Update parquet files (step 31 of 397)

spaces/1368565466ki/ZSTRD/utils.py

@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-  audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-  return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()

spaces/1acneusushi/gradio-2dmoleculeeditor/data/3D Home Design Deluxe 6.exe Utorrent PATCHED.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Game Iso Ps2 Naruto Shippuden Ultimate Ninja 3 High Compressed The Ultimate Naruto PS2 Game that You Can Download and Play in Minutes.md

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-<p>If you are a fan of anime and manga, you probably know about Naruto, the ninja who dreams of becoming the Hokage, the leader of his village. Naruto has a long-running series that spans over 700 episodes and 70 volumes of manga. One of the most popular arcs in the series is Naruto Shippuden, which follows Naruto and his friends as they face new enemies and challenges in their quest to save the world.</p>
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-<p>One of the best ways to experience the Naruto Shippuden story is by playing Naruto Shippuden Ultimate Ninja 3, a video game that was released for the PlayStation 2 in 2008. This game lets you control your favorite characters from the anime and manga, and fight against other ninjas in various modes. You can also explore the hidden leaf village and interact with other characters.</p>
-<p>But how can you play this game if you don't have a PlayStation 2 console? Don't worry, there is a solution. You can download game iso ps2 naruto shippuden ultimate ninja 3 high compressed and play it on your PC or smartphone using an emulator. In this article, we will show you how to do that, and also tell you more about the features, graphics, sound, and gameplay of this amazing game. Let's get started!</p>
- <h2>Introduction</h2>
-<h3>What is Naruto Shippuden Ultimate Ninja 3?</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 is a fighting game that was developed by CyberConnect2 and published by Bandai Namco Games. It is based on the Naruto Shippuden anime and manga series, which is a sequel to the original Naruto series. The game covers the events from the beginning of Naruto Shippuden up to the end of the Sasuke Retrieval arc.</p>
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- <h2>Features of Naruto Shippuden Ultimate Ninja 3</h2>
-<h3>Gameplay</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has four main modes of gameplay: story mode, free battle mode, ultimate contest mode, and mission mode. Each mode offers a different way to enjoy the game and its features.</p>
- <h4>Story mode</h4>
-<p>In story mode, you can follow the plot of Naruto Shippuden from episode 1 to episode 135. You can choose to play as either Naruto or Sasuke, and switch between them at certain points in the story. You can also unlock other characters as you progress through the story. You will have to fight against various enemies and bosses in different locations from the anime and manga. You will also be able to watch cutscenes that recreate some of the most memorable scenes from the series.</p>
- <h4>Free battle mode</h4>
-<p>In free battle mode, you can choose any character you have unlocked and fight against another character controlled by either the computer or another player. You can customize your character's appearance, skills, items, and support characters before each battle. You can also choose from different stages and settings for each battle. You can play in single-player mode or multiplayer mode using either split-screen or online connection.</p>
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- <h4>Ultimate contest mode</h4>
-<p>In ultimate contest mode, you can participate in a tournament that involves all the characters from Naruto Shippuden. You will have to compete against other ninjas in various challenges and mini-games to earn points and rank up. You will also be able to explore the hidden leaf village and interact with other characters from the series. You can unlock new items, costumes, accessories, and characters by completing certain tasks and missions in this mode.</p>
- <h4>Mission mode</h4>
-<p>In mission mode, you can take on various missions that are assigned by different characters from Naruto Shippuden. These missions range from simple tasks like collecting items or defeating enemies to more complex ones like solving puzzles or stealth missions. You will earn rewards such as money, items, skills, or characters by completing these missions. You can also replay any mission you have completed before to improve your score or rank.</p>
- <h3>Graphics</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has impressive graphics that capture the style and atmosphere of Naruto Shippuden. The characters are well-designed and animated with smooth movements and expressions. The stages are detailed and colorful with dynamic backgrounds and effects. The cutscenes are cinematic and realistic with high-quality voice acting and sound effects. The game also supports widescreen resolution and progressive scan for better visual quality.</p>
- <h3>Sound and music</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has excellent sound and music that enhance the gameplay experience. The game features original soundtracks from Naruto Shippuden composed by Yasuharu Takanashi. The music matches the mood and tone of each scene and situation in the game. The game also features voice acting from both Japanese and English cast members of Naruto Shippuden anime series. The voice actors deliver their lines with emotion and personality that match their characters.</p>
- <h3>Characters</h3>
-```html <h2>Tips and tricks for playing Naruto Shippuden Ultimate Ninja 3</h2>
-<p>Naruto Shippuden Ultimate Ninja 3 is a fun and exciting game that can challenge your skills and strategy. To help you enjoy the game more and improve your performance, here are some tips and tricks that you can use:</p>
- <h3>Master the chakra system</h3>
-<p>Chakra is the energy that powers your character's skills and abilities in the game. You can see your chakra gauge at the bottom of the screen. You can charge your chakra by holding the triangle button, but this will leave you vulnerable to attacks. You can also gain chakra by attacking or being attacked by your opponent. You can use your chakra to perform various actions such as:</p>
-<ul>
-<li>Using jutsu: These are special techniques that can deal damage, heal, or buff your character. You can use jutsu by pressing the circle button and a direction on the D-pad. Each character has different jutsu that require different amounts of chakra.</li>
-<li>Using ultimate jutsu: These are powerful attacks that can deal massive damage to your opponent. You can use ultimate jutsu by pressing the circle button twice when your chakra gauge is full. Each character has a unique ultimate jutsu that can trigger a cinematic cutscene.</li>
-<li>Using awakening: This is a state that boosts your character's stats and abilities for a limited time. You can use awakening by pressing the R1 button when your health gauge is low. Each character has a different awakening that can change their appearance and moveset.</li>
-</ul>
-<p>You should learn how to manage your chakra wisely and use it at the right time and situation. You should also pay attention to your opponent's chakra gauge and prevent them from using their jutsu or ultimate jutsu.</p>
- <h3>Use the substitution jutsu wisely</h3>
-<p>The substitution jutsu is a defensive technique that allows you to dodge an incoming attack and teleport behind your opponent. You can use the substitution jutsu by pressing the L2 button right before you get hit. However, you can only use this technique a limited number of times, as indicated by the blue orbs around your character's portrait. The orbs will regenerate over time or by charging your chakra.</p>
-<p>You should use the substitution jutsu sparingly and strategically. You should not waste it on minor attacks or spam it randomly. You should save it for avoiding major attacks or counterattacking your opponent. You should also watch out for your opponent's substitution jutsu and anticipate their moves.</p>
- <h3>Experiment with different characters and teams</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has a large variety of characters that you can choose from. Each character has their own strengths, weaknesses, styles, and strategies. You should try out different characters and see which ones suit your preferences and skills. You should also learn their movesets, combos, jutsus, ultimate jutsus, and awakenings.</p>
-<p>You can also choose two support characters to assist you in battle. You can call them by pressing the L1 or R1 buttons. Each support character has a different role and ability that can help you in different ways. Some support characters can attack, defend, heal, or buff you or your opponent. You should choose support characters that complement your main character and create synergy with them.</p>
- <h3>Unlock hidden content</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has a lot of hidden content that you can unlock by playing the game. Some of the content includes:</p>
-<ul>
-<li>New characters: You can unlock new characters by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode. Some characters are hidden behind passwords that you can find online or in magazines.</li>
-<li>New costumes: You can unlock new costumes for your characters by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode. Some costumes are also available as downloadable content (DLC).</li>
-<li>New items: You can unlock new items for your characters by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode. Some items are also available as DLC.</li>
-<li>New stages: You can unlock new stages for free battle mode by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode.</li>
-<li>New music: You can unlock new music for free battle mode by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Naruto Shippuden Ultimate Ninja 3 is a great game for fans of Naruto Shippuden anime and manga series. It offers a rich and immersive gameplay experience that lets you relive the epic story of Naruto Shippuden with stunning graphics and sound. It also features a large roster of characters that you can play as or fight against in various modes of gameplay. It also has a lot of hidden content that you can unlock by playing the game.</p>
-<p>If you want to play this game but don't have a PlayStation 2 console, you can download game iso ps2 naruto shippuden ultimate ninja 3 high compressed and play it on your PC or smartphone using an emulator. This way, you can enjoy the same quality and performance as playing on a PlayStation 2 console.</p>
-<p>We hope this article has helped you learn more about Naruto Shippuden Ultimate Ninja 3 and how to download game iso ps2 naruto shippuden ultimate ninja 3 high compressed. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Naruto Shippuden Ultimate Ninja 3:</p>
- <h4>Q: Is Naruto Shippuden Ultimate Ninja 3 compatible with PlayStation 3?</h4>
-<p>A: Yes, Naruto Shippuden Ultimate Ninja 3 is compatible with PlayStation 3 as long as it supports backward compatibility with PlayStation 2 games.</p>
- <h4>Q: How many characters are there in Naruto Shippuden Ultimate Ninja 3?</h4>
-<p>A: There are over 40 playable characters in Naruto Shippuden Ultimate Ninja 3, plus over 20 support characters.</p>
- <h4>Q: What is the difference between Naruto Shippuden Ultimate Ninja 4 and Naruto Shippuden Ultimate Ninja 5?</h4>
-<p>A: Naruto Shippuden Ultimate Ninja 4 and Naruto Shippuden Ultimate Ninja 5 are two different games that were released for PlayStation 2 in 2009 and 2010 respectively. They are both based on Naruto Shippuden anime and manga series, but they cover different arcs and events in the story. They also have different features, graphics, sound, gameplay modes, and characters.</p>
- <h4>Q: What is the best emulator to play Naruto Shippuden Ultimate Ninja 3 on PC?</h4>
-<p>A: The best emulator to play Naruto Shippuden Ultimate Ninja 3 on PC is PCSX2, which is a free and open-source emulator that can run PlayStation 2 games on Windows, Linux, and Mac OS.</p>
- <h4>Q: What is the best emulator to play Naruto Shippuden Ultimate Ninja 3 on Android?</h4>
-<p>A: The best emulator to play Naruto Shippuden Ultimate Ninja 3 on Android is DamonPS2, which is a paid emulator that can run PlayStation 2 games on Android devices with high compatibility and performance.</p>
- </p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO Aprende los Conceptos Bsicos de la Fsica con este Libro.md

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-<h1>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO: A Comprehensive Guide</h1>
- <p>If you are a physics student looking for a reliable and effective resource to help you master the subject, you have come to the right place. In this article, we will introduce you to FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO, a complete and comprehensive solution manual for the sixth edition of the popular textbook Física by Wilson, Buffa and Lou. We will explain what FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is, why it is useful for physics students, how to access it online or offline, what are its main features and benefits, and how to get your copy today. By the end of this article, you will have a clear idea of how FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO can help you achieve your academic goals and excel in physics.</p>
- <h2>Introduction</h2>
- <h3>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h3>
- <p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou. The textbook covers all the topics of introductory physics, such as mechanics, thermodynamics, waves, optics, electricity, magnetism, modern physics, and more. The solution manual provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. The solution manual is written by the same authors of the textbook, who are experts in physics education and research.</p>
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- <h3>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h3>
- <p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts and principles of physics better, to apply them to solve various problems, to check their answers and correct their mistakes, to reinforce their learning and retention, and to prepare for exams and assignments. The solution manual is designed to complement the textbook and to enhance the learning experience of the students. It follows the same structure and organization of the textbook, making it easy to use and follow. It also provides tips, hints, strategies, examples, applications, summaries, reviews, and more.</p>
- <h3>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h3>
- <p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h2>Main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO</h2>
- <h3>Detailed solutions and explanations for every exercise</h3>
- <p>One of the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it provides detailed solutions and explanations for every exercise in the textbook. The solutions show all the steps involved in solving the problems, as well as the reasoning behind them. The explanations clarify the concepts and formulas used in the solutions, as well as their physical meaning and significance. The solutions also include graphs, diagrams, tables, figures, equations, units, symbols, notations, conversions, constants, data, references, and more.</p>
- <h3>Step-by-step approach to problem-solving</h3>
- <p>Another feature of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it follows a step-by-step approach to problem-solving. The solutions are organized into four steps: given information, required information, solution plan, and solution execution. The given information lists all the known data and conditions of the problem. The required information lists all the unknown data and conditions that need to be found. The solution plan outlines the strategy or method that will be used to solve the problem. The solution execution shows how to apply the strategy or method to obtain the final answer.</p>
- <h3>Clear and concise presentation of concepts and formulas</h3>
- <p>A third feature of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it presents  the concepts  and formulas  of physics  in a clear  and concise way. The solutions use simple language  and terminology  that are easy  to understand  and follow. The formulas are derived from first principles  or fundamental laws  of physics. The formulas are also stated clearly with their names, symbols, units, variables, assumptions, limitations, conditions, and applications.</p>
- <h3>Interactive and engaging examples and applications</h3>
- <p>A fourth feature of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it provides interactive  and engaging examples  and applications  of physics. The solutions include real-world scenarios  and situations  that illustrate  the relevance  and importance  of physics  in everyday life. The solutions also include questions  and exercises  that challenge  the students  to think critically  and creatively  about physics. The solutions also encourage  the students  to explore  further topics  and concepts  related  to physics.</p>
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- <h2>Benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO</h2>
- <h3>Improve your understanding of physics principles and phenomena</h3>
- <p>One of the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you improve your understanding of physics principles <contd...> ...and phenomena. By using  the solution manual, you can learn  how to apply  the principles  and phenomena  of physics  to solve different types  of problems. You can also learn  how to explain  the principles  and phenomena  of physics  in terms  of their physical meaning  and significance. You can also learn  how to relate  the principles  and phenomena  of physics  to other branches  of science, engineering, technology, and society.</p>
- <h3>Enhance your skills and confidence in solving physics problems</h3>
- <p>Another benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you enhance your skills and confidence in solving physics problems. By using the solution manual, you can practice solving various types of problems, ranging from simple to complex, from conceptual to numerical, from qualitative to quantitative, from basic to advanced. You can also check your answers and correct your mistakes, learn from your errors and improve your performance, compare your solutions with the standard ones and identify your strengths and weaknesses, and test your knowledge and understanding of physics concepts and formulas.</p>
- <h3>Prepare for exams and assignments with ease and efficiency</h3>
- <p>A third benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you prepare for exams and assignments with ease and efficiency. By using the solution manual, you can review the main topics and concepts of physics, revise the key formulas and equations of physics, practice solving different types of problems and questions that may appear on exams and assignments, assess your level of preparation and readiness for exams and assignments, and improve your grades and scores on exams and assignments.</p>
- <h3>Learn from the best authors and experts in the field</h3>
- <p>A fourth benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you learn from the best authors and experts in the field. The solution manual is written by Jerry D. Wilson, Anthony J. Buffa, and Bo Lou, who are renowned professors and researchers in physics education and research. They have extensive experience in teaching physics at various levels, writing physics textbooks and solution manuals, conducting physics experiments and investigations, publishing physics papers and articles, participating in physics conferences and workshops, and contributing to the advancement of physics knowledge and pedagogy.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a comprehensive guide that can help you master physics with ease and confidence. It is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou, which covers all the topics of introductory physics. It provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. It follows a step-by-step approach to problem-solving, presents the concepts and formulas of physics in a clear and concise way, and provides interactive and engaging examples and applications of physics. It helps you improve your understanding of physics principles and phenomena, enhance your skills and confidence in solving physics problems, prepare for exams and assignments with ease and efficiency, and learn from the best authors and experts in the field.</p>
- <h3>Call to action: get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</h3>
- <p>If you are interested in getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today, you have several options to choose from. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer. No matter which option you choose, you will not regret getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today. It will be one of the best investments you can make for your physics education and career. So don't wait any longer, get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</p>
- <h2>FAQs</h2>
- <h4>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou.</p>
- <h4>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts and principles of physics better, to apply them to solve various problems, to check their answers and correct their mistakes, to reinforce their learning and retention, and to prepare for exams and assignments.</p>
- <h4>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h4>What are the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. - Step-by-step approach to problem-solving, presenting the given information, required information, solution plan, and solution execution. - Clear and concise presentation of concepts and formulas of physics, stating their names, symbols, units, variables, assumptions, limitations, conditions, and applications. - Interactive and engaging examples and applications of physics, including real-world scenarios and situations, questions and exercises, tips and hints, strategies and methods, examples and applications, summaries and reviews.</p>
- <h4>What are the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Improve your understanding of physics principles and phenomena, learning how to apply them to solve different types of problems, how to explain them in terms of their physical meaning and significance, how to relate them to other branches of science, engineering, technology, and society. - Enhance your skills and confidence in solving physics problems, practicing solving various types of problems, checking your answers <contd...> ...and correct your mistakes, learn from your errors and improve your performance, compare your solutions with the standard ones and identify your strengths and weaknesses, and test your knowledge and understanding of physics concepts and formulas.</p>
- <h3>Prepare for exams and assignments with ease and efficiency</h3>
- <p>A third benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you prepare for exams and assignments with ease and efficiency. By using the solution manual, you can review the main topics and concepts of physics, revise the key formulas and equations of physics, practice solving different types of problems and questions that may appear on exams and assignments, assess your level of preparation and readiness for exams and assignments, and improve your grades and scores on exams and assignments.</p>
- <h3>Learn from the best authors and experts in the field</h3>
- <p>A fourth benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you learn from the best authors and experts in the field. The solution manual is written by Jerry D. Wilson, Anthony J. Buffa, and Bo Lou, who are renowned professors and researchers in physics education and research. They have extensive experience in teaching physics at various levels, writing physics textbooks and solution manuals, conducting physics experiments and investigations, publishing physics papers and articles, participating in physics conferences and workshops, and contributing to the advancement of physics knowledge and pedagogy.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a comprehensive guide that can help you master physics with ease and confidence. It is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou, which covers all the topics of introductory physics. It provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. It follows a step-by-step approach to problem-solving, presents the concepts and formulas of physics in a clear and concise way, and provides interactive and engaging examples and applications of physics. It helps you improve your understanding of physics principles and phenomena, enhance your skills and confidence in solving physics problems, prepare for exams and assignments with ease and efficiency, and learn from the best authors and experts in the field.</p>
- <h3>Call to action: get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</h3>
- <p>If you are interested in getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today, you have several options to choose from. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer. No matter which option you choose, you will not regret getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today. It will be one of the best investments you can make for your physics education and career. So don't wait any longer, get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</p>
- <h2>FAQs</h2>
- <h4>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou.</p>
- <h4>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts and principles of physics better, to apply them to solve various problems, to check their answers and correct their mistakes, to reinforce their learning and retention, and to prepare for exams and assignments.</p>
- <h4>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online  and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h4>What are the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Detailed solutions  and explanations  for every exercise  in the textbook, as well as additional problems  and questions  for practice  and review. - Step-by-step approach  to problem-solving, presenting the given information, required information, solution plan, and solution execution. - Clear and concise presentation  of concepts  and formulas  of physics, stating their names, symbols, units, variables, assumptions, limitations, conditions, and applications. - Interactive and engaging examples  and applications  of physics, including real-world scenarios  and situations, questions  and exercises, tips  and hints, strategies  and methods, examples  and applications, summaries  and reviews.</p>
- <h4>What are the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Improve your understanding  of physics principles  <contd...> ...and phenomena, learning how to apply them to solve different types of problems, how to explain them in terms of their physical meaning and significance, how to relate them to other branches of science, engineering, technology, and society.</p>
- <h3>Enhance your skills and confidence in solving physics problems</h3>
- <p>Another benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you enhance your skills and confidence in solving physics problems. By using the solution manual, you can practice solving various types of problems, ranging from simple to complex, from conceptual to numerical, from qualitative to quantitative, from basic to advanced. You can also check your answers and correct your mistakes, learn from your errors and improve your performance, compare your solutions with the standard ones and identify your strengths and weaknesses, and test your knowledge and understanding of physics concepts and formulas.</p>
- <h3>Prepare for exams and assignments with ease and efficiency</h3>
- <p>A third benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you prepare for exams and assignments with ease and efficiency. By using the solution manual, you can review the main topics and concepts of physics, revise the key formulas and equations of physics, practice solving different types of problems and questions that may appear on exams and assignments, assess your level of preparation and readiness for exams and assignments, and improve your grades and scores on exams and assignments.</p>
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-<p>Kaal is directed by Soham Shah and produced by Shah Rukh Khan's Red Chillies Entertainment and Karan Johar's Dharma Productions. The film was released on April 29, 2005, and was one of the first Bollywood films to use computer-generated imagery (CGI) for creating realistic animal effects. The film also features a special appearance by Shah Rukh Khan himself in an item song called "Kaal Dhamaal".</p>
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-<p>Kaal was shot in Jim Corbett National Park in Uttarakhand, India, where the crew had to face several challenges such as bad weather, difficult terrain, wild animals, and local protests. The film's budget was estimated at ₹150 million ($2 million), which was considered high for a Bollywood film at that time. The film's music was composed by Salim-Sulaiman, with lyrics by Shabbir Ahmed. The film's soundtrack album featured six songs, including "Kaal Dhamaal", "Tauba Tauba", "Nassa Nassa", "Kaal Theme", "Ankhiyan Teriya Ve", and "Garaj Baras".</p>
-<p>Kaal received mixed reviews from critics and audiences alike. Some praised the film's technical aspects, such as the cinematography, editing, sound design, and visual effects. Others criticized the film's weak script, poor direction, inconsistent performances, and lack of originality. The film was also compared unfavorably to Hollywood films such as Jaws, Jurassic Park, Anaconda, The Ghost and the Darkness, etc. The film earned ₹230 million ($3 million) at the domestic box office and ₹70 million ($1 million) overseas, making it a moderate success.</p>
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-<p>In conclusion, Kaal is a 2005 Bollywood natural horror film that tells the story of a group of people who face a deadly threat in a national park in India. The film features an ensemble cast of Ajay Devgn, Vivek Oberoi, John Abraham, Lara Dutta, and Esha Deol. The film was directed by Soham Shah and produced by Shah Rukh Khan and Karan Johar. The film was released on April 29, 2005 and was an average commercial success at the box office. The film has a theme and a message of protecting and respecting the wildlife and culture of India. The film can be watched online in HD quality from various platforms such as Netflix, Amazon Prime Video, iTunes, Google Play Movies, YouTube, etc. The benefits of watching Kaal online are saving time and money, enjoying flexibility and convenience, and accessing more content and features. The tips for streaming Kaal online are choosing a reliable and legal streaming service, checking your internet connection and device compatibility, and adjusting your settings and preferences.</p>
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-<li>A: No, Kaal is not based on a true story. It is a fictional story that was inspired by various Hollywood films such as Jaws, Jurassic Park, Anaconda, The Ghost and the Darkness, etc.</li>
-<li>Q: Who played Kaali Pratap Singh in Kaal?</li>
-<li>A: Ajay Devgn played Kaali Pratap Singh in Kaal. He played a dual role of both Kaali Pratap Singh, the ancestor who was killed by the British for protecting the wildlife, and Kaali Pratap Singh, the descendant who was possessed by his spirit.</li>
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-<li>A: "Kaal Dhamaal" is an item song that features Shah Rukh Khan in a special appearance. The song is composed by Anand Raj Anand and sung by Anand Raj Anand, Ravi Khote, Kunal Ganjawala and Caralisa Monteiro. The song's title means "Time Fun" or "Time Blast" in Hindi.</li>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Dall e Mod APK and Create Amazing AI Artworks.md

