a purpose or a plan.</li>
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| 106 |
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<li>Don't waste your undo moves or hints on trivial or obvious moves.</li>
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<li>Don't ignore the cards in the foundation or the stock, as they may be useful later.</li>
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<li>Don't get frustrated or give up if you lose or get stuck, as every game is different and solvable.</li>
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</ul>
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<h2>Conclusion</h2>
|
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<p>Spider Solitaire is a classic card game that you can download and play online and for free. It is a fun and challenging way to test your logic, patience, and concentration skills. It also has many benefits, such as improving your memory, mood, and mental health. In this article, we have told you everything you need to know about Spider Solitaire, including its history, rules, variations, benefits, tips, tricks, and more. We have also shown you the best websites to download and play Spider Solitaire online and for free. We hope you have enjoyed reading this article and learned something new. Now it's time to put your knowledge into practice and start playing Spider Solitaire online and for free!</p>
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<h3>Call to action</h3>
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<p>If you are ready to download and play Spider Solitaire online and for free, click on one of the links below and start having fun!</p>
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| 114 |
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<ul>
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| 115 |
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<li><a href="">Spider Solitaire - Play Online</a></li>
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| 116 |
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<li><a href="">Spider Solitaire: free online card game, play full-screen without download</a></li>
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<li><a href="">Spider Solitaire (2 Suits)</a></li>
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</ul></p> 401be4b1e0<br />
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spaces/22h/vintedois-diffusion-v0-1/app.py
DELETED
|
@@ -1,135 +0,0 @@
|
|
| 1 |
-
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, EulerAncestralDiscreteScheduler
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import torch
|
| 4 |
-
from PIL import Image
|
| 5 |
-
|
| 6 |
-
model_id = '22h/vintedois-diffusion-v0-1'
|
| 7 |
-
prefix = ''
|
| 8 |
-
|
| 9 |
-
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
|
| 10 |
-
|
| 11 |
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pipe = StableDiffusionPipeline.from_pretrained(
|
| 12 |
-
model_id,
|
| 13 |
-
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
| 14 |
-
scheduler=scheduler)
|
| 15 |
-
|
| 16 |
-
pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
|
| 17 |
-
model_id,
|
| 18 |
-
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
| 19 |
-
scheduler=scheduler)
|
| 20 |
-
|
| 21 |
-
if torch.cuda.is_available():
|
| 22 |
-
pipe = pipe.to("cuda")
|
| 23 |
-
pipe_i2i = pipe_i2i.to("cuda")
|
| 24 |
-
|
| 25 |
-
def error_str(error, title="Error"):
|
| 26 |
-
return f"""#### {title}
|
| 27 |
-
{error}""" if error else ""
|
| 28 |
-
|
| 29 |
-
def inference(prompt, guidance, steps, width=640, height=640, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
|
| 30 |
-
|
| 31 |
-
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
|
| 32 |
-
prompt = f"{prefix} {prompt}" if auto_prefix else prompt
|
| 33 |
-
|
| 34 |
-
try:
|
| 35 |
-
if img is not None:
|
| 36 |
-
return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
|
| 37 |
-
else:
|
| 38 |
-
return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
|
| 39 |
-
except Exception as e:
|
| 40 |
-
return None, error_str(e)
|
| 41 |
-
|
| 42 |
-
def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
|
| 43 |
-
|
| 44 |
-
result = pipe(
|
| 45 |
-
prompt,
|
| 46 |
-
negative_prompt = neg_prompt,
|
| 47 |
-
num_inference_steps = int(steps),
|
| 48 |
-
guidance_scale = guidance,
|
| 49 |
-
width = width,
|
| 50 |
-
height = height,
|
| 51 |
-
generator = generator)
|
| 52 |
-
|
| 53 |
-
return result.images[0]
|
| 54 |
-
|
| 55 |
-
def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
|
| 56 |
-
|
| 57 |
-
ratio = min(height / img.height, width / img.width)
|
| 58 |
-
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
|
| 59 |
-
result = pipe_i2i(
|
| 60 |
-
prompt,
|
| 61 |
-
negative_prompt = neg_prompt,
|
| 62 |
-
image = img,
|
| 63 |
-
num_inference_steps = int(steps),
|
| 64 |
-
strength = strength,
|
| 65 |
-
guidance_scale = guidance,
|
| 66 |
-
generator = generator)
|
| 67 |
-
|
| 68 |
-
return result.images[0]
|
| 69 |
-
|
| 70 |
-
css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
|
| 71 |
-
"""
|
| 72 |
-
with gr.Blocks(css=css) as demo:
|
| 73 |
-
gr.HTML(
|
| 74 |
-
f"""
|
| 75 |
-
<div class="main-div">
|
| 76 |
-
<div>
|
| 77 |
-
<h1>22h Diffusion v0.1</h1>
|
| 78 |
-
</div>
|
| 79 |
-
<p>
|
| 80 |
-
Demo for <a href="https://huggingface.co/22h/vintedois-diffusion-v0-1">22h Diffusion v0-1</a> Stable Diffusion model.<br>
|
| 81 |
-
{"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
|
| 82 |
-
</p>
|
| 83 |
-
Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/22h/vintedois-diffusion-v0-1'>Settings</a></b>"}<br><br>
|
| 84 |
-
<a style="display:inline-block" href="https://huggingface.co/spaces/22h/vintedois-diffusion-v0-1?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
|
| 85 |
-
</div>
|
| 86 |
-
"""
|
| 87 |
-
)
|
| 88 |
-
with gr.Row():
|
| 89 |
-
|
| 90 |
-
with gr.Column(scale=55):
|
| 91 |
-
with gr.Group():
|
| 92 |
-
with gr.Row():
|
| 93 |
-
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
|
| 94 |
-
generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
|
| 95 |
-
|
| 96 |
-
image_out = gr.Image(height=512)
|
| 97 |
-
error_output = gr.Markdown()
|
| 98 |
-
|
| 99 |
-
with gr.Column(scale=45):
|
| 100 |
-
with gr.Tab("Options"):
|
| 101 |
-
with gr.Group():
|
| 102 |
-
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
|
| 103 |
-
auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
|
| 104 |
-
|
| 105 |
-
with gr.Row():
|
| 106 |
-
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
|
| 107 |
-
steps = gr.Slider(label="Steps", value=50, minimum=2, maximum=75, step=1)
|
| 108 |
-
|
| 109 |
-
with gr.Row():
|
| 110 |
-
width = gr.Slider(label="Width", value=640, minimum=64, maximum=1024, step=8)
|
| 111 |
-
height = gr.Slider(label="Height", value=640, minimum=64, maximum=1024, step=8)
|
| 112 |
-
|
| 113 |
-
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
|
| 114 |
-
|
| 115 |
-
with gr.Tab("Image to image"):
|
| 116 |
-
with gr.Group():
|
| 117 |
-
image = gr.Image(label="Image", height=256, tool="editor", type="pil")
|
| 118 |
-
strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
|
| 119 |
-
|
| 120 |
-
auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
|
| 121 |
-
|
| 122 |
-
inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
|
| 123 |
-
outputs = [image_out, error_output]
|
| 124 |
-
prompt.submit(inference, inputs=inputs, outputs=outputs)
|
| 125 |
-
generate.click(inference, inputs=inputs, outputs=outputs)
|
| 126 |
-
|
| 127 |
-
gr.HTML("""
|
| 128 |
-
<div style="border-top: 1px solid #303030;">
|
| 129 |
-
<br>
|
| 130 |
-
<p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
|
| 131 |
-
</div>
|
| 132 |
-
""")
|
| 133 |
-
|
| 134 |
-
demo.queue(concurrency_count=1)
|
| 135 |
-
demo.launch()
|
|
|
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spaces/ADOPLE/ResumeSummarizer/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: ResumeSummarizer
|
| 3 |
-
emoji: 👁
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: gray
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.35.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
duplicated_from: randstad/ResumeSummarizer
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/wav_processors/__init__.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
from . import base_processor
|
| 2 |
-
from . import common_processors
|
|
|
|
|
|
|
|
|
spaces/AIZero2Hero4Health/2-BiomedEntityRecognition-GR/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: 2 BiomedEntityRecognition GR
|
| 3 |
-
emoji: ⚡
|
| 4 |
-
colorFrom: red
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.8.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
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|
spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/settings/+page.server.ts
DELETED
|
@@ -1,33 +0,0 @@
|
|
| 1 |
-
import { base } from "$app/paths";
|
| 2 |
-
import { collections } from "$lib/server/database";
|
| 3 |
-
import { redirect } from "@sveltejs/kit";
|
| 4 |
-
import { z } from "zod";
|
| 5 |
-
import { models, validateModel } from "$lib/server/models";
|
| 6 |
-
import { authCondition } from "$lib/server/auth";
|
| 7 |
-
import { DEFAULT_SETTINGS } from "$lib/types/Settings";
|
| 8 |
-
|
| 9 |
-
export const actions = {
|
| 10 |
-
default: async function ({ request, locals }) {
|
| 11 |
-
const formData = await request.formData();
|
| 12 |
-
|
| 13 |
-
const { ethicsModalAccepted, ...settings } = z
|
| 14 |
-
.object({
|
| 15 |
-
shareConversationsWithModelAuthors: z
|
| 16 |
-
.union([z.literal("true"), z.literal("on"), z.literal("false"), z.null()])
|
| 17 |
-
.transform((value) => {
|
| 18 |
-
return value === "true" || value === "on";
|
| 19 |
-
}),
|
| 20 |
-
ethicsModalAccepted: z.boolean({ coerce: true }).optional(),
|
| 21 |
-
activeModel: validateModel(models),
|
| 22 |
-
customPrompts: z.record(z.string()).default({}),
|
| 23 |
-
})
|
| 24 |
-
.parse({
|
| 25 |
-
shareConversationsWithModelAuthors: formData.get("shareConversationsWithModelAuthors"),
|
| 26 |
-
ethicsModalAccepted: formData.get("ethicsModalAccepted"),
|
| 27 |
-
activeModel: formData.get("activeModel") ?? DEFAULT_SETTINGS.activeModel,
|
| 28 |
-
customPrompts: JSON.parse(formData.get("customPrompts")?.toString() ?? "{}"),
|
| 29 |
-
});
|
| 30 |
-
|
| 31 |
-
throw redirect(303, request.headers.get("referer") || `${base}/`);
|
| 32 |
-
},
|
| 33 |
-
};
|
|
|
|
|
|
|
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|
spaces/Adapter/T2I-Adapter/ldm/modules/encoders/modules.py
DELETED
|
@@ -1,441 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
import math
|
| 4 |
-
from torch.utils.checkpoint import checkpoint
|
| 5 |
-
|
| 6 |
-
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, CLIPModel
|
| 7 |
-
|
| 8 |
-
import open_clip
|
| 9 |
-
import re
|
| 10 |
-
from ldm.util import default, count_params
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class AbstractEncoder(nn.Module):
|
| 14 |
-
def __init__(self):
|
| 15 |
-
super().__init__()
|
| 16 |
-
|
| 17 |
-
def encode(self, *args, **kwargs):
|
| 18 |
-
raise NotImplementedError
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
class IdentityEncoder(AbstractEncoder):
|
| 22 |
-
|
| 23 |
-
def encode(self, x):
|
| 24 |
-
return x
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class ClassEmbedder(nn.Module):
|
| 28 |
-
def __init__(self, embed_dim, n_classes=1000, key='class'):
|
| 29 |
-
super().__init__()
|
| 30 |
-
self.key = key
|
| 31 |
-
self.embedding = nn.Embedding(n_classes, embed_dim)
|
| 32 |
-
|
| 33 |
-
def forward(self, batch, key=None):
|
| 34 |
-
if key is None:
|
| 35 |
-
key = self.key
|
| 36 |
-
# this is for use in crossattn
|
| 37 |
-
c = batch[key][:, None]
|
| 38 |
-
c = self.embedding(c)
|
| 39 |
-
return c
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
class FrozenT5Embedder(AbstractEncoder):
|
| 43 |
-
"""Uses the T5 transformer encoder for text"""
|
| 44 |
-
def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
|
| 45 |
-
super().__init__()
|
| 46 |
-
self.tokenizer = T5Tokenizer.from_pretrained(version)
|
| 47 |
-
self.transformer = T5EncoderModel.from_pretrained(version)
|
| 48 |
-
self.device = device
|
| 49 |
-
self.max_length = max_length # TODO: typical value?
|
| 50 |
-
if freeze:
|
| 51 |
-
self.freeze()
|
| 52 |
-
|
| 53 |
-
def freeze(self):
|
| 54 |
-
self.transformer = self.transformer.eval()
|
| 55 |
-
#self.train = disabled_train
|
| 56 |
-
for param in self.parameters():
|
| 57 |
-
param.requires_grad = False
|
| 58 |
-
|
| 59 |
-
def forward(self, text):
|
| 60 |
-
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
|
| 61 |
-
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
|
| 62 |
-
tokens = batch_encoding["input_ids"].to(self.device)
|
| 63 |
-
outputs = self.transformer(input_ids=tokens)
|
| 64 |
-
|
| 65 |
-
z = outputs.last_hidden_state
|
| 66 |
-
return z
|
| 67 |
-
|
| 68 |
-
def encode(self, text):
|
| 69 |
-
return self(text)
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
class FrozenCLIPEmbedder(AbstractEncoder):
|
| 73 |
-
"""Uses the CLIP transformer encoder for text (from huggingface)"""
|
| 74 |
-
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
|
| 75 |
-
freeze=True, layer="last"): # clip-vit-base-patch32
|
| 76 |
-
super().__init__()
|
| 77 |
-
self.tokenizer = CLIPTokenizer.from_pretrained(version)
|
| 78 |
-
self.transformer = CLIPModel.from_pretrained(version).text_model
|
| 79 |
-
self.device = device
|
| 80 |
-
self.max_length = max_length
|
| 81 |
-
if freeze:
|
| 82 |
-
self.freeze()
|
| 83 |
-
self.layer = layer
|
| 84 |
-
|
| 85 |
-
def freeze(self):
|
| 86 |
-
self.transformer = self.transformer.eval()
|
| 87 |
-
for param in self.parameters():
|
| 88 |
-
param.requires_grad = False
|
| 89 |
-
|
| 90 |
-
def forward(self, text):
|
| 91 |
-
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
|
| 92 |
-
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
|
| 93 |
-
tokens = batch_encoding["input_ids"].to(self.device)
|
| 94 |
-
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer != 'last')
|
| 95 |
-
|
| 96 |
-
if self.layer == 'penultimate':
|
| 97 |
-
z = outputs.hidden_states[-2]
|
| 98 |
-
z = self.transformer.final_layer_norm(z)
|
| 99 |
-
else:
|
| 100 |
-
z = outputs.last_hidden_state
|
| 101 |
-
return z
|
| 102 |
-
|
| 103 |
-
def encode(self, text):
|
| 104 |
-
return self(text)
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
class FrozenOpenCLIPEmbedder(AbstractEncoder):
|
| 108 |
-
"""
|
| 109 |
-
Uses the OpenCLIP transformer encoder for text
|
| 110 |
-
"""
|
| 111 |
-
LAYERS = [
|
| 112 |
-
#"pooled",
|
| 113 |
-
"last",
|
| 114 |
-
"penultimate"
|
| 115 |
-
]
|
| 116 |
-
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
|
| 117 |
-
freeze=True, layer="last"):
|
| 118 |
-
super().__init__()
|
| 119 |
-
assert layer in self.LAYERS
|
| 120 |
-
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
|
| 121 |
-
del model.visual
|
| 122 |
-
self.model = model
|
| 123 |
-
|
| 124 |
-
self.device = device
|
| 125 |
-
self.max_length = max_length
|
| 126 |
-
if freeze:
|
| 127 |
-
self.freeze()
|
| 128 |
-
self.layer = layer
|
| 129 |
-
if self.layer == "last":
|
| 130 |
-
self.layer_idx = 0
|
| 131 |
-
elif self.layer == "penultimate":
|
| 132 |
-
self.layer_idx = 1
|
| 133 |
-
else:
|
| 134 |
-
raise NotImplementedError()
|
| 135 |
-
|
| 136 |
-
def freeze(self):
|
| 137 |
-
self.model = self.model.eval()
|
| 138 |
-
for param in self.parameters():
|
| 139 |
-
param.requires_grad = False
|
| 140 |
-
|
| 141 |
-
def forward(self, text):
|
| 142 |
-
tokens = open_clip.tokenize(text)
|
| 143 |
-
z = self.encode_with_transformer(tokens.to(self.device))
|
| 144 |
-
return z
|
| 145 |
-
|
| 146 |
-
def encode_with_transformer(self, text):
|
| 147 |
-
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
|
| 148 |
-
x = x + self.model.positional_embedding
|
| 149 |
-
x = x.permute(1, 0, 2) # NLD -> LND
|
| 150 |
-
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
|
| 151 |
-
x = x.permute(1, 0, 2) # LND -> NLD
|
| 152 |
-
x = self.model.ln_final(x)
|
| 153 |
-
return x
|
| 154 |
-
|
| 155 |
-
def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
|
| 156 |
-
for i, r in enumerate(self.model.transformer.resblocks):
|
| 157 |
-
if i == len(self.model.transformer.resblocks) - self.layer_idx:
|
| 158 |
-
break
|
| 159 |
-
if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
|
| 160 |
-
x = checkpoint(r, x, attn_mask)
|
| 161 |
-
else:
|
| 162 |
-
x = r(x, attn_mask=attn_mask)
|
| 163 |
-
return x
|
| 164 |
-
|
| 165 |
-
def encode(self, text):
|
| 166 |
-
return self(text)
|
| 167 |
-
|
| 168 |
-
|
| 169 |
-
class FrozenCLIPT5Encoder(AbstractEncoder):
|
| 170 |
-
def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
|
| 171 |
-
clip_max_length=77, t5_max_length=77):
|
| 172 |
-
super().__init__()
|
| 173 |
-
self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
|
| 174 |
-
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
|
| 175 |
-
print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
|
| 176 |
-
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
|
| 177 |
-
|
| 178 |
-
def encode(self, text):
|
| 179 |
-
return self(text)
|
| 180 |
-
|
| 181 |
-
def forward(self, text):
|
| 182 |
-
clip_z = self.clip_encoder.encode(text)
|
| 183 |
-
t5_z = self.t5_encoder.encode(text)
|
| 184 |
-
return [clip_z, t5_z]
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
# code from sd-webui
|
| 188 |
-
re_attention = re.compile(r"""
|
| 189 |
-
\\\(|
|
| 190 |
-
\\\)|
|
| 191 |
-
\\\[|
|
| 192 |
-
\\]|
|
| 193 |
-
\\\\|
|
| 194 |
-
\\|
|
| 195 |
-
\(|
|
| 196 |
-
\[|
|
| 197 |
-
:([+-]?[.\d]+)\)|
|
| 198 |
-
\)|
|
| 199 |
-
]|
|
| 200 |
-
[^\\()\[\]:]+|
|
| 201 |
-
:
|
| 202 |
-
""", re.X)
|
| 203 |
-
|
| 204 |
-
|
| 205 |
-
def parse_prompt_attention(text):
|
| 206 |
-
"""
|
| 207 |
-
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
|
| 208 |
-
Accepted tokens are:
|
| 209 |
-
(abc) - increases attention to abc by a multiplier of 1.1
|
| 210 |
-
(abc:3.12) - increases attention to abc by a multiplier of 3.12
|
| 211 |
-
[abc] - decreases attention to abc by a multiplier of 1.1
|
| 212 |
-
\( - literal character '('
|
| 213 |
-
\[ - literal character '['
|
| 214 |
-
\) - literal character ')'
|
| 215 |
-
\] - literal character ']'
|
| 216 |
-
\\ - literal character '\'
|
| 217 |
-
anything else - just text
|
| 218 |
-
|
| 219 |
-
>>> parse_prompt_attention('normal text')
|
| 220 |
-
[['normal text', 1.0]]
|
| 221 |
-
>>> parse_prompt_attention('an (important) word')
|
| 222 |
-
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
|
| 223 |
-
>>> parse_prompt_attention('(unbalanced')
|
| 224 |
-
[['unbalanced', 1.1]]
|
| 225 |
-
>>> parse_prompt_attention('\(literal\]')
|
| 226 |
-
[['(literal]', 1.0]]
|
| 227 |
-
>>> parse_prompt_attention('(unnecessary)(parens)')
|
| 228 |
-
[['unnecessaryparens', 1.1]]
|
| 229 |
-
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
|
| 230 |
-
[['a ', 1.0],
|
| 231 |
-
['house', 1.5730000000000004],
|
| 232 |
-
[' ', 1.1],
|
| 233 |
-
['on', 1.0],
|
| 234 |
-
[' a ', 1.1],
|
| 235 |
-
['hill', 0.55],
|
| 236 |
-
[', sun, ', 1.1],
|
| 237 |
-
['sky', 1.4641000000000006],
|
| 238 |
-
['.', 1.1]]
|
| 239 |
-
"""
|
| 240 |
-
|
| 241 |
-
res = []
|
| 242 |
-
round_brackets = []
|
| 243 |
-
square_brackets = []
|
| 244 |
-
|
| 245 |
-
round_bracket_multiplier = 1.1
|
| 246 |
-
square_bracket_multiplier = 1 / 1.1
|
| 247 |
-
|
| 248 |
-
def multiply_range(start_position, multiplier):
|
| 249 |
-
for p in range(start_position, len(res)):
|
| 250 |
-
res[p][1] *= multiplier
|
| 251 |
-
|
| 252 |
-
for m in re_attention.finditer(text):
|
| 253 |
-
text = m.group(0)
|
| 254 |
-
weight = m.group(1)
|
| 255 |
-
|
| 256 |
-
if text.startswith('\\'):
|
| 257 |
-
res.append([text[1:], 1.0])
|
| 258 |
-
elif text == '(':
|
| 259 |
-
round_brackets.append(len(res))
|
| 260 |
-
elif text == '[':
|
| 261 |
-
square_brackets.append(len(res))
|
| 262 |
-
elif weight is not None and len(round_brackets) > 0:
|
| 263 |
-
multiply_range(round_brackets.pop(), float(weight))
|
| 264 |
-
elif text == ')' and len(round_brackets) > 0:
|
| 265 |
-
multiply_range(round_brackets.pop(), round_bracket_multiplier)
|
| 266 |
-
elif text == ']' and len(square_brackets) > 0:
|
| 267 |
-
multiply_range(square_brackets.pop(), square_bracket_multiplier)
|
| 268 |
-
else:
|
| 269 |
-
res.append([text, 1.0])
|
| 270 |
-
|
| 271 |
-
for pos in round_brackets:
|
| 272 |
-
multiply_range(pos, round_bracket_multiplier)
|
| 273 |
-
|
| 274 |
-
for pos in square_brackets:
|
| 275 |
-
multiply_range(pos, square_bracket_multiplier)
|
| 276 |
-
|
| 277 |
-
if len(res) == 0:
|
| 278 |
-
res = [["", 1.0]]
|
| 279 |
-
|
| 280 |
-
# merge runs of identical weights
|
| 281 |
-
i = 0
|
| 282 |
-
while i + 1 < len(res):
|
| 283 |
-
if res[i][1] == res[i + 1][1]:
|
| 284 |
-
res[i][0] += res[i + 1][0]
|
| 285 |
-
res.pop(i + 1)
|
| 286 |
-
else:
|
| 287 |
-
i += 1
|
| 288 |
-
|
| 289 |
-
return res
|
| 290 |
-
|
| 291 |
-
class WebUIFrozenCLIPEmebedder(AbstractEncoder):
|
| 292 |
-
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", freeze=True, layer="penultimate"):
|
| 293 |
-
super(WebUIFrozenCLIPEmebedder, self).__init__()
|
| 294 |
-
self.tokenizer = CLIPTokenizer.from_pretrained(version)
|
| 295 |
-
self.transformer = CLIPModel.from_pretrained(version).text_model
|
| 296 |
-
self.device = device
|
| 297 |
-
self.layer = layer
|
| 298 |
-
if freeze:
|
| 299 |
-
self.freeze()
|
| 300 |
-
|
| 301 |
-
self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
|
| 302 |
-
self.comma_padding_backtrack = 20
|
| 303 |
-
|
| 304 |
-
def freeze(self):
|
| 305 |
-
self.transformer = self.transformer.eval()
|
| 306 |
-
for param in self.parameters():
|
| 307 |
-
param.requires_grad = False
|
| 308 |
-
|
| 309 |
-
def tokenize(self, texts):
|
| 310 |
-
tokenized = self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
|
| 311 |
-
return tokenized
|
| 312 |
-
|
| 313 |
-
def encode_with_transformers(self, tokens):
|
| 314 |
-
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer!='last')
|
| 315 |
-
|
| 316 |
-
if self.layer == 'penultimate':
|
| 317 |
-
z = outputs.hidden_states[-2]
|
| 318 |
-
z = self.transformer.final_layer_norm(z)
|
| 319 |
-
else:
|
| 320 |
-
z = outputs.last_hidden_state
|
| 321 |
-
|
| 322 |
-
return z
|
| 323 |
-
|
| 324 |
-
def tokenize_line(self, line):
|
| 325 |
-
parsed = parse_prompt_attention(line)
|
| 326 |
-
# print(parsed)
|
| 327 |
-
|
| 328 |
-
tokenized = self.tokenize([text for text, _ in parsed])
|
| 329 |
-
|
| 330 |
-
remade_tokens = []
|
| 331 |
-
multipliers = []
|
| 332 |
-
last_comma = -1
|
| 333 |
-
|
| 334 |
-
for tokens, (text, weight) in zip(tokenized, parsed):
|
| 335 |
-
i = 0
|
| 336 |
-
while i < len(tokens):
|
| 337 |
-
token = tokens[i]
|
| 338 |
-
|
| 339 |
-
if token == self.comma_token:
|
| 340 |
-
last_comma = len(remade_tokens)
|
| 341 |
-
elif self.comma_padding_backtrack != 0 and max(len(remade_tokens),
|
| 342 |
-
1) % 75 == 0 and last_comma != -1 and len(
|
| 343 |
-
remade_tokens) - last_comma <= self.comma_padding_backtrack:
|
| 344 |
-
last_comma += 1
|
| 345 |
-
reloc_tokens = remade_tokens[last_comma:]
|
| 346 |
-
reloc_mults = multipliers[last_comma:]
|
| 347 |
-
|
| 348 |
-
remade_tokens = remade_tokens[:last_comma]
|
| 349 |
-
length = len(remade_tokens)
|
| 350 |
-
|
| 351 |
-
rem = int(math.ceil(length / 75)) * 75 - length
|
| 352 |
-
remade_tokens += [self.tokenizer.eos_token_id] * rem + reloc_tokens
|
| 353 |
-
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
|
| 354 |
-
|
| 355 |
-
remade_tokens.append(token)
|
| 356 |
-
multipliers.append(weight)
|
| 357 |
-
i += 1
|
| 358 |
-
|
| 359 |
-
token_count = len(remade_tokens)
|
| 360 |
-
prompt_target_length = math.ceil(max(token_count, 1) / 75) * 75
|
| 361 |
-
tokens_to_add = prompt_target_length - len(remade_tokens)
|
| 362 |
-
|
| 363 |
-
remade_tokens = remade_tokens + [self.tokenizer.eos_token_id] * tokens_to_add
|
| 364 |
-
multipliers = multipliers + [1.0] * tokens_to_add
|
| 365 |
-
|
| 366 |
-
return remade_tokens, multipliers, token_count
|
| 367 |
-
|
| 368 |
-
def process_text(self, texts):
|
| 369 |
-
remade_batch_tokens = []
|
| 370 |
-
token_count = 0
|
| 371 |
-
|
| 372 |
-
cache = {}
|
| 373 |
-
batch_multipliers = []
|
| 374 |
-
for line in texts:
|
| 375 |
-
if line in cache:
|
| 376 |
-
remade_tokens, multipliers = cache[line]
|
| 377 |
-
else:
|
| 378 |
-
remade_tokens, multipliers, current_token_count = self.tokenize_line(line)
|
| 379 |
-
token_count = max(current_token_count, token_count)
|
| 380 |
-
|
| 381 |
-
cache[line] = (remade_tokens, multipliers)
|
| 382 |
-
|
| 383 |
-
remade_batch_tokens.append(remade_tokens)
|
| 384 |
-
batch_multipliers.append(multipliers)
|
| 385 |
-
|
| 386 |
-
return batch_multipliers, remade_batch_tokens, token_count
|
| 387 |
-
|
| 388 |
-
def process_tokens(self, remade_batch_tokens, batch_multipliers):
|
| 389 |
-
remade_batch_tokens = [[self.tokenizer.bos_token_id] + x[:75] + [self.tokenizer.eos_token_id] for x in remade_batch_tokens]
|
| 390 |
-
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
|
| 391 |
-
|
| 392 |
-
tokens = torch.asarray(remade_batch_tokens).to(self.device)
|
| 393 |
-
|
| 394 |
-
z = self.encode_with_transformers(tokens)
|
| 395 |
-
|
| 396 |
-
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
| 397 |
-
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
|
| 398 |
-
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(self.device)
|
| 399 |
-
original_mean = z.mean()
|
| 400 |
-
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
| 401 |
-
new_mean = z.mean()
|
| 402 |
-
z *= original_mean / new_mean
|
| 403 |
-
|
| 404 |
-
return z
|
| 405 |
-
|
| 406 |
-
def forward(self, text):
|
| 407 |
-
batch_multipliers, remade_batch_tokens, token_count = self.process_text(text)
|
| 408 |
-
|
| 409 |
-
z = None
|
| 410 |
-
i = 0
|
| 411 |
-
while max(map(len, remade_batch_tokens)) != 0:
|
| 412 |
-
rem_tokens = [x[75:] for x in remade_batch_tokens]
|
| 413 |
-
rem_multipliers = [x[75:] for x in batch_multipliers]
|
| 414 |
-
|
| 415 |
-
tokens = []
|
| 416 |
-
multipliers = []
|
| 417 |
-
for j in range(len(remade_batch_tokens)):
|
| 418 |
-
if len(remade_batch_tokens[j]) > 0:
|
| 419 |
-
tokens.append(remade_batch_tokens[j][:75])
|
| 420 |
-
multipliers.append(batch_multipliers[j][:75])
|
| 421 |
-
else:
|
| 422 |
-
tokens.append([self.tokenizer.eos_token_id] * 75)
|
| 423 |
-
multipliers.append([1.0] * 75)
|
| 424 |
-
|
| 425 |
-
z1 = self.process_tokens(tokens, multipliers)
|
| 426 |
-
z = z1 if z is None else torch.cat((z, z1), axis=-2)
|
| 427 |
-
|
| 428 |
-
remade_batch_tokens = rem_tokens
|
| 429 |
-
batch_multipliers = rem_multipliers
|
| 430 |
-
i += 1
|
| 431 |
-
|
| 432 |
-
return z
|
| 433 |
-
|
| 434 |
-
def encode(self, text):
|
| 435 |
-
return self(text)
|
| 436 |
-
|
| 437 |
-
|
| 438 |
-
|
| 439 |
-
if __name__ == "__main__":
|
| 440 |
-
model = FrozenCLIPEmbedder()
|
| 441 |
-
count_params(model, verbose=True)
|
|
|
|
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/moveto-plugin.d.ts
DELETED
|
@@ -1,9 +0,0 @@
|
|
| 1 |
-
import MoveTo from './moveto';
|
| 2 |
-
|
| 3 |
-
export default class MoveToPlugin extends Phaser.Plugins.BasePlugin {
|
| 4 |
-
add(
|
| 5 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 6 |
-
config?: MoveTo.IConfig
|
| 7 |
-
): MoveTo;
|
| 8 |
-
|
| 9 |
-
}
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/custom/Factory.js
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
import Custom from './Custom.js';
|
| 2 |
-
import ObjectFactory from '../ObjectFactory.js';
|
| 3 |
-
import SetValue from '../../../plugins/utils/object/SetValue.js';
|
| 4 |
-
|
| 5 |
-
ObjectFactory.register('custom', function (config) {
|
| 6 |
-
var gameObject = new Custom(this.scene, config);
|
| 7 |
-
this.scene.add.existing(gameObject);
|
| 8 |
-
return gameObject;
|
| 9 |
-
});
|
| 10 |
-
|
| 11 |
-
SetValue(window, 'RexPlugins.Spinner.Custom', Custom);
|
| 12 |
-
|
| 13 |
-
export default Custom;
|
|
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/RunWidthWrap.js
DELETED
|
@@ -1,23 +0,0 @@
|
|
| 1 |
-
// Default method
|
| 2 |
-
var RunWidthWrap = function (parentWidth) {
|
| 3 |
-
var child, childWidth;
|
| 4 |
-
for (var i in this.sizerChildren) {
|
| 5 |
-
child = this.sizerChildren[i];
|
| 6 |
-
if (
|
| 7 |
-
(!child) ||
|
| 8 |
-
(child.isRexSizer && child.ignoreLayout) ||
|
| 9 |
-
(!child.runWidthWrap)
|
| 10 |
-
) {
|
| 11 |
-
continue;
|
| 12 |
-
}
|
| 13 |
-
|
| 14 |
-
childWidth = this.getExpandedChildWidth(child, parentWidth);
|
| 15 |
-
if (child.isRexSizer) {
|
| 16 |
-
childWidth = child.resolveWidth(childWidth);
|
| 17 |
-
}
|
| 18 |
-
child.runWidthWrap(childWidth);
|
| 19 |
-
}
|
| 20 |
-
return this;
|
| 21 |
-
}
|
| 22 |
-
|
| 23 |
-
export default RunWidthWrap;
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetChildrenSizers.js
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
var GetChildrenSizers = function (out) {
|
| 2 |
-
if (out === undefined) {
|
| 3 |
-
out = [];
|
| 4 |
-
}
|
| 5 |
-
var children = this.sizerChildren,
|
| 6 |
-
child;
|
| 7 |
-
for (var i = 0, cnt = children.length; i < cnt; i++) {
|
| 8 |
-
child = children[i];
|
| 9 |
-
if (child && child.isRexSizer) {
|
| 10 |
-
out.push(child);
|
| 11 |
-
}
|
| 12 |
-
}
|
| 13 |
-
return out;
|
| 14 |
-
}
|
| 15 |
-
export default GetChildrenSizers;
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateScrollBar.js
DELETED
|
@@ -1,26 +0,0 @@
|
|
| 1 |
-
import MergeStyle from './utils/MergeStyle.js';
|
| 2 |
-
import ScrollBar from '../../scrollbar/ScrollBar.js';
|
| 3 |
-
import CreateChild from './utils/CreateChild.js';
|
| 4 |
-
import ReplaceSliderConfig from './utils/ReplaceSliderConfig.js';
|
| 5 |
-
|
| 6 |
-
var CreateScrollBar = function (scene, data, view, styles, customBuilders) {
|
| 7 |
-
data = MergeStyle(data, styles);
|
| 8 |
-
|
| 9 |
-
// Replace data by child game object
|
| 10 |
-
CreateChild(scene, data, 'background', view, styles, customBuilders);
|
| 11 |
-
ReplaceSliderConfig(scene, data.slider, view, styles, customBuilders);
|
| 12 |
-
|
| 13 |
-
var buttonsConfig = data.buttons;
|
| 14 |
-
if (buttonsConfig) {
|
| 15 |
-
CreateChild(scene, buttonsConfig, 'top', view, styles, customBuilders);
|
| 16 |
-
CreateChild(scene, buttonsConfig, 'bottom', view, styles, customBuilders);
|
| 17 |
-
CreateChild(scene, buttonsConfig, 'left', view, styles, customBuilders);
|
| 18 |
-
CreateChild(scene, buttonsConfig, 'right', view, styles, customBuilders);
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
var gameObject = new ScrollBar(scene, data);
|
| 22 |
-
scene.add.existing(gameObject);
|
| 23 |
-
return gameObject;
|
| 24 |
-
};
|
| 25 |
-
|
| 26 |
-
export default CreateScrollBar;
|
|
|
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|
spaces/Aki004/herta-so-vits/preprocess_hubert_f0.py
DELETED
|
@@ -1,101 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import multiprocessing
|
| 3 |
-
import os
|
| 4 |
-
import argparse
|
| 5 |
-
from random import shuffle
|
| 6 |
-
|
| 7 |
-
import torch
|
| 8 |
-
from glob import glob
|
| 9 |
-
from tqdm import tqdm
|
| 10 |
-
from modules.mel_processing import spectrogram_torch
|
| 11 |
-
|
| 12 |
-
import utils
|
| 13 |
-
import logging
|
| 14 |
-
|
| 15 |
-
logging.getLogger("numba").setLevel(logging.WARNING)
|
| 16 |
-
import librosa
|
| 17 |
-
import numpy as np
|
| 18 |
-
|
| 19 |
-
hps = utils.get_hparams_from_file("configs/config.json")
|
| 20 |
-
sampling_rate = hps.data.sampling_rate
|
| 21 |
-
hop_length = hps.data.hop_length
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def process_one(filename, hmodel):
|
| 25 |
-
# print(filename)
|
| 26 |
-
wav, sr = librosa.load(filename, sr=sampling_rate)
|
| 27 |
-
soft_path = filename + ".soft.pt"
|
| 28 |
-
if not os.path.exists(soft_path):
|
| 29 |
-
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 30 |
-
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
|
| 31 |
-
wav16k = torch.from_numpy(wav16k).to(device)
|
| 32 |
-
c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
|
| 33 |
-
torch.save(c.cpu(), soft_path)
|
| 34 |
-
|
| 35 |
-
f0_path = filename + ".f0.npy"
|
| 36 |
-
if not os.path.exists(f0_path):
|
| 37 |
-
f0 = utils.compute_f0_dio(
|
| 38 |
-
wav, sampling_rate=sampling_rate, hop_length=hop_length
|
| 39 |
-
)
|
| 40 |
-
np.save(f0_path, f0)
|
| 41 |
-
|
| 42 |
-
spec_path = filename.replace(".wav", ".spec.pt")
|
| 43 |
-
if not os.path.exists(spec_path):
|
| 44 |
-
# Process spectrogram
|
| 45 |
-
# The following code can't be replaced by torch.FloatTensor(wav)
|
| 46 |
-
# because load_wav_to_torch return a tensor that need to be normalized
|
| 47 |
-
|
| 48 |
-
audio, sr = utils.load_wav_to_torch(filename)
|
| 49 |
-
if sr != hps.data.sampling_rate:
|
| 50 |
-
raise ValueError(
|
| 51 |
-
"{} SR doesn't match target {} SR".format(
|
| 52 |
-
sr, hps.data.sampling_rate
|
| 53 |
-
)
|
| 54 |
-
)
|
| 55 |
-
|
| 56 |
-
audio_norm = audio / hps.data.max_wav_value
|
| 57 |
-
audio_norm = audio_norm.unsqueeze(0)
|
| 58 |
-
|
| 59 |
-
spec = spectrogram_torch(
|
| 60 |
-
audio_norm,
|
| 61 |
-
hps.data.filter_length,
|
| 62 |
-
hps.data.sampling_rate,
|
| 63 |
-
hps.data.hop_length,
|
| 64 |
-
hps.data.win_length,
|
| 65 |
-
center=False,
|
| 66 |
-
)
|
| 67 |
-
spec = torch.squeeze(spec, 0)
|
| 68 |
-
torch.save(spec, spec_path)
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
def process_batch(filenames):
|
| 72 |
-
print("Loading hubert for content...")