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-<h1>What is Dall E and Why You Need a Mod APK?</h1>
-<p>If you are a fan of art and creativity, you might have heard of Dall E, an AI-powered art generator that can create stunning images from any text input. But did you know that you can enhance your experience with Dall E by using a mod APK? In this article, we will explain what Dall E is, how it works, and what you can create with it. We will also show you what a mod APK is, how to install it, and what benefits it can bring you. Finally, we will give you some tips on how to find and download the best Dall E mod APK from trusted sources.</p>
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- <h2>Dall E: An AI-Powered Art Generator</h2>
-<p>Dall E is a web-based application that uses a deep learning model to generate images from text descriptions. It was created by OpenAI, a research company that aims to create artificial intelligence that can benefit humanity. Dall E is named after the artist Salvador Dali and the Pixar character WALL-E.</p>
- <h3>How Dall E works</h3>
-<p>To use Dall E, you simply need to type in a text prompt that describes what you want to see, such as "a cat wearing a hat" or "a pineapple pizza". Then, you can click on the generate button and wait for a few seconds. Dall E will then show you 32 different images that match your prompt. You can also refine your prompt by adding more details or changing some words. For example, you can change "a cat wearing a hat" to "a cat wearing a cowboy hat" or "a cat wearing a hat made of cheese".</p>
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-<p>The possibilities are endless with Dall E. You can create realistic or surreal images, mix and match different objects, animals, and people, or even invent new things that don't exist in reality. For example, you can create "a snail made of harp", "a skyscraper that looks like a giraffe", or "a portrait of Albert Einstein in the style of Picasso". You can also use Dall E for fun, education, or inspiration. You can make memes, cartoons, logos, illustrations, or wallpapers. You can learn about different cultures, languages, and history. You can spark your imagination and creativity.</p>
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-<p>As amazing as Dall E is, it also has some limitations. One of them is that you need to have credits to use it. Credits are tokens that allow you to generate images. You get 10 free credits when you sign up for an account, and then you need to pay $10 for 100 credits or $50 for 1000 credits. Another limitation is that you might encounter some ads or watermarks on the images that you generate.</p>
- <h3>What is a mod APK and how to install it</h3>
-<p>A mod APK is a modified version of an Android application that has been altered by someone to add or remove some features. For example, a mod APK of Dall E might give you unlimited credits, remove ads and watermarks, or add some extra options or filters. To install a mod APK, you need to download the APK file from a reliable source, and then enable the installation of apps from unknown sources on your device. Then, you can open the APK file and follow the instructions to install it. You might also need to uninstall the original Dall E app before installing the mod APK.</p>
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-<li>You can access some extra features or options that might not be available in the original app, such as more filters, styles, or formats.</li>
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-<p>However, you should also be aware of some risks and drawbacks of using a mod APK for Dall E, which we will discuss in the next section.</p>
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-<p>Now that you know what a mod APK is and what benefits it can bring you, you might be wondering how to find and download one for Dall E. However, you should also be careful about where you get your mod APK from, as not all sources are trustworthy and safe. In this section, we will explain some of the risks of downloading untrusted mod APKs, and some of the features to look for in a good mod APK. We will also give you some recommendations on where to find and download the best Dall E mod APK.</p>
- <h2>The Risks of Downloading Untrusted Mod APKs</h2>
-<p>While using a mod APK for Dall E might sound tempting, you should also be aware of some potential dangers that might come with it. Some of the risks of downloading untrusted mod APKs are:</p>
- <h3>Malware and viruses</h3>
-<p>Some mod APKs might contain malicious code that can harm your device or steal your personal information. For example, they might install spyware, ransomware, or keyloggers on your device, or access your camera, microphone, contacts, or messages without your permission. They might also display unwanted pop-ups or redirect you to phishing sites that can trick you into giving away your passwords or credit card details.</p>
- <h3>Account suspension and legal issues</h3>
-<p>Some mod APKs might violate the terms and conditions of Dall E or OpenAI, which can result in your account being suspended or banned. For example, they might use unauthorized methods to bypass the credit system or access restricted features. They might also infringe on the intellectual property rights of Dall E or OpenAI, which can lead to legal action or lawsuits.</p>
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-<p>To avoid these risks and enjoy a safe and satisfying experience with Dall E, you should look for a good mod APK that has the following features:</p>
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-<p>A good mod APK should give you unlimited credits and uses for Dall E, so that you can generate as many images as you want without paying for them or running out of them. This way, you can unleash your creativity and explore different possibilities with Dall E.</p>
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-<p>Dall E is an amazing AI-powered art generator that can create stunning images from any text input. However, it also has some limitations, such as the need for credits and the presence of ads and watermarks. To overcome these limitations, you can use a mod APK, which is a modified version of the app that can unlock more features and benefits. However, you should also be careful about the risks of downloading untrusted mod APKs, such as malware and account suspension. To avoid these risks, you should look for a good mod APK that has unlimited credits and uses, no ads and watermarks, and other extra features. You should also download your mod APK from trusted sources, such as Reddit or APKCombo.</p>
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spaces/1phancelerku/anime-remove-background/Cmo cambiar la barra de navegacin en MIUI 12 con estos sencillos pasos.md

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-<h1>Introduction</h1>
-<p>MIUI 12 is the latest version of Xiaomi's Android skin, which was announced in April 2020 and has been rolling out to various devices since then. MIUI 12 is based on Android 10 or Android 11, depending on the device model, and brings a host of new features and visual improvements to enhance the user experience.</p>
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-<p>MIUI 12 is important because it shows Xiaomi's commitment to providing a rich and customizable interface that caters to different user preferences and needs. MIUI 12 also addresses some of the issues and criticisms that previous versions faced, such as privacy concerns, bloatware, ads, and performance issues.</p>
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-<p>MIUI 12 offers a lot of features and changes that make it stand out from other Android skins. Here are some of the main ones:</p>
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-<p>Dark mode is a popular feature that reduces eye strain and saves battery life by using darker colors for the UI elements. MIUI 12 improves the dark mode experience by adding wallpaper dimming, font adjustment, and contrast optimization.</p>
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-<p>MIUI 12 also brings new animations for UI transitions and gestures. The animations are smoother, more realistic, and more responsive than before. They <h1>Review</h1>
-<p>MIUI 12 is a major upgrade from the previous version of Xiaomi's Android skin. It offers a lot of new features, visual improvements, and performance enhancements that make it one of the best Android interfaces available. However, it also has some drawbacks and limitations that may not suit everyone's preferences and needs.</p>
-<h2>Pros</h2>
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-<li><strong>Smooth and realistic animations:</strong> MIUI 12 has revamped the animations for UI transitions and gestures, making them more fluid, responsive, and natural. The animations add a touch of elegance and dynamism to the interface, and also provide useful feedback to the user's actions.</li>
-<li><strong>Stunning super wallpapers:</strong> MIUI 12 introduces super wallpapers, which are live wallpapers that show high-precision 3D models of planets based on NASA data. The super wallpapers change according to the time of day and the dark mode, and also have zoom-in and zoom-out effects when unlocking and locking the screen. They are a great way to spice up the look of your device and impress your friends.</li>
-<li><strong>Enhanced dark mode:</strong> MIUI 12 improves the dark mode experience by adding wallpaper dimming, font adjustment, and contrast optimization. These features make the dark mode more comfortable for the eyes and more consistent across the interface. The dark mode also works with third-party apps, thanks to Xiaomi's force dark mode option.</li>
-<li><strong>Improved privacy and security:</strong> MIUI 12 addresses some of the privacy and security concerns that users had with previous versions. It provides a comprehensive overview of app permissions and behaviors, and allows users to set up alerts and restrictions for sensitive permissions. It also has features like hidden mask mode, which prevents sensitive information from being displayed on other apps' screens.</li>
-<li><strong>Floating windows for multitasking:</strong> MIUI 12 enables multitasking and picture-in-picture mode with floating windows. Users can open multiple apps in small windows that can be moved, resized, and minimized on the screen. This is useful for doing things like watching videos, chatting with friends, or browsing the web while using another app.</li>
-</ul>
-<h2>Cons</h2>
-<ul>
-<li><strong>No zoom camera:</strong> MIUI 12 does not have a telephoto lens for zoom photography, which is a feature that many flagship phones offer. This means that users can only rely on digital zoom or cropping zoom, which results in lower quality images when zooming in. This is a disappointment for users who want to take photos of distant objects or scenes.</li>
-<li><strong>Gets hot when gaming:</strong> MIUI 12 tends to get hot when gaming, especially on high-end devices like the Xiaomi Mi 10T Pro. This may affect the performance and battery life of the device, as well as the comfort of the user. Users may want to lower the graphics settings or use a cooling fan to avoid overheating issues.</li>
-<li><strong>Irritating software issues:</strong> MIUI 12 still has some software issues that may annoy some users. For example, some users have reported problems with notifications not showing up properly, apps crashing or freezing randomly, or settings not being saved correctly. These issues may vary depending on the device model and region, but they may affect the user experience and satisfaction.</li>
-</ul>
-<h1>Conclusion</h1>
-<p>In conclusion, MIUI 12 is a delight to use for most users who want a feature-rich and customizable Android interface. It has a lot of advantages over other Android skins, such as smooth animations, stunning super wallpapers, enhanced dark mode, improved privacy and security, and floating windows for multitasking. However, it also has some disadvantages that may deter some users, such as no zoom camera, overheating when gaming, and irritating software issues. Therefore, users should weigh the pros and cons of MIUI 12 before deciding whether to update their devices or not.</p>
-<h1>FAQs</h1>
-<ul>
-<li><strong>Which devices are eligible for MIUI 12 update?</strong></li>
-<p>A: MIUI 12 update is available for most Xiaomi, Redmi, and POCO devices released in the last 15 months. You can check the list of compatible devices here . Older devices may also receive the update in the future.</p>
-<li><strong>How to download and install MIUI 12 on your Xiaomi device?</strong></li>
-<p>A: You can download and install MIUI 12 on your Xiaomi device by following these steps :</p>
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-<li>Unlock your bootloader by Mi Unlock tool .</li>
-<li>Download our ROM zip file BETA or STABLE.</li>
-<li>If you are on windows: Right click on downloaded zip - Settings - Unblock zip <li>If you are on Linux or Mac OS: Unzip the file and remove the first folder with name "META-INF".</li>
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-<li>Reboot to TWRP Recovery .</li>
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-<li>Reboot your device.</li>
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-<p>Note: You can also use the System Updater app on your device to check for OTA updates and download them directly. However, this method may not work for all devices and regions.</p>
-<li><strong>What is the navigation bar in MIUI 12?</strong></li>
-<p>A: The navigation bar is the bottom part of the screen that shows the navigation buttons, such as back, home, and recent apps. MIUI 12 allows you to customize the navigation bar according to your preference. You can choose between three styles: buttons, gestures, or full screen gestures.</p>
-<p>Buttons are the traditional navigation buttons that you can tap to perform actions. Gestures are swipe-based navigation gestures that you can use instead of buttons. Full screen gestures are similar to gestures, but they hide the navigation bar completely and give you more screen space.</p>
-<li><strong>How to change the navigation bar style in MIUI 12?</strong></li>
-<p>A: You can change the navigation bar style in MIUI 12 by following these steps:</p>
-<ol>
-<li>Go to Settings > Display > Full screen display.</li>
-<li>Select the navigation style that you want: buttons, gestures, or full screen gestures.</li>
-<li>If you choose gestures or full screen gestures, you can also customize the gesture settings, such as swipe sensitivity, swipe direction, and swipe area.</li>
-</ol>
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spaces/1yukikaze/img-to-music/share_btn.py

@@ -1,104 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    async function getOutputMusicFile(audioEL){
-        const res = await fetch(audioEL.src);
-        const blob = await res.blob();
-        const audioId = Date.now() % 200;
-        const fileName = `img-to-music-${{audioId}}.wav`;
-        const musicBlob = new File([blob], fileName, { type: 'audio/wav' });
-        console.log(musicBlob);
-        return musicBlob;
-	}
-    
-    async function audioToBase64(audioFile) {
-        return new Promise((resolve, reject) => {
-            let reader = new FileReader();
-            reader.readAsDataURL(audioFile);
-            reader.onload = () => resolve(reader.result);
-            reader.onerror = error => reject(error);
-          
-        });
-      }
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const prompts = gradioEl.querySelector('#prompts_out textarea').value;
-    const outputMusic = gradioEl.querySelector('#music-output audio');
-    const outputMusic_src = gradioEl.querySelector('#music-output audio').src;
-    const outputMusic_name = outputMusic_src.split('/').pop();
-    let titleTxt = outputMusic_name;
-    //if(titleTxt.length > 100){
-    //    titleTxt = titleTxt.slice(0, 100) + ' ...';
-    //}
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputMusic){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const inputFile = await getInputImgFile(inputImgEl);
-    const urlInputImg = await uploadFile(inputFile);
-    const musicFile = await getOutputMusicFile(outputMusic);
-	const dataOutputMusic = await uploadFile(musicFile);
-    
-    const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-
-#### Prompts out: 
-${prompts}
-
-#### Music:
-
-<audio controls>
-  <source src="${dataOutputMusic}" type="audio/wav">
-Your browser does not support the audio element.
-</audio>
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/img-to-music/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""

spaces/52Hz/CMFNet_deblurring/model/CMFNet.py

@@ -1,191 +0,0 @@
-import torch
-import torch.nn as nn
-from model.block import SAB, CAB, PAB, conv, SAM, conv3x3, conv_down
-
-##########################################################################
-## U-Net
-bn = 2  # block number-1
-
-class Encoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Encoder, self).__init__()
-        if block == 'CAB':
-            self.encoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.encoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.encoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.encoder_level1 = nn.Sequential(*self.encoder_level1)
-        self.encoder_level2 = nn.Sequential(*self.encoder_level2)
-        self.encoder_level3 = nn.Sequential(*self.encoder_level3)
-        self.down12 = DownSample(n_feat, scale_unetfeats)
-        self.down23 = DownSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, x):
-        enc1 = self.encoder_level1(x)
-        x = self.down12(enc1)
-        enc2 = self.encoder_level2(x)
-        x = self.down23(enc2)
-        enc3 = self.encoder_level3(x)
-        return [enc1, enc2, enc3]
-
-class Decoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Decoder, self).__init__()
-        if block == 'CAB':
-            self.decoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.decoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.decoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.decoder_level1 = nn.Sequential(*self.decoder_level1)
-        self.decoder_level2 = nn.Sequential(*self.decoder_level2)
-        self.decoder_level3 = nn.Sequential(*self.decoder_level3)
-        if block == 'CAB':
-            self.skip_attn1 = CAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'PAB':
-            self.skip_attn1 = PAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'SAB':
-            self.skip_attn1 = SAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        self.up21 = SkipUpSample(n_feat, scale_unetfeats)
-        self.up32 = SkipUpSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, outs):
-        enc1, enc2, enc3 = outs
-        dec3 = self.decoder_level3(enc3)
-        x = self.up32(dec3, self.skip_attn2(enc2))
-        dec2 = self.decoder_level2(x)
-        x = self.up21(dec2, self.skip_attn1(enc1))
-        dec1 = self.decoder_level1(x)
-        return [dec1, dec2, dec3]
-
-##########################################################################
-##---------- Resizing Modules ----------
-class DownSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(DownSample, self).__init__()
-        self.down = nn.Sequential(nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=False),
-                                  nn.Conv2d(in_channels, in_channels + s_factor, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.down(x)
-        return x
-
-class UpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(UpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.up(x)
-        return x
-
-class SkipUpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(SkipUpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x, y):
-        x = self.up(x)
-        x = x + y
-        return x
-
-##########################################################################
-# Mixed Residual Module
-class Mix(nn.Module):
-    def __init__(self, m=1):
-        super(Mix, self).__init__()
-        w = nn.Parameter(torch.FloatTensor([m]), requires_grad=True)
-        w = nn.Parameter(w, requires_grad=True)
-        self.w = w
-        self.mix_block = nn.Sigmoid()
-
-    def forward(self, fea1, fea2, feat3):
-        factor = self.mix_block(self.w)
-        other = (1 - factor)/2
-        output = fea1 * other.expand_as(fea1) + fea2 * factor.expand_as(fea2) + feat3 * other.expand_as(feat3)
-        return output, factor
-
-##########################################################################
-# Architecture
-class CMFNet(nn.Module):
-    def __init__(self, in_c=3, out_c=3, n_feat=96, scale_unetfeats=48, kernel_size=3, reduction=4, bias=False):
-        super(CMFNet, self).__init__()
-
-        p_act = nn.PReLU()
-        self.shallow_feat1 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat2 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat3 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-
-        self.stage1_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-        self.stage1_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-
-        self.stage2_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-        self.stage2_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-
-        self.stage3_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-        self.stage3_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-
-        self.sam1o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam2o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam3o = SAM(n_feat, kernel_size=3, bias=bias)
-
-        self.mix = Mix(1)
-        self.add123 = conv(out_c, out_c, kernel_size, bias=bias)
-        self.concat123 = conv(n_feat*3, n_feat, kernel_size, bias=bias)
-        self.tail = conv(n_feat, out_c, kernel_size, bias=bias)
-
-
-    def forward(self, x):
-        ## Compute Shallow Features
-        shallow1 = self.shallow_feat1(x)
-        shallow2 = self.shallow_feat2(x)
-        shallow3 = self.shallow_feat3(x)
-
-        ## Enter the UNet-CAB
-        x1 = self.stage1_encoder(shallow1)
-        x1_D = self.stage1_decoder(x1)
-        ## Apply SAM
-        x1_out, x1_img = self.sam1o(x1_D[0], x)
-
-        ## Enter the UNet-PAB
-        x2 = self.stage2_encoder(shallow2)
-        x2_D = self.stage2_decoder(x2)
-        ## Apply SAM
-        x2_out, x2_img = self.sam2o(x2_D[0], x)
-
-        ## Enter the UNet-SAB
-        x3 = self.stage3_encoder(shallow3)
-        x3_D = self.stage3_decoder(x3)
-        ## Apply SAM
-        x3_out, x3_img = self.sam3o(x3_D[0], x)
-
-        ## Aggregate SAM features of Stage 1, Stage 2 and Stage 3
-        mix_r = self.mix(x1_img, x2_img, x3_img)
-        mixed_img = self.add123(mix_r[0])
-
-        ## Concat SAM features of Stage 1, Stage 2 and Stage 3
-        concat_feat = self.concat123(torch.cat([x1_out, x2_out, x3_out], 1))
-        x_final = self.tail(concat_feat)
-
-        return x_final + mixed_img

spaces/52Hz/HWMNet_lowlight_enhancement/WT/__int__.py

@@ -1 +0,0 @@
-from .transform import *

spaces/55dgxxx558/anime-remove-background/app.py

@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()

spaces/AFischer1985/AI-Interface/README.md

@@ -1,12 +0,0 @@
-
----
-title: AI-Interface
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.47.1
-app_file: run.py
-pinned: false
-hf_oauth: true
----

spaces/AI-Hobbyist/Hoyo-RVC/infer/train-index.py

@@ -1,36 +0,0 @@
-"""
-格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-"""
-import faiss, numpy as np, os
-
-# ###########如果是原始特征要先写save
-inp_root = r"E:\codes\py39\dataset\mi\2-co256"
-npys = []
-for name in sorted(list(os.listdir(inp_root))):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-print(big_npy.shape)  # (6196072, 192)#fp32#4.43G
-np.save("infer/big_src_feature_mi.npy", big_npy)
-
-##################train+add
-# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
-print(big_npy.shape)
-index = faiss.index_factory(256, "IVF512,Flat")  # mi
-print("training")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 9
-index.train(big_npy)
-faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
-print("adding")
-index.add(big_npy)
-faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/__init__.py

@@ -1 +0,0 @@
-from .stft_loss import *  # NOQA

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/midas_net_custom.py

@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name

spaces/AILab-CVC/SEED-LLaMA/models/llama_xformer.py

@@ -1,906 +0,0 @@
-# coding=utf-8
-# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch LLaMA model."""
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from transformers.models.llama.configuration_llama import LlamaConfig
-import xformers.ops as xops
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full(
-        (tgt_len, tgt_len),
-        torch.tensor(torch.finfo(dtype).min, device=device),
-        device=device,
-    )
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat(
-            [
-                torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device),
-                mask,
-            ],
-            dim=-1,
-        )
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-class LlamaRMSNorm(nn.Module):
-
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
-        # convert into half-precision if necessary
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-
-        return self.weight * hidden_states
-
-
-class LlamaRotaryEmbedding(torch.nn.Module):
-
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-        inv_freq = 1.0 / (base**(torch.arange(0, dim, 2).float().to(device) / dim))
-        self.register_buffer("inv_freq", inv_freq)
-
-        # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(
-            self.max_seq_len_cached,
-            device=self.inv_freq.device,
-            dtype=self.inv_freq.dtype,
-        )
-        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
-        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
-        if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
-            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            # Different from paper, but it uses a different permutation in order to obtain the same calculation
-            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
-            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., :x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2:]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class LlamaMLP(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-    ):
-        super().__init__()
-        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
-        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.act_fn = ACT2FN[hidden_act]
-
-    def forward(self, x):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-class LlamaAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.max_position_embeddings = config.max_position_embeddings
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                             f" and `num_heads`: {self.num_heads}).")
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-        # [bsz, nh, t, hd]
-
-        if past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else None
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        if self.training:
-            attn_output = xops.memory_efficient_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_bias=xops.LowerTriangularMask(),
-            )
-        else:
-            attn_output = xops.memory_efficient_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_bias=None if attention_mask.sum() == 0 else xops.LowerTriangularMask(),
-            )
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class LlamaDecoderLayer(nn.Module):
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = LlamaAttention(config=config)
-        self.mlp = LlamaMLP(
-            hidden_size=self.hidden_size,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-        )
-        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        """
-
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states, )
-
-        if output_attentions:
-            outputs += (self_attn_weights, )
-
-        if use_cache:
-            outputs += (present_key_value, )
-
-        return outputs
-
-
-LLAMA_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`LlamaConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaPreTrainedModel(PreTrainedModel):
-    config_class = LlamaConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["LlamaDecoderLayer"]
-    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, LlamaModel):
-            module.gradient_checkpointing = value
-
-
-LLAMA_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaModel(LlamaPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
-
-    Args:
-        config: LlamaConfig
-    """
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype,
-                                              tgt_len=input_shape[-1]).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-
-        return combined_attention_mask
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-
-        if past_key_values is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-        # embed positions
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                (batch_size, seq_length_with_past),
-                dtype=torch.bool,
-                device=inputs_embeds.device,
-            )
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-        )
-
-        hidden_states = inputs_embeds
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
-                use_cache = False
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
-
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states, )
-
-            past_key_value = past_key_values[idx] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, output_attentions, None)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1], )
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1], )
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states, )
-
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-
-class LlamaForCausalLM(LlamaPreTrainedModel):
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = LlamaModel(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LlamaForCausalLM
-
-        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
-        >>> prompt = "Hey, are you consciours? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
-        ```"""
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits, ) + outputs[1:]
-            return (loss, ) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        **kwargs,
-    ):
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update({
-            "position_ids": position_ids,
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "attention_mask": attention_mask,
-        })
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past), )
-        return reordered_past
-
-
-@add_start_docstrings(
-    """
-    The LLaMa Model transformer with a sequence classification head on top (linear layer).
-
-    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    LLAMA_START_DOCSTRING,
-)
-class LlamaForSequenceClassification(LlamaPreTrainedModel):
-    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = LlamaModel(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits, ) + transformer_outputs[1:]
-            return ((loss, ) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-

spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/app.py

@@ -1,24 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = "Transformers 📗 Sentence to Paragraph ❤️ For Mindfulness"
-examples = [
-    ["Feel better physically by"],
-    ["Practicing mindfulness each day"],
-    ["Be happier by"],
-    ["Meditation can improve health"],
-    ["Spending time outdoors"],
-    ["Stress is relieved by quieting your mind, getting exercise and time with nature"],
-    ["Break the cycle of stress and anxiety"],
-    ["Feel calm in stressful situations"],
-    ["Deal with work pressure"],
-    ["Learn to reduce feelings of overwhelmed"]
-]
-from gradio import inputs
-from gradio.inputs import Textbox
-from gradio import outputs
-
-generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B")
-generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-generator1 = gr.Interface.load("huggingface/gpt2-large")
-gr.Parallel(generator1, generator2, generator3, inputs=gr.inputs.Textbox(lines=5, label="Enter a sentence to get another sentence."),
-            title=title, examples=examples).launch(share=False)

spaces/Adapter/T2I-Adapter/ldm/data/dataset_wikiart.py

@@ -1,67 +0,0 @@
-import json
-import os.path
-
-from PIL import Image
-from torch.utils.data import DataLoader
-
-from transformers import CLIPProcessor
-from torchvision.transforms import transforms
-
-import pytorch_lightning as pl
-
-
-class WikiArtDataset():
-    def __init__(self, meta_file):
-        super(WikiArtDataset, self).__init__()
-
-        self.files = []
-        with open(meta_file, 'r') as f:
-            js = json.load(f)
-            for img_path in js:
-                img_name = os.path.splitext(os.path.basename(img_path))[0]
-                caption = img_name.split('_')[-1]
-                caption = caption.split('-')
-                j = len(caption) - 1
-                while j >= 0:
-                    if not caption[j].isdigit():
-                        break
-                    j -= 1
-                if j < 0:
-                    continue
-                sentence = ' '.join(caption[:j + 1])
-                self.files.append({'img_path': os.path.join('datasets/wikiart', img_path), 'sentence': sentence})
-
-        version = 'openai/clip-vit-large-patch14'
-        self.processor = CLIPProcessor.from_pretrained(version)
-
-        self.jpg_transform = transforms.Compose([
-            transforms.Resize(512),
-            transforms.RandomCrop(512),
-            transforms.ToTensor(),
-        ])
-
-    def __getitem__(self, idx):
-        file = self.files[idx]
-
-        im = Image.open(file['img_path'])
-
-        im_tensor = self.jpg_transform(im)
-
-        clip_im = self.processor(images=im, return_tensors="pt")['pixel_values'][0]
-
-        return {'jpg': im_tensor, 'style': clip_im, 'txt': file['sentence']}
-
-    def __len__(self):
-        return len(self.files)
-
-
-class WikiArtDataModule(pl.LightningDataModule):
-    def __init__(self, meta_file, batch_size, num_workers):
-        super(WikiArtDataModule, self).__init__()
-        self.train_dataset = WikiArtDataset(meta_file)
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-
-    def train_dataloader(self):
-        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers,
-                          pin_memory=True)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/match/GetMatchN.js

@@ -1,6 +0,0 @@
-var GetMatchN = function (n, callback, scope) {
-    this.match.match(n, callback, scope);
-    return this;
-}
-
-export default GetMatchN;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/filechooser/FileChooser.js

@@ -1,2 +0,0 @@
-import { OpenFileChooser, FileChooser } from '../../../plugins/filechooser.js';
-export { OpenFileChooser, FileChooser };

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch.js

@@ -1,15 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import NinePatch from '../../ninepatch/NinePatch.js';
-import SetTextureProperties from './utils/SetTextureProperties.js';
-
-var CreateNinePatch = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    var gameObject = new NinePatch(scene, data);
-
-    SetTextureProperties(gameObject, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-export default CreateNinePatch;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Factory.js

@@ -1,13 +0,0 @@
-import Sizer from './Sizer.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('sizer', function (x, y, minWidth, minHeight, orientation, config) {
-    var gameObject = new Sizer(this.scene, x, y, minWidth, minHeight, orientation, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Sizer', Sizer);
-
-export default Sizer;

spaces/AlexWang/lama/saicinpainting/training/losses/feature_matching.py

@@ -1,33 +0,0 @@
-from typing import List
-
-import torch
-import torch.nn.functional as F
-
-
-def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l2).mean()
-
-
-def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l1).mean()
-
-
-def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
-    if mask is None:
-        res = torch.stack([F.mse_loss(fake_feat, target_feat)
-                           for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
-    else:
-        res = 0
-        norm = 0
-        for fake_feat, target_feat in zip(fake_features, target_features):
-            cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
-            error_weights = 1 - cur_mask
-            cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
-            res = res + cur_val
-            norm += 1
-        res = res / norm
-    return res

spaces/Alfaxad/BioGalacticModels/style.css

@@ -1,20 +0,0 @@
-h1 {
-    text-align: center;
-  }
-  table a {
-    background-color: transparent;
-    color: #58a6ff;
-    text-decoration: none;
-  }
-  a:active,
-  a:hover {
-    outline-width: 0;
-  }
-  a:hover {
-    text-decoration: underline;
-  }
-  table, th, td {
-    border: 1px solid;
-  }
-
-  

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/utils.py

@@ -1,34 +0,0 @@
-import torch
-
-def compute_accumulated_transmittance(alphas):
-    accumulated_transmittance = torch.cumprod(alphas, 1)
-    return torch.cat((torch.ones((accumulated_transmittance.shape[0], 1), device=alphas.device),
-                      accumulated_transmittance[:, :-1]), dim=-1)
-
-def render_rays(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192):
-    device = ray_origins.device
-
-    t = torch.linspace(hn, hf, nb_bins, device=device).expand(ray_origins.shape[0], nb_bins)
-    # Perturb sampling along each ray.
-    mid = (t[:, :-1] + t[:, 1:]) / 2.
-    lower = torch.cat((t[:, :1], mid), -1)
-    upper = torch.cat((mid, t[:, -1:]), -1)
-    u = torch.rand(t.shape, device=device)
-    t = lower + (upper - lower) * u  # [batch_size, nb_bins]
-    delta = torch.cat((t[:, 1:] - t[:, :-1], torch.tensor([1e10], device=device).expand(ray_origins.shape[0], 1)), -1)
-
-    # Compute the 3D points along each ray
-    x = ray_origins.unsqueeze(1) + t.unsqueeze(2) * ray_directions.unsqueeze(1)  # [batch_size, nb_bins, 3]
-    # Expand the ray_directions tensor to match the shape of x
-    ray_directions = ray_directions.expand(nb_bins, ray_directions.shape[0], 3).transpose(0, 1)
-
-    colors, sigma = nerf_model(x.reshape(-1, 3), ray_directions.reshape(-1, 3))
-    colors = colors.reshape(x.shape)
-    sigma = sigma.reshape(x.shape[:-1])
-
-    alpha = 1 - torch.exp(-sigma * delta)  # [batch_size, nb_bins]
-    weights = compute_accumulated_transmittance(1 - alpha).unsqueeze(2) * alpha.unsqueeze(2)
-    # Compute the pixel values as a weighted sum of colors along each ray
-    c = (weights * colors).sum(dim=1)
-    weight_sum = weights.sum(-1).sum(-1)  # Regularization for white background
-    return c + 1 - weight_sum.unsqueeze(-1)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/controlnet.md

@@ -1,350 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# ControlNet
-
-[Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
-
-Using a pretrained model, we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.
-
-The abstract from the paper is:
-
-*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.*
-
-This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️
-
-The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet).
-
-## Usage example
-
-In the following we give a simple example of how to use a *ControlNet* checkpoint with Diffusers for inference.
-The inference pipeline is the same for all pipelines:
-
-* 1. Take an image and run it through a pre-conditioning processor.
-* 2. Run the pre-processed image through the [`StableDiffusionControlNetPipeline`].
-
-Let's have a look at a simple example using the [Canny Edge ControlNet](https://huggingface.co/lllyasviel/sd-controlnet-canny).
-
-```python
-from diffusers import StableDiffusionControlNetPipeline
-from diffusers.utils import load_image
-
-# Let's load the popular vermeer image
-image = load_image(
-    "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
-)
-```
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
-
-Next, we process the image to get the canny image. This is step *1.* - running the pre-conditioning processor. The pre-conditioning processor is different for every ControlNet. Please see the model cards of the [official checkpoints](#controlnet-with-stable-diffusion-1.5) for more information about other models.
-
-First, we need to install opencv:
-
-```
-pip install opencv-contrib-python
-```
-
-Next, let's also install all required Hugging Face libraries:
-
-```
-pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
-```
-
-Then we can retrieve the canny edges of the image.
-
-```python
-import cv2
-from PIL import Image
-import numpy as np
-
-image = np.array(image)
-
-low_threshold = 100
-high_threshold = 200
-
-image = cv2.Canny(image, low_threshold, high_threshold)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-canny_image = Image.fromarray(image)
-```
-
-Let's take a look at the processed image.
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png)
-
-Now, we load the official [Stable Diffusion 1.5 Model](runwayml/stable-diffusion-v1-5) as well as the ControlNet for canny edges.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-)
-```
-
-To speed-up things and reduce memory, let's enable model offloading and use the fast [`UniPCMultistepScheduler`].
-
-```py
-from diffusers import UniPCMultistepScheduler
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-# this command loads the individual model components on GPU on-demand.
-pipe.enable_model_cpu_offload()
-```
-
-Finally, we can run the pipeline:
-
-```py
-generator = torch.manual_seed(0)
-
-out_image = pipe(
-    "disco dancer with colorful lights", num_inference_steps=20, generator=generator, image=canny_image
-).images[0]
-```
-
-This should take only around 3-4 seconds on GPU (depending on hardware). The output image then looks as follows:
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_disco_dancing.png)
-
-
-**Note**: To see how to run all other ControlNet checkpoints, please have a look at [ControlNet with Stable Diffusion 1.5](#controlnet-with-stable-diffusion-1.5).
-
-<!-- TODO: add space -->
-
-## Combining multiple conditionings
-
-Multiple ControlNet conditionings can be combined for a single image generation. Pass a list of ControlNets to the pipeline's constructor and a corresponding list of conditionings to `__call__`.
-
-When combining conditionings, it is helpful to mask conditionings such that they do not overlap. In the example, we mask the middle of the canny map where the pose conditioning is located.
-
-It can also be helpful to vary the `controlnet_conditioning_scales` to emphasize one conditioning over the other.
-
-### Canny conditioning
-
-The original image:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"/>
-
-Prepare the conditioning:
-
-```python 
-from diffusers.utils import load_image
-from PIL import Image
-import cv2
-import numpy as np
-from diffusers.utils import load_image
-
-canny_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
-)
-canny_image = np.array(canny_image)
-
-low_threshold = 100
-high_threshold = 200
-
-canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
-
-# zero out middle columns of image where pose will be overlayed
-zero_start = canny_image.shape[1] // 4
-zero_end = zero_start + canny_image.shape[1] // 2
-canny_image[:, zero_start:zero_end] = 0
-
-canny_image = canny_image[:, :, None]
-canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
-canny_image = Image.fromarray(canny_image)
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/landscape_canny_masked.png"/>
-
-### Openpose conditioning
-
-The original image:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png" width=600/>
-
-Prepare the conditioning:
-
-```python
-from controlnet_aux import OpenposeDetector
-from diffusers.utils import load_image
-
-openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
-
-openpose_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
-)
-openpose_image = openpose(openpose_image)
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/person_pose.png" width=600/>
-
-### Running ControlNet with multiple conditionings
-
-```python
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-import torch
-
-controlnet = [
-    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16),
-    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16),
-]
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-pipe.enable_xformers_memory_efficient_attention()
-pipe.enable_model_cpu_offload()
-
-prompt = "a giant standing in a fantasy landscape, best quality"
-negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-generator = torch.Generator(device="cpu").manual_seed(1)
-
-images = [openpose_image, canny_image]
-
-image = pipe(
-    prompt,
-    images,
-    num_inference_steps=20,
-    generator=generator,
-    negative_prompt=negative_prompt,
-    controlnet_conditioning_scale=[1.0, 0.8],
-).images[0]
-
-image.save("./multi_controlnet_output.png")
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/multi_controlnet_output.png" width=600/>
-
-### Guess Mode
-
-Guess Mode is [a ControlNet feature that was implemented](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) after the publication of [the paper](https://arxiv.org/abs/2302.05543). The description states:
-
->In this mode, the ControlNet encoder will try best to recognize the content of the input control map, like depth map, edge map, scribbles, etc, even if you remove all prompts.
-
-#### The core implementation:
-
-It adjusts the scale of the output residuals from ControlNet by a fixed ratio depending on the block depth. The shallowest DownBlock corresponds to `0.1`. As the blocks get deeper, the scale increases exponentially, and the scale for the output of the MidBlock becomes `1.0`. 
-
-Since the core implementation is just this, **it does not have any impact on prompt conditioning**. While it is common to use it without specifying any prompts, it is also possible to provide prompts if desired.
-
-#### Usage:
-
-Just specify `guess_mode=True` in the pipe() function. A `guidance_scale` between 3.0 and 5.0 is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode).
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet).to(
-    "cuda"
-)
-image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
-image.save("guess_mode_generated.png")
-```
-
-#### Output image comparison:
-Canny Control Example
-
-|no guess_mode with prompt|guess_mode without prompt|
-|---|---|
-|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/></a>|
-
-
-## Available checkpoints
-
-ControlNet requires a *control image* in addition to the text-to-image *prompt*. 
-Each pretrained model is trained using a different conditioning method that requires different images for conditioning the generated outputs. For example, Canny edge conditioning requires the control image to be the output of a Canny filter, while depth conditioning requires the control image to be a depth map. See the overview and image examples below to know more.
-
-All checkpoints can be found under the authors' namespace [lllyasviel](https://huggingface.co/lllyasviel).
-
-**13.04.2024 Update**: The author has released improved controlnet checkpoints v1.1 - see [here](#controlnet-v1.1).
-
-### ControlNet v1.0
-
-| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
-|---|---|---|---|
-|[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>|
-|[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth)<br/> *Trained with Midas depth estimation*  |A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>|
-|[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed)<br/> *Trained with HED edge detection (soft edge)*  |A monochrome image with white soft edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> |
-|[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)<br/> *Trained with M-LSD line detection*  |A monochrome image composed only of white straight lines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>|
-|[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal)<br/> *Trained with normal map*  |A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>|
-|[lllyasviel/sd-controlnet-openpose](https://huggingface.co/lllyasviel/sd-controlnet_openpose)<br/> *Trained with OpenPose bone image*  |A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>|
-|[lllyasviel/sd-controlnet-scribble](https://huggingface.co/lllyasviel/sd-controlnet_scribble)<br/> *Trained with human scribbles*  |A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> |
-|[lllyasviel/sd-controlnet-seg](https://huggingface.co/lllyasviel/sd-controlnet_seg)<br/>*Trained with semantic segmentation*  |An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> |
-
-### ControlNet v1.1
-
-| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |
-|---|---|---|---|---|
-|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>|
-|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>|
-
-## StableDiffusionControlNetPipeline
-[[autodoc]] StableDiffusionControlNetPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## StableDiffusionControlNetImg2ImgPipeline
-[[autodoc]] StableDiffusionControlNetImg2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## StableDiffusionControlNetInpaintPipeline
-[[autodoc]] StableDiffusionControlNetInpaintPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## FlaxStableDiffusionControlNetPipeline
-[[autodoc]] FlaxStableDiffusionControlNetPipeline
-	- all
-	- __call__
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/checkpoint_merger.py

@@ -1,286 +0,0 @@
-import glob
-import os
-from typing import Dict, List, Union
-
-import torch
-
-from diffusers.utils import is_safetensors_available
-
-
-if is_safetensors_available():
-    import safetensors.torch
-
-from huggingface_hub import snapshot_download
-
-from diffusers import DiffusionPipeline, __version__
-from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
-from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
-
-
-class CheckpointMergerPipeline(DiffusionPipeline):
-    """
-    A class that that supports merging diffusion models based on the discussion here:
-    https://github.com/huggingface/diffusers/issues/877
-
-    Example usage:-
-
-    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
-
-    merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
-
-    merged_pipe.to('cuda')
-
-    prompt = "An astronaut riding a unicycle on Mars"
-
-    results = merged_pipe(prompt)
-
-    ## For more details, see the docstring for the merge method.
-
-    """
-
-    def __init__(self):
-        self.register_to_config()
-        super().__init__()
-
-    def _compare_model_configs(self, dict0, dict1):
-        if dict0 == dict1:
-            return True
-        else:
-            config0, meta_keys0 = self._remove_meta_keys(dict0)
-            config1, meta_keys1 = self._remove_meta_keys(dict1)
-            if config0 == config1:
-                print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.")
-                return True
-        return False
-
-    def _remove_meta_keys(self, config_dict: Dict):
-        meta_keys = []
-        temp_dict = config_dict.copy()
-        for key in config_dict.keys():
-            if key.startswith("_"):
-                temp_dict.pop(key)
-                meta_keys.append(key)
-        return (temp_dict, meta_keys)
-
-    @torch.no_grad()
-    def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs):
-        """
-        Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed
-        in the argument 'pretrained_model_name_or_path_list' as a list.
-
-        Parameters:
-        -----------
-            pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.
-
-            **kwargs:
-                Supports all the default DiffusionPipeline.get_config_dict kwargs viz..
-
-                cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map.
-
-                alpha - The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
-                    would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
-
-                interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_diff" and None.