|
| 73 |
-
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 74 |
-
hmodel = utils.get_hubert_model().to(device)
|
| 75 |
-
print("Loaded hubert.")
|
| 76 |
-
for filename in tqdm(filenames):
|
| 77 |
-
process_one(filename, hmodel)
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
if __name__ == "__main__":
|
| 81 |
-
parser = argparse.ArgumentParser()
|
| 82 |
-
parser.add_argument(
|
| 83 |
-
"--in_dir", type=str, default="dataset/44k", help="path to input dir"
|
| 84 |
-
)
|
| 85 |
-
|
| 86 |
-
args = parser.parse_args()
|
| 87 |
-
filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10]
|
| 88 |
-
shuffle(filenames)
|
| 89 |
-
multiprocessing.set_start_method("spawn", force=True)
|
| 90 |
-
|
| 91 |
-
num_processes = 1
|
| 92 |
-
chunk_size = int(math.ceil(len(filenames) / num_processes))
|
| 93 |
-
chunks = [
|
| 94 |
-
filenames[i : i + chunk_size] for i in range(0, len(filenames), chunk_size)
|
| 95 |
-
]
|
| 96 |
-
print([len(c) for c in chunks])
|
| 97 |
-
processes = [
|
| 98 |
-
multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks
|
| 99 |
-
]
|
| 100 |
-
for p in processes:
|
| 101 |
-
p.start()
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|
spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/optimizer.py
DELETED
|
@@ -1,372 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 4 |
-
# and proprietary rights in and to this software, related documentation
|
| 5 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 6 |
-
# distribution of this software and related documentation without an express
|
| 7 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 8 |
-
|
| 9 |
-
"""Helper wrapper for a Tensorflow optimizer."""
|
| 10 |
-
|
| 11 |
-
import platform
|
| 12 |
-
import numpy as np
|
| 13 |
-
import tensorflow as tf
|
| 14 |
-
|
| 15 |
-
from collections import OrderedDict
|
| 16 |
-
from typing import List, Union
|
| 17 |
-
|
| 18 |
-
from . import autosummary
|
| 19 |
-
from . import tfutil
|
| 20 |
-
from .. import util
|
| 21 |
-
|
| 22 |
-
from .tfutil import TfExpression, TfExpressionEx
|
| 23 |
-
|
| 24 |
-
_collective_ops_warning_printed = False
|
| 25 |
-
_collective_ops_group_key = 831766147
|
| 26 |
-
_collective_ops_instance_key = 436340067
|
| 27 |
-
|
| 28 |
-
class Optimizer:
|
| 29 |
-
"""A Wrapper for tf.train.Optimizer.
|
| 30 |
-
|
| 31 |
-
Automatically takes care of:
|
| 32 |
-
- Gradient averaging for multi-GPU training.
|
| 33 |
-
- Gradient accumulation for arbitrarily large minibatches.
|
| 34 |
-
- Dynamic loss scaling and typecasts for FP16 training.
|
| 35 |
-
- Ignoring corrupted gradients that contain NaNs/Infs.
|
| 36 |
-
- Reporting statistics.
|
| 37 |
-
- Well-chosen default settings.
|
| 38 |
-
"""
|
| 39 |
-
|
| 40 |
-
def __init__(self,
|
| 41 |
-
name: str = "Train", # Name string that will appear in TensorFlow graph.
|
| 42 |
-
tf_optimizer: str = "tf.train.AdamOptimizer", # Underlying optimizer class.
|
| 43 |
-
learning_rate: TfExpressionEx = 0.001, # Learning rate. Can vary over time.
|
| 44 |
-
minibatch_multiplier: TfExpressionEx = None, # Treat N consecutive minibatches as one by accumulating gradients.
|
| 45 |
-
share: "Optimizer" = None, # Share internal state with a previously created optimizer?
|
| 46 |
-
use_loss_scaling: bool = False, # Enable dynamic loss scaling for robust mixed-precision training?
|
| 47 |
-
loss_scaling_init: float = 64.0, # Log2 of initial loss scaling factor.
|
| 48 |
-
loss_scaling_inc: float = 0.0005, # Log2 of per-minibatch loss scaling increment when there is no overflow.
|
| 49 |
-
loss_scaling_dec: float = 1.0, # Log2 of per-minibatch loss scaling decrement when there is an overflow.
|
| 50 |
-
report_mem_usage: bool = False, # Report fine-grained memory usage statistics in TensorBoard?
|
| 51 |
-
**kwargs):
|
| 52 |
-
|
| 53 |
-
# Public fields.
|
| 54 |
-
self.name = name
|
| 55 |
-
self.learning_rate = learning_rate
|
| 56 |
-
self.minibatch_multiplier = minibatch_multiplier
|
| 57 |
-
self.id = self.name.replace("/", ".")
|
| 58 |
-
self.scope = tf.get_default_graph().unique_name(self.id)
|
| 59 |
-
self.optimizer_class = util.get_obj_by_name(tf_optimizer)
|
| 60 |
-
self.optimizer_kwargs = dict(kwargs)
|
| 61 |
-
self.use_loss_scaling = use_loss_scaling
|
| 62 |
-
self.loss_scaling_init = loss_scaling_init
|
| 63 |
-
self.loss_scaling_inc = loss_scaling_inc
|
| 64 |
-
self.loss_scaling_dec = loss_scaling_dec
|
| 65 |
-
|
| 66 |
-
# Private fields.
|
| 67 |
-
self._updates_applied = False
|
| 68 |
-
self._devices = OrderedDict() # device_name => EasyDict()
|
| 69 |
-
self._shared_optimizers = OrderedDict() # device_name => optimizer_class
|
| 70 |
-
self._gradient_shapes = None # [shape, ...]
|
| 71 |
-
self._report_mem_usage = report_mem_usage
|
| 72 |
-
|
| 73 |
-
# Validate arguments.
|
| 74 |
-
assert callable(self.optimizer_class)
|
| 75 |
-
|
| 76 |
-
# Share internal state if requested.
|
| 77 |
-
if share is not None:
|
| 78 |
-
assert isinstance(share, Optimizer)
|
| 79 |
-
assert self.optimizer_class is share.optimizer_class
|
| 80 |
-
assert self.learning_rate is share.learning_rate
|
| 81 |
-
assert self.optimizer_kwargs == share.optimizer_kwargs
|
| 82 |
-
self._shared_optimizers = share._shared_optimizers # pylint: disable=protected-access
|
| 83 |
-
|
| 84 |
-
def _get_device(self, device_name: str):
|
| 85 |
-
"""Get internal state for the given TensorFlow device."""
|
| 86 |
-
tfutil.assert_tf_initialized()
|
| 87 |
-
if device_name in self._devices:
|
| 88 |
-
return self._devices[device_name]
|
| 89 |
-
|
| 90 |
-
# Initialize fields.
|
| 91 |
-
device = util.EasyDict()
|
| 92 |
-
device.name = device_name
|
| 93 |
-
device.optimizer = None # Underlying optimizer: optimizer_class
|
| 94 |
-
device.loss_scaling_var = None # Log2 of loss scaling: tf.Variable
|
| 95 |
-
device.grad_raw = OrderedDict() # Raw gradients: var => [grad, ...]
|
| 96 |
-
device.grad_clean = OrderedDict() # Clean gradients: var => grad
|
| 97 |
-
device.grad_acc_vars = OrderedDict() # Accumulation sums: var => tf.Variable
|
| 98 |
-
device.grad_acc_count = None # Accumulation counter: tf.Variable
|
| 99 |
-
device.grad_acc = OrderedDict() # Accumulated gradients: var => grad
|
| 100 |
-
|
| 101 |
-
# Setup TensorFlow objects.
|
| 102 |
-
with tfutil.absolute_name_scope(self.scope + "/Devices"), tf.device(device_name), tf.control_dependencies(None):
|
| 103 |
-
if device_name not in self._shared_optimizers:
|
| 104 |
-
optimizer_name = self.scope.replace("/", "_") + "_opt%d" % len(self._shared_optimizers)
|
| 105 |
-
self._shared_optimizers[device_name] = self.optimizer_class(name=optimizer_name, learning_rate=self.learning_rate, **self.optimizer_kwargs)
|
| 106 |
-
device.optimizer = self._shared_optimizers[device_name]
|
| 107 |
-
if self.use_loss_scaling:
|
| 108 |
-
device.loss_scaling_var = tf.Variable(np.float32(self.loss_scaling_init), trainable=False, name="loss_scaling_var")
|
| 109 |
-
|
| 110 |
-
# Register device.
|
| 111 |
-
self._devices[device_name] = device
|
| 112 |
-
return device
|
| 113 |
-
|
| 114 |
-
def register_gradients(self, loss: TfExpression, trainable_vars: Union[List, dict]) -> None:
|
| 115 |
-
"""Register the gradients of the given loss function with respect to the given variables.
|
| 116 |
-
Intended to be called once per GPU."""
|
| 117 |
-
tfutil.assert_tf_initialized()
|
| 118 |
-
assert not self._updates_applied
|
| 119 |
-
device = self._get_device(loss.device)
|
| 120 |
-
|
| 121 |
-
# Validate trainables.
|
| 122 |
-
if isinstance(trainable_vars, dict):
|
| 123 |
-
trainable_vars = list(trainable_vars.values()) # allow passing in Network.trainables as vars
|
| 124 |
-
assert isinstance(trainable_vars, list) and len(trainable_vars) >= 1
|
| 125 |
-
assert all(tfutil.is_tf_expression(expr) for expr in trainable_vars + [loss])
|
| 126 |
-
assert all(var.device == device.name for var in trainable_vars)
|
| 127 |
-
|
| 128 |
-
# Validate shapes.
|
| 129 |
-
if self._gradient_shapes is None:
|
| 130 |
-
self._gradient_shapes = [var.shape.as_list() for var in trainable_vars]
|
| 131 |
-
assert len(trainable_vars) == len(self._gradient_shapes)
|
| 132 |
-
assert all(var.shape.as_list() == var_shape for var, var_shape in zip(trainable_vars, self._gradient_shapes))
|
| 133 |
-
|
| 134 |
-
# Report memory usage if requested.
|
| 135 |
-
deps = [loss]
|
| 136 |
-
if self._report_mem_usage:
|
| 137 |
-
self._report_mem_usage = False
|
| 138 |
-
try:
|
| 139 |
-
with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]):
|
| 140 |
-
deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30))
|
| 141 |
-
except tf.errors.NotFoundError:
|
| 142 |
-
pass
|
| 143 |
-
|
| 144 |
-
# Compute gradients.
|
| 145 |
-
with tf.name_scope(self.id + "_grad"), tf.device(device.name), tf.control_dependencies(deps):
|
| 146 |
-
loss = self.apply_loss_scaling(tf.cast(loss, tf.float32))
|
| 147 |
-
gate = tf.train.Optimizer.GATE_NONE # disable gating to reduce memory usage
|
| 148 |
-
grad_list = device.optimizer.compute_gradients(loss=loss, var_list=trainable_vars, gate_gradients=gate)
|
| 149 |
-
|
| 150 |
-
# Register gradients.
|
| 151 |
-
for grad, var in grad_list:
|
| 152 |
-
if var not in device.grad_raw:
|
| 153 |
-
device.grad_raw[var] = []
|
| 154 |
-
device.grad_raw[var].append(grad)
|
| 155 |
-
|
| 156 |
-
def apply_updates(self, allow_no_op: bool = False) -> tf.Operation:
|
| 157 |
-
"""Construct training op to update the registered variables based on their gradients."""
|
| 158 |
-
tfutil.assert_tf_initialized()
|
| 159 |
-
assert not self._updates_applied
|
| 160 |
-
self._updates_applied = True
|
| 161 |
-
all_ops = []
|
| 162 |
-
|
| 163 |
-
# Check for no-op.
|
| 164 |
-
if allow_no_op and len(self._devices) == 0:
|
| 165 |
-
with tfutil.absolute_name_scope(self.scope):
|
| 166 |
-
return tf.no_op(name='TrainingOp')
|
| 167 |
-
|
| 168 |
-
# Clean up gradients.
|
| 169 |
-
for device_idx, device in enumerate(self._devices.values()):
|
| 170 |
-
with tfutil.absolute_name_scope(self.scope + "/Clean%d" % device_idx), tf.device(device.name):
|
| 171 |
-
for var, grad in device.grad_raw.items():
|
| 172 |
-
|
| 173 |
-
# Filter out disconnected gradients and convert to float32.
|
| 174 |
-
grad = [g for g in grad if g is not None]
|
| 175 |
-
grad = [tf.cast(g, tf.float32) for g in grad]
|
| 176 |
-
|
| 177 |
-
# Sum within the device.
|
| 178 |
-
if len(grad) == 0:
|
| 179 |
-
grad = tf.zeros(var.shape) # No gradients => zero.
|
| 180 |
-
elif len(grad) == 1:
|
| 181 |
-
grad = grad[0] # Single gradient => use as is.
|
| 182 |
-
else:
|
| 183 |
-
grad = tf.add_n(grad) # Multiple gradients => sum.
|
| 184 |
-
|
| 185 |
-
# Scale as needed.
|
| 186 |
-
scale = 1.0 / len(device.grad_raw[var]) / len(self._devices)
|
| 187 |
-
scale = tf.constant(scale, dtype=tf.float32, name="scale")
|
| 188 |
-
if self.minibatch_multiplier is not None:
|
| 189 |
-
scale /= tf.cast(self.minibatch_multiplier, tf.float32)
|
| 190 |
-
scale = self.undo_loss_scaling(scale)
|
| 191 |
-
device.grad_clean[var] = grad * scale
|
| 192 |
-
|
| 193 |
-
# Sum gradients across devices.
|
| 194 |
-
if len(self._devices) > 1:
|
| 195 |
-
with tfutil.absolute_name_scope(self.scope + "/Broadcast"), tf.device(None):
|
| 196 |
-
if platform.system() == "Windows": # Windows => NCCL ops are not available.
|
| 197 |
-
self._broadcast_fallback()
|
| 198 |
-
elif tf.VERSION.startswith("1.15."): # TF 1.15 => NCCL ops are broken: https://github.com/tensorflow/tensorflow/issues/41539
|
| 199 |
-
self._broadcast_fallback()
|
| 200 |
-
else: # Otherwise => NCCL ops are safe to use.
|
| 201 |
-
self._broadcast_nccl()
|
| 202 |
-
|
| 203 |
-
# Apply updates separately on each device.
|
| 204 |
-
for device_idx, device in enumerate(self._devices.values()):
|
| 205 |
-
with tfutil.absolute_name_scope(self.scope + "/Apply%d" % device_idx), tf.device(device.name):
|
| 206 |
-
# pylint: disable=cell-var-from-loop
|
| 207 |
-
|
| 208 |
-
# Accumulate gradients over time.
|
| 209 |
-
if self.minibatch_multiplier is None:
|
| 210 |
-
acc_ok = tf.constant(True, name='acc_ok')
|
| 211 |
-
device.grad_acc = OrderedDict(device.grad_clean)
|
| 212 |
-
else:
|
| 213 |
-
# Create variables.
|
| 214 |
-
with tf.control_dependencies(None):
|
| 215 |
-
for var in device.grad_clean.keys():
|
| 216 |
-
device.grad_acc_vars[var] = tf.Variable(tf.zeros(var.shape), trainable=False, name="grad_acc_var")
|
| 217 |
-
device.grad_acc_count = tf.Variable(tf.zeros([]), trainable=False, name="grad_acc_count")
|
| 218 |
-
|
| 219 |
-
# Track counter.
|
| 220 |
-
count_cur = device.grad_acc_count + 1.0
|
| 221 |
-
count_inc_op = lambda: tf.assign(device.grad_acc_count, count_cur)
|
| 222 |
-
count_reset_op = lambda: tf.assign(device.grad_acc_count, tf.zeros([]))
|
| 223 |
-
acc_ok = (count_cur >= tf.cast(self.minibatch_multiplier, tf.float32))
|
| 224 |
-
all_ops.append(tf.cond(acc_ok, count_reset_op, count_inc_op))
|
| 225 |
-
|
| 226 |
-
# Track gradients.
|
| 227 |
-
for var, grad in device.grad_clean.items():
|
| 228 |
-
acc_var = device.grad_acc_vars[var]
|
| 229 |
-
acc_cur = acc_var + grad
|
| 230 |
-
device.grad_acc[var] = acc_cur
|
| 231 |
-
with tf.control_dependencies([acc_cur]):
|
| 232 |
-
acc_inc_op = lambda: tf.assign(acc_var, acc_cur)
|
| 233 |
-
acc_reset_op = lambda: tf.assign(acc_var, tf.zeros(var.shape))
|
| 234 |
-
all_ops.append(tf.cond(acc_ok, acc_reset_op, acc_inc_op))
|
| 235 |
-
|
| 236 |
-
# No overflow => apply gradients.
|
| 237 |
-
all_ok = tf.reduce_all(tf.stack([acc_ok] + [tf.reduce_all(tf.is_finite(g)) for g in device.grad_acc.values()]))
|
| 238 |
-
apply_op = lambda: device.optimizer.apply_gradients([(tf.cast(grad, var.dtype), var) for var, grad in device.grad_acc.items()])
|
| 239 |
-
all_ops.append(tf.cond(all_ok, apply_op, tf.no_op))
|
| 240 |
-
|
| 241 |
-
# Adjust loss scaling.
|
| 242 |
-
if self.use_loss_scaling:
|
| 243 |
-
ls_inc_op = lambda: tf.assign_add(device.loss_scaling_var, self.loss_scaling_inc)
|
| 244 |
-
ls_dec_op = lambda: tf.assign_sub(device.loss_scaling_var, self.loss_scaling_dec)
|
| 245 |
-
ls_update_op = lambda: tf.group(tf.cond(all_ok, ls_inc_op, ls_dec_op))
|
| 246 |
-
all_ops.append(tf.cond(acc_ok, ls_update_op, tf.no_op))
|
| 247 |
-
|
| 248 |
-
# Last device => report statistics.
|
| 249 |
-
if device_idx == len(self._devices) - 1:
|
| 250 |
-
all_ops.append(autosummary.autosummary(self.id + "/learning_rate", tf.convert_to_tensor(self.learning_rate)))
|
| 251 |
-
all_ops.append(autosummary.autosummary(self.id + "/overflow_frequency", tf.where(all_ok, 0, 1), condition=acc_ok))
|
| 252 |
-
if self.use_loss_scaling:
|
| 253 |
-
all_ops.append(autosummary.autosummary(self.id + "/loss_scaling_log2", device.loss_scaling_var))
|
| 254 |
-
|
| 255 |
-
# Initialize variables.
|
| 256 |
-
self.reset_optimizer_state()
|
| 257 |
-
if self.use_loss_scaling:
|
| 258 |
-
tfutil.init_uninitialized_vars([device.loss_scaling_var for device in self._devices.values()])
|
| 259 |
-
if self.minibatch_multiplier is not None:
|
| 260 |
-
tfutil.run([var.initializer for device in self._devices.values() for var in list(device.grad_acc_vars.values()) + [device.grad_acc_count]])
|
| 261 |
-
|
| 262 |
-
# Group everything into a single op.
|
| 263 |
-
with tfutil.absolute_name_scope(self.scope):
|
| 264 |
-
return tf.group(*all_ops, name="TrainingOp")
|
| 265 |
-
|
| 266 |
-
def reset_optimizer_state(self) -> None:
|
| 267 |
-
"""Reset internal state of the underlying optimizer."""
|
| 268 |
-
tfutil.assert_tf_initialized()
|
| 269 |
-
tfutil.run([var.initializer for device in self._devices.values() for var in device.optimizer.variables()])
|
| 270 |
-
|
| 271 |
-
def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]:
|
| 272 |
-
"""Get or create variable representing log2 of the current dynamic loss scaling factor."""
|
| 273 |
-
return self._get_device(device).loss_scaling_var
|
| 274 |
-
|
| 275 |
-
def apply_loss_scaling(self, value: TfExpression) -> TfExpression:
|
| 276 |
-
"""Apply dynamic loss scaling for the given expression."""
|
| 277 |
-
assert tfutil.is_tf_expression(value)
|
| 278 |
-
if not self.use_loss_scaling:
|
| 279 |
-
return value
|
| 280 |
-
return value * tfutil.exp2(self.get_loss_scaling_var(value.device))
|
| 281 |
-
|
| 282 |
-
def undo_loss_scaling(self, value: TfExpression) -> TfExpression:
|
| 283 |
-
"""Undo the effect of dynamic loss scaling for the given expression."""
|
| 284 |
-
assert tfutil.is_tf_expression(value)
|
| 285 |
-
if not self.use_loss_scaling:
|
| 286 |
-
return value
|
| 287 |
-
return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type
|
| 288 |
-
|
| 289 |
-
def _broadcast_nccl(self):
|
| 290 |
-
"""Sum gradients across devices using NCCL ops (fast path)."""
|
| 291 |
-
from tensorflow.python.ops import nccl_ops # pylint: disable=no-name-in-module
|
| 292 |
-
for all_vars in zip(*[device.grad_clean.keys() for device in self._devices.values()]):
|
| 293 |
-
if any(x.shape.num_elements() > 0 for x in all_vars):
|
| 294 |
-
all_grads = [device.grad_clean[var] for device, var in zip(self._devices.values(), all_vars)]
|
| 295 |
-
all_grads = nccl_ops.all_sum(all_grads)
|
| 296 |
-
for device, var, grad in zip(self._devices.values(), all_vars, all_grads):
|
| 297 |
-
device.grad_clean[var] = grad
|
| 298 |
-
|
| 299 |
-
def _broadcast_fallback(self):
|
| 300 |
-
"""Sum gradients across devices using TensorFlow collective ops (slow fallback path)."""
|
| 301 |
-
from tensorflow.python.ops import collective_ops # pylint: disable=no-name-in-module
|
| 302 |
-
global _collective_ops_warning_printed, _collective_ops_group_key, _collective_ops_instance_key
|
| 303 |
-
if all(x.shape.num_elements() == 0 for device in self._devices.values() for x in device.grad_clean.values()):
|
| 304 |
-
return
|
| 305 |
-
if not _collective_ops_warning_printed:
|
| 306 |
-
print("------------------------------------------------------------------------")
|
| 307 |
-
print("WARNING: Using slow fallback implementation for inter-GPU communication.")
|
| 308 |
-
print("Please use TensorFlow 1.14 on Linux for optimal training performance.")
|
| 309 |
-
print("------------------------------------------------------------------------")
|
| 310 |
-
_collective_ops_warning_printed = True
|
| 311 |
-
for device in self._devices.values():
|
| 312 |
-
with tf.device(device.name):
|
| 313 |
-
combo = [tf.reshape(x, [x.shape.num_elements()]) for x in device.grad_clean.values()]
|
| 314 |
-
combo = tf.concat(combo, axis=0)
|
| 315 |
-
combo = collective_ops.all_reduce(combo, merge_op='Add', final_op='Id',
|
| 316 |
-
group_size=len(self._devices), group_key=_collective_ops_group_key,
|
| 317 |
-
instance_key=_collective_ops_instance_key)
|
| 318 |
-
cur_ofs = 0
|
| 319 |
-
for var, grad_old in device.grad_clean.items():
|
| 320 |
-
grad_new = tf.reshape(combo[cur_ofs : cur_ofs + grad_old.shape.num_elements()], grad_old.shape)
|
| 321 |
-
cur_ofs += grad_old.shape.num_elements()
|
| 322 |
-
device.grad_clean[var] = grad_new
|
| 323 |
-
_collective_ops_instance_key += 1
|
| 324 |
-
|
| 325 |
-
|
| 326 |
-
class SimpleAdam:
|
| 327 |
-
"""Simplified version of tf.train.AdamOptimizer that behaves identically when used with dnnlib.tflib.Optimizer."""
|
| 328 |
-
|
| 329 |
-
def __init__(self, name="Adam", learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8):
|
| 330 |
-
self.name = name
|
| 331 |
-
self.learning_rate = learning_rate
|
| 332 |
-
self.beta1 = beta1
|
| 333 |
-
self.beta2 = beta2
|
| 334 |
-
self.epsilon = epsilon
|
| 335 |
-
self.all_state_vars = []
|
| 336 |
-
|
| 337 |
-
def variables(self):
|
| 338 |
-
return self.all_state_vars
|
| 339 |
-
|
| 340 |
-
def compute_gradients(self, loss, var_list, gate_gradients=tf.train.Optimizer.GATE_NONE):
|
| 341 |
-
assert gate_gradients == tf.train.Optimizer.GATE_NONE
|
| 342 |
-
return list(zip(tf.gradients(loss, var_list), var_list))
|
| 343 |
-
|
| 344 |
-
def apply_gradients(self, grads_and_vars):
|
| 345 |
-
with tf.name_scope(self.name):
|
| 346 |
-
state_vars = []
|
| 347 |
-
update_ops = []
|
| 348 |
-
|
| 349 |
-
# Adjust learning rate to deal with startup bias.
|
| 350 |
-
with tf.control_dependencies(None):
|
| 351 |
-
b1pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False)
|
| 352 |
-
b2pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False)
|
| 353 |
-
state_vars += [b1pow_var, b2pow_var]
|
| 354 |
-
b1pow_new = b1pow_var * self.beta1
|
| 355 |
-
b2pow_new = b2pow_var * self.beta2
|
| 356 |
-
update_ops += [tf.assign(b1pow_var, b1pow_new), tf.assign(b2pow_var, b2pow_new)]
|
| 357 |
-
lr_new = self.learning_rate * tf.sqrt(1 - b2pow_new) / (1 - b1pow_new)
|
| 358 |
-
|
| 359 |
-
# Construct ops to update each variable.
|
| 360 |
-
for grad, var in grads_and_vars:
|
| 361 |
-
with tf.control_dependencies(None):
|
| 362 |
-
m_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False)
|
| 363 |
-
v_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False)
|
| 364 |
-
state_vars += [m_var, v_var]
|
| 365 |
-
m_new = self.beta1 * m_var + (1 - self.beta1) * grad
|
| 366 |
-
v_new = self.beta2 * v_var + (1 - self.beta2) * tf.square(grad)
|
| 367 |
-
var_delta = lr_new * m_new / (tf.sqrt(v_new) + self.epsilon)
|
| 368 |
-
update_ops += [tf.assign(m_var, m_new), tf.assign(v_var, v_new), tf.assign_sub(var, var_delta)]
|
| 369 |
-
|
| 370 |
-
# Group everything together.
|
| 371 |
-
self.all_state_vars += state_vars
|
| 372 |
-
return tf.group(*update_ops)
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docker/diffusers-pytorch-cpu/Dockerfile
DELETED
|
@@ -1,45 +0,0 @@
|
|
| 1 |
-
FROM ubuntu:20.04
|
| 2 |
-
LABEL maintainer="Hugging Face"
|
| 3 |
-
LABEL repository="diffusers"
|
| 4 |
-
|
| 5 |
-
ENV DEBIAN_FRONTEND=noninteractive
|
| 6 |
-
|
| 7 |
-
RUN apt update && \
|
| 8 |
-
apt install -y bash \
|
| 9 |
-
build-essential \
|
| 10 |
-
git \
|
| 11 |
-
git-lfs \
|
| 12 |
-
curl \
|
| 13 |
-
ca-certificates \
|
| 14 |
-
libsndfile1-dev \
|
| 15 |
-
python3.8 \
|
| 16 |
-
python3-pip \
|
| 17 |
-
libgl1 \
|
| 18 |
-
python3.8-venv && \
|
| 19 |
-
rm -rf /var/lib/apt/lists
|
| 20 |
-
|
| 21 |
-
# make sure to use venv
|
| 22 |
-
RUN python3 -m venv /opt/venv
|
| 23 |
-
ENV PATH="/opt/venv/bin:$PATH"
|
| 24 |
-
|
| 25 |
-
# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
|
| 26 |
-
RUN python3 -m pip install --no-cache-dir --upgrade pip && \
|
| 27 |
-
python3 -m pip install --no-cache-dir \
|
| 28 |
-
torch \
|
| 29 |
-
torchvision \
|
| 30 |
-
torchaudio \
|
| 31 |
-
invisible_watermark \
|
| 32 |
-
--extra-index-url https://download.pytorch.org/whl/cpu && \
|
| 33 |
-
python3 -m pip install --no-cache-dir \
|
| 34 |
-
accelerate \
|
| 35 |
-
datasets \
|
| 36 |
-
hf-doc-builder \
|
| 37 |
-
huggingface-hub \
|
| 38 |
-
Jinja2 \
|
| 39 |
-
librosa \
|
| 40 |
-
numpy \
|
| 41 |
-
scipy \
|
| 42 |
-
tensorboard \
|
| 43 |
-
transformers
|
| 44 |
-
|
| 45 |
-
CMD ["/bin/bash"]
|
|
|
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|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/unclip/pipeline_unclip.py
DELETED
|
@@ -1,489 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 Kakao Brain and The HuggingFace Team. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
import inspect
|
| 16 |
-
from typing import List, Optional, Tuple, Union
|
| 17 |
-
|
| 18 |
-
import torch
|
| 19 |
-
from torch.nn import functional as F
|
| 20 |
-
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
|
| 21 |
-
from transformers.models.clip.modeling_clip import CLIPTextModelOutput
|
| 22 |
-
|
| 23 |
-
from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel
|
| 24 |
-
from ...schedulers import UnCLIPScheduler
|
| 25 |
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from ...utils import logging, randn_tensor
|
| 26 |
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from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
|
| 27 |
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from .text_proj import UnCLIPTextProjModel
|
| 28 |
-
|
| 29 |
-
|
| 30 |
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
| 31 |
-
|
| 32 |
-
|
| 33 |
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class UnCLIPPipeline(DiffusionPipeline):
|
| 34 |
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"""
|
| 35 |
-
Pipeline for text-to-image generation using unCLIP.
|
| 36 |
-
|
| 37 |
-
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
| 38 |
-
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
| 39 |
-
|
| 40 |
-
Args:
|
| 41 |
-
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
|
| 42 |
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Frozen text-encoder.
|
| 43 |
-
tokenizer ([`~transformers.CLIPTokenizer`]):
|
| 44 |
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A `CLIPTokenizer` to tokenize text.
|
| 45 |
-
prior ([`PriorTransformer`]):
|
| 46 |
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The canonical unCLIP prior to approximate the image embedding from the text embedding.
|
| 47 |
-
text_proj ([`UnCLIPTextProjModel`]):
|
| 48 |
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Utility class to prepare and combine the embeddings before they are passed to the decoder.
|
| 49 |
-
decoder ([`UNet2DConditionModel`]):
|
| 50 |
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The decoder to invert the image embedding into an image.
|
| 51 |
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super_res_first ([`UNet2DModel`]):
|
| 52 |
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Super resolution UNet. Used in all but the last step of the super resolution diffusion process.
|
| 53 |
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super_res_last ([`UNet2DModel`]):
|
| 54 |
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Super resolution UNet. Used in the last step of the super resolution diffusion process.
|
| 55 |
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prior_scheduler ([`UnCLIPScheduler`]):
|
| 56 |
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Scheduler used in the prior denoising process (a modified [`DDPMScheduler`]).
|
| 57 |
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decoder_scheduler ([`UnCLIPScheduler`]):
|
| 58 |
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Scheduler used in the decoder denoising process (a modified [`DDPMScheduler`]).
|
| 59 |
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super_res_scheduler ([`UnCLIPScheduler`]):
|
| 60 |
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Scheduler used in the super resolution denoising process (a modified [`DDPMScheduler`]).