-                    Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_diff" is supported.
-
-                force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
-
-        """
-        # Default kwargs from DiffusionPipeline
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        resume_download = kwargs.pop("resume_download", False)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        device_map = kwargs.pop("device_map", None)
-
-        alpha = kwargs.pop("alpha", 0.5)
-        interp = kwargs.pop("interp", None)
-
-        print("Received list", pretrained_model_name_or_path_list)
-        print(f"Combining with alpha={alpha}, interpolation mode={interp}")
-
-        checkpoint_count = len(pretrained_model_name_or_path_list)
-        # Ignore result from model_index_json comparision of the two checkpoints
-        force = kwargs.pop("force", False)
-
-        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
-        if checkpoint_count > 3 or checkpoint_count < 2:
-            raise ValueError(
-                "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
-                " passed."
-            )
-
-        print("Received the right number of checkpoints")
-        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
-        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
-
-        # Validate that the checkpoints can be merged
-        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
-        config_dicts = []
-        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
-            config_dict = DiffusionPipeline.load_config(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                force_download=force_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-            )
-            config_dicts.append(config_dict)
-
-        comparison_result = True
-        for idx in range(1, len(config_dicts)):
-            comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx])
-            if not force and comparison_result is False:
-                raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.")
-                print(config_dicts[0], config_dicts[1])
-        print("Compatible model_index.json files found")
-        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
-        cached_folders = []
-        for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts):
-            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
-            allow_patterns = [os.path.join(k, "*") for k in folder_names]
-            allow_patterns += [
-                WEIGHTS_NAME,
-                SCHEDULER_CONFIG_NAME,
-                CONFIG_NAME,
-                ONNX_WEIGHTS_NAME,
-                DiffusionPipeline.config_name,
-            ]
-            requested_pipeline_class = config_dict.get("_class_name")
-            user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
-
-            cached_folder = (
-                pretrained_model_name_or_path
-                if os.path.isdir(pretrained_model_name_or_path)
-                else snapshot_download(
-                    pretrained_model_name_or_path,
-                    cache_dir=cache_dir,
-                    resume_download=resume_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    revision=revision,
-                    allow_patterns=allow_patterns,
-                    user_agent=user_agent,
-                )
-            )
-            print("Cached Folder", cached_folder)
-            cached_folders.append(cached_folder)
-
-        # Step 3:-
-        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
-        final_pipe = DiffusionPipeline.from_pretrained(
-            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
-        )
-        final_pipe.to(self.device)
-
-        checkpoint_path_2 = None
-        if len(cached_folders) > 2:
-            checkpoint_path_2 = os.path.join(cached_folders[2])
-
-        if interp == "sigmoid":
-            theta_func = CheckpointMergerPipeline.sigmoid
-        elif interp == "inv_sigmoid":
-            theta_func = CheckpointMergerPipeline.inv_sigmoid
-        elif interp == "add_diff":
-            theta_func = CheckpointMergerPipeline.add_difference
-        else:
-            theta_func = CheckpointMergerPipeline.weighted_sum
-
-        # Find each module's state dict.
-        for attr in final_pipe.config.keys():
-            if not attr.startswith("_"):
-                checkpoint_path_1 = os.path.join(cached_folders[1], attr)
-                if os.path.exists(checkpoint_path_1):
-                    files = [
-                        *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
-                        *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
-                    ]
-                    checkpoint_path_1 = files[0] if len(files) > 0 else None
-                if len(cached_folders) < 3:
-                    checkpoint_path_2 = None
-                else:
-                    checkpoint_path_2 = os.path.join(cached_folders[2], attr)
-                    if os.path.exists(checkpoint_path_2):
-                        files = [
-                            *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
-                            *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
-                        ]
-                        checkpoint_path_2 = files[0] if len(files) > 0 else None
-                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
-                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
-                if checkpoint_path_1 is None and checkpoint_path_2 is None:
-                    print(f"Skipping {attr}: not present in 2nd or 3d model")
-                    continue
-                try:
-                    module = getattr(final_pipe, attr)
-                    if isinstance(module, bool):  # ignore requires_safety_checker boolean
-                        continue
-                    theta_0 = getattr(module, "state_dict")
-                    theta_0 = theta_0()
-
-                    update_theta_0 = getattr(module, "load_state_dict")
-                    theta_1 = (
-                        safetensors.torch.load_file(checkpoint_path_1)
-                        if (is_safetensors_available() and checkpoint_path_1.endswith(".safetensors"))
-                        else torch.load(checkpoint_path_1, map_location="cpu")
-                    )
-                    theta_2 = None
-                    if checkpoint_path_2:
-                        theta_2 = (
-                            safetensors.torch.load_file(checkpoint_path_2)
-                            if (is_safetensors_available() and checkpoint_path_2.endswith(".safetensors"))
-                            else torch.load(checkpoint_path_2, map_location="cpu")
-                        )
-
-                    if not theta_0.keys() == theta_1.keys():
-                        print(f"Skipping {attr}: key mismatch")
-                        continue
-                    if theta_2 and not theta_1.keys() == theta_2.keys():
-                        print(f"Skipping {attr}:y mismatch")
-                except Exception as e:
-                    print(f"Skipping {attr} do to an unexpected error: {str(e)}")
-                    continue
-                print(f"MERGING {attr}")
-
-                for key in theta_0.keys():
-                    if theta_2:
-                        theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha)
-                    else:
-                        theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha)
-
-                del theta_1
-                del theta_2
-                update_theta_0(theta_0)
-
-                del theta_0
-        return final_pipe
-
-    @staticmethod
-    def weighted_sum(theta0, theta1, theta2, alpha):
-        return ((1 - alpha) * theta0) + (alpha * theta1)
-
-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    @staticmethod
-    def sigmoid(theta0, theta1, theta2, alpha):
-        alpha = alpha * alpha * (3 - (2 * alpha))
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    @staticmethod
-    def inv_sigmoid(theta0, theta1, theta2, alpha):
-        import math
-
-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    @staticmethod
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * (1.0 - alpha)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/train_controlnet_flax.py

@@ -1,1146 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import logging
-import math
-import os
-import random
-import time
-from pathlib import Path
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-import torch
-import torch.utils.checkpoint
-import transformers
-from datasets import load_dataset, load_from_disk
-from flax import jax_utils
-from flax.core.frozen_dict import unfreeze
-from flax.training import train_state
-from flax.training.common_utils import shard
-from huggingface_hub import create_repo, upload_folder
-from PIL import Image, PngImagePlugin
-from torch.utils.data import IterableDataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed
-
-from diffusers import (
-    FlaxAutoencoderKL,
-    FlaxControlNetModel,
-    FlaxDDPMScheduler,
-    FlaxStableDiffusionControlNetPipeline,
-    FlaxUNet2DConditionModel,
-)
-from diffusers.utils import check_min_version, is_wandb_available
-
-
-# To prevent an error that occurs when there are abnormally large compressed data chunk in the png image
-# see more https://github.com/python-pillow/Pillow/issues/5610
-LARGE_ENOUGH_NUMBER = 100
-PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
-
-if is_wandb_available():
-    import wandb
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = logging.getLogger(__name__)
-
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-    grid_w, grid_h = grid.size
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-
-def log_validation(pipeline, pipeline_params, controlnet_params, tokenizer, args, rng, weight_dtype):
-    logger.info("Running validation...")
-
-    pipeline_params = pipeline_params.copy()
-    pipeline_params["controlnet"] = controlnet_params
-
-    num_samples = jax.device_count()
-    prng_seed = jax.random.split(rng, jax.device_count())
-
-    if len(args.validation_image) == len(args.validation_prompt):
-        validation_images = args.validation_image
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_image) == 1:
-        validation_images = args.validation_image * len(args.validation_prompt)
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_prompt) == 1:
-        validation_images = args.validation_image
-        validation_prompts = args.validation_prompt * len(args.validation_image)
-    else:
-        raise ValueError(
-            "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
-        )
-
-    image_logs = []
-
-    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
-        prompts = num_samples * [validation_prompt]
-        prompt_ids = pipeline.prepare_text_inputs(prompts)
-        prompt_ids = shard(prompt_ids)
-
-        validation_image = Image.open(validation_image).convert("RGB")
-        processed_image = pipeline.prepare_image_inputs(num_samples * [validation_image])
-        processed_image = shard(processed_image)
-        images = pipeline(
-            prompt_ids=prompt_ids,
-            image=processed_image,
-            params=pipeline_params,
-            prng_seed=prng_seed,
-            num_inference_steps=50,
-            jit=True,
-        ).images
-
-        images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
-        images = pipeline.numpy_to_pil(images)
-
-        image_logs.append(
-            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
-        )
-
-    if args.report_to == "wandb":
-        formatted_images = []
-        for log in image_logs:
-            images = log["images"]
-            validation_prompt = log["validation_prompt"]
-            validation_image = log["validation_image"]
-
-            formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
-            for image in images:
-                image = wandb.Image(image, caption=validation_prompt)
-                formatted_images.append(image)
-
-        wandb.log({"validation": formatted_images})
-    else:
-        logger.warn(f"image logging not implemented for {args.report_to}")
-
-    return image_logs
-
-
-def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
-    img_str = ""
-    if image_logs is not None:
-        for i, log in enumerate(image_logs):
-            images = log["images"]
-            validation_prompt = log["validation_prompt"]
-            validation_image = log["validation_image"]
-            validation_image.save(os.path.join(repo_folder, "image_control.png"))
-            img_str += f"prompt: {validation_prompt}\n"
-            images = [validation_image] + images
-            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
-            img_str += f"![images_{i})](./images_{i}.png)\n"
-
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- controlnet
-- jax-diffusers-event
-inference: true
----
-    """
-    model_card = f"""
-# controlnet- {repo_id}
-
-These are controlnet weights trained on {base_model} with new type of conditioning. You can find some example images in the following. \n
-{img_str}
-"""
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--controlnet_model_name_or_path",
-        type=str,
-        default=None,
-        help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
-        " If not specified controlnet weights are initialized from unet.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--from_pt",
-        action="store_true",
-        help="Load the pretrained model from a PyTorch checkpoint.",
-    )
-    parser.add_argument(
-        "--controlnet_revision",
-        type=str,
-        default=None,
-        help="Revision of controlnet model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--profile_steps",
-        type=int,
-        default=0,
-        help="How many training steps to profile in the beginning.",
-    )
-    parser.add_argument(
-        "--profile_validation",
-        action="store_true",
-        help="Whether to profile the (last) validation.",
-    )
-    parser.add_argument(
-        "--profile_memory",
-        action="store_true",
-        help="Whether to dump an initial (before training loop) and a final (at program end) memory profile.",
-    )
-    parser.add_argument(
-        "--ccache",
-        type=str,
-        default=None,
-        help="Enables compilation cache.",
-    )
-    parser.add_argument(
-        "--controlnet_from_pt",
-        action="store_true",
-        help="Load the controlnet model from a PyTorch checkpoint.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="runs/{timestamp}",
-        help="The output directory where the model predictions and checkpoints will be written. "
-        "Can contain placeholders: {timestamp}.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=5000,
-        help=("Save a checkpoint of the training state every X updates."),
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--snr_gamma",
-        type=float,
-        default=None,
-        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
-        "More details here: https://arxiv.org/abs/2303.09556.",
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_steps",
-        type=int,
-        default=100,
-        help=("log training metric every X steps to `--report_t`"),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="wandb",
-        help=('The integration to report the results and logs to. Currently only supported platforms are `"wandb"`'),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument("--streaming", action="store_true", help="To stream a large dataset from Hub.")
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training dataset. By default it will use `load_dataset` method to load a custom dataset from the folder."
-            "Folder must contain a dataset script as described here https://huggingface.co/docs/datasets/dataset_script) ."
-            "If `--load_from_disk` flag is passed, it will use `load_from_disk` method instead. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--load_from_disk",
-        action="store_true",
-        help=(
-            "If True, will load a dataset that was previously saved using `save_to_disk` from `--train_data_dir`"
-            "See more https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.load_from_disk"
-        ),
-    )
-    parser.add_argument(
-        "--image_column", type=str, default="image", help="The column of the dataset containing the target image."
-    )
-    parser.add_argument(
-        "--conditioning_image_column",
-        type=str,
-        default="conditioning_image",
-        help="The column of the dataset containing the controlnet conditioning image.",
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default="text",
-        help="The column of the dataset containing a caption or a list of captions.",
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set. Needed if `streaming` is set to True."
-        ),
-    )
-    parser.add_argument(
-        "--proportion_empty_prompts",
-        type=float,
-        default=0,
-        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
-    )
-    parser.add_argument(
-        "--validation_prompt",
-        type=str,
-        default=None,
-        nargs="+",
-        help=(
-            "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
-            " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
-            " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
-        ),
-    )
-    parser.add_argument(
-        "--validation_image",
-        type=str,
-        default=None,
-        nargs="+",
-        help=(
-            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
-            " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
-            " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
-            " `--validation_image` that will be used with all `--validation_prompt`s."
-        ),
-    )
-    parser.add_argument(
-        "--validation_steps",
-        type=int,
-        default=100,
-        help=(
-            "Run validation every X steps. Validation consists of running the prompt"
-            " `args.validation_prompt` and logging the images."
-        ),
-    )
-    parser.add_argument("--wandb_entity", type=str, default=None, help=("The wandb entity to use (for teams)."))
-    parser.add_argument(
-        "--tracker_project_name",
-        type=str,
-        default="train_controlnet_flax",
-        help=("The `project` argument passed to wandb"),
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps", type=int, default=1, help="Number of steps to accumulate gradients over"
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    args.output_dir = args.output_dir.replace("{timestamp}", time.strftime("%Y%m%d_%H%M%S"))
-
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Need either a dataset name or a training folder.")
-    if args.dataset_name is not None and args.train_data_dir is not None:
-        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
-
-    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
-        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
-
-    if args.validation_prompt is not None and args.validation_image is None:
-        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
-
-    if args.validation_prompt is None and args.validation_image is not None:
-        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
-
-    if (
-        args.validation_image is not None
-        and args.validation_prompt is not None
-        and len(args.validation_image) != 1
-        and len(args.validation_prompt) != 1
-        and len(args.validation_image) != len(args.validation_prompt)
-    ):
-        raise ValueError(
-            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
-            " or the same number of `--validation_prompt`s and `--validation_image`s"
-        )
-
-    # This idea comes from
-    # https://github.com/borisdayma/dalle-mini/blob/d2be512d4a6a9cda2d63ba04afc33038f98f705f/src/dalle_mini/data.py#L370
-    if args.streaming and args.max_train_samples is None:
-        raise ValueError("You must specify `max_train_samples` when using dataset streaming.")
-
-    return args
-
-
-def make_train_dataset(args, tokenizer, batch_size=None):
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-            streaming=args.streaming,
-        )
-    else:
-        if args.train_data_dir is not None:
-            if args.load_from_disk:
-                dataset = load_from_disk(
-                    args.train_data_dir,
-                )
-            else:
-                dataset = load_dataset(
-                    args.train_data_dir,
-                    cache_dir=args.cache_dir,
-                )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    if isinstance(dataset["train"], IterableDataset):
-        column_names = next(iter(dataset["train"])).keys()
-    else:
-        column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    if args.image_column is None:
-        image_column = column_names[0]
-        logger.info(f"image column defaulting to {image_column}")
-    else:
-        image_column = args.image_column
-        if image_column not in column_names:
-            raise ValueError(
-                f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    if args.caption_column is None:
-        caption_column = column_names[1]
-        logger.info(f"caption column defaulting to {caption_column}")
-    else:
-        caption_column = args.caption_column
-        if caption_column not in column_names:
-            raise ValueError(
-                f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    if args.conditioning_image_column is None:
-        conditioning_image_column = column_names[2]
-        logger.info(f"conditioning image column defaulting to {caption_column}")
-    else:
-        conditioning_image_column = args.conditioning_image_column
-        if conditioning_image_column not in column_names:
-            raise ValueError(
-                f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    def tokenize_captions(examples, is_train=True):
-        captions = []
-        for caption in examples[caption_column]:
-            if random.random() < args.proportion_empty_prompts:
-                captions.append("")
-            elif isinstance(caption, str):
-                captions.append(caption)
-            elif isinstance(caption, (list, np.ndarray)):
-                # take a random caption if there are multiple
-                captions.append(random.choice(caption) if is_train else caption[0])
-            else:
-                raise ValueError(
-                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
-                )
-        inputs = tokenizer(
-            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-        )
-        return inputs.input_ids
-
-    image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ]
-    )
-
-    conditioning_image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution),
-            transforms.ToTensor(),
-        ]
-    )
-
-    def preprocess_train(examples):
-        images = [image.convert("RGB") for image in examples[image_column]]
-        images = [image_transforms(image) for image in images]
-
-        conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]]
-        conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
-
-        examples["pixel_values"] = images
-        examples["conditioning_pixel_values"] = conditioning_images
-        examples["input_ids"] = tokenize_captions(examples)
-
-        return examples
-
-    if jax.process_index() == 0:
-        if args.max_train_samples is not None:
-            if args.streaming:
-                dataset["train"] = dataset["train"].shuffle(seed=args.seed).take(args.max_train_samples)
-            else:
-                dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-        if args.streaming:
-            train_dataset = dataset["train"].map(
-                preprocess_train,
-                batched=True,
-                batch_size=batch_size,
-                remove_columns=list(dataset["train"].features.keys()),
-            )
-        else:
-            train_dataset = dataset["train"].with_transform(preprocess_train)
-
-    return train_dataset
-
-
-def collate_fn(examples):
-    pixel_values = torch.stack([example["pixel_values"] for example in examples])
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
-    conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    input_ids = torch.stack([example["input_ids"] for example in examples])
-
-    batch = {
-        "pixel_values": pixel_values,
-        "conditioning_pixel_values": conditioning_pixel_values,
-        "input_ids": input_ids,
-    }
-    batch = {k: v.numpy() for k, v in batch.items()}
-    return batch
-
-
-def get_params_to_save(params):
-    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
-
-
-def main():
-    args = parse_args()
-
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    # Setup logging, we only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-
-    # wandb init
-    if jax.process_index() == 0 and args.report_to == "wandb":
-        wandb.init(
-            entity=args.wandb_entity,
-            project=args.tracker_project_name,
-            job_type="train",
-            config=args,
-        )
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    rng = jax.random.PRNGKey(0)
-
-    # Handle the repository creation
-    if jax.process_index() == 0:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer and add the placeholder token as a additional special token
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
-        )
-    else:
-        raise NotImplementedError("No tokenizer specified!")
-
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    total_train_batch_size = args.train_batch_size * jax.local_device_count() * args.gradient_accumulation_steps
-    train_dataset = make_train_dataset(args, tokenizer, batch_size=total_train_batch_size)
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        shuffle=not args.streaming,
-        collate_fn=collate_fn,
-        batch_size=total_train_batch_size,
-        num_workers=args.dataloader_num_workers,
-        drop_last=True,
-    )
-
-    weight_dtype = jnp.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = jnp.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = jnp.bfloat16
-
-    # Load models and create wrapper for stable diffusion
-    text_encoder = FlaxCLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-    vae, vae_params = FlaxAutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path,
-        revision=args.revision,
-        subfolder="vae",
-        dtype=weight_dtype,
-        from_pt=args.from_pt,
-    )
-    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="unet",
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-
-    if args.controlnet_model_name_or_path:
-        logger.info("Loading existing controlnet weights")
-        controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-            args.controlnet_model_name_or_path,
-            revision=args.controlnet_revision,
-            from_pt=args.controlnet_from_pt,
-            dtype=jnp.float32,
-        )
-    else:
-        logger.info("Initializing controlnet weights from unet")
-        rng, rng_params = jax.random.split(rng)
-
-        controlnet = FlaxControlNetModel(
-            in_channels=unet.config.in_channels,
-            down_block_types=unet.config.down_block_types,
-            only_cross_attention=unet.config.only_cross_attention,
-            block_out_channels=unet.config.block_out_channels,
-            layers_per_block=unet.config.layers_per_block,
-            attention_head_dim=unet.config.attention_head_dim,
-            cross_attention_dim=unet.config.cross_attention_dim,
-            use_linear_projection=unet.config.use_linear_projection,
-            flip_sin_to_cos=unet.config.flip_sin_to_cos,
-            freq_shift=unet.config.freq_shift,
-        )
-        controlnet_params = controlnet.init_weights(rng=rng_params)
-        controlnet_params = unfreeze(controlnet_params)
-        for key in [
-            "conv_in",
-            "time_embedding",