|
| 61 |
-
|
| 62 |
-
"""
|
| 63 |
-
|
| 64 |
-
_exclude_from_cpu_offload = ["prior"]
|
| 65 |
-
|
| 66 |
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prior: PriorTransformer
|
| 67 |
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decoder: UNet2DConditionModel
|
| 68 |
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text_proj: UnCLIPTextProjModel
|
| 69 |
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text_encoder: CLIPTextModelWithProjection
|
| 70 |
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tokenizer: CLIPTokenizer
|
| 71 |
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super_res_first: UNet2DModel
|
| 72 |
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super_res_last: UNet2DModel
|
| 73 |
-
|
| 74 |
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prior_scheduler: UnCLIPScheduler
|
| 75 |
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decoder_scheduler: UnCLIPScheduler
|
| 76 |
-
super_res_scheduler: UnCLIPScheduler
|
| 77 |
-
|
| 78 |
-
def __init__(
|
| 79 |
-
self,
|
| 80 |
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prior: PriorTransformer,
|
| 81 |
-
decoder: UNet2DConditionModel,
|
| 82 |
-
text_encoder: CLIPTextModelWithProjection,
|
| 83 |
-
tokenizer: CLIPTokenizer,
|
| 84 |
-
text_proj: UnCLIPTextProjModel,
|
| 85 |
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super_res_first: UNet2DModel,
|
| 86 |
-
super_res_last: UNet2DModel,
|
| 87 |
-
prior_scheduler: UnCLIPScheduler,
|
| 88 |
-
decoder_scheduler: UnCLIPScheduler,
|
| 89 |
-
super_res_scheduler: UnCLIPScheduler,
|
| 90 |
-
):
|
| 91 |
-
super().__init__()
|
| 92 |
-
|
| 93 |
-
self.register_modules(
|
| 94 |
-
prior=prior,
|
| 95 |
-
decoder=decoder,
|
| 96 |
-
text_encoder=text_encoder,
|
| 97 |
-
tokenizer=tokenizer,
|
| 98 |
-
text_proj=text_proj,
|
| 99 |
-
super_res_first=super_res_first,
|
| 100 |
-
super_res_last=super_res_last,
|
| 101 |
-
prior_scheduler=prior_scheduler,
|
| 102 |
-
decoder_scheduler=decoder_scheduler,
|
| 103 |
-
super_res_scheduler=super_res_scheduler,
|
| 104 |
-
)
|
| 105 |
-
|
| 106 |
-
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
|
| 107 |
-
if latents is None:
|
| 108 |
-
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
| 109 |
-
else:
|
| 110 |
-
if latents.shape != shape:
|
| 111 |
-
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
|
| 112 |
-
latents = latents.to(device)
|
| 113 |
-
|
| 114 |
-
latents = latents * scheduler.init_noise_sigma
|
| 115 |
-
return latents
|
| 116 |
-
|
| 117 |
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def _encode_prompt(
|
| 118 |
-
self,
|
| 119 |
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prompt,
|
| 120 |
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device,
|
| 121 |
-
num_images_per_prompt,
|
| 122 |
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do_classifier_free_guidance,
|
| 123 |
-
text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
|
| 124 |
-
text_attention_mask: Optional[torch.Tensor] = None,
|
| 125 |
-
):
|
| 126 |
-
if text_model_output is None:
|
| 127 |
-
batch_size = len(prompt) if isinstance(prompt, list) else 1
|
| 128 |
-
# get prompt text embeddings
|
| 129 |
-
text_inputs = self.tokenizer(
|
| 130 |
-
prompt,
|
| 131 |
-
padding="max_length",
|
| 132 |
-
max_length=self.tokenizer.model_max_length,
|
| 133 |
-
truncation=True,
|
| 134 |
-
return_tensors="pt",
|
| 135 |
-
)
|
| 136 |
-
text_input_ids = text_inputs.input_ids
|
| 137 |
-
text_mask = text_inputs.attention_mask.bool().to(device)
|
| 138 |
-
|
| 139 |
-
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
| 140 |
-
|
| 141 |
-
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
| 142 |
-
text_input_ids, untruncated_ids
|
| 143 |
-
):
|
| 144 |
-
removed_text = self.tokenizer.batch_decode(
|
| 145 |
-
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
| 146 |
-
)
|
| 147 |
-
logger.warning(
|
| 148 |
-
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
| 149 |
-
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
| 150 |
-
)
|
| 151 |
-
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
|
| 152 |
-
|
| 153 |
-
text_encoder_output = self.text_encoder(text_input_ids.to(device))
|
| 154 |
-
|
| 155 |
-
prompt_embeds = text_encoder_output.text_embeds
|
| 156 |
-
text_encoder_hidden_states = text_encoder_output.last_hidden_state
|
| 157 |
-
|
| 158 |
-
else:
|
| 159 |
-
batch_size = text_model_output[0].shape[0]
|
| 160 |
-
prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
|
| 161 |
-
text_mask = text_attention_mask
|
| 162 |
-
|
| 163 |
-
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
| 164 |
-
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
| 165 |
-
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
|
| 166 |
-
|
| 167 |
-
if do_classifier_free_guidance:
|
| 168 |
-
uncond_tokens = [""] * batch_size
|
| 169 |
-
|
| 170 |
-
uncond_input = self.tokenizer(
|
| 171 |
-
uncond_tokens,
|
| 172 |
-
padding="max_length",
|
| 173 |
-
max_length=self.tokenizer.model_max_length,
|
| 174 |
-
truncation=True,
|
| 175 |
-
return_tensors="pt",
|
| 176 |
-
)
|
| 177 |
-
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
|
| 178 |
-
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
|
| 179 |
-
|
| 180 |
-
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
|
| 181 |
-
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
|
| 182 |
-
|
| 183 |
-
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
| 184 |
-
|
| 185 |
-
seq_len = negative_prompt_embeds.shape[1]
|
| 186 |
-
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
|
| 187 |
-
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
|
| 188 |
-
|
| 189 |
-
seq_len = uncond_text_encoder_hidden_states.shape[1]
|
| 190 |
-
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
|
| 191 |
-
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
|
| 192 |
-
batch_size * num_images_per_prompt, seq_len, -1
|
| 193 |
-
)
|
| 194 |
-
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
|
| 195 |
-
|
| 196 |
-
# done duplicates
|
| 197 |
-
|
| 198 |
-
# For classifier free guidance, we need to do two forward passes.
|
| 199 |
-
# Here we concatenate the unconditional and text embeddings into a single batch
|
| 200 |
-
# to avoid doing two forward passes
|
| 201 |
-
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
| 202 |
-
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
|
| 203 |
-
|
| 204 |
-
text_mask = torch.cat([uncond_text_mask, text_mask])
|
| 205 |
-
|
| 206 |
-
return prompt_embeds, text_encoder_hidden_states, text_mask
|
| 207 |
-
|
| 208 |
-
@torch.no_grad()
|
| 209 |
-
def __call__(
|
| 210 |
-
self,
|
| 211 |
-
prompt: Optional[Union[str, List[str]]] = None,
|
| 212 |
-
num_images_per_prompt: int = 1,
|
| 213 |
-
prior_num_inference_steps: int = 25,
|
| 214 |
-
decoder_num_inference_steps: int = 25,
|
| 215 |
-
super_res_num_inference_steps: int = 7,
|
| 216 |
-
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
| 217 |
-
prior_latents: Optional[torch.FloatTensor] = None,
|
| 218 |
-
decoder_latents: Optional[torch.FloatTensor] = None,
|
| 219 |
-
super_res_latents: Optional[torch.FloatTensor] = None,
|
| 220 |
-
text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
|
| 221 |
-
text_attention_mask: Optional[torch.Tensor] = None,
|
| 222 |
-
prior_guidance_scale: float = 4.0,
|
| 223 |
-
decoder_guidance_scale: float = 8.0,
|
| 224 |
-
output_type: Optional[str] = "pil",
|
| 225 |
-
return_dict: bool = True,
|
| 226 |
-
):
|
| 227 |
-
"""
|
| 228 |
-
The call function to the pipeline for generation.
|
| 229 |
-
|
| 230 |
-
Args:
|
| 231 |
-
prompt (`str` or `List[str]`):
|
| 232 |
-
The prompt or prompts to guide image generation. This can only be left undefined if `text_model_output`
|
| 233 |
-
and `text_attention_mask` is passed.
|
| 234 |
-
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
| 235 |
-
The number of images to generate per prompt.
|
| 236 |
-
prior_num_inference_steps (`int`, *optional*, defaults to 25):
|
| 237 |
-
The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
|
| 238 |
-
image at the expense of slower inference.
|
| 239 |
-
decoder_num_inference_steps (`int`, *optional*, defaults to 25):
|
| 240 |
-
The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
|
| 241 |
-
image at the expense of slower inference.
|
| 242 |
-
super_res_num_inference_steps (`int`, *optional*, defaults to 7):
|
| 243 |
-
The number of denoising steps for super resolution. More denoising steps usually lead to a higher
|
| 244 |
-
quality image at the expense of slower inference.
|
| 245 |
-
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
| 246 |
-
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
| 247 |
-
generation deterministic.
|
| 248 |
-
prior_latents (`torch.FloatTensor` of shape (batch size, embeddings dimension), *optional*):
|
| 249 |
-
Pre-generated noisy latents to be used as inputs for the prior.
|
| 250 |
-
decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
|
| 251 |
-
Pre-generated noisy latents to be used as inputs for the decoder.
|
| 252 |
-
super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
|
| 253 |
-
Pre-generated noisy latents to be used as inputs for the decoder.
|
| 254 |
-
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
|
| 255 |
-
A higher guidance scale value encourages the model to generate images closely linked to the text
|
| 256 |
-
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
| 257 |
-
decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
|
| 258 |
-
A higher guidance scale value encourages the model to generate images closely linked to the text
|
| 259 |
-
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
| 260 |
-
text_model_output (`CLIPTextModelOutput`, *optional*):
|
| 261 |
-
Pre-defined [`CLIPTextModel`] outputs that can be derived from the text encoder. Pre-defined text
|
| 262 |
-
outputs can be passed for tasks like text embedding interpolations. Make sure to also pass
|
| 263 |
-
`text_attention_mask` in this case. `prompt` can the be left `None`.
|
| 264 |
-
text_attention_mask (`torch.Tensor`, *optional*):
|
| 265 |
-
Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention
|
| 266 |
-
masks are necessary when passing `text_model_output`.
|
| 267 |
-
output_type (`str`, *optional*, defaults to `"pil"`):
|
| 268 |
-
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
| 269 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 270 |
-
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
|
| 271 |
-
|
| 272 |
-
Returns:
|
| 273 |
-
[`~pipelines.ImagePipelineOutput`] or `tuple`:
|
| 274 |
-
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
|
| 275 |
-
returned where the first element is a list with the generated images.
|
| 276 |
-
"""
|
| 277 |
-
if prompt is not None:
|
| 278 |
-
if isinstance(prompt, str):
|
| 279 |
-
batch_size = 1
|
| 280 |
-
elif isinstance(prompt, list):
|
| 281 |
-
batch_size = len(prompt)
|
| 282 |
-
else:
|
| 283 |
-
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
| 284 |
-
else:
|
| 285 |
-
batch_size = text_model_output[0].shape[0]
|
| 286 |
-
|
| 287 |
-
device = self._execution_device
|
| 288 |
-
|
| 289 |
-
batch_size = batch_size * num_images_per_prompt
|
| 290 |
-
|
| 291 |
-
do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
|
| 292 |
-
|
| 293 |
-
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
|
| 294 |
-
prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
|
| 295 |
-
)
|
| 296 |
-
|
| 297 |
-
# prior
|
| 298 |
-
|
| 299 |
-
self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
|
| 300 |
-
prior_timesteps_tensor = self.prior_scheduler.timesteps
|
| 301 |
-
|
| 302 |
-
embedding_dim = self.prior.config.embedding_dim
|
| 303 |
-
|
| 304 |
-
prior_latents = self.prepare_latents(
|
| 305 |
-
(batch_size, embedding_dim),
|
| 306 |
-
prompt_embeds.dtype,
|
| 307 |
-
device,
|
| 308 |
-
generator,
|
| 309 |
-
prior_latents,
|
| 310 |
-
self.prior_scheduler,
|
| 311 |
-
)
|
| 312 |
-
|
| 313 |
-
for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
|
| 314 |
-
# expand the latents if we are doing classifier free guidance
|
| 315 |
-
latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
|
| 316 |
-
|
| 317 |
-
predicted_image_embedding = self.prior(
|
| 318 |
-
latent_model_input,
|
| 319 |
-
timestep=t,
|
| 320 |
-
proj_embedding=prompt_embeds,
|
| 321 |
-
encoder_hidden_states=text_encoder_hidden_states,
|
| 322 |
-
attention_mask=text_mask,
|
| 323 |
-
).predicted_image_embedding
|
| 324 |
-
|
| 325 |
-
if do_classifier_free_guidance:
|
| 326 |
-
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
|
| 327 |
-
predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
|
| 328 |
-
predicted_image_embedding_text - predicted_image_embedding_uncond
|
| 329 |
-
)
|
| 330 |
-
|
| 331 |
-
if i + 1 == prior_timesteps_tensor.shape[0]:
|
| 332 |
-
prev_timestep = None
|
| 333 |
-
else:
|
| 334 |
-
prev_timestep = prior_timesteps_tensor[i + 1]
|
| 335 |
-
|
| 336 |
-
prior_latents = self.prior_scheduler.step(
|
| 337 |
-
predicted_image_embedding,
|
| 338 |
-
timestep=t,
|
| 339 |
-
sample=prior_latents,
|
| 340 |
-
generator=generator,
|
| 341 |
-
prev_timestep=prev_timestep,
|
| 342 |
-
).prev_sample
|
| 343 |
-
|
| 344 |
-
prior_latents = self.prior.post_process_latents(prior_latents)
|
| 345 |
-
|
| 346 |
-
image_embeddings = prior_latents
|
| 347 |
-
|
| 348 |
-
# done prior
|
| 349 |
-
|
| 350 |
-
# decoder
|
| 351 |
-
|
| 352 |
-
text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
|
| 353 |
-
image_embeddings=image_embeddings,
|
| 354 |
-
prompt_embeds=prompt_embeds,
|
| 355 |
-
text_encoder_hidden_states=text_encoder_hidden_states,
|
| 356 |
-
do_classifier_free_guidance=do_classifier_free_guidance,
|
| 357 |
-
)
|
| 358 |
-
|
| 359 |
-
if device.type == "mps":
|
| 360 |
-
# HACK: MPS: There is a panic when padding bool tensors,
|
| 361 |
-
# so cast to int tensor for the pad and back to bool afterwards
|
| 362 |
-
text_mask = text_mask.type(torch.int)
|
| 363 |
-
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
|
| 364 |
-
decoder_text_mask = decoder_text_mask.type(torch.bool)
|
| 365 |
-
else:
|
| 366 |
-
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
|
| 367 |
-
|
| 368 |
-
self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
|
| 369 |
-
decoder_timesteps_tensor = self.decoder_scheduler.timesteps
|
| 370 |
-
|
| 371 |
-
num_channels_latents = self.decoder.config.in_channels
|
| 372 |
-
height = self.decoder.config.sample_size
|
| 373 |
-
width = self.decoder.config.sample_size
|
| 374 |
-
|
| 375 |
-
decoder_latents = self.prepare_latents(
|
| 376 |
-
(batch_size, num_channels_latents, height, width),
|
| 377 |
-
text_encoder_hidden_states.dtype,
|
| 378 |
-
device,
|
| 379 |
-
generator,
|
| 380 |
-
decoder_latents,
|
| 381 |
-
self.decoder_scheduler,
|
| 382 |
-
)
|
| 383 |
-
|
| 384 |
-
for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
|
| 385 |
-
# expand the latents if we are doing classifier free guidance
|
| 386 |
-
latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
|
| 387 |
-
|
| 388 |
-
noise_pred = self.decoder(
|
| 389 |
-
sample=latent_model_input,
|
| 390 |
-
timestep=t,
|
| 391 |
-
encoder_hidden_states=text_encoder_hidden_states,
|
| 392 |
-
class_labels=additive_clip_time_embeddings,
|
| 393 |
-
attention_mask=decoder_text_mask,
|
| 394 |
-
).sample
|
| 395 |
-
|
| 396 |
-
if do_classifier_free_guidance:
|
| 397 |
-
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
| 398 |
-
noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
|
| 399 |
-
noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
|
| 400 |
-
noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
|
| 401 |
-
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
|
| 402 |
-
|
| 403 |
-
if i + 1 == decoder_timesteps_tensor.shape[0]:
|
| 404 |
-
prev_timestep = None
|
| 405 |
-
else:
|
| 406 |
-
prev_timestep = decoder_timesteps_tensor[i + 1]
|
| 407 |
-
|
| 408 |
-
# compute the previous noisy sample x_t -> x_t-1
|
| 409 |
-
decoder_latents = self.decoder_scheduler.step(
|
| 410 |
-
noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
|
| 411 |
-
).prev_sample
|
| 412 |
-
|
| 413 |
-
decoder_latents = decoder_latents.clamp(-1, 1)
|
| 414 |
-
|
| 415 |
-
image_small = decoder_latents
|
| 416 |
-
|
| 417 |
-
# done decoder
|
| 418 |
-
|
| 419 |
-
# super res
|
| 420 |
-
|
| 421 |
-
self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
|
| 422 |
-
super_res_timesteps_tensor = self.super_res_scheduler.timesteps
|
| 423 |
-
|
| 424 |
-
channels = self.super_res_first.config.in_channels // 2
|
| 425 |
-
height = self.super_res_first.config.sample_size
|
| 426 |
-
width = self.super_res_first.config.sample_size
|
| 427 |
-
|
| 428 |
-
super_res_latents = self.prepare_latents(
|
| 429 |
-
(batch_size, channels, height, width),
|
| 430 |
-
image_small.dtype,
|
| 431 |
-
device,
|
| 432 |
-
generator,
|
| 433 |
-
super_res_latents,
|
| 434 |
-
self.super_res_scheduler,
|
| 435 |
-
)
|
| 436 |
-
|
| 437 |
-
if device.type == "mps":
|
| 438 |
-
# MPS does not support many interpolations
|
| 439 |
-
image_upscaled = F.interpolate(image_small, size=[height, width])
|
| 440 |
-
else:
|
| 441 |
-
interpolate_antialias = {}
|
| 442 |
-
if "antialias" in inspect.signature(F.interpolate).parameters:
|
| 443 |
-
interpolate_antialias["antialias"] = True
|
| 444 |
-
|
| 445 |
-
image_upscaled = F.interpolate(
|
| 446 |
-
image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
|
| 447 |
-
)
|
| 448 |
-
|
| 449 |
-
for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
|
| 450 |
-
# no classifier free guidance
|
| 451 |
-
|
| 452 |
-
if i == super_res_timesteps_tensor.shape[0] - 1:
|
| 453 |
-
unet = self.super_res_last
|
| 454 |
-
else:
|
| 455 |
-
unet = self.super_res_first
|
| 456 |
-
|
| 457 |
-
latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
|
| 458 |
-
|
| 459 |
-
noise_pred = unet(
|
| 460 |
-
sample=latent_model_input,
|
| 461 |
-
timestep=t,
|
| 462 |
-
).sample
|
| 463 |
-
|
| 464 |
-
if i + 1 == super_res_timesteps_tensor.shape[0]:
|
| 465 |
-
prev_timestep = None
|
| 466 |
-
else:
|
| 467 |
-
prev_timestep = super_res_timesteps_tensor[i + 1]
|
| 468 |
-
|
| 469 |
-
# compute the previous noisy sample x_t -> x_t-1
|
| 470 |
-
super_res_latents = self.super_res_scheduler.step(
|
| 471 |
-
noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
|
| 472 |
-
).prev_sample
|
| 473 |
-
|
| 474 |
-
image = super_res_latents
|
| 475 |
-
# done super res
|
| 476 |
-
|
| 477 |
-
# post processing
|
| 478 |
-
|
| 479 |
-
image = image * 0.5 + 0.5
|
| 480 |
-
image = image.clamp(0, 1)
|
| 481 |
-
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
| 482 |
-
|
| 483 |
-
if output_type == "pil":
|
| 484 |
-
image = self.numpy_to_pil(image)
|
| 485 |
-
|
| 486 |
-
if not return_dict:
|
| 487 |
-
return (image,)
|
| 488 |
-
|
| 489 |
-
return ImagePipelineOutput(images=image)
|
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|
spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
|
| 2 |
-
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
|
|
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|
spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
pretrained='open-mmlab://detectron2/resnext101_32x8d',
|
| 4 |
-
backbone=dict(
|
| 5 |
-
type='ResNeXt',
|
| 6 |
-
depth=101,
|
| 7 |
-
groups=32,
|
| 8 |
-
base_width=8,
|
| 9 |
-
num_stages=4,
|
| 10 |
-
out_indices=(0, 1, 2, 3),
|
| 11 |
-
frozen_stages=1,
|
| 12 |
-
norm_cfg=dict(type='BN', requires_grad=False),
|
| 13 |
-
style='pytorch'))
|
| 14 |
-
|
| 15 |
-
dataset_type = 'CocoDataset'
|
| 16 |
-
data_root = 'data/coco/'
|
| 17 |
-
img_norm_cfg = dict(
|
| 18 |
-
mean=[103.530, 116.280, 123.675],
|
| 19 |
-
std=[57.375, 57.120, 58.395],
|
| 20 |
-
to_rgb=False)
|
| 21 |
-
train_pipeline = [
|
| 22 |
-
dict(type='LoadImageFromFile'),
|
| 23 |
-
dict(
|
| 24 |
-
type='LoadAnnotations',
|
| 25 |
-
with_bbox=True,
|
| 26 |
-
with_mask=True,
|
| 27 |
-
poly2mask=False),
|
| 28 |
-
dict(
|
| 29 |
-
type='Resize',
|
| 30 |
-
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
|
| 31 |
-
(1333, 768), (1333, 800)],
|
| 32 |
-
multiscale_mode='value',
|
| 33 |
-
keep_ratio=True),
|
| 34 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 35 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 36 |
-
dict(type='Pad', size_divisor=32),
|
| 37 |
-
dict(type='DefaultFormatBundle'),
|
| 38 |
-
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
|
| 39 |
-
]
|
| 40 |
-
test_pipeline = [
|
| 41 |
-
dict(type='LoadImageFromFile'),
|
| 42 |
-
dict(
|
| 43 |
-
type='MultiScaleFlipAug',
|
| 44 |
-
img_scale=(1333, 800),
|
| 45 |
-
flip=False,
|
| 46 |
-
transforms=[
|
| 47 |
-
dict(type='Resize', keep_ratio=True),
|
| 48 |
-
dict(type='RandomFlip'),
|
| 49 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 50 |
-
dict(type='Pad', size_divisor=32),
|
| 51 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 52 |
-
dict(type='Collect', keys=['img']),
|
| 53 |
-
])
|
| 54 |
-
]
|
| 55 |
-
data = dict(
|
| 56 |
-
train=dict(pipeline=train_pipeline),
|
| 57 |
-
val=dict(pipeline=test_pipeline),
|
| 58 |
-
test=dict(pipeline=test_pipeline))
|
| 59 |
-
|
| 60 |
-
lr_config = dict(step=[28, 34])
|
| 61 |
-
runner = dict(type='EpochBasedRunner', max_epochs=36)
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spaces/Andy1621/uniformer_image_detection/configs/yolact/yolact_r50_8x8_coco.py
DELETED
|
@@ -1,11 +0,0 @@
|
|
| 1 |
-
_base_ = 'yolact_r50_1x8_coco.py'
|
| 2 |
-
|
| 3 |
-
optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
|
| 4 |
-
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
|
| 5 |
-
# learning policy
|
| 6 |
-
lr_config = dict(
|
| 7 |
-
policy='step',
|
| 8 |
-
warmup='linear',
|
| 9 |
-
warmup_iters=1000,
|
| 10 |
-
warmup_ratio=0.1,
|
| 11 |
-
step=[20, 42, 49, 52])
|
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/ext_loader.py
DELETED
|
@@ -1,71 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
import importlib
|
| 3 |
-
import os
|
| 4 |
-
import pkgutil
|
| 5 |
-
import warnings
|
| 6 |
-
from collections import namedtuple
|
| 7 |
-
|
| 8 |
-
import torch
|
| 9 |
-
|
| 10 |
-
if torch.__version__ != 'parrots':
|
| 11 |
-
|
| 12 |
-
def load_ext(name, funcs):
|
| 13 |
-
ext = importlib.import_module('mmcv.' + name)
|
| 14 |
-
for fun in funcs:
|
| 15 |
-
assert hasattr(ext, fun), f'{fun} miss in module {name}'
|
| 16 |
-
return ext
|
| 17 |
-
else:
|
| 18 |
-
from parrots import extension
|
| 19 |
-
from parrots.base import ParrotsException
|
| 20 |
-
|
| 21 |
-
has_return_value_ops = [
|
| 22 |
-
'nms',
|
| 23 |
-
'softnms',
|
| 24 |
-
'nms_match',
|
| 25 |
-
'nms_rotated',
|
| 26 |
-
'top_pool_forward',
|
| 27 |
-
'top_pool_backward',
|
| 28 |
-
'bottom_pool_forward',
|
| 29 |
-
'bottom_pool_backward',
|
| 30 |
-
'left_pool_forward',
|
| 31 |
-
'left_pool_backward',
|
| 32 |
-
'right_pool_forward',
|
| 33 |
-
'right_pool_backward',
|
| 34 |
-
'fused_bias_leakyrelu',
|
| 35 |
-
'upfirdn2d',
|
| 36 |
-
'ms_deform_attn_forward',
|
| 37 |
-
'pixel_group',
|
| 38 |
-
'contour_expand',
|
| 39 |
-
]
|
| 40 |
-
|
| 41 |
-
def get_fake_func(name, e):
|
| 42 |
-
|
| 43 |
-
def fake_func(*args, **kwargs):
|
| 44 |
-
warnings.warn(f'{name} is not supported in parrots now')
|
| 45 |
-
raise e
|
| 46 |
-
|
| 47 |
-
return fake_func
|
| 48 |
-
|
| 49 |
-
def load_ext(name, funcs):
|
| 50 |
-
ExtModule = namedtuple('ExtModule', funcs)
|
| 51 |
-
ext_list = []
|
| 52 |
-
lib_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
|
| 53 |
-
for fun in funcs:
|
| 54 |
-
try:
|
| 55 |
-
ext_fun = extension.load(fun, name, lib_dir=lib_root)
|
| 56 |
-
except ParrotsException as e:
|
| 57 |
-
if 'No element registered' not in e.message:
|
| 58 |
-
warnings.warn(e.message)
|
| 59 |
-
ext_fun = get_fake_func(fun, e)
|
| 60 |
-
ext_list.append(ext_fun)
|
| 61 |
-
else:
|
| 62 |
-
if fun in has_return_value_ops:
|
| 63 |
-
ext_list.append(ext_fun.op)
|
| 64 |
-
else:
|
| 65 |
-
ext_list.append(ext_fun.op_)
|
| 66 |
-
return ExtModule(*ext_list)
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
def check_ops_exist():
|
| 70 |
-
ext_loader = pkgutil.find_loader('mmcv._ext')
|
| 71 |
-
return ext_loader is not None
|
|
|
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|
spaces/Artrajz/vits-simple-api/utils/data_utils.py
DELETED
|
@@ -1,97 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import logging
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class HParams():
|
| 6 |
-
def __init__(self, **kwargs):
|
| 7 |
-
for k, v in kwargs.items():
|
| 8 |
-
if type(v) == dict:
|
| 9 |
-
v = HParams(**v)
|
| 10 |
-
self[k] = v
|
| 11 |
-
|
| 12 |
-
def keys(self):
|
| 13 |
-
return self.__dict__.keys()
|
| 14 |
-
|
| 15 |
-
def items(self):
|
| 16 |
-
return self.__dict__.items()
|
| 17 |
-
|
| 18 |
-
def values(self):
|
| 19 |
-
return self.__dict__.values()
|
| 20 |
-
|
| 21 |
-
def __len__(self):
|
| 22 |
-
return len(self.__dict__)
|
| 23 |
-
|
| 24 |
-
def __getitem__(self, key):
|
| 25 |
-
return getattr(self, key)
|
| 26 |
-
|
| 27 |
-
def __setitem__(self, key, value):
|
| 28 |
-
return setattr(self, key, value)
|
| 29 |
-
|
| 30 |
-
def __contains__(self, key):
|
| 31 |
-
return key in self.__dict__
|
| 32 |
-
|
| 33 |
-
def __repr__(self):
|
| 34 |
-
return self.__dict__.__repr__()
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def load_checkpoint(checkpoint_path, model):
|
| 38 |
-
from torch import load
|
| 39 |
-
checkpoint_dict = load(checkpoint_path, map_location='cpu')
|
| 40 |
-
iteration = checkpoint_dict.get('iteration', None)
|
| 41 |
-
saved_state_dict = checkpoint_dict['model']
|
| 42 |
-
if hasattr(model, 'module'):
|
| 43 |
-
state_dict = model.module.state_dict()
|
| 44 |
-
else:
|
| 45 |
-
state_dict = model.state_dict()
|
| 46 |
-
new_state_dict = {}
|
| 47 |
-
for k, v in state_dict.items():
|
| 48 |
-
try:
|
| 49 |
-
new_state_dict[k] = saved_state_dict[k]
|
| 50 |
-
except:
|
| 51 |
-
logging.info(f"{k} is not in the checkpoint")
|
| 52 |
-
new_state_dict[k] = v
|
| 53 |
-
if hasattr(model, 'module'):
|
| 54 |
-
model.module.load_state_dict(new_state_dict)
|
| 55 |
-
else:
|
| 56 |
-
model.load_state_dict(new_state_dict)
|
| 57 |
-
if iteration:
|
| 58 |
-
logging.info(f"Loaded checkpoint '{checkpoint_path}' (iteration {iteration})")
|
| 59 |
-
else:
|
| 60 |
-
logging.info(f"Loaded checkpoint '{checkpoint_path}'")
|
| 61 |
-
return
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
def get_hparams_from_file(config_path):
|
| 65 |
-
from json import loads
|
| 66 |
-
with open(config_path, 'r', encoding='utf-8') as f:
|
| 67 |
-
data = f.read()
|
| 68 |
-
config = loads(data)
|
| 69 |
-
|
| 70 |
-
hparams = HParams(**config)
|
| 71 |
-
return hparams
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
def load_audio_to_torch(full_path, target_sampling_rate):
|
| 75 |
-
import librosa
|
| 76 |
-
from torch import FloatTensor
|
| 77 |
-
from numpy import float32
|
| 78 |
-
audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
|
| 79 |
-
return FloatTensor(audio.astype(float32))
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
def clean_folder(folder_path):
|
| 83 |
-
for filename in os.listdir(folder_path):
|
| 84 |
-
file_path = os.path.join(folder_path, filename)
|
| 85 |
-
# 如果是文件，则删除文件。如果是文件夹则跳过。
|
| 86 |
-
if os.path.isfile(file_path):
|
| 87 |
-
os.remove(file_path)
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
def check_is_none(item) -> bool:
|
| 91 |
-
# none -> True, not none -> False
|
| 92 |
-
return item is None or (isinstance(item, str) and str(item).isspace()) or str(item) == ""
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
def save_audio(audio, path):
|
| 96 |
-
with open(path, "wb") as f:
|
| 97 |
-
f.write(audio)
|
|
|
|
|
|
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/align.py
DELETED
|
@@ -1,311 +0,0 @@
|
|
| 1 |
-
import sys
|
| 2 |
-
from itertools import chain
|
| 3 |
-
from typing import TYPE_CHECKING, Iterable, Optional
|
| 4 |
-
|
| 5 |
-
if sys.version_info >= (3, 8):
|
| 6 |
-
from typing import Literal
|
| 7 |
-
else:
|
| 8 |
-
from pip._vendor.typing_extensions import Literal # pragma: no cover
|
| 9 |
-
|
| 10 |
-
from .constrain import Constrain
|
| 11 |
-
from .jupyter import JupyterMixin
|
| 12 |
-
from .measure import Measurement
|
| 13 |
-
from .segment import Segment
|
| 14 |
-
from .style import StyleType
|
| 15 |
-
|
| 16 |
-
if TYPE_CHECKING:
|
| 17 |
-
from .console import Console, ConsoleOptions, RenderableType, RenderResult
|
| 18 |
-
|
| 19 |
-
AlignMethod = Literal["left", "center", "right"]
|
| 20 |
-
VerticalAlignMethod = Literal["top", "middle", "bottom"]
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
class Align(JupyterMixin):
|
| 24 |
-
"""Align a renderable by adding spaces if necessary.
|
| 25 |
-
|
| 26 |
-
Args:
|
| 27 |
-
renderable (RenderableType): A console renderable.
|
| 28 |
-
align (AlignMethod): One of "left", "center", or "right""
|
| 29 |
-
style (StyleType, optional): An optional style to apply to the background.
|
| 30 |
-
vertical (Optional[VerticalAlginMethod], optional): Optional vertical align, one of "top", "middle", or "bottom". Defaults to None.
|
| 31 |
-
pad (bool, optional): Pad the right with spaces. Defaults to True.
|
| 32 |
-
width (int, optional): Restrict contents to given width, or None to use default width. Defaults to None.
|
| 33 |
-
height (int, optional): Set height of align renderable, or None to fit to contents. Defaults to None.
|
| 34 |
-
|
| 35 |
-
Raises:
|
| 36 |
-
ValueError: if ``align`` is not one of the expected values.
|
| 37 |
-
"""
|
| 38 |
-
|
| 39 |
-
def __init__(
|
| 40 |
-
self,
|
| 41 |
-
renderable: "RenderableType",
|
| 42 |
-
align: AlignMethod = "left",
|
| 43 |
-
style: Optional[StyleType] = None,
|
| 44 |
-
*,
|
| 45 |
-
vertical: Optional[VerticalAlignMethod] = None,
|
| 46 |
-
pad: bool = True,
|
| 47 |
-
width: Optional[int] = None,
|
| 48 |
-
height: Optional[int] = None,
|
| 49 |
-
) -> None:
|
| 50 |
-
if align not in ("left", "center", "right"):
|
| 51 |
-
raise ValueError(
|
| 52 |
-
f'invalid value for align, expected "left", "center", or "right" (not {align!r})'
|
| 53 |
-
)
|
| 54 |
-
if vertical is not None and vertical not in ("top", "middle", "bottom"):
|
| 55 |
-
raise ValueError(
|
| 56 |
-
f'invalid value for vertical, expected "top", "middle", or "bottom" (not {vertical!r})'
|
| 57 |
-
)
|
| 58 |
-
self.renderable = renderable
|
| 59 |
-
self.align = align
|
| 60 |
-
self.style = style
|
| 61 |
-
self.vertical = vertical
|
| 62 |
-
self.pad = pad
|
| 63 |
-
self.width = width
|
| 64 |
-
self.height = height
|
| 65 |
-
|
| 66 |
-
def __repr__(self) -> str:
|
| 67 |
-
return f"Align({self.renderable!r}, {self.align!r})"
|
| 68 |
-
|
| 69 |
-
@classmethod
|
| 70 |
-
def left(
|
| 71 |
-
cls,
|
| 72 |
-
renderable: "RenderableType",
|
| 73 |
-
style: Optional[StyleType] = None,
|
| 74 |
-
*,
|
| 75 |
-
vertical: Optional[VerticalAlignMethod] = None,
|
| 76 |
-
pad: bool = True,
|
| 77 |
-
width: Optional[int] = None,
|
| 78 |
-
height: Optional[int] = None,
|
| 79 |
-
) -> "Align":
|
| 80 |
-
"""Align a renderable to the left."""
|
| 81 |
-
return cls(
|
| 82 |
-
renderable,
|
| 83 |
-
"left",
|
| 84 |
-
style=style,
|
| 85 |
-
vertical=vertical,
|
| 86 |
-
pad=pad,
|
| 87 |
-
width=width,
|
| 88 |
-
height=height,
|
| 89 |
-
)
|
| 90 |
-
|
| 91 |
-
@classmethod
|
| 92 |
-
def center(
|
| 93 |
-
cls,
|
| 94 |
-
renderable: "RenderableType",
|
| 95 |
-
style: Optional[StyleType] = None,
|
| 96 |
-
*,
|
| 97 |
-
vertical: Optional[VerticalAlignMethod] = None,
|
| 98 |
-
pad: bool = True,
|
| 99 |
-
width: Optional[int] = None,
|
| 100 |
-
height: Optional[int] = None,
|
| 101 |
-
) -> "Align":
|
| 102 |
-
"""Align a renderable to the center."""
|
| 103 |
-
return cls(
|
| 104 |
-
renderable,
|
| 105 |
-
"center",
|
| 106 |
-
style=style,
|
| 107 |
-
vertical=vertical,
|
| 108 |
-
pad=pad,
|
| 109 |
-
width=width,
|
| 110 |
-
height=height,
|
| 111 |
-
)
|
| 112 |
-
|
| 113 |
-
@classmethod
|
| 114 |
-
def right(
|
| 115 |
-
cls,
|
| 116 |
-
renderable: "RenderableType",
|
| 117 |
-
style: Optional[StyleType] = None,
|
| 118 |
-
*,
|
| 119 |
-
vertical: Optional[VerticalAlignMethod] = None,
|
| 120 |
-
pad: bool = True,
|
| 121 |
-
width: Optional[int] = None,
|
| 122 |
-
height: Optional[int] = None,
|
| 123 |
-
) -> "Align":
|
| 124 |
-
"""Align a renderable to the right."""