-            "down_blocks_0",
-            "down_blocks_1",
-            "down_blocks_2",
-            "down_blocks_3",
-            "mid_block",
-        ]:
-            controlnet_params[key] = unet_params[key]
-
-    pipeline, pipeline_params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        tokenizer=tokenizer,
-        controlnet=controlnet,
-        safety_checker=None,
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-    pipeline_params = jax_utils.replicate(pipeline_params)
-
-    # Optimization
-    if args.scale_lr:
-        args.learning_rate = args.learning_rate * total_train_batch_size
-
-    constant_scheduler = optax.constant_schedule(args.learning_rate)
-
-    adamw = optax.adamw(
-        learning_rate=constant_scheduler,
-        b1=args.adam_beta1,
-        b2=args.adam_beta2,
-        eps=args.adam_epsilon,
-        weight_decay=args.adam_weight_decay,
-    )
-
-    optimizer = optax.chain(
-        optax.clip_by_global_norm(args.max_grad_norm),
-        adamw,
-    )
-
-    state = train_state.TrainState.create(apply_fn=controlnet.__call__, params=controlnet_params, tx=optimizer)
-
-    noise_scheduler, noise_scheduler_state = FlaxDDPMScheduler.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="scheduler"
-    )
-
-    # Initialize our training
-    validation_rng, train_rngs = jax.random.split(rng)
-    train_rngs = jax.random.split(train_rngs, jax.local_device_count())
-
-    def compute_snr(timesteps):
-        """
-        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-        """
-        alphas_cumprod = noise_scheduler_state.common.alphas_cumprod
-        sqrt_alphas_cumprod = alphas_cumprod**0.5
-        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-        alpha = sqrt_alphas_cumprod[timesteps]
-        sigma = sqrt_one_minus_alphas_cumprod[timesteps]
-        # Compute SNR.
-        snr = (alpha / sigma) ** 2
-        return snr
-
-    def train_step(state, unet_params, text_encoder_params, vae_params, batch, train_rng):
-        # reshape batch, add grad_step_dim if gradient_accumulation_steps > 1
-        if args.gradient_accumulation_steps > 1:
-            grad_steps = args.gradient_accumulation_steps
-            batch = jax.tree_map(lambda x: x.reshape((grad_steps, x.shape[0] // grad_steps) + x.shape[1:]), batch)
-
-        def compute_loss(params, minibatch, sample_rng):
-            # Convert images to latent space
-            vae_outputs = vae.apply(
-                {"params": vae_params}, minibatch["pixel_values"], deterministic=True, method=vae.encode
-            )
-            latents = vae_outputs.latent_dist.sample(sample_rng)
-            # (NHWC) -> (NCHW)
-            latents = jnp.transpose(latents, (0, 3, 1, 2))
-            latents = latents * vae.config.scaling_factor
-
-            # Sample noise that we'll add to the latents
-            noise_rng, timestep_rng = jax.random.split(sample_rng)
-            noise = jax.random.normal(noise_rng, latents.shape)
-            # Sample a random timestep for each image
-            bsz = latents.shape[0]
-            timesteps = jax.random.randint(
-                timestep_rng,
-                (bsz,),
-                0,
-                noise_scheduler.config.num_train_timesteps,
-            )
-
-            # Add noise to the latents according to the noise magnitude at each timestep
-            # (this is the forward diffusion process)
-            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
-
-            # Get the text embedding for conditioning
-            encoder_hidden_states = text_encoder(
-                minibatch["input_ids"],
-                params=text_encoder_params,
-                train=False,
-            )[0]
-
-            controlnet_cond = minibatch["conditioning_pixel_values"]
-
-            # Predict the noise residual and compute loss
-            down_block_res_samples, mid_block_res_sample = controlnet.apply(
-                {"params": params},
-                noisy_latents,
-                timesteps,
-                encoder_hidden_states,
-                controlnet_cond,
-                train=True,
-                return_dict=False,
-            )
-
-            model_pred = unet.apply(
-                {"params": unet_params},
-                noisy_latents,
-                timesteps,
-                encoder_hidden_states,
-                down_block_additional_residuals=down_block_res_samples,
-                mid_block_additional_residual=mid_block_res_sample,
-            ).sample
-
-            # Get the target for loss depending on the prediction type
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            loss = (target - model_pred) ** 2
-
-            if args.snr_gamma is not None:
-                snr = jnp.array(compute_snr(timesteps))
-                snr_loss_weights = jnp.where(snr < args.snr_gamma, snr, jnp.ones_like(snr) * args.snr_gamma) / snr
-                loss = loss * snr_loss_weights
-
-            loss = loss.mean()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(compute_loss)
-
-        # get a minibatch (one gradient accumulation slice)
-        def get_minibatch(batch, grad_idx):
-            return jax.tree_util.tree_map(
-                lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False),
-                batch,
-            )
-
-        def loss_and_grad(grad_idx, train_rng):
-            # create minibatch for the grad step
-            minibatch = get_minibatch(batch, grad_idx) if grad_idx is not None else batch
-            sample_rng, train_rng = jax.random.split(train_rng, 2)
-            loss, grad = grad_fn(state.params, minibatch, sample_rng)
-            return loss, grad, train_rng
-
-        if args.gradient_accumulation_steps == 1:
-            loss, grad, new_train_rng = loss_and_grad(None, train_rng)
-        else:
-            init_loss_grad_rng = (
-                0.0,  # initial value for cumul_loss
-                jax.tree_map(jnp.zeros_like, state.params),  # initial value for cumul_grad
-                train_rng,  # initial value for train_rng
-            )
-
-            def cumul_grad_step(grad_idx, loss_grad_rng):
-                cumul_loss, cumul_grad, train_rng = loss_grad_rng
-                loss, grad, new_train_rng = loss_and_grad(grad_idx, train_rng)
-                cumul_loss, cumul_grad = jax.tree_map(jnp.add, (cumul_loss, cumul_grad), (loss, grad))
-                return cumul_loss, cumul_grad, new_train_rng
-
-            loss, grad, new_train_rng = jax.lax.fori_loop(
-                0,
-                args.gradient_accumulation_steps,
-                cumul_grad_step,
-                init_loss_grad_rng,
-            )
-            loss, grad = jax.tree_map(lambda x: x / args.gradient_accumulation_steps, (loss, grad))
-
-        grad = jax.lax.pmean(grad, "batch")
-
-        new_state = state.apply_gradients(grads=grad)
-
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-
-        def l2(xs):
-            return jnp.sqrt(sum([jnp.vdot(x, x) for x in jax.tree_util.tree_leaves(xs)]))
-
-        metrics["l2_grads"] = l2(jax.tree_util.tree_leaves(grad))
-
-        return new_state, metrics, new_train_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-    unet_params = jax_utils.replicate(unet_params)
-    text_encoder_params = jax_utils.replicate(text_encoder.params)
-    vae_params = jax_utils.replicate(vae_params)
-
-    # Train!
-    if args.streaming:
-        dataset_length = args.max_train_samples
-    else:
-        dataset_length = len(train_dataloader)
-    num_update_steps_per_epoch = math.ceil(dataset_length / args.gradient_accumulation_steps)
-
-    # Scheduler and math around the number of training steps.
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {args.max_train_samples if args.streaming else len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
-    logger.info(f"  Total optimization steps = {args.num_train_epochs * num_update_steps_per_epoch}")
-
-    if jax.process_index() == 0 and args.report_to == "wandb":
-        wandb.define_metric("*", step_metric="train/step")
-        wandb.define_metric("train/step", step_metric="walltime")
-        wandb.config.update(
-            {
-                "num_train_examples": args.max_train_samples if args.streaming else len(train_dataset),
-                "total_train_batch_size": total_train_batch_size,
-                "total_optimization_step": args.num_train_epochs * num_update_steps_per_epoch,
-                "num_devices": jax.device_count(),
-                "controlnet_params": sum(np.prod(x.shape) for x in jax.tree_util.tree_leaves(state.params)),
-            }
-        )
-
-    global_step = step0 = 0
-    epochs = tqdm(
-        range(args.num_train_epochs),
-        desc="Epoch ... ",
-        position=0,
-        disable=jax.process_index() > 0,
-    )
-    if args.profile_memory:
-        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_initial.prof"))
-    t00 = t0 = time.monotonic()
-    for epoch in epochs:
-        # ======================== Training ================================
-
-        train_metrics = []
-        train_metric = None
-
-        steps_per_epoch = (
-            args.max_train_samples // total_train_batch_size
-            if args.streaming or args.max_train_samples
-            else len(train_dataset) // total_train_batch_size
-        )
-        train_step_progress_bar = tqdm(
-            total=steps_per_epoch,
-            desc="Training...",
-            position=1,
-            leave=False,
-            disable=jax.process_index() > 0,
-        )
-        # train
-        for batch in train_dataloader:
-            if args.profile_steps and global_step == 1:
-                train_metric["loss"].block_until_ready()
-                jax.profiler.start_trace(args.output_dir)
-            if args.profile_steps and global_step == 1 + args.profile_steps:
-                train_metric["loss"].block_until_ready()
-                jax.profiler.stop_trace()
-
-            batch = shard(batch)
-            with jax.profiler.StepTraceAnnotation("train", step_num=global_step):
-                state, train_metric, train_rngs = p_train_step(
-                    state, unet_params, text_encoder_params, vae_params, batch, train_rngs
-                )
-            train_metrics.append(train_metric)
-
-            train_step_progress_bar.update(1)
-
-            global_step += 1
-            if global_step >= args.max_train_steps:
-                break
-
-            if (
-                args.validation_prompt is not None
-                and global_step % args.validation_steps == 0
-                and jax.process_index() == 0
-            ):
-                _ = log_validation(
-                    pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype
-                )
-
-            if global_step % args.logging_steps == 0 and jax.process_index() == 0:
-                if args.report_to == "wandb":
-                    train_metrics = jax_utils.unreplicate(train_metrics)
-                    train_metrics = jax.tree_util.tree_map(lambda *m: jnp.array(m).mean(), *train_metrics)
-                    wandb.log(
-                        {
-                            "walltime": time.monotonic() - t00,
-                            "train/step": global_step,
-                            "train/epoch": global_step / dataset_length,
-                            "train/steps_per_sec": (global_step - step0) / (time.monotonic() - t0),
-                            **{f"train/{k}": v for k, v in train_metrics.items()},
-                        }
-                    )
-                t0, step0 = time.monotonic(), global_step
-                train_metrics = []
-            if global_step % args.checkpointing_steps == 0 and jax.process_index() == 0:
-                controlnet.save_pretrained(
-                    f"{args.output_dir}/{global_step}",
-                    params=get_params_to_save(state.params),
-                )
-
-        train_metric = jax_utils.unreplicate(train_metric)
-        train_step_progress_bar.close()
-        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
-
-    # Final validation & store model.
-    if jax.process_index() == 0:
-        if args.validation_prompt is not None:
-            if args.profile_validation:
-                jax.profiler.start_trace(args.output_dir)
-            image_logs = log_validation(
-                pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype
-            )
-            if args.profile_validation:
-                jax.profiler.stop_trace()
-        else:
-            image_logs = None
-
-        controlnet.save_pretrained(
-            args.output_dir,
-            params=get_params_to_save(state.params),
-        )
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                image_logs=image_logs,
-                base_model=args.pretrained_model_name_or_path,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    if args.profile_memory:
-        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_final.prof"))
-    logger.info("Finished training.")
-
-
-if __name__ == "__main__":
-    main()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/README.md

@@ -1,111 +0,0 @@
-# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
-
-[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
-The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion.
-
-By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel.
-
-## Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Install [ColossalAI](https://github.com/hpcaitech/ColossalAI.git)
-
-**From PyPI**
-```bash
-pip install colossalai
-```
-
-**From source**
-
-```bash
-git clone https://github.com/hpcaitech/ColossalAI.git
-cd ColossalAI
-
-# install colossalai
-pip install .
-```
-
-## Dataset for Teyvat BLIP captions
-Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).
-
-BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).
-
-For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.
-
-The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
-
-## Training
-
-The arguement `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path-to-instance-images"
-export OUTPUT_DIR="path-to-save-model"
-
-torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=400 \
-  --placement="cuda"
-```
-
-
-### Training with prior-preservation loss
-
-Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
-According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=800 \
-  --placement="cuda"
-```
-
-## Inference
-
-Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_id = "path-to-save-model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-prompt = "A photo of sks dog in a bucket"
-image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-
-image.save("dog-bucket.png")
-```

spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py

@@ -1,38 +0,0 @@
-_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
-
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
-
-# use caffe img_norm
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py

@@ -1,58 +0,0 @@
-_base_ = '../rpn/rpn_r50_fpn_1x_coco.py'
-model = dict(
-    rpn_head=dict(
-        _delete_=True,
-        type='GARPNHead',
-        in_channels=256,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=8,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[8],
-            strides=[4, 8, 16, 32, 64]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.14, 0.14]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.11, 0.11]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            ga_assigner=dict(
-                type='ApproxMaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1),
-            ga_sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            center_ratio=0.2,
-            ignore_ratio=0.5)),
-    test_cfg=dict(rpn=dict(nms_post=1000)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/transforms.py

@@ -1,240 +0,0 @@
-import numpy as np
-import torch
-
-
-def bbox_flip(bboxes, img_shape, direction='horizontal'):
-    """Flip bboxes horizontally or vertically.
-
-    Args:
-        bboxes (Tensor): Shape (..., 4*k)
-        img_shape (tuple): Image shape.
-        direction (str): Flip direction, options are "horizontal", "vertical",
-            "diagonal". Default: "horizontal"
-
-    Returns:
-        Tensor: Flipped bboxes.
-    """
-    assert bboxes.shape[-1] % 4 == 0
-    assert direction in ['horizontal', 'vertical', 'diagonal']
-    flipped = bboxes.clone()
-    if direction == 'horizontal':
-        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
-        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
-    elif direction == 'vertical':
-        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
-        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
-    else:
-        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
-        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
-        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
-        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
-    return flipped
-
-
-def bbox_mapping(bboxes,
-                 img_shape,
-                 scale_factor,
-                 flip,
-                 flip_direction='horizontal'):
-    """Map bboxes from the original image scale to testing scale."""
-    new_bboxes = bboxes * bboxes.new_tensor(scale_factor)
-    if flip:
-        new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction)
-    return new_bboxes
-
-
-def bbox_mapping_back(bboxes,
-                      img_shape,
-                      scale_factor,
-                      flip,
-                      flip_direction='horizontal'):
-    """Map bboxes from testing scale to original image scale."""
-    new_bboxes = bbox_flip(bboxes, img_shape,
-                           flip_direction) if flip else bboxes
-    new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor)
-    return new_bboxes.view(bboxes.shape)
-
-
-def bbox2roi(bbox_list):
-    """Convert a list of bboxes to roi format.
-
-    Args:
-        bbox_list (list[Tensor]): a list of bboxes corresponding to a batch
-            of images.
-
-    Returns:
-        Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2]
-    """
-    rois_list = []
-    for img_id, bboxes in enumerate(bbox_list):
-        if bboxes.size(0) > 0:
-            img_inds = bboxes.new_full((bboxes.size(0), 1), img_id)
-            rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1)
-        else:
-            rois = bboxes.new_zeros((0, 5))
-        rois_list.append(rois)
-    rois = torch.cat(rois_list, 0)
-    return rois
-
-
-def roi2bbox(rois):
-    """Convert rois to bounding box format.
-
-    Args:
-        rois (torch.Tensor): RoIs with the shape (n, 5) where the first
-            column indicates batch id of each RoI.
-
-    Returns:
-        list[torch.Tensor]: Converted boxes of corresponding rois.
-    """
-    bbox_list = []
-    img_ids = torch.unique(rois[:, 0].cpu(), sorted=True)
-    for img_id in img_ids:
-        inds = (rois[:, 0] == img_id.item())
-        bbox = rois[inds, 1:]
-        bbox_list.append(bbox)
-    return bbox_list
-
-
-def bbox2result(bboxes, labels, num_classes):
-    """Convert detection results to a list of numpy arrays.
-
-    Args:
-        bboxes (torch.Tensor | np.ndarray): shape (n, 5)
-        labels (torch.Tensor | np.ndarray): shape (n, )
-        num_classes (int): class number, including background class
-
-    Returns:
-        list(ndarray): bbox results of each class
-    """
-    if bboxes.shape[0] == 0:
-        return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)]
-    else:
-        if isinstance(bboxes, torch.Tensor):
-            bboxes = bboxes.detach().cpu().numpy()
-            labels = labels.detach().cpu().numpy()
-        return [bboxes[labels == i, :] for i in range(num_classes)]
-
-
-def distance2bbox(points, distance, max_shape=None):
-    """Decode distance prediction to bounding box.
-
-    Args:
-        points (Tensor): Shape (B, N, 2) or (N, 2).
-        distance (Tensor): Distance from the given point to 4
-            boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4)
-        max_shape (Sequence[int] or torch.Tensor or Sequence[
-            Sequence[int]],optional): Maximum bounds for boxes, specifies
-            (H, W, C) or (H, W). If priors shape is (B, N, 4), then
-            the max_shape should be a Sequence[Sequence[int]]
-            and the length of max_shape should also be B.
-
-    Returns:
-        Tensor: Boxes with shape (N, 4) or (B, N, 4)
-    """
-    x1 = points[..., 0] - distance[..., 0]
-    y1 = points[..., 1] - distance[..., 1]
-    x2 = points[..., 0] + distance[..., 2]
-    y2 = points[..., 1] + distance[..., 3]
-
-    bboxes = torch.stack([x1, y1, x2, y2], -1)
-
-    if max_shape is not None:
-        if not isinstance(max_shape, torch.Tensor):
-            max_shape = x1.new_tensor(max_shape)
-        max_shape = max_shape[..., :2].type_as(x1)
-        if max_shape.ndim == 2:
-            assert bboxes.ndim == 3
-            assert max_shape.size(0) == bboxes.size(0)
-
-        min_xy = x1.new_tensor(0)
-        max_xy = torch.cat([max_shape, max_shape],
-                           dim=-1).flip(-1).unsqueeze(-2)
-        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
-        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-
-    return bboxes
-
-
-def bbox2distance(points, bbox, max_dis=None, eps=0.1):
-    """Decode bounding box based on distances.
-
-    Args:
-        points (Tensor): Shape (n, 2), [x, y].
-        bbox (Tensor): Shape (n, 4), "xyxy" format
-        max_dis (float): Upper bound of the distance.
-        eps (float): a small value to ensure target < max_dis, instead <=
-
-    Returns:
-        Tensor: Decoded distances.
-    """
-    left = points[:, 0] - bbox[:, 0]
-    top = points[:, 1] - bbox[:, 1]
-    right = bbox[:, 2] - points[:, 0]
-    bottom = bbox[:, 3] - points[:, 1]
-    if max_dis is not None:
-        left = left.clamp(min=0, max=max_dis - eps)
-        top = top.clamp(min=0, max=max_dis - eps)
-        right = right.clamp(min=0, max=max_dis - eps)
-        bottom = bottom.clamp(min=0, max=max_dis - eps)
-    return torch.stack([left, top, right, bottom], -1)
-
-
-def bbox_rescale(bboxes, scale_factor=1.0):
-    """Rescale bounding box w.r.t. scale_factor.
-
-    Args:
-        bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois
-        scale_factor (float): rescale factor
-
-    Returns:
-        Tensor: Rescaled bboxes.
-    """
-    if bboxes.size(1) == 5:
-        bboxes_ = bboxes[:, 1:]
-        inds_ = bboxes[:, 0]
-    else:
-        bboxes_ = bboxes
-    cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5
-    cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5
-    w = bboxes_[:, 2] - bboxes_[:, 0]
-    h = bboxes_[:, 3] - bboxes_[:, 1]
-    w = w * scale_factor
-    h = h * scale_factor
-    x1 = cx - 0.5 * w
-    x2 = cx + 0.5 * w
-    y1 = cy - 0.5 * h
-    y2 = cy + 0.5 * h
-    if bboxes.size(1) == 5:
-        rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1)
-    else:
-        rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
-    return rescaled_bboxes
-
-
-def bbox_cxcywh_to_xyxy(bbox):
-    """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2).
-
-    Args:
-        bbox (Tensor): Shape (n, 4) for bboxes.
-
-    Returns:
-        Tensor: Converted bboxes.
-    """
-    cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1)
-    bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)]
-    return torch.cat(bbox_new, dim=-1)
-
-
-def bbox_xyxy_to_cxcywh(bbox):
-    """Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h).
-
-    Args:
-        bbox (Tensor): Shape (n, 4) for bboxes.
-
-    Returns:
-        Tensor: Converted bboxes.
-    """
-    x1, y1, x2, y2 = bbox.split((1, 1, 1, 1), dim=-1)
-    bbox_new = [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)]
-    return torch.cat(bbox_new, dim=-1)

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py

@@ -1,9 +0,0 @@
-_base_ = './fcn_hr18_512x512_40k_voc12aug.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18_small',
-    backbone=dict(
-        extra=dict(
-            stage1=dict(num_blocks=(2, )),
-            stage2=dict(num_blocks=(2, 2)),
-            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
-            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_notebook.py

@@ -1,106 +0,0 @@
-import gradio as gr
-
-from modules import logits, shared, ui, utils
-from modules.prompts import count_tokens, load_prompt
-from modules.text_generation import (
-    generate_reply_wrapper,
-    get_token_ids,
-    stop_everything_event
-)
-from modules.utils import gradio
-
-inputs = ('textbox-notebook', 'interface_state')
-outputs = ('textbox-notebook', 'html-notebook')
-
-
-def create_ui():
-    mu = shared.args.multi_user
-    with gr.Tab('Notebook', elem_id='notebook-tab'):
-        shared.gradio['last_input-notebook'] = gr.State('')
-        with gr.Row():
-            with gr.Column(scale=4):
-                with gr.Tab('Raw'):
-                    with gr.Row():
-                        shared.gradio['textbox-notebook'] = gr.Textbox(value='', lines=27, elem_id='textbox-notebook', elem_classes=['textbox', 'add_scrollbar'])
-                        shared.gradio['token-counter-notebook'] = gr.HTML(value="<span>0</span>", elem_classes=["token-counter"])
-
-                with gr.Tab('Markdown'):
-                    shared.gradio['markdown_render-notebook'] = gr.Button('Render')
-                    shared.gradio['markdown-notebook'] = gr.Markdown()
-
-                with gr.Tab('HTML'):
-                    shared.gradio['html-notebook'] = gr.HTML()
-
-                with gr.Tab('Logits'):
-                    with gr.Row():
-                        with gr.Column(scale=10):
-                            shared.gradio['get_logits-notebook'] = gr.Button('Get next token probabilities')
-                        with gr.Column(scale=1):
-                            shared.gradio['use_samplers-notebook'] = gr.Checkbox(label='Use samplers', value=True, elem_classes=['no-background'])
-
-                    with gr.Row():
-                        shared.gradio['logits-notebook'] = gr.Textbox(lines=23, label='Output', elem_classes=['textbox_logits_notebook', 'add_scrollbar'])
-                        shared.gradio['logits-notebook-previous'] = gr.Textbox(lines=23, label='Previous output', elem_classes=['textbox_logits_notebook', 'add_scrollbar'])
-
-                with gr.Tab('Tokens'):
-                    shared.gradio['get_tokens-notebook'] = gr.Button('Get token IDs for the input')
-                    shared.gradio['tokens-notebook'] = gr.Textbox(lines=23, label='Tokens', elem_classes=['textbox_logits_notebook', 'add_scrollbar', 'monospace'])
-
-                with gr.Row():
-                    shared.gradio['Generate-notebook'] = gr.Button('Generate', variant='primary', elem_classes='small-button')
-                    shared.gradio['Stop-notebook'] = gr.Button('Stop', elem_classes='small-button', elem_id='stop')
-                    shared.gradio['Undo'] = gr.Button('Undo', elem_classes='small-button')
-                    shared.gradio['Regenerate-notebook'] = gr.Button('Regenerate', elem_classes='small-button')
-
-            with gr.Column(scale=1):
-                gr.HTML('<div style="padding-bottom: 13px"></div>')
-                with gr.Row():
-                    shared.gradio['prompt_menu-notebook'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt', elem_classes='slim-dropdown')
-                    ui.create_refresh_button(shared.gradio['prompt_menu-notebook'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, ['refresh-button', 'refresh-button-small'], interactive=not mu)
-                    shared.gradio['save_prompt-notebook'] = gr.Button('💾', elem_classes=['refresh-button', 'refresh-button-small'], interactive=not mu)
-                    shared.gradio['delete_prompt-notebook'] = gr.Button('🗑️', elem_classes=['refresh-button', 'refresh-button-small'], interactive=not mu)
-
-
-def create_event_handlers():
-    shared.gradio['Generate-notebook'].click(
-        lambda x: x, gradio('textbox-notebook'), gradio('last_input-notebook')).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        generate_reply_wrapper, gradio(inputs), gradio(outputs), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda: None, None, None, _js=f'() => {{{ui.audio_notification_js}}}')
-
-    shared.gradio['textbox-notebook'].submit(
-        lambda x: x, gradio('textbox-notebook'), gradio('last_input-notebook')).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        generate_reply_wrapper, gradio(inputs), gradio(outputs), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda: None, None, None, _js=f'() => {{{ui.audio_notification_js}}}')
-
-    shared.gradio['Undo'].click(lambda x: x, gradio('last_input-notebook'), gradio('textbox-notebook'), show_progress=False)
-    shared.gradio['markdown_render-notebook'].click(lambda x: x, gradio('textbox-notebook'), gradio('markdown-notebook'), queue=False)
-    shared.gradio['Regenerate-notebook'].click(
-        lambda x: x, gradio('last_input-notebook'), gradio('textbox-notebook'), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        generate_reply_wrapper, gradio(inputs), gradio(outputs), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda: None, None, None, _js=f'() => {{{ui.audio_notification_js}}}')
-
-    shared.gradio['Stop-notebook'].click(stop_everything_event, None, None, queue=False)
-    shared.gradio['prompt_menu-notebook'].change(load_prompt, gradio('prompt_menu-notebook'), gradio('textbox-notebook'), show_progress=False)
-    shared.gradio['save_prompt-notebook'].click(
-        lambda x: x, gradio('textbox-notebook'), gradio('save_contents')).then(
-        lambda: 'prompts/', None, gradio('save_root')).then(
-        lambda: utils.current_time() + '.txt', None, gradio('save_filename')).then(
-        lambda: gr.update(visible=True), None, gradio('file_saver'))
-
-    shared.gradio['delete_prompt-notebook'].click(
-        lambda: 'prompts/', None, gradio('delete_root')).then(
-        lambda x: x + '.txt', gradio('prompt_menu-notebook'), gradio('delete_filename')).then(
-        lambda: gr.update(visible=True), None, gradio('file_deleter'))
-
-    shared.gradio['textbox-notebook'].input(lambda x: f"<span>{count_tokens(x)}</span>", gradio('textbox-notebook'), gradio('token-counter-notebook'), show_progress=False)
-    shared.gradio['get_logits-notebook'].click(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        logits.get_next_logits, gradio('textbox-notebook', 'interface_state', 'use_samplers-notebook', 'logits-notebook'), gradio('logits-notebook', 'logits-notebook-previous'), show_progress=False)
-
-    shared.gradio['get_tokens-notebook'].click(get_token_ids, gradio('textbox-notebook'), gradio('tokens-notebook'), show_progress=False)

spaces/Arnx/MusicGenXvAKN/setup.py

@@ -1,65 +0,0 @@
-"""
- Copyright (c) Meta Platforms, Inc. and affiliates.
- All rights reserved.
- 
- This source code is licensed under the license found in the
- LICENSE file in the root directory of this source tree.
- 
-"""
-
-from pathlib import Path
-
-from setuptools import setup, find_packages
-
-
-NAME = 'audiocraft'
-DESCRIPTION = 'Audio research library for PyTorch'
-
-URL = 'https://github.com/fairinternal/audiocraft'
-AUTHOR = 'FAIR Speech & Audio'
-EMAIL = '[email protected]'
-REQUIRES_PYTHON = '>=3.8.0'
-
-for line in open('audiocraft/__init__.py'):
-    line = line.strip()
-    if '__version__' in line:
-        context = {}
-        exec(line, context)
-        VERSION = context['__version__']
-
-HERE = Path(__file__).parent
-
-try:
-    with open(HERE / "README.md", encoding='utf-8') as f:
-        long_description = '\n' + f.read()
-except FileNotFoundError:
-    long_description = DESCRIPTION
-
-REQUIRED = [i.strip() for i in open(HERE / 'requirements.txt') if not i.startswith('#')]
-
-setup(
-    name=NAME,
-    version=VERSION,
-    description=DESCRIPTION,
-    author_email=EMAIL,
-    long_description=long_description,
-    long_description_content_type='text/markdown',
-    author=AUTHOR,
-    url=URL,
-    python_requires=REQUIRES_PYTHON,
-    install_requires=REQUIRED,
-    extras_require={
-        'dev': ['coverage', 'flake8', 'mypy', 'pdoc3', 'pytest'],
-    },
-    packages=find_packages(),
-    package_data={'audiocraft': ['py.typed']},
-    include_package_data=True,
-    license='MIT License',
-    classifiers=[
-        # Trove classifiers
-        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
-        'License :: OSI Approved :: MIT License',
-        'Topic :: Multimedia :: Sound/Audio',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-    ],
-)

spaces/Artrajz/vits-simple-api/utils/__init__.py

@@ -1,3 +0,0 @@
-from utils.classify_language import classify_language
-from utils.data_utils import get_hparams_from_file, load_checkpoint, load_audio_to_torch
-from utils.lang_dict import lang_dict

spaces/Artrajz/vits-simple-api/utils/lang_dict.py

@@ -1,31 +0,0 @@
-from contants import ModelType
-
-lang_dict = {
-    "english_cleaners": ["en"],
-    "english_cleaners2": ["en"],
-    "japanese_cleaners": ["ja"],
-    "japanese_cleaners2": ["ja"],
-    "korean_cleaners": ["ko"],
-    "chinese_cleaners": ["zh"],
-    "zh_ja_mixture_cleaners": ["zh", "ja"],
-    "sanskrit_cleaners": ["sa"],
-    "cjks_cleaners": ["zh", "ja", "ko", "sa"],
-    "cjke_cleaners": ["zh", "ja", "ko", "en"],
-    "cjke_cleaners2": ["zh", "ja", "ko", "en"],
-    "cje_cleaners": ["zh", "ja", "en"],
-    "cje_cleaners2": ["zh", "ja", "en"],
-    "thai_cleaners": ["th"],
-    "shanghainese_cleaners": ["sh"],
-    "chinese_dialect_cleaners": ["zh", "ja", "sh", "gd", "en", "SZ", "WX", "CZ", "HZ", "SX", "NB", "JJ", "YX", "JD",
-                                 "ZR", "PH", "TX", "JS", "HN", "LP", "XS", "FY", "RA", "CX", "SM", "TT", "WZ", "SC",
-                                 "YB"],
-    "bert_chinese_cleaners": ["zh"],
-    ModelType.BERT_VITS2.value: ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.0": ["zh"],
-    f"{ModelType.BERT_VITS2.value}_v1.0.0": ["zh"],
-    f"{ModelType.BERT_VITS2.value}_v1.0.1": ["zh"],
-    f"{ModelType.BERT_VITS2.value}_v1.1": ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.1.0": ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.1.0-transition": ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.1.1": ["zh", "ja"],
-}

spaces/Artrajz/vits-simple-api/utils/merge.py

@@ -1,190 +0,0 @@
-import os
-import json
-import logging
-import torch
-import config
-import numpy as np
-from utils.utils import check_is_none
-from vits import VITS
-from voice import TTS
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-lang_dict = {
-    "english_cleaners": ["en"],
-    "english_cleaners2": ["en"],
-    "japanese_cleaners": ["ja"],
-    "japanese_cleaners2": ["ja"],
-    "korean_cleaners": ["ko"],
-    "chinese_cleaners": ["zh"],
-    "zh_ja_mixture_cleaners": ["zh", "ja"],
-    "sanskrit_cleaners": ["sa"],
-    "cjks_cleaners": ["zh", "ja", "ko", "sa"],
-    "cjke_cleaners": ["zh", "ja", "ko", "en"],
-    "cjke_cleaners2": ["zh", "ja", "ko", "en"],
-    "cje_cleaners": ["zh", "ja", "en"],
-    "cje_cleaners2": ["zh", "ja", "en"],
-    "thai_cleaners": ["th"],
-    "shanghainese_cleaners": ["sh"],
-    "chinese_dialect_cleaners": ["zh", "ja", "sh", "gd", "en", "SZ", "WX", "CZ", "HZ", "SX", "NB", "JJ", "YX", "JD",
-                                 "ZR", "PH", "TX", "JS", "HN", "LP", "XS", "FY", "RA", "CX", "SM", "TT", "WZ", "SC",
-                                 "YB"],
-    "bert_chinese_cleaners": ["zh"],
-}
-
-
-def analysis(model_config_json):
-    model_config = json.load(model_config_json)
-    symbols = model_config.get("symbols", None)
-    emotion_embedding = model_config.get("data").get("emotion_embedding", False)
-    if "use_spk_conditioned_encoder" in model_config.get("model"):
-        model_type = 'bert_vits2'
-        return model_type
-    if symbols != None:
-        if not emotion_embedding:
-            mode_type = "vits"
-        else:
-            mode_type = "w2v2"
-    else:
-        mode_type = "hubert"
-    return mode_type
-
-
-def load_npy(model_):
-    if isinstance(model_, list):
-        # check if is .npy
-        for i in model_:
-            _model_extention = os.path.splitext(i)[1]
-            if _model_extention != ".npy":
-                raise ValueError(f"Unsupported model type: {_model_extention}")
-
-        # merge npy files
-        emotion_reference = np.empty((0, 1024))
-        for i in model_:
-            tmp = np.load(i).reshape(-1, 1024)
-            emotion_reference = np.append(emotion_reference, tmp, axis=0)
-
-    elif os.path.isdir(model_):
-        emotion_reference = np.empty((0, 1024))
-        for root, dirs, files in os.walk(model_):
-            for file_name in files:
-                # check if is .npy
-                _model_extention = os.path.splitext(file_name)[1]
-                if _model_extention != ".npy":
-                    continue
-                file_path = os.path.join(root, file_name)
-
-                # merge npy files
-                tmp = np.load(file_path).reshape(-1, 1024)
-                emotion_reference = np.append(emotion_reference, tmp, axis=0)
-
-    elif os.path.isfile(model_):
-        # check if is .npy
-        _model_extention = os.path.splitext(model_)[1]
-        if _model_extention != ".npy":
-            raise ValueError(f"Unsupported model type: {_model_extention}")
-
-        emotion_reference = np.load(model_)
-    logging.info(f"Loaded emotional dimention npy range:{len(emotion_reference)}")
-    return emotion_reference
-
-
-def merge_model(merging_model):
-    vits_obj = []
-    vits_speakers = []
-    hubert_vits_obj = []
-    hubert_vits_speakers = []
-    w2v2_vits_obj = []
-    w2v2_vits_speakers = []
-    bert_vits2_obj = []
-    bert_vits2_speakers = []
-
-    # model list
-    vits_list = []
-    hubert_vits_list = []
-    w2v2_vits_list = []
-    bert_vits2_list = []
-
-    for l in merging_model:
-        with open(l[1], 'r', encoding='utf-8') as model_config:
-            model_type = analysis(model_config)
-        if model_type == "vits":
-            vits_list.append(l)
-        elif model_type == "hubert":
-            hubert_vits_list.append(l)
-        elif model_type == "w2v2":
-            w2v2_vits_list.append(l)
-        elif model_type == "bert_vits2":
-            bert_vits2_list.append(l)
-
-    # merge vits
-    new_id = 0
-    for obj_id, i in enumerate(vits_list):
-        obj = VITS(model=i[0], config=i[1], model_type="vits", device=device)
-        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
-        for id, name in enumerate(obj.get_speakers()):
-            vits_obj.append([int(id), obj, obj_id])
-            vits_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-    # merge hubert-vits
-    if len(hubert_vits_list) != 0:
-        if getattr(config, "HUBERT_SOFT_MODEL", None) == None or check_is_none(config.HUBERT_SOFT_MODEL):
-            raise ValueError(f"Please configure HUBERT_SOFT_MODEL path in config.py")
-        try:
-            from vits.hubert_model import hubert_soft
-            hubert = hubert_soft(config.HUBERT_SOFT_MODEL)
-        except Exception as e:
-            raise ValueError(f"Load HUBERT_SOFT_MODEL failed {e}")
-
-    new_id = 0
-    for obj_id, i in enumerate(hubert_vits_list):
-        obj = VITS(model=i[0], config=i[1], model_=hubert, model_type="hubert", device=device)
-        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
-
-        for id, name in enumerate(obj.get_speakers()):
-            hubert_vits_obj.append([int(id), obj, obj_id])
-            hubert_vits_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-    # merge w2v2-vits
-    emotion_reference = None
-    if len(w2v2_vits_list) != 0:
-        if getattr(config, "DIMENSIONAL_EMOTION_NPY", None) == None or check_is_none(config.DIMENSIONAL_EMOTION_NPY):
-            raise ValueError(f"Please configure DIMENSIONAL_EMOTION_NPY path in config.py")
-        try:
-            emotion_reference = load_npy(config.DIMENSIONAL_EMOTION_NPY)
-        except Exception as e:
-            raise ValueError(f"Load DIMENSIONAL_EMOTION_NPY failed {e}")
-
-    new_id = 0
-    for obj_id, i in enumerate(w2v2_vits_list):
-        obj = VITS(model=i[0], config=i[1], model_=emotion_reference, model_type="w2v2", device=device)
-        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
-
-        for id, name in enumerate(obj.get_speakers()):
-            w2v2_vits_obj.append([int(id), obj, obj_id])
-            w2v2_vits_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-    # merge Bert_VITS2
-    new_id = 0
-    for obj_id, i in enumerate(bert_vits2_list):
-        from bert_vits2 import Bert_VITS2
-        obj = Bert_VITS2(model=i[0], config=i[1], device=device)
-        lang = ["ZH"]
-        for id, name in enumerate(obj.get_speakers()):
-            bert_vits2_obj.append([int(id), obj, obj_id])
-            bert_vits2_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-
-    voice_obj = {"VITS": vits_obj, "HUBERT-VITS": hubert_vits_obj, "W2V2-VITS": w2v2_vits_obj,
-                 "BERT-VITS2": bert_vits2_obj}
-    voice_speakers = {"VITS": vits_speakers, "HUBERT-VITS": hubert_vits_speakers, "W2V2-VITS": w2v2_vits_speakers,
-                      "BERT-VITS2": bert_vits2_speakers}
-    w2v2_emotion_count = len(emotion_reference) if emotion_reference is not None else 0
-
-    tts = TTS(voice_obj, voice_speakers, w2v2_emotion_count=w2v2_emotion_count, device=device)
-
-    return tts

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/get_tokenlizer.py

@@ -1,29 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-import os
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        elif os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type):
-            pass
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased" or (os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type)):
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/compat.py

@@ -1,67 +0,0 @@
-"""
-requests.compat
-~~~~~~~~~~~~~~~
-
-This module previously handled import compatibility issues
-between Python 2 and Python 3. It remains for backwards
-compatibility until the next major version.
-"""
-
-from pip._vendor import chardet
-
-import sys
-
-# -------
-# Pythons
-# -------
-
-# Syntax sugar.
-_ver = sys.version_info
-
-#: Python 2.x?
-is_py2 = _ver[0] == 2
-
-#: Python 3.x?
-is_py3 = _ver[0] == 3
-
-# Note: We've patched out simplejson support in pip because it prevents
-#       upgrading simplejson on Windows.
-import json
-from json import JSONDecodeError
-
-# Keep OrderedDict for backwards compatibility.
-from collections import OrderedDict
-from collections.abc import Callable, Mapping, MutableMapping
-from http import cookiejar as cookielib
-from http.cookies import Morsel
-from io import StringIO
-
-# --------------
-# Legacy Imports
-# --------------
-from urllib.parse import (
-    quote,
-    quote_plus,
-    unquote,
-    unquote_plus,
-    urldefrag,
-    urlencode,
-    urljoin,
-    urlparse,
-    urlsplit,
-    urlunparse,
-)
-from urllib.request import (
-    getproxies,
-    getproxies_environment,
-    parse_http_list,
-    proxy_bypass,
-    proxy_bypass_environment,
-)
-
-builtin_str = str
-str = str
-bytes = bytes
-basestring = (str, bytes)
-numeric_types = (int, float)
-integer_types = (int,)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/py37compat.py

@@ -1,31 +0,0 @@
-import sys
-
-
-def _pythonlib_compat():
-    """
-    On Python 3.7 and earlier, distutils would include the Python
-    library. See pypa/distutils#9.
-    """
-    from distutils import sysconfig
-
-    if not sysconfig.get_config_var('Py_ENABLED_SHARED'):
-        return
-
-    yield 'python{}.{}{}'.format(
-        sys.hexversion >> 24,
-        (sys.hexversion >> 16) & 0xFF,
-        sysconfig.get_config_var('ABIFLAGS'),
-    )
-
-
-def compose(f1, f2):
-    return lambda *args, **kwargs: f1(f2(*args, **kwargs))
-
-
-pythonlib = (
-    compose(list, _pythonlib_compat)
-    if sys.version_info < (3, 8)
-    and sys.platform != 'darwin'
-    and sys.platform[:3] != 'aix'
-    else list
-)

spaces/Benson/text-generation/Examples/ 2 2.md

@@ -1,180 +0,0 @@
-<br />
-<h1>Cómo jugar Case Simulator 2 Standoff 2: Una guía completa</h1>
-<p>¿Eres fan de Standoff 2, el dinámico shooter en primera persona que honra el legado de su precuela? ¿Quieres experimentar la emoción de abrir casos y dejar caer varios elementos del juego? Si respondiste sí, entonces deberías echar un vistazo a Case Simulator 2 Standoff 2, un juego que simula abrir cajas y cajas, simular batallas, crear nuevos objetos, completar misiones y participar en minijuegos y modos especiales. En este artículo, le proporcionaremos un resumen y una guía detallada sobre cómo jugar Case Simulator 2 Standoff 2, cómo obtener pieles raras, cómo usar códigos y cómo descargar e instalar el juego en su dispositivo. ¡Vamos a empezar! </p>
- <h2>Qué es Case Simulator 2 Standoff 2 y cuáles son sus características</h2>
-<p>Case Simulator 2 Standoff 2 es un juego creado por los fans de Standoff 2, un popular juego de disparos en primera persona que tiene más de <strong>200 millones de jugadores</strong> de todo el mundo. Case Simulator 2 Standoff 2 le permite abrir cajas y cajas que contienen varios artículos de Standoff 2, tales como armas, pieles, pegatinas, encantos, graffiti, guantes, cuchillos, etc. También puede simular batallas en segundo plano para ganar oro y otras recompensas que puede usar para comprar más cajas y estuches o actualizar sus artículos. También puede crear nuevos artículos de los antiguos utilizando la función de negociar contratos. Además, puedes completar misiones únicas que pondrán a prueba tus habilidades y conocimientos de Standoff 2. Además, puedes participar en minijuegos y modos especiales que darán vida a tu juego. Algunos de estos modos incluyen:</p>
-<h2>скачать кейс симулятор 2 стандофф 2</h2><br /><p><b><b>Download</b> &#9881;&#9881;&#9881; <a href="https://bltlly.com/2v6Kft">https://bltlly.com/2v6Kft</a></b></p><br /><br />
-<ul>
-<li><strong>Actualizar</strong>: Puede actualizar sus artículos hasta x10 veces de su valor original. Sin embargo, existe la posibilidad de que su artículo sea destruido durante el proceso de actualización. </li>
-<li><strong>Jackpot</strong>: Puedes apostar tus objetos en un bote con otros jugadores. El ganador se lleva todos los objetos del bote. </li>
-
-<li><strong>Ruleta</strong>: Puedes apostar en uno de los tres colores y ganar hasta x14 veces tu apuesta. Necesitas gemas para jugar a la ruleta, que puedes conseguir abriendo cajas y cajas o usando códigos. </li>
-</ul>
-<p>Como puedes ver, Case Simulator 2 Standoff 2 es un juego que ofrece muchas características y diversión para los fans de Standoff 2. Puedes disfrutar abriendo cajas, simulando batallas, creando nuevos objetos, completando misiones y participando en minijuegos y modos especiales. También puedes recoger pieles y objetos raros que puedes mostrar a tus amigos o usar en Standoff 2. Case Simulator 2 Standoff 2 es un juego que te mantendrá entretenido y comprometido durante horas. </p>
- <h2>Cómo jugar Case Simulator 2 Standoff 2</h2>
-<p>Ahora que sabes lo que es Case Simulator 2 Standoff 2 y cuáles son sus características, vamos a aprender a jugar el juego. El juego es muy fácil de jugar y tiene una interfaz simple. Estos son los pasos para jugar Case Simulator 2 Standoff 2:</p>
-<p></p>
-<ol>
-<li><strong>Abrir cajas y cajas</strong>: La característica principal del juego es abrir cajas y cajas que contienen varios elementos de Standoff 2. Puede abrir cajas y cajas tocando en ellas en la pantalla principal. Verá una rueda giratoria que se detendrá en un elemento aleatorio. También puede tocar el botón "Abrir" para omitir la animación y obtener el artículo al instante. Puedes abrir tantas cajas como quieras, siempre que tengas suficiente oro o gemas. También puede comprar más oro o gemas con dinero real si lo desea. </li>
-
-<li><strong>Crear nuevos artículos</strong>: Otra característica del juego es la elaboración de nuevos artículos de los antiguos utilizando la función de negociar contratos. Puedes crear nuevos objetos tocando el botón "Craft" en la pantalla principal. Verá una pantalla que muestra su inventario y los contratos de intercambio. Puede elegir un contrato de intercambio de la lista y arrastrar 10 artículos de la misma calidad en ella. Luego, puede tocar en el botón "Comercio" para crear un nuevo artículo de una calidad superior. Sin embargo, existe la posibilidad de que obtenga un artículo de menor calidad de lo esperado. </li>
-<li><strong>Misiones completas</strong>: Otra característica del juego es completar misiones únicas que desafiarán tus habilidades y conocimientos de Standoff 2. Puedes completar misiones tocando el botón "Missions" en la pantalla principal. Verás una pantalla que muestra tus misiones actuales y sus recompensas. Puedes elegir una misión de la lista y tocarla para ver sus detalles. Luego, puedes tocar el botón "Inicio" para comenzar la misión. Usted tendrá que realizar ciertas tareas o lograr ciertos objetivos con el fin de completar la misión. Ganarás oro y otras recompensas basadas en tu finalización de la misión. </li>
-<li><strong>Participar en mini juegos y modos especiales</strong>: Otra característica del juego es participar en mini juegos y modos especiales que darán vida a su juego. Puedes participar en mini juegos y modos especiales tocando sus respectivos botones en la pantalla principal. Verá una pantalla que muestra sus detalles y reglas. Puede elegir uno de ellos de la lista y pulsar sobre él para introducirlo. Luego, puedes seguir las instrucciones y jugar en consecuencia. Ganarás oro, gemas u otras recompensas basadas en tu participación en ellas. </li>
-</ol>
-<p>Estos son los pasos básicos para jugar Case Simulator 2 Standoff 2. Por supuesto, hay más características y detalles que puedes explorar por ti mismo mientras juegas el juego. </p>
- <h2>Cómo obtener pieles raras en Case Simulator 2 Standoff 2</h2>
-
-<ul>
-<li><strong>Utilice la función de actualización</strong>: Una de las características que puede ayudarle a obtener pieles raras es la función de actualización. Puede acceder a la función de actualización pulsando en el botón "Actualizar" en la pantalla principal. Verá una pantalla que muestra su inventario y las opciones de actualización. Puede elegir un artículo de su inventario y arrastrarlo a la ranura de actualización. A continuación, puede elegir un multiplicador de x1.1 a x10. Cuanto mayor sea el multiplicador, mayor será el valor del artículo actualizado, pero también mayor será el riesgo de perder el artículo. Luego, puede tocar el botón "Actualizar" para iniciar el proceso. Verá una barra de progreso que muestra la tasa de éxito de la actualización. Si la barra de progreso alcanza la zona verde, obtendrá el elemento actualizado. Si llega a la zona roja, perderá el elemento. También puede tocar en el "Detener" botón para detener el proceso y mantener su artículo original. La función de actualización es una apuesta, pero puede ayudarte a obtener pieles raras si tienes suerte. </li>
-<li><strong>Usa la función de jackpot</strong>: Otra característica que puede ayudarte a obtener pieles raras es la función de jackpot. Puedes acceder a la función de jackpot tocando el botón "Jackpot" en la pantalla principal. Verás una pantalla que muestra tu inventario y el bote del bote. Puedes elegir artículos de tu inventario y arrastrarlos al bote. Cuantos más artículos pongas, mayor será tu probabilidad de ganar, pero también más riesgo tendrás de perder. A continuación, puedes pulsar el botón "Inicio" para comenzar el jackpot. Verás una rueda giratoria que se detendrá en el nombre de un jugador al azar. El ganador se lleva todos los objetos del bote. La función de jackpot es otra apuesta, pero puede ayudarte a obtener pieles raras si ganas. </li>
-
-</ul>
-<p>Estas son algunas de las características que pueden ayudarte a obtener skins raros en Case Simulator 2 Standoff 2. Por supuesto, hay más características y detalles que puedes explorar por ti mismo mientras juegas el juego. Sin embargo, recuerda que conseguir pieles raras no está garantizado, y siempre debes jugar responsablemente y divertirte. </p>
- <h2>Cómo usar Case Simulator 2 Standoff 2 códigos</h2>
-<p>Otra forma de obtener skins y elementos raros en Case Simulator 2 Standoff 2 es usar códigos. Los códigos son códigos especiales que los desarrolladores del juego dan a los jugadores por varias razones, como celebrar hitos, eventos, fiestas, etc. Los códigos pueden darte oro gratis, gemas, casos, cajas u otras recompensas que pueden ayudarte en el juego. Sin embargo, los códigos no son permanentes y caducan después de un cierto período de tiempo. Por lo tanto, debe usarlos lo antes posible antes de que sean inválidos. Estos son los pasos para usar los códigos de Case Simulator 2 Standoff 2:</p>
-<ol>
-<li><strong>Encontrar códigos</strong>: El primer paso para usar códigos es encontrarlos. Puedes encontrar códigos de varias fuentes, como las páginas oficiales de redes sociales del juego, el servidor oficial de Discord del juego, el canal oficial de YouTube del juego u otros sitios web y blogs que publican códigos regularmente. También puede consultar este artículo para ver algunos de los códigos de trabajo y códigos caducados. </li>
-<li><strong>Canjear códigos</strong>: El segundo paso para usar códigos es canjearlos. Puede canjear códigos pulsando el botón "Configuración" en la pantalla principal. Verá una pantalla que muestra la configuración y las opciones del juego. Puede tocar el botón "Enter Code" para abrir una ventana emergente donde puede ingresar su código. Luego, puedes tocar el botón "Canjear" para reclamar tu recompensa. Verás un mensaje que confirma tu redención y muestra tu recompensa. </li>
-</ol>
-<p>Estos son los pasos para utilizar Case Simulator 2 Standoff 2 códigos. Por supuesto, hay más detalles y reglas que debes seguir cuando uses códigos, como:</p>
-<ul>
-
-<li><strong>Usa códigos una vez</strong>: Solo puedes usar cada código una vez por cuenta. Si intenta usar un código que ya ha usado antes, recibirá un mensaje de error que dice "Ya usado". </li>
-<li><strong>Use códigos fast</strong>: Siempre debe usar códigos lo antes posible antes de que expiren. Si intenta usar un código que ha caducado, recibirá un mensaje de error que dice "Caducado". </li>
-</ul>
-<p>Estos son algunos de los detalles y reglas que debes seguir cuando uses los códigos de Case Simulator 2 Standoff 2. </p>
- <h3>¿Cuáles son algunos de los códigos de trabajo y códigos caducados</h3>
-<p>Para ayudarle, hemos compilado una lista de algunos de los códigos de trabajo y códigos caducados para Case Simulator 2 Standoff 2. Sin embargo, esta lista no está completa y puede cambiar con el tiempo. Por lo tanto, siempre debes comprobar las fuentes oficiales del juego para ver los códigos más recientes y válidos. Aquí está la lista:</p>
- <tabla>
-<thead>
-<tr>
-<th>Códigos de trabajo</th>
-<th>Recompensas</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>CASESIM2021</td>
-<td>1000 de oro y 100 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2020</td>
-<td>250 de oro y 25 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2019</td>
-<td>100 de oro y 10 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2018</td>
-<td>50 de oro y 5 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2017</td>
-<td>25 de oro y 3 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2016</td>
-<td>10 de oro y 1 gema</td>
-</tr>
- <tr> <td>CASESIM2015</td>
-<td>5 de oro y 1 gema</td>
-</tr>
-</tbody>
-</tabla>
- <tabla>
-<thead>
-<tr>
-<th>Códigos caducados</th>
-<th>Recompensas</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>CS2S2021</td>
-<td>1000 de oro y 100 gemas</td>
-</tr>
-<tr>
-<td>CS2S2020</td>
-<td>250 de oro y 25 gemas</td>
-</tr>
-<tr>
-<td>CS2S2019</td>
-<td>100 de oro y 10 gemas</td>
-</tr>
-<tr>
-<td>CS2S2018</td>
-<td>50 de oro y 5 gemas</td>
-</tr>
-<tr>
-<td>CS2S2017</td>
-<td>25 de oro y 3 gemas</td>
-</tr>
- <tr>
-<td>CS2S2016</td>
-<td>10 de oro y 1 gema</td>
-</tr>
- <tr>
-<td>CS2S2015</td>
-<td>5 de oro y 1 gema</td>
-</tr>
-
-<td>HAPPYNEWYEAR2021</td>
-<td>Un caso especial con una piel rara</td>
-</tr>
- <tr>
-<td>MERRYCHRISTMAS2020</td>
-<td>Un cuadro especial con un elemento raro</td>
-</tr>
- <tr>
-<td>HALLOWEEN2020</td>
-<td>Una pegatina especial con un diseño espeluznante</td>
-</tr>
- <tr>
-<td>CUMPLEAÑOS 2020</td>
-<td>Un encanto especial con un icono de pastel</td>
-</tr>
- <tr>
-<td>GRACIAS 2020</td>