|
| 125 |
-
return cls(
|
| 126 |
-
renderable,
|
| 127 |
-
"right",
|
| 128 |
-
style=style,
|
| 129 |
-
vertical=vertical,
|
| 130 |
-
pad=pad,
|
| 131 |
-
width=width,
|
| 132 |
-
height=height,
|
| 133 |
-
)
|
| 134 |
-
|
| 135 |
-
def __rich_console__(
|
| 136 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 137 |
-
) -> "RenderResult":
|
| 138 |
-
align = self.align
|
| 139 |
-
width = console.measure(self.renderable, options=options).maximum
|
| 140 |
-
rendered = console.render(
|
| 141 |
-
Constrain(
|
| 142 |
-
self.renderable, width if self.width is None else min(width, self.width)
|
| 143 |
-
),
|
| 144 |
-
options.update(height=None),
|
| 145 |
-
)
|
| 146 |
-
lines = list(Segment.split_lines(rendered))
|
| 147 |
-
width, height = Segment.get_shape(lines)
|
| 148 |
-
lines = Segment.set_shape(lines, width, height)
|
| 149 |
-
new_line = Segment.line()
|
| 150 |
-
excess_space = options.max_width - width
|
| 151 |
-
style = console.get_style(self.style) if self.style is not None else None
|
| 152 |
-
|
| 153 |
-
def generate_segments() -> Iterable[Segment]:
|
| 154 |
-
if excess_space <= 0:
|
| 155 |
-
# Exact fit
|
| 156 |
-
for line in lines:
|
| 157 |
-
yield from line
|
| 158 |
-
yield new_line
|
| 159 |
-
|
| 160 |
-
elif align == "left":
|
| 161 |
-
# Pad on the right
|
| 162 |
-
pad = Segment(" " * excess_space, style) if self.pad else None
|
| 163 |
-
for line in lines:
|
| 164 |
-
yield from line
|
| 165 |
-
if pad:
|
| 166 |
-
yield pad
|
| 167 |
-
yield new_line
|
| 168 |
-
|
| 169 |
-
elif align == "center":
|
| 170 |
-
# Pad left and right
|
| 171 |
-
left = excess_space // 2
|
| 172 |
-
pad = Segment(" " * left, style)
|
| 173 |
-
pad_right = (
|
| 174 |
-
Segment(" " * (excess_space - left), style) if self.pad else None
|
| 175 |
-
)
|
| 176 |
-
for line in lines:
|
| 177 |
-
if left:
|
| 178 |
-
yield pad
|
| 179 |
-
yield from line
|
| 180 |
-
if pad_right:
|
| 181 |
-
yield pad_right
|
| 182 |
-
yield new_line
|
| 183 |
-
|
| 184 |
-
elif align == "right":
|
| 185 |
-
# Padding on left
|
| 186 |
-
pad = Segment(" " * excess_space, style)
|
| 187 |
-
for line in lines:
|
| 188 |
-
yield pad
|
| 189 |
-
yield from line
|
| 190 |
-
yield new_line
|
| 191 |
-
|
| 192 |
-
blank_line = (
|
| 193 |
-
Segment(f"{' ' * (self.width or options.max_width)}\n", style)
|
| 194 |
-
if self.pad
|
| 195 |
-
else Segment("\n")
|
| 196 |
-
)
|
| 197 |
-
|
| 198 |
-
def blank_lines(count: int) -> Iterable[Segment]:
|
| 199 |
-
if count > 0:
|
| 200 |
-
for _ in range(count):
|
| 201 |
-
yield blank_line
|
| 202 |
-
|
| 203 |
-
vertical_height = self.height or options.height
|
| 204 |
-
iter_segments: Iterable[Segment]
|
| 205 |
-
if self.vertical and vertical_height is not None:
|
| 206 |
-
if self.vertical == "top":
|
| 207 |
-
bottom_space = vertical_height - height
|
| 208 |
-
iter_segments = chain(generate_segments(), blank_lines(bottom_space))
|
| 209 |
-
elif self.vertical == "middle":
|
| 210 |
-
top_space = (vertical_height - height) // 2
|
| 211 |
-
bottom_space = vertical_height - top_space - height
|
| 212 |
-
iter_segments = chain(
|
| 213 |
-
blank_lines(top_space),
|
| 214 |
-
generate_segments(),
|
| 215 |
-
blank_lines(bottom_space),
|
| 216 |
-
)
|
| 217 |
-
else: # self.vertical == "bottom":
|
| 218 |
-
top_space = vertical_height - height
|
| 219 |
-
iter_segments = chain(blank_lines(top_space), generate_segments())
|
| 220 |
-
else:
|
| 221 |
-
iter_segments = generate_segments()
|
| 222 |
-
if self.style:
|
| 223 |
-
style = console.get_style(self.style)
|
| 224 |
-
iter_segments = Segment.apply_style(iter_segments, style)
|
| 225 |
-
yield from iter_segments
|
| 226 |
-
|
| 227 |
-
def __rich_measure__(
|
| 228 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 229 |
-
) -> Measurement:
|
| 230 |
-
measurement = Measurement.get(console, options, self.renderable)
|
| 231 |
-
return measurement
|
| 232 |
-
|
| 233 |
-
|
| 234 |
-
class VerticalCenter(JupyterMixin):
|
| 235 |
-
"""Vertically aligns a renderable.
|
| 236 |
-
|
| 237 |
-
Warn:
|
| 238 |
-
This class is deprecated and may be removed in a future version. Use Align class with
|
| 239 |
-
`vertical="middle"`.
|
| 240 |
-
|
| 241 |
-
Args:
|
| 242 |
-
renderable (RenderableType): A renderable object.
|
| 243 |
-
"""
|
| 244 |
-
|
| 245 |
-
def __init__(
|
| 246 |
-
self,
|
| 247 |
-
renderable: "RenderableType",
|
| 248 |
-
style: Optional[StyleType] = None,
|
| 249 |
-
) -> None:
|
| 250 |
-
self.renderable = renderable
|
| 251 |
-
self.style = style
|
| 252 |
-
|
| 253 |
-
def __repr__(self) -> str:
|
| 254 |
-
return f"VerticalCenter({self.renderable!r})"
|
| 255 |
-
|
| 256 |
-
def __rich_console__(
|
| 257 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 258 |
-
) -> "RenderResult":
|
| 259 |
-
style = console.get_style(self.style) if self.style is not None else None
|
| 260 |
-
lines = console.render_lines(
|
| 261 |
-
self.renderable, options.update(height=None), pad=False
|
| 262 |
-
)
|
| 263 |
-
width, _height = Segment.get_shape(lines)
|
| 264 |
-
new_line = Segment.line()
|
| 265 |
-
height = options.height or options.size.height
|
| 266 |
-
top_space = (height - len(lines)) // 2
|
| 267 |
-
bottom_space = height - top_space - len(lines)
|
| 268 |
-
blank_line = Segment(f"{' ' * width}", style)
|
| 269 |
-
|
| 270 |
-
def blank_lines(count: int) -> Iterable[Segment]:
|
| 271 |
-
for _ in range(count):
|
| 272 |
-
yield blank_line
|
| 273 |
-
yield new_line
|
| 274 |
-
|
| 275 |
-
if top_space > 0:
|
| 276 |
-
yield from blank_lines(top_space)
|
| 277 |
-
for line in lines:
|
| 278 |
-
yield from line
|
| 279 |
-
yield new_line
|
| 280 |
-
if bottom_space > 0:
|
| 281 |
-
yield from blank_lines(bottom_space)
|
| 282 |
-
|
| 283 |
-
def __rich_measure__(
|
| 284 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 285 |
-
) -> Measurement:
|
| 286 |
-
measurement = Measurement.get(console, options, self.renderable)
|
| 287 |
-
return measurement
|
| 288 |
-
|
| 289 |
-
|
| 290 |
-
if __name__ == "__main__": # pragma: no cover
|
| 291 |
-
from pip._vendor.rich.console import Console, Group
|
| 292 |
-
from pip._vendor.rich.highlighter import ReprHighlighter
|
| 293 |
-
from pip._vendor.rich.panel import Panel
|
| 294 |
-
|
| 295 |
-
highlighter = ReprHighlighter()
|
| 296 |
-
console = Console()
|
| 297 |
-
|
| 298 |
-
panel = Panel(
|
| 299 |
-
Group(
|
| 300 |
-
Align.left(highlighter("align='left'")),
|
| 301 |
-
Align.center(highlighter("align='center'")),
|
| 302 |
-
Align.right(highlighter("align='right'")),
|
| 303 |
-
),
|
| 304 |
-
width=60,
|
| 305 |
-
style="on dark_blue",
|
| 306 |
-
title="Align",
|
| 307 |
-
)
|
| 308 |
-
|
| 309 |
-
console.print(
|
| 310 |
-
Align.center(panel, vertical="middle", style="on red", height=console.height)
|
| 311 |
-
)
|
|
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/styled.py
DELETED
|
@@ -1,42 +0,0 @@
|
|
| 1 |
-
from typing import TYPE_CHECKING
|
| 2 |
-
|
| 3 |
-
from .measure import Measurement
|
| 4 |
-
from .segment import Segment
|
| 5 |
-
from .style import StyleType
|
| 6 |
-
|
| 7 |
-
if TYPE_CHECKING:
|
| 8 |
-
from .console import Console, ConsoleOptions, RenderResult, RenderableType
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class Styled:
|
| 12 |
-
"""Apply a style to a renderable.
|
| 13 |
-
|
| 14 |
-
Args:
|
| 15 |
-
renderable (RenderableType): Any renderable.
|
| 16 |
-
style (StyleType): A style to apply across the entire renderable.
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
def __init__(self, renderable: "RenderableType", style: "StyleType") -> None:
|
| 20 |
-
self.renderable = renderable
|
| 21 |
-
self.style = style
|
| 22 |
-
|
| 23 |
-
def __rich_console__(
|
| 24 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 25 |
-
) -> "RenderResult":
|
| 26 |
-
style = console.get_style(self.style)
|
| 27 |
-
rendered_segments = console.render(self.renderable, options)
|
| 28 |
-
segments = Segment.apply_style(rendered_segments, style)
|
| 29 |
-
return segments
|
| 30 |
-
|
| 31 |
-
def __rich_measure__(
|
| 32 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 33 |
-
) -> Measurement:
|
| 34 |
-
return Measurement.get(console, options, self.renderable)
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
if __name__ == "__main__": # pragma: no cover
|
| 38 |
-
from pip._vendor.rich import print
|
| 39 |
-
from pip._vendor.rich.panel import Panel
|
| 40 |
-
|
| 41 |
-
panel = Styled(Panel("hello"), "on blue")
|
| 42 |
-
print(panel)
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spaces/Autopixel/blurry-faces/app.py
DELETED
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@@ -1,59 +0,0 @@
|
|
| 1 |
-
import cv2
|
| 2 |
-
import gradio as gr
|
| 3 |
-
from typing import Union, Tuple
|
| 4 |
-
from PIL import Image, ImageOps
|
| 5 |
-
import numpy as np
|
| 6 |
-
import torch
|
| 7 |
-
|
| 8 |
-
model = torch.jit.load('./model/model.pt').eval()
|
| 9 |
-
|
| 10 |
-
def resize_with_padding(img: Image.Image, expected_size: Tuple[int, int]) -> Image.Image:
|
| 11 |
-
img.thumbnail((expected_size[0], expected_size[1]))
|
| 12 |
-
delta_width = expected_size[0] - img.size[0]
|
| 13 |
-
delta_height = expected_size[1] - img.size[1]
|
| 14 |
-
pad_width = delta_width // 2
|
| 15 |
-
pad_height = delta_height // 2
|
| 16 |
-
padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
|
| 17 |
-
return ImageOps.expand(img, padding), padding
|
| 18 |
-
|
| 19 |
-
def preprocess_image(img: Image.Image, size: int = 512) -> Tuple[Image.Image, torch.tensor, Tuple[int]]:
|
| 20 |
-
pil_img, padding = resize_with_padding(img, (size, size))
|
| 21 |
-
|
| 22 |
-
img = (np.array(pil_img).astype(np.float32) / 255) - np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3)
|
| 23 |
-
img = img / np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3)
|
| 24 |
-
img = np.transpose(img, (2, 0, 1))
|
| 25 |
-
|
| 26 |
-
return pil_img, torch.tensor(img[None]), padding
|
| 27 |
-
|
| 28 |
-
def soft_blur_with_mask(image: Image.Image, mask: torch.tensor, padding: Tuple[int]) -> Image.Image:
|
| 29 |
-
image = np.array(image)
|
| 30 |
-
# Create a blurred copy of the original image.
|
| 31 |
-
blurred_image = cv2.GaussianBlur(image, (221, 221), sigmaX=20, sigmaY=20)
|
| 32 |
-
image_height, image_width = image.shape[:2]
|
| 33 |
-
mask = cv2.resize(mask.astype(np.uint8), (image_width, image_height), interpolation=cv2.INTER_NEAREST)
|
| 34 |
-
# Blurring the mask itself to get a softer mask with no firm edges
|
| 35 |
-
mask = cv2.GaussianBlur(mask.astype(np.float32), (11, 11), 10, 10)[:, :, None]
|
| 36 |
-
|
| 37 |
-
# Take the blurred image where the mask it positive, and the original image where the image is original
|
| 38 |
-
image = (mask * blurred_image + (1.0 - mask) * image)
|
| 39 |
-
pad_w, pad_h, _, _ = padding
|
| 40 |
-
img_w, img_h, _ = image.shape
|
| 41 |
-
image = image[(pad_h):(img_h-pad_h), (pad_w):(img_w-pad_w), :]
|
| 42 |
-
return Image.fromarray(image.astype(np.uint8))
|
| 43 |
-
|
| 44 |
-
def run(image, size):
|
| 45 |
-
pil_image, torch_image, padding = preprocess_image(image, size=size)
|
| 46 |
-
|
| 47 |
-
with torch.inference_mode():
|
| 48 |
-
mask = model(torch_image)
|
| 49 |
-
mask = mask.argmax(dim=1).numpy().squeeze()
|
| 50 |
-
|
| 51 |
-
return soft_blur_with_mask(pil_image, mask, padding)
|
| 52 |
-
|
| 53 |
-
content_image_input = gr.inputs.Image(label="Entrada", type="pil")
|
| 54 |
-
model_image_size = gr.inputs.Radio([256, 384, 512, 1024], type="value", default=512, label="Ajustar nivel de inferencia")
|
| 55 |
-
|
| 56 |
-
app_interface = gr.Interface(fn=run,
|
| 57 |
-
inputs=[content_image_input, model_image_size],
|
| 58 |
-
outputs="image")
|
| 59 |
-
app_interface.launch()
|
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|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/model_zoo/model_zoo.py
DELETED
|
@@ -1,213 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import os
|
| 3 |
-
from typing import Optional
|
| 4 |
-
import pkg_resources
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
from detectron2.checkpoint import DetectionCheckpointer
|
| 8 |
-
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
|
| 9 |
-
from detectron2.modeling import build_model
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class _ModelZooUrls(object):
|
| 13 |
-
"""
|
| 14 |
-
Mapping from names to officially released Detectron2 pre-trained models.
|
| 15 |
-
"""
|
| 16 |
-
|
| 17 |
-
S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
|
| 18 |
-
|
| 19 |
-
# format: {config_path.yaml} -> model_id/model_final_{commit}.pkl
|
| 20 |
-
CONFIG_PATH_TO_URL_SUFFIX = {
|
| 21 |
-
# COCO Detection with Faster R-CNN
|
| 22 |
-
"COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl",
|
| 23 |
-
"COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl",
|
| 24 |
-
"COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl",
|
| 25 |
-
"COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl",
|
| 26 |
-
"COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl",
|
| 27 |
-
"COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl",
|
| 28 |
-
"COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl",
|
| 29 |
-
"COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl",
|
| 30 |
-
"COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl",
|
| 31 |
-
"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl",
|
| 32 |
-
# COCO Detection with RetinaNet
|
| 33 |
-
"COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl",
|
| 34 |
-
"COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl",
|
| 35 |
-
"COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl",
|
| 36 |
-
# COCO Detection with RPN and Fast R-CNN
|
| 37 |
-
"COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl",
|
| 38 |
-
"COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl",
|
| 39 |
-
"COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl",
|
| 40 |
-
# COCO Instance Segmentation Baselines with Mask R-CNN
|
| 41 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl",
|
| 42 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl",
|
| 43 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl",
|
| 44 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl",
|
| 45 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl",
|
| 46 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl",
|
| 47 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl",
|
| 48 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl",
|
| 49 |
-
"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl",
|
| 50 |
-
"COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl", # noqa
|
| 51 |
-
# New baselines using Large-Scale Jitter and Longer Training Schedule
|
| 52 |
-
"new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ": "42047764/model_final_bb69de.pkl",
|
| 53 |
-
"new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ": "42047638/model_final_89a8d3.pkl",
|
| 54 |
-
"new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ": "42019571/model_final_14d201.pkl",
|
| 55 |
-
"new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ": "42025812/model_final_4f7b58.pkl",
|
| 56 |
-
"new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ": "42131867/model_final_0bb7ae.pkl",
|
| 57 |
-
"new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ": "42073830/model_final_f96b26.pkl",
|
| 58 |
-
"new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ": "42047771/model_final_b7fbab.pkl", # noqa
|
| 59 |
-
"new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ": "42132721/model_final_5d87c1.pkl", # noqa
|
| 60 |
-
"new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ": "42025447/model_final_f1362d.pkl", # noqa
|
| 61 |
-
"new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ": "42047784/model_final_6ba57e.pkl", # noqa
|
| 62 |
-
"new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ": "42047642/model_final_27b9c1.pkl", # noqa
|
| 63 |
-
"new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ": "42045954/model_final_ef3a80.pkl", # noqa
|
| 64 |
-
# COCO Person Keypoint Detection Baselines with Keypoint R-CNN
|
| 65 |
-
"COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl",
|
| 66 |
-
"COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl",
|
| 67 |
-
"COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl",
|
| 68 |
-
"COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl",
|
| 69 |
-
# COCO Panoptic Segmentation Baselines with Panoptic FPN
|
| 70 |
-
"COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl",
|
| 71 |
-
"COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl",
|
| 72 |
-
"COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl",
|
| 73 |
-
# LVIS Instance Segmentation Baselines with Mask R-CNN
|
| 74 |
-
"LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl", # noqa
|
| 75 |
-
"LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl", # noqa
|
| 76 |
-
"LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl", # noqa
|
| 77 |
-
# Cityscapes & Pascal VOC Baselines
|
| 78 |
-
"Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl",
|
| 79 |
-
"PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl",
|
| 80 |
-
# Other Settings
|
| 81 |
-
"Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl",
|
| 82 |
-
"Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl",
|
| 83 |
-
"Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl",
|
| 84 |
-
"Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl",
|
| 85 |
-
"Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl",
|
| 86 |
-
"Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl",
|
| 87 |
-
"Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl",
|
| 88 |
-
"Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl",
|
| 89 |
-
"Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl",
|
| 90 |
-
"Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl",
|
| 91 |
-
"Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl", # noqa
|
| 92 |
-
# D1 Comparisons
|
| 93 |
-
"Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl", # noqa
|
| 94 |
-
"Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl", # noqa
|
| 95 |
-
"Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl",
|
| 96 |
-
}
|
| 97 |
-
|
| 98 |
-
@staticmethod
|
| 99 |
-
def query(config_path: str) -> Optional[str]:
|
| 100 |
-
"""
|
| 101 |
-
Args:
|
| 102 |
-
config_path: relative config filename
|
| 103 |
-
"""
|
| 104 |
-
name = config_path.replace(".yaml", "").replace(".py", "")
|
| 105 |
-
if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX:
|
| 106 |
-
suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name]
|
| 107 |
-
return _ModelZooUrls.S3_PREFIX + name + "/" + suffix
|
| 108 |
-
return None
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def get_checkpoint_url(config_path):
|
| 112 |
-
"""
|
| 113 |
-
Returns the URL to the model trained using the given config
|
| 114 |
-
|
| 115 |
-
Args:
|
| 116 |
-
config_path (str): config file name relative to detectron2's "configs/"
|
| 117 |
-
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
|
| 118 |
-
|
| 119 |
-
Returns:
|
| 120 |
-
str: a URL to the model
|
| 121 |
-
"""
|
| 122 |
-
url = _ModelZooUrls.query(config_path)
|
| 123 |
-
if url is None:
|
| 124 |
-
raise RuntimeError("Pretrained model for {} is not available!".format(config_path))
|
| 125 |
-
return url
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
def get_config_file(config_path):
|
| 129 |
-
"""
|
| 130 |
-
Returns path to a builtin config file.
|
| 131 |
-
|
| 132 |
-
Args:
|
| 133 |
-
config_path (str): config file name relative to detectron2's "configs/"
|
| 134 |
-
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
|
| 135 |
-
|
| 136 |
-
Returns:
|
| 137 |
-
str: the real path to the config file.
|
| 138 |
-
"""
|
| 139 |
-
cfg_file = pkg_resources.resource_filename(
|
| 140 |
-
"detectron2.model_zoo", os.path.join("configs", config_path)
|
| 141 |
-
)
|
| 142 |
-
if not os.path.exists(cfg_file):
|
| 143 |
-
raise RuntimeError("{} not available in Model Zoo!".format(config_path))
|
| 144 |
-
return cfg_file
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
def get_config(config_path, trained: bool = False):
|
| 148 |
-
"""
|
| 149 |
-
Returns a config object for a model in model zoo.
|
| 150 |
-
|
| 151 |
-
Args:
|
| 152 |
-
config_path (str): config file name relative to detectron2's "configs/"
|
| 153 |
-
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
|
| 154 |
-
trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights.
|
| 155 |
-
If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used
|
| 156 |
-
instead; this will typically (though not always) initialize a subset of weights using
|
| 157 |
-
an ImageNet pre-trained model, while randomly initializing the other weights.
|
| 158 |
-
|
| 159 |
-
Returns:
|
| 160 |
-
CfgNode or omegaconf.DictConfig: a config object
|
| 161 |
-
"""
|
| 162 |
-
cfg_file = get_config_file(config_path)
|
| 163 |
-
if cfg_file.endswith(".yaml"):
|
| 164 |
-
cfg = get_cfg()
|
| 165 |
-
cfg.merge_from_file(cfg_file)
|
| 166 |
-
if trained:
|
| 167 |
-
cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path)
|
| 168 |
-
return cfg
|
| 169 |
-
elif cfg_file.endswith(".py"):
|
| 170 |
-
cfg = LazyConfig.load(cfg_file)
|
| 171 |
-
if trained:
|
| 172 |
-
url = get_checkpoint_url(config_path)
|
| 173 |
-
if "train" in cfg and "init_checkpoint" in cfg.train:
|
| 174 |
-
cfg.train.init_checkpoint = url
|
| 175 |
-
else:
|
| 176 |
-
raise NotImplementedError
|
| 177 |
-
return cfg
|
| 178 |
-
|
| 179 |
-
|
| 180 |
-
def get(config_path, trained: bool = False, device: Optional[str] = None):
|
| 181 |
-
"""
|
| 182 |
-
Get a model specified by relative path under Detectron2's official ``configs/`` directory.
|
| 183 |
-
|
| 184 |
-
Args:
|
| 185 |
-
config_path (str): config file name relative to detectron2's "configs/"
|
| 186 |
-
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
|
| 187 |
-
trained (bool): see :func:`get_config`.
|
| 188 |
-
device (str or None): overwrite the device in config, if given.
|
| 189 |
-
|
| 190 |
-
Returns:
|
| 191 |
-
nn.Module: a detectron2 model. Will be in training mode.
|
| 192 |
-
|
| 193 |
-
Example:
|
| 194 |
-
::
|
| 195 |
-
from detectron2 import model_zoo
|
| 196 |
-
model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True)
|
| 197 |
-
"""
|
| 198 |
-
cfg = get_config(config_path, trained)
|
| 199 |
-
if device is None and not torch.cuda.is_available():
|
| 200 |
-
device = "cpu"
|
| 201 |
-
if device is not None and isinstance(cfg, CfgNode):
|
| 202 |
-
cfg.MODEL.DEVICE = device
|
| 203 |
-
|
| 204 |
-
if isinstance(cfg, CfgNode):
|
| 205 |
-
model = build_model(cfg)
|
| 206 |
-
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
|
| 207 |
-
else:
|
| 208 |
-
model = instantiate(cfg.model)
|
| 209 |
-
if device is not None:
|
| 210 |
-
model = model.to(device)
|
| 211 |
-
if "train" in cfg and "init_checkpoint" in cfg.train:
|
| 212 |
-
DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
|
| 213 |
-
return model
|
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|
spaces/Banbri/zcvzcv/src/lib/replaceWhiteWithTransparent.ts
DELETED
|
@@ -1,37 +0,0 @@
|
|
| 1 |
-
export function replaceWhiteWithTransparent(imageBase64: string): Promise<string> {
|
| 2 |
-
return new Promise((resolve, reject) => {
|
| 3 |
-
const img = new Image();
|
| 4 |
-
img.onload = () => {
|
| 5 |
-
const canvas = document.createElement('canvas');
|
| 6 |
-
canvas.width = img.width;
|
| 7 |
-
canvas.height = img.height;
|
| 8 |
-
|
| 9 |
-
const ctx = canvas.getContext('2d');
|
| 10 |
-
if (!ctx) {
|
| 11 |
-
reject('Unable to get canvas 2D context');
|
| 12 |
-
return;
|
| 13 |
-
}
|
| 14 |
-
|
| 15 |
-
ctx.drawImage(img, 0, 0);
|
| 16 |
-
|
| 17 |
-
const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
|
| 18 |
-
const data = imageData.data;
|
| 19 |
-
|
| 20 |
-
for (let i = 0; i < data.length; i += 4) {
|
| 21 |
-
if (data[i] === 255 && data[i + 1] === 255 && data[i + 2] === 255) {
|
| 22 |
-
data[i + 3] = 0;
|
| 23 |
-
}
|
| 24 |
-
}
|
| 25 |
-
|
| 26 |
-
ctx.putImageData(imageData, 0, 0);
|
| 27 |
-
|
| 28 |
-
resolve(canvas.toDataURL());
|
| 29 |
-
};
|
| 30 |
-
|
| 31 |
-
img.onerror = (err) => {
|
| 32 |
-
reject(err);
|
| 33 |
-
};
|
| 34 |
-
|
| 35 |
-
img.src = imageBase64;
|
| 36 |
-
});
|
| 37 |
-
}
|
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|
spaces/BigSalmon/GPT2Mask/app.py
DELETED
|
@@ -1,68 +0,0 @@
|
|
| 1 |
-
import streamlit as st
|
| 2 |
-
import numpy as np
|
| 3 |
-
import pandas as pd
|
| 4 |
-
import os
|
| 5 |
-
import torch
|
| 6 |
-
import torch.nn as nn
|
| 7 |
-
from transformers.activations import get_activation
|
| 8 |
-
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 9 |
-
from transformers import AutoTokenizer, AutoModel
|
| 10 |
-
from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
st.title('GPT2:')
|
| 14 |
-
|
| 15 |
-
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 16 |
-
|
| 17 |
-
@st.cache(allow_output_mutation=True)
|
| 18 |
-
def get_model():
|
| 19 |
-
#tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MASKGPT2")
|
| 20 |
-
#model = AutoModelForCausalLM.from_pretrained("BigSalmon/MASKGPT2")
|
| 21 |
-
#tokenizer = GPTNeoXTokenizerFast.from_pretrained("CarperAI/FIM-NeoX-1.3B")
|
| 22 |
-
#model = GPTNeoXForCausalLM.from_pretrained("BigSalmon/FormalInformalConcise-FIM-NeoX-1.3B")
|
| 23 |
-
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/FamilyFeud")
|
| 24 |
-
model = AutoModelForCausalLM.from_pretrained("BigSalmon/FamilyFeud")
|
| 25 |
-
return model, tokenizer
|
| 26 |
-
|
| 27 |
-
model, tokenizer = get_model()
|
| 28 |
-
|
| 29 |
-
g = """***
|
| 30 |
-
|
| 31 |
-
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
|
| 32 |
-
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
|
| 33 |
-
|
| 34 |
-
***
|
| 35 |
-
|
| 36 |
-
original:"""
|
| 37 |
-
|
| 38 |
-
def prefix_format(sentence):
|
| 39 |
-
words = sentence.split()
|
| 40 |
-
if "[MASK]" in sentence:
|
| 41 |
-
words2 = words.index("[MASK]")
|
| 42 |
-
#print(words2)
|
| 43 |
-
output = ("<|SUF|> " + ' '.join(words[words2+1:]) + " <|PRE|> " + ' '.join(words[:words2]) + " <|MID|>")
|
| 44 |
-
st.write(output)
|
| 45 |
-
else:
|
| 46 |
-
st.write("Add [MASK] to sentence")
|
| 47 |
-
|
| 48 |
-
with st.form(key='my_form'):
|
| 49 |
-
prompt = st.text_area(label='Enter sentence', value=g)
|
| 50 |
-
submit_button = st.form_submit_button(label='Submit')
|
| 51 |
-
submit_button6 = st.form_submit_button(label='Turn Into Infill Format. Just add [MASK] where you want it infilled')
|
| 52 |
-
if submit_button:
|
| 53 |
-
with torch.no_grad():
|
| 54 |
-
text = tokenizer.encode(prompt)
|
| 55 |
-
myinput, past_key_values = torch.tensor([text]), None
|
| 56 |
-
myinput = myinput
|
| 57 |
-
myinput= myinput
|
| 58 |
-
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
|
| 59 |
-
logits = logits[0,-1]
|
| 60 |
-
probabilities = torch.nn.functional.softmax(logits)
|
| 61 |
-
best_logits, best_indices = logits.topk(250)
|
| 62 |
-
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
|
| 63 |
-
text.append(best_indices[0].item())
|
| 64 |
-
best_probabilities = probabilities[best_indices].tolist()
|
| 65 |
-
words = []
|
| 66 |
-
st.write(best_words)
|
| 67 |
-
if submit_button6:
|
| 68 |
-
prefix_format(prompt)
|
|
|
|
|
|
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|
|
spaces/CVH-vn1210/make_hair/minigpt4/tasks/image_text_pretrain.py
DELETED
|
@@ -1,18 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
Copyright (c) 2022, salesforce.com, inc.
|
| 3 |
-
All rights reserved.
|
| 4 |
-
SPDX-License-Identifier: BSD-3-Clause
|
| 5 |
-
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
| 6 |
-
"""
|
| 7 |
-
|
| 8 |
-
from minigpt4.common.registry import registry
|
| 9 |
-
from minigpt4.tasks.base_task import BaseTask
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
@registry.register_task("image_text_pretrain")
|
| 13 |
-
class ImageTextPretrainTask(BaseTask):
|
| 14 |
-
def __init__(self):
|
| 15 |
-
super().__init__()
|
| 16 |
-
|
| 17 |
-
def evaluation(self, model, data_loader, cuda_enabled=True):
|
| 18 |
-
pass
|
|
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|
spaces/CVPR/WALT/mmdet/core/bbox/coder/__init__.py
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
from .base_bbox_coder import BaseBBoxCoder
|
| 2 |
-
from .bucketing_bbox_coder import BucketingBBoxCoder
|
| 3 |
-
from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
|
| 4 |
-
from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
|
| 5 |
-
from .pseudo_bbox_coder import PseudoBBoxCoder
|
| 6 |
-
from .tblr_bbox_coder import TBLRBBoxCoder
|
| 7 |
-
from .yolo_bbox_coder import YOLOBBoxCoder
|
| 8 |
-
|
| 9 |
-
__all__ = [
|
| 10 |
-
'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder',
|
| 11 |
-
'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder',
|
| 12 |
-
'BucketingBBoxCoder'
|
| 13 |
-
]
|
|
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|
spaces/CVPR/WALT/mmdet/models/roi_heads/dynamic_roi_head.py
DELETED
|
@@ -1,154 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import torch
|
| 3 |
-
|
| 4 |
-
from mmdet.core import bbox2roi
|
| 5 |
-
from mmdet.models.losses import SmoothL1Loss
|
| 6 |
-
from ..builder import HEADS
|
| 7 |
-
from .standard_roi_head import StandardRoIHead
|
| 8 |
-
|
| 9 |
-
EPS = 1e-15
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
@HEADS.register_module()
|
| 13 |
-
class DynamicRoIHead(StandardRoIHead):
|
| 14 |
-
"""RoI head for `Dynamic R-CNN <https://arxiv.org/abs/2004.06002>`_."""
|
| 15 |
-
|
| 16 |
-
def __init__(self, **kwargs):
|
| 17 |
-
super(DynamicRoIHead, self).__init__(**kwargs)
|
| 18 |
-
assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
|
| 19 |
-
# the IoU history of the past `update_iter_interval` iterations
|
| 20 |
-
self.iou_history = []
|
| 21 |
-
# the beta history of the past `update_iter_interval` iterations
|
| 22 |
-
self.beta_history = []
|
| 23 |
-
|
| 24 |
-
def forward_train(self,
|
| 25 |
-
x,
|
| 26 |
-
img_metas,
|
| 27 |
-
proposal_list,
|
| 28 |
-
gt_bboxes,
|
| 29 |
-
gt_labels,
|
| 30 |
-
gt_bboxes_ignore=None,
|
| 31 |
-
gt_masks=None):
|
| 32 |
-
"""Forward function for training.
|
| 33 |
-
|
| 34 |
-
Args:
|
| 35 |
-
x (list[Tensor]): list of multi-level img features.
|
| 36 |
-
|
| 37 |
-
img_metas (list[dict]): list of image info dict where each dict
|
| 38 |
-
has: 'img_shape', 'scale_factor', 'flip', and may also contain
|
| 39 |
-
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
|
| 40 |
-
For details on the values of these keys see
|
| 41 |
-
`mmdet/datasets/pipelines/formatting.py:Collect`.
|
| 42 |
-
|
| 43 |
-
proposals (list[Tensors]): list of region proposals.
|
| 44 |
-
|
| 45 |
-
gt_bboxes (list[Tensor]): each item are the truth boxes for each
|
| 46 |
-
image in [tl_x, tl_y, br_x, br_y] format.
|
| 47 |
-
|
| 48 |
-
gt_labels (list[Tensor]): class indices corresponding to each box
|
| 49 |
-
|
| 50 |
-
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
|
| 51 |
-
boxes can be ignored when computing the loss.
|
| 52 |
-
|
| 53 |
-
gt_masks (None | Tensor) : true segmentation masks for each box
|
| 54 |
-
used if the architecture supports a segmentation task.
|
| 55 |
-
|
| 56 |
-
Returns:
|
| 57 |
-
dict[str, Tensor]: a dictionary of loss components
|
| 58 |
-
"""
|
| 59 |
-
# assign gts and sample proposals
|
| 60 |
-
if self.with_bbox or self.with_mask:
|
| 61 |
-
num_imgs = len(img_metas)
|
| 62 |
-
if gt_bboxes_ignore is None:
|
| 63 |
-
gt_bboxes_ignore = [None for _ in range(num_imgs)]
|
| 64 |
-
sampling_results = []
|
| 65 |
-
cur_iou = []
|
| 66 |
-
for i in range(num_imgs):
|
| 67 |
-
assign_result = self.bbox_assigner.assign(
|
| 68 |
-
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
|
| 69 |
-
gt_labels[i])
|
| 70 |
-
sampling_result = self.bbox_sampler.sample(
|
| 71 |
-
assign_result,
|
| 72 |
-
proposal_list[i],
|
| 73 |
-
gt_bboxes[i],
|
| 74 |
-
gt_labels[i],
|
| 75 |
-
feats=[lvl_feat[i][None] for lvl_feat in x])
|
| 76 |
-
# record the `iou_topk`-th largest IoU in an image
|
| 77 |
-
iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
|
| 78 |
-
len(assign_result.max_overlaps))
|
| 79 |
-
ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
|
| 80 |
-
cur_iou.append(ious[-1].item())
|
| 81 |
-
sampling_results.append(sampling_result)
|
| 82 |
-
# average the current IoUs over images
|
| 83 |
-
cur_iou = np.mean(cur_iou)
|
| 84 |
-
self.iou_history.append(cur_iou)
|
| 85 |
-
|
| 86 |
-
losses = dict()
|
| 87 |
-
# bbox head forward and loss
|
| 88 |
-
if self.with_bbox:
|
| 89 |
-
bbox_results = self._bbox_forward_train(x, sampling_results,
|
| 90 |
-
gt_bboxes, gt_labels,
|
| 91 |
-
img_metas)
|
| 92 |
-
losses.update(bbox_results['loss_bbox'])
|
| 93 |
-
|
| 94 |
-
# mask head forward and loss
|
| 95 |
-
if self.with_mask:
|
| 96 |
-
mask_results = self._mask_forward_train(x, sampling_results,
|
| 97 |
-
bbox_results['bbox_feats'],
|
| 98 |
-
gt_masks, img_metas)
|
| 99 |
-
losses.update(mask_results['loss_mask'])
|
| 100 |
-
|
| 101 |
-
# update IoU threshold and SmoothL1 beta
|
| 102 |
-
update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
|
| 103 |
-
if len(self.iou_history) % update_iter_interval == 0:
|
| 104 |
-
new_iou_thr, new_beta = self.update_hyperparameters()
|
| 105 |
-
|
| 106 |
-
return losses
|
| 107 |
-
|
| 108 |
-
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
|
| 109 |
-
img_metas):
|
| 110 |
-
num_imgs = len(img_metas)
|
| 111 |
-
rois = bbox2roi([res.bboxes for res in sampling_results])
|
| 112 |
-
bbox_results = self._bbox_forward(x, rois)
|
| 113 |
-
|
| 114 |
-
bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
|
| 115 |
-
gt_labels, self.train_cfg)
|
| 116 |
-
# record the `beta_topk`-th smallest target
|
| 117 |
-
# `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
|
| 118 |
-
# and bbox_weights, respectively
|
| 119 |
-
pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
|
| 120 |
-
num_pos = len(pos_inds)
|
| 121 |
-
cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
|
| 122 |
-
beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
|
| 123 |
-
num_pos)
|
| 124 |
-
cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
|
| 125 |
-
self.beta_history.append(cur_target)
|
| 126 |
-
loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
|
| 127 |
-
bbox_results['bbox_pred'], rois,
|
| 128 |
-
*bbox_targets)
|
| 129 |
-
|
| 130 |
-
bbox_results.update(loss_bbox=loss_bbox)
|
| 131 |
-
return bbox_results
|
| 132 |
-
|
| 133 |
-
def update_hyperparameters(self):
|
| 134 |
-
"""Update hyperparameters like IoU thresholds for assigner and beta for
|
| 135 |
-
SmoothL1 loss based on the training statistics.
|
| 136 |
-
|
| 137 |
-
Returns:
|
| 138 |
-
tuple[float]: the updated ``iou_thr`` and ``beta``.
|
| 139 |
-
"""
|
| 140 |
-
new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
|
| 141 |
-
np.mean(self.iou_history))
|
| 142 |
-
self.iou_history = []
|
| 143 |
-
self.bbox_assigner.pos_iou_thr = new_iou_thr
|
| 144 |
-
self.bbox_assigner.neg_iou_thr = new_iou_thr
|
| 145 |
-
self.bbox_assigner.min_pos_iou = new_iou_thr
|
| 146 |
-
if (np.median(self.beta_history) < EPS):
|
| 147 |
-
# avoid 0 or too small value for new_beta
|
| 148 |
-
new_beta = self.bbox_head.loss_bbox.beta
|
| 149 |
-
else:
|
| 150 |
-
new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
|
| 151 |
-
np.median(self.beta_history))
|
| 152 |
-
self.beta_history = []
|
| 153 |
-
self.bbox_head.loss_bbox.beta = new_beta
|
| 154 |
-
return new_iou_thr, new_beta
|
|
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|
spaces/ChrisPreston/diff-svc_minato_aqua/modules/nsf_hifigan/env.py
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import shutil
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class AttrDict(dict):
|
| 6 |
-
def __init__(self, *args, **kwargs):
|
| 7 |
-
super(AttrDict, self).__init__(*args, **kwargs)
|
| 8 |
-
self.__dict__ = self
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def build_env(config, config_name, path):
|
| 12 |
-
t_path = os.path.join(path, config_name)
|
| 13 |
-
if config != t_path:
|
| 14 |
-
os.makedirs(path, exist_ok=True)
|
| 15 |
-
shutil.copyfile(config, os.path.join(path, config_name))
|
|
|
|
|
|
|
|
|
|
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|
|
spaces/CosmicSage/Linaqruf-anything-v3.0/app.py
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
|
| 3 |
-
gr.Interface.load("models/Linaqruf/anything-v3.0").launch()
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/D_S_I_G_.py
DELETED
|
@@ -1,151 +0,0 @@
|
|
| 1 |
-
from fontTools.misc.textTools import bytesjoin, strjoin, tobytes, tostr, safeEval
|
| 2 |
-
from fontTools.misc import sstruct
|
| 3 |
-
from . import DefaultTable
|
| 4 |
-
import base64
|
| 5 |
-
|
| 6 |
-
DSIG_HeaderFormat = """
|
| 7 |
-
> # big endian
|
| 8 |
-
ulVersion: L
|
| 9 |
-
usNumSigs: H
|
| 10 |
-
usFlag: H
|
| 11 |
-
"""
|
| 12 |
-
# followed by an array of usNumSigs DSIG_Signature records
|
| 13 |
-
DSIG_SignatureFormat = """
|
| 14 |
-
> # big endian
|
| 15 |
-
ulFormat: L
|
| 16 |
-
ulLength: L # length includes DSIG_SignatureBlock header
|
| 17 |
-
ulOffset: L
|
| 18 |
-
"""
|
| 19 |
-
# followed by an array of usNumSigs DSIG_SignatureBlock records,
|
| 20 |
-
# each followed immediately by the pkcs7 bytes
|
| 21 |
-
DSIG_SignatureBlockFormat = """
|
| 22 |
-
> # big endian
|
| 23 |
-
usReserved1: H
|
| 24 |
-
usReserved2: H
|
| 25 |
-
cbSignature: l # length of following raw pkcs7 data
|
| 26 |
-
"""
|
| 27 |
-
|
| 28 |
-
#
|
| 29 |
-
# NOTE
|
| 30 |
-
# the DSIG table format allows for SignatureBlocks residing
|
| 31 |
-
# anywhere in the table and possibly in a different order as
|
| 32 |
-
# listed in the array after the first table header
|
| 33 |
-
#
|
| 34 |
-
# this implementation does not keep track of any gaps and/or data
|
| 35 |
-
# before or after the actual signature blocks while decompiling,
|
| 36 |
-
# and puts them in the same physical order as listed in the header
|
| 37 |
-
# on compilation with no padding whatsoever.
|
| 38 |
-
#
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
class table_D_S_I_G_(DefaultTable.DefaultTable):
|
| 42 |
-
def decompile(self, data, ttFont):
|
| 43 |
-
dummy, newData = sstruct.unpack2(DSIG_HeaderFormat, data, self)
|
| 44 |
-
assert self.ulVersion == 1, "DSIG ulVersion must be 1"
|
| 45 |
-
assert self.usFlag & ~1 == 0, "DSIG usFlag must be 0x1 or 0x0"
|
| 46 |
-
self.signatureRecords = sigrecs = []
|
| 47 |
-
for n in range(self.usNumSigs):
|
| 48 |
-
sigrec, newData = sstruct.unpack2(
|
| 49 |
-
DSIG_SignatureFormat, newData, SignatureRecord()
|
| 50 |
-
)
|
| 51 |
-
assert sigrec.ulFormat == 1, (
|
| 52 |
-
"DSIG signature record #%d ulFormat must be 1" % n
|
| 53 |
-
)
|
| 54 |
-
sigrecs.append(sigrec)
|
| 55 |
-
for sigrec in sigrecs:
|
| 56 |
-
dummy, newData = sstruct.unpack2(
|
| 57 |
-
DSIG_SignatureBlockFormat, data[sigrec.ulOffset :], sigrec
|
| 58 |
-
)
|
| 59 |
-
assert sigrec.usReserved1 == 0, (
|
| 60 |
-
"DSIG signature record #%d usReserverd1 must be 0" % n
|
| 61 |
-
)
|
| 62 |
-
assert sigrec.usReserved2 == 0, (
|
| 63 |
-
"DSIG signature record #%d usReserverd2 must be 0" % n
|
| 64 |
-
)
|
| 65 |
-
sigrec.pkcs7 = newData[: sigrec.cbSignature]
|
| 66 |
-
|
| 67 |
-
def compile(self, ttFont):
|
| 68 |
-
packed = sstruct.pack(DSIG_HeaderFormat, self)
|
| 69 |
-
headers = [packed]
|
| 70 |
-
offset = len(packed) + self.usNumSigs * sstruct.calcsize(DSIG_SignatureFormat)
|
| 71 |
-
data = []
|
| 72 |
-
for sigrec in self.signatureRecords:
|
| 73 |
-
# first pack signature block
|
| 74 |
-
sigrec.cbSignature = len(sigrec.pkcs7)
|
| 75 |
-
packed = sstruct.pack(DSIG_SignatureBlockFormat, sigrec) + sigrec.pkcs7
|
| 76 |
-
data.append(packed)
|
| 77 |
-
# update redundant length field
|
| 78 |
-
sigrec.ulLength = len(packed)
|
| 79 |
-
# update running table offset
|
| 80 |
-
sigrec.ulOffset = offset
|
| 81 |
-
headers.append(sstruct.pack(DSIG_SignatureFormat, sigrec))
|
| 82 |
-
offset += sigrec.ulLength
|
| 83 |
-
if offset % 2:
|
| 84 |
-
# Pad to even bytes
|
| 85 |
-
data.append(b"\0")
|
| 86 |
-
return bytesjoin(headers + data)
|
| 87 |
-
|
| 88 |
-
def toXML(self, xmlWriter, ttFont):
|
| 89 |
-
xmlWriter.comment(
|
| 90 |
-
"note that the Digital Signature will be invalid after recompilation!"
|
| 91 |
-
)
|
| 92 |
-
xmlWriter.newline()
|
| 93 |
-
xmlWriter.simpletag(
|
| 94 |
-
"tableHeader",
|
| 95 |
-
version=self.ulVersion,
|
| 96 |
-
numSigs=self.usNumSigs,
|
| 97 |
-
flag="0x%X" % self.usFlag,
|
| 98 |
-
)
|
| 99 |
-
for sigrec in self.signatureRecords:
|
| 100 |
-
xmlWriter.newline()
|
| 101 |
-
sigrec.toXML(xmlWriter, ttFont)
|
| 102 |
-
xmlWriter.newline()
|
| 103 |
-
|
| 104 |
-
def fromXML(self, name, attrs, content, ttFont):
|
| 105 |
-
if name == "tableHeader":
|
| 106 |
-
self.signatureRecords = []
|
| 107 |
-
self.ulVersion = safeEval(attrs["version"])
|
| 108 |
-
self.usNumSigs = safeEval(attrs["numSigs"])
|
| 109 |
-
self.usFlag = safeEval(attrs["flag"])
|
| 110 |
-
return
|
| 111 |
-
if name == "SignatureRecord":
|
| 112 |
-
sigrec = SignatureRecord()
|
| 113 |
-
sigrec.fromXML(name, attrs, content, ttFont)
|
| 114 |
-
self.signatureRecords.append(sigrec)
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
pem_spam = lambda l, spam={
|
| 118 |
-
"-----BEGIN PKCS7-----": True,
|
| 119 |
-
"-----END PKCS7-----": True,
|
| 120 |
-
"": True,
|
| 121 |
-
}: not spam.get(l.strip())
|
| 122 |
-
|
| 123 |
-
|
| 124 |
-
def b64encode(b):
|
| 125 |
-
s = base64.b64encode(b)
|
| 126 |
-
# Line-break at 76 chars.
|
| 127 |
-
items = []
|
| 128 |
-
while s:
|
| 129 |
-
items.append(tostr(s[:76]))
|
| 130 |
-
items.append("\n")
|
| 131 |
-
s = s[76:]
|
| 132 |
-
return strjoin(items)
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
class SignatureRecord(object):
|
| 136 |
-
def __repr__(self):
|
| 137 |
-
return "<%s: %s>" % (self.__class__.__name__, self.__dict__)
|
| 138 |
-
|
| 139 |
-
def toXML(self, writer, ttFont):
|
| 140 |
-
writer.begintag(self.__class__.__name__, format=self.ulFormat)
|
| 141 |
-
writer.newline()
|
| 142 |
-
writer.write_noindent("-----BEGIN PKCS7-----\n")
|
| 143 |
-
writer.write_noindent(b64encode(self.pkcs7))
|
| 144 |
-
writer.write_noindent("-----END PKCS7-----\n")
|
| 145 |
-
writer.endtag(self.__class__.__name__)
|
| 146 |
-
|
| 147 |
-
def fromXML(self, name, attrs, content, ttFont):
|
| 148 |
-
self.ulFormat = safeEval(attrs["format"])
|
| 149 |
-
self.usReserved1 = safeEval(attrs.get("reserved1", "0"))
|
| 150 |
-
self.usReserved2 = safeEval(attrs.get("reserved2", "0"))
|
| 151 |
-
self.pkcs7 = base64.b64decode(tobytes(strjoin(filter(pem_spam, content))))
|
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|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/themes/base.py
DELETED
|
@@ -1,1815 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
import json
|
| 4 |
-
import re
|
| 5 |
-
import tempfile
|
| 6 |
-
import textwrap
|
| 7 |
-
from pathlib import Path
|
| 8 |
-
from typing import Iterable
|
| 9 |
-
|
| 10 |
-
import huggingface_hub
|
| 11 |
-
import requests
|
| 12 |
-
import semantic_version as semver
|
| 13 |
-
from gradio_client.documentation import document, set_documentation_group
|
| 14 |
-
from huggingface_hub import CommitOperationAdd
|
| 15 |
-
|
| 16 |
-
from gradio.themes.utils import (
|
| 17 |
-
colors,
|
| 18 |
-
fonts,
|
| 19 |
-
get_matching_version,
|
| 20 |
-
get_theme_assets,
|
| 21 |
-
sizes,
|
| 22 |
-
)
|
| 23 |
-
from gradio.themes.utils.readme_content import README_CONTENT
|
| 24 |
-
|
| 25 |
-
set_documentation_group("themes")
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
class ThemeClass:
|
| 29 |
-
def __init__(self):
|
| 30 |
-
self._stylesheets = []
|
| 31 |
-
self.name = None
|
| 32 |
-
|
| 33 |
-
def _get_theme_css(self):
|
| 34 |
-
css = {}
|
| 35 |
-
dark_css = {}
|
| 36 |
-
|
| 37 |
-
for attr, val in self.__dict__.items():
|
| 38 |
-
if attr.startswith("_"):
|
| 39 |
-
continue
|
| 40 |
-
if val is None:
|
| 41 |
-
if attr.endswith("_dark"):
|
| 42 |
-
dark_css[attr[:-5]] = None
|
| 43 |
-
continue
|
| 44 |
-
else:
|
| 45 |
-
raise ValueError(
|
| 46 |
-
f"Cannot set '{attr}' to None - only dark mode variables can be None."
|
| 47 |
-
)
|
| 48 |
-
val = str(val)
|
| 49 |
-
pattern = r"(\*)([\w_]+)(\b)"
|
| 50 |
-
|
| 51 |
-
def repl_func(match):
|
| 52 |
-
full_match = match.group(0)
|
| 53 |
-
if full_match.startswith("*") and full_match.endswith("_dark"):
|
| 54 |
-
raise ValueError(
|
| 55 |
-
f"Cannot refer '{attr}' to '{val}' - dark variable references are automatically used for dark mode attributes, so do not use the _dark suffix in the value."
|
| 56 |
-
)
|
| 57 |
-
if (
|
| 58 |
-
attr.endswith("_dark")
|
| 59 |
-
and full_match.startswith("*")
|
| 60 |
-
and attr[:-5] == full_match[1:]
|
| 61 |
-
):
|
| 62 |
-
raise ValueError(
|
| 63 |
-
f"Cannot refer '{attr}' to '{val}' - if dark and light mode values are the same, set dark mode version to None."
|
| 64 |
-
)
|
| 65 |
-
|
| 66 |
-
word = match.group(2)
|
| 67 |
-
word = word.replace("_", "-")
|
| 68 |
-
return f"var(--{word})"
|
| 69 |
-
|
| 70 |
-
val = re.sub(pattern, repl_func, val)
|
| 71 |
-
|
| 72 |
-
attr = attr.replace("_", "-")
|
| 73 |
-
|
| 74 |
-
if attr.endswith("-dark"):
|
| 75 |
-
attr = attr[:-5]
|
| 76 |
-
dark_css[attr] = val
|
| 77 |
-
else:
|
| 78 |
-
css[attr] = val
|
| 79 |
-
|
| 80 |
-
for attr, val in css.items():
|
| 81 |
-
if attr not in dark_css:
|
| 82 |
-
dark_css[attr] = val
|
| 83 |
-
|
| 84 |
-
css_code = (
|
| 85 |
-
":root {\n"
|
| 86 |
-
+ "\n".join([f" --{attr}: {val};" for attr, val in css.items()])
|
| 87 |
-
+ "\n}"
|
| 88 |
-
)
|
| 89 |
-
dark_css_code = (
|
| 90 |
-
".dark {\n"
|
| 91 |
-
+ "\n".join([f" --{attr}: {val};" for attr, val in dark_css.items()])
|
| 92 |
-
+ "\n}"
|
| 93 |
-
)
|
| 94 |
-
|
| 95 |
-
return f"{css_code}\n{dark_css_code}"
|
| 96 |
-
|
| 97 |
-
def to_dict(self):
|
| 98 |
-
"""Convert the theme into a python dictionary."""
|
| 99 |
-
schema = {"theme": {}}
|
| 100 |
-
for prop in dir(self):
|
| 101 |
-
if (
|
| 102 |
-
not prop.startswith("_")
|
| 103 |
-
or prop.startswith("_font")
|
| 104 |
-
or prop == "_stylesheets"
|
| 105 |
-
or prop == "name"
|
| 106 |
-
) and isinstance(getattr(self, prop), (list, str)):
|
| 107 |
-
schema["theme"][prop] = getattr(self, prop)
|
| 108 |
-
return schema
|
| 109 |
-
|
| 110 |
-
@classmethod
|
| 111 |
-
def load(cls, path: str) -> ThemeClass:
|
| 112 |
-
"""Load a theme from a json file.
|
| 113 |
-
|
| 114 |
-
Parameters:
|
| 115 |
-
path: The filepath to read.
|
| 116 |
-
"""
|
| 117 |
-
with open(path) as fp:
|
| 118 |
-
return cls.from_dict(json.load(fp, object_hook=fonts.as_font))
|
| 119 |
-
|
| 120 |
-
@classmethod
|
| 121 |
-
def from_dict(cls, theme: dict[str, dict[str, str]]) -> ThemeClass:
|
| 122 |
-
"""Create a theme instance from a dictionary representation.
|
| 123 |
-
|
| 124 |
-
Parameters:
|
| 125 |
-
theme: The dictionary representation of the theme.
|
| 126 |
-
"""
|
| 127 |
-
new_theme = cls()
|
| 128 |
-
for prop, value in theme["theme"].items():
|
| 129 |
-
setattr(new_theme, prop, value)
|
| 130 |
-
|
| 131 |
-
# For backwards compatibility, load attributes in base theme not in the loaded theme from the base theme.
|
| 132 |
-
base = Base()
|
| 133 |
-
for attr in base.__dict__:
|
| 134 |
-
if not attr.startswith("_") and not hasattr(new_theme, attr):
|
| 135 |
-
setattr(new_theme, attr, getattr(base, attr))
|
| 136 |
-
|
| 137 |
-
return new_theme
|
| 138 |
-
|
| 139 |
-
def dump(self, filename: str):
|
| 140 |
-
"""Write the theme to a json file.
|
| 141 |
-
|
| 142 |
-
Parameters:
|
| 143 |
-
filename: The path to write the theme too
|
| 144 |
-
"""
|
| 145 |
-
Path(filename).write_text(json.dumps(self.to_dict(), cls=fonts.FontEncoder))
|
| 146 |
-
|
| 147 |
-
@classmethod
|
| 148 |
-
def from_hub(cls, repo_name: str, hf_token: str | None = None):
|
| 149 |
-
"""Load a theme from the hub.
|
| 150 |
-
|
| 151 |
-
This DOES NOT require a HuggingFace account for downloading publicly available themes.
|
| 152 |
-
|
| 153 |
-
Parameters:
|
| 154 |
-
repo_name: string of the form <author>/<theme-name>@<semantic-version-expression>. If a semantic version expression is omitted, the latest version will be fetched.
|
| 155 |
-
hf_token: HuggingFace Token. Only needed to download private themes.
|
| 156 |
-
"""
|
| 157 |
-
if "@" not in repo_name:
|
| 158 |
-
name, version = repo_name, None
|
| 159 |
-
else:
|
| 160 |
-
name, version = repo_name.split("@")
|
| 161 |
-
|
| 162 |
-
api = huggingface_hub.HfApi(token=hf_token)
|
| 163 |
-
|
| 164 |
-
try:
|
| 165 |
-
space_info = api.space_info(name)
|
| 166 |
-
except requests.HTTPError as e:
|
| 167 |
-
raise ValueError(f"The space {name} does not exist") from e
|
| 168 |
-
|
| 169 |
-
assets = get_theme_assets(space_info)
|
| 170 |
-
matching_version = get_matching_version(assets, version)
|
| 171 |
-
|
| 172 |
-
if not matching_version:
|
| 173 |
-
raise ValueError(
|
| 174 |
-
f"Cannot find a matching version for expression {version} "
|
| 175 |
-
f"from files {[f.filename for f in assets]}"
|
| 176 |
-
)
|
| 177 |
-
|
| 178 |
-
theme_file = huggingface_hub.hf_hub_download(
|
| 179 |
-
repo_id=name,
|
| 180 |
-
repo_type="space",
|
| 181 |
-
filename=f"themes/theme_schema@{matching_version.version}.json",
|
| 182 |
-
)
|
| 183 |
-
theme = cls.load(theme_file)
|
| 184 |
-
theme.name = name
|
| 185 |
-
return theme
|
| 186 |
-
|
| 187 |
-
@staticmethod
|
| 188 |
-
def _get_next_version(space_info: huggingface_hub.hf_api.SpaceInfo) -> str:
|
| 189 |
-
assets = get_theme_assets(space_info)
|
| 190 |
-
latest_version = max(assets, key=lambda asset: asset.version).version
|
| 191 |
-
return str(latest_version.next_patch())
|
| 192 |
-
|
| 193 |
-
@staticmethod
|
| 194 |
-
def _theme_version_exists(
|
| 195 |
-
space_info: huggingface_hub.hf_api.SpaceInfo, version: str
|
| 196 |
-
) -> bool:
|
| 197 |
-
assets = get_theme_assets(space_info)
|
| 198 |
-
return any(a.version == semver.Version(version) for a in assets)
|
| 199 |
-
|
| 200 |
-
def push_to_hub(
|
| 201 |
-
self,
|
| 202 |
-
repo_name: str,
|
| 203 |
-
org_name: str | None = None,
|
| 204 |
-
version: str | None = None,
|
| 205 |
-
hf_token: str | None = None,
|
| 206 |
-
theme_name: str | None = None,
|
| 207 |
-
description: str | None = None,
|
| 208 |
-
private: bool = False,
|
| 209 |
-
):
|
| 210 |
-
"""Upload a theme to the HuggingFace hub.
|
| 211 |
-
|
| 212 |
-
This requires a HuggingFace account.
|
| 213 |
-
|
| 214 |
-
Parameters:
|
| 215 |
-
repo_name: The name of the repository to store the theme assets, e.g. 'my_theme' or 'sunset'.
|
| 216 |
-
org_name: The name of the org to save the space in. If None (the default), the username corresponding to the logged in user, or hƒ_token is used.
|
| 217 |
-
version: A semantic version tag for theme. Bumping the version tag lets you publish updates to a theme without changing the look of applications that already loaded your theme.
|
| 218 |
-
hf_token: API token for your HuggingFace account
|
| 219 |
-
theme_name: Name for the name. If None, defaults to repo_name
|
| 220 |
-
description: A long form description to your theme.
|
| 221 |
-
"""
|
| 222 |
-
|
| 223 |
-
from gradio import __version__
|
| 224 |
-
|
| 225 |
-
api = huggingface_hub.HfApi()
|
| 226 |
-
|
| 227 |
-
if not hf_token:
|
| 228 |
-
try:
|
| 229 |
-
author = huggingface_hub.whoami()["name"]
|
| 230 |
-
except OSError as e:
|
| 231 |
-
raise ValueError(
|
| 232 |
-
"In order to push to hub, log in via `huggingface-cli login` "
|
| 233 |
-
"or provide a theme_token to push_to_hub. For more information "
|
| 234 |
-
"see https://huggingface.co/docs/huggingface_hub/quick-start#login"
|
| 235 |
-
) from e
|
| 236 |
-
else:
|
| 237 |
-
author = huggingface_hub.whoami(token=hf_token)["name"]
|
| 238 |
-
|
| 239 |
-
space_id = f"{org_name or author}/{repo_name}"
|
| 240 |
-
|
| 241 |
-
try:
|
| 242 |
-
space_info = api.space_info(space_id)
|
| 243 |
-
except requests.HTTPError:
|
| 244 |
-
space_info = None
|
| 245 |
-
|
| 246 |
-
space_exists = space_info is not None
|
| 247 |
-
|
| 248 |
-
# If no version, set the version to next patch release
|
| 249 |
-
if not version:
|
| 250 |
-
version = self._get_next_version(space_info) if space_exists else "0.0.1"
|
| 251 |
-
else:
|
| 252 |
-
_ = semver.Version(version)
|
| 253 |
-
|
| 254 |
-
if space_exists and self._theme_version_exists(space_info, version):
|
| 255 |
-
raise ValueError(
|
| 256 |
-
f"The space {space_id} already has a "
|
| 257 |
-
f"theme with version {version}. See: themes/theme_schema@{version}.json. "
|
| 258 |
-
"To manually override this version, use the HuggingFace hub UI."
|
| 259 |
-
)
|
| 260 |
-
|
| 261 |
-
theme_name = theme_name or repo_name
|
| 262 |
-
|
| 263 |
-
with tempfile.NamedTemporaryFile(
|
| 264 |
-
mode="w", delete=False, suffix=".json"
|
| 265 |
-
) as css_file:
|
| 266 |
-
contents = self.to_dict()
|
| 267 |
-
contents["version"] = version
|
| 268 |
-
json.dump(contents, css_file, cls=fonts.FontEncoder)
|
| 269 |
-
with tempfile.NamedTemporaryFile(mode="w", delete=False) as readme_file:
|
| 270 |
-
readme_content = README_CONTENT.format(
|
| 271 |
-
theme_name=theme_name,
|
| 272 |
-
description=description or "Add a description of this theme here!",
|
| 273 |
-
author=author,
|
| 274 |
-
gradio_version=__version__,
|
| 275 |
-
)
|
| 276 |
-
readme_file.write(textwrap.dedent(readme_content))
|
| 277 |
-
with tempfile.NamedTemporaryFile(mode="w", delete=False) as app_file:
|
| 278 |
-
contents = (Path(__file__).parent / "app.py").read_text()
|
| 279 |
-
contents = re.sub(
|
| 280 |
-
r"theme=gr.themes.Default\(\)",
|
| 281 |
-
f"theme='{space_id}'",
|
| 282 |
-
contents,
|
| 283 |
-
)
|
| 284 |
-
contents = re.sub(r"{THEME}", theme_name or repo_name, contents)
|
| 285 |
-
contents = re.sub(r"{AUTHOR}", org_name or author, contents)
|
| 286 |
-
contents = re.sub(r"{SPACE_NAME}", repo_name, contents)
|
| 287 |
-
app_file.write(contents)
|
| 288 |
-
|
| 289 |
-
operations = [
|
| 290 |
-
CommitOperationAdd(
|
| 291 |
-
path_in_repo=f"themes/theme_schema@{version}.json",
|
| 292 |
-
path_or_fileobj=css_file.name,
|
| 293 |
-
),
|
| 294 |
-
CommitOperationAdd(
|
| 295 |
-
path_in_repo="README.md", path_or_fileobj=readme_file.name
|
| 296 |
-
),
|
| 297 |
-
CommitOperationAdd(path_in_repo="app.py", path_or_fileobj=app_file.name),
|
| 298 |
-
]
|
| 299 |
-
|
| 300 |
-
huggingface_hub.create_repo(
|
| 301 |
-
space_id,
|
| 302 |
-
repo_type="space",
|
| 303 |
-
space_sdk="gradio",
|
| 304 |
-
token=hf_token,
|
| 305 |
-
exist_ok=True,
|
| 306 |
-
private=private,
|
| 307 |
-
)
|
| 308 |
-
|
| 309 |
-
api.create_commit(
|
| 310 |
-
repo_id=space_id,
|
| 311 |
-
commit_message="Updating theme",
|
| 312 |
-
repo_type="space",
|
| 313 |
-
operations=operations,
|
| 314 |
-
token=hf_token,
|
| 315 |
-
)
|
| 316 |
-
url = f"https://huggingface.co/spaces/{space_id}"
|
| 317 |
-
print(f"See your theme here! {url}")
|
| 318 |
-
return url
|
| 319 |
-
|
| 320 |
-
|
| 321 |
-
@document("push_to_hub", "from_hub", "load", "dump", "from_dict", "to_dict")
|
| 322 |
-
class Base(ThemeClass):
|
| 323 |
-
def __init__(
|
| 324 |
-
self,
|
| 325 |
-
*,
|
| 326 |
-
primary_hue: colors.Color | str = colors.blue,
|
| 327 |
-
secondary_hue: colors.Color | str = colors.blue,
|
| 328 |
-
neutral_hue: colors.Color | str = colors.gray,
|
| 329 |
-
text_size: sizes.Size | str = sizes.text_md,
|
| 330 |
-
spacing_size: sizes.Size | str = sizes.spacing_md,
|
| 331 |
-
radius_size: sizes.Size | str = sizes.radius_md,
|
| 332 |
-
font: fonts.Font
|
| 333 |
-
| str
|
| 334 |
-
| Iterable[fonts.Font | str] = (
|
| 335 |
-
fonts.GoogleFont("Source Sans Pro"),
|
| 336 |
-
"ui-sans-serif",
|
| 337 |
-
"system-ui",
|
| 338 |
-
"sans-serif",
|
| 339 |
-
),
|
| 340 |
-
font_mono: fonts.Font
|
| 341 |
-
| str
|
| 342 |
-
| Iterable[fonts.Font | str] = (
|
| 343 |
-
fonts.GoogleFont("IBM Plex Mono"),
|
| 344 |
-
"ui-monospace",
|
| 345 |
-
"Consolas",
|
| 346 |
-
"monospace",
|
| 347 |
-
),
|
| 348 |
-
):
|
| 349 |
-
"""
|
| 350 |
-
Parameters:
|
| 351 |
-
primary_hue: The primary hue of the theme. Load a preset, like gradio.themes.colors.green (or just the string "green"), or pass your own gradio.themes.utils.Color object.
|
| 352 |
-
secondary_hue: The secondary hue of the theme. Load a preset, like gradio.themes.colors.green (or just the string "green"), or pass your own gradio.themes.utils.Color object.
|
| 353 |
-
neutral_hue: The neutral hue of the theme, used . Load a preset, like gradio.themes.colors.green (or just the string "green"), or pass your own gradio.themes.utils.Color object.
|
| 354 |
-
text_size: The size of the text. Load a preset, like gradio.themes.sizes.text_sm (or just the string "sm"), or pass your own gradio.themes.utils.Size object.
|
| 355 |
-
spacing_size: The size of the spacing. Load a preset, like gradio.themes.sizes.spacing_sm (or just the string "sm"), or pass your own gradio.themes.utils.Size object.
|
| 356 |
-
radius_size: The radius size of corners. Load a preset, like gradio.themes.sizes.radius_sm (or just the string "sm"), or pass your own gradio.themes.utils.Size object.
|
| 357 |
-
font: The primary font to use for the theme. Pass a string for a system font, or a gradio.themes.font.GoogleFont object to load a font from Google Fonts. Pass a list of fonts for fallbacks.
|
| 358 |
-
font_mono: The monospace font to use for the theme, applies to code. Pass a string for a system font, or a gradio.themes.font.GoogleFont object to load a font from Google Fonts. Pass a list of fonts for fallbacks.
|
| 359 |
-
"""
|
| 360 |
-
|
| 361 |
-
self.name = "base"
|
| 362 |
-
|
| 363 |
-
def expand_shortcut(shortcut, mode="color", prefix=None):
|
| 364 |
-
if not isinstance(shortcut, str):
|
| 365 |
-
return shortcut
|
| 366 |
-
if mode == "color":
|
| 367 |
-
for color in colors.Color.all:
|
| 368 |
-
if color.name == shortcut:
|
| 369 |
-
return color
|
| 370 |
-
raise ValueError(f"Color shortcut {shortcut} not found.")
|
| 371 |
-
elif mode == "size":
|
| 372 |
-
for size in sizes.Size.all:
|
| 373 |
-
if size.name == f"{prefix}_{shortcut}":
|
| 374 |
-
return size
|
| 375 |
-
raise ValueError(f"Size shortcut {shortcut} not found.")