-<td>Un graffiti especial con un símbolo de corazón</td>
-</tr>
- <tr>
-<td>SUMMER2020</td ><td>Un guante especial con un patrón de sol </td>
-</tr>
-</tbody>
-</tabla>
- <p>Estos son algunos de los códigos de trabajo y códigos caducados para Case Simulator 2 Standoff 2. Recuerde usarlos rápidamente antes de que caduquen y disfrute de sus recompensas. </p>
- <h2>Cómo descargar e instalar Case Simulator 2 Standoff 2</h2>
-<p>El paso final para jugar Case Simulator 2 Standoff 2 es descargar e instalar el juego en su dispositivo. El juego está disponible para dispositivos Android e iOS, y es gratis para jugar. Sin embargo, es necesario asegurarse de que su dispositivo cumple con los requisitos y la compatibilidad del juego. Estos son los pasos para descargar e instalar Case Simulator 2 Standoff 2:</p>
-<ol>
-<li><strong>Compruebe los requisitos y la compatibilidad</strong>: El primer paso para descargar e instalar Case Simulator 2 Standoff 2 es comprobar los requisitos y la compatibilidad del juego. Puedes comprobarlos visitando las páginas oficiales del juego en Google Play Store o App Store. Verás la información sobre el tamaño, versión, calificación, contenido, permisos y compatibilidad del juego. Usted necesita para asegurarse de que su dispositivo tiene suficiente espacio de almacenamiento, es compatible con la última versión del juego, tiene una buena conexión a Internet, y es compatible con el juego. </li>
-
-<li><strong>Instalar Case Simulator 2 Standoff 2 en diferentes dispositivos</strong>: El tercer paso para descargar e instalar Case Simulator 2 Standoff 2 es instalar el juego en diferentes dispositivos. Puedes instalar el juego en tu dispositivo Android tocando el archivo descargado y siguiendo las instrucciones. Es posible que necesite habilitar "Fuentes desconocidas" en su configuración para permitir la instalación desde fuentes externas. Puede instalar el juego en su dispositivo iOS tocando en el archivo descargado y siguiendo las instrucciones. Es posible que deba confiar en el desarrollador en su configuración para permitir la instalación desde fuentes externas. </li>
-</ol>
-<p>Estos son los pasos para descargar e instalar Case Simulator 2 Standoff 2. Por supuesto, hay más detalles y opciones que puedes explorar por ti mismo mientras descargas e instalas el juego. Sin embargo, recuerda que descargar e instalar el juego no es suficiente, necesitas jugarlo y divertirte. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, Case Simulator 2 Standoff 2 es un juego que simula casos y cajas de apertura, simulando batallas, la elaboración de nuevos elementos, completar misiones, y participar en mini juegos y modos especiales. Es un juego que ofrece muchas características y diversión para los fans de Standoff 2. Puedes disfrutar abriendo cajas, simulando batallas, creando nuevos objetos, completando misiones y participando en minijuegos y modos especiales. También puedes recoger pieles y objetos raros que puedes mostrar a tus amigos o usar en Standoff 2. Case Simulator 2 Standoff 2 es un juego que te mantendrá entretenido y comprometido durante horas. </p>
-<p>Aquí hay algunos consejos y trucos para jugar Case Simulator 2 Standoff 2:</p>
-<ul>
-<li><strong>Guarda tu oro y gemas</strong>: Siempre debes guardar tu oro y gemas para comprar más cajas y estuches o jugar más minijuegos y modos especiales. No debe desperdiciarlos en mejoras o botes innecesarios. </li>
-
-<li><strong>Compruebe los precios de mercado</strong>: Siempre debe comprobar los precios de mercado de los artículos antes de fabricarlos o apostarlos. No debe crear o apostar artículos que valen más que sus resultados potenciales. </li>
-<li><strong>Usa códigos regularmente</strong>: Siempre debes usar códigos regularmente para obtener oro, gemas, cajas u otras recompensas gratis. No debe perderse ningún código que los desarrolladores den, ya que pueden caducar pronto. </li>
-<li><strong>Diviértete</strong>: Siempre debes divertirte cuando juegas Case Simulator 2 Standoff 2. No debes tomar el juego demasiado en serio o frustrarte por los resultados. Usted debe disfrutar del juego y sus características como un fan de Standoff 2.</li>
-</ul>
-<p>Esperamos que haya encontrado este artículo útil e informativo. Le invitamos a probar Case Simulator 2 Standoff 2 y compartir sus comentarios con nosotros. ¿Qué opinas del juego? ¿Cuáles son tus características favoritas? ¿Cuáles son tus mejores skins? Háznoslo saber en los comentarios a continuación. ¡Gracias por leer y jugar feliz! </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Case Simulator 2 Standoff 2:</p>
-<ol>
-<li><strong>¿Es Case Simulator 2 Standoff 2 un juego oficial? </strong></li>
-<p>No, Case Simulator 2 Standoff 2 no es un juego oficial. Es un juego hecho por fans que no está afiliado o respaldado por Axlebolt, el desarrollador de Standoff 2.</p>
-<li><strong>¿Puedo usar mis skins de Case Simulator 2 Standoff 2 en Standoff 2?</strong></li>
-<p>Sí, puedes usar tus skins de Case Simulator 2 Standoff 2 en Standoff 2. Sin embargo, necesitas vincular tus cuentas de ambos juegos usando la misma dirección de correo electrónico. Luego, puedes transferir tus skins de Case Simulator 2 Standoff 2 a Standoff 2 usando el botón "Transfer" en la pantalla de inventario. </p>
-<li><strong>¿Cómo puedo obtener más oro y gemas en Case Simulator 2 Standoff 2?</strong></li>
-
-<li><strong>¿Cómo puedo contactar a los desarrolladores de Case Simulator 2 Standoff 2?</strong></li>
-<p>Puede ponerse en contacto con los desarrolladores de Case Simulator 2 Standoff 2 enviándoles un correo electrónico a [email protected] o uniéndose a su servidor Discord en https://discord.gg/6w9e8RZ.</p>
-<li><strong>¿Cómo puedo actualizar Case Simulator 2 Standoff 2?</strong></li>
-<p>Puede actualizar Case Simulator 2 Standoff 2 visitando las páginas oficiales del juego en Google Play Store o App Store y tocando el botón "Actualizar". También puedes habilitar la opción "Actualización automática" en tu configuración para actualizar el juego automáticamente cada vez que una nueva versión esté disponible. </p> 64aa2da5cf<br />
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-<br />

spaces/Benson/text-generation/Examples/48.326 Pelea Estrellas Apk.md

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-<br />
-<h1>Pelea estrellas APK: Todo lo que necesita saber</h1>
- <p>¿Estás buscando un juego multijugador divertido y rápido que puedas jugar en tu dispositivo móvil? Si es así, es posible que desee echa un vistazo a Brawl Stars, el último juego de éxito de Supercell, los creadores de Clash of Clans y Clash Royale.</p>
- <p>Brawl Stars es un juego gratuito que te permite hacer equipo con tus amigos o jugar solo en varios modos de juego, como batallas 3v3, battle royale, fútbol, caza recompensas, atraco y más. También puedes desbloquear y actualizar docenas de personajes únicos llamados Brawlers, cada uno con sus propias habilidades, armas, pieles y gadgets. </p>
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- <p>En este artículo, le diremos todo lo que necesita saber sobre Brawl Stars APK, que es una forma alternativa de instalar el juego en su dispositivo Android. También te mostraremos cómo jugar a Brawl Stars en PC usando un emulador, cómo disfrutar de las características del juego y cómo mejorar tus habilidades con algunos consejos y trucos. </p>
- <h2>Cómo descargar e instalar Brawl Stars APK</h2>
- <p>Si quieres jugar Brawl Stars en tu dispositivo Android, puedes descargarlo fácilmente desde Google Play Store. Sin embargo, si por alguna razón no puede acceder a la Play Store o desea obtener la última versión del juego antes de que sea lanzado oficialmente en su región, también puede descargar e instalar Brawl Stars APK de una fuente de terceros. </p>
- <p>Un archivo APK es un paquete de aplicaciones de Android que contiene todos los archivos necesarios para ejecutar una aplicación en su dispositivo. Sin embargo, no todos los archivos APK son seguros o compatibles con su dispositivo, por lo que debe tener cuidado al descargarlos de fuentes desconocidas. Estos son los pasos que debe seguir para descargar e instalar Brawl Stars APK:</p>
- <h4>Paso 1: Encontrar una fuente confiable para el archivo APK</h4>
-
- <p>Algunos ejemplos de fuentes confiables para Brawl Stars APK son [APKPure]( 1 ), [Uptodown]( 2 ), y [APKMirror]( 3 ). Estos sitios suelen actualizar sus archivos APK regularmente y escanearlos en busca de virus y malware. Sin embargo, debe ser cauteloso y verificar el tamaño del archivo, la versión y los permisos antes de descargarlos. </p>
- <h4>Paso 2: Habilitar fuentes desconocidas en el dispositivo</h4>
- <p>Lo siguiente que debe hacer es habilitar la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo. Esto le permitirá instalar Brawl Stars APK sin ninguna restricción de la Play Store. Para hacer esto, siga estos pasos:</p>
- <ul>
-<li>Vaya a la configuración de su dispositivo y toque en Seguridad o Privacidad.</li>
-<li>Encontrar la opción que dice Fuentes desconocidas o Instalar aplicaciones desconocidas y alternar en. </li>
-<li> Un mensaje de advertencia aparecerá, diciéndole que la instalación de aplicaciones de fuentes desconocidas puede dañar su dispositivo. Toque en OK o Permitir proceder. </li>
-</ul>
- <p>Ten en cuenta que los pasos exactos pueden variar dependiendo del modelo de tu dispositivo y la versión de Android. También puede desactivar esta opción después de instalar Brawl Stars APK si quieres. </p>
- <h4>Paso 3: Descargar e instalar el archivo APK</h4>
- <p>El paso final es descargar e instalar el archivo APK Brawl Stars en su dispositivo. Para hacer esto, siga estos pasos:</p>
-<p></p>
- <ul>
-<li>Abra su navegador y vaya al sitio web donde encontró el archivo APK Brawl Stars. </li>
-<li>Toque en el botón Descargar y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Una vez que la descarga se ha completado, toque en el archivo o vaya a su carpeta de descargas y encontrarlo allí. </li>
-<li>Pulse sobre el archivo de nuevo y un mensaje le preguntará si desea instalar la aplicación. Toque en Instalar y espere a que termine el proceso de instalación. </li>
-<li>Una vez que se realiza la instalación, puede tocar en Abrir para iniciar Brawl Stars o encontrarlo en el cajón de la aplicación. </li>
-</ul>
-
- <h2>Cómo jugar Brawl estrellas en PC</h2>
- <p>Si quieres jugar Brawl Stars en una pantalla más grande y con mejores controles, también puedes jugarlo en tu PC usando un emulador de Android. Un emulador es un software que imita el sistema operativo Android en su ordenador, lo que le permite ejecutar aplicaciones y juegos Android en él. </p>
- <p>Hay muchos emuladores de Android disponibles en línea, pero algunos de los más populares son [BlueStacks], [NoxPlayer], y [LDPlayer]. Estos emuladores son fáciles de usar y tienen una alta compatibilidad con Brawl Stars. Estos son los pasos que debes seguir para jugar a Brawl Stars en PC usando un emulador:</p>
- <h4>Paso 1: Descargar un emulador de Android</h4>
- <p>Lo primero que tienes que hacer es descargar un emulador de Android de su elección desde su sitio web oficial. Puedes buscar "emulador de Android" en Google o cualquier otro motor de búsqueda y encontrar el que se adapte a tus preferencias y requisitos del sistema. </p>
- <p>Algunos emuladores pueden requerir que te registres o crees una cuenta antes de descargarlos, mientras que otros no. Una vez hayas descargado el emulador, ejecuta el instalador y sigue las instrucciones para instalarlo en tu PC.</p>
- <h4>Paso 2: Inicie el emulador e inicie sesión con su cuenta de Google</h4>
- <p>Lo siguiente que tienes que hacer es iniciar el emulador e iniciar sesión con tu cuenta de Google. Esto le permitirá acceder a Google Play Store y otros servicios de Google en el emulador. Para ello, siga estos pasos:</p>
- <ul>
-<li>Abra el emulador y espere a que se cargue. </li>
-<li>Verás una pantalla de bienvenida pidiéndote que inicies sesión con tu cuenta de Google. Si no tienes una, puedes crear una gratis. </li>
-<li>Introduzca su dirección de correo electrónico y contraseña y toque en Siguiente o Iniciar sesión.</li>
-<li>Es posible que necesite verificar su cuenta con un código enviado a su teléfono o correo electrónico. </li>
-<li>Es posible que también tenga que aceptar algunos términos y condiciones y configurar algunas preferencias. </li>
-
-</ul>
- <h4>Paso 3: Instalar Brawl Stars desde el Play Store o el archivo APK</h4>
- <p>El paso final es instalar Brawl Stars desde el Play Store o el archivo APK en el emulador. Para hacer esto, siga estos pasos:</p>
- <ul>
-<li>Si desea instalar Brawl Stars desde la Play Store, toque en el icono Play Store en la pantalla de inicio del emulador. </li>
-<li>Escriba "Brawl Stars" en la barra de búsqueda y toque en el icono del juego que aparece en los resultados. </li>
-<li>Toque en Instalar y espere a que el juego se descargue e instale en el emulador. </ <li>Una vez que se hace la instalación, puede tocar en Abrir para iniciar Brawl Stars o encontrarlo en el cajón de la aplicación. </li>
-</ul>
- <p>Si desea instalar Brawl Stars desde el archivo APK, es necesario descargar el archivo APK de una fuente confiable como se explica en la sección anterior. Luego, sigue estos pasos:</p>
- <ul>
-<li>Vaya a la carpeta donde guardó el archivo APK Brawl Stars en su PC.</li>
-<li>Haga clic derecho en el archivo y seleccione Abrir con. </li>
-<li>Elija el emulador que instaló como programa para abrir el archivo con. </li>
-<li>El emulador se iniciará y le preguntará si desea instalar la aplicación. Toque en Instalar y espere a que termine el proceso de instalación. </li>
-<li>Una vez que se realiza la instalación, puede tocar en Abrir para iniciar Brawl Stars o encontrarlo en el cajón de la aplicación. </li>
-</ul>
- <p>¡Felicidades! Has instalado y jugado con éxito Brawl Stars en tu PC usando un emulador. Ahora puedes disfrutar jugando con una pantalla más grande y mejores controles. </p>
- <h2>Características del juego Brawl Stars</h2>
- <p>Brawl Stars es un juego que ofrece muchas características divertidas y emocionantes para sus jugadores. Estas son algunas de las principales características que puedes disfrutar en Brawl Stars:</p>
- <h3>Brawlers: Los personajes de las estrellas Brawl</h3>
-
- <p>Los luchadores se dividen en diferentes rarezas: comunes, raros, súper raros, épicos, míticos, legendarios y cromáticos. Cuanto más alta es la rareza, más difícil es desbloquearlos. Los luchadores también pertenecen a diferentes clases: Luchador, Francotirador, Peso Pesado, Lanzador, Sanador, Apoyo, Asesino, Escaramuza y Chirrido. Cada clase tiene sus propias fortalezas y debilidades, por lo que necesitas elegir el mejor Brawler para cada modo de juego y situación. </p>
- <p>Aquí hay una tabla que muestra algunos de los Brawlers más populares en cada clase:</p>
-
-segundos | Puñetazos: Cura el 25% del daño que hace con sus ataques y súper | Aterrizaje duro: Inflige 1000 daños adicionales a los enemigos por debajo del 50% de salud cuando aterriza | | Chirrido | Chillido | Manchas pegajosas | Big Blob: Lanza una bomba pegajosa masiva que explota después de un retraso, infligir daño y dejar atrás bombas más pequeñas | Windup: Aumenta el alcance de su próximo ataque y la velocidad del proyectil en un 50% | Reacción en cadena: Inflige un 10% más de daño por cada enemigo golpeado por su ataque o súper | <p>Como puedes ver, Los luchadores son muy diversos y tienen diferentes roles y estilos de juego. Puedes experimentar con diferentes Brawlers y encontrar los que se adapten a tus preferencias y estrategias. </p>
- <h3>Modos de juego: Las diferentes formas de pelea</h3>
- <p>Brawl Stars ofrece una variedad de modos de juego que puedes jugar con tus amigos o en solitario. Cada modo de juego tiene sus propias reglas, objetivos y mapas. Puedes elegir entre los siguientes modos de juego:</p>
- <ul>
-<li>Gem Grab: Un modo 3v3 donde tienes que recoger y mantener 10 gemas durante 15 segundos para ganar. Las gemas aparecen en el centro del mapa y caen cuando un jugador muere. </li>
-<li>Showdown: Un modo solo o dúo donde tienes que sobrevivir contra 9 o 4 otros jugadores en una arena que se reduce. Puedes encontrar cubos de poder que aumentan tu salud y daño. El último jugador o equipo en pie gana. </li>
-<li>Brawl Ball: Un modo 3v3 donde tienes que anotar dos goles con un balón de fútbol antes que el otro equipo. Puedes patear, pasar o llevar la pelota, pero la dejas caer cuando usas tu súper o mueres. </li>
-<li>Bounty: Un modo 3v3 donde tienes que matar a tantos enemigos como sea posible mientras evitas que te maten. Cada muerte te da una estrella, lo que aumenta tu recompensa. El equipo con más estrellas al final del partido gana. </li>
-
-<li>Zona caliente: Un modo 3v3 donde tienes que controlar una o más zonas en el mapa quedándote dentro de ellas. Cuantos más jugadores haya en una zona, más rápido se llenará. Gana el equipo que llene más zonas o tenga más porcentaje al final del partido. </li>
-<li>Siege: Un modo 3v3 donde tienes que recoger los pernos que aparecen en el centro del mapa y utilizarlos para construir un poderoso robot que ataca la torreta IKE del enemigo. El equipo que destruye la torreta IKE del enemigo o tiene más salud en su torreta IKE al final del partido gana. </li>
-<li>Knockout: Un modo 3v3 donde tienes que eliminar a todos los enemigos en una ronda al mejor de tres. Cada jugador tiene solo una vida por ronda, y el equipo que mata a todos los enemigos primero gana la ronda. </li>
-</ul>
- <p>Estos son solo algunos de los modos de juego que ofrece Brawl Stars. También hay eventos especiales, como Boss Fight, Robo Rumble, Super City Rampage, Big Game y Power Play, que ofrecen diferentes desafíos y recompensas. También puedes crear tus propios mapas personalizados y modos de juego usando la función Map Maker. </p>
- <h3>Brawl Pass: El sistema de recompensas de temporada</h3>
- <p>Brawl Pass es una función que te permite ganar recompensas jugando Brawl Stars. Cada temporada dura unos dos meses y tiene un tema, como Starr Force, Jurassic Splash o Starr Park. Puedes progresar a través del Brawl Pass al ganar fichas al jugar partidas, completar misiones o ver anuncios. </p>
- <p>The Brawl Pass tiene dos pistas: una pista gratuita y una pista premium. La pista gratuita te da recompensas como monedas, puntos de poder, cajas, pines y ocasionalmente Brawlers. La pista premium te da más recompensas, como gemas, pieles, pines exclusivos y luchadores garantizados. Para acceder a la pista premium, necesitas comprar el Brawl Pass por 169 gemas por temporada. </p>
-
- <p>The Brawl Pass es una gran manera de obtener más recompensas y contenido de jugar Brawl Stars. También puedes comprar niveles adicionales con gemas si quieres acelerar tu progreso o obtener las recompensas antes. </p>
- <h2>Consejos y trucos de Brawl Stars</h2>
- <p>Brawl Stars es un juego que requiere habilidad, estrategia y trabajo en equipo para ganar. Estos son algunos consejos y trucos que pueden ayudarte a mejorar tu rendimiento y divertirte más en Brawl Stars:</p>
- <h3>Cómo elegir el mejor luchador para cada modo</h3>
- <p>Como mencionamos antes, los Brawlers tienen diferentes clases, habilidades y estilos de juego que los hacen más o menos adecuados para ciertos modos de juego. Por ejemplo, los curanderos son buenos para apoyar a los compañeros de equipo en Gem Grab o Siege, mientras que los asesinos son buenos para cazar enemigos en Showdown o Bounty. También debes considerar el diseño del mapa, la composición del equipo enemigo y tu preferencia personal al elegir un Brawler.</p>
- <p>Aquí hay algunas pautas generales para elegir el mejor Brawler para cada modo:</p>
- <ul>
-<li>Gem Grab: Elija Brawlers que pueden controlar el área central, proteger el portador de la gema, o escapar con las gemas. Los ejemplos son Poco, Pam, Sandy, Nita, Tara, Gene y Max.</li>
-<li>Enfrentamiento: Elige Luchadores que puedan sobrevivir por su cuenta, hacer mucho daño o esconderse en los arbustos. Los ejemplos son Leon, Edgar, Crow, Colt, Brock, Bea y Bibi.</li>
-<li>Brawl Ball: Elige Brawlers que pueden marcar goles, romper paredes, o detener al enemigo de anotar. Ejemplos son El Primo, Rosa, Frank, Spike, Rico, Mortis y Darryl.</li>
-<li>Bounty: Elige Brawlers que pueden disparar a los enemigos desde la distancia, evitar ser asesinado, o recoger estrellas. Algunos ejemplos son Piper, Brock, Bo, Tick, 8-Bit, Colette y Byron.</li>
-<li>Atraco: Elige Luchadores que puedan infligir un alto daño a la caja fuerte, defender tu propia caja fuerte o romper la defensa del enemigo. Los ejemplos son Bull, Barley, Dynamike, Colt, Rico, Nani y Amber.</li>
-
-<li>Sitio: Elija Brawlers que pueden recoger pernos, construir robots, o dañar la torreta IKE. Algunos ejemplos son Jessie, Penny, Carl, Barley, Dynamike, Lou y Stu.</li>
-<li>Knockout: Elige Brawlers que puedan eliminar enemigos rápidamente, sobrevivir más tiempo o apoyar a tus compañeros de equipo. Algunos ejemplos son Piper, Brock, Bo, Tick, 8-Bit, Colette y Byron.</li>
-</ul>
- <p>Por supuesto, estos no son los únicos Brawlers que pueden funcionar bien en cada modo. También puedes probar diferentes combinaciones y estrategias y ver qué funciona mejor para ti y tu equipo. </p>
- <h3>Cómo desbloquear nuevos luchadores y pieles</h3>
- <p>Uno de los aspectos más emocionantes de Brawl Stars es desbloquear nuevos Brawlers y skins que cambian su apariencia y a veces sus animaciones y sonidos. Hay varias formas de desbloquear nuevos Brawlers y skins en Brawl Stars:</p>
- <ul>
-<li>Cajas de pelea: Estas son la principal fuente de desbloqueo de nuevos Brawlers. Puedes conseguir cajas de pelea jugando partidos, completando misiones o comprándolas con gemas. Hay tres tipos de cajas de pelea: Normal, Grande y Mega. Cuanto mayor sea el tipo, más recompensas y posibilidades de obtener un nuevo Brawler que se obtiene. </li>
-<li>Brawl Pass: Como mencionamos antes, el Brawl Pass te da recompensas jugando Brawl Stars. Algunas de estas recompensas incluyen nuevos luchadores y pieles que son exclusivos para cada temporada. Puede obtenerlos alcanzando ciertos niveles en la pista gratuita o premium del Brawl Pass.</li>
-<li>Tienda: La tienda es donde usted puede comprar varios artículos con gemas o monedas. Algunos de estos artículos incluyen nuevos luchadores y pieles que están disponibles por un tiempo limitado o de forma permanente. También puede encontrar ofertas especiales y descuentos en la tienda.</li>
-<li>Puntos estelares: Los puntos estelares son una moneda especial que puedes ganar clasificando a tus luchadores o jugando Power Play. Puedes usar Star Points para comprar skins o cajas exclusivas en la Star Shop.</li>
-</ul>
-
- <h3>Cómo usar tu súper habilidad y gadgets con eficacia</h3>
- <p>Además de sus ataques normales, cada Brawler tiene una súper habilidad y un gadget que puede darles una ventaja en la batalla. Una súper habilidad es un movimiento poderoso que se carga mientras haces o recibes daño. Un gadget es un artículo especial que puedes usar una o dos veces por partido dependiendo del gadget. Puedes desbloquear gadgets abriendo cajas cuando tu Brawler alcance el nivel de potencia 7.</p>
- <p>Usar tu súper habilidad y gadget de manera efectiva puede hacer una gran diferencia en tu rendimiento y resultado del partido. Aquí hay algunos consejos sobre cómo usarlos:</p>
- <ul>
-<li>Sepa cuándo usarlos: No pierda su súper habilidad o gadget en situaciones o objetivos innecesarios. Guárdelos para cuando puedan tener el mayor impacto o cuando realmente los necesite. Por ejemplo, usa tu súper habilidad para acabar con un enemigo, escapar del peligro o asegurar un objetivo. Usa tu dispositivo para curarte, aumentar tu daño o sorprender a tu enemigo. </li>
-<li>Saber cómo usarlos: No solo spam su súper capacidad o gadget sin apuntar o sincronizar correctamente. Aprende cómo funcionan y qué hacen exactamente. Por ejemplo, algunas súper habilidades tienen un retardo o un límite de rango antes de activarse. Algunos aparatos tienen un tiempo de reutilización o una duración antes de que expiren. Algunas súper habilidades y gadgets también pueden afectar a tus aliados o enemigos positiva o negativamente. </li>
-<li>Saber en quién usarlos: No utilice su súper habilidad o gadget en el objetivo equivocado o en el momento equivocado. Aprende quiénes son los mejores objetivos para tu súper habilidad o gadget y quiénes son los peores. Por ejemplo, algunas súper habilidades y gadgets son más efectivos contra ciertas clases o tipos de enemigos que otros. Algunas súper habilidades y gadgets también pueden ser contrarrestados o esquivados por otras súper habilidades o gadgets. </li>
-</ul>
-
- <h2>Conclusión</h2>
- <p>Brawl Stars es un juego que ofrece mucha diversión y emoción para sus jugadores. Ya sea que quieras jugar solo o con tus amigos, puedes encontrar un modo de juego que se adapte a tus preferencias y habilidades. También puedes desbloquear y actualizar diferentes Brawlers y skins que añaden más variedad y personalidad a tu juego. </p>
- <p>En este artículo, le hemos dicho todo lo que necesita saber sobre Brawl Stars APK , que es una forma alternativa de instalar el juego en su dispositivo Android. También te hemos mostrado cómo jugar a Brawl Stars en PC usando un emulador, cómo disfrutar de las características del juego y cómo mejorar tus habilidades con algunos consejos y trucos. </p>
- <p>Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer y feliz pelea! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Brawl Stars APK:</p>
- <h4> ¿Es seguro descargar e instalar Brawl Stars APK? </h4>
- <p>Brawl Stars APK es generalmente seguro para descargar e instalar, siempre y cuando lo obtenga de una fuente confiable y permita fuentes desconocidas en su dispositivo. Sin embargo, siempre debes tener cuidado y verificar el tamaño del archivo, la versión y los permisos antes de descargar e instalar cualquier archivo APK. También debe escanear el archivo en busca de virus y malware utilizando una aplicación antivirus de buena reputación. </p>
- <h4> ¿Es Brawl Stars APK legal de usar? </h4>
- <p>Brawl Stars APK es legal de usar, siempre y cuando no modificar o hackear el juego de ninguna manera. Modificar o hackear el juego puede resultar en una prohibición de Supercell o una acción legal de ellos. También debe respetar los términos de servicio y la política de privacidad de Supercell al jugar Brawl Stars.</p>
- <h4> ¿Cuáles son los beneficios de usar Brawl Stars APK? </h4>
- <p>Brawl Stars APK tiene algunos beneficios sobre la versión oficial del juego de la Play Store. Algunos de estos beneficios son:</p>
- <ul>
-
-<li> Puede omitir cualquier restricción o limitación que su dispositivo o región pueda tener. </li>
-<li> Puede ahorrar algo de espacio de almacenamiento en su dispositivo mediante la eliminación de la versión Play Store del juego. </li>
-</ul>
- <h4> ¿Cuáles son los inconvenientes de usar Brawl Stars APK? </h4>
- <p>Brawl Stars APK también tiene algunos inconvenientes en comparación con la versión oficial del juego de la Play Store. Algunos de estos inconvenientes son:</p>
- <ul>
-<li>Es posible que encuentre algunos errores o fallos que aún no se han corregido. </li>
-<li>Es posible que no pueda acceder a algunas características o eventos que son exclusivos de la versión de Play Store del juego. </li>
-<li>Es posible que no pueda actualizar el juego de forma automática o fácil. </li>
-</ul>
- <h4> ¿Cómo puedo actualizar Brawl Stars APK? </h4>
- <p>Si desea actualizar Brawl Stars APK, es necesario descargar e instalar la última versión del archivo APK de una fuente confiable. Puede seguir los mismos pasos que se describen en la sección anterior sobre cómo descargar e instalar Brawl Stars APK. Es posible que tenga que desinstalar la versión anterior del juego antes de instalar el nuevo, dependiendo de la fuente y la actualización. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Cmo Descargar Whatsapp Negocios En El Ordenador Porttil.md

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-<h1>Cómo Descargar WhatsApp Business en Laptop</h1>
-<p>WhatsApp Business es una herramienta para que las empresas interactúen con los clientes a través de la plataforma. Está construido sobre WhatsApp Messenger e incluye todas las características en las que confías, como multimedia, llamadas gratuitas y chat en grupo. Hay dos formas de usar WhatsApp para negocios: WhatsApp Business App y WhatsApp Business Platform. La aplicación es para pequeñas empresas que gestionan personalmente las conversaciones con los clientes. La plataforma es para medianas y grandes empresas que se comunican con los clientes a escala a través del acceso programático. </p>