|
| 376 |
-
|
| 377 |
-
primary_hue = expand_shortcut(primary_hue, mode="color")
|
| 378 |
-
secondary_hue = expand_shortcut(secondary_hue, mode="color")
|
| 379 |
-
neutral_hue = expand_shortcut(neutral_hue, mode="color")
|
| 380 |
-
text_size = expand_shortcut(text_size, mode="size", prefix="text")
|
| 381 |
-
spacing_size = expand_shortcut(spacing_size, mode="size", prefix="spacing")
|
| 382 |
-
radius_size = expand_shortcut(radius_size, mode="size", prefix="radius")
|
| 383 |
-
|
| 384 |
-
# Hue ranges
|
| 385 |
-
self.primary_50 = primary_hue.c50
|
| 386 |
-
self.primary_100 = primary_hue.c100
|
| 387 |
-
self.primary_200 = primary_hue.c200
|
| 388 |
-
self.primary_300 = primary_hue.c300
|
| 389 |
-
self.primary_400 = primary_hue.c400
|
| 390 |
-
self.primary_500 = primary_hue.c500
|
| 391 |
-
self.primary_600 = primary_hue.c600
|
| 392 |
-
self.primary_700 = primary_hue.c700
|
| 393 |
-
self.primary_800 = primary_hue.c800
|
| 394 |
-
self.primary_900 = primary_hue.c900
|
| 395 |
-
self.primary_950 = primary_hue.c950
|
| 396 |
-
|
| 397 |
-
self.secondary_50 = secondary_hue.c50
|
| 398 |
-
self.secondary_100 = secondary_hue.c100
|
| 399 |
-
self.secondary_200 = secondary_hue.c200
|
| 400 |
-
self.secondary_300 = secondary_hue.c300
|
| 401 |
-
self.secondary_400 = secondary_hue.c400
|
| 402 |
-
self.secondary_500 = secondary_hue.c500
|
| 403 |
-
self.secondary_600 = secondary_hue.c600
|
| 404 |
-
self.secondary_700 = secondary_hue.c700
|
| 405 |
-
self.secondary_800 = secondary_hue.c800
|
| 406 |
-
self.secondary_900 = secondary_hue.c900
|
| 407 |
-
self.secondary_950 = secondary_hue.c950
|
| 408 |
-
|
| 409 |
-
self.neutral_50 = neutral_hue.c50
|
| 410 |
-
self.neutral_100 = neutral_hue.c100
|
| 411 |
-
self.neutral_200 = neutral_hue.c200
|
| 412 |
-
self.neutral_300 = neutral_hue.c300
|
| 413 |
-
self.neutral_400 = neutral_hue.c400
|
| 414 |
-
self.neutral_500 = neutral_hue.c500
|
| 415 |
-
self.neutral_600 = neutral_hue.c600
|
| 416 |
-
self.neutral_700 = neutral_hue.c700
|
| 417 |
-
self.neutral_800 = neutral_hue.c800
|
| 418 |
-
self.neutral_900 = neutral_hue.c900
|
| 419 |
-
self.neutral_950 = neutral_hue.c950
|
| 420 |
-
|
| 421 |
-
# Spacing
|
| 422 |
-
self.spacing_xxs = spacing_size.xxs
|
| 423 |
-
self.spacing_xs = spacing_size.xs
|
| 424 |
-
self.spacing_sm = spacing_size.sm
|
| 425 |
-
self.spacing_md = spacing_size.md
|
| 426 |
-
self.spacing_lg = spacing_size.lg
|
| 427 |
-
self.spacing_xl = spacing_size.xl
|
| 428 |
-
self.spacing_xxl = spacing_size.xxl
|
| 429 |
-
|
| 430 |
-
self.radius_xxs = radius_size.xxs
|
| 431 |
-
self.radius_xs = radius_size.xs
|
| 432 |
-
self.radius_sm = radius_size.sm
|
| 433 |
-
self.radius_md = radius_size.md
|
| 434 |
-
self.radius_lg = radius_size.lg
|
| 435 |
-
self.radius_xl = radius_size.xl
|
| 436 |
-
self.radius_xxl = radius_size.xxl
|
| 437 |
-
|
| 438 |
-
self.text_xxs = text_size.xxs
|
| 439 |
-
self.text_xs = text_size.xs
|
| 440 |
-
self.text_sm = text_size.sm
|
| 441 |
-
self.text_md = text_size.md
|
| 442 |
-
self.text_lg = text_size.lg
|
| 443 |
-
self.text_xl = text_size.xl
|
| 444 |
-
self.text_xxl = text_size.xxl
|
| 445 |
-
|
| 446 |
-
# Font
|
| 447 |
-
if not isinstance(font, Iterable):
|
| 448 |
-
font = [font]
|
| 449 |
-
self._font = [
|
| 450 |
-
fontfam if isinstance(fontfam, fonts.Font) else fonts.Font(fontfam)
|
| 451 |
-
for fontfam in font
|
| 452 |
-
]
|
| 453 |
-
if not isinstance(font_mono, Iterable):
|
| 454 |
-
font_mono = [font_mono]
|
| 455 |
-
self._font_mono = [
|
| 456 |
-
fontfam if isinstance(fontfam, fonts.Font) else fonts.Font(fontfam)
|
| 457 |
-
for fontfam in font_mono
|
| 458 |
-
]
|
| 459 |
-
self.font = ", ".join(str(font) for font in self._font)
|
| 460 |
-
self.font_mono = ", ".join(str(font) for font in self._font_mono)
|
| 461 |
-
|
| 462 |
-
self._stylesheets = []
|
| 463 |
-
for font in self._font + self._font_mono:
|
| 464 |
-
font_stylesheet = font.stylesheet()
|
| 465 |
-
if font_stylesheet:
|
| 466 |
-
self._stylesheets.append(font_stylesheet)
|
| 467 |
-
|
| 468 |
-
self.set()
|
| 469 |
-
|
| 470 |
-
def set(
|
| 471 |
-
self,
|
| 472 |
-
*,
|
| 473 |
-
# Body Attributes: These set set the values for the entire body of the app.
|
| 474 |
-
body_background_fill=None,
|
| 475 |
-
body_background_fill_dark=None,
|
| 476 |
-
body_text_color=None,
|
| 477 |
-
body_text_color_dark=None,
|
| 478 |
-
body_text_size=None,
|
| 479 |
-
body_text_color_subdued=None,
|
| 480 |
-
body_text_color_subdued_dark=None,
|
| 481 |
-
body_text_weight=None,
|
| 482 |
-
embed_radius=None,
|
| 483 |
-
# Element Colors: These set the colors for common elements.
|
| 484 |
-
background_fill_primary=None,
|
| 485 |
-
background_fill_primary_dark=None,
|
| 486 |
-
background_fill_secondary=None,
|
| 487 |
-
background_fill_secondary_dark=None,
|
| 488 |
-
border_color_accent=None,
|
| 489 |
-
border_color_accent_dark=None,
|
| 490 |
-
border_color_primary=None,
|
| 491 |
-
border_color_primary_dark=None,
|
| 492 |
-
color_accent=None,
|
| 493 |
-
color_accent_soft=None,
|
| 494 |
-
color_accent_soft_dark=None,
|
| 495 |
-
# Text: This sets the text styling for text elements.
|
| 496 |
-
link_text_color=None,
|
| 497 |
-
link_text_color_dark=None,
|
| 498 |
-
link_text_color_active=None,
|
| 499 |
-
link_text_color_active_dark=None,
|
| 500 |
-
link_text_color_hover=None,
|
| 501 |
-
link_text_color_hover_dark=None,
|
| 502 |
-
link_text_color_visited=None,
|
| 503 |
-
link_text_color_visited_dark=None,
|
| 504 |
-
prose_text_size=None,
|
| 505 |
-
prose_text_weight=None,
|
| 506 |
-
prose_header_text_weight=None,
|
| 507 |
-
# Shadows: These set the high-level shadow rendering styles. These variables are often referenced by other component-specific shadow variables.
|
| 508 |
-
shadow_drop=None,
|
| 509 |
-
shadow_drop_lg=None,
|
| 510 |
-
shadow_inset=None,
|
| 511 |
-
shadow_spread=None,
|
| 512 |
-
shadow_spread_dark=None,
|
| 513 |
-
# Layout Atoms: These set the style for common layout elements, such as the blocks that wrap components.
|
| 514 |
-
block_background_fill=None,
|
| 515 |
-
block_background_fill_dark=None,
|
| 516 |
-
block_border_color=None,
|
| 517 |
-
block_border_color_dark=None,
|
| 518 |
-
block_border_width=None,
|
| 519 |
-
block_border_width_dark=None,
|
| 520 |
-
block_info_text_color=None,
|
| 521 |
-
block_info_text_color_dark=None,
|
| 522 |
-
block_info_text_size=None,
|
| 523 |
-
block_info_text_weight=None,
|
| 524 |
-
block_label_background_fill=None,
|
| 525 |
-
block_label_background_fill_dark=None,
|
| 526 |
-
block_label_border_color=None,
|
| 527 |
-
block_label_border_color_dark=None,
|
| 528 |
-
block_label_border_width=None,
|
| 529 |
-
block_label_border_width_dark=None,
|
| 530 |
-
block_label_shadow=None,
|
| 531 |
-
block_label_text_color=None,
|
| 532 |
-
block_label_text_color_dark=None,
|
| 533 |
-
block_label_margin=None,
|
| 534 |
-
block_label_padding=None,
|
| 535 |
-
block_label_radius=None,
|
| 536 |
-
block_label_right_radius=None,
|
| 537 |
-
block_label_text_size=None,
|
| 538 |
-
block_label_text_weight=None,
|
| 539 |
-
block_padding=None,
|
| 540 |
-
block_radius=None,
|
| 541 |
-
block_shadow=None,
|
| 542 |
-
block_shadow_dark=None,
|
| 543 |
-
block_title_background_fill=None,
|
| 544 |
-
block_title_background_fill_dark=None,
|
| 545 |
-
block_title_border_color=None,
|
| 546 |
-
block_title_border_color_dark=None,
|
| 547 |
-
block_title_border_width=None,
|
| 548 |
-
block_title_border_width_dark=None,
|
| 549 |
-
block_title_text_color=None,
|
| 550 |
-
block_title_text_color_dark=None,
|
| 551 |
-
block_title_padding=None,
|
| 552 |
-
block_title_radius=None,
|
| 553 |
-
block_title_text_size=None,
|
| 554 |
-
block_title_text_weight=None,
|
| 555 |
-
container_radius=None,
|
| 556 |
-
form_gap_width=None,
|
| 557 |
-
layout_gap=None,
|
| 558 |
-
panel_background_fill=None,
|
| 559 |
-
panel_background_fill_dark=None,
|
| 560 |
-
panel_border_color=None,
|
| 561 |
-
panel_border_color_dark=None,
|
| 562 |
-
panel_border_width=None,
|
| 563 |
-
panel_border_width_dark=None,
|
| 564 |
-
section_header_text_size=None,
|
| 565 |
-
section_header_text_weight=None,
|
| 566 |
-
# Component Atoms: These set the style for elements within components.
|
| 567 |
-
chatbot_code_background_color=None,
|
| 568 |
-
chatbot_code_background_color_dark=None,
|
| 569 |
-
checkbox_background_color=None,
|
| 570 |
-
checkbox_background_color_dark=None,
|
| 571 |
-
checkbox_background_color_focus=None,
|
| 572 |
-
checkbox_background_color_focus_dark=None,
|
| 573 |
-
checkbox_background_color_hover=None,
|
| 574 |
-
checkbox_background_color_hover_dark=None,
|
| 575 |
-
checkbox_background_color_selected=None,
|
| 576 |
-
checkbox_background_color_selected_dark=None,
|
| 577 |
-
checkbox_border_color=None,
|
| 578 |
-
checkbox_border_color_dark=None,
|
| 579 |
-
checkbox_border_color_focus=None,
|
| 580 |
-
checkbox_border_color_focus_dark=None,
|
| 581 |
-
checkbox_border_color_hover=None,
|
| 582 |
-
checkbox_border_color_hover_dark=None,
|
| 583 |
-
checkbox_border_color_selected=None,
|
| 584 |
-
checkbox_border_color_selected_dark=None,
|
| 585 |
-
checkbox_border_radius=None,
|
| 586 |
-
checkbox_border_width=None,
|
| 587 |
-
checkbox_border_width_dark=None,
|
| 588 |
-
checkbox_check=None,
|
| 589 |
-
radio_circle=None,
|
| 590 |
-
checkbox_shadow=None,
|
| 591 |
-
checkbox_label_background_fill=None,
|
| 592 |
-
checkbox_label_background_fill_dark=None,
|
| 593 |
-
checkbox_label_background_fill_hover=None,
|
| 594 |
-
checkbox_label_background_fill_hover_dark=None,
|
| 595 |
-
checkbox_label_background_fill_selected=None,
|
| 596 |
-
checkbox_label_background_fill_selected_dark=None,
|
| 597 |
-
checkbox_label_border_color=None,
|
| 598 |
-
checkbox_label_border_color_dark=None,
|
| 599 |
-
checkbox_label_border_color_hover=None,
|
| 600 |
-
checkbox_label_border_color_hover_dark=None,
|
| 601 |
-
checkbox_label_border_width=None,
|
| 602 |
-
checkbox_label_border_width_dark=None,
|
| 603 |
-
checkbox_label_gap=None,
|
| 604 |
-
checkbox_label_padding=None,
|
| 605 |
-
checkbox_label_shadow=None,
|
| 606 |
-
checkbox_label_text_size=None,
|
| 607 |
-
checkbox_label_text_weight=None,
|
| 608 |
-
checkbox_label_text_color=None,
|
| 609 |
-
checkbox_label_text_color_dark=None,
|
| 610 |
-
checkbox_label_text_color_selected=None,
|
| 611 |
-
checkbox_label_text_color_selected_dark=None,
|
| 612 |
-
error_background_fill=None,
|
| 613 |
-
error_background_fill_dark=None,
|
| 614 |
-
error_border_color=None,
|
| 615 |
-
error_border_color_dark=None,
|
| 616 |
-
error_border_width=None,
|
| 617 |
-
error_border_width_dark=None,
|
| 618 |
-
error_text_color=None,
|
| 619 |
-
error_text_color_dark=None,
|
| 620 |
-
error_icon_color=None,
|
| 621 |
-
error_icon_color_dark=None,
|
| 622 |
-
input_background_fill=None,
|
| 623 |
-
input_background_fill_dark=None,
|
| 624 |
-
input_background_fill_focus=None,
|
| 625 |
-
input_background_fill_focus_dark=None,
|
| 626 |
-
input_background_fill_hover=None,
|
| 627 |
-
input_background_fill_hover_dark=None,
|
| 628 |
-
input_border_color=None,
|
| 629 |
-
input_border_color_dark=None,
|
| 630 |
-
input_border_color_focus=None,
|
| 631 |
-
input_border_color_focus_dark=None,
|
| 632 |
-
input_border_color_hover=None,
|
| 633 |
-
input_border_color_hover_dark=None,
|
| 634 |
-
input_border_width=None,
|
| 635 |
-
input_border_width_dark=None,
|
| 636 |
-
input_padding=None,
|
| 637 |
-
input_placeholder_color=None,
|
| 638 |
-
input_placeholder_color_dark=None,
|
| 639 |
-
input_radius=None,
|
| 640 |
-
input_shadow=None,
|
| 641 |
-
input_shadow_dark=None,
|
| 642 |
-
input_shadow_focus=None,
|
| 643 |
-
input_shadow_focus_dark=None,
|
| 644 |
-
input_text_size=None,
|
| 645 |
-
input_text_weight=None,
|
| 646 |
-
loader_color=None,
|
| 647 |
-
loader_color_dark=None,
|
| 648 |
-
slider_color=None,
|
| 649 |
-
slider_color_dark=None,
|
| 650 |
-
stat_background_fill=None,
|
| 651 |
-
stat_background_fill_dark=None,
|
| 652 |
-
table_border_color=None,
|
| 653 |
-
table_border_color_dark=None,
|
| 654 |
-
table_even_background_fill=None,
|
| 655 |
-
table_even_background_fill_dark=None,
|
| 656 |
-
table_odd_background_fill=None,
|
| 657 |
-
table_odd_background_fill_dark=None,
|
| 658 |
-
table_radius=None,
|
| 659 |
-
table_row_focus=None,
|
| 660 |
-
table_row_focus_dark=None,
|
| 661 |
-
# Buttons: These set the style for buttons.
|
| 662 |
-
button_border_width=None,
|
| 663 |
-
button_border_width_dark=None,
|
| 664 |
-
button_shadow=None,
|
| 665 |
-
button_shadow_active=None,
|
| 666 |
-
button_shadow_hover=None,
|
| 667 |
-
button_transition=None,
|
| 668 |
-
button_large_padding=None,
|
| 669 |
-
button_large_radius=None,
|
| 670 |
-
button_large_text_size=None,
|
| 671 |
-
button_large_text_weight=None,
|
| 672 |
-
button_small_padding=None,
|
| 673 |
-
button_small_radius=None,
|
| 674 |
-
button_small_text_size=None,
|
| 675 |
-
button_small_text_weight=None,
|
| 676 |
-
button_primary_background_fill=None,
|
| 677 |
-
button_primary_background_fill_dark=None,
|
| 678 |
-
button_primary_background_fill_hover=None,
|
| 679 |
-
button_primary_background_fill_hover_dark=None,
|
| 680 |
-
button_primary_border_color=None,
|
| 681 |
-
button_primary_border_color_dark=None,
|
| 682 |
-
button_primary_border_color_hover=None,
|
| 683 |
-
button_primary_border_color_hover_dark=None,
|
| 684 |
-
button_primary_text_color=None,
|
| 685 |
-
button_primary_text_color_dark=None,
|
| 686 |
-
button_primary_text_color_hover=None,
|
| 687 |
-
button_primary_text_color_hover_dark=None,
|
| 688 |
-
button_secondary_background_fill=None,
|
| 689 |
-
button_secondary_background_fill_dark=None,
|
| 690 |
-
button_secondary_background_fill_hover=None,
|
| 691 |
-
button_secondary_background_fill_hover_dark=None,
|
| 692 |
-
button_secondary_border_color=None,
|
| 693 |
-
button_secondary_border_color_dark=None,
|
| 694 |
-
button_secondary_border_color_hover=None,
|
| 695 |
-
button_secondary_border_color_hover_dark=None,
|
| 696 |
-
button_secondary_text_color=None,
|
| 697 |
-
button_secondary_text_color_dark=None,
|
| 698 |
-
button_secondary_text_color_hover=None,
|
| 699 |
-
button_secondary_text_color_hover_dark=None,
|
| 700 |
-
button_cancel_background_fill=None,
|
| 701 |
-
button_cancel_background_fill_dark=None,
|
| 702 |
-
button_cancel_background_fill_hover=None,
|
| 703 |
-
button_cancel_background_fill_hover_dark=None,
|
| 704 |
-
button_cancel_border_color=None,
|
| 705 |
-
button_cancel_border_color_dark=None,
|
| 706 |
-
button_cancel_border_color_hover=None,
|
| 707 |
-
button_cancel_border_color_hover_dark=None,
|
| 708 |
-
button_cancel_text_color=None,
|
| 709 |
-
button_cancel_text_color_dark=None,
|
| 710 |
-
button_cancel_text_color_hover=None,
|
| 711 |
-
button_cancel_text_color_hover_dark=None,
|
| 712 |
-
) -> Base:
|
| 713 |
-
"""
|
| 714 |
-
Parameters:
|
| 715 |
-
body_background_fill: The background of the entire app.
|
| 716 |
-
body_background_fill_dark: The background of the entire app in dark mode.
|
| 717 |
-
body_text_color: The default text color.
|
| 718 |
-
body_text_color_dark: The default text color in dark mode.
|
| 719 |
-
body_text_size: The default text size.
|
| 720 |
-
body_text_color_subdued: The text color used for softer, less important text.
|
| 721 |
-
body_text_color_subdued_dark: The text color used for softer, less important text in dark mode.
|
| 722 |
-
body_text_weight: The default text weight.
|
| 723 |
-
embed_radius: The corner radius used for embedding when the app is embedded within a page.
|
| 724 |
-
background_fill_primary: The background primarily used for items placed directly on the page.
|
| 725 |
-
background_fill_primary_dark: The background primarily used for items placed directly on the page in dark mode.
|
| 726 |
-
background_fill_secondary: The background primarily used for items placed on top of another item.
|
| 727 |
-
background_fill_secondary_dark: The background primarily used for items placed on top of another item in dark mode.
|
| 728 |
-
border_color_accent: The border color used for accented items.
|
| 729 |
-
border_color_accent_dark: The border color used for accented items in dark mode.
|
| 730 |
-
border_color_primary: The border color primarily used for items placed directly on the page.
|
| 731 |
-
border_color_primary_dark: The border color primarily used for items placed directly on the page in dark mode.
|
| 732 |
-
color_accent: The color used for accented items.
|
| 733 |
-
color_accent_soft: The softer color used for accented items.
|
| 734 |
-
color_accent_soft_dark: The softer color used for accented items in dark mode.
|
| 735 |
-
link_text_color: The text color used for links.
|
| 736 |
-
link_text_color_dark: The text color used for links in dark mode.
|
| 737 |
-
link_text_color_active: The text color used for links when they are active.
|
| 738 |
-
link_text_color_active_dark: The text color used for links when they are active in dark mode.
|
| 739 |
-
link_text_color_hover: The text color used for links when they are hovered over.
|
| 740 |
-
link_text_color_hover_dark: The text color used for links when they are hovered over in dark mode.
|
| 741 |
-
link_text_color_visited: The text color used for links when they have been visited.
|
| 742 |
-
link_text_color_visited_dark: The text color used for links when they have been visited in dark mode.
|
| 743 |
-
prose_text_size: The text size used for markdown and other prose.
|
| 744 |
-
prose_text_weight: The text weight used for markdown and other prose.
|
| 745 |
-
prose_header_text_weight: The text weight of a header used for markdown and other prose.
|
| 746 |
-
shadow_drop: Drop shadow used by other shadowed items.
|
| 747 |
-
shadow_drop_lg: Larger drop shadow used by other shadowed items.
|
| 748 |
-
shadow_inset: Inset shadow used by other shadowed items.
|
| 749 |
-
shadow_spread: Size of shadow spread used by shadowed items.
|
| 750 |
-
shadow_spread_dark: Size of shadow spread used by shadowed items in dark mode.
|
| 751 |
-
block_background_fill: The background around an item.
|
| 752 |
-
block_background_fill_dark: The background around an item in dark mode.
|
| 753 |
-
block_border_color: The border color around an item.
|
| 754 |
-
block_border_color_dark: The border color around an item in dark mode.
|
| 755 |
-
block_border_width: The border width around an item.
|
| 756 |
-
block_border_width_dark: The border width around an item in dark mode.
|
| 757 |
-
block_info_text_color: The color of the info text.
|
| 758 |
-
block_info_text_color_dark: The color of the info text in dark mode.
|
| 759 |
-
block_info_text_size: The size of the info text.
|
| 760 |
-
block_info_text_weight: The weight of the info text.
|
| 761 |
-
block_label_background_fill: The background of the title label of a media element (e.g. image).
|
| 762 |
-
block_label_background_fill_dark: The background of the title label of a media element (e.g. image) in dark mode.
|
| 763 |
-
block_label_border_color: The border color of the title label of a media element (e.g. image).
|
| 764 |
-
block_label_border_color_dark: The border color of the title label of a media element (e.g. image) in dark mode.
|
| 765 |
-
block_label_border_width: The border width of the title label of a media element (e.g. image).
|
| 766 |
-
block_label_border_width_dark: The border width of the title label of a media element (e.g. image) in dark mode.
|
| 767 |
-
block_label_shadow: The shadow of the title label of a media element (e.g. image).
|
| 768 |
-
block_label_text_color: The text color of the title label of a media element (e.g. image).
|
| 769 |
-
block_label_text_color_dark: The text color of the title label of a media element (e.g. image) in dark mode.
|
| 770 |
-
block_label_margin: The margin of the title label of a media element (e.g. image) from its surrounding container.
|
| 771 |
-
block_label_padding: The padding of the title label of a media element (e.g. image).
|
| 772 |
-
block_label_radius: The corner radius of the title label of a media element (e.g. image).
|
| 773 |
-
block_label_right_radius: The corner radius of a right-aligned helper label.
|
| 774 |
-
block_label_text_size: The text size of the title label of a media element (e.g. image).
|
| 775 |
-
block_label_text_weight: The text weight of the title label of a media element (e.g. image).
|
| 776 |
-
block_padding: The padding around an item.
|
| 777 |
-
block_radius: The corner radius around an item.
|
| 778 |
-
block_shadow: The shadow under an item.
|
| 779 |
-
block_shadow_dark: The shadow under an item in dark mode.
|
| 780 |
-
block_title_background_fill: The background of the title of a form element (e.g. textbox).
|
| 781 |
-
block_title_background_fill_dark: The background of the title of a form element (e.g. textbox) in dark mode.
|
| 782 |
-
block_title_border_color: The border color of the title of a form element (e.g. textbox).
|
| 783 |
-
block_title_border_color_dark: The border color of the title of a form element (e.g. textbox) in dark mode.
|
| 784 |
-
block_title_border_width: The border width of the title of a form element (e.g. textbox).
|
| 785 |
-
block_title_border_width_dark: The border width of the title of a form element (e.g. textbox) in dark mode.
|
| 786 |
-
block_title_text_color: The text color of the title of a form element (e.g. textbox).
|
| 787 |
-
block_title_text_color_dark: The text color of the title of a form element (e.g. textbox) in dark mode.
|
| 788 |
-
block_title_padding: The padding of the title of a form element (e.g. textbox).
|
| 789 |
-
block_title_radius: The corner radius of the title of a form element (e.g. textbox).
|
| 790 |
-
block_title_text_size: The text size of the title of a form element (e.g. textbox).
|
| 791 |
-
block_title_text_weight: The text weight of the title of a form element (e.g. textbox).
|
| 792 |
-
container_radius: The corner radius of a layout component that holds other content.
|
| 793 |
-
form_gap_width: The border gap between form elements, (e.g. consecutive textboxes).
|
| 794 |
-
layout_gap: The gap between items within a row or column.
|
| 795 |
-
panel_background_fill: The background of a panel.
|
| 796 |
-
panel_background_fill_dark: The background of a panel in dark mode.
|
| 797 |
-
panel_border_color: The border color of a panel.
|
| 798 |
-
panel_border_color_dark: The border color of a panel in dark mode.
|
| 799 |
-
panel_border_width: The border width of a panel.
|
| 800 |
-
panel_border_width_dark: The border width of a panel in dark mode.
|
| 801 |
-
section_header_text_size: The text size of a section header (e.g. tab name).
|
| 802 |
-
section_header_text_weight: The text weight of a section header (e.g. tab name).
|
| 803 |
-
chatbot_code_background_color: The background color of code blocks in the chatbot.
|
| 804 |
-
chatbot_code_background_color_dark: The background color of code blocks in the chatbot in dark mode.
|
| 805 |
-
checkbox_background_color: The background of a checkbox square or radio circle.
|
| 806 |
-
checkbox_background_color_dark: The background of a checkbox square or radio circle in dark mode.
|
| 807 |
-
checkbox_background_color_focus: The background of a checkbox square or radio circle when focused.
|
| 808 |
-
checkbox_background_color_focus_dark: The background of a checkbox square or radio circle when focused in dark mode.
|
| 809 |
-
checkbox_background_color_hover: The background of a checkbox square or radio circle when hovered over.
|
| 810 |
-
checkbox_background_color_hover_dark: The background of a checkbox square or radio circle when hovered over in dark mode.
|
| 811 |
-
checkbox_background_color_selected: The background of a checkbox square or radio circle when selected.
|
| 812 |
-
checkbox_background_color_selected_dark: The background of a checkbox square or radio circle when selected in dark mode.
|
| 813 |
-
checkbox_border_color: The border color of a checkbox square or radio circle.
|
| 814 |
-
checkbox_border_color_dark: The border color of a checkbox square or radio circle in dark mode.
|
| 815 |
-
checkbox_border_color_focus: The border color of a checkbox square or radio circle when focused.
|
| 816 |
-
checkbox_border_color_focus_dark: The border color of a checkbox square or radio circle when focused in dark mode.
|
| 817 |
-
checkbox_border_color_hover: The border color of a checkbox square or radio circle when hovered over.
|
| 818 |
-
checkbox_border_color_hover_dark: The border color of a checkbox square or radio circle when hovered over in dark mode.
|
| 819 |
-
checkbox_border_color_selected: The border color of a checkbox square or radio circle when selected.
|
| 820 |
-
checkbox_border_color_selected_dark: The border color of a checkbox square or radio circle when selected in dark mode.
|
| 821 |
-
checkbox_border_radius: The corner radius of a checkbox square.
|
| 822 |
-
checkbox_border_width: The border width of a checkbox square or radio circle.
|
| 823 |
-
checkbox_border_width_dark: The border width of a checkbox square or radio circle in dark mode.
|
| 824 |
-
checkbox_check: The checkmark visual of a checkbox square.
|
| 825 |
-
radio_circle: The circle visual of a radio circle.
|
| 826 |
-
checkbox_shadow: The shadow of a checkbox square or radio circle.
|
| 827 |
-
checkbox_label_background_fill: The background of the surrounding button of a checkbox or radio element.
|
| 828 |
-
checkbox_label_background_fill_dark: The background of the surrounding button of a checkbox or radio element in dark mode.
|
| 829 |
-
checkbox_label_background_fill_hover: The background of the surrounding button of a checkbox or radio element when hovered over.
|
| 830 |
-
checkbox_label_background_fill_hover_dark: The background of the surrounding button of a checkbox or radio element when hovered over in dark mode.
|
| 831 |
-
checkbox_label_background_fill_selected: The background of the surrounding button of a checkbox or radio element when selected.
|
| 832 |
-
checkbox_label_background_fill_selected_dark: The background of the surrounding button of a checkbox or radio element when selected in dark mode.
|
| 833 |
-
checkbox_label_border_color: The border color of the surrounding button of a checkbox or radio element.
|
| 834 |
-
checkbox_label_border_color_dark: The border color of the surrounding button of a checkbox or radio element in dark mode.
|
| 835 |
-
checkbox_label_border_color_hover: The border color of the surrounding button of a checkbox or radio element when hovered over.
|
| 836 |
-
checkbox_label_border_color_hover_dark: The border color of the surrounding button of a checkbox or radio element when hovered over in dark mode.
|
| 837 |
-
checkbox_label_border_width: The border width of the surrounding button of a checkbox or radio element.
|
| 838 |
-
checkbox_label_border_width_dark: The border width of the surrounding button of a checkbox or radio element in dark mode.
|
| 839 |
-
checkbox_label_gap: The gap consecutive checkbox or radio elements.
|
| 840 |
-
checkbox_label_padding: The padding of the surrounding button of a checkbox or radio element.
|
| 841 |
-
checkbox_label_shadow: The shadow of the surrounding button of a checkbox or radio element.
|
| 842 |
-
checkbox_label_text_size: The text size of the label accompanying a checkbox or radio element.
|
| 843 |
-
checkbox_label_text_weight: The text weight of the label accompanying a checkbox or radio element.
|
| 844 |
-
checkbox_label_text_color: The text color of the label accompanying a checkbox or radio element.
|
| 845 |
-
checkbox_label_text_color_dark: The text color of the label accompanying a checkbox or radio element in dark mode.
|
| 846 |
-
checkbox_label_text_color_selected: The text color of the label accompanying a checkbox or radio element when selected.
|
| 847 |
-
checkbox_label_text_color_selected_dark: The text color of the label accompanying a checkbox or radio element when selected in dark mode.
|
| 848 |
-
error_background_fill: The background of an error message.
|
| 849 |
-
error_background_fill_dark: The background of an error message in dark mode.
|
| 850 |
-
error_border_color: The border color of an error message.
|
| 851 |
-
error_border_color_dark: The border color of an error message in dark mode.
|
| 852 |
-
error_border_width: The border width of an error message.
|
| 853 |
-
error_border_width_dark: The border width of an error message in dark mode.
|
| 854 |
-
error_text_color: The text color of an error message.
|
| 855 |
-
error_text_color_dark: The text color of an error message in dark mode.
|
| 856 |
-
input_background_fill: The background of an input field.
|
| 857 |
-
input_background_fill_dark: The background of an input field in dark mode.
|
| 858 |
-
input_background_fill_focus: The background of an input field when focused.
|
| 859 |
-
input_background_fill_focus_dark: The background of an input field when focused in dark mode.
|
| 860 |
-
input_background_fill_hover: The background of an input field when hovered over.
|
| 861 |
-
input_background_fill_hover_dark: The background of an input field when hovered over in dark mode.
|
| 862 |
-
input_border_color: The border color of an input field.
|
| 863 |
-
input_border_color_dark: The border color of an input field in dark mode.
|
| 864 |
-
input_border_color_focus: The border color of an input field when focused.
|
| 865 |
-
input_border_color_focus_dark: The border color of an input field when focused in dark mode.
|
| 866 |
-
input_border_color_hover: The border color of an input field when hovered over.
|
| 867 |
-
input_border_color_hover_dark: The border color of an input field when hovered over in dark mode.
|
| 868 |
-
input_border_width: The border width of an input field.
|
| 869 |
-
input_border_width_dark: The border width of an input field in dark mode.
|
| 870 |
-
input_padding: The padding of an input field.
|
| 871 |
-
input_placeholder_color: The placeholder text color of an input field.
|
| 872 |
-
input_placeholder_color_dark: The placeholder text color of an input field in dark mode.
|
| 873 |
-
input_radius: The corner radius of an input field.
|
| 874 |
-
input_shadow: The shadow of an input field.
|
| 875 |
-
input_shadow_dark: The shadow of an input field in dark mode.
|
| 876 |
-
input_shadow_focus: The shadow of an input field when focused.
|
| 877 |
-
input_shadow_focus_dark: The shadow of an input field when focused in dark mode.
|
| 878 |
-
input_text_size: The text size of an input field.
|
| 879 |
-
input_text_weight: The text weight of an input field.
|
| 880 |
-
loader_color: The color of the loading animation while a request is pending.
|
| 881 |
-
loader_color_dark: The color of the loading animation while a request is pending in dark mode.
|
| 882 |
-
slider_color: The color of the slider in a range element.
|
| 883 |
-
slider_color_dark: The color of the slider in a range element in dark mode.
|
| 884 |
-
stat_background_fill: The background used for stats visuals (e.g. confidence bars in label).
|
| 885 |
-
stat_background_fill_dark: The background used for stats visuals (e.g. confidence bars in label) in dark mode.
|
| 886 |
-
table_border_color: The border color of a table.
|
| 887 |
-
table_border_color_dark: The border color of a table in dark mode.
|
| 888 |
-
table_even_background_fill: The background of even rows in a table.
|
| 889 |
-
table_even_background_fill_dark: The background of even rows in a table in dark mode.
|
| 890 |
-
table_odd_background_fill: The background of odd rows in a table.
|
| 891 |
-
table_odd_background_fill_dark: The background of odd rows in a table in dark mode.
|
| 892 |
-
table_radius: The corner radius of a table.
|
| 893 |
-
table_row_focus: The background of a focused row in a table.
|
| 894 |
-
table_row_focus_dark: The background of a focused row in a table in dark mode.
|
| 895 |
-
button_border_width: The border width of a button.
|
| 896 |
-
button_border_width_dark: The border width of a button in dark mode.
|
| 897 |
-
button_cancel_background_fill: The background of a button of "cancel" variant.
|
| 898 |
-
button_cancel_background_fill_dark: The background of a button of "cancel" variant in dark mode.
|
| 899 |
-
button_cancel_background_fill_hover: The background of a button of "cancel" variant when hovered over.
|
| 900 |
-
button_cancel_background_fill_hover_dark: The background of a button of "cancel" variant when hovered over in dark mode.
|
| 901 |
-
button_cancel_border_color: The border color of a button of "cancel" variant.
|
| 902 |
-
button_cancel_border_color_dark: The border color of a button of "cancel" variant in dark mode.
|
| 903 |
-
button_cancel_border_color_hover: The border color of a button of "cancel" variant when hovered over.
|
| 904 |
-
button_cancel_border_color_hover_dark: The border color of a button of "cancel" variant when hovered over in dark mode.
|
| 905 |
-
button_cancel_text_color: The text color of a button of "cancel" variant.
|
| 906 |
-
button_cancel_text_color_dark: The text color of a button of "cancel" variant in dark mode.
|
| 907 |
-
button_cancel_text_color_hover: The text color of a button of "cancel" variant when hovered over.
|
| 908 |
-
button_cancel_text_color_hover_dark: The text color of a button of "cancel" variant when hovered over in dark mode.
|
| 909 |
-
button_large_padding: The padding of a button with the default "large" size.
|
| 910 |
-
button_large_radius: The corner radius of a button with the default "large" size.
|
| 911 |
-
button_large_text_size: The text size of a button with the default "large" size.
|
| 912 |
-
button_large_text_weight: The text weight of a button with the default "large" size.
|
| 913 |
-
button_primary_background_fill: The background of a button of "primary" variant.
|
| 914 |
-
button_primary_background_fill_dark: The background of a button of "primary" variant in dark mode.
|
| 915 |
-
button_primary_background_fill_hover: The background of a button of "primary" variant when hovered over.
|
| 916 |
-
button_primary_background_fill_hover_dark: The background of a button of "primary" variant when hovered over in dark mode.
|
| 917 |
-
button_primary_border_color: The border color of a button of "primary" variant.
|
| 918 |
-
button_primary_border_color_dark: The border color of a button of "primary" variant in dark mode.
|
| 919 |
-
button_primary_border_color_hover: The border color of a button of "primary" variant when hovered over.
|
| 920 |
-
button_primary_border_color_hover_dark: The border color of a button of "primary" variant when hovered over in dark mode.
|
| 921 |
-
button_primary_text_color: The text color of a button of "primary" variant.
|
| 922 |
-
button_primary_text_color_dark: The text color of a button of "primary" variant in dark mode.
|
| 923 |
-
button_primary_text_color_hover: The text color of a button of "primary" variant when hovered over.
|
| 924 |
-
button_primary_text_color_hover_dark: The text color of a button of "primary" variant when hovered over in dark mode.
|
| 925 |
-
button_secondary_background_fill: The background of a button of default "secondary" variant.
|
| 926 |
-
button_secondary_background_fill_dark: The background of a button of default "secondary" variant in dark mode.
|
| 927 |
-
button_secondary_background_fill_hover: The background of a button of default "secondary" variant when hovered over.
|
| 928 |
-
button_secondary_background_fill_hover_dark: The background of a button of default "secondary" variant when hovered over in dark mode.
|
| 929 |
-
button_secondary_border_color: The border color of a button of default "secondary" variant.
|
| 930 |
-
button_secondary_border_color_dark: The border color of a button of default "secondary" variant in dark mode.
|
| 931 |
-
button_secondary_border_color_hover: The border color of a button of default "secondary" variant when hovered over.
|
| 932 |
-
button_secondary_border_color_hover_dark: The border color of a button of default "secondary" variant when hovered over in dark mode.
|
| 933 |
-
button_secondary_text_color: The text color of a button of default "secondary" variant.
|
| 934 |
-
button_secondary_text_color_dark: The text color of a button of default "secondary" variant in dark mode.
|
| 935 |
-
button_secondary_text_color_hover: The text color of a button of default "secondary" variant when hovered over.
|
| 936 |
-
button_secondary_text_color_hover_dark: The text color of a button of default "secondary" variant when hovered over in dark mode.
|
| 937 |
-
button_shadow: The shadow under a button.
|
| 938 |
-
button_shadow_active: The shadow under a button when pressed.
|
| 939 |
-
button_shadow_hover: The shadow under a button when hovered over.
|
| 940 |
-
button_small_padding: The padding of a button set to "small" size.
|
| 941 |
-
button_small_radius: The corner radius of a button set to "small" size.
|
| 942 |
-
button_small_text_size: The text size of a button set to "small" size.
|
| 943 |
-
button_small_text_weight: The text weight of a button set to "small" size.
|
| 944 |
-
button_transition: The transition animation duration of a button between regular, hover, and focused states.
|
| 945 |
-
"""
|
| 946 |
-
|
| 947 |
-
# Body
|
| 948 |
-
self.body_background_fill = body_background_fill or getattr(
|
| 949 |
-
self, "body_background_fill", "*background_fill_primary"
|
| 950 |
-
)
|
| 951 |
-
self.body_background_fill_dark = body_background_fill_dark or getattr(
|
| 952 |
-
self, "body_background_fill_dark", "*background_fill_primary"
|
| 953 |
-
)
|
| 954 |
-
self.body_text_color = body_text_color or getattr(
|
| 955 |
-
self, "body_text_color", "*neutral_800"
|
| 956 |
-
)
|
| 957 |
-
self.body_text_color_dark = body_text_color_dark or getattr(
|
| 958 |
-
self, "body_text_color_dark", "*neutral_100"
|
| 959 |
-
)
|
| 960 |
-
self.body_text_size = body_text_size or getattr(
|
| 961 |
-
self, "body_text_size", "*text_md"
|
| 962 |
-
)
|
| 963 |
-
self.body_text_weight = body_text_weight or getattr(
|
| 964 |
-
self, "body_text_weight", "400"
|
| 965 |
-
)
|
| 966 |
-
self.embed_radius = embed_radius or getattr(self, "embed_radius", "*radius_lg")
|
| 967 |
-
# Core Colors
|
| 968 |
-
self.color_accent = color_accent or getattr(
|
| 969 |
-
self, "color_accent", "*primary_500"
|
| 970 |
-
)
|
| 971 |
-
self.color_accent_soft = color_accent_soft or getattr(
|
| 972 |
-
self, "color_accent_soft", "*primary_50"
|
| 973 |
-
)
|
| 974 |
-
self.color_accent_soft_dark = color_accent_soft_dark or getattr(
|
| 975 |
-
self, "color_accent_soft_dark", "*neutral_700"
|
| 976 |
-
)
|
| 977 |
-
self.background_fill_primary = background_fill_primary or getattr(
|
| 978 |
-
self, "background_primary", "white"
|
| 979 |
-
)
|
| 980 |
-
self.background_fill_primary_dark = background_fill_primary_dark or getattr(
|
| 981 |
-
self, "background_primary_dark", "*neutral_950"
|
| 982 |
-
)
|
| 983 |
-
self.background_fill_secondary = background_fill_secondary or getattr(
|
| 984 |
-
self, "background_secondary", "*neutral_50"
|
| 985 |
-
)
|
| 986 |
-
self.background_fill_secondary_dark = background_fill_secondary_dark or getattr(
|
| 987 |
-
self, "background_secondary_dark", "*neutral_900"
|
| 988 |
-
)
|
| 989 |
-
self.border_color_accent = border_color_accent or getattr(
|
| 990 |
-
self, "border_color_accent", "*primary_300"
|
| 991 |
-
)
|
| 992 |
-
self.border_color_accent_dark = border_color_accent_dark or getattr(
|
| 993 |
-
self, "border_color_accent_dark", "*neutral_600"
|
| 994 |
-
)
|
| 995 |
-
self.border_color_primary = border_color_primary or getattr(
|
| 996 |
-
self, "border_color_primary", "*neutral_200"
|
| 997 |
-
)
|
| 998 |
-
self.border_color_primary_dark = border_color_primary_dark or getattr(
|
| 999 |
-
self, "border_color_primary_dark", "*neutral_700"
|
| 1000 |
-
)
|
| 1001 |
-
# Text Colors
|
| 1002 |
-
self.link_text_color = link_text_color or getattr(
|
| 1003 |
-
self, "link_text_color", "*secondary_600"
|
| 1004 |
-
)
|
| 1005 |
-
self.link_text_color_active = link_text_color_active or getattr(
|
| 1006 |
-
self, "link_text_color_active", "*secondary_600"
|
| 1007 |
-
)
|
| 1008 |
-
self.link_text_color_active_dark = link_text_color_active_dark or getattr(
|
| 1009 |
-
self, "link_text_color_active_dark", "*secondary_500"
|
| 1010 |
-
)
|
| 1011 |
-
self.link_text_color_dark = link_text_color_dark or getattr(
|
| 1012 |
-
self, "link_text_color_dark", "*secondary_500"
|
| 1013 |
-
)
|
| 1014 |
-
self.link_text_color_hover = link_text_color_hover or getattr(
|
| 1015 |
-
self, "link_text_color_hover", "*secondary_700"
|
| 1016 |
-
)
|
| 1017 |
-
self.link_text_color_hover_dark = link_text_color_hover_dark or getattr(
|
| 1018 |
-
self, "link_text_color_hover_dark", "*secondary_400"
|
| 1019 |
-
)
|
| 1020 |
-
self.link_text_color_visited = link_text_color_visited or getattr(
|
| 1021 |
-
self, "link_text_color_visited", "*secondary_500"
|
| 1022 |
-
)
|
| 1023 |
-
self.link_text_color_visited_dark = link_text_color_visited_dark or getattr(
|
| 1024 |
-
self, "link_text_color_visited_dark", "*secondary_600"
|
| 1025 |
-
)
|
| 1026 |
-
self.body_text_color_subdued = body_text_color_subdued or getattr(
|
| 1027 |
-
self, "body_text_color_subdued", "*neutral_400"
|
| 1028 |
-
)
|
| 1029 |
-
self.body_text_color_subdued_dark = body_text_color_subdued_dark or getattr(
|
| 1030 |
-
self, "body_text_color_subdued_dark", "*neutral_400"
|
| 1031 |
-
)
|
| 1032 |
-
# Shadows
|
| 1033 |
-
self.shadow_drop = shadow_drop or getattr(
|
| 1034 |
-
self, "shadow_drop", "rgba(0,0,0,0.05) 0px 1px 2px 0px"
|
| 1035 |
-
)
|
| 1036 |
-
self.shadow_drop_lg = shadow_drop_lg or getattr(
|
| 1037 |
-
self,
|
| 1038 |
-
"shadow_drop_lg",
|
| 1039 |
-
"0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)",
|
| 1040 |
-
)
|
| 1041 |
-
self.shadow_inset = shadow_inset or getattr(
|
| 1042 |
-
self, "shadow_inset", "rgba(0,0,0,0.05) 0px 2px 4px 0px inset"
|
| 1043 |
-
)
|
| 1044 |
-
self.shadow_spread = shadow_spread or getattr(self, "shadow_spread", "3px")
|
| 1045 |
-
self.shadow_spread_dark = shadow_spread_dark or getattr(
|
| 1046 |
-
self, "shadow_spread_dark", "1px"
|
| 1047 |
-
)
|
| 1048 |
-
# Layout Atoms
|
| 1049 |
-
self.block_background_fill = block_background_fill or getattr(
|
| 1050 |
-
self, "block_background_fill", "*background_fill_primary"
|
| 1051 |
-
)
|
| 1052 |
-
self.block_background_fill_dark = block_background_fill_dark or getattr(
|
| 1053 |
-
self, "block_background_fill_dark", "*neutral_800"
|
| 1054 |
-
)
|
| 1055 |
-
self.block_border_color = block_border_color or getattr(
|
| 1056 |
-
self, "block_border_color", "*border_color_primary"
|
| 1057 |
-
)
|
| 1058 |
-
self.block_border_color_dark = block_border_color_dark or getattr(
|
| 1059 |
-
self, "block_border_color_dark", "*border_color_primary"
|
| 1060 |
-
)
|
| 1061 |
-
self.block_border_width = block_border_width or getattr(
|
| 1062 |
-
self, "block_border_width", "1px"
|
| 1063 |
-
)
|
| 1064 |
-
self.block_border_width_dark = block_border_width_dark or getattr(
|
| 1065 |
-
self, "block_border_width_dark", None
|
| 1066 |
-
)
|
| 1067 |
-
self.block_info_text_color = block_info_text_color or getattr(
|
| 1068 |
-
self, "block_info_text_color", "*body_text_color_subdued"
|
| 1069 |
-
)
|
| 1070 |
-
self.block_info_text_color_dark = block_info_text_color_dark or getattr(
|
| 1071 |
-
self, "block_info_text_color_dark", "*body_text_color_subdued"
|
| 1072 |
-
)
|
| 1073 |
-
self.block_info_text_size = block_info_text_size or getattr(
|
| 1074 |
-
self, "block_info_text_size", "*text_sm"
|
| 1075 |
-
)
|
| 1076 |
-
self.block_info_text_weight = block_info_text_weight or getattr(
|
| 1077 |
-
self, "block_info_text_weight", "400"
|
| 1078 |
-
)
|
| 1079 |
-
self.block_label_background_fill = block_label_background_fill or getattr(
|
| 1080 |
-
self, "block_label_background_fill", "*background_fill_primary"
|
| 1081 |
-
)
|
| 1082 |
-
self.block_label_background_fill_dark = (
|
| 1083 |
-
block_label_background_fill_dark
|
| 1084 |
-
or getattr(
|
| 1085 |
-
self, "block_label_background_fill_dark", "*background_fill_secondary"
|
| 1086 |
-
)
|
| 1087 |
-
)
|
| 1088 |
-
self.block_label_border_color = block_label_border_color or getattr(
|
| 1089 |
-
self, "block_label_border_color", "*border_color_primary"
|
| 1090 |
-
)
|
| 1091 |
-
self.block_label_border_color_dark = block_label_border_color_dark or getattr(
|
| 1092 |
-
self, "block_label_border_color_dark", "*border_color_primary"
|
| 1093 |
-
)
|
| 1094 |
-
self.block_label_border_width = block_label_border_width or getattr(
|
| 1095 |
-
self, "block_label_border_width", "1px"
|
| 1096 |
-
)
|
| 1097 |
-
self.block_label_border_width_dark = block_label_border_width_dark or getattr(
|
| 1098 |
-
self, "block_label_border_width_dark", None
|
| 1099 |
-
)
|
| 1100 |
-
self.block_label_shadow = block_label_shadow or getattr(
|
| 1101 |
-
self, "block_label_shadow", "*block_shadow"
|
| 1102 |
-
)
|
| 1103 |
-
self.block_label_text_color = block_label_text_color or getattr(
|
| 1104 |
-
self, "block_label_text_color", "*neutral_500"
|
| 1105 |
-
)
|
| 1106 |
-
self.block_label_text_color_dark = block_label_text_color_dark or getattr(
|
| 1107 |
-
self, "block_label_text_color_dark", "*neutral_200"
|
| 1108 |
-
)
|
| 1109 |
-
self.block_label_margin = block_label_margin or getattr(
|
| 1110 |
-
self, "block_label_margin", "0"
|
| 1111 |
-
)
|
| 1112 |
-
self.block_label_padding = block_label_padding or getattr(
|
| 1113 |
-
self, "block_label_padding", "*spacing_sm *spacing_lg"
|
| 1114 |
-
)
|
| 1115 |
-
self.block_label_radius = block_label_radius or getattr(
|
| 1116 |
-
self,
|
| 1117 |
-
"block_label_radius",
|
| 1118 |
-
"calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0",
|
| 1119 |
-
)
|
| 1120 |
-
self.block_label_right_radius = block_label_right_radius or getattr(
|
| 1121 |
-
self,
|
| 1122 |
-
"block_label_right_radius",
|
| 1123 |
-
"0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)",
|
| 1124 |
-
)
|
| 1125 |
-
self.block_label_text_size = block_label_text_size or getattr(
|
| 1126 |
-
self, "block_label_text_size", "*text_sm"
|
| 1127 |
-
)
|
| 1128 |
-
self.block_label_text_weight = block_label_text_weight or getattr(
|
| 1129 |
-
self, "block_label_text_weight", "400"
|
| 1130 |
-
)
|
| 1131 |
-
self.block_padding = block_padding or getattr(
|
| 1132 |
-
self, "block_padding", "*spacing_xl calc(*spacing_xl + 2px)"
|
| 1133 |
-
)
|
| 1134 |
-
self.block_radius = block_radius or getattr(self, "block_radius", "*radius_lg")
|
| 1135 |
-
self.block_shadow = block_shadow or getattr(self, "block_shadow", "none")
|
| 1136 |
-
self.block_shadow_dark = block_shadow_dark or getattr(
|
| 1137 |
-
self, "block_shadow_dark", None
|
| 1138 |
-
)
|
| 1139 |
-
self.block_title_background_fill = block_title_background_fill or getattr(
|
| 1140 |
-
self, "block_title_background_fill", "none"
|
| 1141 |
-
)
|
| 1142 |
-
self.block_title_background_fill_dark = (
|
| 1143 |
-
block_title_background_fill_dark
|
| 1144 |
-
or getattr(self, "block_title_background_fill_dark", None)
|
| 1145 |
-
)
|
| 1146 |
-
self.block_title_border_color = block_title_border_color or getattr(
|
| 1147 |
-
self, "block_title_border_color", "none"
|
| 1148 |
-
)
|
| 1149 |
-
self.block_title_border_color_dark = block_title_border_color_dark or getattr(
|
| 1150 |
-
self, "block_title_border_color_dark", None
|
| 1151 |
-
)
|
| 1152 |
-
self.block_title_border_width = block_title_border_width or getattr(
|
| 1153 |
-
self, "block_title_border_width", "0px"
|
| 1154 |
-
)
|
| 1155 |
-
self.block_title_border_width_dark = block_title_border_width_dark or getattr(
|
| 1156 |
-
self, "block_title_border_width_dark", None
|
| 1157 |
-
)
|
| 1158 |
-
self.block_title_text_color = block_title_text_color or getattr(
|
| 1159 |
-
self, "block_title_text_color", "*neutral_500"
|
| 1160 |
-
)
|
| 1161 |
-
self.block_title_text_color_dark = block_title_text_color_dark or getattr(
|
| 1162 |
-
self, "block_title_text_color_dark", "*neutral_200"
|
| 1163 |
-
)
|
| 1164 |
-
self.block_title_padding = block_title_padding or getattr(
|
| 1165 |
-
self, "block_title_padding", "0"
|
| 1166 |
-
)
|
| 1167 |
-
self.block_title_radius = block_title_radius or getattr(
|
| 1168 |
-
self, "block_title_radius", "none"
|
| 1169 |
-
)
|
| 1170 |
-
self.block_title_text_size = block_title_text_size or getattr(
|
| 1171 |
-
self, "block_title_text_size", "*text_md"
|
| 1172 |
-
)
|
| 1173 |
-
self.block_title_text_weight = block_title_text_weight or getattr(
|
| 1174 |
-
self, "block_title_text_weight", "400"
|
| 1175 |
-
)
|
| 1176 |
-
self.container_radius = container_radius or getattr(
|
| 1177 |
-
self, "container_radius", "*radius_lg"
|
| 1178 |
-
)
|
| 1179 |
-
self.form_gap_width = form_gap_width or getattr(self, "form_gap_width", "0px")
|
| 1180 |
-
self.layout_gap = layout_gap or getattr(self, "layout_gap", "*spacing_xxl")
|
| 1181 |
-
self.panel_background_fill = panel_background_fill or getattr(
|
| 1182 |
-
self, "panel_background_fill", "*background_fill_secondary"
|
| 1183 |
-
)
|
| 1184 |
-
self.panel_background_fill_dark = panel_background_fill_dark or getattr(
|
| 1185 |
-
self, "panel_background_fill_dark", "*background_fill_secondary"
|
| 1186 |
-
)
|
| 1187 |
-
self.panel_border_color = panel_border_color or getattr(
|
| 1188 |
-
self, "panel_border_color", "*border_color_primary"
|
| 1189 |
-
)
|
| 1190 |
-
self.panel_border_color_dark = panel_border_color_dark or getattr(
|
| 1191 |
-
self, "panel_border_color_dark", "*border_color_primary"
|
| 1192 |
-
)
|
| 1193 |
-
self.panel_border_width = panel_border_width or getattr(
|
| 1194 |
-
self, "panel_border_width", "0"
|
| 1195 |
-
)
|
| 1196 |
-
self.panel_border_width_dark = panel_border_width_dark or getattr(
|
| 1197 |
-
self, "panel_border_width_dark", None
|
| 1198 |
-
)
|
| 1199 |
-
self.section_header_text_size = section_header_text_size or getattr(
|
| 1200 |
-
self, "section_header_text_size", "*text_md"
|
| 1201 |
-
)
|
| 1202 |
-
self.section_header_text_weight = section_header_text_weight or getattr(
|
| 1203 |
-
self, "section_header_text_weight", "400"
|
| 1204 |
-
)
|
| 1205 |
-
# Component Atoms
|
| 1206 |
-
self.chatbot_code_background_color = chatbot_code_background_color or getattr(
|
| 1207 |
-
self, "chatbot_code_background_color", "*neutral_100"
|
| 1208 |
-
)
|
| 1209 |
-
self.chatbot_code_background_color_dark = (
|
| 1210 |
-
chatbot_code_background_color_dark
|
| 1211 |
-
or getattr(self, "chatbot_code_background_color_dark", "*neutral_800")
|
| 1212 |
-
)
|
| 1213 |
-
self.checkbox_background_color = checkbox_background_color or getattr(
|
| 1214 |
-
self, "checkbox_background_color", "*background_fill_primary"
|
| 1215 |
-
)
|
| 1216 |
-
self.checkbox_background_color_dark = checkbox_background_color_dark or getattr(
|
| 1217 |
-
self, "checkbox_background_color_dark", "*neutral_800"
|
| 1218 |
-
)
|
| 1219 |
-
self.checkbox_background_color_focus = (
|
| 1220 |
-
checkbox_background_color_focus
|
| 1221 |
-
or getattr(
|
| 1222 |
-
self, "checkbox_background_color_focus", "*checkbox_background_color"
|
| 1223 |
-
)
|
| 1224 |
-
)
|
| 1225 |
-
self.checkbox_background_color_focus_dark = (
|
| 1226 |
-
checkbox_background_color_focus_dark
|
| 1227 |
-
or getattr(
|
| 1228 |
-
self,
|
| 1229 |
-
"checkbox_background_color_focus_dark",
|
| 1230 |
-
"*checkbox_background_color",
|
| 1231 |
-
)
|
| 1232 |
-
)
|
| 1233 |
-
self.checkbox_background_color_hover = (
|
| 1234 |
-
checkbox_background_color_hover
|
| 1235 |
-
or getattr(
|
| 1236 |
-
self, "checkbox_background_color_hover", "*checkbox_background_color"
|
| 1237 |
-
)
|
| 1238 |
-
)
|
| 1239 |
-
self.checkbox_background_color_hover_dark = (
|
| 1240 |
-
checkbox_background_color_hover_dark
|
| 1241 |
-
or getattr(
|
| 1242 |
-
self,
|
| 1243 |
-
"checkbox_background_color_hover_dark",
|
| 1244 |
-
"*checkbox_background_color",
|
| 1245 |
-
)
|
| 1246 |
-
)
|
| 1247 |
-
self.checkbox_background_color_selected = (
|
| 1248 |
-
checkbox_background_color_selected
|
| 1249 |
-
or getattr(self, "checkbox_background_color_selected", "*secondary_600")
|
| 1250 |
-
)
|
| 1251 |
-
self.checkbox_background_color_selected_dark = (
|
| 1252 |
-
checkbox_background_color_selected_dark
|
| 1253 |
-
or getattr(
|
| 1254 |
-
self, "checkbox_background_color_selected_dark", "*secondary_600"
|
| 1255 |
-
)
|
| 1256 |
-
)
|
| 1257 |
-
self.checkbox_border_color = checkbox_border_color or getattr(
|
| 1258 |
-
self, "checkbox_border_color", "*neutral_300"
|
| 1259 |
-
)
|
| 1260 |
-
self.checkbox_border_color_dark = checkbox_border_color_dark or getattr(
|
| 1261 |
-
self, "checkbox_border_color_dark", "*neutral_700"
|
| 1262 |
-
)
|
| 1263 |
-
self.checkbox_border_color_focus = checkbox_border_color_focus or getattr(
|
| 1264 |
-
self, "checkbox_border_color_focus", "*secondary_500"
|
| 1265 |
-
)
|
| 1266 |
-
self.checkbox_border_color_focus_dark = (
|
| 1267 |
-
checkbox_border_color_focus_dark
|
| 1268 |
-
or getattr(self, "checkbox_border_color_focus_dark", "*secondary_500")
|
| 1269 |
-
)
|
| 1270 |
-
self.checkbox_border_color_hover = checkbox_border_color_hover or getattr(
|
| 1271 |
-
self, "checkbox_border_color_hover", "*neutral_300"
|
| 1272 |
-
)
|
| 1273 |
-
self.checkbox_border_color_hover_dark = (
|
| 1274 |
-
checkbox_border_color_hover_dark
|
| 1275 |
-
or getattr(self, "checkbox_border_color_hover_dark", "*neutral_600")
|
| 1276 |
-
)
|
| 1277 |
-
self.checkbox_border_color_selected = checkbox_border_color_selected or getattr(
|
| 1278 |
-
self, "checkbox_border_color_selected", "*secondary_600"
|
| 1279 |
-
)
|
| 1280 |
-
self.checkbox_border_color_selected_dark = (
|
| 1281 |
-
checkbox_border_color_selected_dark
|
| 1282 |
-
or getattr(self, "checkbox_border_color_selected_dark", "*secondary_600")
|
| 1283 |
-
)
|
| 1284 |
-
self.checkbox_border_radius = checkbox_border_radius or getattr(
|
| 1285 |
-
self, "checkbox_border_radius", "*radius_sm"
|
| 1286 |
-
)
|
| 1287 |
-
self.checkbox_border_width = checkbox_border_width or getattr(
|
| 1288 |
-
self, "checkbox_border_width", "*input_border_width"
|
| 1289 |
-
)
|
| 1290 |
-
self.checkbox_border_width_dark = checkbox_border_width_dark or getattr(
|
| 1291 |
-
self, "checkbox_border_width_dark", "*input_border_width"
|
| 1292 |
-
)
|
| 1293 |
-
self.checkbox_label_background_fill = checkbox_label_background_fill or getattr(
|
| 1294 |
-
self, "checkbox_label_background_fill", "*button_secondary_background_fill"
|
| 1295 |
-
)
|
| 1296 |
-
self.checkbox_label_background_fill_dark = (
|
| 1297 |
-
checkbox_label_background_fill_dark
|
| 1298 |
-
or getattr(
|
| 1299 |
-
self,
|
| 1300 |
-
"checkbox_label_background_fill_dark",
|
| 1301 |
-
"*button_secondary_background_fill",
|
| 1302 |
-
)
|
| 1303 |
-
)
|
| 1304 |
-
self.checkbox_label_background_fill_hover = (
|
| 1305 |
-
checkbox_label_background_fill_hover
|
| 1306 |
-
or getattr(
|
| 1307 |
-
self,
|
| 1308 |
-
"checkbox_label_background_fill_hover",
|
| 1309 |
-
"*button_secondary_background_fill_hover",
|
| 1310 |
-
)
|
| 1311 |
-
)
|
| 1312 |
-
self.checkbox_label_background_fill_hover_dark = (
|
| 1313 |
-
checkbox_label_background_fill_hover_dark
|
| 1314 |
-
or getattr(
|
| 1315 |
-
self,
|
| 1316 |
-
"checkbox_label_background_fill_hover_dark",
|
| 1317 |
-
"*button_secondary_background_fill_hover",
|
| 1318 |
-
)
|
| 1319 |
-
)
|
| 1320 |
-
self.checkbox_label_background_fill_selected = (
|
| 1321 |
-
checkbox_label_background_fill_selected
|
| 1322 |
-
or getattr(
|
| 1323 |
-
self,
|
| 1324 |
-
"checkbox_label_background_fill_selected",
|
| 1325 |
-
"*checkbox_label_background_fill",
|
| 1326 |
-
)
|
| 1327 |
-
)
|
| 1328 |
-
self.checkbox_label_background_fill_selected_dark = (
|
| 1329 |
-
checkbox_label_background_fill_selected_dark
|
| 1330 |
-
or getattr(
|
| 1331 |
-
self,
|
| 1332 |
-
"checkbox_label_background_fill_selected_dark",
|
| 1333 |
-
"*checkbox_label_background_fill",
|
| 1334 |
-
)
|
| 1335 |
-
)
|
| 1336 |
-
self.checkbox_label_border_color = checkbox_label_border_color or getattr(
|
| 1337 |
-
self, "checkbox_label_border_color", "*border_color_primary"
|
| 1338 |
-
)
|
| 1339 |
-
self.checkbox_label_border_color_dark = (
|
| 1340 |
-
checkbox_label_border_color_dark
|
| 1341 |
-
or getattr(
|
| 1342 |
-
self, "checkbox_label_border_color_dark", "*border_color_primary"
|
| 1343 |
-
)
|
| 1344 |
-
)
|
| 1345 |
-
self.checkbox_label_border_color_hover = (
|
| 1346 |
-
checkbox_label_border_color_hover
|
| 1347 |
-
or getattr(
|
| 1348 |
-
self,
|
| 1349 |
-
"checkbox_label_border_color_hover",
|
| 1350 |
-
"*checkbox_label_border_color",
|
| 1351 |
-
)
|
| 1352 |
-
)
|
| 1353 |
-
self.checkbox_label_border_color_hover_dark = (
|
| 1354 |
-
checkbox_label_border_color_hover_dark
|
| 1355 |
-
or getattr(
|
| 1356 |
-
self,
|
| 1357 |
-
"checkbox_label_border_color_hover_dark",
|
| 1358 |
-
"*checkbox_label_border_color",
|
| 1359 |
-
)
|
| 1360 |
-
)
|
| 1361 |
-
self.checkbox_label_border_width = checkbox_label_border_width or getattr(
|
| 1362 |
-
self, "checkbox_label_border_width", "*input_border_width"
|
| 1363 |
-
)
|
| 1364 |
-
self.checkbox_label_border_width_dark = (
|
| 1365 |
-
checkbox_label_border_width_dark
|
| 1366 |
-
or getattr(self, "checkbox_label_border_width_dark", "*input_border_width")
|
| 1367 |
-
)
|
| 1368 |
-
self.checkbox_label_gap = checkbox_label_gap or getattr(
|
| 1369 |
-
self, "checkbox_label_gap", "*spacing_lg"
|
| 1370 |
-
)
|
| 1371 |
-
self.checkbox_label_padding = checkbox_label_padding or getattr(
|
| 1372 |
-
self, "checkbox_label_padding", "*spacing_md calc(2 * *spacing_md)"
|
| 1373 |
-
)
|
| 1374 |
-
self.checkbox_label_shadow = checkbox_label_shadow or getattr(
|
| 1375 |
-
self, "checkbox_label_shadow", "none"
|
| 1376 |
-
)
|
| 1377 |
-
self.checkbox_label_text_size = checkbox_label_text_size or getattr(
|
| 1378 |
-
self, "checkbox_label_text_size", "*text_md"
|
| 1379 |
-
)
|
| 1380 |
-
self.checkbox_label_text_weight = checkbox_label_text_weight or getattr(
|
| 1381 |
-
self, "checkbox_label_text_weight", "400"
|
| 1382 |
-
)
|
| 1383 |
-
self.checkbox_check = checkbox_check or getattr(
|
| 1384 |
-
self,
|
| 1385 |
-
"checkbox_check",
|
| 1386 |
-
"""url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e")""",
|
| 1387 |
-
)
|
| 1388 |
-
self.radio_circle = radio_circle or getattr(
|
| 1389 |
-
self,
|
| 1390 |
-
"radio_circle",
|
| 1391 |
-
"""url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e")""",
|
| 1392 |
-
)
|
| 1393 |
-
self.checkbox_shadow = checkbox_shadow or getattr(
|
| 1394 |
-
self, "checkbox_shadow", "*input_shadow"
|
| 1395 |
-
)
|
| 1396 |
-
self.checkbox_label_text_color = checkbox_label_text_color or getattr(
|
| 1397 |
-
self, "checkbox_label_text_color", "*body_text_color"
|
| 1398 |
-
)
|
| 1399 |
-
self.checkbox_label_text_color_dark = checkbox_label_text_color_dark or getattr(
|
| 1400 |
-
self, "checkbox_label_text_color_dark", "*body_text_color"
|
| 1401 |
-
)
|
| 1402 |
-
self.checkbox_label_text_color_selected = (
|
| 1403 |
-
checkbox_label_text_color_selected
|
| 1404 |
-
or getattr(
|
| 1405 |
-
self, "checkbox_label_text_color_selected", "*checkbox_label_text_color"
|
| 1406 |
-
)
|
| 1407 |
-
)
|
| 1408 |
-
self.checkbox_label_text_color_selected_dark = (
|
| 1409 |
-
checkbox_label_text_color_selected_dark
|
| 1410 |
-
or getattr(
|
| 1411 |
-
self,
|
| 1412 |
-
"checkbox_label_text_color_selected_dark",
|
| 1413 |
-
"*checkbox_label_text_color",
|
| 1414 |
-
)
|
| 1415 |
-
)
|
| 1416 |
-
self.error_background_fill = error_background_fill or getattr(
|
| 1417 |
-
self, "error_background_fill", colors.red.c50
|
| 1418 |
-
)
|
| 1419 |
-
self.error_background_fill_dark = error_background_fill_dark or getattr(
|
| 1420 |
-
self, "error_background_fill_dark", "*background_fill_primary"
|
| 1421 |
-
)
|
| 1422 |
-
self.error_border_color = error_border_color or getattr(
|
| 1423 |
-
self, "error_border_color", colors.red.c700
|
| 1424 |
-
)
|
| 1425 |
-
self.error_border_color_dark = error_border_color_dark or getattr(
|
| 1426 |
-
self, "error_border_color_dark", colors.red.c500
|
| 1427 |
-
)
|
| 1428 |
-
self.error_border_width = error_border_width or getattr(
|
| 1429 |
-
self, "error_border_width", "1px"
|
| 1430 |
-
)
|
| 1431 |
-
self.error_border_width_dark = error_border_width_dark or getattr(
|
| 1432 |
-
self, "error_border_width_dark", None
|
| 1433 |
-
)
|
| 1434 |
-
self.error_text_color = error_text_color or getattr(
|
| 1435 |
-
self, "error_text_color", colors.red.c700
|
| 1436 |
-
)
|
| 1437 |
-
self.error_text_color_dark = error_text_color_dark or getattr(
|
| 1438 |
-
self, "error_text_color_dark", colors.red.c50
|
| 1439 |
-
)
|
| 1440 |
-
self.error_icon_color = error_icon_color or getattr(
|
| 1441 |
-
self, "error_icon_color", colors.red.c700
|
| 1442 |
-
)
|
| 1443 |
-
self.error_icon_color_dark = error_icon_color_dark or getattr(
|
| 1444 |
-
self, "error_icon_color_dark", colors.red.c500
|
| 1445 |
-
)
|
| 1446 |
-
self.input_background_fill = input_background_fill or getattr(
|
| 1447 |
-
self, "input_background_fill", "*neutral_100"
|
| 1448 |
-
)
|
| 1449 |
-
self.input_background_fill_dark = input_background_fill_dark or getattr(
|
| 1450 |
-
self, "input_background_fill_dark", "*neutral_700"
|
| 1451 |
-
)
|
| 1452 |
-
self.input_background_fill_focus = input_background_fill_focus or getattr(
|
| 1453 |
-
self, "input_background_fill_focus", "*secondary_500"
|
| 1454 |
-
)
|
| 1455 |
-
self.input_background_fill_focus_dark = (
|
| 1456 |
-
input_background_fill_focus_dark
|
| 1457 |
-
or getattr(self, "input_background_fill_focus_dark", "*secondary_600")
|
| 1458 |
-
)
|
| 1459 |
-
self.input_background_fill_hover = input_background_fill_hover or getattr(
|
| 1460 |
-
self, "input_background_fill_hover", "*input_background_fill"
|
| 1461 |
-
)
|
| 1462 |
-
self.input_background_fill_hover_dark = (
|
| 1463 |
-
input_background_fill_hover_dark
|
| 1464 |
-
or getattr(
|
| 1465 |
-
self, "input_background_fill_hover_dark", "*input_background_fill"
|
| 1466 |
-
)
|
| 1467 |
-
)
|
| 1468 |
-
self.input_border_color = input_border_color or getattr(
|
| 1469 |
-
self, "input_border_color", "*border_color_primary"
|
| 1470 |
-
)
|
| 1471 |
-
self.input_border_color_dark = input_border_color_dark or getattr(
|
| 1472 |
-
self, "input_border_color_dark", "*border_color_primary"
|
| 1473 |
-
)
|
| 1474 |
-
self.input_border_color_focus = input_border_color_focus or getattr(
|
| 1475 |
-
self, "input_border_color_focus", "*secondary_300"
|
| 1476 |
-
)
|
| 1477 |
-
self.input_border_color_focus_dark = input_border_color_focus_dark or getattr(
|
| 1478 |
-
self, "input_border_color_focus_dark", "*neutral_700"
|
| 1479 |
-
)
|
| 1480 |
-
self.input_border_color_hover = input_border_color_hover or getattr(
|
| 1481 |
-
self, "input_border_color_hover", "*input_border_color"
|
| 1482 |
-
)
|
| 1483 |
-
self.input_border_color_hover_dark = input_border_color_hover_dark or getattr(
|
| 1484 |
-
self, "input_border_color_hover_dark", "*input_border_color"
|
| 1485 |
-
)
|
| 1486 |
-
self.input_border_width = input_border_width or getattr(
|
| 1487 |
-
self, "input_border_width", "0px"
|
| 1488 |
-
)
|
| 1489 |
-
self.input_border_width_dark = input_border_width_dark or getattr(
|
| 1490 |
-
self, "input_border_width_dark", None
|
| 1491 |
-
)
|
| 1492 |
-
self.input_padding = input_padding or getattr(
|
| 1493 |
-
self, "input_padding", "*spacing_xl"
|
| 1494 |
-
)
|
| 1495 |
-
self.input_placeholder_color = input_placeholder_color or getattr(
|
| 1496 |
-
self, "input_placeholder_color", "*neutral_400"
|
| 1497 |
-
)
|
| 1498 |
-
self.input_placeholder_color_dark = input_placeholder_color_dark or getattr(
|
| 1499 |
-
self, "input_placeholder_color_dark", "*neutral_500"
|
| 1500 |
-
)
|
| 1501 |
-
self.input_radius = input_radius or getattr(self, "input_radius", "*radius_lg")
|
| 1502 |
-
self.input_shadow = input_shadow or getattr(self, "input_shadow", "none")
|
| 1503 |
-
self.input_shadow_dark = input_shadow_dark or getattr(
|
| 1504 |
-
self, "input_shadow_dark", None
|
| 1505 |
-
)
|
| 1506 |
-
self.input_shadow_focus = input_shadow_focus or getattr(
|
| 1507 |
-
self, "input_shadow_focus", "*input_shadow"
|
| 1508 |
-
)
|
| 1509 |
-
self.input_shadow_focus_dark = input_shadow_focus_dark or getattr(
|
| 1510 |
-
self, "input_shadow_focus_dark", None
|
| 1511 |
-
)
|
| 1512 |
-
self.input_text_size = input_text_size or getattr(
|
| 1513 |
-
self, "input_text_size", "*text_md"
|
| 1514 |
-
)
|
| 1515 |
-
self.input_text_weight = input_text_weight or getattr(
|
| 1516 |
-
self, "input_text_weight", "400"
|
| 1517 |
-
)
|
| 1518 |
-
self.loader_color = loader_color or getattr(
|
| 1519 |
-
self, "loader_color", "*color_accent"
|
| 1520 |
-
)
|
| 1521 |
-
self.loader_color_dark = loader_color_dark or getattr(
|
| 1522 |
-
self, "loader_color_dark", None
|
| 1523 |
-
)
|
| 1524 |
-
self.prose_text_size = prose_text_size or getattr(
|
| 1525 |
-
self, "prose_text_size", "*text_md"
|
| 1526 |
-
)
|
| 1527 |
-
self.prose_text_weight = prose_text_weight or getattr(
|
| 1528 |
-
self, "prose_text_weight", "400"
|
| 1529 |
-
)
|
| 1530 |
-
self.prose_header_text_weight = prose_header_text_weight or getattr(
|
| 1531 |
-
self, "prose_header_text_weight", "600"
|
| 1532 |
-
)
|
| 1533 |
-
self.slider_color = slider_color or getattr(self, "slider_color", "auto")
|
| 1534 |
-
self.slider_color_dark = slider_color_dark or getattr(
|
| 1535 |
-
self, "slider_color_dark", None
|
| 1536 |
-
)
|
| 1537 |
-
self.stat_background_fill = stat_background_fill or getattr(
|
| 1538 |
-
self, "stat_background_fill", "*primary_300"
|
| 1539 |
-
)
|
| 1540 |
-
self.stat_background_fill_dark = stat_background_fill_dark or getattr(
|
| 1541 |
-
self, "stat_background_fill_dark", "*primary_500"
|
| 1542 |
-
)
|
| 1543 |
-
self.table_border_color = table_border_color or getattr(
|
| 1544 |
-
self, "table_border_color", "*neutral_300"
|
| 1545 |
-
)
|
| 1546 |
-
self.table_border_color_dark = table_border_color_dark or getattr(
|
| 1547 |
-
self, "table_border_color_dark", "*neutral_700"
|
| 1548 |
-
)
|
| 1549 |
-
self.table_even_background_fill = table_even_background_fill or getattr(
|
| 1550 |
-
self, "table_even_background_fill", "white"
|
| 1551 |
-
)
|
| 1552 |
-
self.table_even_background_fill_dark = (
|
| 1553 |
-
table_even_background_fill_dark
|
| 1554 |
-
or getattr(self, "table_even_background_fill_dark", "*neutral_950")
|
| 1555 |
-
)
|
| 1556 |
-
self.table_odd_background_fill = table_odd_background_fill or getattr(
|
| 1557 |
-
self, "table_odd_background_fill", "*neutral_50"
|
| 1558 |
-
)
|
| 1559 |
-
self.table_odd_background_fill_dark = table_odd_background_fill_dark or getattr(
|
| 1560 |
-
self, "table_odd_background_fill_dark", "*neutral_900"
|
| 1561 |
-
)
|
| 1562 |
-
self.table_radius = table_radius or getattr(self, "table_radius", "*radius_lg")
|
| 1563 |
-
self.table_row_focus = table_row_focus or getattr(
|
| 1564 |
-
self, "table_row_focus", "*color_accent_soft"
|
| 1565 |
-
)
|
| 1566 |
-
self.table_row_focus_dark = table_row_focus_dark or getattr(