-<h2>Cómo descargar whatsapp negocios en el ordenador portátil</h2><br /><p><b><b>Download</b> &gt;&gt;&gt; <a href="https://bltlly.com/2v6MKX">https://bltlly.com/2v6MKX</a></b></p><br /><br />
-<p>En este artículo, le mostraremos cómo descargar WhatsApp Business en una computadora portátil usando un emulador. Un emulador es un software que le permite ejecutar aplicaciones Android en su PC o Mac. De esta manera, puedes usar WhatsApp Business en tu portátil sin tener que cambiar de dispositivo o usar tu número de teléfono. </p>
-<h2>¿Qué es WhatsApp Business y por qué usarlo? </h2>
-<p>WhatsApp Business es una aplicación de descarga gratuita para pequeñas empresas que quieren conectarse con sus clientes de una manera rápida y conveniente. Puede crear un perfil empresarial con su logotipo, sitio web, dirección y catálogo de productos o servicios. También puede utilizar herramientas especiales para automatizar, ordenar y responder rápidamente a los mensajes. También puede usar etiquetas para organizar sus chats y contactos. </p>
-<h3>WhatsApp Business App vs WhatsApp Business Platform</h3>
-<p>La aplicación WhatsApp Business está diseñada para pequeñas empresas que quieren gestionar sus propias conversaciones con los clientes. Puedes descargar la aplicación desde Google Play Store o Apple App Store y verificar el número de teléfono de tu empresa. Puedes usar simultáneamente la aplicación WhatsApp Business y WhatsApp Messenger siempre y cuando las cuentas estén vinculadas a diferentes números de teléfono. </p>
-<p></p>
-
-<h3>Características y beneficios de WhatsApp</h3>
-<p>Algunas de las características y beneficios de usar WhatsApp Business son:</p>
-<ul>
-<li>Puedes conocer clientes donde ya están. WhatsApp tiene más de 2 mil millones de usuarios en todo el mundo que lo utilizan diariamente para fines personales y profesionales. </li>
-<li>Puede impulsar los resultados del negocio al aumentar la visibilidad, automatizar la comunicación y mantener organizado su flujo de trabajo. </li>
-<li>Usted puede construir relaciones duraderas con los clientes proporcionando soporte rápido y personalizado, enviando actualizaciones y ofertas, y recogiendo comentarios. </li>
-<li>Puedes aprovechar la seguridad y fiabilidad de WhatsApp. Cada mensaje está cifrado de extremo a extremo, lo que significa que solo usted y la persona con la que se está comunicando pueden ver la información. También tienes control sobre quién puede enviarte mensajes y bloquear contactos no deseados. </li>
-</ul>
-<h3>Limitaciones y alternativas de negocio de WhatsApp</h3>
-<p>A pesar de sus ventajas, WhatsApp Business también tiene algunas limitaciones que debes conocer:</p>
-<ul>
-<li>La aplicación está limitada a un solo dispositivo y no se puede compartir con los miembros de tu equipo. </li>
-<li>La plataforma requiere un proceso de aprobación estricto y puede no estar disponible en algunos países o regiones. </li>
-<li>Los límites de mensajería determinan el número máximo de conversaciones iniciadas por el negocio que puede comenzar con cada uno de sus números de teléfono en un período de 24 horas. </li>
-<li>Las restricciones de difusión y métricas limitadas pueden afectar sus capacidades de marketing y análisis. </li>
-</ul>
-<p>Si estás buscando alternativas a WhatsApp Business, puedes considerar algunas de estas opciones:</p>
-<ul>
-<li>Instagram: Una popular plataforma de redes sociales que te permite mostrar tus productos y servicios, interactuar con tus seguidores y usar mensajes directos para la atención al cliente. </li>
-<li>Facebook Messenger: Una aplicación de mensajería ampliamente utilizada que - le permite crear una página de negocios, enviar mensajes automatizados y usar chatbots para el servicio al cliente. </li>
-
-<li>Correo electrónico: Una forma tradicional pero eficaz de comunicarse con sus clientes, enviar boletines y realizar un seguimiento de su rendimiento. </li>
-</ul>
-<h2>Cómo instalar WhatsApp Business en un ordenador portátil usando un emulador</h2>
-<p>Si quieres usar WhatsApp Business en tu portátil, necesitarás usar un emulador. Un emulador es un software que imita la funcionalidad de un dispositivo Android en su PC o Mac. De esta manera, puede ejecutar cualquier aplicación de Android en su ordenador portátil sin tener que poseer un dispositivo real. </p>
-<h3>¿Qué es un emulador y cómo funciona? </h3>
-<p>Un emulador es un programa que crea un entorno virtual que simula el hardware y el software de otro dispositivo. Por ejemplo, un emulador de Android puede hacer que su computadora portátil actúe como un teléfono o tableta Android. A continuación, puede instalar y ejecutar cualquier aplicación de Android en su ordenador portátil como si estuviera utilizando un dispositivo real. </p>
-<p>Hay muchos emuladores disponibles para diferentes propósitos y plataformas. Algunos de los más populares son:</p>
-<ul>
-<li>BlueStacks: Un emulador gratuito y fácil de usar que admite Windows y Mac. Tiene una gran biblioteca de aplicaciones y juegos que puedes descargar desde Google Play Store o su propia tienda de aplicaciones. </li>
-<li>NoxPlayer: Un emulador potente y personalizable que también es compatible con Windows y Mac. Tiene características avanzadas como mapeo de teclado, soporte de gamepad y grabación de pantalla. </li>
-<li>MEmu: Un emulador ligero y rápido que está optimizado para juegos. Es compatible solo con Windows y tiene una interfaz sencilla que le permite acceder a la Google Play Store y otras tiendas de aplicaciones. </li>
-</ul>
-<h3>Pasos para descargar e instalar un emulador</h3>
-<p>Para descargar e instalar un emulador en su computadora portátil, siga estos pasos:</p>
-<ol>
-<li>Elige un emulador que se adapte a tus necesidades y preferencias. Puede comparar las características, el rendimiento y la compatibilidad de diferentes emuladores en línea. </li>
-
-<li>Ejecute el archivo de instalación y siga las instrucciones en la pantalla. Es posible que necesite conceder algunos permisos o aceptar algunos términos y condiciones. </li>
-<li>Espere a que el proceso de instalación se complete. Puede tomar algún tiempo dependiendo de la velocidad de Internet y las especificaciones del sistema. </li>
-<li>Inicie el emulador e inicie sesión con su cuenta de Google. Esto le permitirá acceder a la Google Play Store y otros servicios de Google. </li>
-</ol>
-<h3>Pasos para descargar e instalar WhatsApp Business en emulador</h3>
-<p>Para descargar e instalar WhatsApp Business en tu emulador, sigue estos pasos:</p>
-<ol>
-<li>Abra la aplicación Google Play Store en su emulador. Puede encontrarla en la pantalla de inicio o en el cajón de aplicaciones. </li>
-<li>Buscar WhatsApp Business en la barra de búsqueda. También puede navegar por las categorías o recomendaciones para encontrarlo. </li>
-<li>Seleccione WhatsApp Business de los resultados de búsqueda y toque en Instalar. Es posible que necesite aceptar algunos permisos o aceptar algunos términos y condiciones. </li>
-<li>Espere a que termine el proceso de descarga e instalación. Puede tardar unos minutos dependiendo de la velocidad de Internet y el rendimiento del emulador. </li>
-<li>Abra WhatsApp Business en su emulador. Puede encontrarlo en la pantalla de inicio o en el cajón de aplicaciones. </li>
-<li>Verifique su número de teléfono de negocios ingresando en el campo y tocando en Siguiente. Recibirás un código de verificación vía SMS o llamada telefónica que necesitas introducir en la app. </li>
-<li>Cree su perfil de negocio ingresando su nombre de negocio, categoría, descripción, dirección, sitio web, correo electrónico y horas de operación. También puede subir su logotipo o imagen de perfil. </li>
-<li>Comience a usar WhatsApp Business en su computadora portátil. Puede enviar y recibir mensajes, crear etiquetas, configurar respuestas automatizadas, ver estadísticas y más. </li>
-</ol>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre WhatsApp Business:</p>
- <h4>Q: ¿Puedo usar WhatsApp Business en varios dispositivos? </h4>
- <p>A: No, solo puedes usar WhatsApp Business en un dispositivo a la vez. Si intenta iniciar sesión en otro dispositivo, se cerrará la sesión del anterior. </p>
- <h4> Q: ¿Puedo usar WhatsApp Business y WhatsApp Messenger con el mismo número de teléfono? </h4>
- <p>A: No, necesitas tener un número de teléfono separado para cada aplicación. Puedes usar tu número de teléfono existente para WhatsApp Messenger y uno diferente para WhatsApp Business, o viceversa. </p>
- <h4>Q: ¿Cómo puedo copia de seguridad y restaurar mis datos de WhatsApp Business? </h4>
- <p>A: Puede hacer copias de seguridad y restaurar los datos de WhatsApp Business utilizando Google Drive o un almacenamiento local. Para hacer una copia de seguridad de sus datos, vaya a Configuración > Chats > Copia de seguridad de chat y elija la frecuencia, cuenta y red que desea usar. Para restaurar sus datos, desinstalar y reinstalar WhatsApp Business y siga las instrucciones para restaurar desde Google Drive o una copia de seguridad local. </p>
- <h4>Q: ¿Cómo puedo eliminar mi cuenta de WhatsApp Business? </h4>
- <p>A: Para eliminar tu cuenta de WhatsApp Business, ve a Configuración > Cuenta > Eliminar mi cuenta e ingresa tu número de teléfono. Esto eliminará su cuenta, perfil, chats, grupos y configuraciones. También perderá el acceso a las copias de seguridad y los datos asociados con su cuenta. </p>
- <h4>Q: ¿Cómo puedo contactar con el soporte de WhatsApp Business? </h4>
- <p>A: Puede ponerse en contacto con el soporte de WhatsApp Business enviando un correo electrónico a [email protected] o utilizando la función de ayuda en la aplicación. Para usar la función de ayuda en la aplicación, ve a Configuración > Ayuda > Contáctanos y llena el formulario con tu pregunta o problema. Recibirás una respuesta en 24 horas. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Coche Usado Magnate Juego Mod Apk 20.1.md

@@ -1,73 +0,0 @@
-<br />
-<h1>Magnate de coches usados juego Mod APK 20.1: Un juego de simulación divertido y adictivo</h1>
-<p>¿Te gustan los coches y quieres dirigir tu propio concesionario de coches? Si es así, entonces deberías probar Used Car Tycoon Game, un juego de simulación donde puedes comprar y vender coches usados, mejorar tu garaje y sala de exposición, contratar personal y administrar tu negocio. En este juego, puedes experimentar la emoción de ser un magnate del automóvil y hacer tu sueño realidad. </p>
-<h2>coche usado magnate juego mod apk 20.1</h2><br /><p><b><b>Download</b> &rArr; <a href="https://bltlly.com/2v6IQ4">https://bltlly.com/2v6IQ4</a></b></p><br /><br />
-<p>Pero ¿qué pasa si quieres tener más dinero y disfrutar del juego sin anuncios? Bueno, hay una solución para eso. Puede descargar Used Car Tycoon Game Mod APK 20.1, una versión modificada del juego que le da dinero ilimitado, sin anuncios, y fácil instalación. En este artículo, le diremos más sobre este juego, sus características, por qué debe descargar la versión apk mod, y cómo hacerlo. Así que, vamos a empezar! </p>
-<h2>¿Qué es un juego de coches usados? </h2>
-<p>Used Car Tycoon Game es un juego de simulación desarrollado por Dragon Fly Entertainment. Fue lanzado en 2020 y tiene más de 10 millones de descargas en Google Play Store. El juego tiene una calificación de 4.3 de 5 estrellas y es adecuado para todos. </p>
-<p>En este juego, puedes comprar y vender autos usados de diferentes marcas, modelos y condiciones. También puede actualizar su garaje y sala de exposición para atraer a más clientes y aumentar sus ganancias. Puede contratar personal como mecánicos, vendedores, limpiadores y gerentes para ayudarlo a administrar su negocio sin problemas. También puede competir con otros concesionarios de automóviles en la ciudad y convertirse en el mejor magnate del automóvil en la ciudad. </p>
-<h3>Características del juego de magnate de coches usados</h3>
-<h4>Comprar y vender coches usados</h4>
-<p>La característica principal de este juego es la compra y venta de coches usados. Usted puede navegar a través de cientos de coches de diferentes categorías, tales como sedanes, SUV, camiones, coches deportivos, coches de lujo, y más. Usted puede comprobar la condición, kilometraje, precio, y la historia de cada coche antes de comprarlo. También puede negociar con los vendedores para obtener el mejor trato posible. </p>
-
-<h4>Mejora tu garaje y sala de exposición</h4>
-<p>Otra característica de este juego es la mejora de su garaje y sala de exposición. Puede ampliar su garaje para almacenar más coches y mejorar sus instalaciones, como ascensores, herramientas, máquinas, etc. También puede actualizar su sala de exposición para mostrar más coches y hacer que se vea más profesional y atractivo. </p>
-<p>Al actualizar su garaje y sala de exposición, puede aumentar su reputación y la satisfacción del cliente. También puede desbloquear nuevas características como subastas, préstamos, seguros, etc. También puede acceder a nuevas ubicaciones como suburbios, centro de la ciudad, playas, etc.</p>
-<p></p>
-<h4>Contrata personal y gestiona tu negocio</h4>
-<p>La última característica de este juego es la contratación de personal y la gestión de su negocio. Puede contratar personal como mecánicos, vendedores, limpiadores y gerentes para ayudarlo a administrar su concesionario de automóviles de manera eficiente. Cada miembro del personal tiene sus propias habilidades, habilidades, salarios y personalidades. Puedes entrenarlos para mejorar su desempeño y lealtad. </p>
-<p <p>También puede administrar su negocio estableciendo sus precios, presupuesto, marketing, inventario, etc. También puede monitorear sus ingresos, gastos, ganancias, flujo de efectivo, etc. También puede lidiar con varios eventos y desafíos como quejas de clientes, problemas del personal, competidores, tendencias del mercado, etc. También puede obtener logros y recompensas por su rendimiento y progreso. </p>
-<h3>¿Por qué descargar usado coche magnate juego Mod APK 20.1? </h3>
-<p>Used Car Tycoon Game es un divertido y adictivo juego de simulación que te mantendrá entretenido durante horas. Sin embargo, si quieres disfrutar del juego más, usted debe descargar Used Car Tycoon Game Mod APK 20.1, una versión modificada del juego que le da algunos beneficios adicionales. Aquí hay algunas razones por las que debe descargar esta versión apk mod:</p>
-<h4>Dinero ilimitado</h4>
-
-<p>Con esta versión apk mod, usted no tiene que preocuparse por el dinero más. Tendrás dinero ilimitado desde el inicio del juego, y puedes gastarlo todo lo que quieras sin consecuencias. Usted puede comprar cualquier coche que desee, actualizar su garaje y sala de exposición al nivel máximo, contratar al mejor personal, etc. También puede experimentar con diferentes estrategias y opciones sin ningún riesgo. </p>
-<h4>No hay anuncios</h4>
-<p>Otra razón para descargar esta versión apk mod es que elimina todos los anuncios del juego. Los anuncios son molestos y distraen, especialmente cuando aparecen en el medio del juego o cuando intentas disfrutar del juego. También pueden arruinar tu inmersión y estado de ánimo. </p>
-<p>Con esta versión apk mod, no tienes que ver ningún anuncio en el juego. Puede jugar el juego sin problemas y pacíficamente sin interrupciones ni distracciones. También puede guardar sus datos y batería al no cargar ningún anuncio. </p>
-<h4>Fácil instalación</h4>
-<p>La última razón para descargar esta versión apk mod es que es fácil de instalar y usar. Usted don’t necesidad de raíz de su dispositivo o hacer cualquier complicado pasos para instalar esta versión apk mod. Solo tienes que seguir unos sencillos pasos que explicaremos más adelante en este artículo. </p>
-<p>Con esta versión apk mod, usted no tiene que preocuparse por cualquier problema de compatibilidad o seguridad. Puede instalar esta versión apk mod en cualquier dispositivo Android y disfrutar del juego sin ningún problema. </p>
-<h2> ¿Cómo descargar e instalar el juego de coches usados Tycoon Mod APK 20.1? </h2>
-<p>Si usted está convencido por los beneficios de la descarga de Used Car Tycoon Game Mod APK 20.1, usted puede preguntarse cómo hacerlo. Bueno, no te preocupes, porque te guiaremos a través del proceso paso a paso. Aquí es cómo descargar e instalar usado coche magnate juego Mod APK 20.1:</p>
-<h3>Paso 1: Descargar el archivo apk mod desde el enlace de abajo</h3>
-
-<p><a href="">Descargar Usado Tycoon Juego Mod APK 20.1 aquí</a></p>
-<h3>Paso 2: Habilitar fuentes desconocidas en el dispositivo</h3>
-<p>El segundo paso es habilitar fuentes desconocidas en su dispositivo. Esto es necesario porque este archivo apk mod no es de la tienda oficial de Google Play, por lo que debe permitir que su dispositivo para instalar aplicaciones de fuentes desconocidas. </p>
-<p>Para habilitar fuentes desconocidas en su dispositivo, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </p>
-<h3>Paso 3: Instalar el archivo apk mod y disfrutar del juego</h3>
-<p>El tercer y último paso es instalar el archivo apk mod y disfrutar del juego. Para instalar el archivo apk mod, ir a su administrador de archivos y localizar el archivo apk mod descargado. Toque en él y siga las instrucciones en la pantalla para instalarlo. </p>
-<p>Una vez realizada la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutarlo con dinero ilimitado, sin anuncios y fácil instalación. </p>
-<h2>Conclusión</h2>
-<p>Used Car Tycoon Game es un divertido y adictivo juego de simulación donde puedes comprar y vender coches usados, mejorar tu garaje y sala de exposición, contratar personal y gestionar tu negocio. También puede descargar Used Car Tycoon Game Mod APK 20.1, una versión modificada del juego que le da dinero ilimitado, sin anuncios, y fácil instalación. En este artículo, le hemos dicho más sobre este juego, sus características, por qué debe descargar la versión apk mod, y cómo hacerlo. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer y jugar feliz! </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Used Car Tycoon Game Mod APK 20.1:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>¿Es seguro descargar y usar el juego de magnate de coches usados Mod APK 20.1? </td>
-
-</tr>
-<tr>
-<td>¿El juego de magnate de coches usados Mod APK 20.1 funciona en todos los dispositivos Android? </td>
-<td>Sí, Usado coche magnate juego Mod APK 20.1 funciona en todos los dispositivos Android que soportan Android 4.4 y por encima. Es compatible con la mayoría de los teléfonos y tabletas Android. </td>
-</tr>
-<tr>
-<td>¿Me prohibirán el juego si uso Used Car Tycoon Game Mod APK 20.1? </td>
-<td>No, no se le prohibió el juego si se utiliza el coche usado Tycoon Game Mod APK 20.1. Esta versión apk mod no interfiere con los servidores del juego o características en línea. Solo modifica las funciones offline del juego como dinero y anuncios. </td>
-</tr>
-<tr>
-<td>¿Puedo actualizar el juego si uso APK 20.1? </td>
-<td>No, no se puede actualizar el juego si se utiliza Used Car Tycoon Game Mod APK 20.1. Esta versión apk mod se basa en la versión original del juego, que puede no ser compatible con las últimas actualizaciones. Si desea actualizar el juego, usted tendrá que desinstalar la versión apk mod e instalar la versión oficial de la Google Play Store.</td>
-</tr>
-<tr>
-<td>¿Puedo jugar el juego sin conexión si uso Used Car Tycoon Game Mod APK 20.1? </td>
-<td>Sí, se puede jugar el juego sin conexión si se utiliza Used Car Tycoon Game Mod APK 20.1. Esta versión mod apk no requiere ninguna conexión a Internet para jugar el juego. Puede disfrutar del juego sin interrupciones o limitaciones. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar 16.4.1.md

@@ -1,72 +0,0 @@
-<br />
-<h1>Plantas vs Zombies Descargar 1: Cómo jugar el clásico juego de defensa de la torre en su PC</h1>
-<h2>Introducción</h2>
-<p>Plants vs Zombies es uno de los juegos de defensa de torres más populares y adictivos jamás creados. Fue desarrollado por PopCap Games y lanzado en 2009 para Windows y Mac OS X. El juego ha ganado varios premios y ha sido elogiado por su humor, jugabilidad y gráficos. </p>
-<h2>descargar 16.4.1</h2><br /><p><b><b>Download Zip</b> &#9989; <a href="https://bltlly.com/2v6LDQ">https://bltlly.com/2v6LDQ</a></b></p><br /><br />
-<p>En Plants vs Zombies, tienes que proteger tu casa de las olas de zombies que quieren comerse tu cerebro. Haces esto plantando varios tipos de plantas que pueden disparar, explotar o ralentizar a los zombies. El juego tiene 50 niveles en el modo Aventura, además de otros modos como Supervivencia, Puzzle y Mini-Games. También puedes desbloquear diferentes plantas, zombies y logros a medida que avanzas. </p>
-<p>Si eres un fan de Plants vs Zombies, o si quieres probarlo por primera vez, es posible que te estés preguntando cómo jugarlo en tu PC. En este artículo, te mostraremos dos formas fáciles de descargar e instalar Plants vs Zombies en tu PC, para que puedas disfrutar de este clásico juego en una pantalla más grande. </p>
-<h2>Cómo descargar e instalar Plants vs Zombies en PC</h2>
-<h3>Opción 1: Descarga desde Google Play Store usando el emulador de BlueStacks</h3>
-<p>Una de las formas más fáciles de jugar Plants vs Zombies en tu PC es usar un emulador de Android como BlueStacks. BlueStacks es un software que te permite ejecutar aplicaciones y juegos Android en tu PC. Puedes descargarlo gratis desde [BlueStacks.com]( 2 ). </p>
-<h4>Paso 1: Descargar e instalar BlueStacks en su PC</h4>
-<p>Vaya a [BlueStacks.com]( 2 ) y haga clic en el botón de descarga. La descarga se iniciará automáticamente. Una vez finalizada la descarga, ejecute el archivo de instalación y siga las instrucciones para instalar BlueStacks en su PC.</p>
-<h4>Paso 2: Inicie BlueStacks e inicie sesión con su cuenta de Google</h4>
-
-<h4>Paso 3: Búsqueda de plantas vs zombies en la Google Play Store</h4>
-<p>Una vez que haya iniciado sesión, verá la pantalla de inicio de BlueStacks. En la esquina superior derecha, verá un icono de búsqueda. Haz clic en él y escribe "Plants vs Zombies" en la barra de búsqueda. Verás una lista de resultados. Haga clic en el que dice "Plants vs. Zombies=" por ELECTRONIC ARTS.</p>
-<p></p>
-<h4>Paso 4: Instalar plantas vs zombies y disfrutar jugando en su PC</h4>
-<p>Serás llevado a la página de aplicaciones de Plants vs Zombies en la Google Play Store. Haga clic en el botón de instalación y espere a que la instalación termine <p>Después de que la instalación se haya completado, verá un botón abierto. Haga clic en él y podrá jugar Plants vs Zombies en su PC. También puede encontrar el icono del juego en la pantalla de inicio de BlueStacks o en el escritorio. Puede utilizar el ratón y el teclado para controlar el juego, o personalizar la configuración a su preferencia. </p>
-<h3>Opción 2: Descargar desde Filehippo.com usando un archivo de instalación</h3>
-<p>Otra forma de jugar Plants vs Zombies en tu PC es descargarlo desde un sitio web que ofrece archivos de instalación para juegos de PC. Uno de los sitios web que puedes utilizar es [Filehippo.com]. Filehippo.com es una fuente confiable y confiable de descargas de software libre para Windows, Mac y Android. Puedes descargar Plants vs Zombies de Filehippo.com gratis y sin virus ni malware. </p>
-<h4>Paso 1: Ir a Filehippo.com y buscar plantas vs zombies</h4>
-<p>Abra su navegador web y vaya a [Filehippo.com]. En la esquina superior derecha, verá un cuadro de búsqueda. Escribe "Plants vs Zombies" en el cuadro de búsqueda y pulsa enter. Verás una lista de resultados. Haga clic en el que dice "Plants vs. Zombies Game Of The Year Edition 1.2.0.1073 for PC Windows". </p>
-<h4>Paso 2: Haga clic en el botón de descarga y guarde el archivo de instalación en su PC</h4>
-
-<h4>Paso 3: Ejecute el archivo de instalación y siga las instrucciones para instalar Plants vs Zombies en su PC</h4>
-<p>Una vez completada la descarga, vaya a la ubicación donde guardó el archivo de instalación y haga doble clic en él. Aparecerá una ventana pidiéndole que confirme si desea ejecutar el archivo. Haz clic en sí y sigue las instrucciones para instalar Plants vs Zombies en tu PC. Es posible que tenga que aceptar los términos y condiciones y elegir una carpeta de destino para el juego. </p>
-<h4>Paso 4: Plantas de lanzamiento vs zombies y divertirse jugando en su PC</h4>
-<p>Una vez completada la instalación, verá un icono de acceso directo para Plants vs Zombies en su escritorio o menú de inicio. Haz clic en él y podrás jugar Plants vs Zombies en tu PC. Puedes usar el ratón y el teclado para controlar el juego, o ajustar la configuración a tu gusto. </p>
-<h2>Conclusión</h2>
-<p>Plants vs Zombies es un clásico juego de torre de defensa que puedes jugar en tu PC usando un emulador de Android como BlueStacks o un archivo de instalación de Filehippo.com. Ambos métodos son fáciles y gratuitos, y te permiten disfrutar de este divertido y adictivo juego en una pantalla más grande. Ya sea que quieras revivir tus recuerdos de infancia o descubrir este juego por primera vez, Plants vs Zombies es una gran opción para cualquiera que ame la estrategia, el humor y los zombies. </p>
-<p>Si estás listo para jugar Plants vs Zombies en tu PC, elige una de las opciones de arriba y sigue los pasos que te proporcionamos. Usted será capaz de descargar e instalar Plants vs Zombies en ningún momento, y empezar a plantar sus defensas contra los invasores muertos vivientes. Diviértete! </p>
-<h3>Preguntas frecuentes</h3>
-<ul>
-<li><b>¿Es libre Plants vs Zombies? </b></li>
-<p>Sí, Plants vs Zombies es gratis para descargar y jugar en tu PC usando BlueStacks o Filehippo.com. Sin embargo, puede haber algunas compras en la aplicación o anuncios en el juego que puedes ignorar o comprar. </p>
-<li><b>¿Son seguras las plantas contra los zombis? </b></li>
-
-<li><b>¿Cuáles son los requisitos del sistema para Plantas vs Zombies? </b></li>
-<p>Los requisitos mínimos del sistema para Plantas vs Zombies son:</p>
-<tabla>
-<tr><td>OS</td><td>Windows XP/Vista/7/8/10</td></tr>
-<tr><td>CPU</td><td>procesador de 1,2 GHz</td></tr>
-<tr><td>RAM</td><td>512 MB</td></tr>
-<tr><td>HDD</td><td>65 MB de espacio libre</td></tr>
-<tr><td>Gráficos</td <td>DirectX 8 o posterior</td></tr>
-<tr><td>Sonido</td><td>Tarjeta de sonido compatible con DirectX</td></tr>
-</tabla>
-<p>Los requisitos de sistema recomendados para Plants vs Zombies son:</p>
-<tabla>
-<tr><td>OS</td><td>Windows XP/Vista/7/8/10</td></tr>
-<tr><td>CPU</td><td>procesador de 1,5 GHz</td></tr>
-<tr><td>RAM</td><td>1 GB</td></tr>
-<tr><td>HDD</td><td>65 MB de espacio libre</td></tr>
-<tr><td>Gráficos</td><td>DirectX 9 o posterior</td></tr>
-<tr><td>Sonido</td><td>Tarjeta de sonido compatible con DirectX</td></tr>
-</tabla>
-<li><b>¿Cuántas plantas y zombies hay en Plants vs Zombies? </b></li>
-<p>Hay 49 plantas diferentes y 26 zombis diferentes en Plants vs Zombies. Cada planta y zombi tiene sus propias habilidades y características únicas. Puedes desbloquear más plantas y zombies mientras juegas el juego y completas los niveles. </p>
-<li><b>¿Cuáles son los otros modos en Plants vs Zombies? </b></li>
-<p>Además del modo Aventura, que tiene 50 niveles, también hay otros modos en Plants vs Zombies que puedes jugar para más diversión y desafío. Estos modos son:</p>
-<ul>
-<li>Modo de supervivencia: Tienes que sobrevivir a interminables oleadas de zombies con recursos limitados. </li>
-<li>Modo de rompecabezas: Tienes que resolver varios puzzles que involucran plantas y zombies. </li>
-Modo de minijuegos: Tienes que jugar varios minijuegos que tienen diferentes reglas y objetivos. </li>
-<li>Modo de jardín zen: Tienes que crecer y cuidar de tus propias plantas en un jardín relajante. </li>
-<li>Crazy Dave’s Shop: Puedes comprar varios artículos y mejoras de Crazy Dave, el vecino excéntrico que te ayuda a lo largo del juego. </li>
-</ul>
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-<p>Sí, hay una secuela de Plants vs Zombies llamada Plants vs. Zombies 2: It’s About Time. Fue lanzado en 2013 para dispositivos iOS y Android. La secuela cuenta con nuevas plantas, zombies, mundos, niveles y modos. También tiene un tema de viaje en el tiempo que le permite visitar diferentes períodos históricos y luchar contra zombies allí. </p> 64aa2da5cf<br />
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