|
| 1567 |
-
self, "table_row_focus_dark", "*color_accent_soft"
|
| 1568 |
-
)
|
| 1569 |
-
# Buttons
|
| 1570 |
-
self.button_border_width = button_border_width or getattr(
|
| 1571 |
-
self, "button_border_width", "*input_border_width"
|
| 1572 |
-
)
|
| 1573 |
-
self.button_border_width_dark = button_border_width_dark or getattr(
|
| 1574 |
-
self, "button_border_width_dark", "*input_border_width"
|
| 1575 |
-
)
|
| 1576 |
-
self.button_cancel_background_fill = button_cancel_background_fill or getattr(
|
| 1577 |
-
self, "button_cancel_background_fill", "*button_secondary_background_fill"
|
| 1578 |
-
)
|
| 1579 |
-
self.button_cancel_background_fill_dark = (
|
| 1580 |
-
button_cancel_background_fill_dark
|
| 1581 |
-
or getattr(
|
| 1582 |
-
self,
|
| 1583 |
-
"button_cancel_background_fill_dark",
|
| 1584 |
-
"*button_secondary_background_fill",
|
| 1585 |
-
)
|
| 1586 |
-
)
|
| 1587 |
-
self.button_cancel_background_fill_hover = (
|
| 1588 |
-
button_cancel_background_fill_hover
|
| 1589 |
-
or getattr(
|
| 1590 |
-
self,
|
| 1591 |
-
"button_cancel_background_fill_hover",
|
| 1592 |
-
"*button_cancel_background_fill",
|
| 1593 |
-
)
|
| 1594 |
-
)
|
| 1595 |
-
self.button_cancel_background_fill_hover_dark = (
|
| 1596 |
-
button_cancel_background_fill_hover_dark
|
| 1597 |
-
or getattr(
|
| 1598 |
-
self,
|
| 1599 |
-
"button_cancel_background_fill_hover_dark",
|
| 1600 |
-
"*button_cancel_background_fill",
|
| 1601 |
-
)
|
| 1602 |
-
)
|
| 1603 |
-
self.button_cancel_border_color = button_cancel_border_color or getattr(
|
| 1604 |
-
self, "button_cancel_border_color", "*button_secondary_border_color"
|
| 1605 |
-
)
|
| 1606 |
-
self.button_cancel_border_color_dark = (
|
| 1607 |
-
button_cancel_border_color_dark
|
| 1608 |
-
or getattr(
|
| 1609 |
-
self,
|
| 1610 |
-
"button_cancel_border_color_dark",
|
| 1611 |
-
"*button_secondary_border_color",
|
| 1612 |
-
)
|
| 1613 |
-
)
|
| 1614 |
-
self.button_cancel_border_color_hover = (
|
| 1615 |
-
button_cancel_border_color_hover
|
| 1616 |
-
or getattr(
|
| 1617 |
-
self,
|
| 1618 |
-
"button_cancel_border_color_hover",
|
| 1619 |
-
"*button_cancel_border_color",
|
| 1620 |
-
)
|
| 1621 |
-
)
|
| 1622 |
-
self.button_cancel_border_color_hover_dark = (
|
| 1623 |
-
button_cancel_border_color_hover_dark
|
| 1624 |
-
or getattr(
|
| 1625 |
-
self,
|
| 1626 |
-
"button_cancel_border_color_hover_dark",
|
| 1627 |
-
"*button_cancel_border_color",
|
| 1628 |
-
)
|
| 1629 |
-
)
|
| 1630 |
-
self.button_cancel_text_color = button_cancel_text_color or getattr(
|
| 1631 |
-
self, "button_cancel_text_color", "*button_secondary_text_color"
|
| 1632 |
-
)
|
| 1633 |
-
self.button_cancel_text_color_dark = button_cancel_text_color_dark or getattr(
|
| 1634 |
-
self, "button_cancel_text_color_dark", "*button_secondary_text_color"
|
| 1635 |
-
)
|
| 1636 |
-
self.button_cancel_text_color_hover = button_cancel_text_color_hover or getattr(
|
| 1637 |
-
self, "button_cancel_text_color_hover", "*button_cancel_text_color"
|
| 1638 |
-
)
|
| 1639 |
-
self.button_cancel_text_color_hover_dark = (
|
| 1640 |
-
button_cancel_text_color_hover_dark
|
| 1641 |
-
or getattr(
|
| 1642 |
-
self, "button_cancel_text_color_hover_dark", "*button_cancel_text_color"
|
| 1643 |
-
)
|
| 1644 |
-
)
|
| 1645 |
-
self.button_large_padding = button_large_padding or getattr(
|
| 1646 |
-
self, "button_large_padding", "*spacing_lg calc(2 * *spacing_lg)"
|
| 1647 |
-
)
|
| 1648 |
-
self.button_large_radius = button_large_radius or getattr(
|
| 1649 |
-
self, "button_large_radius", "*radius_lg"
|
| 1650 |
-
)
|
| 1651 |
-
self.button_large_text_size = button_large_text_size or getattr(
|
| 1652 |
-
self, "button_large_text_size", "*text_lg"
|
| 1653 |
-
)
|
| 1654 |
-
self.button_large_text_weight = button_large_text_weight or getattr(
|
| 1655 |
-
self, "button_large_text_weight", "600"
|
| 1656 |
-
)
|
| 1657 |
-
self.button_primary_background_fill = button_primary_background_fill or getattr(
|
| 1658 |
-
self, "button_primary_background_fill", "*primary_200"
|
| 1659 |
-
)
|
| 1660 |
-
self.button_primary_background_fill_dark = (
|
| 1661 |
-
button_primary_background_fill_dark
|
| 1662 |
-
or getattr(self, "button_primary_background_fill_dark", "*primary_700")
|
| 1663 |
-
)
|
| 1664 |
-
self.button_primary_background_fill_hover = (
|
| 1665 |
-
button_primary_background_fill_hover
|
| 1666 |
-
or getattr(
|
| 1667 |
-
self,
|
| 1668 |
-
"button_primary_background_fill_hover",
|
| 1669 |
-
"*button_primary_background_fill",
|
| 1670 |
-
)
|
| 1671 |
-
)
|
| 1672 |
-
self.button_primary_background_fill_hover_dark = (
|
| 1673 |
-
button_primary_background_fill_hover_dark
|
| 1674 |
-
or getattr(
|
| 1675 |
-
self,
|
| 1676 |
-
"button_primary_background_fill_hover_dark",
|
| 1677 |
-
"*button_primary_background_fill",
|
| 1678 |
-
)
|
| 1679 |
-
)
|
| 1680 |
-
self.button_primary_border_color = button_primary_border_color or getattr(
|
| 1681 |
-
self, "button_primary_border_color", "*primary_200"
|
| 1682 |
-
)
|
| 1683 |
-
self.button_primary_border_color_dark = (
|
| 1684 |
-
button_primary_border_color_dark
|
| 1685 |
-
or getattr(self, "button_primary_border_color_dark", "*primary_600")
|
| 1686 |
-
)
|
| 1687 |
-
self.button_primary_border_color_hover = (
|
| 1688 |
-
button_primary_border_color_hover
|
| 1689 |
-
or getattr(
|
| 1690 |
-
self,
|
| 1691 |
-
"button_primary_border_color_hover",
|
| 1692 |
-
"*button_primary_border_color",
|
| 1693 |
-
)
|
| 1694 |
-
)
|
| 1695 |
-
self.button_primary_border_color_hover_dark = (
|
| 1696 |
-
button_primary_border_color_hover_dark
|
| 1697 |
-
or getattr(
|
| 1698 |
-
self,
|
| 1699 |
-
"button_primary_border_color_hover_dark",
|
| 1700 |
-
"*button_primary_border_color",
|
| 1701 |
-
)
|
| 1702 |
-
)
|
| 1703 |
-
self.button_primary_text_color = button_primary_text_color or getattr(
|
| 1704 |
-
self, "button_primary_text_color", "*primary_600"
|
| 1705 |
-
)
|
| 1706 |
-
self.button_primary_text_color_dark = button_primary_text_color_dark or getattr(
|
| 1707 |
-
self, "button_primary_text_color_dark", "white"
|
| 1708 |
-
)
|
| 1709 |
-
self.button_primary_text_color_hover = (
|
| 1710 |
-
button_primary_text_color_hover
|
| 1711 |
-
or getattr(
|
| 1712 |
-
self, "button_primary_text_color_hover", "*button_primary_text_color"
|
| 1713 |
-
)
|
| 1714 |
-
)
|
| 1715 |
-
self.button_primary_text_color_hover_dark = (
|
| 1716 |
-
button_primary_text_color_hover_dark
|
| 1717 |
-
or getattr(
|
| 1718 |
-
self,
|
| 1719 |
-
"button_primary_text_color_hover_dark",
|
| 1720 |
-
"*button_primary_text_color",
|
| 1721 |
-
)
|
| 1722 |
-
)
|
| 1723 |
-
self.button_secondary_background_fill = (
|
| 1724 |
-
button_secondary_background_fill
|
| 1725 |
-
or getattr(self, "button_secondary_background_fill", "*neutral_200")
|
| 1726 |
-
)
|
| 1727 |
-
self.button_secondary_background_fill_dark = (
|
| 1728 |
-
button_secondary_background_fill_dark
|
| 1729 |
-
or getattr(self, "button_secondary_background_fill_dark", "*neutral_600")
|
| 1730 |
-
)
|
| 1731 |
-
self.button_secondary_background_fill_hover = (
|
| 1732 |
-
button_secondary_background_fill_hover
|
| 1733 |
-
or getattr(
|
| 1734 |
-
self,
|
| 1735 |
-
"button_secondary_background_fill_hover",
|
| 1736 |
-
"*button_secondary_background_fill",
|
| 1737 |
-
)
|
| 1738 |
-
)
|
| 1739 |
-
self.button_secondary_background_fill_hover_dark = (
|
| 1740 |
-
button_secondary_background_fill_hover_dark
|
| 1741 |
-
or getattr(
|
| 1742 |
-
self,
|
| 1743 |
-
"button_secondary_background_fill_hover_dark",
|
| 1744 |
-
"*button_secondary_background_fill",
|
| 1745 |
-
)
|
| 1746 |
-
)
|
| 1747 |
-
self.button_secondary_border_color = button_secondary_border_color or getattr(
|
| 1748 |
-
self, "button_secondary_border_color", "*neutral_200"
|
| 1749 |
-
)
|
| 1750 |
-
self.button_secondary_border_color_dark = (
|
| 1751 |
-
button_secondary_border_color_dark
|
| 1752 |
-
or getattr(self, "button_secondary_border_color_dark", "*neutral_600")
|
| 1753 |
-
)
|
| 1754 |
-
self.button_secondary_border_color_hover = (
|
| 1755 |
-
button_secondary_border_color_hover
|
| 1756 |
-
or getattr(
|
| 1757 |
-
self,
|
| 1758 |
-
"button_secondary_border_color_hover",
|
| 1759 |
-
"*button_secondary_border_color",
|
| 1760 |
-
)
|
| 1761 |
-
)
|
| 1762 |
-
self.button_secondary_border_color_hover_dark = (
|
| 1763 |
-
button_secondary_border_color_hover_dark
|
| 1764 |
-
or getattr(
|
| 1765 |
-
self,
|
| 1766 |
-
"button_secondary_border_color_hover_dark",
|
| 1767 |
-
"*button_secondary_border_color",
|
| 1768 |
-
)
|
| 1769 |
-
)
|
| 1770 |
-
self.button_secondary_text_color = button_secondary_text_color or getattr(
|
| 1771 |
-
self, "button_secondary_text_color", "*neutral_700"
|
| 1772 |
-
)
|
| 1773 |
-
self.button_secondary_text_color_dark = (
|
| 1774 |
-
button_secondary_text_color_dark
|
| 1775 |
-
or getattr(self, "button_secondary_text_color_dark", "white")
|
| 1776 |
-
)
|
| 1777 |
-
self.button_secondary_text_color_hover = (
|
| 1778 |
-
button_secondary_text_color_hover
|
| 1779 |
-
or getattr(
|
| 1780 |
-
self,
|
| 1781 |
-
"button_secondary_text_color_hover",
|
| 1782 |
-
"*button_secondary_text_color",
|
| 1783 |
-
)
|
| 1784 |
-
)
|
| 1785 |
-
self.button_secondary_text_color_hover_dark = (
|
| 1786 |
-
button_secondary_text_color_hover_dark
|
| 1787 |
-
or getattr(
|
| 1788 |
-
self,
|
| 1789 |
-
"button_secondary_text_color_hover_dark",
|
| 1790 |
-
"*button_secondary_text_color",
|
| 1791 |
-
)
|
| 1792 |
-
)
|
| 1793 |
-
self.button_shadow = button_shadow or getattr(self, "button_shadow", "none")
|
| 1794 |
-
self.button_shadow_active = button_shadow_active or getattr(
|
| 1795 |
-
self, "button_shadow_active", "none"
|
| 1796 |
-
)
|
| 1797 |
-
self.button_shadow_hover = button_shadow_hover or getattr(
|
| 1798 |
-
self, "button_shadow_hover", "none"
|
| 1799 |
-
)
|
| 1800 |
-
self.button_small_padding = button_small_padding or getattr(
|
| 1801 |
-
self, "button_small_padding", "*spacing_sm calc(2 * *spacing_sm)"
|
| 1802 |
-
)
|
| 1803 |
-
self.button_small_radius = button_small_radius or getattr(
|
| 1804 |
-
self, "button_small_radius", "*radius_lg"
|
| 1805 |
-
)
|
| 1806 |
-
self.button_small_text_size = button_small_text_size or getattr(
|
| 1807 |
-
self, "button_small_text_size", "*text_md"
|
| 1808 |
-
)
|
| 1809 |
-
self.button_small_text_weight = button_small_text_weight or getattr(
|
| 1810 |
-
self, "button_small_text_weight", "400"
|
| 1811 |
-
)
|
| 1812 |
-
self.button_transition = button_transition or getattr(
|
| 1813 |
-
self, "button_transition", "background-color 0.2s ease"
|
| 1814 |
-
)
|
| 1815 |
-
return self
|
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|
spaces/DeepFloyd/IF/style.css
DELETED
|
@@ -1,238 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
This CSS file is modified from:
|
| 3 |
-
https://huggingface.co/spaces/stabilityai/stable-diffusion/blob/2794a3c3ba66115c307075098e713f572b08bf80/app.py
|
| 4 |
-
*/
|
| 5 |
-
|
| 6 |
-
h1 {
|
| 7 |
-
text-align: center;
|
| 8 |
-
}
|
| 9 |
-
|
| 10 |
-
.gradio-container {
|
| 11 |
-
font-family: 'IBM Plex Sans', sans-serif;
|
| 12 |
-
}
|
| 13 |
-
|
| 14 |
-
.gr-button {
|
| 15 |
-
color: white;
|
| 16 |
-
border-color: black;
|
| 17 |
-
background: black;
|
| 18 |
-
}
|
| 19 |
-
|
| 20 |
-
input[type='range'] {
|
| 21 |
-
accent-color: black;
|
| 22 |
-
}
|
| 23 |
-
|
| 24 |
-
.dark input[type='range'] {
|
| 25 |
-
accent-color: #dfdfdf;
|
| 26 |
-
}
|
| 27 |
-
|
| 28 |
-
.container {
|
| 29 |
-
max-width: 730px;
|
| 30 |
-
margin: auto;
|
| 31 |
-
padding-top: 1.5rem;
|
| 32 |
-
}
|
| 33 |
-
|
| 34 |
-
#gallery {
|
| 35 |
-
min-height: auto;
|
| 36 |
-
height: 185px;
|
| 37 |
-
margin-top: 15px;
|
| 38 |
-
margin-left: auto;
|
| 39 |
-
margin-right: auto;
|
| 40 |
-
border-bottom-right-radius: .5rem !important;
|
| 41 |
-
border-bottom-left-radius: .5rem !important;
|
| 42 |
-
}
|
| 43 |
-
#gallery .grid-wrap, #gallery .empty{
|
| 44 |
-
height: 185px;
|
| 45 |
-
min-height: 185px;
|
| 46 |
-
}
|
| 47 |
-
#gallery .preview{
|
| 48 |
-
height: 185px;
|
| 49 |
-
min-height: 185px!important;
|
| 50 |
-
}
|
| 51 |
-
#gallery>div>.h-full {
|
| 52 |
-
min-height: 20rem;
|
| 53 |
-
}
|
| 54 |
-
|
| 55 |
-
.details:hover {
|
| 56 |
-
text-decoration: underline;
|
| 57 |
-
}
|
| 58 |
-
|
| 59 |
-
.gr-button {
|
| 60 |
-
white-space: nowrap;
|
| 61 |
-
}
|
| 62 |
-
|
| 63 |
-
.gr-button:focus {
|
| 64 |
-
border-color: rgb(147 197 253 / var(--tw-border-opacity));
|
| 65 |
-
outline: none;
|
| 66 |
-
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
|
| 67 |
-
--tw-border-opacity: 1;
|
| 68 |
-
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
|
| 69 |
-
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
|
| 70 |
-
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
|
| 71 |
-
--tw-ring-opacity: .5;
|
| 72 |
-
}
|
| 73 |
-
|
| 74 |
-
#advanced-btn {
|
| 75 |
-
font-size: .7rem !important;
|
| 76 |
-
line-height: 19px;
|
| 77 |
-
margin-top: 12px;
|
| 78 |
-
margin-bottom: 12px;
|
| 79 |
-
padding: 2px 8px;
|
| 80 |
-
border-radius: 14px !important;
|
| 81 |
-
}
|
| 82 |
-
|
| 83 |
-
#advanced-options {
|
| 84 |
-
display: none;
|
| 85 |
-
margin-bottom: 20px;
|
| 86 |
-
}
|
| 87 |
-
|
| 88 |
-
.footer {
|
| 89 |
-
margin-bottom: 45px;
|
| 90 |
-
margin-top: 35px;
|
| 91 |
-
text-align: center;
|
| 92 |
-
border-bottom: 1px solid #e5e5e5;
|
| 93 |
-
}
|
| 94 |
-
|
| 95 |
-
.footer>p {
|
| 96 |
-
font-size: .8rem;
|
| 97 |
-
display: inline-block;
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| 98 |
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from {
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to {
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|
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|
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margin-left: auto;
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|
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all: initial;
|
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color: #ffffff;
|
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|
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right: 0;
|
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|
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|
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all: unset;
|
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|
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|
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|
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|
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|
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flex: 1 1 50%;
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gap: 0;
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|
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|
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|
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padding: .45rem 0.625rem
|
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|
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|
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|
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border-top-width: 1px !important;
|
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margin-top: 1em
|
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}
|
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|
| 189 |
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.image_duplication {
|
| 190 |
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position: absolute;
|
| 191 |
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width: 100px;
|
| 192 |
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left: 50px
|
| 193 |
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}
|
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|
| 195 |
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#component-0 {
|
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max-width: 730px;
|
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margin: auto;
|
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padding-top: 1.5rem;
|
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}
|
| 200 |
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|
| 201 |
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#upscaled-image img {
|
| 202 |
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object-fit: scale-down;
|
| 203 |
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}
|
| 204 |
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/* share button */
|
| 205 |
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#share-btn-container {
|
| 206 |
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display: flex;
|
| 207 |
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padding-left: 0.5rem !important;
|
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padding-right: 0.5rem !important;
|
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background-color: #000000;
|
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justify-content: center;
|
| 211 |
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align-items: center;
|
| 212 |
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border-radius: 9999px !important;
|
| 213 |
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width: 13rem;
|
| 214 |
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margin-top: 10px;
|
| 215 |
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margin-left: auto;
|
| 216 |
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flex: unset !important;
|
| 217 |
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}
|
| 218 |
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#share-btn {
|
| 219 |
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all: initial;
|
| 220 |
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color: #ffffff;
|
| 221 |
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font-weight: 600;
|
| 222 |
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cursor: pointer;
|
| 223 |
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font-family: 'IBM Plex Sans', sans-serif;
|
| 224 |
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margin-left: 0.5rem !important;
|
| 225 |
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padding-top: 0.25rem !important;
|
| 226 |
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padding-bottom: 0.25rem !important;
|
| 227 |
-
right:0;
|
| 228 |
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}
|
| 229 |
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#share-btn * {
|
| 230 |
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all: unset !important;
|
| 231 |
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}
|
| 232 |
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#share-btn-container div:nth-child(-n+2){
|
| 233 |
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width: auto !important;
|
| 234 |
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min-height: 0px !important;
|
| 235 |
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}
|
| 236 |
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#share-btn-container .wrap {
|
| 237 |
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display: none !important;
|
| 238 |
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|
spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: 語音質檢+噪音去除：Meta Denoiser
|
| 3 |
-
emoji: 📉
|
| 4 |
-
colorFrom: gray
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.22.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: mit
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/DragGan/DragGan/stylegan_human/torch_utils/op_edit/__init__.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
# Copyright (c) SenseTime Research. All rights reserved.
|
| 2 |
-
|
| 3 |
-
from .fused_act import FusedLeakyReLU, fused_leaky_relu
|
| 4 |
-
from .upfirdn2d import upfirdn2d
|
|
|
|
|
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|
spaces/Dragonnnext/Unicorn-proxy/README.md
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Unicorn OAI Proxy
|
| 3 |
-
emoji: 🦄
|
| 4 |
-
sdk: docker
|
| 5 |
-
colorFrom: gray
|
| 6 |
-
colorTo: gray
|
| 7 |
-
pinned: false
|
| 8 |
-
---
|
| 9 |
-
|
| 10 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/Duskfallcrew/duskfall-s-general-digital-art-model/app.py
DELETED
|
@@ -1,137 +0,0 @@
|
|
| 1 |
-
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import torch
|
| 4 |
-
from PIL import Image
|
| 5 |
-
|
| 6 |
-
model_id = 'MultiversexPeeps/duskfall-s-general-digital-art-model'
|
| 7 |
-
prefix = 'gendigi'
|
| 8 |
-
|
| 9 |
-
scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
|
| 10 |
-
|
| 11 |
-
pipe = StableDiffusionPipeline.from_pretrained(
|
| 12 |
-
model_id,
|
| 13 |
-
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
| 14 |
-
scheduler=scheduler)
|
| 15 |
-
|
| 16 |
-
pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
|
| 17 |
-
model_id,
|
| 18 |
-
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
| 19 |
-
scheduler=scheduler)
|
| 20 |
-
|
| 21 |
-
if torch.cuda.is_available():
|
| 22 |
-
pipe = pipe.to("cuda")
|
| 23 |
-
pipe_i2i = pipe_i2i.to("cuda")
|
| 24 |
-
|
| 25 |
-
def error_str(error, title="Error"):
|
| 26 |
-
return f"""#### {title}
|
| 27 |
-
{error}""" if error else ""
|
| 28 |
-
|
| 29 |
-
def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
|
| 30 |
-
|
| 31 |
-
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
|
| 32 |
-
prompt = f"{prefix} {prompt}" if auto_prefix else prompt
|
| 33 |
-
|
| 34 |
-
try:
|
| 35 |
-
if img is not None:
|
| 36 |
-
return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
|
| 37 |
-
else:
|
| 38 |
-
return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
|
| 39 |
-
except Exception as e:
|
| 40 |
-
return None, error_str(e)
|
| 41 |
-
|
| 42 |
-
def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
|
| 43 |
-
|
| 44 |
-
result = pipe(
|
| 45 |
-
prompt,
|
| 46 |
-
negative_prompt = neg_prompt,
|
| 47 |
-
num_inference_steps = int(steps),
|
| 48 |
-
guidance_scale = guidance,
|
| 49 |
-
width = width,
|
| 50 |
-
height = height,
|
| 51 |
-
generator = generator)
|
| 52 |
-
|
| 53 |
-
return result.images[0]
|
| 54 |
-
|
| 55 |
-
def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
|
| 56 |
-
|
| 57 |
-
ratio = min(height / img.height, width / img.width)
|
| 58 |
-
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
|
| 59 |
-
result = pipe_i2i(
|
| 60 |
-
prompt,
|
| 61 |
-
negative_prompt = neg_prompt,
|
| 62 |
-
init_image = img,
|
| 63 |
-
num_inference_steps = int(steps),
|
| 64 |
-
strength = strength,
|
| 65 |
-
guidance_scale = guidance,
|
| 66 |
-
width = width,
|
| 67 |
-
height = height,
|
| 68 |
-
generator = generator)
|
| 69 |
-
|
| 70 |
-
return result.images[0]
|
| 71 |
-
|
| 72 |
-
css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
|
| 73 |
-
"""
|
| 74 |
-
with gr.Blocks(css=css) as demo:
|
| 75 |
-
gr.HTML(
|
| 76 |
-
f"""
|
| 77 |
-
<div class="main-div">
|
| 78 |
-
<div>
|
| 79 |
-
<h1>Duskfall S General Digital Art Model</h1>
|
| 80 |
-
</div>
|
| 81 |
-
<p>
|
| 82 |
-
Demo for <a href="https://huggingface.co/MultiversexPeeps/duskfall-s-general-digital-art-model">Duskfall S General Digital Art Model</a> Stable Diffusion model. <br> If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew / All samples and info are here: https://civitai.com/user/duskfallcrew <br> Use " gendigi " as your prefix token.<br>
|
| 83 |
-
{"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
|
| 84 |
-
</p>
|
| 85 |
-
Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/Duskfallcrew/duskfall-s-general-digital-art-model/settings'>Settings</a></b>"} after duplicating the space<br><br>
|
| 86 |
-
<a style="display:inline-block" href="https://huggingface.co/spaces/Duskfallcrew/duskfall-s-general-digital-art-model?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
|
| 87 |
-
</div>
|
| 88 |
-
"""
|
| 89 |
-
)
|
| 90 |
-
with gr.Row():
|
| 91 |
-
|
| 92 |
-
with gr.Column(scale=55):
|
| 93 |
-
with gr.Group():
|
| 94 |
-
with gr.Row():
|
| 95 |
-
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
|
| 96 |
-
generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
|
| 97 |
-
|
| 98 |
-
image_out = gr.Image(height=512)
|
| 99 |
-
error_output = gr.Markdown()
|
| 100 |
-
|
| 101 |
-
with gr.Column(scale=45):
|
| 102 |
-
with gr.Tab("Options"):
|
| 103 |
-
with gr.Group():
|
| 104 |
-
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
|
| 105 |
-
auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (gendigi)", value=prefix, visible=prefix)
|
| 106 |
-
|
| 107 |
-
with gr.Row():
|
| 108 |
-
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
|
| 109 |
-
steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
|
| 110 |
-
|
| 111 |
-
with gr.Row():
|
| 112 |
-
width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
|
| 113 |
-
height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
|
| 114 |
-
|
| 115 |
-
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
|
| 116 |
-
|
| 117 |
-
with gr.Tab("Image to image"):
|
| 118 |
-
with gr.Group():
|
| 119 |
-
image = gr.Image(label="Image", height=256, tool="editor", type="pil")
|
| 120 |
-
strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
|
| 121 |
-
|
| 122 |
-
auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
|
| 123 |
-
|
| 124 |
-
inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
|
| 125 |
-
outputs = [image_out, error_output]
|
| 126 |
-
prompt.submit(inference, inputs=inputs, outputs=outputs)
|
| 127 |
-
generate.click(inference, inputs=inputs, outputs=outputs)
|
| 128 |
-
|
| 129 |
-
gr.HTML("""
|
| 130 |
-
<div style="border-top: 1px solid #303030;">
|
| 131 |
-
<br>
|
| 132 |
-
<p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
|
| 133 |
-
</div>
|
| 134 |
-
""")
|
| 135 |
-
|
| 136 |
-
demo.queue(concurrency_count=1)
|
| 137 |
-
demo.launch()
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|
spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/archs/discriminator_arch.py
DELETED
|
@@ -1,67 +0,0 @@
|
|
| 1 |
-
from basicsr.utils.registry import ARCH_REGISTRY
|
| 2 |
-
from torch import nn as nn
|
| 3 |
-
from torch.nn import functional as F
|
| 4 |
-
from torch.nn.utils import spectral_norm
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
@ARCH_REGISTRY.register()
|
| 8 |
-
class UNetDiscriminatorSN(nn.Module):
|
| 9 |
-
"""Defines a U-Net discriminator with spectral normalization (SN)
|
| 10 |
-
|
| 11 |
-
It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
|
| 12 |
-
|
| 13 |
-
Arg:
|
| 14 |
-
num_in_ch (int): Channel number of inputs. Default: 3.
|
| 15 |
-
num_feat (int): Channel number of base intermediate features. Default: 64.
|
| 16 |
-
skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
|
| 20 |
-
super(UNetDiscriminatorSN, self).__init__()
|
| 21 |
-
self.skip_connection = skip_connection
|
| 22 |
-
norm = spectral_norm
|
| 23 |
-
# the first convolution
|
| 24 |
-
self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
|
| 25 |
-
# downsample
|
| 26 |
-
self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
|
| 27 |
-
self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
|
| 28 |
-
self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
|
| 29 |
-
# upsample
|
| 30 |
-
self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
|
| 31 |
-
self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
|
| 32 |
-
self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
|
| 33 |
-
# extra convolutions
|
| 34 |
-
self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
|
| 35 |
-
self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
|
| 36 |
-
self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)
|
| 37 |
-
|
| 38 |
-
def forward(self, x):
|
| 39 |
-
# downsample
|
| 40 |
-
x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
|
| 41 |
-
x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
|
| 42 |
-
x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
|
| 43 |
-
x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)
|
| 44 |
-
|
| 45 |
-
# upsample
|
| 46 |
-
x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
|
| 47 |
-
x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)
|
| 48 |
-
|
| 49 |
-
if self.skip_connection:
|
| 50 |
-
x4 = x4 + x2
|
| 51 |
-
x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
|
| 52 |
-
x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)
|
| 53 |
-
|
| 54 |
-
if self.skip_connection:
|
| 55 |
-
x5 = x5 + x1
|
| 56 |
-
x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
|
| 57 |
-
x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)
|
| 58 |
-
|
| 59 |
-
if self.skip_connection:
|
| 60 |
-
x6 = x6 + x0
|
| 61 |
-
|
| 62 |
-
# extra convolutions
|
| 63 |
-
out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
|
| 64 |
-
out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
|
| 65 |
-
out = self.conv9(out)
|
| 66 |
-
|
| 67 |
-
return out
|
|
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|
spaces/EvanMarie/faces_three/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Happy, Sad, or Angry
|
| 3 |
-
emoji: 😀 😥 😡
|
| 4 |
-
colorFrom: purple
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.9.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: apache-2.0
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
|
spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/models/experimental.py
DELETED
|
@@ -1,45 +0,0 @@
|
|
| 1 |
-
# # This file contains experimental modules
|
| 2 |
-
|
| 3 |
-
import numpy as np
|
| 4 |
-
import torch
|
| 5 |
-
from torch import nn
|
| 6 |
-
|
| 7 |
-
from facelib.detection.yolov5face.models.common import Conv
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
class CrossConv(nn.Module):
|
| 11 |
-
# Cross Convolution Downsample
|
| 12 |
-
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
|
| 13 |
-
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
|
| 14 |
-
super().__init__()
|
| 15 |
-
c_ = int(c2 * e) # hidden channels
|
| 16 |
-
self.cv1 = Conv(c1, c_, (1, k), (1, s))
|
| 17 |
-
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
|
| 18 |
-
self.add = shortcut and c1 == c2
|
| 19 |
-
|
| 20 |
-
def forward(self, x):
|
| 21 |
-
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
class MixConv2d(nn.Module):
|
| 25 |
-
# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
|
| 26 |
-
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
|
| 27 |
-
super().__init__()
|
| 28 |
-
groups = len(k)
|
| 29 |
-
if equal_ch: # equal c_ per group
|
| 30 |
-
i = torch.linspace(0, groups - 1e-6, c2).floor() # c2 indices
|
| 31 |
-
c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
|
| 32 |
-
else: # equal weight.numel() per group
|
| 33 |
-
b = [c2] + [0] * groups
|
| 34 |
-
a = np.eye(groups + 1, groups, k=-1)
|
| 35 |
-
a -= np.roll(a, 1, axis=1)
|
| 36 |
-
a *= np.array(k) ** 2
|
| 37 |
-
a[0] = 1
|
| 38 |
-
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
|
| 39 |
-
|
| 40 |
-
self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
|
| 41 |
-
self.bn = nn.BatchNorm2d(c2)
|
| 42 |
-
self.act = nn.LeakyReLU(0.1, inplace=True)
|
| 43 |
-
|
| 44 |
-
def forward(self, x):
|
| 45 |
-
return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
|
|
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